National Biodiversity Assessment 2018opus.sanbi.org/jspui/bitstream/20.500.12143/6370/4/... · National Biodiversity Institute (SANBI) in terms of allocating some staff time and funding

National Biodiversity Assessment 2018 Technical Report Vol. 1: Terrestrial Realm

i

National Biodiversity Assessment 2018

TECHNICAL REPORT

Volume 1:

Terrestrial Realm

REPORT NUMBER: http://hdl.handle.net/20.500.12143/6370

http://hdl.handle.net/20.500.12143/6370


2

CITATION FOR THIS REPORT

Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). 2019. South African National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6370

CHAPTER CITATIONS

Chapter 1: Skowno, A.L. Raimondo, D.C. & Poole, C.J. 2019. ‘Chapter 1: Introduction and Approach’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L. Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 2: Poole, C.J., Raimondo, D. & Driver, A. (eds.). 2019. ‘Chapter 2: Benefits of Biodiversity in the Terrestrial Realm’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby JA (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 3: Skowno, A.L., Raimondo, D.C., Driver, A., Powrie, L.W., Hoffman, M.T., Van de Merwe S., Hlahane, K., Fizzotti, B. & Variawa, T. 2019. ‘Chapter 3: Pressures and Drivers I – General’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 4: van Wilgen, B.W., Wilson, J.R., Faulkner, K.T., Mnikathi, Z., Morapi, T., Munyai, T., Rahlao, S. & Zengeya, T.A. 2019. ‘Chapter 4: Pressures and Drives II – Biological Invasions’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 5: Foden, W., Midgley, G., Kelly, C., Stevens, N. & Robinson, J.2019. ‘Chapter 5: Pressures and Drivers III – Climate Change’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 6: Skowno, A.L., Raimondo, D.C., Dayaram, A. & Kirkwood, D. 2019. ‘Chapter 6: Input Data’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 7: Skowno, A.L., Matlala, M.S., Kirkwood, D. & Slingsby. J.A. 2019. ‘Chapter 7: Ecosystem Assessments’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 8: Raimondo, D., Von Staden, L., Van der Colff, D., Child, M., Tolley, K.A., Edge, D., Kirkman, S., Measey, J., Taylor, M., Retief, E., Weeber, J., Roxburgh, L. & Fizzotti, B. 2019. ‘Chapter 8: Indigenous Species Assessments’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 9: Skowno, A.L., Raimondo, D.C. & Fizzotti, B. 2019. ‘Chapter 9: Biome Summaries’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J. & Fizzotti, B. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 10: Tolley, K.A, da Silva, J. & Van Vuuren, B. 2019. ‘Chapter 10: Benefits, Trends and Risks to Genetic Diversity’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 11: Skowno, A.L., Daniels, F., Driver, A., Midgely, G., Foden, W., Stevens, N., Van Wilgen, B.W., Wilson, J.R., Faulkner, K.T., Mnikathi, Z., Morapi, T., Munyai, T., Rahlao, S., Zengeya, T.A., Poole, C.J. & Pfab, M. 2019. ‘Chapter 11: Responses to Pressures’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Chapter 12: Skowno, A.L., Poole, C.J. 2019. ‘Chapter 11: Knowledge gaps and research priorities’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

This report uses the names of the government departments confirmed in June 2019. Please refer to

www.gov.za to see all the changes in government departments and ministries.



3

This report forms part of a set of reports that make up the South African National Biodiversity Assessment 2018.

SYNTHESIS REPORT NBA 2018

For reference in non-scientific publications

South African National Biodiversity Institute (SANBI). 2019. National Biodiversity Assessment 2018: The status of South Africa’s ecosystems and biodiversity. Synthesis Report. South African National Biodiversity Institute, an entity of the Department of Environment, Forestry and Fisheries, Pretoria. http://hdl.handle.net/20.500.12143/6362

For reference in scientific publications

Skowno, A.L., Poole, C.J., Raimondo, D.C., Sink, K.J., Van Deventer, H., Van Niekerk, L., Harris, L.R., Smith-Adao, L.B., Tolley, K.A., Zengeya, T.A., Foden, W.B., Midgley, G.F. & Driver, A. 2019. National Biodiversity Assessment 2018: The status of South Africa’s ecosystems and biodiversity. Synthesis Report. South African National Biodiversity Institute, an entity of the Department of Environment, Forestry and Fisheries Pretoria. http://hdl.handle.net/20.500.12143/6362

TECHNICAL REPORTS NBA 2018

1. Terrestrial Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B., Slingsby, J.A. (eds). 2019. South African National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6370 2. Inland Aquatic (Freshwater) Van Deventer, H., Smith-Adao, L., Collins, N.B., Grenfell, M., Grundling, A., Grundling, P-L., Impson, D., Job, N., Lötter, M., Ollis, D., Petersen, C., Scherman, P., Sieben, E., Snaddon, K., Tererai, F. & Van der Colff, D. 2019. South African National Biodiversity Assessment 2018: Technical Report. Volume 2: Inland Aquatic (Freshwater) Realm. CSIR report number CSIR/NRE/ECOS/IR/2019/0004/A. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6230 3. Estuarine Van Niekerk, L., Adams, J.B., Lamberth, S.J., MacKay, F., Taljaard, S., Turpie, J.K., Weerts S. & Raimondo, D.C., 2019 (eds). South African National Biodiversity Assessment 2018: Technical Report. Volume 3: Estuarine Realm. CSIR report number CSIR/SPLA/EM/EXP/2019/0062/A. South African National Biodiversity Institute, Pretoria. Report Number: SANBI/NAT/NBA2018/2019/Vol3/A. http://hdl.handle.net/20.500.12143/6373 4. Marine Sink, K.J., Van der Bank, M.G., Majiedt, P.A., Harris, L., Atkinson, L., Kirkman, S. & Karenyi, N. (eds). 2019. South African National Biodiversity Assessment 2018 Technical Report Volume 4: Marine Realm. South African National Biodiversity Institute, Pretoria. South Africa. http://hdl.handle.net/20.500.12143/6372 5. Coast Harris, L.R., Sink, K.J., Skowno, A.L. & Van Niekerk, L. (eds). 2019. South African National Biodiversity Assessment 2018: Technical Report. Volume 5: Coast. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6374 6. Sub-Antarctic Territory Whitehead, T.O., Von der Meden, C., Skowno, A.L., Sink, K.J., Van der Merwe, S., Adams, R. & Holness, S. (eds). 2019. South African National Biodiversity Assessment 2018 Technical Report Volume 6: Sub-Antarctic Territory. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6375 7. Genetic Diversity Tolley, K.A., Da Silva, J.M. & Jansen Van Vuuren, B. 2019. South African National Biodiversity Assessment 2018 Technical Report Volume 7: Genetic Diversity. South African National Biodiversity Institute, Pretoria. http://hdl.handle.net/20.500.12143/6376











4

ACKNOWLEDGEMENTS

The terrestrial assessment of the National Biodiversity Assessment 2018 was funded by the South African

National Biodiversity Institute (SANBI) in terms of allocating some staff time and funding for workshops.

However, the terrestrial assessment would not have been possible without the substantial in-kind

contributions of time, data, analyses and sometimes actual monetary contributions (in terms of travel to

workshops) from numerous individuals and institutions. An estimated 10 000 person hours was spent on the

terrestrial assessment during the period April 2015 to April 2019.

The assessment was led by Dr Andrew Skowno from SANBI, with a small team of SANBI staff who worked

part-time on the assessment. Technical guidance and review was provided from a Terrestrial Reference

Group, the Provincial & Metro Planning Working Group (a group of biodiversity planners that meets on an

annual basis), and from the NBA Core Reference Group (which consisted of the various component leads for

NBA 2018). The National Vegetation Map Committee provided technical guidance for the foundational

ecosystem layer used in the terrestrial realm – the National Vegetation Map. Numerous species experts

contributed their knowledge and time to the species assessments.

The authors for each chapter are listed in the chapter citations at the top of each chapter. The editors thank

these individuals for their commitment to the terrestrial assessment. The editors would particularly like to

thank the following people for their contributions to the report.

Reviewers of the terrestrial technical report

Name Institution

Warrick Stewart Resilience Environmental Advice

Debbie Jewitt Ezemvelo KZN Wildlife

SANBI staff, interns and research assistants involved in the terrestrial assessment

Name Role

Amanda Driver Senior Policy Advisor

Andrew Skowno NBA Lead, lead of terrestrial assessment

Anisha Dayaram Vegetation Scientist

Bianca Fizzotti Research Assistant

Carol Poole NBA Project Manager, lead of the benefits of biodiversity component

Deshni Pillay Director: Biodiversity Assessment and Monitoring

Dewidine Van der Colff Animal Red List Officer

Domitilla Raimondo Manager: Threatened Species Unit

Fahiema Daniels Biodiversity Planning

Given Leballo GIS technician

Jeffrey Manuel Director: Biodiversity Information Management and Planning

Keneilwe Hlahane Intern and Research Assistant

Leslie Powrie Biodiversity Information Specialist

Lize von Staden Plant Red List Officer

Maphale Matlala Ecosystem Assessment Scientist

Mcebisi Qabaqaba Vegetation map intern

Mutsinda Ramavhunga GIS technician

Norma Malajti GIS support for protected areas

Smiso Bhengu GIS technician

Stephni Van der Merwe Vegetation map intern

Sephelele Zondo GIS technician


5

Members of the Provincial & Metro Biodiversity Planning Working Group*

Name Institution

Mervyn Lotter Mpumalanga Tourism and Parks Agency

Boyd Escott Ezemvelo KZN Wildlife

Philip Desmet Independent consultant

Warrick Stewart Independent consultant: Resilience Environmental Advice

Donovan Kirkwood Independent consultant

Stephen Holness Independent consultant and Nelson Mandela University

Genevieve Pence CapeNature

Ray Schaller North-West DACE

Nacelle Collins Free State Department of Economic, Small Business Development, Tourism and Environmental Affairs

Enrico Oosthuysen Northern Cape Department of Environment and Nature Conservation

Linda Harris Nelson Mandela University

Kagiso Mangwale Eastern Cape Parks and Tourism Authority

Greer Hawley Coastal Environmental Services EOH

*Note: At the first meeting of the Terrestrial Reference Group in 2016, it was confirmed that there would be a session at each annual Provincial &

Metro Biodiversity Planning Working Group meeting that relates to the terrestrial report, and therefore no separate meetings would be needed going

forward.

Contributors to the National Vegetation Map version used in this assessment, with an indication of the

nature of the contribution made. Members of the National Vegetation Map Committee are indicated.

Name Contribution Institution

Adriaan Grobler Strategic and Data Nelson Mandela University

Alastair Potts Strategic (Committee member) and Data Nelson Mandela University

Andrew Skowno Strategic (Committee member) and Data South African National Biodiversity Institute (SANBI)

Anisha Dayaram Strategic (Committee member) and Data South African National Biodiversity Institute (SANBI)

Cameron Mclean Data Ethekwini municipality

Coert Geldenhuys Strategic (Committee member) and Data Independent consultant

Debbie Jewitt Strategic (Committee member) and Data Ezemvelo KZN Wildlife

Donovan Kirkwood Strategic and Data Independent consultant

Erwin Sieben Strategic (Committee member) and Data University of KwaZulu-Natal

Fahiema Daniels Strategic South African National Biodiversity Institute (SANBI)

Hugo Bezuidenhout Strategic (Committee member) and Data SANPARKS

Jan Vlok Data Independent consultant

Johan Bester Strategic (Committee member) and Data Department of Agriculture, Forestry and Fisheries

Johann du Preez Strategic (Committee member) Independent

Johanna Makinta Strategic (Committee member) and Data Department of Agriculture, Forestry and Fisheries

Keneilwe Hlahane Data South African National Biodiversity Institute (SANBI)

Laco Mucina Strategic (Committee member) University of Western Australia

Les Powrie Strategic (Committee member) South African National Biodiversity Institute (SANBI)

Linda Harris Data Nelson Mandela University

Maphale Matlala Strategic (Committee member) South African National Biodiversity Institute (SANBI)

Mcebisi Qabaqaba Data South African National Biodiversity Institute (SANBI)

Mervyn Lotter Strategic (Committee member) and Data Mpumalanga Tourism and Parks Agency

Philip Desmet Strategic (Committee member) and Data Independent consultant

Pieter Winter Data South African National Biodiversity Institute (SANBI)

Richard Boon Data eThekwini municipality

Richard Cowling Data Nelson Mandela University

Simon Todd Strategic (Committee member) Independent consultant, SAEON, University of Cape Town

Stephni Van der Merwe Data South African National Biodiversity Institute (SANBI)

Taryn Riddin Data Nelson Mandela University

Tony Rebelo Strategic (Committee member) and Data SANBI

Vincent Egan Data Limpopo Dept. of Economic Development, Environment & Tourism

Species experts

Name Institution

Alexander Rebelo Bayworld Museum

Andrew Turner CapeNature

Bryan Maritz University of the Western Cape

Charles Haddad University of the Free State


6

Christa Thirion Department of Water and Sanitation

Denham Parker University of Cape Town

Dewidine Van Der Colff South African National Biodiversity Institute (SANBI)

Domitilla Raimondo South African National Biodiversity Institute (SANBI)

Emmanuel Dolinhsan Independent

Ernst Retief BirdLife South Africa

Fiona Mackay Oceanographics Research Institute

Francois Roux Mpumalanga Parks and Tourism Agency

Graham Alexander University of Witwatersrand

Harriet Davies-Mostert Endangered Wildlife Trust

Heather Terrapon South African National Biodiversity Institute (SANBI)

Henning Winker Department of Agriculture, Forestry and Fisheries

Hlengiwe Mtshali Botanical Society

Jeanne Tarrant Endangered Wildlife Trust

John Measey Stellenbosch University

Kerry Sink South African National Biodiversity Institute (SANBI)

Krystal Tolley South African National Biodiversity Institute (SANBI)

Lizanne Roxburgh Endangered Wildlife Trust

Lize von Staden South African National Biodiversity Institute (SANBI)

Louw Kyss Knysna Basin Project

Martin Taylor BirdLife South Africa

Martine Jordaan CapeNature

Matthew Child South African National Biodiversity Institute (SANBI)

Michael Samways Stellenbosch University

Mike Bates National Museum Bloemfontein

Mohlamatsane Mokhatla South African National Parks

Petro Marais Agricultural Research Council

Prideel Majiedt South African National Biodiversity Institute (SANBI)

Res Altwegg University of Cape Town

Robin Lyle Agricultural Research Council

Rose Thornycroft South African National Biodiversity Institute (SANBI)

Samantha Page-Nicholson Endangered Wildlife Trust

John Simaika Stellenbosch University

Skhumbuzo Kubheka Ezemvelo KZN Wildlife

Stefan Foord University of Venda

Stephen Lamberth Department of Agriculture, Forestry and Fisheries

Werner Conradie Bayworld Museum

A list of meetings held for the work leading to the production of this technical report of NBA 2018 can be

found as Appendix A.


7

SHORT CONTENTS

Acknowledgements ........................................................................................................................................... 4

Short contents ................................................................................................................................................... 7

Executive summary............................................................................................................................................ 8

1. Introduction and Approach ................................................................................................................. 13

2. Benefits of Biodiversity in the Terrestrial Realm ................................................................................. 22

3. Pressures and Drivers I – General ........................................................................................................ 36

4. Pressures and Drivers II – Biological Invasions .................................................................................... 59

5. Pressures and Drivers III - Climate Change .......................................................................................... 72

6. Input Data ............................................................................................................................................ 91

7. Ecosystem Assessments .................................................................................................................... 100

8. Indigenous Species Assessments ....................................................................................................... 117

9. Biome Summaries .............................................................................................................................. 139

10. Benefits, Trends and Risks to Genetic Diversity ................................................................................ 148

11. Sector Actions and Responses ........................................................................................................... 162

12. Knowledge gaps and research priorities for the terrestrial realm .................................................... 176

13. References ......................................................................................................................................... 181

14. List of appendices .............................................................................................................................. 192

15. List of annexures (separate documents AND datasets) .................................................................... 192

16. List of acronyms, abbreviations, initialisms and symbols ................................................................. 193

17. Glossary of terms ............................................................................................................................... 194

Appendices .................................................................................................................................................... 197


8

EXECUTIVE SUMMARY

The National Biodiversity Assessment (NBA) 2018 is a collaborative effort to synthesise the best available

science on South Africa’s biodiversity. The overarching aim of the NBA is to inform policy and decision-making

in a range of sectors, and contribute to national development priorities. The NBA is used to inform policy in

the biodiversity sector and other sectors that rely on or impact on natural resources, such as water,

agriculture, mining and human settlements. The NBA provides information to help prioritise resources for

managing and conserving biodiversity, and provides context and information that underpins biodiversity

inputs to land use planning processes. A range of national and international level monitoring, reporting and

assessment processes rely on information gathered during the NBA. The NBA is also a key reference and

educational product relevant to scientists, students, consultants and decision makers, and acts as a national

level platform for collaboration, information sharing and capacity building in the biodiversity sector in South

Africa. This report focusses on the Terrestrial Realm with similar reports covering the Marine, Coastal, Inland

Aquatic and Estuarine Realms respectively. There are also special reports on Genetic Diversity and on the

Prince Edward Islands and surrounding seas in the NBA 2018.

South Africa’s terrestrial realm is recognised globally for its biodiversity and high levels of endemism. The

unique and diverse fauna and flora, together with the wide range of ecosystems, underpins South Africa’s

vibrant and growing tourism and wildlife industries, culturally and economically important traditional

medicine practices, extensive livestock farming industry, and the functioning of water catchment areas.

Together these industries and functions provide hundreds of thousands of jobs and contribute to food and

water security.

South Africa has globally exceptional biodiversity that provides a wide array of benefits to the economy,

society and human wellbeing (established but incomplete). Biodiversity-related jobs rival the mining sector

in terms of numbers, and the biodiversity-based tourism industry is worth R31 billion per year. Intact

ecosystems and high species diversity are essential for ecosystem services, healthy populations of crop

pollinators and natural predators of crop pests, as well as for the survival of wild relatives of crops and for

the increased carrying-capacity of natural rangelands for both livestock farming and wildlife ranching (the

latter worth R14 billion per year). The harvesting of edible plants, edible insects and medicinal plants from

the wild is widely practiced in South Africa and is particularly important as part of the rural economy. Natural

ecosystems, plants and animals have influenced people’s cultural and spiritual development, and are woven

into languages, place names, religion and folklore. This web of associations with biodiversity forms an

important part of South Africans’ national identity and heritage.

Biodiversity-related

employment =

~418 000 jobs

© SA Tourism


9

Medicinal plants are essential to the work of some

200 000 Traditional Health Practitioners and provide a

further ~93 000 income generating activities in the

informal sector for harvesters and traders (established

but incomplete). It is estimated that the informal

African Traditional Medicine (ATM) industry is valued at

about R18 billion per year and that ~70% of the

population use ATM, often in combination with

allopathic medicine. The most recent Red List

assessment (2013) recorded that 134 (20%) of the 656

commonly-traded medicinal plant species are of

conservation concern (declining rapidly). Evidence from

medicinal plant markets indicate that the size of the

traded components is decreasing and supply lines are

becoming increasingly irregular, which has stimulated

trade in plant material from neighbouring countries.

This decline not only represents a loss in biodiversity,

but is ultimately linked to a loss in health benefits and

the attrition of livelihoods. Urgent work is needed to

determine which of the approximately 150 medicinal

plant species considered heavily-utilised are under

increasing pressure both from trade and from habitat loss. Interdepartmental cooperation is required to

stimulate small and large scale cultivation efforts, and an increased focus on research and long-term

monitoring of trade in medicinal plants to better understand patterns and the value of use.

Terrestrial ecosystems and species face

pressures from a range of human activities,

including loss and degradation of natural

habitat, biological invasions, pollution and

waste, unsustainable natural resource use

and climate change. These pressures

interact in complex ways that undermine

biodiversity and ecological infrastructure,

which are important foundations of the

country’s social and economic systems. The

key drivers of habitat loss are land clearing

for croplands, human settlements,

plantation forestry, mining and

infrastructure development. These activities have led to the loss of 21% of South Africa’s natural terrestrial

ecosystem extent. Other key pressures include invasive species (plants in particular), overutilisation of

rangelands, disrupted fire regimes and climate change. These have not yet been mapped and quantified at

an adequate scale to gauge and track their impacts on biodiversity nationally, and this situation needs to be

addressed urgently.

© CapeNature

© Geoff Spiby


10

Habitat loss is occurring at an increasing

rate across South Africa, especially in the

more mesic regions. The KZN coastal belt in

particular has very high historic and recent

rates of habitat loss. The Grassland, Fynbos

and Savanna biomes have also seen high

levels of land clearing for croplands and

human settlements both historically and

recently. Unchecked, habitat loss and

fragmentation could ultimately lead to

ecosystem collapse and widespread

biodiversity loss.

A lack of appropriate data on ecosystem condition limits our ability to assess ecosystem types

comprehensively. Habitat loss is a simple measure of

ecological condition that is reliably collected using land

cover change datasets, however, there is a major gap in our

ability to measure the subtler forms of habitat modification

and estimate ecosystem condition. As a result of this we

tend to over-estimate the extent of natural and near-natural

habitat in South African rangelands and mountain

catchments in particular. This in turn leads to

underestimation of ecosystem threat status of these

regions. To counter this, we need to develop techniques to

estimate and map ecosystem condition across all biomes

and develop a better understanding of land degradation

from a biodiversity point of view.

Biological invasions represent a major threat to biodiversity. Intentional introduction pathways are

declining but accidental introduction pathways are increasing due to international trade and travel. There

are 775 invasive species in South Africa, most of which are plants or terrestrial invertebrates. Of these, 107

species (the majority of which are plants) are considered to be having a severe impact on biodiversity and/or

human wellbeing. There are more invasive species in the mesic regions than the arid interior, and density of

woody invasive plants tends to be highest in coastal areas and Fynbos mountain areas, though Prosopis sp.

are a problem in arid riparian areas. The negative impacts of invasive species on biodiversity are felt in all

biomes but are thought to be most severe in the Fynbos biome. Our understanding of the current extent and

severity of invasions, and the impacts of the invasions on biodiversity is not adequate. In addition to the

ongoing clearing and rapid detection and eradication programmes, focussed monitoring of invasive species

distribution and abundance is urgently required to better understand and manage biological invasions and

their threats to biodiversity and human wellbeing.

There is evidence that South Africa’s climate is changing but the natural variability of our climate

(especially rainfall) makes future projections of the impacts on biodiversity difficult. Mean temperature

increases of more than 1 °C have been observed in the last 100 years, and this trend has already been

accompanied by increases in extreme events including drought, heavy rainfall events, coastal storm surges,

strong winds and wildfires. Climate change vulnerability assessments and focused monitoring of species and

ecosystems is required to enhance the detection and attribution of climate change impacts on biodiversity.

© DEDEAT


11

Bush encroachment is increasing across the

Grassland and Savanna biomes and is partly

driven by global change. Over the past century one

of the most pervasive structural changes observed

has been an increase in the density and spread of

woody species. This global trend, known as bush

encroachment or woody thickening, is widespread

in southern Africa grasslands, open savannas and

mixed grass or shrub ecosystems. Research has

shown that a changing climate and rising CO2 are

probable background drivers of extensive and

broad-scale switches towards greater woody plant cover, but that other important drivers (fire and grazing

or browsing) influence the rate of this change. These widespread ecological shifts have triggered plant and

animal community reorganisations, net declines in biodiversity and changes in land use activities. These

alarming shifts drive the urgent need for climate change mitigation and management of interacting change

drivers.

Almost a quarter of South Africa’s terrestrial ecosystem

types are threatened. This is a clear indicator of mounting

pressures on biodiversity and ecosystems. These pressures

should be closely monitored and the data required to do

this (principally ecological condition data) should be

acquired as a matter of priority. There are 35 Critically

Endangered, 39 Endangered and 29 Vulnerable terrestrial

ecosystem types. The Indian Ocean Coastal Belt, Fynbos and

Grassland biomes have the highest proportion of

threatened ecosystem types including 27 Critically

Endangered and 29 Endangered types between them. Since

most land that has not been cleared is considered

natural/near natural, the assessment generally underestimates ecosystem modification and some ecosystem

types may be in significantly worse condition (and at higher risk of collapse) than the available data suggest.

Improved invasive alien plant and land degradation mapping is required to address this shortcoming. The

innovative steps taken to incorporate threatened ecosystem types into systematic biodiversity plans and

land-use decision making processes should be continued.

Of the 22 667 terrestrial taxa assessed, 3 024 (14%) are threatened. Mammals have 17% of taxa threatened

with extinction; plants have 14%, amphibians 13%, butterflies 10%, birds 9% and reptiles 5%. South Africa

has very high levels of endemism (64%) and one in five

of these endemics are threatened with extinction. The

trend in species status over time has been measured for

the first time using the Red List Index (RLI). Groups for

which sufficient time series data existed included all

terrestrial vertebrates, a sample of 900 plants and one

invertebrate group, butterflies. Similar levels of decline

were observed for all taxa. The decline observed for

butterflies highlights the need to assess and monitor

additional invertebrate groups.

© Andrew Skowno


12

Despite there being an overall increase in risk of extinction for all six taxonomic groups, 10 mammal taxa

have genuinely improved in status since 2004. Eight of these have experienced increases in population size

due to effective protection and the control of

poaching and hunting, while three have increased

as a result of reintroductions. The Honey Badger,

Mellivora capensis has an improved status as a

result of successful implementation of the badger

friendly certification schemes and linked best

practice guidelines. The African Lion, Panthera Leo,

was downlisted from Vulnerable to Least Concern

as a result of its population increasing in well

managed reserves and new private and state own

protected areas where it has been reintroduced.

The terrestrial protected area estate of South Africa increased by 11% between 2010 and 2018 – now

covering almost 9% of the mainland. The placement of these new protected areas has resulted in overall

improvement in ecosystem protection levels for all biomes. A quarter of the terrestrial ecosystem types are

Well Protected and a quarter are Not Protected. Biodiversity stewardship programmes have contributed

towards the majority of this increase and continue to be the most cost effective mechanism for protected

area expansion. Efforts should be made to support and expand biodiversity stewardship programmes and

address those ecosystems types that are Not Protected.

Protection levels for species were assessed for the first

time – using an indicator developed specifically for the

NBA – and show that birds and reptiles are relatively well

protected by South Africa’s protected areas network, while

butterflies, mammals, plants and amphibians are under-

protected (i.e., Not Protected, Poorly Protected or

Moderately Protected). Over 85% of bird and reptile taxa

qualify as Well Protected, while only 72% of amphibians,

63% of plants, 57% of butterflies and 56% of mammals are

Well Protected. Plants have the highest proportion of under-

protected taxa with 17% in the category Not Protected. Even for relatively Well Protected groups, like

reptiles, the most threatened species often remain unrepresented in protected areas. Threatened or

endemic taxa should therefore also be considered, along with under-represented taxa, to be targeted in

protected area expansion efforts.

A lack of knowledge and techniques limits our ability to assess the risks to the genetic component of

biodiversity. The maintenance of genetic diversity is of the utmost importance as it equates to evolutionary

potential and thus allows species or populations to respond or adapt to an ever-changing environment. Risks

to genetic diversity include genetic erosion through habitat fragmentation, reduced population sizes and

connectivity, hybridization and inbreeding, unsustainable use, and the disruption of co-adapted gene

complexes through translocations. There is a lack of temporal genetic datasets, as well as a lack of genetic

diversity indicators and thresholds, with which data can be compared. To assist future genetic monitoring

programmes and studies, a genetic monitoring framework is required that outlines how to prioritise species

for monitoring, what genetic markers to use, how often populations should be monitored and which metrics

to consider.

© Markus Lilje


13

1. INTRODUCTION AND APPROACH

Chapter 1: Skowno, A.L. Raimondo, D.C. & Poole, C.J. 2019. ‘Chapter 1: Introduction and Approach’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L. Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

The National Biodiversity Assessment (NBA) is a collaborative effort to synthesise and present the best

available science on South Africa’s biodiversity. It aims to inform policy, planning and decision making in a

range of sectors for the conservation and sustainable use of biodiversity.

The NBA is a platform for reporting on the current

state of biodiversity within South Africa. It describes

the key pressures on biodiversity and, where possible,

identifies important trends. It covers the terrestrial,

inland aquatic1, estuarine and marine realms, as well

as the coast and South Africa’s sub-Antarctic territory

as cross-realm zones. The NBA is used to illustrate the

benefits that biodiversity and intact ecosystems

provide to the economy, society and human

wellbeing. Finally, the systematic approach of the

NBA allows us to identify important national

knowledge gaps and research priorities linked to

biodiversity.

1 Inland aquatic realm refers to rivers and inland wetlands. The term ‘freshwater realm’ is regularly used in the biodiversity sector but since numerous inland saline wetland ecosystems occur in South Africa the term ‘inland aquatic’ is preferred. The term ‘inland wetland’ is used to distinguish these ecosystems from estuarine or marine wetlands which are considered part of the estuarine and marine realms respectively.

Biodiversity is defined as the ‘variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and across ecosystems’ (Convention on Biological Diversity).

Biodiversity incorporates diversity at the genetic, species and ecosystem level – which together form the foundation of ecosystem services and are integrally linked to human wellbeing.

The NBA covers all four realms: terrestrial, inland aquatic (freshwater), estuarine and marine; and the coast.


14

1.1. Purpose and structure of the NBA The NBA is the primary tool for monitoring and reporting on the state of biodiversity in South Africa. It is

prepared as part of the South African National Biodiversity Institute’s (SANBI) mandate2 to monitor and report

regularly on the status of South Africa’s biodiversity, and is a collaborative effort from many institutions and

individuals. The NBA focusses primarily on assessing biodiversity at the ecosystem and species level, with

efforts being made to include genetic level assessments. Two headline indicators that are applied to both

ecosystems and species are used in the NBA: threat status and protection level. The products of the NBA

include seven technical reports, a technical synthesis report and several popular outputs.

The primary purpose of the NBA is to provide a high-level summary of the state of South Africa’s biodiversity

at regular points in time, with a strong focus on spatial information. Each NBA builds on decades of research

and innovation by South African scientists, and makes that science available in a useful form to users both

inside and outside of the biodiversity sector. As a body of work the NBA is not prescriptive; it presents

important information that can be adopted by government and civil society in various decision-making

processes to support socio-economic imperatives, human wellbeing, and the best management and

conservation of South Africa’s biodiversity.

Like the previous assessments in 2004 and 2011, this third iteration of the NBA will feed into a range of

processes within the environmental sector and beyond (Figure 1). Key applications include:

Informing policies and strategies in the biodiversity sector (e.g. National Biodiversity Framework, National Protected Area Expansion Strategy), and other key sectors responsible for natural resources utilisation and/or protection, such as the water, agriculture, fisheries, and mining sectors (e.g. Mining and Biodiversity Guidelines).

Providing information to help prioritise the often limited resources for managing and conserving biodiversity; including datasets that feed into site and regional level planning and assessment (e.g. Strategic Environmental Assessments and Environmental Impact Assessments), provincial and municipal Bioregional Plans and Marine Spatial Plans (i.e. systematic biodiversity planning).

Creating a key reference and educational work for use by scientists, students, consultants, decision makers and funders.

Serving as an effective national level platform for encouraging and facilitating collaboration, information sharing and, importantly, capacity building in the biodiversity sector in South Africa.

Providing information for a range of national and international level monitoring, reporting and assessment processes such as state of environment reporting and reporting on commitments to international conventions (e.g. linked to the United Nations Convention on Biological Diversity [CBD], the Sustainable Development Goals [SDGs] and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services [IPBES]).

2 SANBI’s mandate is outlined in the National Environmental Management: Biodiversity Act (10 of 2004), hereafter referred to as the

‘Biodiversity Act’.


15

Figure 1. International reporting processes and channels into which the NBA is a key informant. Including international conventions signed by the South African Government and voluntary processes.

1.1.1. Navigating the NBA products

The NBA has a varied audience each with different needs, hence the NBA is presented in various forms. The

NBA website is the primary portal through which you can access all information and products

[http://biodiversityadvisory.sanbi.org]. The NBA website also provides factsheets and presentations

summarising the NBA for non-technical audiences, using graphics and accessible language.

The NBA 2018 has seven technical reports: one for each realm consisting of a terrestrial (this report), inland

aquatic, estuarine and marine; two cross-realm technical reports (the coast and South Africa’s sub-Antarctic

territory); and a technical report on genetic diversity. The technical reports are comprehensive volumes

covering all input data used for the assessments, detailed explanations of methods and approach, full results

and discussion, key messages for decision makers, limitations and knowledge gaps, and priorities for the

future. These reports are for a scientific and technical audience, and are fully referenced and peer reviewed.

The technical reports refer to various supplementary technical documents, maps and datasets; all of which

are available through the NBA website with accompanying metadata.

The synthesis report focuses on the main findings and key messages from each of the seven technical reports.

As the technical reports give full details of the methods and input data used for the NBA, the synthesis report

only briefly discusses the building blocks and approach used on a broad level. The synthesis report is divided

into four parts.


16

1.1.2. The NBA process

The breadth and scope of the NBA make collaboration between

multiple institutions and individuals an essential part of the process.

SANBI plays the lead role and facilitates contributions by a large pool

of experts. The collaboration ensures that the best available science

underpins the NBA, promotes collective ownership of the NBA

products by the biodiversity community in South Africa, and helps

ensure a common vision for action following the assessment. The

vast majority of contributions to the NBA are voluntary, and the few

formal funded contributions involve significant co-financing.

Without these voluntary contributions from experts and institutions

outside of SANBI, the NBA would not be possible. While the reliance

on experts to contribute voluntarily does present significant risks to

the process, paid alternatives bring their own challenges and budget

constraints.

Various internal and external governance structures were put in place in 2015 to guide the NBA 2018 process,

ensure the project received adequate oversight, and provide structures for the consultation of a wide range

of experts in each specific biodiversity field (Figure 2). The reference groups included researchers, experts

and officials with technical roles, while the steering and advisory committees included senior officials. The

NBA 2018 process focused particularly on increasing cross-realm collaboration, which led to better alignment

between realms for input data, assessment approaches and explanation of areas for improvement.

1.1.3. Units of assessment and headline indicators

Headline indicators: threat status and protection level for species and ecosystems

Biodiversity indicators have received renewed attention recently amid calls to slow global losses of

biodiversity (Nicholson et al. 2012; Tittensor et al. 2014; Geijzendorffer et al. 2015). Indicators in general, are

a tool to i) improve general awareness and gain public attention for biodiversity; ii) meet international

reporting requirements; iii) monitor conservation actions; and iv) inform policy and decision-making by

governments (Nicholson et al. 2012, 2015; Keith et al. 2013, 2015; Geijzendorffer et al. 2015; Tanentzap,

Walker & Stephens 2017).

The NBA 2018 required approximately 135 000 person hours, contributed by more than 465 individuals, from approximately 90 institutions. The Council for Scientific and Industrial Research (CSIR) led the inland aquatic and estuarine components, and the Nelson Mandela University led the coastal component.

Figure 2. Committees and reference groups established for the NBA 2018. The purple panel includes the oversight structures for the NBA management team. The orange panel is the reference committees for the technical elements. The green panel indicates foundational ecosystem and species assessment work that underpins the NBA; these exist in parallel to the NBA and are intended to continue between assessments.


17

The NBA relies on two headline indicators that can be applied to both ecosystems and species; threat status

and protection level. The first indicator (threat status) is based on the IUCN risk assessment framework for

species (Red List of Species) (IUCN, 2012a) and ecosystems (Red List of Ecosystems) (Bland & Keith et al.

2017).

The IUCN Red List of Species is well established globally and in South Africa and has formed a part of the NBA

reporting since 2005 (Driver et al. 2005). The IUCN Red List of Ecosystems (RLE) is relatively new (v1.0

released in 2016) and prior to its development South Africa developed its own ecosystem threat status

assessment framework between 2004 and 2008 (RSA 2011). The second indicator, protection level, was

developed in South Africa for national reporting (Driver et al. 2004) and addresses the extent to which

ecosystems and species are protected. In the 2004 and 2011 national assessments protection level was only

applied to ecosystems, but over the last two years SANBI’s Threatened Species Unit has applied the indicator

to species and it will be reported on for the first time in the NBA 2018. These headline indicators provide a

way of comparing results meaningfully across the different realms, and also provides a standardised

framework that links with policy and legislation in South Africa, thus facilitating the interface between science

and policy. There is growing recognition within government and other institutions of this framework and the

need to respond to these headline indicators in planning and decision making.

Ecosystem indicators

Ecosystem threat status tells us about the degree to which ecosystems are still intact or alternatively losing

vital aspects of their structure, function and composition, on which their ability to provide ecosystem services

ultimately depends (Figure 3). The conceptual ‘end point’ of decline for an ecosystem is termed ‘collapse’

and is equivalent to extinction in the species Red Listing framework.

Ecosystem types are categorised as Critically Endangered (CR), Endangered (EN), Vulnerable (VU) or Least

Concern (LC), based on the proportion of each ecosystem type that remains in good ecological condition

relative to a series of thresholds. For the NBA 2018 the IUCN Red List of Ecosystems was used as the risk

assessment framework for terrestrial ecosystems (Bland et al. 2017). The previous national biodiversity

assessments (2004 and 2011) predated the development of the IUCN Red List of Ecosystems and used the

South African Threatened Ecosystem Framework (Driver et al. 2004, 2012; RSA, 2011); making South Africa

one of the pioneers globally of this approach to ecosystem assessment.

Ecosystem protection level tells us whether ecosystems are adequately protected or under-protected.

Ecosystem types are categorised as Not Protected, Poorly Protected, Moderately Protected or Well

Protected, based on the proportion of each ecosystem type that occurs within a protected area recognised

in the National Environmental Management: Protected Areas Act (Act 57 of 2003)3.

The ability to map and classify ecosystems into different ecosystem types is essential in order to assess threat

status and protection levels and track trends over time. South Africa has an emerging national ecosystem

classification system, including vegetation types, river ecosystem types, wetland ecosystem types, estuary

ecosystem types, and marine and coastal ecosystem types, which provides an essential scientific basis for

ecosystem-level monitoring, assessment and planning.

3 Hereafter referred to as the ‘Protected Areas Act’


18

Figure 3. Steps in assessing ecosystem threat status and ecosystem protection level. Note the link between ecosystem condition and protection level – only natural habitat contributes to protection level targets (e.g. the airport within a large protected area would not contribute to protection targets as the natural habitat has been lost).

Species indicators

Threat status of species tells us which species in South Africa are at risk of extinction. Threatened species are

those with high risk of extinction and are classified in three categories of increasing risk of extinction

Vulnerable (VU), Endangered (EN) and Critically Endangered (CR). Levels of threat are determined against

quantitative threshold-based criteria. South Africa uses the latest version of the IUCN Red List Categories and

Criteria, version 3.1. (IUCN, 2012a).

Protection level of species is presented for the first time in this NBA and has no global equivalent indicator.

Protection level of species measures progress towards effective protection of a population persistence target

for each species. The indicator consists of two components. The first measures how well represented each

species is within the protected area network, based on the number of individuals of a species or area of

suitable habitat protected relative to the persistence target set for that species. This component allows the

identification of which species require further protection, where species not represented or poorly

represented within protected area network are prioritised for inclusion in spatial planning for protected area

expansion. Component two includes a measure of how well a protected area is mitigating threats to each

species and when combined with protected area representation provides an overall (effective) protection

level measure for each species.

Both the threat status and protection status indicators for species allow South Africa to report against Aichi

Target 12 (https://www.cbd.int/sp/targets/rationale/target-12/), while the protection status also provides a

measure of how well South Africa’s protected areas are meeting the ecological representation requirement

of Aichi Target 11 (https://www.cbd.int/sp/targets/rationale/target-11/).

Indices of change

The headline indicators of the NBA can form the basis for an index that tracks change over time. One such

index, developed by the IUCN, is the Red List Index for species (Butchart et al., 2007). The Red List Index

tracks genuine changes in extinction risk across entire species groups (i.e. it is based on only changes in

extinction risk between Red List assessments due to actual improvements or deteriorations in extinction risk).

It is used to generate 15 of the indicators used to track progress towards the Aichi Targets

(UNEP/CBD/SBSTTA/20), mobilised through the Biodiversity Indicators Partnership as well as serving as the

official UN Indicator 15.5.1 for Sustainable Development Goal 15.5 (Brooks et al. 2015).

Habitat loss indicators

While a suite of comprehensive and inclusive biodiversity indicators are under development - through

initiatives such as Biodiversity Indicators Partnership, the Group on Earth Observation: Biodiversity

Ecosystem type

Ecosystem condition

Evaluate proportion in good condition against

series of thresholds

Ecosystem threat status

Location of protected areas

Compare proportion protected with

biodiversity target

Ecosystem protection

level

https://www.cbd.int/sp/targets/rationale/target-12/

https://www.cbd.int/sp/targets/rationale/target-11/default.shtml

https://www.cbd.int/doc/meetings/sbstta/sbstta-20/official/sbstta-20-13-en.pdf

https://www.bipindicators.net/indicators/red-list-index

https://unstats.un.org/sdgs/indicators/database/?indicator=15.5.1


19

Observation Network (GEOBON) and others - there is a need to report nationally and sub nationally on rates

of natural habitat loss (Rowland et al. in review). To fill this gap two simple indicators of terrestrial habitat

loss or ecosystem extent have been developed for the NBA 2018. The land cover change data (discussed in

Chapter 3), which is available for the first time at a national scale in South Africa, makes it possible to compute

the rate of loss of natural habitat to anthropogenic activities between 1990 and 2014 (expressed as

percentage of the 1990 extent per year). When this rate of habitat loss (RoL) is combined with the extent of

natural remaining in 1990 it is possible to estimate the number of years to ecosystem collapse4 (YtC).

Indicators for biological invasions

Recent work by Wilson et al. (2018) developed a theoretical framework for reporting on biological invasions

at a national level. The framework explicitly considers biological invasions in terms of pathways, species

(taxa), sites and interventions (separated into inputs, outputs and outcomes). The indicators are described

in Chapter 4.

Global biodiversity indicators

The Aichi Biodiversity Targets were structured around the CBDs 2011-2020 Strategic Plan for Biodiversity.

Strategic Goals B (Reduce the direct pressures on biodiversity and promote sustainable use; Aichi targets 5-

10) and Goal C (Improve the status of biodiversity by safeguarding ecosystems, species and genetic diversity;

Aichi targets 11-13) are the most closely linked to the NBA 2018. Table 1 shows the links between the NBA

headline indicators and the indicators for the Aichi Targets and Sustainable Development Goals. Ecosystem

threat status, in particular, is not well captured in the Aichi Targets or SDGs. The SDGs linked to biodiversity

propose Key Biodiversity Areas (KBAs) as a possible focal ‘unit of assessment’ for various indicators and it is

likely that assessing the status of KBAs will become a global indicator of biodiversity that can be added to the

NBA headline indicators. To facilitate this SANBI has initiated a project in partnership with BirdLife South

Africa and co-funded by the WWF Nedbank Green Trust to identify and delineate a preliminary KBA network

for the South Africa based on the species and ecosystem information gathered in the NBA.

Table 1. Links between NBA headline indicators and Aichi Targets and SDGs.

NBA indicator Aichi target

Sustainable Development Goal

Comment

Ecosystem threat status 5 15.1 This powerful indicator of threat to ecosystems is not adequately captured in Aichi and SDG related indicators

Ecosystem protection level 11 15.1 A version of protection level is used in SDG 15.1 reporting

Species threat status 12 15.5 Good links between national indicators and the Aichi and SDG indicators

Species protection level 12 15.1 Not adequately captured in Aichi and SDG related indicators

Ecosystem extent / habitat Loss 5 15.1, 6.6.1 Limited to forest and wetland extent in the SDGs

Biological Invasion Indicators (species, Areas and Pathways)

9 15.8 Reasonable links between national indicators and Aichi and SDG indicators

4 Ecosystem collapse is a term linked to the IUCN Red List of Ecosystems risk assessment framework (Keith et al. 2013), and occurs

when the entire historical extent of an ecosystem type has lost its characteristic biota and ecosystem function, and has been replaced by an anthropogenic landscape (e.g. croplands, urban settlements) or novel ecosystem (Bland et al. 2018; Rowland et al. 2018).


20

1.2. About the terrestrial environment South Africa is one of 17 megadiverse nations, recognised globally for its high biodiversity and high levels of

endemism (Mittermeier, Robles-Gil & Mittermeier,1997; Mittermeier et al. 2011). The unique and diverse

flora of the Cape Floristic Region makes it a global biodiversity hotspot along with the Succulent Karoo biome

and the Maputaland-Pondoland-Albany region (Mittermeier, Robles-Gil & Mittermeier 1997; Mittermeier et

al. 2011). This exceptional diversity of unique species is complimented by a wide range of bioclimatic zones

and geological settings. This results in a wide array of biomes and ecosystems across the terrestrial, inland

aquatic, estuarine, coastal and marine realms.

South Africa has nine terrestrial biomes (as defined and described by Rutherford et al. 2006) (Figure 4). The

moist, winter-rainfall region in the southwest of the country is home to the unique Fynbos biome (a distinct

floral kingdom). Adjacent to this lies the Succulent Karoo biome, an arid winter-rainfall biome with the

highest diversity of succulent plants in the world. The Nama-Karoo biome covers the arid, summer-rainfall,

western interior of the country. The Savanna biome dominates the northern and eastern summer rainfall

regions of South Africa, and is the largest biome in Africa. The Grassland biome occurs mostly on the cooler

high lying central plateau and has high levels of endemism. The Albany Thicket biome occurs in the eastern

and southern cape and contains a unique combination of plant forms with an Eocene origin and unique

evolutionary history (Cowling et al., 2005). The Forest biome is characterised by small patches distributed

across the winter and summer rainfall areas of the country, and globally are considered warm-temperate.

The Indian Ocean Coastal Belt biome represents the southernmost extent of the wet tropical seaboard of

East Africa. The Desert biome occupies a small portion of the extreme north west of the country, forming the

southernmost extent of the Namib Desert of Namibia and Angola.

Figure 4. Biomes in South Africa, Lesotho and Swaziland. Biomes are broad groupings of vegetation types that share similar ecological characteristics. Some biomes have a richer array of vegetation types than others, with the Fynbos biome having the highest number of vegetation types.


21

South Africa has the world’s richest temperate flora, with 20 401 recorded indigenous vascular plant taxa.

With the current size of the global flora at ~304 000 vascular plant taxa (Christenhusz & Byng 2016), 7% of

the world’s plant diversity is represented within South African borders. In addition, some 13 763 taxa (65%

of the country’s and >4% of the globe’s flora) are endemic.

An estimated 67 000 species of animals have been recorded from South Africa, and each year about 250 new

species are described, adding to this total. The greatest diversity is in the insects, which represent over 72%

of animal species. The largest insect group is the beetles with over 17 000 species. South Africa has a high

proportion of the world’s birds with 8.5% represented (732 species), but only 38 of these are endemic to

South Africa. South Africa’s reptile fauna is diverse, with 404 species and half are endemic (Bates et al. 2014).

This places South Africa’s reptile fauna within the top 10% of diverse reptile faunas globally (Tolley et al. in

prep). Amphibian levels of endemism are also high, with 50% of the 125 taxa found in South Africa being

endemic. South Africa has 336 mammal species of which 57 are endemic. A few of the terrestrial invertebrate

groups have high richness relative to the global fauna. For example, 13% of the world’s sunspiders

(Solifugida), ticks (Ixiodidae) and silverfish or fishmoth (Zygentoma) species occur in South Africa. In most

cases the percentage of the global richness for groups is less than 6%, with an average across all groups of

9%. Many of the invertebrate groups are, however, poorly studied in South Africa and so the figures for

richness are likely to be gross underestimates of true richness.

Six well studied taxonomic groups are included in this terrestrial assessment: birds, mammals, reptiles,

amphibians, butterflies and plants. For the five animal groups, species richness is highest in the moist north

eastern parts of the country. Both reptiles and amphibians also have high richness in the Cape Fold

Mountains. Being well adapted to arid conditions reptile species richness is high in the Succulent Karoo

Biome. For plants, richness patterns are more varied. High concentrations of plant species are found in the

Fynbos region particularly in the Cape Fold Mountains and the transition between the Fynbos and Albany

Thicket biomes in the Eastern Cape. Further hotspots of plant species richness are found in Pondoland, the

southern foothills of the Drakensberg, the northern Drakensberg escarpment that extends from Mpumalanga

up to the Wolkberg in Limpopo, as well as the isolated Soutpansberg and Blouberg Mountains of Limpopo.

Prince Edward and Marion Islands – South Africa’s

sub-Antarctic territory

The NBA 2018 also covers South Africa’s sub-Antarctic

territory of Prince Edward and Marion Islands. Situated 1 700

km south east of the mainland, these tiny islands have a very

different biodiversity profile to that of the mainland (Figure

5). Volcanic in origin, they experience a cold temperate

climate with a strong oceanic influence. The state of the

terrestrial biodiversity of the islands and their surrounding

seas are the focus of a dedicated technical volume of the NBA

2018 (Sink et al. 2019).

Figure 5. Geographic location of Prince Edward and Marion islands, 1 700km south east of the mainland. The 200nm Exclusive Economic Zone (EEZ) for the mainland and sub-Antarctic territory are shown with dashed lines.


22

2. BENEFITS OF BIODIVERSITY IN THE TERRESTRIAL REALM

Chapter 2: Poole, C.J., Raimondo, D. & Driver, A. (eds.). 2019. ‘Chapter 2: Benefits of Biodiversity in the Terrestrial Realm’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A,L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby JA (eds.). South African National Biodiversity Institute, Pretoria.

2.1. Introduction South Africa has immensely diverse and unique biodiversity, and terrestrial biodiversity that is absolutely

essential for human survival, wellbeing and the country’s long-term economic potential. Biodiversity provides

tangible benefits including food, medicine and materials. It also provides the healthy soils, pollinators and

pest control critical for food security and improved production. Biodiversity provides employment in the

tourism, wildlife ranching and associated industries as well as in sectors of the economy that restore or

conserve biodiversity. Healthy terrestrial ecosystems assist wellbeing for both rural and urban dwellers by

providing natural spaces for recreational, spiritual and cultural activities. Intact ecosystems slow down floods

and store water for times of drought, protecting people from natural hazards. A consistent supply of clean

water which has become an ever-more critical issue in South Africa depends on terrestrial ecosystems

surrounding rivers and wetlands being in good ecological condition.

For the NBA 2018 a Compendium of Benefits of Biodiversity (SANBI 2019) has been produced that explores

the range of ways in which biodiversity supports human wellbeing. While this work is by no means

comprehensive, it demonstrates the importance of biodiversity and outlines a number of examples of how

biodiversity contributes to the objectives in the National Development Plan 2030. Objectives such as

improving the economy and employment, building an inclusive rural economy, health care for all, and many

others rely on biodiversity assets, ecological infrastructure and environmental sustainability and resilience.

2.2. Biodiversity protection and sustainable utilisation creates employment The contribution of biodiversity to the economy can be partly

illustrated through biodiversity-related employment. Recent work

by SANBI in partnership with the Development Policy Research Unit

at the University of Cape Town has developed a conceptual

framework for defining biodiversity-related employment and made

an initial estimate of biodiversity-related jobs. The methodology draws on a combination of three different

data sources: administrative data, national survey data, and existing estimates for particular biodiversity-

related sectors. Estimates are that there are more than 418 000 biodiversity-related jobs in South Africa

(SANBI 2019). To put this in context, this total can be compared with approximately 434 000 jobs in the

mining sector, 843 000 jobs in the agricultural sector, 1.7 million jobs in manufacturing (Stats SA 2017) and

722 000 jobs in tourism (Stats SA 2018). Of the 418 539 total jobs, 17% (71 989) are in Category A: Conserving

biodiversity and 83% (346 550) are in Category B: Using biodiversity (which includes both non-consumptive

and extractive use), giving a ratio of approximately 1:5. This suggests that for every job dedicated to

conserving biodiversity (i.e. jobs that protect, manage, restore or maintain biodiversity, or jobs in biodiversity

research), there are approximately five jobs that depend directly on using biodiversity (i.e. extractive use

such as medicinal plant harvesting and trade or non-consumptive use such as biodiversity-based tourism).

Investment in conserving biodiversity assets and ecological infrastructure, and growing the category of jobs

related to conserving biodiversity, is worthwhile as these jobs can leverage socio-economic development and

further employment at a scale of national significance. Many of the biodiversity-related jobs are located

outside major urban centres, and therefore play an important role in supporting rural development and

associated poverty reduction. Many of the sub-categories are labour-intensive, with a substantial proportion

Biodiversity-related employment =

~418 000 jobs


23

of less-skilled jobs that can contribute to labour absorption. There is more potential for job growth in the

‘Using Biodiversity’ category than in traditional sectors such as manufacturing, agriculture and mining. These

results suggest strong potential for biodiversity assets to support long-term inclusive growth and

employment outside major urban centres, with further work needed to quantify this potential and to

determine how best it can be enabled.

2.3. Biodiversity-based tourism is contributing substantially to economic growth Tourism contributes considerably towards the South African

economy. While domestic tourism tends to fluctuate, currently

decreasing due to economic constraints and ‘belt-tightening’ by

South African citizens, foreign tourism to South Africa is increasing.

In 2016, total foreign tourist numbers totalled over 10 million, an average annual increase of 4% from 2010.

Tourism’s total impact on the South African economy ranges around 9.3% of Gross Domestic Product, and

nearly 10% of all employment opportunities in South Africa are to some extent influenced by the tourism

sector (Bac & Tlholoe 2017).

Tourists to South Africa vary considerably in terms of the aim of their tourism, with most tourists combining

a variety of experiences in their visit. Tourism in South Africa is strongly linked to South Africa’s environmental

features – protected areas, natural landscapes, wild animals and beaches. Research undertaken on behalf of

SANBI by Grant Thornton (Pty) Ltd revealed that biodiversity-based tourism is equivalent to 12% of total

tourism demand in 2015 (R31 billion) – and domestic tourism accounted for 52% of this demand (R16 billion)

and foreign or inbound tourism for 48% or R14.9 billion. In 2016, a third of all overnight stays and a quarter

of all day trips incorporate activities that are based on biodiversity assets. Approximately 45% of tourists from

the Americas and Europe already participate in these key activities or attractions, while visitors from other

African countries largely do not participate in the country’s wealth of biodiversity assets. There is therefore

much scope for growth of biodiversity-based tourism in South Africa, and improving data collection relating

to the extent of the tourism market and visitors’ wishes would assist in identifying opportunities (Bac &

Tlholoe 2017).

2.4. Most of South Africa is used for

rangelands and wildlife ranching As much as 70% of South Africa’s land is used for grazing or

browsing areas for livestock or game (i.e. as ‘rangeland’;

Scholtz et al. 2013), with only approximately 11% of South

Africa suitable for cultivation (RMRD 2016). Given that

rangelands make up large parts of the South African

landscape, the healthy functioning of these rangelands is

crucial, not only in providing grazing for the livestock or game

and as habitat for useful species like medicinal plants or

pollinators, but also because healthy rangelands provide

ecosystem services like improving water quality, erosion

control and carbon sequestration.

The livestock and game sectors in South Africa are

undeniably important to the country’s economy, and are

collectively estimated to provide about 245 000 jobs on

commercial farms (Meissner, Scholtz & Palmer 2013).

What is a ‘rangeland’? The term Rangeland refers to any extensive area of land that is occupied by native herbaceous or shrubby vegetation that is grazed by domestic or wild herbivores (Encyclopaedia Britannica). Globally, they span several biomes and include grasslands, savannas, shrublands, deserts and marshes. Rangelands are important for people and nature the world over as ecological infrastructure. We have relied on rangelands for millennia, primarily as grazing for livestock and wildlife, and for harvesting medicinal and edible plants from the land. Healthy rangelands maintain soil stability, improve water infiltration and foster plant diversity.

Biodiversity-based tourism =

R31 billion /year


24

South Africa’s rangelands seem to be supporting progressively fewer livestock over time, likely driven by a

change from livestock to game farming and probably linked to degradation of the state of rangelands (Milton

& Dean 1995). Worryingly, extensive rangeland degradation has been reported at a municipal scale (Hoffman

& Ashwell 2001), and quantifying this degradation at spatial scale that suits ecosystem assessment remains

a major challenge.

Rangelands with a healthy mix of indigenous species have better soil

stability and reduced soil erosion, and likely provide better quality

grazing and carrying capacity. Reduced erosion in turn means less

sediment in water run-off, resulting in better water quality both for

human use as well as for the functioning of aquatic ecosystems.

Diversity is also important for resilience: more diverse rangelands can

bounce back faster after drought (Tilman & Downing 1994; Van

Ruijven & Berendse 2010). Once species diversity is lost, bringing it

back needs time as well as active and costly intervention. Initiatives such as ‘Meat Naturally’ in the

uMzimvubu District provide a model for restoring rangelands that could be rolled out to other parts of South

Africa.

Private ownership of wildlife, known as wildlife ranching, is

one of the reasons why southern Africa is the only region on

the continent with stable or increasing large mammal

populations (Craigie et al. 2010). Wildlife ranching can be

defined as all privately or community-owned land areas that

derive commercial benefit from wildlife, encompassing a range

of management approaches from active to passive (Taylor,

Lindsey & Davies-Mostert 2015). Wildlife ranching as a land use

may not have the expressive objective of biodiversity

conservation. Ranches range between intensive agriculture

and extensive biodiversity conservation and are founded on four (often overlapping and integrated)

economic pillars: 1) animal husbandry (breeding and live sales), 2) hunting (both for venison and trophy), 3)

ecotourism and 4) game products (e.g. meat and skins) (Cloete, Van der Merwe & Saayman 2015; Taylor,

Lindsey & Davies-Mostert 2015).

There is some debate as to the magnitude of the industry.

Estimates of the extent of wildlife ranching in South Africa range

from a lesser footprint of 170 419 km2 (13.9% of the SA land

surface) comprising 8 979 ranches and 5.9 million head of wildlife

(Taylor, Lindsey & Davies-Mostert 2015); to larger estimates of

205 000 km2 (16.6% of the SA land surface), comprised of at least 10 000 ranches, with an estimated 2.5 to

18 million head of wildlife (Bothma & du Toit 2015). Despite variable figures, even the minimum estimated

area under wildlife ranching is more than the coverage of formally protected areas (108 061 km2).

The most recent estimate for the total economic contribution of wildlife ranching is R14.4 billion (R9.3 billion

direct value generation and R5.1 billion purchasing inputs from other sectors), which accounted for 0.3% of

Gross Domestic Product in 2015. It is likely that the return on assets for wildlife ranching is higher than

livestock farming, and possibly more stable over time (especially under variable climatic conditions).

Currently, the wildlife ranching industry employs over 65 000 people (Taylor, Lindsey and Davies-Mostert,

2015), and is set to expand given government investment and infrastructure development. The draft

Biodiversity Economy Strategy for South Africa (DEA, 2015a) mentions ambitious targets, including 60 000

What is wildlife? The term ‘wildlife’ seems undefined as yet. A narrow definition might be limited to indigenous animals living in natural habitats. Broad definitions might encompass plants, unmanaged populations of non-native species and other elements of natural ecosystems. In this summary ‘wildlife’ means primarily indigenous large mammal species.

Wildlife ranching =

R14.4 billion /year

The Meat Naturally initiative provides support to rural South Africans on rangeland management, alien clearing, and livestock husbandry, and incentivises positive rangeland management in exchange for access to mobile auctions with low commissions.

https://www.meatnaturallyafrica.com/


25

additional jobs created by the wildlife sector by 2030. However, this must be carefully coordinated so as to

actively work towards social inclusiveness and benefit-sharing from wildlife ownership, as well as positive

environmental benefits and biodiversity outcomes (Spierenburg and Brooks, 2014). Conservancy models may

be most appropriate for taking this land-use model forward as a sustainable development tool (Lindsey,

Romanach & Davies-Mostert 2009), as it entails multiple land-owners working together towards a shared

vision whilst unlocking greater economic opportunity over wider land-areas.

Wildlife ranching inherently relies on the diversity and adaptations of indigenous ungulates, and reflects

South Africa’s natural heritage. The average number of herbivore species on wildlife ranches is 15 (Taylor,

Lindsey & Davies-Mostert 2015), including many rare species or species of conservation concern (Child et al.

2016). However, regulation and coordination of the industry is needed to align the commercial objectives

with broader species and ecosystem conservation goals. Work is currently underway to design frameworks

that can categorise the biodiversity and economic contributions of wildlife ranches that could be used to

support green certification schemes and tax incentives. Such frameworks are examining aspects of on-farm

management activities such as water management, landscape permeability linked to fencing, the level of

management of species, and the management of the natural vegetation. When examined through a lens of

four dimensions of impact (intensity, frequency, persistence and extent), these on-farm activities can be

categorised in terms of their on-farm and landscape-level ecological impacts and support a more refined

approach to incentives (such as a certification scheme) and regulation.

2.5. Biodiversity supports crop agriculture Crop agriculture currently utilises about 12% of the South African landscape with approximately 16% having

been cultivated at some stage. Subsistence and small-scale crop farming is critical for South Africans living in

rural areas. Biodiversity plays an important role in maintaining agro-ecosystems, albeit sometimes hidden

from view, and two examples are pollination and natural pest control.

Pollination of crops: Most of the cereal crops that provide the bulk in human diets across the world like rice,

wheat and sorghum are wind-pollinated, but animal-pollinated crops including many fruits and vegetables

are essential to good nutrition. Animal-pollinated crops are responsible for 90% of vitamin C, and the majority

of vitamin A and related carotenoids (Eilers et al. 2011). In addition, treats like coffee, chocolate and vanilla,

are also animal pollinated. The majority of pollinators are insects, but some birds and mammals also play this

role.

In South Africa, the indigenous Honey Bee (Apis mellifera) is the most important crop pollinator, and the two

sub-species (both indigenous) are managed by beekeepers in different parts of the country to provide the

pollination service to farmers at the correct time (Allsopp, de Lange & Veldtman 2008; de Lange, Veldtman

& Allsopp 2013). The advantage of using indigenous bee species as managed pollinators is that these colonies

are far more disease-resistant than managed honey bees used in other parts of the globe (Dietemann, Pirk

& Crewe 2009; Human et al. 2011). In South Africa, the primary way of replacing lost colonies or increasing

colony numbers is to trap wild swarms. This trapping of wild swarms and the fact that both managed and

wild populations of honey bees utilise indigenous vegetation as a food supply (collecting nectar for

carbohydrates and pollen for protein from most flowering plants) means that biodiversity is an important

part of the crop pollination system. Beekeepers across South Africa have extensive knowledge of flowering

plants, and usually utilise a mixture of indigenous wild vegetation, eucalyptus plantations, commercial crops

and even roadside weeds at different times of the year to ensure that their bees have the nutrition they need

for colony development and the strength to provide pollination services.

Not only do honey bees provide a pollination service, as other bees, flies, beetles, butterflies, bats and even

some types of rodents are also involved. Work done in Hoedspruit mango fields found that yield was best


26

predicted by distance to natural vegetation, which is the source of wild pollinators. Large fields in which trees

were far from natural vegetation had significantly lower fruit production per tree (Bartomeus et al. 2013).

Analysis of these and other similar data, generated for over 40 crop species across the globe, found that a

diversity of pollinators yields much better fruit production than using honey bees alone (Carvalheiro et al.

2010, 2011).

By carefully planning new urban development and farms, managing landscapes with the pollinators in mind,

and the use of pollinator-friendly products and practices (e.g. using pesticides appropriately, keeping natural

vegetation in amongst crops), pollinator’s habitats and their food resources can be simultaneously protected.

Natural pest control: Reducing pests that cause damage or spread diseases on crops is important in food

production. Controlling crop pests such as insects, mites and weeds using other organisms that feed on the

pests (e.g. wasps, ants, mites) is a clever solution and a hidden ecosystem service. For example, control of

spider mites by predaceous mite species in South African apple orchards saves the farmer money by reducing

the number of miticide applications required, and helps reduce the pests forming resistance to the miticides

(Pringle 2001; Pringle & Heunis 2006). Another example is the introduction of the parasitic wasp that has co-

evolved with fruit flies into production areas, as they serve to control the fruit flies that cause significant

production losses if left unchecked (Wharton 1989; Wong et al. 1992; Ekesi et al. 2016). Preliminary surveys

made in South Africa have highlighted a high diversity of parasitic wasps associated with various fruit fly

species, and there is potential to use such local wasp biodiversity to deliver a pest control ecosystem service.

Crop agriculture using indigenous South African species as the crop plant is increasing in popularity in South

Africa. Some well-known examples are:

Rooibos (Aspalathus linearis) is indigenous to South Africa’s Fynbos biome and makes a caffeine-free

tea that has become very popular globally because of its various health benefits. While the ‘Nortier’

variety is now widely grown by commercial farmers and is the most commonly consumed rooibos,

wild rooibos is more genetically diverse and drought resistant than the Nortier variety and has a long

history of being harvested from the wild for high value small scale commercial sale.

Honeybush is another caffeine-free hot beverage, made from a number of species from the genus

Cyclopia (also indigenous to South Africa’s Fynbos biome). Unlike Rooibos, honeybush has enjoyed

limited commercial interest and most biomass is still harvested from the wild. Recently the

production and distribution of honeybush tea has undergone considerable growth and has now

entered the market more formally, with associated commercial growing enterprises.

As the market for indigenous crops increase, the trend is to move from wild harvested to large commercial

production areas with likely concomitant negative impacts on biodiversity. Rooibos commercial farming is a

good example, as rooibos farms are plagued by several pest organisms and farmers require new fields

(typically ploughed into virgin Fynbos) after three years to maintain yields. Clearing for rooibos tea cultivation

has resulted in 150 indigenous plant taxa being listed as threatened (SANBI 2017a). However, there is much

research ongoing in South Africa about ecologically-friendly farming practices. In contrast to rooibos

production, the honeybush industry is typically done in smaller fields that keep rows of natural vegetation

and encourages beneficial organisms that better control pests to the crop.

Genetic material that supports commercial agriculture, or ‘crop wild relatives’, is another important aspect

of biodiversity’s contribution to agriculture. Crop wild relatives are wild species of plants that are closely

related to commercial crops. They are recognised as a vital component of agricultural biodiversity. Crop wild

relatives collectively constitute an enormous reservoir of genetic variation that can be used in plant breeding

and are a vital resource in meeting the challenge of providing food security, enhancing agricultural

production and sustaining productivity in the context of a rapidly growing world population and accelerated

climate change (Maxted et al. 2006). They have been used to improve the yields and nutritional quality of


27

crops since the beginnings of agriculture. Farmers often plant wild relatives alongside domesticated crops to

promote natural crossing of beneficial traits. Genes from wild plants have also provided cultivars with

resistance against pests and diseases and improved tolerance to abiotic stresses.

The checklist of food and fodder crop wild relatives for South Africa lists 1 593 species, subspecies and

varieties of which 258 are of high priority for conservation (Holness et al. 2018). Sweet potato with 48

Ipomoea taxa, eggplant with 44 Solanum taxa and rooibos tea with 41 Aspalathus taxa are the crops with the

highest number of crop wild relatives in South Africa. The priority crop wild relatives list includes 220

indigenous taxa, 91 of which are endemic to South Africa. The northern summer rainfall parts of South Africa

are important for crop wild relative diversity. The Kruger National Park in Limpopo and Mpumalanga Province

and the Isimangaliso Wetland Park in KwaZulu-Natal all exhibit high levels of crop wild relative diversity. The

Magaliesberg Mountains in Gauteng, the Cedarberg Wilderness Area and the Cape Fold Mountains of the

Western Cape are also important areas for crop wild relative richness (Figure 6).

Figure 6. Richness patterns of priority crop wild relatives (CWR) in South Africa. Darker areas refer to higher richness (higher number of priority CWR). Protected areas important to CWR are shown.

The complementary conservation of crop wild relatives both in situ and ex situ is the best strategy to

safeguard and make available the diversity of crop wild relatives, as well as to ensure their continued

evolution. South Africa has responded to global calls for national governments to ensure they have plans in

place for the conservation and use of crop wild relatives, and has produced a National Strategic Action Plan

for the Conservation and Sustainable Use of Crop Wild Relatives in South Africa (DAFF 2016) which identifies

priority actions for both in situ and ex situ conservation.

Much work has been done since 2004 to promote sustainable agricultural production within the agricultural

sectors with a range of biodiversity and business initiatives set up for wine, potatoes, rooibos tea, sugar,

indigenous cut flowers and fruit producers. Not all of these initiatives have been sustained, and currently

(2018) active initiatives exist only for fruit, wine, sugar and cut flowers. Lessons need to be learned from


28

these initiatives regarding what works and does not work to promote and incentivise sustainable crop

agriculture.

2.6. South Africans harvest medicine and food directly from the wild In impoverished rural areas, where the cash economy is a sporadic trickle, natural capital contributes

significantly to people’s direct daily consumption (such as food, clean water, fuel wood and building

material), income generation (such as the sale of medicinal plants and reed mats) and is a crucial safety net

for households in times of shock or need. This contribution from the natural environment is seldom

considered, yet it holds substantial value. Small reductions in these ecosystem services can have large welfare

impacts.

Medicinal plants: Many South Africans depend heavily on wild indigenous plant species for health care. There

are 2 062 (10.1% of national flora) plant species used for medicine in the country, and 656 are recorded in

common trade (Williams, Victor & Crouch 2013). The informal

trade of medicinal plant species is summarised as approximately

40 885 tonnes of raw material from the wild, and the value of

African Traditional Medicine (ATM) industry is estimated to be

approximately R17.96 billion per year. This can be broken down

into:

• 29 347 tonnes dispensed from Traditional Health Practitioners (THPs) as part of consultations. The

monetary value of THP visits and the medication they dispense is ~R16.8 billion per year;

• 2 345 tonnes purchased independently by the public from muthi shops (an estimated R149.4 million

per year);

• 9 193 tonnes purchased independently by the public from street vendors or markets (an estimated

R1.02 billion per year).

These figures have been extrapolated5 from surveys conducted by (Cunningham 1988; Mander 1998; Mander

et al. 2007). The extent of overlap between THPs, shops and street vendors is unknown. There is thus a great

need for national primary research to obtain up-to-date statistics. It is also not known what quantities of the

medicinal plant material either sold or dispensed within South Africa are sourced beyond national borders.

It is known that there is significant cross-border trade due to the increasing scarcity of some medicinal plants

species in South Africa (Mander et al. 2007; Rasethe 2017).

In terms of health benefits, it is estimated that 70-72% of

South Africa’s population use ATM, although it is known

that many citizens use a combination of allopathic and

ATM. As South Africa has an average of only 64 allopathic

doctors per 100 000 lives (the world average is 152

(Econex, 2016)), and only 17.4% of its population has a

formal medical aid (Statistics South Africa 2017), the use of

ATM and the related dependence on medicinal plant

material is very high. Medicinal plants play a crucial role in

the healthcare of South Africans.

Whilst the gathering and application of traditional

medicines in South Africa has been ongoing for centuries, the combination of population growth and the

5 In report from Ground Level Landscapes and Zuplex Botanicals (consultancy contract to SANBI entitled ‘Undertake a scoping exercise

and review regarding the value of medicinal plants to the South African Economy (Q5338/2016)’).

Value of informal African Traditional Medicine industry =

R17.9 billion /year

Medicinal plants not only provide a substantial economic value in themselves, but are essential for the work of an estimated 200 000 Traditional Health Practitioners in South Africa. A further ~93 000 income generating activities (i.e. plant harvesters and traders on the street or in shops) existed in the informal sector in 2017. Medicinal plants therefore provide substantial livelihoods benefits in South Africa as well as health benefits.


29

change in patterns of demand linked to urbanization is resulting in widespread unsustainable harvesting

pressure with growing impacts on the resource base.

The most recent Red List assessment for medicinal plants (Williams, Victor & Crouch 2013) confirms that

informal supply chains involving indigenous medicinal plants are under pressure and 134 of the 656

commonly-traded species (20.4%) are of conservation concern (declining rapidly), 56 of the traded species

are threatened (7 are Critically Endangered), and 78 species are classified as Near Threatened, Data Deficient,

Rare or Critically Rare, or as Least Concern but with evidence of population decline.

An increasing number of species in high demand have experienced local extirpations in the past 10 years and

are being imported from Mozambique, Swaziland and Zimbabwe (e.g. Siphonochilus aethiopicus, Warburgia

salutaris, Alepidea spp.) while many species once used traditionally only within their natural distribution

range are now appearing in markets outside of their range. A 2017 survey of 300 traditional medicine

practitioners from Limpopo Province had 60% of practitioners reporting an inability to access desired

material due to overharvesting (Rasethe 2017). Further evidence of the pressure on the resource includes:

reports documenting the reduction in the size of the traded components (e.g. bulbs are smaller or juvenile),

harvesters reporting that the distances to harvesting sites are increasing, and supply becoming increasingly

irregular and/or a number of species now available only in certain markets.

Urgent work is needed to determine which of the approximately 150 medicinal plant species considered

heavily-utilised are under increasing pressure both from trade and from habitat loss. The decline in medicinal

plants represents not only a loss of biodiversity but is intimately linked to a loss of health benefits, the erosion

of livelihoods of harvesters, traders and THPs, and damage to job and wealth creation opportunities. In

addition, the loss of indigenous resources limits the ability to use these resources for research on issues such

as antibiotic and anti-retroviral resistance. In this regard, the genetic value of the medicinal plant resources

has not yet been quantified in a similar manner to the crop wild relative value. The issue of sustainability of

wild harvesting of medicinal plant resources in South Africa is presently inadequately addressed, and the

growth paths speculated in the current biodiversity economy or bioeconomy strategies may be unrealistic.

The management of the medicinal resource needs an integrated management response involving traditional

healers, government (provincial and national), non-governmental organisations and industry, with

investigation required into which species can continue to be wild harvested and which require active

cultivation. A feasibility study of a range of different cultivation models needs to be undertaken to determine

the most economically viability option that contributes optimally to job creation.

Edible plants: Some South Africans obtain a substantial portion of their daily food from the wild, contributing

to food security in rural areas and as a means of making a livelihood. Based on a comprehensive literature

review (Welcome & Van Wyk 2019), there are ±1 300 species (6% of the total South African flora) that have

edible roots, stems, leaves, flowers, fruits, seeds or gums. The most nutritious species are usually the most

popular, which is why the Baobab (Adansonia digitata) and Marula (Sclerocarya birrea) are so well known.

They both have fruits with high vitamin C content as well as leaves high in calcium (Baobab) and seeds high

in protein (Marula).

According to the 2017 State of the World’s Plants report, 80% of food derived from plants comes from 17

plant families, with the most important being: Poaceae (grasses and cereals), Fabaceae (legumes),

Brassicaceae (cabbage / kale family) and Rosaceae (rose and deciduous fruit family). In South Africa, and

Africa as a whole, there is a change to this pattern with Apocynaceae (the milkweed family, including Hoodia

gordonii), Iridaceae (the gladiolus and watsonia family of bulbs) and Asteraceae (the daisies) in the top five

along with Fabaceae (legumes) and Poaceae (grasses and cereals). The different cultural groups of South

Africa tend to have their own preferences of edible plant species, but there are also many species commonly


30

used by all – one example is the Buffalo Thorn (Ziziphus mucronata), which has mealy and sweet fruits that

can be eaten raw or boiled, ground into a meal to make porridge, roasted as a coffee substitute, or fermented

into an alcoholic beverage. There are also many species recorded as being sold on the local market, these

include: fruits of the sour figs (Carpobrotus edulis and Carpobrotus muirii) and the Transvaal Milkplum

(Englerophytum magalismontanum), roots of the Shepherd's Tree (Boscia albitrunca), seeds of the narra

melon (Acanthosicyos horridus), and the roasted nuts of the Gemsbuck or Marama Bean (Tylosema

esculentum). There are also a few South African food plants that generate income on a larger scale. The well-

known waterblommetjie or Cape Pondweed (Aponogeton distachyos) is popular in local restaurants, is sold

in food stores as a fresh or canned product, and has a festival dedicated to it (Welcome & Van Wyk 2019)

Edible insects: The consumption of insects (entomophagy) is prevalent in Mpumalanga, North West, Limpopo

and Gauteng (Teffo, Toms & Eloff 2007). A comprehensive review of edible insects in South Africa is not yet

available, but some studies indicate over 20 species of edible insects have been documented and people

from deep rural areas in northern South Africa all have access to various unique edible insect species specific

to their region. Mopane worms and termites are known and loved by all people in these areas, whereas

others such as the lesser known ‘bophetha’ (Hemijana variegata), a hairy caterpillar feeding on Canthium

armatum (armed turkey-berry), are known only to rural people of Venda, Capricorn and Sekhukhune in

Limpopo Province (Egan 2013).

In South Africa, the best known edible insect is the mopane worm, also known by its Venda name of

“Mašotša” (Ditlhogo et al. 1996). Mopane worms are edible caterpillars of the Emperor Moth (Imbrasia

belina) (Lepidoptera: Saturniidae). They have a long history of being an important traditional delicacy in

southern African countries (Stack et al. 2003), and are valuable economically, socially and nutritionally (Stack

et al. 2003; Hope et al. 2009; Thomas 2013). For example, Makhado et al.(2014) estimated the annual trade

in Mopane worms in South Africa at USD30-50 million (~R450-750 million) (Box 1). There is however a trend

of decrease of Mopane worm harvests in South Africa (Baiyegunhi & Oppong 2016) and at present, much of

the Mopane worm produce appears to be imported from Zimbabwe and Botswana. This is likely a combined

consequence of declining Mopane tree availability due to agricultural and urban development, changing

climate conditions increasing the mismatch between rainfall, leaf flush and the moth’s egg laying period and

overharvesting of Mopane worm populations.

Box 1. Is semi-domestication of the Mopane worm the answer?

Wild harvesting of Mopane worms in South Africa is ecologically unsustainable. South Africa Mopane worm has sporadic population outbreaks that are harvested, but these outbreaks depend on certain climatic conditions, habitat, prior harvesting activities, predation, parasites and diseases. The outbreaks are unpredictable, and this confounds the development of sustainable harvesting practices crucial for harvesters and traders.

Over-harvesting of early life-cycle worm (perhaps due to harvesting being done without an understanding of the worm’s ecology), and the extreme variation in Mopane worm supply due to several other factors together translate into an erratic food resource. The University of Venda is researching the management practices at various life stages of the worm that could help reduce the variability in mopane worm supply for harvesting. A potential spinoff from this research could be a method for local residents to rear Mopane worms on Mopane trees on community land, so as not to solely rely on wild harvesting to obtain worms for personal consumption.


31

2.7. Innovative use of biodiversity could bring further economic growth So many smaller aspects of biodiversity could be used

sustainably to promote economic growth, as people often

only think about using the whole species (e.g. growing or

harvesting an indigenous plant for food), the whole

ecosystem (e.g. grasslands for grazing livestock) or the whole

environment (e.g. tourism) – as discussed in the previous

sections. South Africans need to think more innovatively

about the benefits of biodiversity and broaden concepts of

these benefits to encompass emerging fields like biomimicry

and bioprospecting.

Horticultural trade and cut flowers: South Africa’s indigenous flora are a benefit of biodiversity that has not

reached its full potential as a biotrade industry in South Africa, whether wild harvested or cultivated.

The global horticultural interest in South African plants started in the late seventeenth and early eighteenth

centuries, when major plant collections reached Europe. Many plants, especially those used in the

floricultural business, became world famous and have been collected, domesticated, cultivated and,

unfortunately, also exploited by foreign horticulturists and entrepreneurs. Hundreds of plant species have

found their way across the globe and some achieved worldwide popularity due to their iconic appearance,

sweet fragrance, delicate flowers and stunning colours. The unique indigenous Strelitzia reginae (Bird of

Paradise), for example, is not only one the most popular horticultural perennials around the world, it has also

been named the flower of Los Angeles since 1952. Zonal, regal and ivy geraniums have been decorating

window boxes in Europe for centuries, while the Cape primrose, Cape daisies, Strelitzias, Proteas and

Pincushions have all become famous. Several species are also used as the source of genetic material for plant

breeders to produce new and exciting ornamental and cut flower selections. Species and hybrids of South

African genera that are in high demand are: Agapanthus, Arctotis, Crocosmia, Disa, Eucomis, Erica,

Haemanthus, Ixia, Lachenalia, Leucadendron, Leucospermum, Lobelia, Mimetes, Nerine, Nymphaea,

Ornithogalum, Osteospermum, Pelargonium, Protea, Rhodohypoxis, Serruria, Sparaxis, Strelitzia,

Streptocarpus, Tulbaghia, Venidium, Watsonia and Zantedeschia.

Bioprospecting, the process of discovery and commercialisation of new products based on biological

resources (Wikipedia definition), is another area where the South African government is hoping for jobs and

economic growth. A report (DEA 2015b), which was the result of primary data collection from store sampling

and industry reviews, provides a first economic overview of the formal commercial bioprospecting market in

South Africa, with specific emphasis on the biotrade and use of indigenous plant and honey bee products. In

a survey of retail and specialist stores and health shops across the country, 549 retail products were found

to contain South African indigenous plant resources and/or bee products. The products were mostly

cosmetics (including personal hygiene products), followed by complementary medicines and food

flavourants, with oils and fragrances having limited representation in the stores surveyed. Despite the large

number of products, the resources included in these products were limited to only 24 South African species.

The most extensive resource use in products was Aloe ferox (found in 146 products), followed by bee

products (found in 93 products), Aspalathus linearis (rooibos, found in 92 products) and Pelargonium sidoides

(found in 40 products). The study also examined whether the resource was wild harvested or commercially

grown, which revealed that Aloe ferox is 95% wild-sourced and that Rooibos is 99% cultivated. However, this

aspect of the study revealed that there are insufficient data available to determine whether many of the

plant species are wild harvested or cultivated (DEA 2015b).

Put simply, biotrade is the trade in the ‘neat’ or ‘raw’ material – either from the wild or commercially produced; whereas bioprospecting would require some processing of some kind or the use of only the chemical compounds or a portion of the raw material. Biomimicry, on the other hand, merely observes biodiversity and imitates its cleverness in products.


32

Despite South Africa being a remarkably biodiverse country with a large number of plant species that could

potentially provide new medicines, there are currently very few drug leads obtained from South African

plants. This is despite the country having a large informal traditional medicine marketplace that could

potentially help expedite products into the formal medicines market. Those South African species that have

made it into the formal medicinal market have chiefly been developed beyond our borders and largely to the

benefit of other nations (Drewes 2012) – see Box 2.

Box 2: South African species that have either already been commercially developed or show promise in the formal medicine market

South African species that have been used for compounds in the formal medicine market include: - EPs 7630 (Umckaloabo) from the plant Pelargonium sidoides (licensed to treat respiratory tract infections

such as acute bronchitis since the 1990s); - Combretastatin from the plant Combretum caffrum (combretastatin A-4 has been shown to cause vascular

disruptions of tumours in cancer patients and Phase 3 trials were underway in 2015); and

- P57 isolated from Hoodia gordonii (isolated in 1977 by the Council for Scientific and Industrial Research as an appetite suppressant based on indigenous knowledge, although commercial product development subsequently halted).

There are a number of indigenous plant species that are under cultivation or wild harvested for utilisation or

potential use in the essential oils bioprospecting market segment, including Eriocephalus punctulatus (Cape

Chamomile), Eriocephalus africanus (Cape Snowbush), Pelargonium graveolens (Rose-scented pelargonium)

and Lippia javanica (Lemon Bush). Besides Buchu (Agathosma), this segment is very small and the related

industry located mainly in the winter rainfall region of South Africa.

The total revenue produced from value-added products sold in the domestic retail market, and which

contained bio-resources as an ingredient, was approximately R1 470 million in 2011. The importance of

indigenous plant resources and bee products as an ingredient in these value-added product categories is

revealed by the comparative values of retail sales of products with and without these indigenous resources

as an ingredient. Products containing indigenous plant resources and bee products as an ingredient sell

between 50%-100% more by retail value than products without them (Department of Environmental Affairs

Bio-products retail database). This is clear evidence of a strong consumer demand for products containing

indigenous plant resources and bee products as an ingredient.

The bioprospecting industry, based on export trends, has grown, on average, by 6% per year over the period

2001-2011. There is likely large growth potential in this industry (DEA 2015b).

Biomimicry is not as well-known as some of the other benefits of biodiversity. It is the practice of learning

from (not just about) nature and then emulating its forms, processes, and ecosystems to create more

sustainable products, processes and systems. Several South African ecosystems and species have been used

in biomimicry by international companies (SANBI 2019), including bird protection window glass inspired by

spiders’ webs and termite dens inspiring air conditioning systems for tall buildings. A local example being

utilised is the Durban Resilience Strategy. The strategy informs urban planning and design, with the idea that

the urban system will contribute ecosystem services at a level equal to the reference ecological system for

the area. Ecological Performance Standard Targets (e.g. for water yield, carbon storage, biodiversity

protection, sediment retention, etc.) become part of the key performance indicators for the development

and are measured against the same site ecosystem. The idea is that the entire development area through a

combination of both ecological infrastructure, preserved or restored critical biodiversity and mimicking

healthy ecosystems in the built environment will contribute to the performance targets.

2.8. Biodiversity enriches everyday lives There is increasing evidence that interacting with nature brings measurable emotional and mental

benefits to people as well as physical benefits. No matter whether people live in an urban or rural


33

environment, nature and biodiversity play a key role in their overall perception of life. The natural

ecosystems, plants and animals have influenced South African’s cultural and spiritual development. These

influences are woven into languages and place names, as well as the religion and folklore supporting spiritual

and cultural life. This web of associations with biodiversity forms an important part of South Africans’ national

identity (Dold & Cocks 2012).

In this section we briefly celebrate and explore the link between people and nature from the perspectives of

the range of South Africa’s cultures, and fully recognise that there are a substantial number of publications

on this topic.

Several indigenous plants are important in the Xhosa culture, including: Cymbopogon validus grass is used

to make brooms, which are hung above a house door as a talisman against lightning; Tulbaghia violaceace

(locally known as itswele lomlambo, isivumbampunzi, wild garlic) is used via an infusion that is sprinkled

around the home as a protection from the evil spirits; and branches of the sacred wild olive tree (umnquma)

are used as a platter for consecrated meat (intsonyama) of ritually sacrificed animals. In Zululand, young

leaves of ilala (Hyphaene coriacea) are used to make a variety of items for local use and sale to international

tourists such as brooms, baskets, washing baskets, hats, jewellery containers and toys. In the traditional Zulu

wedding, the bride’s gifts to the groom’s family members (umabo) usually consist of sleeping mats (amacanci)

and traditional beer strainers (amahluzo) made from incema (Juncus kraussii). A twig of umlahlankosi or

umphafa (Ziziphus mucronata subsp. mucronata) is wildly used in the Zulu culture to fetch the spirit of a dead

person from the spot where they died and carry it to their home. Leaves and stems of Helichrysum

odoratissimum and H. stenopterum (both species are called impepho) are burned as an incense by both

sangomas and community members to communicate with ancestors. Encephalartos transvenosus, or the

Modjadji cycad, have been protected by the Rain Queens (Modjadji) of the Balobedu tribe of Limpopo

Province for centuries.

South African animals also have spiritual and cultural significance. An interesting example is that of the

Southern Ground-Hornbill (Bucorvus leadbeateri), which is viewed by some cultures as a signifier of

death/destruction/loss/deprivation, while in other cultures it is perceived as a protective influence against

evil spirits, lightning and drought. Snakes are particularly revered – for example Lamprophis fuiginosus

(African house snake) is considered as representative of ancestors by the amaMpondomise (a Xhosa tribe).

In the Zulu culture it is widely believed that if amankankane (Hadedas, Bostrychia hagedash) fly over a

homestead while making their usual loud call, a death will occur in that homestead.

Special places in South Africa also have spiritual and cultural significance, including the Motouleng caves

(meaning 'place of beating drums') located in the mountains of the eastern Free State and Lesotho, which

have served as a spiritual gathering place of prayer for over 800 years. Tha the Vondo, Limpopo Province’s

most beautiful and majestic forest, is regarded as sacred by the local Venda people. Hogsback in the Eastern

Cape is regarded as a place of spiritual upliftment, and Xhosa legend holds that the Hole in the Wall landmark

at the mouth of the Mpako River is the gateway to the world of their ancestors. Even in urban areas, there

are natural spaces popular for rituals and prayer (e.g. Lion’s Head in Cape Town, Melville Koppies in

Johannesburg).

In language, biodiversity plays an important role and is used in place names and sayings. In the Zululand

region there is uMkhanyakude District Municipality, and the name comes from umkhanyakude trees (Fever

tree; Vachellia xanthophloea) that are common in that area. There are a number of isiZulu sayings or proverbs

that are derived from animals, e.g. ‘ingwe idla ngamabala’ (meaning ‘a leopard gets what is due to it because

of its spots’ – i.e. each person lives off his/her talents); ‘zimbiwe insele’ (when something is plentiful and free,

e.g. honey combs have been dug up by a honey badger and anyone can help themselves); and ‘uzulelwa

amanqe’ (‘vultures are circling over you’ – warning someone of impending danger). Both Xhosa and Zulu


34

cultures use the proverb ‘indlovu ayisindwa umboko wayo’ (‘an elephant does not find its trunk too heavy’ –

relating to one’s struggles in life). The isiXhosa names for months come from names of plants or flowers that

grow or seasonal changes that happen at that time of year – e.g. January: EyoMqungu (month of Tambuki

Grass), July: EyeKhala / EyeNtlaba (month of aloes).

In addition to species, places and language, becoming a biodiversity citizen scientist is another way that

people feel connected to South Africa’s biodiversity and enriched in their everyday lives. SANBI and various

other institutions in the sector have long recognised the value of citizen science and several projects and

platforms have emerged to help channel the South African public’s interest and passion for biodiversity

conservation into providing vital assistance to biodiversity science. For example, species monitoring records

collected by the public (in the field in their own time)

and loaded on platforms such as iNaturalist and the

Southern African Bird Atlas Project (SABAP) are used by

scientists to support various biodiversity monitoring

projects as the data feeds into national databases of

species distribution records. iNaturalist offers a free

identification service, as well as tools for other

institutions to run and manage their own citizen

science projects. Another example is that of the

Transcribe system, which enables citizen scientists to

contribute from the comfort of their own home and

digitise the many historical museum, herbaria and field

note records archived in collections around the

country. This digitising provides vital historical

information about species distributions and field trips

undertaken up to 300 years ago.

The Custodians of Rare and Endangered Wildflowers

(CREW) programme involves citizen scientists directly

in field surveys and monitoring key sites for threatened plant species in priority parts of the South African

landscape. The CREW citizen scientists are trained in plant identification and are provided with information

to track down threatened and rare plant species specific to their particular region. The CREW network

comprises around 950 citizen scientists based across South Africa who provide detailed population level data

and threat information that is fed into SANBI’s Plant Red List assessment process and is channelled into land

use decision making. To date 100 570 records for 8 973 plants (44% of the flora), including 2 120 threatened

and rare plants, have been contributed by citizen scientists, with 2 382 field surveys undertaken to 58 under-

sampled areas between 2003 and 2018. This network is now active beyond monitoring threatened species

and contributes to other activities such as collecting seeds of threatened species for the Millennium Seed

Bank Partnership and implementing a number of other components of the South Africa’s Plant Conservation

Strategy.

Interviews of citizen scientists reveal that individuals experience high levels personal enrichment and

fulfilment from contributing to national conservation programmes. Citizen science projects provide

educational benefits such as skills for accurate data collection, critical thinking and scientifically informed

decision-making. This increases scientific capacity, better informs decisions and improves social capital in

South Africa.

iNaturalist South Africa currently has 2 860 observers and 423 000 observations for 24 000 species of plants, animals and moulds. Between 2012 and 2016 in the space of only four years, 250 000 species observations were uploaded.

SABAP2 (run as a joint effort between the University of Cape Town, BirdLife South Africa and SANBI) is the only comprehensive countrywide species dataset that enables scientists to assess trends in distribution and abundance. It contains more than 8 million records of bird distributions.

The Transcribe system is relatively new as it only came online in 2017, but has already digitised more than 30 000 specimen records of the approximately 50 000 records loaded. These statistics change regularly as new specimen images are loaded from museums and herbaria.

https://www.inaturalist.org/places/south-africa

http://sabap2.adu.org.za/

https://transcribe.sanbi.org/

https://www.sanbi.org/biodiversity/building-knowledge/biodiversity-monitoring-assessment/custodians-of-rare-and-endangered-wildflowers-crew-programme/


http://biodiversityadvisor.sanbi.org/planning-and-assessment/plant-conservation-strategy/

http://biodiversityadvisor.sanbi.org/planning-and-assessment/plant-conservation-strategy/


35

2.9. Conclusions and knowledge gaps This chapter clearly illustrates that biodiversity is central to South Africa’s national objectives of increased

economic growth, job creation, and improved service delivery and wellbeing for all its citizens. The main

challenge today and into the future is how to maintain and enhance these beneficial contributions of South

Africa’s unique biodiversity. Biodiversity as a national asset and a powerful contributor to economic

development and job creation is not always fully recognised in South Africa, especially in market transactions,

national accounting, and the allocation of public sector resources. A clear priority action is to ensure that all

sectors better integrate this understanding into their policies and practices.

Care should be taken to allocate resources to conserving biodiversity assets and ecological infrastructure, so

that there can be a greater focus on supporting job creation in sectors that depend on biodiversity such as

biodiversity-based tourism and wildlife ranching. The conserving of biodiversity assets will create the

additional result of ensuring the continuation of ecosystem services like pollination, natural pest control and

reliable grazing, as well as the preservation of natural habitats for edible plants, edible insects and medicinal

plants that are extracted directly from the wild. The conservation of natural habitats will also ensure the

continuation of the natural resource base for crop wild relatives, bioprospecting and other innovations for

the future such as biomimicry. It will also ensure that the citizens of South Africa have the natural spaces and

indigenous species so significant to their psychological wellbeing.

Further primary research is particularly needed in the arena of direct extraction of biodiversity for use and

trade (e.g. medicinal plant, edible plant and edible insect harvesting) to ensure the sustainability of the

benefits derived. Continued research in ecological-friendly farming practices are also vital to inform the

necessary growth in crop and livestock agriculture, as well as for the proposed upscaling of cultivation of

critical species such as medicinal plants and plants used as a bioprospecting resource.


36

3. PRESSURES AND DRIVERS I – GENERAL

Chapter 3: Skowno, A.L., Raimondo, D.C., Driver, A., Powrie, L.W., Hoffman, M.T., Van de Merwe S., Hlahane, K., Fizzotti, B. & Variawa, T. 2019. ‘Chapter 3: Pressures and Drivers I – General’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

3.1. Summary of pressures in the terrestrial realm Terrestrial ecosystems and species face pressures from a range

of human activities, including loss and degradation of natural

habitat, invasive alien species, pollution and waste, natural

resource use and climate change (Table 2). These pressures

interact in complex ways that we are only beginning to

understand (Hassan et al. 2005). Loss of natural habitat is the

single biggest cause of loss of biodiversity and ecosystem

functioning in the terrestrial environment (Hassan et al. 2005).

Outright loss of natural habitat takes place mainly as a result of

conversion of natural vegetation for cultivation, mining,

plantation forestry, infrastructure development and urban

development, which means that patterns of land use have a

great impact on terrestrial biodiversity. Habitat loss is also

usually associated with habitat fragmentation, which further

impacts ecological functioning and viability of species,

particularly in the context of climate change and biological

invasions.

While outright habitat loss is the most intense form of habitat

modification, it is not the most extensive. Large portions of

South Africa’s rangelands have seen extensive modifications

from centuries of livestock farming, and mountain catchment

areas have been modified through invasion by alien woody plant species. The ecological condition in these

modified areas ranges from near natural to heavily modified depending on the degree to which ecosystem

structure, function and composition have been altered. Land degradation, as defined by IPBES (2018),

includes both habitat loss and persistent decline or loss of biodiversity and ecosystem function and services,

thus encompassing the full range of ecological condition. National assessments of land degradation in South

African rangelands in particular have shown that overgrazing and bush encroachment are wide spread

(Hoffman & Ashwell 2001). More recent work suggests that, while there may be a trend towards

improvement in some arid rangelands, bush encroachment is increasingly widespread and severe (Hoffman

et al. 2018; Venter, Cramer & Hawkins 2018).

Biological invasions, especially invasive alien plants, are a major pressure on the biodiversity and ecosystem

structure and functioning of the terrestrial realm. They displace indigenous species, disturb habitats, and

disrupt ecosystem structure and functioning.

Waste generated by mining, agriculture, manufacturing and urban settlements generates water pollution,

soil pollution and air pollution, impacting on ecosystems, species and ecological processes, often substantial

distances away from the original pollution source.

Anthropogenic climate change has been shown to impact on most ecological processes (Scheffers et al.,

2016) with disruptions evident from the genetic level to the landscape level. In addition to acting as a direct

The IPBES Global Assessment of Land Degradation and Restoration (2018) defined land degradation as the ‘many human-caused processes that drive the decline or loss in biodiversity, ecosystem functions or ecosystem services in any terrestrial and associated aquatic ecosystems’. Degraded land is defined as the ‘state of land which results from the persistent decline or loss in biodiversity and ecosystem functions and services that cannot fully recover unaided within decadal time scales. Degraded land takes many forms: in some cases, all biodiversity, ecosystem functions and services are adversely affected; in others, only some aspects are negatively affected while others have been increased. Transforming natural ecosystems into human-oriented production ecosystems—for instance agriculture or managed forests—often creates benefits to society but simultaneously can result in losses of biodiversity and some ecosystem services. Valuing and balancing these trade-offs is a challenge for society as a whole.’

http://www.ipbes.net/


37

driver of species loss and habitat degradation, climate change is widely considered as a multiplier of other

pressures on biodiversity – both exacerbating the effects of other pressures and altering the frequency,

intensity and timing of events (Barger et al. 2018). The various pressures described above can also leave

species and ecosystems more susceptible to climate change and extreme events (Barger et al. 2018).

Biological resource use is a pressure that directly targets specific species and includes hunting, poaching and

trapping of animals and harvesting of wild plants. In South Africa, animals and plants are commonly used as

traditional medicine for both the healing of ailments and for cultural purposes (Whiting, Williams & Hibbitts

2011).

Table 2. IUCN threat classification system cross-walked to the CBD classification of pressures. Sub-threats are listed along with the spatial scale over which the threat generally operates and the intensity of the threat on

biodiversity. The relative importance of the threat to South African ecosystems and species is estimated in the final column. Note,

the assessment chapters include a quantitative analysis of the major threats that drive the IUCN red lists for ecosystems and

species.

Major Threat / Pressure

Sub Threats & South African Examples CBD cross-walk Spatial Scale Intensity Importance in SA

Agriculture Non-timber crops; livestock farming & ranching; wood & pulp plantations.

Habitat loss & degradation

Landscape level to local footprint

Low to High

*****

Aquaculture Land-based aquaculture Pollution (nutrient); habitat loss

Local footprint High *

Biological resource use

Gathering terrestrial plants; hunting & trapping terrestrial animals; wood harvesting

Over exploitation of biological resources

Landscape level Low **

Climate change & severe weather

Droughts; habitat shifting & alteration; other impacts; storms & flooding; temperature extremes

Climate change Landscape level Low ***

Energy production & mining

Mining & quarrying; oil & gas drilling; pipelines; renewable energy; seismic surveys

Habitat loss & degradation; pollution

Local footprint High **

Human intrusions & disturbance

Ammunition dumping; recreational activities; civil unrest & military exercises


Local footprint Low to High

*

Invasive and other problematic species, genes & diseases

Diseases; introduced genetic material; invasive non-native species; problematic native species;

Invasive species; habitat loss & degradation


Low to High

****

Natural system modifications

Abstraction of ground / surface water; erosion; fire & fire suppression; other ecosystem modifications

Habitat loss & degradation


Low to High

***

Pollution

Agricultural & forestry effluents; air-borne pollutants; domestic & urban waste water; garbage & solid waste; industrial & military effluents

Pollution Landscape level to local footprint

Low to High

**

Residential & commercial development

Commercial & industrial areas; housing & urban areas; tourism & recreation areas



Low to High

****

Transportation & service corridors

Flight paths; roads & railroads; shipping lanes; utility & service lines

Habitat loss & degradation; pollution; invasive species

Local footprint Low to High

**

Geological events Avalanches/landslides, earthquakes/tsunamis; volcanoes

Habitat loss & degradation; pollution; invasive species


Low to High

*


38

3.2. Habitat loss

3.2.1. National land cover change layer developed for the NBA 2018

The primary building blocks of the terrestrial ecosystem condition layer used in the assessments in NBA 2018

were the 1990 and 2013/4 national land cover products provided by the National Department of

Environmental Affairs (DEA) (https://egis.environment.gov.za/; GeoTerraImage 2015, 2016). These products

were reclassified and combined into a simple land cover change map, and then refined using additional input

data on historical field crop boundaries extracted from the 1: 50 000 topographical series between 1950 and

1975 (Skowno 2018). The artificial water bodies class was also modified, using a combination of the national

vector data (dams, water works, sewage plants etc.) from the Chief Directorate: National Geospatial

Information (CD:NGI), a Global Water Occurrence Dataset (Pekel et al. 2016) and desktop GIS mapping (Van

Deventer et al. 2018). Land cover information for Lesotho and Swaziland (which is not included in the national

datasets provided by DEA data) was added to the layer using the CCI S2 Prototype 20m Africa Land Cover

Product (http://2016africalandcover20m.esrin.esa.int/download.php) (Skowno 2018). The coastal areas of

the layer were reclassified using a detailed mask of the coastal ecosystems developed for the NBA by Dr Linda

Harris (Harris et al. 2019). The result is a simplified Land Cover Change map for the whole country (Figure 7).

It should be noted that while the map illustrates large portions of the country as ‘natural habitat – no change’,

much of this would actually be near natural or semi natural due to its use as natural rangelands for livestock.

Figure 7. Simplified land cover change map showing natural areas remaining circa 2014, areas where natural habitat was lost between 1990 and 2014, and areas where natural habitat was lost prior to 1990.

http://2016africalandcover20m.esrin.esa.int/download.php


39

3.2.2. Patterns and trends of habitat loss in South Africa

National trends

South Africa’s mainland extent is approximately 1.22 million

km2. Of this croplands6 currently make up over 12%, making

them the largest form of anthropogenic land cover change in

the country. Biodiversity loss linked to this historical and

recent clearing of natural7 habitat for field crops,

horticultural crops and pastures is the largest pressure on

ecosystems and biodiversity in South Africa. Built-up areas

(including rural and urban settlements, industrial and

commercial areas and large infrastructure) also contribute

significantly to natural habitat loss and cover over 2% of the

country. Secondary natural areas are abandoned croplands

or pastures that have recovered some plant cover but have

lost most of their original biodiversity – these areas make up

approximately 4% of the country. Current croplands together with the secondary areas amount to over 16%

of South Africa that has been directly impacted by land clearing and ploughing at some point in South Africa’s

history. Plantation forestry (including non-native pine, eucalyptus and acacia species) is an important driver

of habitat loss, in grassland regions in particular, and cover approximately 2% of South Africa. The impact of

mining as a direct driver of habitat loss is relatively low (0.3% of South Africa), however, the highly uneven

distribution of mining areas means that the impacts are focussed on particular ecosystems, and the impacts

are often persistent.

Based on the national land cover, 80.8% of South Africa (985 559km2) was in a natural state in 1990. By 2014

natural areas are estimated to have declined to 78.8% (961 001 km2) (Figure 7, Table 3). This 24 588 km2 loss

of habitat was driven mostly by land clearing for new croplands (13 706 km2), urban and rural settlements

(3 346 km2), and plantations (2 734 km2). Table 3 and Table 4 show the overall changes per land cover class

and the transitions between classes in the period 1990 to 2014. In this report we focus on the transitions

from natural to non-natural land cover classes which we refer to as habitat loss. There are many other

interesting trends and transitions in Table 4 that warrant further investigation by other sectors and are

beyond the scope of the NBA. For example, the apparent abandonment of croplands and reduction in

plantation forestry areas. Other transitions in Table 4 suggest some classification inconsistencies between

1990 and 2014. For example, the ‘replacement’ of built-up areas with croplands (especially in KZN) is, in some

instances, more likely a situation where the mosaic of rural settlements and croplands was simply classified

as Built-up in 1990 and then as Cropland in 2014, but no real change occurred (Figure 8).

6 In this land cover dataset, the category [Croplands] includes both field crops (maize, soya, sunflowers, wheat etc.), horticultural crops (fruit orchards, vineyards and vegetables etc.) and planted pasture grasses. 7 In this report the term ‘natural’ includes ‘near-natural’ areas in which at least plant species composition, structure and function are

largely intact.

Interpreting land cover change and habitat loss statistics An important consideration when interpreting land cover change and habitat loss statistics is that they can differ slightly in certain circumstances. This occurs when there have been transitions between anthropogenic (non-natural) classes, such cropland being converted to built-up areas or mines being established in secondary natural areas. These transitions do not involve the loss of natural habitat per se, only the original activity caused the habitat loss.


40

Figure 8. Mixed settlement and small scale farming near Kwa Nobhena in KwaZulu-Natal. The 1990 land cover dataset classifies this as built-up area, whereas the 2014 classification separates the built-up and cultivated areas. For this reason, the fine detail of some smaller amounts of transformation may not be mentioned, although they are counted as part of the overall loss of natural habitat.

Table 3. Generalised land cover statistics for South Africa showing extent (km2) of each class in 1990 and 2014.

Land Cover Class (km2)

Extent 1750 (Reference)

Extent 1990

% of SA

Extent 2014

% of SA

Change (1750-2014)

Change (1750 -1990)

Change (1990-2014)

Natural 1219459 985599 81% 961011 79% -258448 -233860 -24588

Artificial water body 0 5629 0.5% 6009 0.5% 6009 5629 379

Built-up 0 27337 2% 27606 2% 27606 27337 270

Cropland 0 141560 12% 150625 12% 150625 141560 9066

Erosion 0 1287 0.1% 1956 0.2% 1956 1287 669

Mine 0 2834 0.2% 3155 0.3% 3155 2834 322

Plantation 0 19135 2% 18591 2% 18591 19135 -544

Secondary natural 0 36079 3% 50506 4% 50506 36079 14427

Table 4. Contingency table showing the extent (km2) per land cover class in 1990 vs. 2014. The values in the matrix represent the transitions between classes from 1990 to 2014. The table can be read left to right for

changes from 1990 to 2014, the top row for example shows the extent of natural areas circa 1990 that transitioned into each

class in 2014.

Land Cover 2014

Nat

ural

Art

ifici

al

wat

er b

ody

Bui

lt-up

Cro

plan

d

Ero

sion

Min

e

Pla

ntat

ion

Sec

onda

ry

natu

ral

Gra

nd

Tot

al

Lan

d C

ove

r 19

90

Natural 961011 305 3284 16591 687 739 2982 0 985599

Artificial water body

0 5629 0 0 0 0 0 0 5629

Built-up 0 0 23137 1765 0 14 136 2284 27337

Cropland 0 23 261 127212 0 357 275 13430 141560

Erosion 0 0 0 18 1268 0 0 0 1287

Mine 0 4 12 24 0 1896 8 889 2834

Plantation 0 11 268 408 0 36 14684 3728 19135

Secondary natural 0 35 643 4607 0 114 506 30174 36079

Grand Total 961011 6009 27606 150625 1956 3155 18591 50506 1219459

2014 1990


41

Habitat loss per biome and per province

The overall extent of natural habitat remaining in 2014 varies greatly per biome; only 36% of the Indian Ocean

Coastal Belt, 60% of Grassland and 69% of Fynbos are in a natural state (Table 5). All the other biomes are in

significantly better overall state with over 80% remaining natural habitat. The key trend from this metric is

that the rates of habitat loss have increased in recent years especially in the Indian Ocean Coastal Belt (152%

increase in rate of habitat loss) and over 60% in Nama-Karoo, Savanna and Desert. The recent rates of habitat

loss are highest in the Indian Ocean Coastal Belt (0.61%/y), Grassland (0.23%/y) and Fynbos (0.15%/y) (Table

5).

Table 5. Loss of natural habitat per biome; based on land cover change data 1990-2014.

Biome Extent 1750 (Reference)

(km2)

Extent 1990 (km2)

Extent 2014 (km2)

Recent rate of habitat loss (%/y) 1990-2014

Albany Thicket 35250 32450 (92%) 32126 (91%) -0.043%

Desert 6260 6179 (99%) 6166 (99%) -0.009%

Forests 4544 3838 (84%) 3754 (83%) -0.095%

Fynbos 81444 57891 (71%) 55865 (69%) -0.152%

Grassland 330861 209239 (63%) 198057 (60%) -0.232%

Indian Ocean CB 11530 4825 (42%) 4148 (36%) -0.610%

Nama-Karoo 249354 245220 (98%) 244526 (98%) -0.012%

Savanna 394159 327852 (83%) 319094 (81%) -0.116%

Succulent Karoo 78203 74907 (96%) 74608 (95%) -0.017%

Azonal Vegetation 26082 21779 (84%) 21303 (82%) -0.095%

The provincial level analysis of habitat loss shows that Gauteng has less than half of its original natural habitat

remaining (Table 6). Mpumalanga, KwaZulu-Natal, the Free State and the North West Province have

approximately 2/3rds remaining (Table 6). The recent rates of habitat loss (1990-2014) are highest in the

Gauteng (0.54%/y), Mpumalanga (0.24%/y), KwaZulu-Natal (0.38%/y) and the Free State (0.15%/y).

Table 6. Loss of natural habitat per Province; based on land cover change data 1990-2014. ‘Extent’ is the extent of natural habitat.

Province Extent 1750 (Reference)

(km2)

Extent 1990 (km2)

Extent 2014 (km2)

Recent rate of habitat loss (%/y) 1990-2014

Eastern Cape 168908 140529 (83%) 138182 (82%) 0.073%

Free State 129823 83678 (65%) 80730 (62%) 0.153%

Gauteng 18179 8958 (49%) 7835 (43%) 0.545%

KwaZulu-Natal 93307 58166 (62%) 53779 (58%) 0.328%

Limpopo 125754 100935 (80%) 97189 (77%) 0.161%

Mpumalanga 76495 46269 (60%) 43764 (57%) 0.235%

North West 104882 72210 (69%) 70367 (67%) 0.111%

Northern Cape 372900 366292 (98%) 365445 (98%) 0.010%

Western Cape 129489 103883 (80%) 102212 (79%) 0.070%


42

Ecosystem extent / habitat loss indicators

The habitat loss metrics based on land cover change (described above) were applied to each individual

terrestrial ecosystem unit (vegetation type) in the computation of two simple indicators of changes in

ecosystem extent.

1) Rate of habitat Loss (RoL) (1990–2014). Computed for each ecosystem type by calculating the decline

in extent between two time points and then dividing this by the extent at first time point, as per

Equation 18.

Equation 1: Recent rate of habitat loss (recent) (%/y): 𝑅𝑜𝐿𝑟 =𝐴𝑟𝑒𝑎1990−𝐴𝑟𝑒𝑎2014

𝑌𝑒𝑎𝑟1990−𝑌𝑒𝑎𝑟2014÷ 𝐴𝑟𝑒𝑎1990

2) The Years to ecosystem Collapse (YtC): combines the recent rate of loss with the current (2014)

extent of natural habitat and estimates – in a ‘business as usual scenario’9 – the number of years until

the ecosystem extent declines to zero10. It is computed following Equation 2.

Equation 3: Years to Collapse: 𝑌𝑡𝐶 = 𝐴𝑟𝑒𝑎1990 ÷ 𝐴𝑟𝑒𝑎1990−𝐴𝑟𝑒𝑎2014

𝑌𝑒𝑎𝑟1990−𝑌𝑒𝑎𝑟2014

Figure 9 shows the recent Rate of Loss (RoL) indicator, and Figure 10 shows the years to collapse (YtC)

indicator. The Cape lowland renosterveld (Fynbos biome), parts of the Grassland biome and whole KZN coast

(Indian Ocean Coastal belt biome) show the highest rates of habitat loss between 1990 and 2014. Expanding

croplands and human settlements are the key drivers of these changes (Figure 9). The YtC indicator shows a

similar spatial pattern (Figure 10) but highlights those ecosystems that have lost a substantial portion of their

original geographic range and are currently experiencing high rates of habitat loss. Five ecosystems types

spread between the eastern Overberg portions of the lowland renosterveld and the KZN coast could collapse

entirely with the next 75 years, if the current rate of habitat loss continues. A further 26 ecosystem types (14

lowland Fynbos, three Grassland, six Savanna, one IOCB and one Azonal type) could be lost within the next

150 years (Table 7). These indicators of decline in ecosystem extent are key to assessing the threat status of

ecosystems using the IUCN Red List of Ecosystems framework (Chapter 7).

Table 7. The number of ecosystem types per biome falling into categories of ‘Years to Collapse’; based on the rate of habitat loss between 1990 and 2014 projected into the future in a business as usual scenario. Collapse in this case represents complete loss.

Biome Less than 75 years

75 to 150 years

150 to 299 years

300 to 599 years

More than 600 years

Azonal Vegetation 1 3 3 11 Succulent Karoo 1 1 1 61 Savanna 6 6 20 59 Nama-Karoo 13 Indian Ocean Coastal Belt 1 1 3 1

Grassland 3 18 17 36 Fynbos 3 14 21 13 71 Forests 1 2 9 Desert 15 Albany Thicket 1 2 5 36

Total 5 26 55 62 311

8 Note: the rate of habitat loss (RoL) described here is identical to the concept of Absolute Rate of Decline (ARD) described in the various guidelines for the IUCN Red List of Ecosystems (see Bland & Keith et al. 2017 and Chapter 7 of this report). 9 This indicator provides a useful metric for comparing the direct pressures on different ecosystems types but the uncertainties of forecasting future rates of change are acknowledged, and the actual values for years to collapse should interpreted with caution. 10 The point at which the geographical extent of an ecosystems declines to zero is referred to as the point of collapse in the IUCN Red List of Ecosystems framework (Bland et al., 2018).


43

Figure 9. Rate of recent habitat loss indicator (RoL) 1990 – 2014 calculated per terrestrial ecosystem type. The Cape lowlands, Mpumalanga Highveld grasslands and KZN coast and adjacent interior have the highest rates of habitat loss between 1990 and 2014, with expanding croplands and human settlements being the key drivers. The terrestrial ecosystem types are provided by the 2018 version of the national vegetation map.

Figure 10. Years to ecosystem Collapse (YtC) indicator based on recent rate of habitat loss and remaining extent of natural habitat. The indicator assumes a ‘business as usual scenario’ where the rate of habitat loss measured between 1990 and 2014 is projected forward unchanged. This is unlikely to occur but the indicator is a useful way to compare relative rates of habitat loss at an ecosystem level.


44

Habitat loss and its impact on species

Changes in land use and associated loss of natural habitat

is impacting negatively on South Africa’s indigenous

species. Figure 11 shows a meta-analysis of pressures

effecting species using data collated during the Red Listing

assessment process for all terrestrial taxa of conservation

concern (TOCC). For all terrestrial species groups assessed

to date, habitat loss for crop cultivation, afforestation,

mining and urban development are the main driver of

species population decline. For birds, loss of habitat to

forestry plantations and crop cultivation are the top two

drivers of decline. Sixty percent (60%) of South Africa’s

mammal species of conservation concern are declining as

a result of habitat loss. Of the 13 mammal species that

have become more threatened since 2004, nine are as a

result of habitat loss. Certain groups of mammals are

particularly impacted, for example 82% of South Africa’s

golden mole species are listed as Threatened or Near

Threatened as a result of habitat loss. South African amphibians are also primarily threatened by habitat loss

with 82% (27) of the 33 TOCC impacted. 64 % of South Africa’s reptiles are threatened by a reduction in

extent and quality of habitat. With crop cultivation, urban development and plantation forestry

concentrated in the south west and eastern parts of the country there are currently high concentrations of

threatened species in these areas. Butterflies also have high concentrations of endemic species in the

lowlands of the western and southern Cape, and along the coast areas of KwaZulu-Natal (Mecenero et al.

2013). With high levels of historic and ongoing transformation for housing development and agriculture in

the lowlands of the Cape and along the KwaZulu-Natal coastline agriculture and residential development are

key pressures impacting South Africa’s butterflies. A very large number of indigenous plant species 2657

(43%) of South Africa’s plant TOCC are threatened directly by loss of habitat.

Taxa of Conservation Concern (TOCC) are species and subspecies that are important for South Africa’s conservation decision-making processes. They include all taxa that are assessed according the IUCN Red List Criteria as Critically Endangered (CR) Endangered (EN), Vulnerable (VU), Data Deficient (DDD) or Near Threatened (NT). They also include range restricted taxa (Extent of Occurrence < 500 km2) that are classified according to South Africa’s national criteria as Rare. Detailed information on the pressures impacting these taxa has been captured during the Red List assessment processes. Throughout the NBA reference to the impact of a particular pressure on a taxonomic groups is determined from the proportion of taxa of conservation concern impacted by that pressure.


45

Figure 11. Pressure matrix for threatened species based on a meta-analysis of the South African Species Red List Database. The size of the bubble corresponds to the percentage of taxa of conservation concern in the taxonomic group that is subject to each pressure.

3.2.1. Habitat / land degradation

South Africa has a large percentage of its land surface dedicated to livestock ranching (~70%); and while this

land use practice is, in many circumstances, compatible with biodiversity conservation it can constitute a

significant pressure. The available land cover change dataset unfortunately does not capture biodiversity


46

impacts linked to unsustainable rangeland management, making it not possible to estimate the extent,

spatial patterns and intensity of the impacts. While historical analysis of livestock numbers (see Section 3.3.1)

indicates significant declines in the last century, overutilisation of rangelands remains a key pressure on

biodiversity across a large portion of South Africa. Metadata analysis of pressures to species shows that

degradation is having a significant impact, for example 35% of TOCC (2 140 plant taxa) are declining as a

result of either livestock overgrazing, inappropriate fire regimes or invasive alien plants. Various studies have

focussed on the biodiversity impacts linked to rangeland management in specific regions or ecosystems, but

as yet no reliable data exists that would allow for a spatially explicit assessment of ecological condition in

rangelands at an ecosystem level. Species data however indicate that 1 477 plant species are listed in a

category of conservation concern as a result of declines linked to livestock overgrazing. Invertebrate taxa are

also being negatively impacted with 37 (26% of butterfly TOCC) declining (Figure 11). For butterflies,

overgrazing by livestock results in degradation of vegetation structure, changes in micro climates, and in

certain cases leads to the loss of host plants and or host ants.

Habitat degradation from inappropriate fire regimes is also significantly impacting certain groups of species.

For butterflies it is the dominant pressure affecting 50 taxa (35% of the TOCC) (Figure 11). For plants this is

an ever increasing pressure (1 005 or 16% taxa of plant TOCC impacted). Inappropriate fire regimes is

especially severe for endemic species restricted to fire dependent Fynbos and Grassland ecosystems whose

life-histories are dependent on natural fire cycles. More frequent fires (which may be linked to climate

change) and increases in ignition sources is causing declines to slow maturing species, which do not to have

enough time to set seed between fires. In other areas fire exclusion is resulting in changes in vegetation

structure in both grassland and Fynbos ecosystems, which results in fire-dependent plant taxa being

outcompeted and displaced by fire sensitive species (e.g. most forest taxa).

Bush encroachment is a well described form of land degradation in South Africa’s Savanna and Grassland

biomes that can negatively impact biodiversity and rangeland potential and other ecosystem functions

(O’Connor, Puttick & Hoffman 2014). The drivers of bush encroachment may be one of, or a combination of,

the following: livestock management, fire management, increased atmospheric CO2, increase in

temperature, and changes in precipitation regime (Skowno et al. 2017; Venter et al. 2018). Bush

encroachment is dealt with in more detail in Chapter 5 (Climate Change).

3.2.2. Limitations of habitat loss analysis

The land cover change datasets used in this assessment have some key limitations that should be addressed

for future assessments, including:

Time gaps between land cover data acquisitions and processing mean that the data reported in this

2018 assessment were captured in 2014. As automated and global scale remote sensing becomes

more accessible, it is hoped that future assessments will not suffer from this long time delay. The

ecosystem assessment will be rerun when new land cover data become available.

Density and distribution of invasive plants is not included in current land cover maps. This is an aspect

of land degradation that should be possible to map with reasonable accuracy given recent advances

in remote sensing.

Land degradation due to inappropriate rangeland management (overstocking rates and fire regimes),

as mentioned above, is not captured in any land cover products.

Classification inconsistencies in mixed rural settlement or subsistence farming landscapes (as per

Figure 8 above) lead to some spurious transitions between classes.


47

3.3. Agriculture Agricultural practices vary in terms of their impact on biodiversity and ecosystems. Cultivation of croplands

and fruit orchards requires the clearing of natural habitats. This form of direct biodiversity pressure is

relatively easy to quantify in a spatially explicit way using remote sensing approaches. As mentioned in the

habitat degradation section above, rangeland livestock farming makes up a large proportion of South Africa

(70%). The impacts of extensive livestock farming on biodiversity are far more difficult to quantify and vary

between different ecosystems and due to different rangeland management practices (Barger et al. 2018;

Prince et al. 2018). There are numerous examples where commercial livestock farming is highly compatible

with biodiversity conservation, and conversely there are examples where even sustainable rangeland land

management has negatively impacted elements of biodiversity (O’Connor & Kuyler 2009; O’Connor et al.

2010; Little, Hockey† & Jansen 2015; Prince et al., 2018). Other pressures from agricultural activities include

fertilizers and pesticides which can impact on neighbouring vegetation.

The key metrics we are able to track for this national assessment are a) the proportion of natural areas that

have been lost to cropland (including horticultural crops) between 1750 and 2014 (historical habitat loss) and

b) the recent loss of natural habitat due to new croplands (between 1990 and 2014). The land cover change

datasets discussed in Chapter 3 show that over 16% (195 413 km2) of the country has been converted to

cropland since ~1750 (calculated by combining secondary natural class and the cropland class in 1990 and

then adding in the additional new croplands developed between 1990 and 2014). The most extensive field

crop in South Africa is maize (covering 26 682 km2 in 2017), followed by sunflowers, soya and wheat (DAFF,

2018)11. Deciduous fruit is the most extensive horticultural crop (covering ~7 970 km2 in 2017) followed by

viticulture and citrus. Considering the period 1990 to 2014, expanding croplands have resulted in habitat loss

of 13 706 km2 (1.1% of South Africa). Statistics linking land clearing directly to crop types are not currently

available so it is difficult to attribute the habitat loss to a particular crop or agricultural practice. The biggest

impact by croplands in terms of habitat loss has been in the Grassland (31%), Indian Ocean Coastal Belt (28%)

and Fynbos biomes (27%) (Figure 12a). The provinces where habitat loss is driven by croplands are Free State

(35%), Gauteng with (31%), North West (29%) and Mpumalanga (27%) (Figure 12b).

Crop cultivation is the dominant pressure to South Africa’s reptiles impacting 64% of TOCC. Mammal and bird

TOCC are also heavily impacted by loss of habitat to crop cultivation. A total of 1 757 plant TOCC (28%) are

declining as a result of loss of habitat to crop cultivation (Figure 11).

Figure 12. Habitat loss per biome (a) and per province (b) driven by cultivation. Historical loss (between 1750 and 2014) shown in grey and recent loss (between 1990 and 2014) shown in black.

11 DAFF (2018) Trends in the Agricultural Sector 2017. Department of Agriculture, Forestry and Fisheries, Pretoria.


48

3.3.1. Historical perspective of agricultural changes 1911-2007

A census of the major crops grown in South Africa, as well as the number of livestock on farms, has been published every five to 10 years, from 1836 until 2007 when the most recent agricultural census was undertaken (Statistics South Africa 2010). The data have been reported at a magisterial district level, which for most of the 20th century numbered 367 districts (Hoffman & Ashwell 2001). Unfortunately, information has been reported on a relatively consistent basis only for the white-owned, ‘commercial’ farming areas which, for the purposes of this report, number 249 districts. Data from the independent ‘homelands’ (Transkei, Bophuthatswana, Venda, Ciskei) and ‘self-governing territories’ (e.g. KwaZulu, Lebowa, Gazankulu, etc.), are only rarely included in the census record and not at all since 1976. These are areas where, according to legislation promulgated by successive colonial and apartheid governments, the majority of black South Africans were expected to live. Because of the lack of a continuous, reliable record is it is not possible to document long term trends in key agricultural production indicators in the ‘communal’ areas of South Africa and they are therefore excluded from this analysis.

Data on the number of hectares cultivated to maize and wheat (Figure 13), as well as the number of livestock (cattle, sheep, goats and equines [horses, donkeys and mules])(Figure 14) reported in each of the 249 ‘commercial’ magisterial districts for the period 1911 to 2007 was collated from the agricultural census records. Using a GIS overlay of the vegetation of South Africa (Mucina & Rutherford 2006) each magisterial district was assigned to a biome based on the dominant biome represented in the district. In about 5% of cases two or more different biomes were present in relatively equal proportions in a magisterial district and no dominant biome was evident. Under these circumstances, a decision on the districts’ biome affinity was made according to the biome in which the major agricultural activity is likely to have occurred. Also, in some biomes (e.g. Desert, Indian Ocean Coastal Belt) agriculture makes a relatively minor contribution to the national statistics and they are therefore not included in the reporting which follows. Only data for the following biomes (and number of magisterial districts) are reported: Albany Thicket (11), Fynbos (35), Grassland (121), Nama-Karoo (29), Savanna (45) and Succulent Karoo (8).

Cultivation: Maize and wheat

Maize and wheat are the main agricultural crops in South Africa and significantly more hectares are planted to these two crops than to others such as rye, barley, lucerne, sunflowers, etc. The largest areas planted to maize are primarily in the Grassland biome and secondarily in the Savanna biome (Figure 13). The remaining biomes contribute relatively little to the hectares of maize sown in South Africa. Most hectares of wheat are sown in the Grassland and Fynbos biomes although some wheat is also sown in the Savanna, Nama-Karoo and Succulent Karoo biomes.

Figure 13. The number of ha planted to maize (a) and wheat (b) in the biomes of the commercial farming areas of South Africa for the period 1911-2007 and as reported in the agricultural census record (e.g. Statistics South Africa, 2010). Note that the y-axis is not the same for the different crops.

(a) (b)


49

The trend for the number of hectares sown to both maize and wheat in all biomes is similar. The number of hectares increased linearly until the 1960s where after the area remained relatively constant until 1988. The 1993 and 2002 census, however, returned significant successive declines in the area cultivated to maize and wheat with 2002 levels only about a third of those when the largest area in South Africa was cultivated to these crops. The 2007 census showed that the area cultivated to maize has increased a little while the area cultivated to wheat is similar to 2002 levels.

These census-derived figures are well aligned with the land cover change study (Chapter 3) which showed that almost 3.6 million hectares of South Africa consists of ‘secondary natural’ areas which are historically cultivated lands (circa 1950-1970s) that have recovered some vegetation cover after being abandoned. While there has been a decline in area cultivated for maize and wheat, agriculture continues to be the main driver of natural habitat loss as new cultivated areas (for crops other than maize and wheat) are established.

The reasons for the decline in area cultivated to maize and wheat are difficult to determine. There is the possibility that values in the census record might not reflect the reality on the ground. Pre-1994 census records relied on an Agricultural Extension Service dedicated to the commercial farming sector. After 1994, however, the priorities of the Department of Agriculture changed and the Agricultural Extension service focused increasingly on emerging and small-scale farmers. The loss of contact with the commercial farmers might have had an influence on the accuracy of the data collected during the agricultural census. Another explanation for the decrease in area cultivated to maize and wheat might include the diversification of crop production amongst commercial farmers. Other crops might have replaced maize and wheat as the primary crops grown in the commercial farming areas of South Africa although the data contained in the 2007 agricultural census record do not reflect this (Statistics South Africa, 2010).

Overall, these changes in area cultivated are significant for the ecology of Grassland, Savanna and Fynbos biomes and to some extent the other biomes as well. The extent to which the extensive fallow lands have been able to regain some measure of natural vegetation structure and composition is worth exploring further.


50

Livestock numbers

Irrespective of the type of livestock, the number of animals in the commercial magisterial districts of South Africa has declined significantly relative to their peak numbers in nearly all biomes over the last century (Figure 14). Most cattle are supported on rangelands in the Grassland and Savanna biomes and following an initial increase in the first part of the 20th century the number of animals in 2007 has declined to just over half their peak value recorded in 1946. Sheep are most abundant in the Grassland and Nama-Karoo biomes and they have similarly declined to only a quarter of the number that they were in the 1930s. In 2007 the total number of goats in South Africa was less than 7% of their peak value which was recorded in 1911. The number of equines (horses, mules and donkeys) in 2002 was just over 1% of the number that was recorded in 1918. If the number of animals in all livestock breeds is added together then, in 2002, when the last census of equines was undertaken, South Africa supported just over a quarter of the total number of animals that was recorded in 1930, when the total number of animals in the commercial farming areas was at its peak.

Figure 14. The number of cattle, sheep, goats and equines (horses, mules, donkeys) in the biomes of the commercial farming areas of South Africa for the period 1911-2007 and as reported in the agricultural census record (e.g. Statistics South Africa, 2010). Note that the y-axis is not the same for the different livestock breeds.

This decline in animal numbers has significant consequences for the vegetation and biodiversity of South Africa (see Section 3.2.1). Large numbers of animals alter the composition and structure of rangelands with important consequences for a range of important ecosystem services such as water provision and carbon sequestration. A synthesis of information for the Succulent and Nama-Karoo biomes (Hoffman et al. 2018) has argued that the increase in vegetation cover on the slopes, plains and ephemeral rivers, documented in many of nearly 300 repeat photograph pairs recorded in the region, has occurred as a direct result of the significant decline in livestock numbers since the mid-20th century in these biomes. The increase in woody plant cover, particularly in the savanna biome (Skowno et al. 2017) has a long history in southern Africa and the role of herbivory, together with fire and the increase in CO2, provides a partial explanation for these changes in some areas (O’Connor et al. 2014) (See Chapter 5 for more detail).

(a) (b)

)

(a)

(c) (d)


51

3.4. Plantation forestry Habitat loss and the associated loss of species driven by

afforestation (typically non-indigenous timber species

planted in Grassland or Fynbos ecosystems) can be

assessed using the existing land cover change datasets.

The historical loss of habitat (1750-2014) driven by

plantation forestry amounts to approximately 20 954 km2

(1.7% of South Africa). Based on the land cover data

available, the extent of plantation forestry declined slightly

between 1990 and 2014, but there was also a total of

2 734 km2 of natural habitat loss from afforestation. These

trends match official forestry industry statistics12 showing

an increase in plantation area between 1990 and 1996 of

2 168 km2, followed by a steady decline in plantation area

between 1997 and 2017 of 2 848 km2. The provinces where

habitat loss is driven by timber production are

Mpumalanga with 11% and KwaZulu-Natal with 8%. Gauteng, the Eastern Cape and the Western Cape have

significant plantation forestry areas, but these amount to less than 3% of the province (Figure 15).

In addition to habitat loss, there are a number of biodiversity impacts from forestry plantations (see review

by Armstrong et al. 1998). Commercial afforestation threatens a host of birds, both through outright

transformation of indigenous Grassland vegetation, as well as through changes to the hydrology as a result

of the concentration of large numbers of trees (which causes the drying up of streams and wetlands).

Afforestation of Grassland in parts of Mpumalanga has had a particularly adverse impact on large terrestrial

species such as cranes and bustards which require vast open landscapes for continued survival.

Afforestation negatively impacts on 45% of South Africa’s birds, 33% of amphibian and 25% of reptile TOCC

(Figure 11). Expansion of plantations in the Eastern Cape and southern parts of KwaZulu-Natal has led to

species such as the Mistbelt Chirping Frog (Anhydrophryne ngongoniensis) and the Amatola Toad

(Vandijkophrynus amatolicus) increasing in threat status between 1990 and 2015.

Figure 15. Habitat loss per biome (a) and per province (b) driven by plantation forestry. Historical loss (between 1750 and 2014) shown in grey and recent loss (between 1990 and 2014) shown in black.

12 South African Forestry and Forest Products Industry Facts File 1980-2016 (http://www.forestry.co.za/statistical-data).

Biodiversity ‘in the matrix’ Plantation forestry established in the natural grasslands ‘matrix’ adjacent to indigenous forest patches presents a novel environment for biodiversity. Studies have shown that forest specialist arthropods (Yekwayo et al. 2016) and small mammals (Wilson et al. 2010) prefer natural grasslands to timer plantations as a matrix. While for forest specialist birds, plantations can facilitate dispersal between indigenous forest patches (Wethered & Lawes 2003). For certain forest plants recruitment under the shade of timber compartments can be higher than in natural matrix grasslands, potentially leading to indigenous forest expansion (Geldenhuys 1997). Source: SA Forestry Online article by Lize Joubert-Van der Merwe.

http://saforestryonline.co.za/articles/indigenous-forest-patches-not-biodiversity-islands/

http://saforestryonline.co.za/articles/indigenous-forest-patches-not-biodiversity-islands/


52

3.5. Infrastructure and settlements

Settlements and associated infrastructure

The development of human settlements and associated infrastructure (equivalent to the land cover class

built-up) have resulted in the loss of 31 666 km2 (2.6% of South Africa) of natural habitat between 1750 and

2014. The provinces where natural habitat is most affected by the development of built-up areas are Gauteng

with 3 770 km2 (21%) and KwaZulu-Natal 9 028 km2 (10%). The majority of recent habitat loss driven by new

built-up areas has taken place in Limpopo (895 km2), followed by Gauteng (437 km2) and the Eastern Cape

(426 km2) (Figure 16a).

The biomes most affected by infrastructure and settlement are Indian Ocean Coastal Belt with 2 567 km2

(22%), Grassland 12 087 km2 (3.7%) and Savanna 13 530 km2 (3.4%). The biomes with the greatest change

between 1990 and 2014 are the Indian Ocean Coastal Belt 96 km2 (0.8%) and Savanna 1 791m2 (0.5%) (Figure

16b).

Urban and coastal development is one of the leading causes of population decline to amphibian species with

39% of TOCC threatened by development. 20% of the taxa that have become more threatened in the past 15

years have their change in status due to urban and coastal development. One example is the spotted Snout-

Burrower (Hemisus guttatus), which has most of its population concentrated on the KZN coast line. It has

experienced extensive loss and fragmentation of its habitat due to coastal development and as a result has

been up listed from Least Concern to Near Threatened.

A total of 70% of the reptile species that have become more threatened over the past 20 years are as a result

of increase in urban and coastal development. Habitat specialists with small ranges that have distributions

limited to coastal or metropolitan areas are particularly vulnerable. Two examples include the Durban Dwarf

Burrowing Skink (Scelotes inornatus) uplisted from Endangered to Critically Endangered due to ongoing loss

of habitat within greater Durban area, and the Southern Adder (Bitis armata) uplisted from Near Threatened

to Vulnerable, due to the rapid increase of coastal development in the Langebaan peninsula and the area

between Danger Bay and the Breede River mouth, both coastal areas where the species is found. Overall 41%

of reptiles of conservation concern are threatened by the development of human settlements while 47% of

mammal TOCC are similarly impacted (Figure 11).

Figure 16. Habitat loss per biome (a) and per province (b) driven by human settlement and commercial & industrial development (these activities fall into the land cover class ‘built-up’). Historical loss (between 1750 and 2014) shown in grey and recent loss (between 1990 and 2014) shown in black.


53

Transport, power and communication networks

The development of road, rail, electricity and telecommunication networks result in relatively low levels of

direct habitat loss, but can have significant impacts on animal movement, seed dispersal, fire spread (Forman

& Alexander 1998). They are generally not well reflected in the land cover data and are not easily quantified

spatially. The electricity grid poses a threat to large terrestrial and wetland birds as well as some smaller,

fast-flying species. Large birds that are relatively ungainly in flight (e.g. flamingos, cranes and bustards) are

particularly vulnerable to flying directly into utility wires and cables. Declines in 15 species of large birds

including iconic species such as Ludwig’s Bustard (Neotis ludwigii) can be directly attributed to these collision

events (Taylor, Peacock & Wanless 2015). Although there are these negative effects, when these networks

cross anthropogenic landscapes they can provide corridors for movement by some animals. For example, if

the road verges or space under electricity infrastructure are in a near natural state, they can be home or

movement corridors to for populations of threatened species.

Energy production

Energy production impacts on biodiversity can be direct impacts linked to habitat loss and indirect impacts

linked to their operation. The habitat loss linked to energy production infrastructure is generally well

represented in the land cover change data, but since the majority of renewable energy facilities have been

built since 2014 (i.e. after the latest land cover time point) these energy facilities are not adequately

represented for in the current land cover change data. This short coming will be addressed in future land

cover products.

The past 10 years has seen a significant increase in South Africa’s renewable energy sector with an excess of

63 new installations operating, 27 under construction and a number more planned13. While renewable

energy projects result in far lower greenhouse gas emissions than other forms of energy production such as

coal, oil and gas, the expansion of this form of energy facility results in the direct loss of habitat (e.g. solar

and wind facility footprints) and in some circumstances it can significantly threaten bird and bat species

through collisions with operation structures (i.e. wind turbines). This is especially true where these obstacles

occur as prominent features in open airspace. Disturbance can lead to birds being displaced and excluded

from areas of suitable habitat – effectively causing loss of habitat for them (Taylor & Peacock 2018). The

effects attributable to wind farms are variable, specific to species, and still poorly understood. Monitoring of

post-construction sites currently underway will feed much needed data into future status assessments, and

will also provide input into best practise for the planning and construction of wind energy projects to

minimise impacts to sensitive species.

13 https://www.ipp-projects.co.za/ProjectDatabase, accessed in November 2018.

https://www.ipp-projects.co.za/ProjectDatabase


54

Box 3. Wildlife ranching and the fencing conundrum

Wildlife ranching and the fencing conundrum

The proliferation of wildlife ranches across South Africa is rightly recognised as a potential boon for both biodiversity and the green economy. However, the tendency for wildlife ranchers to breed commercial valuable game species for high-profit trophy hunting and live animal sales is resulting in the erection of breeding camps and enclosures across swathes of South Africa. While the extent of landscape fragmentation caused by these internal fences is yet to be quantified, it is likely that the practice leads to overstocking and thus habitat degradation, which may cause population crashes and an erosion of social-ecological resilience. The direct effects of fences on species conservation, however, are starting to be documented. Ranchers directly persecute predators, ranging from jackals to leopards, to protect their wildlife assets and this trend is increasing with the expansion of the industry. The electrification of these fences also leads to ‘by-catch’ of other species, ranging from pythons and tortoises to pangolins. For example, the national Red List assessment for the ground Pangolin (Smutsia temminckii) lists one of the primary threats to the species as electric fences where an estimated 377 to 1 028 die after curling round electrified strands each year (Figure 17).

There are several potential solutions to overcome the negative effects of fences. The best would be to encourage the formation of conservancies by dropping internal fences and sharing economic benefits from wildlife between landowners. Installing artificial passageways in fences has been demonstrated to facilitate the movement of species ranging from warthogs to cheetahs between ranches in Namibia.

Similarly, switching to a triple-strand tripwire (rather than the standard single- or double-strand tripwire) has been demonstrated to reduce pangolin mortality by 66%. Implementing simple, cost-effective solutions such as these on a large scale could significantly ameliorate the impact of the expanding wildlife industry.

Matthew Child – South African National Biodiversity Institute

3.6. Mining The ‘Mining and Biodiversity Guidelines’ published in 2013 summarise the range of impacts on biodiversity

typically associated with mining (DEA et al. 2013). Direct impacts linked to clearing natural habitat are

captured in the land cover change dataset. Other direct impacts include on species and ecosystems, such as

water abstraction from natural sources and contamination of water bodies. The indirect impacts, such as

migration of pollutants and induced impacts such as those linked to mine associate industrial and urban

development are only quantified on a site by site basis and are not reflected in the land cover change

analyses.

From a land cover change perspective mining resulted in the loss of at least 3 686 km2 of natural habitat

between 1750 and 2014 (0.3% of South Africa). Between 1990 and 2014, a total of 695 km2 of natural habitat

was lost to direct, mining related land clearing. The land cover data suggests that some mines detected in

the 1990 data now have some vegetation cover (i.e. these were classified in 2014 as secondary natural;

867 km2). The provinces where natural habitat was historically most affected by mining operations are

Gauteng and Mpumalanga which lost 1.6% (294 km2) and 0.8% (641 km2) (Figure 18). The provinces with the

greatest habitat loss due to mining between 1990 and 2014 are Gauteng (46 km2, 0.3%) Mpumalanga

(165 km2, 0.2%) and North West (163 km2, 0.2%).

Ongoing escalation of mining activities in remaining near natural areas in the Grassland biome is placing

significant additional pressure on restricted endemic species. For example, the Steenkampsberg Important

Bird and Biodiversity Area, home to the Critically Endangered the White-winged Flufftail (Sarothrura ayresi),

has seen increased applications for mining (Taylor and Peacock, 2018).

Figure 17. Temminck’s Ground Pangolin (Smutsia temminckii) entangled in an electric fence line) Photo: Darren Pietersen.


55

Certain groups of mammal species are particularly impacted by mining. For example, a number of South

Africa’s golden mole species are threatened or Near Threatened as a result of habitat loss to mining, one

species, De Winton’s Golden Mole (Cryptochloris wintoni), is listed as Critically Endangered Possibly Extinct

as a result of its habitat mainly being transformed for diamond mining in the Port Nolloth region. Van Zyl’s

Golden Mole (Cryptochloris zyli), also a Namaqualand endemic listed as Endangered, is similarly impacted

with dramatic habitat alteration owing to the large-scale mining of coastal sands for alluvial diamonds

occurring on the coastal dune habitats of this species. Mining is also impacting negatively on endemic plants

with many species restricted to the Desert and Succulent Karroo biomes particularly impacted.

Figure 18. Habitat loss per biome (a) and per province (b) driven by mining activities. Historical loss (between 1750 and 2014) shown in grey and recent loss (between 1990 and 2014) shown in black.

3.7. Biological resource use Biological resource use is a pressure that directly targets specific species and includes hunting, poaching and

trapping of animals and harvesting of plants. This type of pressure is not detected in land cover products. In

South Africa, animals and plants are commonly used as traditional medicine for both the healing of ailments

and for spiritual and cultural purposes (Whiting, Williams & Hibbitts 2011). Over 2 000 indigenous plant

species have documented traditional medicinal uses, and just over a quarter of these are traded annually in

the country (Williams, Victor & Crouch 2013). The majority of plant material is obtained from open access

communal lands in various provinces around the country. These resources are collected without any

restrictions and can be for personal use, but most are transported to urban markets where they are sold to

traders and traditional healers. Some 656 medicinal plant species are common in trade and many are

unsustainably harvested, with 184 species declining due to unsustainable use. Of these 656 traded species,

18.9% are of conservation concern with 8.5% (56) listed as threatened (Critically Endangered, Endangered or

Vulnerable), 4.6% (30) listed as Near Threatened and 5.2% (43) listed as declining (Williams, Victor and

Crouch, 2013).


56

A study by Whiting et al. 2011 about South Africa’s main medicinal market, Faraday, showed that 147

vertebrate species representing about 9% of the total number of vertebrate species in South Africa are traded

for traditional medicine. Regularly traded vertebrates included 60 mammal species, 33 reptile species, 53

bird species and one amphibian species. Increases in volumes of animals traded is having an impact of the

threat status of a number of species. For example, there has been a rise in death of vultures for use in the

muthi trade, with 29% of vulture deaths attributable to harvesting of vulture body parts for traditional

medicine (Taylor and Peacock, 2018). Endemic species of reptiles including the iconic sungazer, Smaug

giganteus, are experiencing ongoing declines due to harvesting for traditional medicine.

A meta-analysis of data on pressures as part of the 2016 Mammal Red List assessment (Child et al. 2017)

indicates that hunting, poaching and trapping of animals is causing significant declines to 28 mammal species.

The past decade has seen the rise of the new emerging threat of international wildlife trafficking syndicates

that are beginning to heavily impact on species desired for overseas markets, including Rhinos

(Ceratotherium simum and Diceros bicornis) and Pangolins (Smutsia temminckii). Expansion of human

settlements, especially in areas bordering protected areas has resulted in increased hunting intensity for

bushmeat and/or traditional medicine and cultural regalia, as well as increasing the number of animals killed

incidentally in snares, which impacts species ranging from African Wild Dog (Lycaon pictus) and Leopard

Poisoning of vultures for belief-use (muthi) purposes is one of the most significant threats facing all species of vultures in Africa (Botha et al. 2017). Belief-based use of vultures occurs when the carcasses, body parts and derivatives are used to treat a range of physical and mental ailments, or to bring good fortune. Vultures are sold alongside other species of birds, mammals, reptiles and other taxa at markets specialising in supplying belief-based users. Six African vulture species were included in a group of 19 conservation priority bird species recorded most frequently in markets in 25 African countries surveyed (Williams et al. 2014). With the rapid growth of human populations and more effective harvesting methods (through the use of highly toxic poisons) the impact on vulture populations is increasing.

Due to the range of threats and relatively small populations of most vulture species in South Africa, the current impact of poisoning for and trade in vulture parts for belief-use is not sustainable and could contribute to the rapid decline and even extinction of species such as the white-headed-, hooded- and bearded vulture in the region in the next 20 years while the nationally more common species such as the Cape Vulture (Gyps coprotheres) and African White-backed Vulture (Gyps africanus) due to their social feeding habits will also likely be affected.

A National Vulture Conservation Action Plan needs to be developed and implemented to achieve the following: reduce consumption and demand for vultures through an awareness-building campaign targeting public consumers and current role-players in the trade; develop and implement policies to improve regulation of the vulture trade; improve law enforcement for better regulation of the vulture trade; and improve understanding of the vulture trade to allow more focused interventions including research and monitoring of the use and trade of vultures.

Figure 19.Poisoned vultures (photo: Andre Both) and vulture foot (Photo: Scott Ramsay).

Andre Botha – Endangered Wildlife Trust

Box 4. The impact of muthi practices on threatened vulture populations


57

(Panthera pardus) to Temminck’s Ground Pangolin (Smutsia temminckii) and Mountain Reedbuck (Redunca

fulvorufula), with the Mountain Reedbuck experiencing significant declines resulting in it being up listed from

Vulnerable to Endangered. There has been an increase in the scale of illegal sport hunting with dogs which

directly threatens species, such as Oribi (Ourebia ourebi). The increasing use of leopard skins for cultural

ceremonies has resulted in the leopard being uplisted from Least Concern to Vulnerable. Six mammal species

have increased in threat status between 2004 and 2016 as a result of direct persecution (Child et al., 2017).

3.8. Pollution Pollution in South Africa can be identified in local hotspots, and is usually associated with urban development

and power stations. For example the extent of

the impact of pollution is country-wide, across

multiple ecosystems. In 2017 South Africa

generated 42 million tonnes of general waste

and 38 million tonnes of hazardous waste (DEA

2018). Approximately 11% of the general waste

was recycled, leaving the remainder of the

waste to be put into the landscape and

atmosphere. Urban areas produce the highest

amount of pollution with Gauteng

(761 kg/capita/year) and the Western Cape

(675 kg/capita/year) having the greatest

municipal waste contribution in 2011 (DEA,

2012b).

The exposure to harmful pollutants may cause

harmful effects on species across South Africa.

Birds are the taxa most susceptible to pollution

in the terrestrial realm, which could be a result

of having exposure to multiple pathways of

pollutants, for example, solid waste from

landfill site, or air-borne pollutants.

The majority of hazardous waste generated in

2017 was from fly ash and dust (96.1%)

primarily from coal-fired power stations (DEA

2018). This waste can give rise to air pollution

along with other pollutants emitted from power

stations and landfill sites.

South Africa is heavily reliant on energy from

coal-fired power stations which emit several pollutants into the atmosphere. Pollutants such as Sulfur dioxide

and Nitrogen dioxide (SO2 and NO2) can lead to acid rain which can contaminate ecosystems. Ramall et al.

(2014) observed effects of acid rain on Forest Natal Mahogany tree (Trichilia dregeana) seedlings in South

Africa. They observed stress-related symptoms including leaf tip necrosis, abnormal bilobed leaf tips and

reduced leaf chlorophyll concentration. Acid rain has the potential to alter the establishment success rate of

South African vegetation as pollutants increasingly infiltrate the atmosphere (DEA 2012a). Mercury (Hg) is

another key pollutant emitted by these facilities. Mercury concentrations were sampled from wild hatched

eggshells from two threatened bird species, the Southern Ground-Hornbill (Bucorvus leadbeateri) and the

Wattled Crane (Bugeranus carunculatus), in a study by Daso et al. (2015). Concentrations of Hg higher than

Debris from landfill sites polluting Kelp Gull habitats A study focussing on pollution found in Kelp Gull (Larus dominicanus) nests in the Western Cape shows how landfill sites are the predominant reservoir for debris collection for this species (Witteveen, Brown & Ryan, 2016) Waste was translocated from stranded beach litter for nest construction or regurgitated in nests from neighbouring landfill sites. Dietary-derived debris increased with proximity to urban waste landfill sites as Kelp Gulls scavenge landfill sites for food and commonly ingest debris in the process. The increased consumption and use of waste for nest construction can lead to increased risk of entanglement for chicks and adults. The physical displacement of waste is just one of the pathways for pollution to affect biodiversity.

Figure 20. Pollution in Kelp Gull nests. Soures: Minke Witteveen http://oceanadventures.co.za/kelp_gull/ & http://www.infrastructurene.ws/2017/02/22/improving-landfilling-correct-practices-and-useful-technologies/ .

http://oceanadventures.co.za/kelp_gull/

http://www.infrastructurene.ws/2017/02/22/improving-landfilling-correct-practices-and-useful-technologies/

http://www.infrastructurene.ws/2017/02/22/improving-landfilling-correct-practices-and-useful-technologies/


58

1.5µgg-1 can lead to adverse effects and decrease reproductive success, and both species had higher levels.

Cranes had especially higher concentrations, which could be a result of the Hg bioaccumulation through the

food chain as the cranes forage on aquatic flora and fauna (Olowoyo, Mugivhisa & Busa 2015). High

concentrations of Hg were also found in Nile Crocodile (Crocodylus nyloticus) eggs in the Kruger National Park

(Botha, Van Hoven & Guillette Jr 2011) which illustrates how pollutants can travel extensively through

bioaccumulation in soil and water systems.


59

4. PRESSURES AND DRIVERS II – BIOLOGICAL INVASIONS

Chapter 4: Van Wilgen, B.W., Wilson, J.R., Faulkner, K.T., Mnikathi, Z., Morapi, T., Munyai, T., Rahlao, S. & Zengeya, T.A. 2019. ‘Chapter 4: Pressures and Drives II – Biological Invasions’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Insights from the status report on biological invasions in South Africa

Overview

This section gives some insights on the key findings from the first status report on biological invasions in South

Africa. The report is the first such country-level assessment anywhere in the world that focuses specifically on

biological invasions. The report covers three main aspects of invasions (pathways, species, and areas), as well

as interventions (in terms of both the effectiveness of control measures, and the effectiveness of the

regulations).

Most alien species found in South Africa today were intentionally introduced many years ago, either

deliberately with the goal of establishing populations in nature, or for horticulture, agriculture, forestry or the

pet trade from where some escaped to become invasive. The remainder were introduced accidentally as

commodity contaminants or as stowaways on transport vectors. While the rate of intentional introduction of

high-risk species is expected to decline due to improved regulation, it is also expected that the rate of

unintentional introductions will increase due to increases in trade and tourism. The rate at which species are

arriving in the country appears to be gradually increasing. Once an alien species is introduced to South Africa,

further spread within the country is highly likely and very difficult to stop. There is a thriving trade in alien

species for a variety of purposes within South Africa’s borders. Alien species can also be accidentally

transported along the country’s extensive transport networks, and invasive species can spread naturally.

A total of 556 invasive taxa have been listed in the Alien and Invasive Species Regulations 2016. The actual

number of invasive species is higher, with 775 having been identified to date. Most of these invasive species

are terrestrial and freshwater plants (574 species) or terrestrial invertebrates (107 species). A total of 107

species were considered by experts to be having either major or severe impacts on biodiversity and/or human

wellbeing; the vast majority of these (75%) were terrestrial or freshwater plants.

Alien species richness was highest in the Savanna, Grassland, Indian Ocean Coastal Belt and Fynbos biomes,

with relatively low species richness in the more arid Karoo and Desert biomes. Alien trees and shrubs can

dominate areas such as Fynbos catchments and coastal areas; Mesquite trees (Prosopis spp.) dominate arid

areas; many riparian zones are invaded by trees; many rangelands are invaded by cacti and herbaceous

annual and perennial plants. There are very few studies that cover the combined impacts of invasive species

on particular areas.

Available studies estimate the combined impacts of invasive plants on surface water runoff at between 1 450

to 2 450 million m3 per year. If no remedial action is taken, reductions in water resources could rise to between

2 600 and 3 150 million m3 per year, severely impacting drought-stricken cities like Cape Town. Total

reductions in the productivity of rangelands, and in biodiversity intactness, are low at present (between 1 and

3%), but these impacts are expected to grow rapidly as invasive plants enter a stage of exponential growth.

Biological invasions account for 25% of the reduction in South African biodiversity seen to date.

In terms of control measure inputs, South Africa’s Alien & Invasive Species Regulations are substantial, as they

cover most aspects of the problem. Large sums of money have been spent (currently R1.5 billion per year),

especially explicitly on the control of terrestrial and freshwater plant species. This is almost certainly an

underestimate as it only includes funding from the Department of Environmental Affairs and not from other


60

government or semi government entities or the private sector. Planning coverage is low, and there is little

evidence of adequate levels of goal-setting or monitoring.

Control measure outputs are assessed in terms of the proportion of pathways, species or areas that have been

subjected to control. The Convention on Biological Diversity recognises 44 pathways of introduction, and 34

of these pathways (77.3%) are managed to some extent. Although 556 taxa are listed in the Alien & Invasive

Species regulations, not all of these are subjected to active management. For example, ~126 out of 379 alien

terrestrial and freshwater plant taxa have been targeted for some control, and of these, eight species make

up 80% of the area subjected to treatment. In terms of areas, less than 1% of invaded land has been reported

to have been the subject of control measures.

Data on the outcomes of control measures are sorely lacking. The impact of pathway regulation on rates of

introduction of invasive species cannot yet be determined, given that they have only been in place for a short

time. Control measures have been shown to be effective in some localised areas but not so in others. While

the situation would arguably have been worse had there been no control, current control efforts have not

been effective in preventing the ongoing spread of invasive species when viewed at a national scale.

4.1. Introduction The South African National Biodiversity Institute (SANBI) is mandated by the National Environmental

Management: Biodiversity Act (Act 10 of 2004) and its Regulations (Alien and Invasive Species Regulations,

2014) to monitor and report regularly to the Minister on the status of all listed invasive species. In order to

fulfil this mandate, the SANBI must submit a report to the Minister within three years of the A&IS regulations

coming into effect, and at least every three years thereafter. The report must contain a summary and

assessment of (a) the status of listed invasive species and other species that have been subjected to a risk

assessment; and (b) the effectiveness of the regulations and control measures. SANBI is also expected to

carry out research and monitoring necessary to determine status and effectiveness. The first report was

completed (Van Wilgen & Wilson 2018) and submitted to the Minster in March 2018 (see

https://www.sanbi.org/media/the-status-of-biological-invasions-and-their-management-in-south-africa). It

is the first report globally that provides an assessment of the status of all aspects of biological invasions at a

national level (Van Wilen & Wilson 2018).

The report covers three main aspects of invasions (pathways, species, and areas), as well as interventions (in

terms of both the effectiveness of control measures, and the effectiveness of the regulations). In order to

report on these aspects, a suite of indicators were developed to assess status at a national level (Figure 21)

(Wilson et al. 2018). The indicators for pathways assess the potential dispersal routes into and within the

country and the degree to which each pathway is responsible for spreading organisms. Indicators for species

assess the status, extent, abundance and impact of species. The indicators for sites assess alien species

richness (number of species to be considered), relative invasive abundance (indicates the presence of

dominant alien species) and impact of invasions (provision of ecosystem services using qualitative and

quantitative estimates and conversion into monetary terms of reduction in services due to invasions).

Interventions are split into inputs (quality of regulatory frame work, money spent, planning coverage),

outputs (the degree to which pathways, species and sites that need to be managed are actual subjected to

management interventions, and an assessment of the quality of intervention) and outcomes (effectiveness

of treatments for pathways, species and sites – does the interventions in place make a difference i.e. change

the status of biological invasions?).

In addition, there are four high level indicators that are aggregated from the 20 indicators. These align with

the pressure, state and response framework: a) rate of introduction of unregulated species (pressure); b)

https://www.sanbi.org/media/the-status-of-biological-invasions-and-their-management-in-south-africa


61

number of invasive species that have major impacts (state); c) extent of area that suffers major impacts

(state) and d) level of success in managing invasions (response). Each indicator is modular so that if resources

permit more detailed data may be collected without compromising the ability to compare situations where

fewer data are available. Each indicator is assigned some level of confidence to the metrics (high, medium

and low). If direct evidence is available – high, if the evidence is ambiguous, not clearly documented or

inferred – low. Lastly, for each indicator a fact sheet was developed, outlining how the indicators are to be

measured and providing a method for ascribing a level of confidence when assigning values to indicators.

The main aim of this section is to give some insights of some of the key findings from the status report on

biological invasions in South Africa.

Figure 21. The 20 indicators and four high-level indicators that have been proposed for monitoring and reporting on the status of biological invasions at a national level. (Adapted from Wilson et al. 2018).

4.2. Pathways There are many different potential pathways of introduction to South Africa and the prominence of some of

these pathways has increased markedly over time, in particular with increasing trade. The goods, people and

transport vessels that are related to these pathways can enter the country through 72 official ports of entry.

Alien species are being introduced to South Africa through a wide variety of pathways, and although most

alien taxa have been intentionally imported into the country, many have been accidentally introduced as

commodity contaminants or as stowaways on transport vectors. In addition, some taxa have entered the

Republic from neighbouring countries through natural spread over the 4 862 km long land borderline, but

none have spread into the country through human-built corridors that connect previously unconnected

regions (e.g. canals). Most alien taxa were originally imported intentionally for the ornamental plant trade

and some have subsequently escaped from cultivation. Overall the rate of introduction of new taxa appears

to be increasing.

For many pathways there has been an increase or no major change in introduction rate since the 1990s, and

only a few pathways (e.g. introductions for fishing and aquaculture) are no longer responsible for the

introduction of new alien taxa. Notably, however, it was not possible to ascribe > 50% of alien taxa to an

introduction pathway. South Africa’s extensive and well-functioning transport networks facilitate the

transportation of a large, and increasing, amount of goods and people, and so once an alien taxon has been

5. Number and status of alien species

6. Extent of alien species

7. Abundance of alien speciesSPECIES

SITES

9. Alien species richness

10. Relative invasive abundance

PATHWAYS

1. Introduction pathway prominence

3. Within-country pathway prominence

INTERVENTIONS

11. Impact of invasions

2. Introduction rates

4. Within-country dispersal rates

18

. Eff

ect

ive

ne

sso

f p

ath

wa

y t

rea

tme

nts

OU

TC

OM

ES

8. Impact of alien species

19

. Eff

ect

ive

ne

ss o

f sp

eci

es

tre

atm

en

ts

20

. Eff

ect

ive

ne

ss o

f si

tes

tre

atm

en

ts

15

. P

ath

wa

ys

tre

ate

d

OU

TP

UT

S

16

. S

pe

cie

s tr

ea

ted

17

. A

rea

s tr

ea

ted

12

. Q

ua

lity

of

reg

ula

tory

fra

me

wo

rk

INP

UT

S

13

. M

on

ey s

pe

nt

14

. P

lan

nin

g c

ove

rag

e

HIGH LEVELA. Rate of introduction of new

unregulated species

HIGH LEVELB. Number of invasive species that

have major impacts

HIGH LEVELC. Amount of area that suffers major

impacts from invasions

HIGH LEVELD. Level of success in managing

invasions


62

introduced to South Africa, further dispersal or natural spread is highly likely. Taxa that are indigenous to the

Republic can also be dispersed to parts of the country where they are not indigenous. Commodity

contaminants or stowaways can be dispersed along the extensive transport networks, and there is also a

thriving internal trade in species for a variety of purposes. Alien taxa may also spread naturally within the

country, and utilise human-made corridors like tunnels and canals that connect previously unconnected

regions. For most of the pathways of introduction for which forecasts could be made, an increase in

prominence is expected in the future. For some of these pathways control measures are not in place, and

unless this changes, further increases in the rates of introduction of alien species are likely.

4.3. Species The status of alien species in South Africa was based on data from a wide range of sources (atlas projects,

expert assessments, lists, and published papers and reports). Of the 2 033 alien species recorded (or assumed

to be present) outside of cultivation or captivity in South Africa, 1 865 are found in the terrestrial biome. Of

these 692 are known to be invasive, 116 are known to be naturalised but not invasive, and 558 are present,

but not naturalised (Figure 22). For the remainder (499 species), there is insufficient information to assign

them to an introduction status category. Eight of the alien species recorded as present in the country are

currently listed in the Alien & Invasive Species Regulations as prohibited (i.e. species assumed to be absent

from South Africa and which may not be imported). Large numbers of alien species have relatively restricted

distributions (Figure 22). Only in the case of plants and birds are there widespread species [e.g. found in at

least a quarter (i.e. > 500) of the quarter-degree grid cells (QDGCs) of South Africa]. At least one alien reptile

and two terrestrial invertebrate species are relatively widespread (> 100 QDGCs), although the data coverage

is poor, so there is a low level of confidence in these estimates.

Table 8. The number of alien species known to occur in terrestrial realm in South Africa, assigned to various categories of introduction status. Legal status refers to species listed under the Alien & Invasive Species Regulations, introduced = present in South Africa but is not established outside of captivity or cultivation, Naturalised = established outside of captivity or cultivation, Invasive = established outside of captivity or cultivation and spreading, and NA = Occurs in South Africa but there is insufficient data to assign status.

Taxon Legal category Introduced Naturalised Invasive NA Total

Microbes Prohibited 0 1 0 0 1

Listed 0 0 0 7 7

Unlisted 75 18 6 1 100

Plants Listed 33 4 306 28 371

Unlisted 248 1 255 2 506

Invertebrates Prohibited 1 0 0 0 1

Listed 2 8 0 13 23

Unlisted 135 71 107 262 575

Amphibians Prohibited 2 0 0 0 2

Listed 3 1 2 0 6

Unlisted 12 0 1 0 13

Reptiles Prohibited 1 0 0 0 1

Listed 5 2 0 22 29

Unlisted 16 0 1 81 98

Birds Prohibited 1 0 0 0 1

Listed 4 4 8 7 23

Unlisted 19 2 5 40 66

Mammals Listed 1 4 1 34 40

Unlisted 0 0 0 2 2

Total 558 116 692 499 1865


63

The only data available to estimate the abundance of alien species are those for terrestrial and freshwater

plants (Versfeld, Le Maitre &Champan 1998). These estimates are very crude or over 20 years out of date, so

the level of confidence in these estimates is very low. Estimates made by Kotzé et al. (2010) do not cover the

whole country, are restricted to certain taxa, group some species by genus or family, and there is uncertainty

regarding the methodology employed. It is therefore not possible at this stage to provide estimates of

individual species abundance. There are no comparable data for any other high-level taxa. A systematic

evaluation of the impacts of individual invasive species as per the recently developed international standards

such as the Environmental Impact Classification for Alien Taxa (EICAT) scheme (Blackburn et al. 2014; Hawkins

et al. 2015) and the Socio-Economic Classification of Alien Taxa Scheme (SEICAT) (Bacher et al. 2018) has not

yet been conducted. There was, however, a recent exercise in which experts were asked for their opinion on

the impact of listed species (Zengeyaet al. 2017). Using this scheme, 19 terrestrial species were assessed as

having a severe impact, and 73 as having a major impact (Table 9). Of these 92 species, most (76) are plants,

eight are mammals, five are invertebrates, two are amphibians, and there is one bird species.

Figure 22. The distribution of ranges of terrestrial alien species in South Africa. Note ranges are plotted in on a log scale. QDGC = quarter degree grid scale. Panel (a) plants; (b) mammals; (c) birds; (d) reptiles; (e) amphibians; (f) invertebrates.


64

Most of the 19 species that were assessed by experts as having severe impacts were terrestrial plants (16

species) which included seven species of Australian trees and shrubs in the genus Acacia (Table 11). Acacias

have been implicated in reducing grazing potential and surface water runoff; and negatively impact on

biodiversity. The list also included North American mesquite trees (Honey Mesquite - Prosopis glandulosa

var. torreyana, Velvet Mesquite - Prosopis veluntina) that reduce grazing potential; deplete groundwater

resources; and negatively impact on biodiversity. Herbaceous and succulent species include Triffid Weed

(Chromolaena odorata) that severely reduces rangeland productivity and thus the livelihoods of rural people,

while invasive shrubs include Silky Hakea (Hakea sericea) displaces most other species, increases fire

intensity, leading to soil damage and excessive erosion, and Lantana (Lantana camara) reduces biodiversity

and rangeland productivity. Examples of severe impacts in other high-level taxa include three terrestrial

invertebrate species: Garden Snail - Cornu aspersum (pestiferous, documented for damage to commercial

and ornamental crops, as well as domestic gardens), Tramp Slug - Deroceras invadens (pest of garden

vegetables) and the Argentine Ant (Linepithema humile) that disrupts ant-plant mutualisms that are

responsible for the seed dispersal of indigenous plants, and thus pose serious threats to indigenous

vegetation survival (Table 11).

Overall, alien plants are the most diverse, widespread and damaging group of invaders in South Africa.

Furthermore, it is clear that South Africa has a major alien plant invasion debt. Well over 100 new taxa have

been recorded as naturalised or escaped from cultivation over the past decade, and the recorded range of

almost all plants has increased significantly. This is a significant cause for concern, as it clearly indicates that

problems associated with alien species are set to increase.

Table 9. The number of alien species known to occur in South Africa in the terrestrial biome, assigned to different impact scores based on expert opinion of the impact in South Africa. The impact scores are grouped into five categorises that correspond in spirit to the categories of EICAT and these are [negligible impact (score 1-2) - Minimal Concern (MC); few impacts (3-4) – Minor (MI); some impacts (5-6) – moderate (MO); Major impacts (7-8) – Major (MR); Severe impacts (9-10) – Massive (MV). NE = not evaluated.

Taxon Impact

DD Negligible Some Major Severe NE Totals

Plants 2 161 128 60 16 511 878

Invertebrates 5 110 20 2 3 459 599

Microbes 6 1 101 108

Amphibians 15 3 1 2 21 Reptiles 18 22 8 80 128

Birds 10 8 1 71 90

Mammals 20 11 8 3 42

Totals 40 332 177 73 19 1225 1866


65

Box 5. Indigenous species threatened by biological invasions

Indigenous Species threatened by biological invasions

A meta-analysis of pressures effecting taxa of conservation concern (TOCC) captured during Red List assessments shows that habitat degradation and competition from invasive alien plant species is the dominant threat to plant and amphibian taxa (Figure 11). Amphibians are impacted due to a number of restricted endemics being concentrated in the Cape Fold Mountains, which are experiencing a rapid increase in infestations of alien plants. Infestations cause declines in habitat quality in the form of drying up of seeps and streams and increases in fire frequencies. In addition to invasive plant impacts, an increasing number of indigenous amphibian species are experiencing additional competition from invasive or native amphibian species. A total of 67% of South Africa’s plants that are of conservation concern occur in the Cape Floral Region. Both lowland and mountain regions of the CFR, along with additional plant endemism hot spots - along the coast of KZN, the foothills of the Drakensberg Mountains and along the Mpumalanga escarpment - coincide with areas of high concentrations of invasive plant species. Figure 23 shows the citizen scientist survey areas (documented by the Custodians of Rare and Endangered Wildflowers programme [CREW]) where invasive plant species occur at the same sites as indigenous plant taxa of conservation concern.

Table 10. Number of taxa of conservation concern that are impacted by invasive species

Number of TOCC

impacted % of TOCC impacted

Plants 2037 33

Butterflies 37 26

Amphibians 26 79

Birds 26 29

Mammals 9 10

Reptiles 9 20

Domitilla Raimondo and Dewidine von der Colff – South African National Biodiversity Institute

Figure 23. Overall number of plant TOCC that are impacted by invasive species per land parcel.


66

Table 11. Invasive species assessed to have severe impacts in South Africa. The regulatory category ‘’context specific’’ applies to species that have been placed into various categories depending on their location.

Taxon Species Regulatory category

Extent (QDGCs occupied)

Examples of impacts

Ter

rest

rial p

lant

s

Acacia cyclops (rooikrans)

1b 115 Forms closed-canopy stands, excluding most other species; disrupts natural sand movement in coastal ecosystems; increases fire intensity, leading to soil damage and erosion.

Acacia dealbata (silver wattle)

2 240 Forms closed-canopy stands, excluding most other species, especially in riparian areas; uses excessive amounts of water.

Acacia decurrens and hybrids (green wattle)


Acacia longifolia (long leaved wattle)

1b 53 Forms closed-canopy stands, excluding most other species; uses excessive amounts of water.

Acacia mearnsii and hybrids (black wattle)


Acacia melanoxylon (Australian blackwood)

2 124 Widespread invader in forests and forest ecotones. Excludes other species.

Acacia saligna (Port Jackson willow)

1b 126 Forms closed-canopy stands, excluding most other species.

Agrostis stolonifera (creeping bent grass)

Context specific

Offshore islands

Forms extensive clonal patches by means of long stolons, impacting on indigenous plant species on offshore islands.

Chromolaena odorata (triffid weed)

1b 110 Can dominate in grassland and savanna ecosystems, especially in disturbed areas, and reduces biodiversity and rangeland productivity.

Dolichandra unguis-cati (cat’s claw creeper)

1b 44 A climbing vine that invades forests, woodlands and forest margins, smothering and collapsing trees.

Echium plantagineum (Patterson’s curse)

1b 104 An invader of pastures and cultivated lands.

Eucalyptus camaldulensis (river red gum)

Context specific

136 Forms closed-canopy stands in riparian areas, excluding most other species; uses excessive amounts of water.

Hakea sericea (silky hakea)

1b 39 Forms closed-canopy stands in Fynbos mountain catchments, and displaces most other species. Increases fire intensity, leading to soil damage and excessive erosion.

Lantana camara (lantana)

1b 312 Widespread invasive shrub that can dominate in savanna and grassland regions, and reduces biodiversity and rangeland productivity.

Prosopis glandulosa var. torreyana (honey mesquite)

Context specific

112 Many well-documented impacts on biodiversity, groundwater supplies, rangeland productivity and human livelihoods and health.

Prosopis velutina (velvet mesquite)

Context specific

5 Many well-documented impacts on biodiversity, groundwater supplies, rangeland productivity and human livelihoods.

Ter

rest

rial

inve

rteb

rate

s

Cornu aspersum (garden snail)

Unlisted 115 Pestiferous, documented for damage to commercial and ornamental crops, as well as domestic gardens.

Deroceras invadens (tramp slug)

Unlisted 10 Pest of garden vegetables.

Linepithema humile (Argentine ant)

1b 36 Disrupts seed dispersal mechanisms in Fynbos, potentially leading to collapse of plant reproduction systems.


67

4.4. Sites The status of invaded areas was assessed at

provincial, biome, catchment and quarter-degree

grid-cell scales, where data allow. Invasive plant

species richness was highest in the Savanna,

Grassland, Indian Ocean Coastal Belt and Fynbos

biomes, and lower in the arid biomes (Figure 24).

Similarly, invasive animal species richness was

highest in the relatively humid coastal provinces

(Western and Eastern Cape and KwaZulu-Natal)

and lower in the arid interior provinces (Northern

Cape, Northwest and Free State). Alien species

richness provides an indication of the diversity of

issues that need attention, but it is not a measure

of how large the invasions are – this would require

estimates of cover, biomass or population size.

There are no reliable estimates of these measures,

but crude estimates made in 1998 (Versfeld, Le

Maitre & Champan 1998) confirmed what is

generally accepted – the Western Cape is the most invaded province, followed by Mpumalanga, Northern

Cape and KwaZulu-Natal. These estimates are more than 20 years out of date, and data from an atlas project

suggests both the extent of invasions, and the relative dominance of species, have changed considerably

since then (Henderson & Wilson 2017).

At a national scale, the combined impacts of invasive alien plants on surface water runoff have been

estimated at between 1 444 to 2 444 million m3 per year (Le Maitre, Versfeld & Chapman 2000; Le Maitre et

al. 2016). Primary catchments most affected (> 5% reduction in mean annual runoff) are in the Western and

Eastern Cape, and KwaZulu-Natal. If no remedial action is taken, reductions in water resources could rise to

between 2 589 and 3 153 million m3 per year, about 50% higher than estimated current reductions. Invasive

alien plant infestations are estimated to have reduced the potential for South Africa to support grazing stock

by just over 1%, though this varies between biomes (Van Wilgen et al. 2008). If no remedial action is taken,

however, impacts are projected to become much larger (up to a 71% loss of grazing in some biomes).

Reductions in biodiversity intactness in South Africa’s terrestrial biomes were highest (3%) in the Fynbos

biome (Van Wilgen et al. 2008). Under a scenario where invasive alien plants are allowed to reach their full

potential, biodiversity intactness is predicted to decline dramatically, by around 70% for the Savanna, Fynbos

and Grassland biomes, and even more (by 87% and 96%) for the two Karoo biomes. Invasion of natural

ecosystems by alien plants can change the structure and biomass of vegetation, adding fuel and supporting

fires of higher intensity. Increased fire intensity can in turn increase the damage done by fires, as well as the

difficulty of controlling fires. Although there is very little in the way of documented impacts in South Africa,

these effects have clearly been shown in a limited number of studies (e.g. Kynsna fire; Kraaij et al. 2018).

Estimating the level of invasion by alien species in particular areas could only be made with a low degree of

certainty, given the relative lack of reliable and comprehensive data on invasive species. The same applies to

impacts. However, based on a few existing studies, it appears that impacts are currently relatively low (with

the exception of water resources), but that they are set to grow rapidly as invasive species enter a phase of

exponential growth.

Figure 24. Number of invasive terrestrial plants per quarter degree square (QDS). Data from the Southern African Plant Invaders Atlas, accessed May 2016.


68

4.5. Interventions

4.5.1. Quality of regulations

The effectiveness of the Alien & Invasive Species Regulations under the Biodiversity Act is discussed here in

terms of managing pathways of introduction and dispersal, individual species and specific areas, as well as

on other aspects that are required to be reported on under the Alien & Invasive Species Regulations (e.g.

state-funded research). South Africa is one of the few countries that has comprehensive regulations in place

to manage biological invasions, and many parts of the regulations are innovative. The regulations deal with

most aspects of biological invasions (pathways, species, and areas) and most mechanisms to implement,

update, review, and appeal the regulations are clear, and as such were rated as ‘substantial’. However,

although there are some sections of the legislation that are relevant to the management of some specific

pathways (e.g. the intentional import of alien species for the pet trade). The Alien & Invasive Species

Regulations do not specifically regulate pathways. In addition, there are several factors such as the lack of a

national strategy to manage biological invasions, as well as organisational and human capacity constraints

that limit the implementation of the regulations. The evidence base for listing species was not presented in

a standard, transparent manner prior to the promulgation of the regulations, although some species have

subsequently been assessed. While these assessments are consistent with the regulations, they do not meet

international best practice for risk analyses. A risk analysis framework has been developed but is still to be

implemented (Kumschick et al. 2018).

The regulations have been in place for less than three years, and it is probably premature to expect that their

effectiveness could be assessed at this early stage. However, a number of important points emerge,

including: high levels of non-compliance with some regulations; a shortage of capacity within the DEA to

ensure compliance (although the magnitude of the shortage has not been assessed); the apparent absence

of a strategic approach to implement the regulations in a capacity-constrained environment; and

contestation of the desirability of regulations for particular species. Finally, where there has been activity

and data are available, the data only focussed on outputs (e.g. number of permits issued). Linking these data

to outcomes in terms of the state of biological invasions in South Africa will require the development of

agreed methodologies.

4.5.2. Effectiveness of control measures

Control effectiveness was assessed in terms of inputs, outputs or outcomes for interventions aimed at

pathways, species and areas. The required monitoring data to make such assessments are largely absent,

and therefore the assessment has relied heavily on a limited number of research projects that covered some

pathways, species, and areas.

1.5.2.1 Pathways related control measures

Inputs for the management of the pathways of introduction can be gauged from information on the money

spent to prevent both intentional and unintentional introductions, as well as information on the pathways

for which management plans have been developed. Information on the money spent is currently not

available. A number of government departments are involved in managing pathways [e.g. Department of

Environmental Affairs (DEA), Department of Agriculture, Forestry and Fisheries (DAFF), Department of

Transport (DoT)], and obtaining a more meaningful estimate of the money spent would require data from all

of the departments involved. Planning coverage can be determined based on the number of pathways that

are currently managed and those for which plans have been developed but for which management is not yet

in place. Of the 44 pathways of introduction (CBD subcategories, see Chapter 3), 20 involve the intentional


69

import of organisms, while ten involve the accidental introduction of organisms as contaminants of imported

commodities.

There is currently legislation [e.g. National Environment Management: Biodiversity Act (Act No. 10 of 2004),

Agricultural Pests Act (Act No. 36 of 1983), Animal Diseases Act (Act No. 35 of 1984)] and international

agreements (e.g. IPPC) in place that aim to prevent the introduction of potentially harmful species through

these pathways. There are 11 pathways involved in the accidental introduction of alien species as stowaways

on transport vectors. Under international agreements and regulations (IPPC and International Health

Regulations) wood packaging should be treated to prevent the spread of timber pests, and aircrafts should

be sprayed to kill insect disease vectors (e.g. mosquitos). Cargo and passengers entering South Africa are also

searched for alien organisms, and legislation to prevent the introduction of species through the release of

ballast water by ships has been drafted. Therefore, five of the 11 stowaway pathways currently have

management plans in place. As such we believe that 35 of the 44 pathways of introduction (79.5%) have

plans in place for management, but as this assessment is solely based on the knowledge of experts, our

confidence is low.

Outputs are gauged in terms of the number of pathways requiring management that are managed to some

degree. We determined that all 44 pathways should require management. Although organisms may not have

been introduced through some pathways, changes to socio-economic trends could lead to changes in the

rate of introduction through the pathways. Currently, all pathways with management plans in place are

managed to some degree, except ship or boat ballast water for which the legislation has not yet been passed.

Therefore, 35 pathways of introduction (77.3%) are managed whilst 31.8% of the pathways have partial

management as interventions for pathways that involve the unintentional introduction of alien taxa are not

in place at all ports of entry. As permits are required to import alien taxa, all pathways that involve the

intentional introduction of alien taxa have complete management (45.4% of pathways). However, as this

assessment is solely based on the knowledge of experts, our confidence is low.

Outcomes are gauged on recent changes to the rate of introduction, which are determined by comparing the

rate of introduction in the last full decade (2000–2009) to that of the previous decade (1990–1999). One

pathway of introduction (‘landscape flora or fauna improvement in the wild) has permanent management

(2.3%), as this pathway is no longer present and thus does not require ongoing management. Eight pathways

(18.2%) are effectively managed as there have been no recent introductions or as the rate of introduction

has declined. However, 17 pathways (38.6%) have no management (10 pathways) or management is

ineffective (7 pathways), as there has been either a minimal change or an increase in the rate of introduction.

The management effectiveness of 18 pathways (40.9%) is not known as there are either no introductions

recorded, or the data appears to be inadequate. As this assessment is based on incomplete data and expert

opinion our confidence is low.

1.5.2.2 Species-specific control measures

Inputs for the management of particular species are either in the form of biological control (which uses host

specific biological control agents that target particular species), or eradication projects that target particular

species. In terms of money spent, the Department of Environmental Affairs Natural Resource Management

Programmes currently provides ZAR 55 million/yr in support of biological control projects. There are other

sources of funding (for example from the budgets of the Agricultural Research Councils Plant Protection

Research Institute, and from participating universities), but information about these is not readily available.

In addition, records of funding for species-specific eradication projects are not readily available, so the

estimate of ZAR 55 million/yr is almost certainly an underestimate.


70

In terms of control expenditure per species, available data at a national scale are restricted to a single study

that covers expenses up to 2008 (Van Wilgen et al. 2012b). An extract from this study reads as follows: ”The

largest portion of funding (561.9 million Rands) was spent on the control of Acacia mearnsii. If this is added

to the costs associated with the closely-related wattle species Acacia dealbata (cost of 79.3 million Rands),

the costs of control of these two species accounted for 19.4% of the costs of all alien plant control. A total of

435.5 million Rands was spent on the next most-targeted taxon (Prosopis species), while 237.0 and 183.5

million Rands were spent on Eucalyptus and Pinus species respectively. The remaining taxa in the top 10 (and

costs of control in millions of rands) were Lantana camara (180.6), Chromolaena odorata (171.8), Solanum

mauritianum (121.5), Hakea species (69.0) and A. cyclops (58.0).”

Other relatively recent studies have quantified the costs per species for limited areas. For example, Van

Wilgen et al. (2016) reported that historical control costs in the protected areas of the Cape Floristic Region

amounted to ZAR 564 million (2012 rands), most of which (90%) was expended on the genera Acacia, Pinus

and Hakea in that order. In the Kruger National Park, Van Wilgen et al. (2017) reported that ZAR 350 million

had been spent on invasive alien plant control up to 2015. The following species received most funding:

Lantana camara (Lantana, ZAR 66.6 million), Ricinus communis (Castor Oil Plant, ZAR 36.7 million), Xanthium

spinosum (Spiny Cocklebur, ZAR 27 million), Argemone mexicana (Yellow-Flowered Mexican Poppy, ZAR 18.3

million) and Chromolaena odorata (Triffid Weed, ZAR 11.8). The largest amount spent on a single taxon to

date is the estimated ZAR 1.8 billion for Prosopis species (mesquite) in the Northern Cape Province up to

2016 (R.T. Shackleton unpublished data), although this is probably exceeded by the total amount spent on

Acacia mearnsii (BLACK WATTLE). There is, however, no comprehensive recent assessment of expenditure

per species at a national scale.

Planning coverage can be gauged in terms of the five available invasive species management programmes.

In addition to the two species covered: Parthenium hysterophorus (Famine Weed), and Campuloclinium

macrocephalum (Pompom Weed), plans are available for the genera Acacia (14 species listed in the A&IS

Regulations) and Prosopis (2 species listed) and for the family Cactaceae (35 species listed). These 53 species

are 9.5% of the 556 invasive taxa listed in the NEM:BA A&IS Regulations. Based on the fact that four out of

five of these plans have been peer-reviewed and published, 80% can be regarded as adequate.

Outputs are expressed as the number of species requiring management that are actually managed to some

degree. Of the 556 taxa listed in the NEM:BA A&IS Regulations, 136 (24.3%) are managed to some degree

(i.e. funds have been expended on their control), most 126 species are plants. Management operations only

reach a very small proportion (~1% every year) of the total area invaded by each species, however, in terms

of categories of management, only invasive plants targeted for biological control are known to be under

substantial or complete control. For most other regulated taxa there are few examples of species under

active management, and most species are not managed at all, or the degree of management is not known.

The level of confidence in these estimates is low, given the low confidence in the records of extent of

management.

Outcomes are gauged in terms of the level of control achieved for each species. Of the 556 listed taxa, 36

(6.4%) have either been eradicated or brought under complete or substantial biological control. For most

other species, however, ranges continue to expand. Returns on investment into species-specific control

interventions have been excellent for biological control (where benefit: cost ratios between 8:1 to 3000:1

have been achieved), but this applies only to a small percentage of all species.


71

1.5.2.3 Area specific control measures

Inputs for the management of particular areas are mainly in the form of invasive plant control operations in

catchments, protected areas or on other land. In terms of money spent, the Department of Environmental

Affairs’Natural Resource Management Programmes currently provides ZAR 1 750 million/yr in support of

such projects. There are other sources of funding (for example from provincial conservation agencies,

municipalities and private landowners), but these are not readily available, so the estimate of ZAR 1 750

million/yr is almost certainly an underestimate. Expenditure on alien species control in particular areas is

available for a limited number of areas.

Planning coverage is difficult to gauge at a national level. However, evidence suggests that planning is

generally poor, as there is a lack of clear goals, and almost no allowance for monitoring and evaluation (Van

Wilgen & Wannenburgh 2016; Fill et al. 2017; Van Wilgen et al. 2017). Planning coverage can be gauged by

the area covered by invasive species monitoring, control and eradication plans submitted in terms of the

NEM:BA A&IS Regulations; these plans only cover 4% of the country, and vary in terms of their adequacy.

Outputs are measured in terms of the proportion of land requiring management that is actually managed. In

South Africa, there is approximately 973 643 km2 of untransformed natural vegetation. The only available

estimate of the proportion of this land that is invaded to some degree, and thus requires management, is 8%

(i.e. 77 900 km2, Versfeld, Le Maitre & Chapman 1998). The records of the public works alien plant control

projects indicate that 282 km2 have been treated over 20 years, which is approximately 0.36% of the land

requiring management. This is an underestimate given the lack of information on other control operations,

but the figure is likely to be very low even if other control operations were to be included.

Outcomes are gauged in terms of the Effectiveness of area treatments. Given the absence of formal

monitoring programmes, the level of effectiveness can only be gauged based on available research studies

that have attempted to do this. Of the 12 studies reviewed here, 8% were gauged to be effective, 58%

partially effective and 34% ineffective. The level of confidence in this estimate is therefore low.

4.6. Key high level findings Key high level findings included that approximately seven new alien species have been establishing annually

at a national level. A total of 107 species were considered by experts to be having either major to severe

impacts on biodiversity and/or human wellbeing. The vast majority were terrestrial plants from which 1.4%

of the country was experiencing major impacts, with management success levels were around 5.5%. The level

of confidence in these estimates was low, however, because the data on which they were based were

scattered and incomplete.


72

5. PRESSURES AND DRIVERS III - CLIMATE CHANGE

Chapter 5: Foden, W., Midgley, G., Kelly, C., Stevens, N. & Robinson, J.2019. ‘Chapter 5: Pressures and Drivers III – Climate Change’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

5.1. Overview The biodiversity sector faces a mix of challenges and opportunities under future climate change scenarios,

and these will continue to interact with the socio-economic development trajectory of the country. These

challenges and opportunities involve how best to integrate conservation objectives with climate change

adaptation and mitigation objectives. These are largely centred on land use decisions that are relevant to and

affected by responses in sectors such as water and agriculture.

Average temperatures globally could rise by up to 2.5 oC by mid-century if global mitigation efforts are weakly

implemented, yet observed trends and projections suggest that local warming over African land surfaces may

be double this global rate of warming. In southern Africa, mean temperature increases of more than 1 C

have been observed, and this trend has already been accompanied by increases in extreme events including

dry spell duration, heavy rainfall events, coastal storm surges, strong winds and wildfires. Modelling results

show that several adverse ecological and biodiversity impacts can be avoided by 2050 if global warming is

successfully mitigated and remains within the range of lower risk projections.

Marked climate change impacts have already being observed on a range of species including mammals, birds,

amphibians, reptiles and plants. Ecosystems too have experienced changes including functional and

composition, with climate change impacts exacerbating historical threats such as invasive species, habitat

transformation and degradation and overharvesting. Understanding of structural shifts in vegetation,

including some changes in the distribution of biomes, is informed by new insights from observations of

ecosystem and species changes and improved modelling methods (e.g. Dynamic Global Vegetation

Modelling, DGVM). These are helping to update and enhance earlier species-focused projections developed

through application of correlative niche based modelling (NBM) approaches. Woody encroachment of the

Grassland and Savanna biomes appears to be a major ongoing climate-change related trend, which was not

fully anticipated by earlier modelling efforts. This may be because direct effects of rising atmospheric CO2 on

vegetation are emerging as a potential driver of woody plant encroachment. Apparent expansion of C4

grasses westwards in the Nama-Karoo biome also warrants attention with respect to biodiversity and

ecosystem function implications. Projections of species losses before 2050 for the Succulent Karoo and

Fynbos biomes, dating from the early 2000s, are not yet supported by observations, but this could reflect

inadequate observation effort.

The high inherent variability in southern African rainfall and sometimes contrasting projections of different

impacts modelling methods together reduce the precision of projections of climate change impacts on

biodiversity and ecosystems. A deliberate monitoring program to enhance detection and attribution of

climate change impacts on biodiversity and ecosystems would therefore be a valuable planning and policy

support intervention. To this end, South Africa possesses many of the relevant databases and institutional

structures for rapid implementation. Such a program could be carried out in conjunction with iterative

scenario based planning of conservation responses building on the biome adaptation strategy and expanded

protected areas approaches. These could usefully include nature-based solutions like ecosystem based

adaptation and mitigation that take into account multiple socio-economic and social-ecological needs for

land based adaptation and mitigation responses.


73

The ongoing warming trend observed locally and globally, and slow progress in achieving effective global

mitigation is credible motivation for an increased, nationally coordinated focus on research towards

improved predictive capability and targeted monitoring to test projections and gauge the rate of ongoing

biodiversity and ecosystem change. The development, implementation and assessment of response options

including assessment of adverse implications of land based mitigation responses, are also priorities.

5.2. Introduction Global warming of 0.9 °C has been experienced globally since the 1970s, with each of 1998, 2010, and

2014-2016 successively being the warmest years on record (IPCC 2013). Globally, average temperatures are

predicted to rise by up to 2.5 °C by 2050 (IPCC 2013) but trends in Africa suggest the continent may

experience double this rate of change (Engelbrecht, Thambiran & Davis 2016). In southern Africa, mean

temperature increases are accompanied by increases in extreme events including drought, heavy rainfall

events, coastal storm surges, strong winds and wildfires (Davis-Reddy & Vincent 2017). These changes

themselves impose strong pressures on biodiversity which may become threats to species and ecosystems,

as summarised in Figure 25.

Climate change was identified in the 1990s as an important potential driver of South African terrestrial

ecosystems and biodiversity. Initial work employed correlative modelling approaches that assumed strong

climatic control of species geographic ranges, and these ‘’bioclimatic niche based (NBM)’’ methods were also

applied to biome distribution changes, albeit with careful expression of the assumptions. These efforts raised

concerns that winter rainfall biomes (Fynbos and Succulent Karoo) would be significantly adversely affected,

and that the Grassland biome would shrink due to replacement by Savanna at lower elevations (Rutherford

et al. 1999a). Projections also suggested substantive risk to species richness across most taxa for which

sufficient data existed to develop bioclimatic niche models. These alarming projections were summarised in

South Africa’s Initial National Communication to the United Nations Framework Convention on Climate

Change (UNFCCC), helping to support South Africa’s positions in multi-lateral international agreements to

mitigate greenhouse gas emissions. These projections also sparked a national effort to better understand the

risks of climate change to biodiversity, with research now conducted at multiple universities and state

agencies (ASSAF 2017).

Many new insights have been generated by this increase in research effort, including attempts to identify

trends and changes attributable to aspects of climate change and rising atmospheric CO2 (e.g. Bond,

Midgley & Woodward 2003a; Venter et al. 2018), and the realisation that wildfire may override climate

constraints in determining biome distribution (Bond, Midgley & Woodward 2003b). Projections of changes

in terrestrial ecosystem structure and function are now available, which use relatively newly developed

dynamic global vegetation models that take these CO2 and fire effects into account (e.g. Scheiter & Higgins

2009). These suggest that initial correlation-based species projections had ignored some important

processes and may have overestimated the sensitivity of some elements of biodiversity to climate change,

while underestimating others. Thus the two main approaches followed historically, the species- and biome-

based correlative approach, have increasingly been challenged by the mechanistic approach. In this

chapter, more recent findings resulting from modelling are placed into context with results summarised in

the previous NBA, with the benefit of almost two decades of observed changes that are valuable in

assessing the projections made.


74

Figure 25. A summary of observed and anticipated climate change pressures on South Africa’s biodiversity. These include those from abiotic climate changes and resulting changes in physical processes, biotic systems, as well as in human behaviour in response to climate change.

5.3. How is South Africa’s climate changing?

5.3.1. Temperature

Subtropical southern Africa is one of the three fastest warming areas on the continent and has experienced

an average temperature increase of approximately 0.4 oC per decade (Davis-Reddy & Vincent 2017). Across

greater Southern Africa, average temperatures over the last century have shown a clear and marked increase

(Figure 26), with strongest average warming in summer, then autumn, winter and lastly spring (Kruger &

Nxumalo 2017). Future temperature projections show increases of 2 – 4 oC, with pessimistic projections of

up to 8.5 oC in the interior (CCAM downscalings; Davis-Reddy & Vincent 2017).

Maximum temperatures have also shown a strong increase at most weather stations across South Africa,

particularly in the warmer parts of the country including the west, northeast and extreme east (Kruger &

Sekele 2013) (Figure 27). In addition to their severity, the frequency and duration of very hot days (i.e.

temperatures in excess of 35 degrees) have increased, with even the most conservative models predicting

increases of up to 80 very hot days per year by end of century (Davis-Reddy & Vincent 2017). Correspondingly,

cold spells have decreased in duration and frequency, particularly in the eastern half of country and along

the coast, and in their severity in the Lowveld, east coast, and the dry western interior (Kruger & Sekele

2013). Changes in diurnal temperature ranges remain unclear since New et al. (2006), for example, found

that minimum temperatures were increasing faster than maxima, therefore decreasing the range, but Kruger

and Sekele (2013) and MacKellar et al. (2014) found no consistent pattern.


75

Figure 26. The difference (°C) between mean annual temperature and the long-term temperature average (1961-1990) over southern Africa from 1901 to 2014. Red and yellow represent a positive and negative differences respectively. (Based on CRU TS 3.23 dataset; Davis-Reddy & Vincent 2017).

5.3.2. Rainfall

The high baseline variability in African climate, along with the low and declining number of weather stations,

makes understanding weather patterns challenging. Southern Africa’s rainfall has experienced periods of

above and below average rainfall since 1900, but there has been no overall trend. South Africa saw increases

in annual rainfall totals over the southern interior from 1921 to 2015, but a drying trend in the north and

northeast (Engelbrecht, Thambiran & Davis 2016). Rainfall predictions for Southern Africa are uncertain, and

many disagree on wetting vs. drying trends. Overall, however, the most likely scenario for the region is a

reduction in rainfall, particularly over the south Western Cape and central regions (e.g. northern Botswana,

Namibia, southern Zambia and Zimbabwe).

Intensification of droughts due to reduction in rainfall and/or increased evapotranspiration are made with

medium confidence (Davis-Reddy & Vincent 2017). There has been a general trend of increased frequency of

extreme rainfall events (20 mm of rain falling within 24 hours) in the latter half of the 20th and early 21st

centuries, but these show no clear spatial coherency (De Waal, Chapman & Kemp 2017). Projections indicate

a general trend of increased frequency of heavy rainfall events, but with low confidence (Davis-Reddy &

Vincent 2017).

5.3.3. Extreme events

The number of extreme events including heat waves, droughts and floods has shown a clear increase since

1980, and this trend is expected to continue (Davis-Reddy & Vincent 2017). Heat waves and droughts may

result in increases in the intensity and severity of wildfires, causing increases in areas impacted (IPCC 2012).

Coastal storm surges too are predicted to increase due to sea level rise, increased storm intensity and

increases in wave height (IPCC 2012, 2013) (Figure 27). In the last four decades, southern Africa experienced

491 recorded climate-disasters, resulting in 110 967 human deaths, 2.47 million people becoming homeless,

140 million people impacted and an estimated cost of USD10 billion (EM-DAT, 2016). While the costs of such

extreme events on the region’s biodiversity have not been quantified and are often omitted from projections

of impact, they pose a significant threat to biodiversity.


76

Figure 27. Summary figure of key future climatic risks for each biome and the projected changes in each based on the IPCC Fifth Assessment Report findings for temperature, rainfall, extreme events and sea level rise (taken from DEA 2015c).


77

5.4. How is climate change impacting South Africa’s biodiversity? Eighty-two percent of 94 biological processes identified globally have been found to have been affected by

climate change, with impacts spanning biological levels from genes to populations, species, communities,

ecosystems and their services (Scheffers et al. 2016). In South Africa, however, there is currently no

deliberate and coherent strategy to detect the impacts of anthropogenic climate change on biodiversity,

although there is strong potential to do so through building upon several world-class spatial species and land

cover databases. The current evidence for such impacts is derived from a limited number of individual studies

that have generally identified organisms and processes that are most likely to show sensitivity to climate

change. It is important to note that this targeting of potentially climate-sensitive organisms and processes

introduces the potential for confirmation bias (Hockey & Midgley 2009). Less biased approaches are

increasingly feasible due to the rapid increase in the collection of observational data. Deliberate, planned

efforts may be advanced further through the maturing South African Environmental Observation Network

(SAEON) and Expanded Freshwater and Terrestrial Environmental Observation Network (EFTEON) programs.

Remote sensing approaches are providing useful insights into process shifts, though the value of such

approaches is limited by their relatively short history.

South Africa’s Biome Adaptation Strategy (DEA 2015c) argued that a biome-based approach provides a

coherent framework for understanding climate change impacts and developing adaptation plans. This is

because biomes represent large regions governed by similar processes and occurring under a known range

of climatic and disturbance conditions. The use of biome ‘’switches’’ as clear indicators of impacts is a useful

because structural switches imply ecologically significant shifts in climatic and/or disturbance drivers.

Detection of such switches may be particularly useful at the interfaces between different biomes, providing

a focus for projections and observations of shifts in the extent of biomes, to serve as early warning of

ecologically significant changes in prospect, tests of projected impacts and the models used to generate

them, and to identify areas where adaptation responses may be implemented. However, species, community

and process-based observations may be more valuable in ‘early warning’ detection of incipient ecological

changes, due their finer sensitivity and spatial resolution. In this section, the attribution to climate change

drivers of observed switches in biomes are assessed first, followed by selected observations of species and

processes within and across biomes.

5.4.1. Biome switches

According to Rutherford and Westfall (1994) biome boundaries may be defined by the Summer Aridity Index

and rainfall seasonality. Both measures are strongly affected by shifting rainfall patterns and rising

atmospheric temperatures. However, it is not possible to apply this simple ‘’climate-only’’ understanding to

the likely future distribution of biomes because other critical factors have been shown to affect the ecological

success of the plant life forms that define them. Disturbance has been shown to favour shrubby and

herbaceous life forms relative to taller woody plants, and the interplay of climate and disturbance regime

must be taken into account. An important mechanism underpinning tree-grass interactions is the role of

atmospheric CO2 in favouring the ability of tree saplings to store carbon in their root systems, which permits

them to overcome the disturbance regime imposed upon them by flammable grasslands (Bond, Midgley &

Woodward 2003a). To a lesser extent, soil nutrient status and texture (e.g. sand vs. clay content) are critical

for some species and plant life forms. Assessment of biome switches thus needs to take account of a complex

interplay of climatic, disturbance and soil considerations. Observations since the late 1990s have provided

significantly greater insight into the geographical patterns of possible switches in biome type, and the

processes that underlie these large scale and significant ecosystem changes. Three main shifts are emerging

as either ongoing or imminent; in order of extent and certainty, these are shifts towards greater woody plant

dominance, shifts to C4 grass dominance, and desertification.


78

Switches to woody plant dominance

Over the past century one of the most pervasive biome-switches observed has been an accelerating increase

in the density and spread of woody species (Stevens et al. 2017). This trend is called bush encroachment or

woody thickening and has caused a widespread switch (Figure 28) from Grassland, open Savanna and mixed

grass or shrub shrubland biomes to woody–plant dominated biomes (Archer et al. 2017). African savannas in

particular are vulnerable to encroachment and in the past decades show a continental average annual

increase of 0.25% of woody cover per year (Stevens et al. 2017; Axelsson and Hanan 2018; Venter, Cramer

and Hawkins 2018).

In South African Savanna, Grassland and Nama- and Succulent Karoo, woody cover has doubled across

virtually all land-uses since the first national-scale aerial photography was first undertaken in the 1940s. The

only exception is seen in conservation areas in low-rainfall savannas (MAP<650mm) when elephants were

present (Stevens et al. 2016). More recent spatially extensive studies, using the satellite record, demonstrate

that woody cover extent has increased by 20% in 23 years (1990-2013) (Skowno et al. 2017), with the rate

of increase varying between land-uses. Encroachment is most prevalent in Savanna and Grassland biomes

(Stevens 2015) (Figure 29).

Figure 28. Extent of woody cover change (%/y) from 1986-2015. Green colours show increase in woody cover and brown show a trend of decline. Map used with permission of Zander Venter (Venter, Cramer & Hawkins 2018).


79

Figure 29. Aerial photograph of grassland in NE KZN in 1940 (a), compared to an aerial photograph of the same location taken in 2010 (b). The aerial photograph pair demonstrate how woody cover is increasing in grasslands in South Africa. A photograph from 1922 of grassland: savanna boundary in the mountains near Ntabankulu (photo by du Toit) (c) with a repeat photograph of the same location taken in 2011 (photo by J Puttick) (d). The photographs demonstrate that the altitudinal limit of savannas are increasing causing the spread of savannas into grasslands. Photograph (a&b) courtesy of Nicola Stevens and (c&d) courtesy of Timm Hoffman (http://rephotosa.adu.org.za ).

The drivers of bush encroachment extend beyond atmospheric CO2 alone (e.g. Skowno et al. 2017). When

analysed at the continental level, about half the variability in woody encroachment can be explained by

regional warming, increasing rainfall, land use and reductions in fire intensity and frequency (Venter, Cramer

& Hawkins 2018). Taken together, these observations suggest that a changing climate and rising CO2 are

likely background drivers of extensive and broad-scale switches towards greater woody plant cover, but that

other important drivers (fire and grazing or browsing) may be available as management options to influence

the rate of this change.

There are important ramifications of this biome switch for South Africa’s mitigation and adaptation

objectives, as bush encroachment and fire suppression contributes to carbon sequestration under UNFCCC

regulations. However, such carbon gains need to be balanced against human livelihoods losses (loss of

grazing), changes in iconic indigenous African biodiversity, and potential adverse impacts on ecosystem

services such as water yield (Midgley & Bond 2015). Biome shifts towards a woodland or shrubland

dominated ecosystem generally cause a reorganisation of the community, benefitting woodland species and

often resulting a net decline in biodiversity (McCleery et al. 2018).

http://rephotosa.adu.org.za/


80

Switches to grass dominance

Grasses (Family Poaceae) with the C4 photosynthetic pathway comprise a dominant or co-dominant life form

in Grassland, Savanna and Nama-Karoo biomes. The C4 pathway is derived from the ancestral C3 form of

photosynthesis and most commonly occurs in grasses, which are herbaceous and lack carbon-dense structure

such as stems and branches. The C4 pathway allows C4 grasses an ecological advantage under warm growing

season conditions, and under low atmospheric CO2 levels, as opposed to carbon-dense woody plants that

use the C3 pathway. The Succulent Karoo, Desert, Thicket, Forest and Fynbos biomes have few to no C4

species present (Fynbos is co-dominated by C3 grass-like restioids), and there is a diminishing dominance of

C4 grasses towards the western reaches of the Nama-Karoo biome. Expansion of C4 grasses into these biomes

has the potential to transform them rapidly, especially through acceleration of the fire cycle (Figure 30).

Ecosystem transformation via C4 grasses has been observed in other parts of the world where southern

African grasses have invaded previously grass-free ecosystems. With rising growing season temperatures,

and lengthening of growing season, it is likely that the bioclimatically-suitable conditions for C4 grasses are

expanding in extent.

Figure 30. Unburnt (left) and burnt Karoo veld at the Middelburg Commonage site between two and four years after the fire. Most shrubs on the unburnt section are the nonsprouter Eriocephalus ericoides. The spread of fires into the Karoo can facilitate the extension of grassland into the Karoo. Photo © JCO du Toit, 2015 All Rights Reserved.

Several studies indicate that at least over the last half century, the eastern Nama-Karoo (upper Nama-Karoo

bioregion) experienced an increase in grassiness and a decline in the abundance of short karroid shrubs. The

grass biomass increase has been driven by a sustained increase in tall perennial grasses (Masubelele et al.

2014; du Toit, Van den Berg & O'Connor 2015a; Masubelele, Hoffman & Bond 2015) with the changes most

pronounced at the ecotone of the Grassland and Nama-Karoo biomes (Hoffman & Ashwell 2001). The Nama

Karoo-Grassland boundary appears to have shifted tens to hundreds of kilometres (du Toit & O’Connor 2014).

Additionally, long term patterns of NDVI (1984-2014) indicate that this particular region of the Karoo has

experienced a significant increase in the length of the growing season, with the end of the growing season

becoming more extended (Davis-Reddy 2018).

Evidence strongly thus suggests that the boundary between Nama-Karoo and the Grassland biomes has

changed since the first mapping efforts conducted in the mid-20th century (Acocks, 1953). Contrary to

Acocks’ (1953) early predictions of north eastward expansion of Karoo shrublands into the more mesic

grassland environments (which assumed continued unsustainable land use practices), the opposite appears

to be occurring. A simple attribution of this change to anthropogenic climate shifts cannot be confidently

asserted, because evidence has shown that sustained heavy browsing in the Nama-Karoo resulted in a

significant increase in shrub cover and a decline in grasses (Rutherford, Powrie & Husted 2012; Hanke et al.

2014) and the remedial action of lower stocking rates may have promoted the expansion of grasses within

this region (Masubelele, Hoffman & Bond 2015).

A sustained period of elevated rainfall and a shift in the seasonality of rainfall from late wet season to early

wet season may recently have promoted increased grassiness in the Nama-Karoo biome (du Toit 2010;

du Toit & O’Connor 2014; Masubelele et al. 2014; Masubelele, Hoffman & Bond 2015). Such changes may


81

have been positively reinforced by an increase in fire frequency that accompanied the increase in grassiness

(du Toit, O’Connor & Van den Berg 2015). When fire occurs in the seldom burnt Nama-Karoo vegetation,

indigenous shrubs are often killed, extending the grassland boundary (Figure 30).

Few other clear long term vegetation trends have been consistently detected in other regions of the

Nama-Karoo. Based on NDVI changes (1984-2014), some parts of the southern Nama-Karoo have

experienced a shift in the growing season with an earlier inception date, but no significant extension of the

growing season (Davis-Reddy 2018). It is important to note that growing season trends may be cyclical, as

the Karoo region has a history of long-term climatic cycles (du Toit & O’Connor 2014, 2017).

Switches to Desert

Early projections for the winter rainfall Succulent Karoo and Fynbos were for a potentially catastrophic

decline in biome extent and biodiversity by mid-21st century due to drying and warming (desertification)

trends. These projections are not yet supported by observations, although some early signs are emerging of

population collapse under extreme drought and temperature conditions in the northern Richtersveld region

(personal communication with Domitilla Raimondo, SANBI). Updated climate projections and the application

of more refined modelling methods suggest a strongly reduced risk of aridification impacts by mid-century.

Very few observations of long term change due to climate change are available for the Succulent Karoo

biome, with a far greater focus on the assessment of land use as a driver of observed change. One exception

is the apparent decline of the iconic Quiver tree (Aloidendron dichotomum) Foden et al. (2007) and Guo et

al. (2017) both indicate that conditions in the warmer and drier parts of the range of this species may be

causing a decline in population growth rate and inducing increased adult mortality, while in the cooler

southern parts of its range, populations have positive growth rates, possibly due to anthropogenic warming.

Foden et al. (2007) excluded all reasonable alternative explanations for the observed mortality pattern. This

interpretation has been challenged (Jack et al. 2014), but the analysis they provide sub-divides the sampled

populations by rainfall seasonality and does not directly test for attribution of observed mortality to

anthropogenic warming.

A handful of experiments suggests that the dwarf succulent plant forms of this region are vulnerable to the

magnitude of warming expected by the middle of this century (Musil et al. 2005). Droughting experiments

have shown that both succulent and non-succulent growth forms may be vulnerable as adult plants due to

extreme drought (Midgley & Van Der Heyden 1999, Hoffman et al. 2009), but succulents display extreme

drought tolerance as seedlings (Hoffman et al. 2009). It has long been argued that many endemic species are

dependent on regular winter rainfall, dewfall, and especially coastal fog for moisture, but very few studies

have explicitly tested this idea.

Early projections for the Fynbos biome were also for a considerable contraction of the extent of bioclimatic

conditions suitable for this biome, particularly in the lowlands and in its northern reaches, with the uplands

and mountains providing a stronghold for maintaining species richness under future scenarios. Similar to the

Succulent Karoo, revised projections produced with updated climate scenarios and modelling techniques

project a lower risk, but nonetheless, substantive risks to species remain. This is due to the projected

requirement for geographic range shifts of species with limited range sizes. With fire an important

biome-defining process, the interaction of changing climate and fire risk conditions is emerging as an

important risk (e.g. Slingsby et al. 2017). Initial work on this issue has indicated an increase in the frequency

of extensive fires, possibly due to more frequent high fire danger conditions, which themselves are projected

to increase in frequency. Together with the increased chances of fire ignitions, especially related to increased

human densities, this issue is likely to continue as an important driver of changes in biome functioning and

biodiversity. Mean fire return interval in Cape Floritic Region (CFR) Fynbos has been shown to have


82

decreased, with intervals reduced from 12 to 19 years in 1970, to 6 to 9 years in 2000 (Chalmandrier et al.

2013; Wilson, Latimer & Silander 2015). There is also a critical interaction with alien plant invasions to

consider, with invasive alien species (IAS) increasing fire risk, fire intensity, and fire frequency. The

combination of climate change and plant IAS has the potential to cause very significant biodiversity losses

and ecosystem function changes.

In general, ecosystems representative of the Desert biome are not seen as vulnerable to climate change due

to their assumed inherent resilience under warm dry conditions, but this assumption is untested. This biome,

defined as being dominated by annual plants that are responsive to rainfall inputs, was projected to spread

south and eastwards in South Africa, replacing both Nama-Karoo and Succulent Karoo biomes in their

northern reaches. Revised projections still include at least some expectation of such an expansion by 2050

under a high risk scenario.

5.4.2. Species and community changes

The first impacts of climate change on biodiversity are likely to be revealed in responses at individual species

and community levels. Species- and community-based observations will be valuable in providing an early

warning detection of incipient ecological changes, especially due their sensitivity and spatial resolution, but

attribution of observed responses to climate change depends on demanding analysis in the early phases of

responses. There is unfortunately no national scale, directed effort to monitor biodiversity changes

specifically to detect and attribute such climate change impacts. However, several efforts launched for

purposes of inventory and stock taking, such as the South African National Bird Atlas Program (SABAP), and

the Protea Atlas initiative, can be leveraged through repeat data gathering to serve this purpose. The

maturation of the South African Environmental Observation Network (SAEON) now also provides an

appropriate platform to further this goal. Well-developed spatial data including satellite derived observations

of vegetation are also now yielding important insights into long term vegetation change. There is an

opportunity to develop a far better-integrated effort to monitor, detect and test for the attribution of

changes in ecosystems and biodiversity to climate change drivers.

We summarise a range of studies that have reported climate change impacts at species and community levels

in Table 12. Examples range from reorganisation of bird, reptile and mammal assemblages (linked to changes

in habitat structure due to bush encroachment) to range extension by specific plant species such as Seriphium

plumosum (Bankrupt Bush). Several temperature and possibly rainfall related biodiversity shifts have also

been reported. Because bird species are both generally mobile (and thus likely to adapt rapidly) and well-

studied, this group is very attractive for testing climate change impacts. However, the risk of confirmation

bias in selected studies of apparently responsive birds has been highlighted by Hockey and Midgley (2009),

and also applies to other taxa. They showed several trends in bird range shifts that support a climate change

explanation, but can in fact be attributed to other more likely drivers. Nonetheless, with appropriate

attention to statistical treatment, Altwegg et al. (2011) demonstrated that Barn Swallows (Hirundinidae) have

been shifting their departure dates, with a high likelihood of seasonal climatic shifts serving as the driver of

this change.


83

Table 12. South African based studies focussed on the various impacts of climate change on genes, species and ecosystems.

Observed Impacts Potential mechanisms Predictions G

EN

ES

Population declines of Aloidendron dichotomum in the Northern Cape and Namibia (Foden et al. 2007) potentially affecting unique genotypes as identified by amplified fragment length polymorphism (AFLP) Jack and Josephs 2016 http://www.pcu.uct.ac.za/news/quiver-tree-genetics-window-past-distributions .

Drought and warming causing local population extinctions (Foden et al. 2007).

Potential loss of unique genotypes over the next five decades (Guo et al. 2017).

SPECIES

HER

PET

O

FAU

NA

SA Amphibian range contractions: 70% had range contractions, 53% severe (Botts, Erasmus & Alexander 2015).

Uncertain. 70% loss of climate space for A2a scenario, 60% for B2a for 37 endemic herps in CFR. (Mokhatla, Rödder & Measey 2015).

PLA

NTS

Increased die off of giant euphorbia trees (Van Der Linde et al. 2012).

Decreasing rainfall, rising temperatures, increasing water demand, mammal impacts, fungal pathogens (Van Der Linde et al. 2012).

Die off of quiver tree equatorward populations (no poleward expansion). (Foden et al, 2007).

Densification and range expansion of common Savanna trees Senegalia mellifera, Vachellia tortilis, Tarchonanthus camperatus, Rhiozum trichotomum, Vachellia tortilis, Colophospermum mopane, Dichrostachus cineria, Vachellia karoo, Senegalia erubescens, Terminalia sericea and V.siebierianna.

Reduction in fire intensity, loss of megaherbivores, fewer browsers, elevated CO2, increased rainfall and warming (Venter, Cramer & Hawkins 2018).

Increase in Savanna tree dominance (Scheiter & Higgins 2009).

Range expansion and densification of Seriphium plumosum across karoo and grasslands (Bankrupt bush) (Jordaan & Province 2009; Van Zyl & Avenant, 2018).

Elevated CO2. C3 plants outperform C4 plants under elevated CO2 (Collatz, Berry & Clark 1998).

BIR

DS

Barn swallows: shifting phenology, leaving South Africa 8 days earlier (Altwegg et al. 2011).

Novel climate regime.

Cape rock-jumper & protea canary ranges have decreased and 30% decline in reporting rates over 20 years (Lee & Barnard 2016).

Loss of suitable climate space and increasing temperatures (Lee & Barnard 2016).

Endemic bird species of CFR Fynbos (e.g., nectarivores) will become vulnerable, changing in abundance and composition due to increasing time since fire (Chalmandrier et al. 2013).

Common swift: Rapid range expansion over recent decades. (Guo, Zietsman & Hockey 2016).

Black sparrowhawks: Range expansion, & 3 month earlier breeding season. (Martin et al. 2014).

Novel climate regime.

Increase in extent and abundance of bulbul, nicators and weaver families (Loftie-Eaton, 2014; Péron & Altwegg 2015).

Woody encroachment.

Decline in abundance and range extent of lark and sparrowlark, cisticola, chats and wheatears, wagtails and pipits and widowbird families (Péron & Altwegg 2015).

Woody encroachment.

9 palearctic migrant bird species advanced departure dates from non-breeding grounds (Bussière, Underhill & Altwegg 2015).

Northern hemisphere climate change.

Vultures, secretary birds, ground hornbill declines in populations (Schultz, 2007; Loftie-Eaton, 2014).

Woody encroachment and general population pressures; warming affecting nesting site availability (vultures).

MA

MM

ALS

Declines in the abundance of meso-grazer species in savannas (zebra, blue wildebeest, sable, roan, tsessebe and eland) (Smit & Prins, 2015)

Woody encroachment.

Increase in browsing species (impala, kudu and giraffe) in Savanna parks (Smit & Prins, 2015).

Woody encroachment.

Cheetah population decline (Marker & Dickman 2004) Woody encroachment is contributing factor to reducing suitable hunting habitat.

Decline in bat diversity (McCleery et al. 2018) Woody encroachment.

Decline in rodent diversity (Blaum, Rossmanith and Jeltsch, 2007a; McCleery et al.2018)

Woody encroachment.

http://www.pcu.uct.ac.za/news/quiver-tree-genetics-window-past-distributions

http://www.pcu.uct.ac.za/news/quiver-tree-genetics-window-past-distributions


84

INV

AS

IVE

SP

EC

IES

Multifaceted impacts on almost all aspects of ecosystem function and endemic and indigenous biodiversity.

Multiple mechanisms may be exacerbated by climate change.

Models for 162 non-native trees and shrubs show decrease in suitable climatic habitat (Bezeng et al. 2017).

Rising temperatures change bioclimatic niche space.

By 2080, of the ‘bad five’ aquatic invasives, range expansions of 1 to 10% of current range for three, and contractions of 1.5 to 9% of two species, under warming projections CSIRO-Mk3.0. (Hoveka et al. 2016).

Water temperature and nitrogen concentration, optimum growth rates found at ±30 °C.

Increased ecological success and geographic range of water hyacinth; higher population growth rates to cause faster spread rates within habitats and faster invasion into uninvaded habitats especially those that were previously low temperature limiting (Masters & Norgrove 2010).

ECOSYSTEMS

FY

NB

OS

Functional and composition switch in endemic Fynbos bird species: Shifts from Fynbos specialists/ nectarivorous species to non-Fynbos specialists birds / granivorous (Chalmandrier et al. 2013)

Increase in fire frequency. Endemic bird species of CFR Fynbos (e.g., nectarivores) will become vulnerable, changing in abundance and composition due to increasing time since fire (Chalmandrier et al. 2013).

SA

VA

NN

A

Savanna expansion into grassland (O’Connor, Puttick and Hoffman, 2014; Skowno, et al. 2017).

Elevated CO2, altered disturbance regime, warming, changing rainfall (O’Connor, Puttick & Hoffman, 2014; Venter, Cramer & Hawkins 2018).

Increase in dominance of trees driven by elevated CO2 (Scheiter & Higgins 2009).

Woody encroachment within savannas (Skowno, et al. 2017). Elevated CO2, altered disturbance regime, warming, changing rainfall (O’Connor, Puttick and Hoffman, 2014; Venter, Cramer and Hawkins 2018).

Increase in dominance of trees driven by elevated CO2 (Scheiter & Higgins 2009)

Functional and compositional switch in Savanna bird community with fewer larger-bodied non-passerines, ground-foragers, seed-eaters and birds associated with grasses in open savannas (e.g. coucals, korhaans, whydahs, indigobirds and hornbills) (Sirami and Monadjem, 2012) to a community dominated by small-medium bodied insectivorous passerines (Seymour & Dean 2010) like shrikes, bulbuls, robins, flycatchers and honeyguides (Sirami & Monadjem 2012)

Shift in vegetation structure (Skowno and Bond, 2003; Krook, Bond and Hockey, 2007; Sirami et al., 2009).

Functional and compositional switch in Savanna large-mammal community Decline in community dominated by large bodied meso-grazers with a shift to community characterised by smaller bodied herbivores and browsers (Smit & Prins 2015)

Reducing in grass biomass and change in vegetation structure caused by encroachment.

Functional and compositional switch in Savanna small carnivore populations. Small carnivores like cape fox, striped polecats, suricates, yellow-mongooses, bat-eared foxes and small spotted genets decline with encroachment (Blaum et al., 2007b).

Functional and compositional switch in Savanna arthropod communities A shift in ant communities and a decrease in termite activities with encroachment. Abundance of scorpions and dung beetles increased and abundance of grasshoppers, ground and carrion beetles and solifuges decline. (Blaum et al., 2009).

Change in vegetation structure caused by encroachment

Functional & compositional switch in diurnal lizard species in savannas due to woody encroachment -diurnal lizard species, woody encroachment (Meik et al., 2002).

Change in vegetation structure caused by encroachment

NA

MA

KA

RO

O Grass & vegetation cover increase alongside shrub decrease in the semi-

arid Karoo Midlands.(Mmoto L. Masubelele, Hoffman & Bond 2015). Karoo expansion into grasslands

(Acocks, 1953; Rutherford et al., 1999b)

Increase in fire frequency in Karoo mediated by grass invasion impacting non-fire tolerant Karoo shrubs (du Toit, O’Connor & Van den Berg 2015; du Toit, Van den Berg & O’Connor 2015)

5.4.3. Projected changes in biomes and biodiversity

Early projections starting in the late 1990s made extensive use of the bioclimatic niche modelling approach

to predict the future of South Africa’s biomes (Rutherford et al. 1999a). These models projected significant

Observed Impacts Potential mechanisms Predictions


85

spatial shifts in biomes and their boundaries, and the ingress of novel bioclimatic conditions with no current

matching biome. More recent bioclimatic niche-based models driven by updated climate change scenarios

(Figure 31) predict a far less extreme extent of biome switches and shifts under ‘’low risk’’ future climate

scenarios (e.g. (DEA 2013a), with ‘’low risk’’ representing the 10th percentile combination of rainfall and

temperature change, i.e. the cooler, wetter quartile of the future climate scenario space). Under this

scenario, the only significant biome shifts projected are for an expansion of the Savanna biome into the

Grassland biome. These more recent projections include information for biomes not previously modelled,

with a possible expansion of the Albany Thicket inland, mainly in areas of the Nama-Karoo, but no significant

changes for the Indian Ocean Coastal Belt.

Under so-called ‘’high risk’’ or worst-case climate scenarios (Figure 31, representing the 90th percentile

combination of rainfall and temperature change, i.e. the warmer, drier quartile of the future climate scenario

space), significant expansion of climate conditions suitable for the Desert biome are projected in the

Nama-Karoo, significant Fynbos biome contractions are confirmed to occur, and the contraction of Grassland

biome is projected, being replaced by Savanna conditions. Grassland biome contraction and switches to

Savanna are confirmed, and the substantive expansion of Desert biome into Nama-Karoo range is projected.

However, the previously projected contraction of the Succulent Karoo biome is not confirmed except for a

limited region of Succulent Karoo switch to Desert in its northern reaches, and its expansion in the southern

coastal region to replace parts of the Fynbos biome. Parts of the Albany Thicket are projected to be replaced

by Nama-Karoo and Savanna biomes, and Savanna could impinge significantly onto the Indian Ocean Coastal

Belt. The Grassland biome appears to be most at risk under all scenarios of climate change (DEA 2013).


86

Figure 31. Projections of bioclimatic envelopes under statistically downscaled climate scenarios, looking ahead to approximately 2050. Low Risk map simulates impacts of wetter/low warming future climate projections, High Risk the impacts of drier/hotter projections, Medium Risk the median temperature and rainfall projections (see Methodology Box 6). From (DEA, 2013d).

A contrasting approach to projecting biome shifts has been applied recently, namely the Dynamic Global

Vegetation Model (DGVM). The aDGVM (Scheiter & Higgins 2009), a modelling framework that takes into

account changes in CO2 and accounts for disturbance (e.g. fire), provides both contrasting and confirmatory

projections for South Africa, depending on location. These projections are unfortunately not yet credible for

the winter rainfall and shrub-dominated biomes due to known limitations of this model in simulating crown

fires and the functioning of the shrub and succulent growth forms (Moncrieff et al. 2015) (Figure 32). The

most important projections from this approach, when incorporating the role of fire and elevated CO2, are for

an increase in woody plant dominance in semi-arid regions, and in fire-driven grasslands and savannas

(Scheiter & Higgins 2009). The approach also makes novel projections of an expansion of woody plants into

the Nama-Karoo, and the possible westward expansion of C4 grass into the Succulent Karoo biome, in contrast

to the bioclimatic niche approach. The aDGVM approach thus projects an expansion of woody plant

dominated ecosystems and a thickening of woody plants in the eastern and central reaches of South Africa,

with extensive switches of Grassland and shrubland biomes to a Savanna-like biome. Vegetation cover is


87

projected to decline in the arid north-western parts of South Africa, where biome level modelling suggests

an expansion of desert type bioclimatic conditions (Scheiter & Higgins 2009; DEA 2013a; Moncrieff et al.

2015).

A range of climate change vulnerability assessments has been carried out, including those focusing on species

[e.g. (Erasmus et al. 2002; Thuiller et al. 2006; Midgley et al. 2003; Bomhard et al. 2005; Broennimann et al.

2006; Mokhatla, Rödder & Measey 2015), protected areas [e.g. (Van Wilgen & Herbst 2016; Rutherford et al.

1999b)], biomes (Midgley et al. 2002, 2003) and ecosystem services (Challinor et al. 2007; Gbetibouo &

Ringler 2009). Research has highlighted the variability and extent of predicted impacts. One study of 179

representative animal species in South Africa suggests the ranges of 78% will contract and 2% will become

locally extinct (Erasmus et al. 2002). Biome-level approaches appear to underestimate risks, yet the Fynbos

biome is known to be particularly vulnerable. By 2050 the Fynbos has been projected to lose 51%-65% of its

bioclimatically suitable area (Midgley et al. 2002) with projected impacts on Protea species (Midgley et al.

2002, 2003), amphibians (Mokhatla, Rödder & Measey 2015), and endemic birds (Chalmandrier et al. 2013).

In light of the broad-scale nature of climatic changes, the concept of sustaining species diversity through fixed

protected areas has been called ‘fundamentally flawed’ (Rutherford et al. 1999b). In Augrabies Falls National

Park and Melkbosrand, for example, over a third of analysed plant species are expected to become locally

extinct (Rutherford et al. 1999b). Agricultural landscapes are also vulnerable to the effects of climate change,

particularly in Limpopo, KwaZulu-Natal, and Eastern Cape provinces (Gbetibouo & Ringler 2009).

Vulnerability and variability is intrinsically linked to social and economic development, highlighting the need

for more interdisciplinary research (Gbetibouo & Ringler 2009). Finally, the application of mechanistically

based modelling approaches has shown that rising atmospheric CO2 effects may strongly interact with fire

regime changes, leading to further bush encroachment into the Grassland, Savanna and even the

Nama-Karoo biomes (Moncrieff et al. 2015).

The state of the evidence for projecting biome shifts shows that certain common projections can be identified

that are plausible and would be significant for biodiversity, ecosystem function and ecosystem services. The

replacement of Grassland by Savanna or woodland vegetation, especially at lower elevations, is projected by

both niche-based models (NBM) and DGVM, being driven by a warming climate (NBM and DGVM) and by

rising CO2 and fire regime changes (DGVM). This trend is supported by multiple observations of woody

thickening. Woody thickening in Nama-Karoo, and possible increased success of C4 grasses and conversion to

Savanna type vegetation projected by DGVM is supported by observations, and partly supported by NBM.

The consequences of woody thickening for biodiversity are many fold, and include already-observed shifts in

the dominance of bird functional types, and impacts on mammal, arthropod and herptile populations. It is

therefore critical that the drivers of the woody encroachment trend are correctly identified, to allow

appropriate adaptation strategies to be put in place.


88

Figure 32. The distribution of South African dominant plant functional types in 1900, 2012 and 2100 simulated using the aDGVM (see Scheiter et al. 2012) for model description and biome classification scheme). Simulations were forced with projected changes in climate given by the Max Planck Institute for Meteorology's (Hamburg) ECHAM5 IPCC (2007) projections with atmospheric CO2 from IPCC (2007) SRES A1B projections. Numbers indicate the percentage of grid cells covered by different biome types in each year and arrows indicate transitions between biomes from 1900 to 2012 and from 2012 to 2100. From Moncrieff et al. (2015).

There is little observational evidence yet of obvious widespread adverse impacts of climate change on the

biodiversity of winter rainfall biomes, but this may simply be a reflection of an expected lag phase between

an ongoing bioclimatic change and the anticipated biological response. Revised NBM under ‘’low risk’’ climate

scenarios now even suggest that widespread impacts may be avoided. Under such scenarios, resulting from

an effective global mitigation pathway, the main impacts on South African biomes could be related to

warming and CO2 fertilization effects on C4 grasses and woody elements, and these may be managed at least

to some extent by using fire and browsing/grazing regimes.

The relatively small number of global studies that have compared predictions of climate change impacts on

biodiversity with those observed show varying levels of agreement, ranging from good (e.g. Tingley et al.

2009; Thomas et al. 2011) to poor (Sofaer, Jarnevich & Flather 2018). These highlight the need for further

advances in the emerging field of climate change vulnerability assessment, as well as better application of

methods now available to consider a broad range of potential impact mechanisms including changes in

inter-species interactions, altered phenology, changes in interactions with non-climate change threats and

loss of microhabitat availability (Foden et al. 2018).


89

Box 6. Methodology for low, intermediate and high risk scenarios

Detailed methodology for low, intermediate and high risk scenarios developed by DEA 2013.

In this vulnerability assessment, an effort was made to quantify the inherent uncertainty of climate change projections by modelling the potential impacts of a range of climate scenarios. This was done through defining a median projected climate change scenario, in addition to a high (90th percentile) and low risk (10th percentile) scenario.

The assessment was conducted with climate scenarios generated using the IPCC A2 emissions scenario, which is itself a business-as-usual emissions scenario, consistent with current emissions trends (Nakicenovic 2000). This emission scenario is less extreme than the 2% per annum emissions growth rate assumed in the IS92a emission trajectory in the IPCC’s Second Assessment Report (IPCC, 1996) used in the climate scenario modelling for South Africa’s First National Communication to the UNFCCC (2000). Climate modelling carried out since the Second and Third IPCC Assessment Reports (IPCC, 1996, 2007) has advanced in several respects, including aspects of ocean circulation that are now captured in a dynamic way. These advances are likely to produce more credible projections for southern Africa, a region that is under significant influence of ocean processes.

The modelling work used two sources of local climate scenarios developed from global model projections used in IPCC Fourth Assessment Reports (2007). These two approaches represent distinct climate modelling methodologies, termed ‘statistical downscaling’ and ‘mechanistic downscaling’ respectively. Statistical downscaling uses established correlations between synoptic conditions and local weather patterns to derive projections of future climate (e.g. Hewitson and Crane 2006). Mechanistic downscaling uses a physical dynamic model of the climate that is able to run at a fine spatial scale over the region of interest, thus avoiding a number of limitations of alternative methods (see Engelbrecht et al. 2009 for more information). Both approaches use the primary outputs of physical models of the climate run at a global scale as a basis for their downscaling.

Statistically downscaled scenarios were based on outputs from 15 global climate models, and were processed using a further statistical treatment, to derive three downscaled climate scenarios for South Africa, for approximately 2050 (mean monthly values for the time-slice 2041 – 2060), namely: • Best-case ‘low-risk’ scenario: combining the 10th percentile smallest predicted increases in seasonal temperature and smallest reductions in seasonal rainfall. • Intermediate scenario: middle of the range (median) predicted increases in temperature increases and changes in rainfall. • Worst-case ‘high-risk’ scenario: 90th percentile greatest predicted increases in seasonal temperature and greatest reductions in seasonal rainfall. The results generated represent a broad range of plausible climate futures. It is important to note that they combine climatic conditions for temperature and rainfall that may not naturally occur together, and may indeed not represent any one of the source models for the data. This point notwithstanding, the intention has been to conduct a traceable ‘stress test’ of the ecosystems under investigation, and to explore the possible future climate space with respect to the ‘tails’ of the frequency distribution as well as the median. Due to the significant uncertainties in modelling both climate and impacts, it is worthwhile for impacts and adaptation projections to model such a range of conditions. The range chosen for this study brackets the potential range of outcomes with an 80% confidence, given the current state of information available.

To provide an alternative approach that is traceable to individual climate models, six mechanistically downscaled climate scenarios were also considered to represent the current climatological understanding of possible climate futures. Three of these were used in this analysis, namely MIROC, ECHAM5 and CSIRO, representing the wettest, intermediate and driest members of a selection of six global climate models. Downscaling was conducted using Cubic Conformal Atmospheric Modelling (CCAM) technology and results were averaged for 2050 (mean monthly values for the time slice 2041 – 2060).


90

Four decades of monitoring arid regions in Africa by the BIOTA/SASSCAL observation system have linked the persistent drought in the Richtersveld to unprecedented biodiversity losses. A total of 54 permanent plots of 100m² have been annually recorded in the Richtersveld, with detailed mapping and measuring the size of all individuals of all perennial species. These plots include 11 plots that have been fenced by SANParks and thereby excluding grazing and trampling by larger herbivores. From this monitoring the lack of rainfall is the main driver of the present unprecedented loss of plant diversity. For the last 7 years rainfall was below the average, while the last 2 years were even below 50% of the average. No similar drought occurred between 1980 and 2018 and the earlier droughts in 1980/1981 and 1990/1991 had less severe impact on the vegetation. Combined with impacts from increased stock farming the drought has left large areas of the plains in the northern Richtersveld bare of living plants. With over 250 plant species endemic to the Richtersveld and Gariep Centre's the impact of this drought is devastating to plant diversity in South Africa and to local communities. The drought has had caused further declines to threatened species that had already suffered severe declines from an increase of local stock farming between 2004 and 2015 (e.g. Schlechteranthus maximilianus, Othonna herrei, and Pachypodium namaquanum in the Numees area).

The photos show the decline in the Brownanthus pseudoschlichtianus community on the Koeroegabvlakte between 2004

(left) and 2018 (right).

© Norbert Jürgens – University of Hamburg

2004

Box 7. Unprecedented collapse of Richtersveld biodiversity in 2018: A perfect storm of drought and overexploitation?


91

6. INPUT DATA

Chapter 6: Skowno, A.L., Raimondo, D.C., Dayaram, A. & Kirkwood, D. 2019. ‘Chapter 6: Input Data’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

6.1. Ecosystem classification and mapping

Terrestrial Ecosystem Mapping

In the terrestrial environment, vegetation types provide an excellent way of delineating ecosystems at a

relatively fine scale. Vegetation types are based on a range of factors, such as geology, soil types, rainfall,

temperature and altitude, which determine the composition and structure of plant communities (Mucina &

Rutherford 2006). They provide a good indication of terrestrial biodiversity other than plant species, because

many animals, birds, insects and other organisms are associated with particular vegetation types or groups

of vegetation types (Reyers et al. 2007).

The 2018 update of the Vegetation of South Africa, Lesotho and Swaziland (Mucina & Rutherford 2006;

Dayaram et al. in prep) delineates and describes 458 national vegetation types that provided the main basis

for delineating terrestrial ecosystem types in the NBA 2018. These vegetation types are nested within nine

biomes: Fynbos, Succulent Karoo, Desert, Nama-Karoo, Grassland, Savanna, Albany Thicket, Indian Ocean

Coastal Belt and Forests (Figure 4)14. The number of vegetation types within each biome varies depending on

the diversity of plant communities that have been described and mapped in these systems. For example,

Fynbos has the highest number of types (122) but covers only 6% of the country’s surface area compared to

the Savanna biome that has 91 types but covers 32% of the country.

A map of the historical extent of vegetation

The vegetation map delineates and describes the historical extent of the vegetation types in South Africa,

Lesotho and Swaziland prior to major anthropogenic land conversion (circa 1750) (Mucina & Rutherford

2006). Delineating this reference configuration of the vegetation in intensively cultivated landscape and in

urban centres can be challenging, and experts use various environmental variables (geology, soils, climate,

elevation, aspect and topographic position) and historical photographs to guide the process. Mapping the

historical extent of the vegetation types allows for a comprehensive assessment of habitat loss and for the

clear separation of land cover change and vegetation change.

A history of vegetation mapping

South Africa is fortunate to have a long history of vegetation mapping, going back to 1936 (Figure 33). Over

the years the focus of the maps has shifted from mapping rangeland types for agricultural planning, to

mapping vegetation communities and biodiversity patterns. The current National Vegetation Map builds on

these historical maps and was first published in 2006 using a combination of existing maps, field plot data,

local expertise and remote sensing (Mucina & Rutherford 2006). Since 2006, our knowledge of the vegetation

communities and our tools for mapping them have progressed and allowed us to refine the boundaries and

the vegetation classification system (Dayaram et al. 2017).

14 The vegetation types of Prince Edward and Marion Islands, 1700km south east of mainland South Africa, include an additional Tundra biome. The sub-Antarctic territory of South Africa is the focus of Volume 6 of the National Biodiversity Assessment (Sink et al. 2019).


92

The vegetation map is used by a wide range of professionals in academia, private industry, NGOs and

government conservation sectors. There is an important feedback loop between these users and the

custodians of the map that allows for the continuous refinement of the map as better data becomes

available. In order to ensure that the map remains scientifically defensible, a transparent and rigorous vetting

process has been put in place15. Changes to the map are presented to the National Vegetation Map

Committee which is composed of vegetation experts from across sectors and with knowledge of all nine

biomes (many of whom were involved in the original map). This committee is further supported by an even

larger network of vegetation experts called the National Vegetation Map Associates. Proposed changes to

the map or classification system are peer reviewed by the most suitably qualified committee members or

associates. If the proposed changes are approved by the review process, the National Vegetation Map team

at SANBI implements the changes.

Changes in the ecosystem types since NBA 2011

The NBA 2011 terrestrial assessment was based on the 435 national vegetation types published in 2006 and

included a small number of wetland and estuarine types. In addition to the vegetation types, 136 special

habitat types were identified from various provincial fine scale planning projects. These special habitats have

not been considered in this NBA 2018, and the terrestrial ecosystem assessment now focusses purely on the

458 national vegetation types. Since the 2006 publication, various refinements and changes have been made

to the National Vegetation Map. These include: numerous boundary changes in KwaZulu-Natal, Northern

Cape, Western Cape, Mpumalanga; widespread refinements to forest type boundaries in KwaZulu-Natal,

Limpopo, Mpumalanga and the Eastern Cape; the removal of the wetland types and estuarine types (these

are now fully represented in the Inland Aquatic and Estuarine ecosystem datasets); the refinement of the

coastal vegetation and alignment with seashore types; and a complete revision of the Albany Thicket biomes

(including numerous new vegetation types, and boundary changes) (Dayaram et al., 2017).

For downloadable versions of the map, more details about the project, or information on how to contribute,

please visit http://bgis.sanbi.org/vegmap

Figure 33. Progression of national scale vegetation mapping in South Africa 1936 to 2018.

6.2. Ecosystem condition Along with an ecosystem classification system and a map of the ecosystem types, information on ecosystem

condition is another crucial input into the ecosystem level assessments. For the NBA 2018, terrestrial

ecosystem condition is based primarily on a national land cover change dataset described in Chapter 3. The

IUCN Red List of Ecosystems (discussed below) encourages the use of all available information on ecosystem

condition, including rates of habitat loss, disruption of biotic processes and environmental degradation. Of

these three components of condition, we only have nationwide data for rates of habitat loss (derived from

the land cover change dataset). For the Albany Thicket biome (Lloyd, Van den Berg & Palmer 2002) and the

15 http://bgis.sanbi.org/Projects/Detail/190 - Guidance for updates to vegetation units.

http://www.bgis.sanbi.org/vegmap

http://www.bgis.sanbi.org/vegmap

http://bgis.sanbi.org/Projects/Detail/190


93

Little Karoo (Thompson et al. 2009) region we do have reliable ecosystem degradation data, and these are

included in the ecosystem condition dataset. This presents a significant limitation when assessing ecosystem

types in which the primary threats are in the form of invasive species and or over-utilisation / overgrazing.

6.3. Species data needed for threat assessments Accurate species occurrence data is required to support both threat and protection level assessments. For

each taxon group the following data preparation took place:

I. Collation of information on species occurrences:

Historic and current occurrence records were sourced from:

a. Digitised specimens from collections institutions (museums or herbaria);

b. Citizen science records collected during targeted atlasing projects and online virtual

museums;

c. Provincial conservation agencies and South African National Parks monitoring records; and

d. Additional observation data from species experts not already captured in any of the above

datasets.

II. Georeferencing of occurrence records and spatial verification:

a. For all taxonomic groups, with the exception of birds, historical occurrence records from

museums or herbaria provide much of the available data on species occurrences. These

records often do not have spatial co-ordinates associated with them. Thus, for each group, a

process was undertaken to accurately georeference records based on locality descriptions.

Uncertainty of accuracy of the co-ordinates captured in correspondence with the level of

detail provided in the locality description was included.

b. As georeferencing is typically undertaken by contract staff who are not species experts, an

important step to ensure accuracy of species input data was to run a process whereby

experts checked the validity of occurrence records and identified both errors of incorrectly

georeferenced occurrence records as well as incorrect identification of records from

collections institutions.

III. Species life-history and ecological information:

In order to determine key parameters required for IUCN Red List assessments of threat status and to

set the population persistence targets to determine protection level, information such as habitat,

generation length, life history or growth form, population growth rate etc. was obtained both from

the scientific literature and species experts.

IV. Spatial data:

Land cover data was used to determine causes of habitat loss and was used to calculate decline

parameters for Red List threat assessments. Changes in land cover between 1990 and 2014 (see

section 3.2.1) was the main source of information to determine trends in change of status for reptiles,

amphibians and plants. South Africa’s protected areas layer (see section 6.4.1) was intersected with

species point occurrence data and habitat suitability models generated for each taxon group to

determine the level of representation in the protected area network for each taxon.

Species groups assessed

In order for species assessments to feed into national level indicators such as the IUCN Red List index it is

important that full taxonomic groups are assessed. Groups included in this assessment are those groups

where the taxonomy is stable enough for there to be sufficient knowledge generated from field studies, and

collections to provide the baseline data for each taxon’s to be assessed. In addition, only groups that have


94

had extensive investment in mobilisation, collation and cleaning of foundation information can be assessed.

To date six terrestrial taxon groups have been comprehensively assessed. These include all terrestrial

vertebrates (mammals, birds, reptiles and amphibians), all South African plants, and one speciose group of

invertebrates, butterflies. Box 8 details the data collated and prepared for each taxon group. Work is

underway to increase the number of invertebrate groups that are assessed, with an ongoing assessment

work currently taking place for arachnids (specifically spiders and scorpions). Over the next five years,

invertebrates important for pollination processes (such as bees and specific families of flies) will be included

in assessment processes.

Box 8. Summary of species occurrence and habitat suitability model distribution data used as a primary input data for conducting threat status assessments conducting Red List Indexes used to determine change in trend data (see section 8.1) and to conduct protection level assessments (see section 8.2)

Birds: Occurrence data was sourced from the Southern African Bird Atlas Project (SABAP2) co-ordinated by the University of Cape Town. SABAP2 started in 2007 and data collection is ongoing. For the Red List assessments data collected by SABAP2 until mid-2015 were used and changes in abundance and distribution for birds were obtained by comparing data from the SABAP1 conducted between 1987 and 1991 and SABAP2 2007 onwards. For the Protection level analysis SABAP2 data collected until February 2018 were used. SABAP2 coverage of the PA network is sufficient for the vast majority of Bird Species. SABAP2 data were used to calculate relative abundance of birds in each reserve using reporting rates. Other datasets managed and curated by the ADU including Birds in Reserve Project (BIRP), Co-ordinated Waterbird Counts (CWAC), or specialised datasets obtained from individuals was used in a handful of cases to augment information available in SABAP.

Mammals: Data collation for the mammal assessment began in June 2013 and ran in parallel with other processes up until December 2015. Data collation and cleaning was co-ordinated and conducted by the Endangered Wild Life Trust (EWT) with contributions from the Animal Demography Unit (ADU) at the University of Cape Town’s (UCT’s) MammalMap project. Data contributors included museums, university researchers, statutory conservation agencies, environmental consultancies, private protected areas, landowners and citizen scientists. Overall, 460 931 occurrence records and 41 075 population count records were amassed. In total, there were 104 primary data contributors including 60 institutions and researchers in their private capacity. For details on data contributors see (Child et al. 2016).

Reptiles: Occurrence records were first sourced as part of the Reptile Conservation Assessment (SARCA) that was coordinated by the Animal Demography Unit (ADU) at the University of Cape Town (UCT) between 2004 and 2009 (Bates et al. 2014). As part of this project the 135 512 reptile occurrence records from 20 participating institutions were digitised and collated. The project involved extensive field surveys in gap areas and initiated the first virtual museum for the country where members of the public submitted images and coordinates of reptiles. While extensive digitisation took place as part of SARCA a large proportion of the data was not georeferenced to the scale required for assessment work. The reassessment of South Africa’s Reptiles undertaken by the IUCN Southern African Reptile Red List Authority between 2015 and 2018 ensured accurate georeferencing of all reptiles of conservation concern. Verification of spatial data by experts and the extrapolation of point data to infer occupied distribution ranges that were used both to calculate the species threat statuses and protection level, also took place as part of this project (Tolley et al. in prep).

Amphibians: Occurrence records for South African amphibians are managed by the International Union for Conservation of Nature’s (IUCN’s) Amphibian Specialist Group, working on southern African amphibia (Angola, Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe). The group known as the Southern African Frog Re-assessment Group (SA-FRoG), formed in 2009 have collated 133 667 amphibian occurrence records which includes data from all of South Africa’s major collection institutions and amphibian monitoring projects. Data from 45 projects and institutions and 21


https://en.wikipedia.org/wiki/Southern_African_Bird_Atlas_Project


95

independent researchers are included. Point occurrence data has been extrapolated by the amphibian experts working on Red List assessments to infer occupied distribution maps. Occupied distribution maps in combination with recent occurrence data were used for both threat and protection level assessments.

Butterfly: Occurrence records for South Africa’s ca 800 butterfly taxa were collated as part of the South African Butterfly Conservation Assessment (SABCA) project funded by SANBI and jointly implemented by the University of Cape Town’s Animal Demography Unit and the Lepidoptera Society of Africa (LepSoc) between 2007 and 2011 (Mecenero et al. 2013). Records were digitised or sourced from 15 collections institutions (museums and herbaria) and extensive surveys conducted as part of SABCA. Ongoing updates of these occurrence records has been managed by the LepSoc and housed in the database Lepibase. Occurrence records for 154 species of taxa of conservation concern (CR, EN, VU, DD, NT and LC- national rare) were individually verified by members of the LepSoc members in 2017 as part of the South African Lepidoptera Conservation Assessment project. Additional records were obtained from the literature with two important sources: Articles from the journal Metamorphosis and Otto, H. 2014. Butterflies of the Kruger National PArk and surrounds. Penguin Random house, South Afrca.

Plant: Occurrence data were sourced primarily from digitised herbarium specimen data from South Africa’s six major herbaria which constitute 90% of the country’s ca. 3 263 200 plant specimens: the Bolus Herbarium (BOL); the Selmar Schonland Herbarium (GRA); the Compton Herbarium (NBG); The KwaZulu-Natal Herbarium (NH); the BEWS herbarium (NU); and the National Herbarium (PRE). Additional datasets from provincial conservation authorities’ plant monitoring datasets, and smaller private herbaria and collections were also included. Recent field occurrence data was obtained from two primary sources, from the data collected by the Custodians of Rare and Endangered Wildflowers Programme, a SANBI-led citizen science project that specifically targets surveying South Africa’s rare and threatened plant species and iNaturalist , a virtual museum open to all members of the public. A sample of 900 plant taxa were randomly selected as to investigate trends for South Africa’s 20 401 plant taxa. Habitat suitability models were developed using a combination of vegetation types (assigned to each species to correlate with descriptions of habitat in the literature and specimen labels), altitudinal range, and topographical features, and areas of overlay were clipped to a concave hull around occurrence records. These habitat suitability models were used in the Protection Level analysis and were interested with land cover data to determine proportion of habitat lost between 1990 and 2014, these data were used in the Red List Index calculation.

6.4. Protection level building blocks Protection level analyses require information on the distribution of ecosystems and species, the distribution

and type of protected areas and biodiversity targets for each ecosystem type on which thresholds can be

based. The condition of ecosystems and the population health of species is also taken into account. The

ecosystem types and condition, and the species input data have been discussed above. Below we introduce

the protected areas data and the biodiversity targets.

6.4.1. Protected areas

Protected areas are areas of land or sea that are protected by law and managed mainly for biodiversity

conservation. Protected areas are vital for ecological sustainability and climate change adaptation. They also

serve as the backbone of the ecological infrastructure network, protecting the areas that deliver important

ecosystem services to people. All protected areas recognised in the Protected Areas Act are considered as

protected areas in the NBA. The Protected Areas Act provides for several categories of protected area,

including special nature reserves, national parks, nature reserves, marine protected areas and protected

environments. In addition, it also recognises world heritage core sites, specially protected forest areas, and


https://www.inaturalist.org/places/south-africa


96

mountain catchment areas. Other effective conservation measures that provide for legally binding habitat

protection and management were also included in this analysis: National Botanical Gardens recognised in

terms of the Biodiversity Act, easements with title deed restrictions and management plans signed with

NGOs, and development offset sites also with title deed restrictions. Private nature reserves, contractual

parks and protected environments secured legally under formal biodiversity stewardship programmes are an

important element of the protected areas estate of South Africa, and are typically the most cost efficient

means of expanding the protected areas estate, underpinning the majority of new declarations.

The Protected Areas Act uses a slightly narrower definition of protected areas than the CBD and IUCN, which

acknowledge a broader range of area-based conservation measures in protecting biodiversity. These areas

include conservation areas not formally protected by law but informally protected by the current owners and

users, and managed, at least partly, for biodiversity conservation. These areas are referred to as conservation

areas in South Africa and include a range of other mechanisms such as the intact and conservation zoned

areas of UNESCO biospheres, buffers zones on world heritage sites, areas protected by spatial planning laws

(e.g. zoning for conservation use), and areas protected by conservation servitudes. In the absence of legally

binding measures that prevent loss of natural habitat and require effective management, these other

area-based conservation measures do not provide full and permanent protection and are not always

optimally managed to achieve biodiversity conservation objectives. For this reason, the NBA currently only

evaluates protected level indicators using protected areas and a narrow range of legally secure other

effective conservation measures as meeting protection targets.

Development of the Protected Areas layer for the NBA 2018

A South African Protected Areas Database (SAPAD) is maintained by the DEA and released publicly each

quarter (https://egis.environment.gov.za). This spatial dataset formed the core of the protection level

analysis. However, the database does not yet represent all existing protected areas as described above, and

also requires restructuring and cleaning to allow protection level analysis. The spatial data used for protection

level analysis was produced using the following steps:

SAPAD 2018Q2 was recompiled into non-overlapping protected area classes, maintaining designation

dates, and as far as possible with overlaps and inconsistencies resolved.

Provincial conservation agencies and South African National Parks (SANParks) protected area spatial data

were sourced, compiled and cleaned up. Areas that did not agree with the DEA SAPAD data were

manually checked and validated. Likely valid protected areas missing from SAPAD were appended. In rare

instances likely erroneous SAPAD protected areas were deleted after verification based on cadastre data

and source proclamations.

Reserve sub types were designated as de facto16 where provincial or SANParks datasets indicated areas

being managed as nature reserves but not present in SAPAD, and where gazetted status could not be

easily located online. In most cases these areas are probably formal protected areas in terms of the

Protected Areas Act, but in a few instances it is possible that these areas have no legal status but may

nevertheless be owned and managed by the relevant agency as a nature reserve.

All data sources were merged and cleaned into a single topologically correct layer with a consistent set

of attribute data for analysis (Figure 34).

16 De facto is defined as: existing or holding a specified position in fact but not necessarily by legal right. It is a term commonly used in matters relating to boundaries and borders.

https://egis.environment.gov.za/


97

There are numerous situations where a site has been

designated in more than one protected area type.

For example, the whole Cape Floristic Region World

Heritage Site overlaps with Provincial Nature

Reserves and Forest Reserves, and many Mountain

Catchment Area designations overlap with Provincial

Nature Reserves and Forest Reserves. To make the

reporting simpler we assigned protected area types

as follows:

a) if a site is Provincial Nature Reserve and

Mountain Catchment Area or World

Heritage Site, then it is reported as a

Provincial Nature Reserve;

b) if a site is a Forest Reserve and Mountain

Catchment Area or World Heritage Site then

it is reported as a Forest Reserve.

As a result, the Mountain Catchment Area and World

Heritage Site statistics reflect only those sites that

are designated only as each respectively.

Key protected area statistics for South Africa

Protected areas cover 8.9% (108 173 km2) of mainland South Africa (Figure 35a). Approximately 7% of this

estate (7 063 km2) is made up of non-natural areas (including old farmlands, infrastructure, dams etc.). The

majority of the estate is made up of National Parks, with Kruger National Park alone making up 18%

(Figure 35b). While state owned and managed National Parks and Provincial Nature Reserves make up the

largest portion of the protected area estate, protected area expansion over the last decade has taken

advantage of alternatives to state owned and run nature reserves with use of biodiversity stewardship

programmes. These formal provincial and national biodiversity stewardship programmes provide

mechanisms to proclaim Protected Areas Act compliant nature reserves and protected environments on

private land, usually with the landowner as the management authority. Biodiversity stewardship

programmes underpin over 68% of the expansion in the protected area estate in the last 10 years. NGO or

donor land purchases make up approximately 13% of the expansion and declarations of state land make up

around 19%. Land purchase by the state is now very rarely used in protected area expansion in South Africa.

Figure 34. Terrestrial protected areas estate for mainland South Africa.


98

Figure 35. (a) Increase in the protected area estate for mainland South Africa between 1960 and 2018. Extent shown in km2 and as a percentage of the mainland area; (b) the extent of the major protected area types circa 2018. NR = Nature Reserve, MCA = Mountain Catchment Area and other types include Botanical Gardens. Note, WHS and MCA designations often overlap with Provincial and National Park designations and the figure shows only those MCA and WHS that have no alternative designation. When the overlaps are ignored, 14% of the protected area estate is designated as WHS and 6% as MCA.

It is noteworthy that Private Nature Reserves (PNRs), proclaimed in terms of older provincial ordinance

mostly before the mid-1990s, still makes up a large proportion of the protected area estate. This is of some

concern since, although they are recognised in terms of the Protected Areas Act, these PNRs rarely have any

restrictions on their title deeds and rarely have management plans, thus providing very little protection from

habitat loss. The large area involved suggests that targeting key PNRs in under-protected and threatened

ecosystems are to be upgraded to a more modern protected area status would be a worthwhile strategy.

Mountain Catchment Areas (MCAs) also make up a significant proportion of total protected areas, mostly in

the Western Cape. Like Private Nature Reserves, they are recognised by the Protected Areas Act, but

generally lack a management plan and appropriate conservation management. Although invasive plant data

is not available, it is known that many of these areas are heavily infested with invasive plants, with intended

biodiversity and water production functions substantially compromised. Note that although many Provincial

Nature Reserves and Forest Reserves are also declared as MCAs and this is noted in the underlying spatial

dataset, this category here is only MCA areas that have no additional protected area status.

In the dataset used for this analysis, the protected areas categorised as de facto are likely to have some

formal status but documentation was not available. Some may be actual de facto protected areas, managed

and treated as a protected area, but without any formal declaration recognised in terms of the Act.

Nonetheless, the relatively small areas falling into this sub-category means that there would likely be very

little influence on the overall protection levels if these were excluded from analysis.

6.4.2. Biodiversity targets for ecosystem types used in the protection level analysis

Assessments of ecosystem protection level requires biodiversity targets to be set for ecosystem types. The

biodiversity target is the minimum proportion of each ecosystem type that needs to be kept in a natural or

near-natural state in the long term in order to maintain viable representative samples of all ecosystem types

and the majority of species associated with those ecosystems (Desmet & Cowling 2004; Reyers et al. 2007).

Biodiversity targets should preferably be based on the ecological characteristics of the ecosystem concerned.

For terrestrial ecosystems, the biodiversity target is calculated based on species richness, using the


99

scientifically formulated species-area relationship, and varies between 16% and 36% of the original extent of

each ecosystem type (Desmet & Cowling 2004). In previous national assessments (2005 and 2012) the South

African threatened ecosystem assessment framework was applied (RSA 2011). The SA framework used these

biodiversity targets as thresholds for the threat status categories. Since we have now adopted the IUCN RLE

approach, which used fixed thresholds, biodiversity targets are used only in the protection level assessment

in the NBA.


100

7. ECOSYSTEM ASSESSMENTS

Chapter 7: Skowno, A.L., Matlala, M.S., Kirkwood, D. & Slingsby. J.A. 2019. ‘Chapter 7: Ecosystem Assessments’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

7.1. Ecosystem threat status (Red list of Ecosystems) South Africa is one of several countries to independently develop indicators of ecosystem threat prior to the

IUCN Red List of Ecosystems (RLE) (Keith et al. 2013)(www.iucnrle.org). These indicators met a recognised

need for an indicator similar to the IUCN Red List of Species that could identify risk for higher-levels of

biodiversity organisation such as ecological communities (Keith et al. 2013; Bland et al. 2017).

The South African List of Threatened Ecosystems was conceptualised as a national indicator of ecosystem

conservation status in the early 2000s. From its early applications as a project-based indicator, it progressed

into a legislated national listing of threatened terrestrial ecosystems (RSA 2011, Botts et al. in review) which

entrenched its use in land use planning and decision making (e.g. through the Environmental Impact

Assessment processes). South Africa has also been reporting on the threat status of its ecosystems for more

than a decade, and using this information to focus scarce resources on conservation priorities through a wide

range of government policies (Driver et al. 2004, 2012; Botts et al. in review).

For the NBA 2018, the ecosystem threat assessments were based on the updated national vegetation map

and new ecosystem condition map (based primarily on the land cover change data). Both the 2011 South

Africa method and the new 2017 IUCN RLE methods were implemented with the aim of comparing and

contrasting the results. Overall, the South African method and the IUCN method were similar, but the

benefits of using the IUCN system (i.e. a stronger scientific evidence base than the South African method,

recognition of the resulting RLE by the IUCN and alignment for with international conventions and

assessment processes) tend to outweigh the drawbacks (i.e. deviating from a locally well-established and

accepted method) (Skowno et al. 2018b). Consequently, the NBA 2018 Terrestrial Reference Group decided

that the IUCN RLE approach should be adopted for the NBA 2018 and that the gazetted list of threatened

ecosystems should be updated with the new information as soon as possible.

7.1.1. The IUCN Red List of Ecosystems Framework

Background of the IUCN RLE

The IUCN Red List of Ecosystems is a framework for assessing the risks to ecosystems and identifying where

ecosystems are threatened (Rodríguez et al. 2011). Using the familiar categories from the Red List of Species

(Figure 36), and based on a set of criteria and thresholds developed collaboratively since 2008, the IUCN RLE

was established to ensure that the assessment methods: (i) can be applied systematically across realms and

geographic areas; (ii) are transparent and scientifically rigorous; (iii) are comparable and repeatable; (iv) can

be easily understood by policy makers and the general public; and (v) complement the IUCN Red List of

Threatened Species framework (Rodríguez et al. 2011; Keith et al. 2013; Bland & Keith et al. 2017).

The key concepts and definitions underpinning the RLE have been documented in a number of international

journal publications, notably Nicholson et al. 2009; Rodríguez et al. 2011; Keith et al. 2013, 2015; Bland

et al. 2017b, 2018. There is growing uptake of the IUCN RLE standards (Bland & Keith et al. 2017) with number

of published sub-global assessments (including North America, Philippines, Australia, Colombia, France,

Finland) adopting the RLE approach. Ultimately, national and other sub-global assessments undertaken using

http://www.iucnrle.org/


101

these international standards will contribute towards establishing a global database of threatened

ecosystems equivalent to the global Red List of Species.

Key Concepts

The goal of the IUCN RLE is to identify ecosystems that are at risk of losing their constituent biodiversity.

While there is substantial evidence that the ecosystem function and services are linked with biodiversity

(Bland & Keith et al. 2017), the relationships between these three facets of ecosystems can be complex.

Consequently, the RLE focusses specifically on risks to biodiversity (Keith et al. 2013). The RLE requires

consistent and clearly defined units of assessments (ecosystem types) that can be delineated spatially, while

at the same time needs to be able to effectively assess risks across widely contrasting ecosystems

(Keith et al. 2013). Vegetation types, in particular, have been suggested as appropriate and consistent units

that represent biodiversity and communities at an appropriate scale for use in the RLE (Keith et al. 2013;

Boitani, Mace & Rondinini 2015). The RLE framework used the concept of ecosystem collapse as the ‘end

point’ of ecosystem decline, this is equivalent to species extinction in the RLS, and is defined operationally as

a ‘transformation of identity, loss of defining abiotic or biotic features and characteristic native biota are no

longer sustained’ (Keith et al. 2013).

Criteria and Thresholds

The risk assessment model for the IUCN RLE is illustrated schematically in Figure 36. Declining distributions

(Figure 37-A) and restricted distributions (Figure 37-B) are considered distributional symptoms of decline;

and degradation of abiotic environment (Figure 37-C) and altered biotic function (Figure 37-D) are considered

functional symptoms of decline. It is possible for these mechanisms to interact and produce additional

symptoms of decline (Keith et al. 2013). The mechanisms in the conceptual model (Figure 37) translate into

five rule-based criteria with thresholds for the distributional and functional symptoms. The final threat listing

for each ecosystem is the worst threat category triggered by any of the criteria (i.e. if an ecosystem is listed

CR under any criteria it is listed CR overall, even if it only scores LC or any other category under all other

criteria).

Figure 36. IUCN RLE threat categories, see glossary of terms of definitions. Source: Bland et al. (2017a).

Figure 37. IUCN RLE framework for assessing the risk of ecosystem collapse. Source: Keith et al. (2013).


102

7.1.2. Implementation of the IUCN RLE for the NBA 2018

We applied the IUCN Red List of Ecosystems (RLE) method for the NBA 2018 using a comprehensive

systematic assessment based on IUCN Criteria A&B (criteria linked to spatial configuration and remaining

extent of ecosystems) for all terrestrial ecosystem types (vegetation types). This assessment, referred to as

the ‘core’ assessment, was then supplemented with additional assessments of selected ecosystem types

based on additional data on ecosystem condition including: habitat loss in metropolitan areas, KZN, Western

Cape and Mpumalanga; degradation in the Albany Thicket biome and Western Cape; and degradation from

invasive alien species and overgrazing using data extracted from threatened species assessments.

It is envisaged that the preliminary national RLE resulting from this assessment will be considered as the

baseline for the nation, and that it will be updated as additional information becomes available and be

released annually. Given the general lack of appropriate ecosystem condition data available in South Africa

this base line assessment is likely to have underestimated the risk to numerous ecosystem types – especially

those that are threatened by more subtle ecosystem modification than land clearing. If the threat status of

an ecosystem type is: a) considered an underestimate; or b) if the data used in the assessment is considered

inaccurate or inadequate; or c) if a researcher can develop new datasets to address additional criteria for

selected ecosystems; then further supplementary assessments should be undertaken. The core assessment

will be updated when updated national land cover change data becomes available.

Input data

The national land cover change dataset (Chapter 3) and the national vegetation map (Chapter 4) provided

the ecosystem assessment units and the primary ecosystem condition input to the RLE analysis. Additional

land cover data was sourced for Gauteng (2011), City of Cape Town (2017), Nelson Mandela Bay Metropolitan

Municipality (2015), Mpumalanga (2017), the Western Cape (2016) and KwaZulu-Natal (2011) (Table 13);

these datasets were used to perform supplementary assessments for Criteria A3. The threatened species

database (SANBI, Threatened Species Unit) was used to identify selected limited range ecosystems (Criteria

B) that are experiencing ongoing decline due to habitat loss, overgrazing or invasive plant species. Ecosystem

degradation data for the Albany Thicket biome, Little Karoo region and the Western Cape allowed for a

supplementary assessment of these regions using Criteria D3 (Table 13).

Table 13. Input data sources for the Red List of Ecosystem analysis.

Assessment Dataset Description Reference

All assessments

Terrestrial ecosystem type map

Vegetation map of South Africa, Lesotho and Swaziland 2018 version 6. Polygon feature geodatabase developed and curated by SANBI.

South African National Biodiversity Institute (2006). The Vegetation Map of South Africa, Lesotho and Swaziland, Mucina, L., Rutherford, M.C. and Powrie, L.W. (Editors), Version 2018.6b.

Core assessment: Criteria A3, A2b, B1, B2

National land cover

Land cover change raster developed by SANBI with two timepoints 1990, 2014. Based on national land cover products by GeoTerra Image 2015.

Skowno AL (2018) Terrestrial habitat modification change map (1990-2014) for South Africa: a national scale, two timepoint, land cover derived, map of terrestrial habitat modification - NBA 2018 Technical Report. Pretoria, South Africa. GeoTerraImage (2015) Technical Report: 2013/2014 South African National Land Cover Dataset version 5. Pretoria, 53 pp. GeoTerraImage (2015) Technical Report: 1990 South African National Land Cover Dataset version 5.2. Pretoria, 63 pp.

Supplementary: Criterion A3

City of Cape Town natural vegetation remnants map

2017 Vegetation remnants map produced by City of Cape Town based on remote sensing and in field validation of condition. Provided as a polygon feature geodatabse.

City of Cape Town (2017). Current Indigenous vegetation [Data file]. Retrieved from City of Cape Town Open Data Portal https://web1.capetown.gov.za/web1/opendataportal

Gauteng land cover

A composite raster land cover product that combines very high resolution (2.5m) urban land cover with high resolution (1om) rural land cover for the province.

GeoTerraImage (2011). Gauteng Provincial Land Cover (2009 imagery; 10m raster dataset). http://www.geoterraimage.com/products-landcover.php

https://web1.capetown.gov.za/web1/opendataportal

http://www.geoterraimage.com/products-landcover.php


103

Assessment Dataset Description Reference

GeoTerraImage (2011). Gauteng Urban Land Cover (2010; 2.5m raster dataset). http://www.geoterraimage.com/products-landcover.php

Nelson Mandel Bay Metro natural areas map

Natural areas map from the municipal bioregional planning process, with combination of desk top and field validated ecological condition. Provided as a polygon shapefile.

Stewart, W.I. and Jorgensen, P.J. 2016. Updating of Systematic Biodiversity Plan and development and publication of Bioregional Plan for the Nelson Mandela Bay Municipality: NMBM 2015 Landcover. SRK Consulting, South Africa.

KwaZulu-Natal land cover

2011 provincial raster land cover product (20m resolution) validated by provincial conservation authorities.

Jewitt D, Goodman PS, Erasmus BFN, O’Connor TG, Witkowski ETF (2015) Systematic land cover change in KwaZulu-Natal, South Africa: Implications for biodiversity. South African Journal of Science, 111, 0–9.

Mpumalanga land cover


GeoTerraImage (2018). Mpumalanga Provincial Land Cover (2017 Sentinel 2 imagery; 10m raster dataset).

Western Cape land cover


Pence, G.Q.K. (2017) Western Cape Biodiversity Spatial Plan: Technical Report. Unpublished Report. Western Cape Nature Conservation Board (Cape Nature), Cape Town.

Supplementary assessment: Criteria B1, B2

Ongoing decline - invasive plants and overgrazing

Evidence of ongoing decline for selected limited range ecosystems with very high numbers of threatened plant species – drawn from Red List of Species assessments.

Threatened species database of South Africa (SANBI, Threatened Species Unit).

Supplementary assessment: Criterion D3

Western Cape ecosystem degradation data

2015 provincial raster land cover and ecological condition product (10m resolution) validated by provincial conservation authorities.

Pence, G.Q.K. (2017) Western Cape Biodiversity Spatial Plan: Technical Report. Unpublished Report. Western Cape Nature Conservation Board (Cape Nature), Cape Town.

Albany Thicket biome degradation data

2002 biome-wide Landsat TM 5 based raster ecosystem degradation product (30m). Developed and field validated as part of the Subtropical Thicket Ecosystem Project (STEP) by the Agricultural Research Council.

Lloyd JW, Van den Berg EC, Palmer AR (2002) Patterns of transformation and degradation in the Thicket Biome, South Africa. Terrestrial Ecology Research Unit, University of Port Elizabeth.

Little Karoo degradation data

2005 MODIS based degradation map of Little Karoo region.

Thompson M, Vlok J, Rouget M, Hoffman MT, Balmford A, Cowling RM (2009) Mapping grazing-induced degradation in a semi-arid environment: A rapid and cost effective approach for assessment and monitoring. Environmental Management, 43, 585–596.

Core assessment

Criteria A2b and A3 – historical and future reductions in geographic range

The ecosystem type data (vegetation map version 2018) and the ecosystem condition data (land cover based)

were cross tabulated within a geographic information system and changes in natural extent from the

reference condition (circa 1750) to 1990 and 2014 were computed for each ecosystem type. The remaining

natural extent of each ecosystem type in 2014 was subtracted from the historical reference extent (circa

1750) and expressed as a percentage of the historical extent; allowing for the application of the thresholds

for Criterion A3 (historical reductions in geographic range). The absolute rate of decline in natural habitat

between 1990 and 2014 (Equation 1) was used to estimate the natural extent of each ecosystem type in 2040

(Equation 2), this projected value was then subtracted from the 1990 extent and expressed as a percentage

of the 1990 extent; allowing for the application of the Criterion A2b (past-present-future reductions in

geographic range).

Equation 1: Absolute Rate of Decline17: 𝐴𝑅𝐷 =𝐴𝑟𝑒𝑎1990 − 𝐴𝑟𝑒𝑎2014

𝑌𝑒𝑎𝑟1990 − 𝑌𝑒𝑎𝑟2014

Equation 2: Natural Extent 2040: 𝐴𝑟𝑒𝑎2040 = 𝐴𝑟𝑒𝑎2014 − (𝐴𝑅𝐷 × (𝑌𝑒𝑎𝑟2014 − 𝑌𝑒𝑎𝑟2040))

17 Absolute Rate of Decline ARD is the term used by the IUCN Red List of Ecosystems Guidelines; it is equivalent to rate of habitat

loss, and to rate of reduction in ecosystem extent used in previous chapters. ARD / rate of habitat loss underpin the ecosystem extent indicators discussed in Chapter 1 and 3.


104

Criteria B1i and B2i – Restricted geographic range

The first step for the assessment under this criterion was to combine the habitat modification data for 2014

with the ecosystem type data to produce an ‘ecosystem remnants’ layer circa 2014. This layer (in geotiff

format) was used to compute the Extent of Occurrence (EOO) and Area of Occupancy (AOO) for each

ecosystem type with the package [redlistr] (Lee & Murray 2017) within the statistical software R (R Core Team

2014). Ecosystem types only qualify for consideration under Criterion B if they are experiencing ongoing

declines in extent or condition (observed or inferred). For the core assessment, an absolute rate of decline

(ARD, Equation 1) threshold of 0.4%/y was used to identify ecosystems qualifying for Criterion B in terms of

ongoing decline. Ecosystems with ARD above this threshold have lost approximately 10% of their natural

remaining extent in the last 25 years. This then allowed for the assessment of Sub-criterion B1 (i) and Sub-

criterion B2 (i) for all qualifying ecosystem types (Table 14).

Supplementary assessments

To complement the core assessment a number of additional datasets were compiled to ensure the ecosystem

risk assessments were based on the best available data. This is a first version of the RLE and going forward

this is an approach that will allow for reassessments of selected ecosystem types as new and improved data

is collected or additional existing data comes to light. The supplementary assessment used Criteria A, B and

D.

Criterion D - Disruption of biotic processes (supplementary)

The Sub Tropical Ecosystem Project (STEP) and Little Karoo (LK) ecosystem degradation datasets (Lloyd, Van

den Berg & Palmer 2002; Rouget et al. 2003; Thompson et al. 2009) were used to assess the ecosystem types

of the Albany Thicket biome and Little Karoo region using Criterion D3 (biotic disruption since 1750) (Table

14). This criterion uses both the severity of disruption (50%, 70% or 90%) and the extent of the disruption

(50%, 70% or 90%) to categorize ecosystems. The STEP and LK degradation class ‘severe’ was considered as

90% severity due to large scale disruption of a wide range of biotic process including vegetation structure,

species composition, richness, biomass (Lloyd, Van den Berg & Palmer 2002; Thompson et al. 2009). The

extent of severely degraded land within in each ecosystem type was expressed as a percentage of the natural

remaining extent and the thresholds as per Table 14 were applied.

Criterion A3 - Historical reductions in geographic range (supplementary)

High resolution and high confidence land cover data exist for certain regions within South Africa including

Gauteng Province, City of Cape Town, Nelson Mandela Bay Metro, Mpumalanga, the Western Cape Province

and KwaZulu-Natal Province (Table 13). The ecosystem type data (vegetation map version 2018) and the high

resolution land cover data were cross tabulated within a geographic information system and changes in

natural extent from the reference condition (circa 1750) were computed for each ecosystem type. The

remaining extent of each ecosystem type was expressed as a percentage of the original extent of the

ecosystem type (circa 1750), allowing for application of Criteria A3 (historical reductions in geographic range).

Criteria B1iii and B2iii – Restricted geographic range (supplementary)

A key challenge in the application of the RLE is the poor availability of spatially explicit ecosystem degradation

data. As a result, the risk of collapse of many ecosystem types may have been underestimated in the core

assessment. A supplementary assessment of Criterion B was undertaken using the threatening processes

data from the Threatened Plant Species Database (SANBI Threatened Species Unit), the most reliable source

of data on functional symptoms of decline in South Africa. For the supplementary assessment of Criterion B,

the qualifying criteria (i.e. evidence of biotic disruption) was a quantitative assessment of threatening


105

processes listed for the threatened species occurring in each ecosystem type. To do this each threatened

plant species was assigned to an ecosystem type or ecosystem types using their spatial position and

descriptions of preferred habitat (personal communication with SANBI Threatened Species Unit). We then

calculated the number of species per ecosystem type that are threatened by a) poor rangeland management

(over grazing), b) invasive alien species and c) inappropriate fire management. The qualifier for biotic

disruption Criteria B1iii and B2iii in the supplementary assessment was set to: ecosystems that contained >

40 threatened plant species, of which > 60% were threatened due to major biotic disruptions. This is a

preliminary solution while additional data on biotic disruption, severity and extent are collected.

Table 14. full list of IUCN RLE criteria and thresholds (Rodríguez et al. 2011); for the list of criteria used in the South African implementation of the RLE see Table 15.


106

Table 15. IUCN RLE criteria and thresholds used in the South African assessment 2018.

Criteria & Sub-criteria CR EN VU

Criteria A: Reduced geographic distribution

Sub-criterion A2b - Loss of habitat over a 50 year period including past present and future For the NBA 2018, the absolute rate of decline in natural habitat between 1990 and 2014 (Equation 1) was used to estimate the natural extent of each ecosystem type in 2040 (Equation 2), this projected value was then subtracted from the 1990 extent and expressed as a percentage of the 1990 extent; allowing for the application of the Criterion A2b (past-present-future reductions in geographic range).

≥ 80% ≥ 50% ≥ 30%

Sub-criterion A3 – Historical loss of habitat (since ~1750) For the NBA 2018, the remaining natural extent of each ecosystem type in 2014 was subtracted from the historical reference extent (circa 1750) and expressed as a percentage of the historical extent; allowing for the application of the thresholds for Criterion A3 (historical reductions in geographic range). Equivalent supplementary assessments utilised higher resolution land cover products available for KwaZulu-Natal province, Mpumalanga province, Western Cape province and three large metropolitan areas.

≥ 90% ≥ 70% ≥ 50%

Criteria B: Restricted distribution & continuing declines in geographic distribution

Sub-criterion B1 (i) - Extent of a minimum convex polygon (km2) enclosing all

occurrences (EOO) & an observed or inferred continuing decline in spatial extent. For the NBA 2018, the absolute rate of habitat loss was used to identify ecosystems with significant ongoing decline in the extent of natural habitat (> 0.4%/y). Supplementary assessments used expert input and the threatened species database to identify restricted distribution ecosystems with very high levels of biotic disruption from over grazing, invasive species and poor fire management - B1(iii).

≤ 2 000 km2 ≤ 20 000

km2

≤ 50 000

km2

Sub-criterion B2 (i) - The number of 10×10 km grid cells occupied (AOO) & an observed or inferred continuing decline in spatial extent. For the NBA 2018, the absolute rate of habitat loss was used to identify ecosystems with significant ongoing decline in the extent of natural habitat (> 0.4%/y). Supplementary assessments used expert input and the threatened species database to identify restricted distribution ecosystems with very high levels of biotic disruption from over grazing, invasive species and poor fire management - B2(iii).

≤ 2 ≤ 20 ≤ 50

Criteria D: Disruption of biotic processes or interactions

Sub-criterion D3 – Disruption of biotic processes, since 1750, based on change in a biotic variable affecting a fraction of the extent of the ecosystem and with relative severity, as indicated by the table on the right. For the NBA 2018, ecosystem degradation data from the Albany Thicket biome and Little Karoo region were used. The severely degraded class in these datasets was considered to be ≥ 90% severity, the extent of severe degradation was expressed as a percentage of the remaining habitat circa 2014.

Relative severity (%)

Ext

ent (

%)

≥ 90 ≥ 70 ≥ 50

≥ 90 CR EN VU

≥ 70 EN VU

≥ 50 VU

7.1.3. Results of the ecosystem threat assessment

The first implementation of the IUCN RLE for South African terrestrial ecosystems (458 vegetation types) for

the NBA 2018 resulted in the listing of 35 Critically Endangered, 39 Endangered and 29 Vulnerable ecosystems

(Table 16) (Figure 38). While eight percent of ecosystem types are Critically Endangered, this amounts to less

than one percent of the extent of natural remaining habitat in South Africa. Endangered ecosystems make

up 8.5% of ecosystems by type and 3% by extent remaining. Vulnerable ecosystems make up 6.3% of

ecosystem by type, amounting to 4% of the natural remaining habitat of South Africa (Table 16) (Figure 39).

The most influential criterion in the RLE assessment was Criterion B1 (restricted distribution & continuing

declines in geographic distribution) which contributed to the listing of 53/103 ecosystem types and Criterion

A3 (historical loss of habitat) which contributed to the listing of 28/103 ecosystem types. The supplementary

assessment of Criterion B1 (iii) using the threatened species pressures database contributed to the listing of

25/103 ecosystem types, of which 12 were listed purely due to this criterion. Criterion D3 (biotic disruption

– based on ecosystem degradation) resulted in the listing of only one (Vulnerable) ecosystem type.


107

Table 16. Summary of the assessment outcomes; including the number of ecosystem types per category & proportion of the natural areas remaining per category.

Category (IUCN RLE) Number of ecosystems

Extent of natural Habitat (km2)

Percentage of natural remaining habitat of SA

Critically Endangered 35 5 904 0.6%

Endangered 39 28 982 3%

Vulnerable 29 42 459 4.4%

Least Concern 355 882 820 92%

Total for South Africa 458 960 167 100%

Figure 38. Map showing the distribution of threatened ecosystems according to the IUCN Red List of Ecosystems. The map shows the historical extent of the ecosystem types (based on the National Vegetation Map 2018). The inset graph shows the percentage of ecosystem types that falls within each threat category (download spatial data at BGIS).

Results per biome

The Fynbos biome has the highest number of threatened ecosystems types (53), followed by Grassland (21)

and Savanna (11) and these make up 20%, 24% and 3% of the natural remaining habitat of the biome

respectively (Figure 40, Table 17). Of the six of the ecosystems types making up the Indian Ocean Coastal Belt

biome, 4 are threatened and 62% of the natural habitat remaining in the biome is threatened. The arid

regions of the country have less threatened ecosystems (by type and by remaining extent); the Succulent

Karoo has two threatened ecosystems (amounting to 0.2% of the natural habitat) and the Nama-Karoo has

no threatened ecosystems. The full terrestrial threatened ecosystem database, including information on land

cover change and the RLE criteria for each ecosystem type, is available online on the Biodiversity GIS website

(http://bgis.sanbi.org/Projects/Detail/221).




108

Figure 39. Map showing the natural remaining extent (circa 2014) of threatened ecosystems according to the IUCN Red List of Ecosystems. The inset graph shows the percentage of the total natural habitat remaining in South Africa (960 167 km2) that falls within each threat category (download spatial data at BGIS).

Table 17. Percentage natural remaining habitat within each IUCN RLE threat category, listed per biome. The number of ecosystem types per threat category, per biome is shown in parenthesis (Appendix C contains a full list of ecosystem types).

Biome Critically

Endangered Endangered Vulnerable

Threatened Ecosystem Types (CR, EN, VU)

Least Concern

Total

Albany Thicket 0.6% (3) - 17% (3) 18% (6) 82% (38) (44)

Desert - 0.03% (1) - 0.03% (1) 99% (14) (15)

Forests - - 3% (1) % (1) 97% (11) (12)

Fynbos 8% (25) 10% (18) 2% (10) 20% (53) 80% (69) (122)

Grassland 0.2% (2) 7% (18) 13% (11) 21% (21) 79% (52) (73)

Indian Ocean Coastal Belt

- 51% (3) 11% (1) 62% (4) 38% (2) (6)

Nama-Karoo - - - - 100% (13) (13)

Savanna 0.2% (2) 1% (6) 2% (3) 3% (11) 97% (80) (91)

Succulent Karoo 0.2% (2) - - 0.2% (2) 99% (62) (64)

Azonal Vegetation 0.003% (1) 3% (3) - 3% (4) 97% (14) (18)

Total 0.6% (35) 2.9% (39) 4.3% (29) 7.8% (103) 92% (355) (458)



109

Figure 40. Threatened ecosystem types per biome, showing (a) the percentage of ecosystem types per biome that fall within each threat category, and (b) the percentage of the total natural habitat remaining in each biome that falls within each threat category (Critically Endangered – CR; Endangered – EN; Vulnerable – VU; Least Concern – LC) (Appendix C contains a full list of ecosystem types).

Provincial summary

Threatened ecosystems are not evenly distributed across South Africa’s provinces. While a large proportion

of the remaining natural habitat in Gauteng (45%), KwaZulu-Natal (30%) and Mpumalanga (30%) is

threatened, a much smaller percentage is listed as Critically Endangered (3%, 1% and 0% respectively) (Figure

41, Table 18). This pattern is mirrored in Limpopo, Free State, Eastern Cape and North West provinces. The

Western Cape has a slightly different pattern with Critically Endangered, Endangered and Vulnerable

ecosystem types are being more even in terms of extent. The results of the ecosystem assessment are closely

linked to the land cover change patterns of the provinces. The high population density of Gauteng, and high

agriculture potential and high population density of KwaZulu-Natal and Mpumalanga are the drivers of the

high rates of habitat loss. Despite the high number of threatened ecosystems in the Western Cape (54), linked

to high ecosystem diversity of the Fynbos biome, only 11% of the natural remaining ecosystem extent of the

province is threatened.

Table 18. Table showing the percentage of the natural remaining habitat in each province that falls with each IUCN RLE category; in parenthesis is the number of ecosystem types per category per province (note these do not sum to a national number of threatened ecosystems as ecosystem types cross provincial boundaries.

Province Critically Endangered

Endangered Vulnerable Threatened Ecosystem Types

Least Concern

Total

Eastern Cape 0.2% (4) 0.3% (3) 7% (11) 8% (18) 92% (83) (101)

Free State - 4% (1) 8% (4) 12% (5) 88% (30) (35)

Gauteng 3% (1) 10% (2) 33% (4) 45% (7) 55% (8) (15)

KwaZulu-Natal 1% (2) 19% (11) 10% (5) 30% (18) 70% (38) (56) Limpopo 0.1% (1) 1% (2) 3% (4) 5% (7) 95% (44) (51) Mpumalanga - 5% (4) 25% (5) 30% (9) 71% (43) (52)

North West - 8% (3) 5% (4) 12% (7) 88% (27) (34)

Northern Cape 0.1% (3) 0.2% (1) - 0.1% (4) 99% (115) (119)

Western Cape 4% (25) 6% (20) 1% (9) 11% (54) 90% (112) (166)


110

Figure 41. Threatened ecosystems per province, showing (a) the percentage of ecosystem types per province that fall within each threat category, and (b) the percentage of the total natural habitat remaining in each province that falls within each threat category (Critically Endangered – CR; Endangered – EN; Vulnerable – VU; Least Concern – LC).

7.1.4. Ecosystem threat status trends

The IUCN RLE methodology relies heavily on land cover change data (which inform Criteria A2b, A3, B1 and

B2). The two time point data available in South Africa are well suited to the RLE assessment but do not allow

for a complete application of the RLE to the earlier time points (a retrospective analysis). A partial application

of the RLE to the 1990 time point is possible, but it would be restricted to Criteria A3 (historical loss of habitat)

only. The national land cover of South Africa is due for an update in 2018, and on the release of this data the

RLE will be updated. This will then lay the foundation for a Red List Index for Ecosystems, which will track

changes in ecosystem threat status over time. As such, this RLE for South Africa represents a new baseline

for threatened ecosystems.

A direct comparison of this 2018 RLE analysis with the 2011 National List of Threatened Terrestrial

Ecosystems (RSA 2011) is of limited utility (Table 19). The input datasets have changed (i.e. a new vegetation

map, and new land cover data have been used), the input data have expanded (i.e. there is land cover change

data available for the first time, unlocking many dormant criteria), and the threat assessment methodology

has changed [i.e. the IUCN RLE framework and guidelines (Bland & Keith et al. 2017) have been released, and

the NBA 2018 utilises this framework]. Changes in ecosystem status between 2011 and 2018 could then be

attributed to any one or a combination of these factors.

Table 19. Comparison of the results of the 2018 Red List of Ecosystems and the 2011 National List of Threatened Terrestrial Ecosystems [note: the methods and input data were not the same at each time point, so this does not represent a trend analysis]. The table includes the number of ecosystem types per category and the proportion of the natural habitat of South Africa within each category. The 2011 assessment included 438 vegetation types and 108 ‘special ecosystem types’; the 2018 assessment was applied to an updated vegetation map with 458 units.

Category (IUCN RLE)

2011 2018

Number of ecosystems

Percentage of natural habitat of SA

Number of ecosystems

Percentage of natural habitat of SA

Critically Endangered 53 1% 35 0.6%

Endangered 64 2% 39 3%

Vulnerable 108 7% 29 4.4%


111

7.1.5. Ecosystems of special concern

The IUCN RLE is a risk assessment framework for

consistently identifying ecosystems that are at risk of

collapse. Since the RLE is designed to be applied across

realms and across the globe, there are certain local

ecosystems that do not meet the thresholds for the

threat categories but are considered ‘of special

concern’ for a number of reasons. This concept has

been successfully applied to the Red List of Species in

South Africa, including range restricted rare species of

the mountainous regions in particular. For the NBA

2018, all Forest ecosystem types (which, in South

Africa are naturally rare, of limited extent and highly

fragmented) are classified as ecosystem of special

concern (Figure 42). Dedicated legislation is in place to

protect natural forests in South Africa (e.g. National

Forests Act (Act 84 of 1998)), and assigning these

ecosystems to the category Ecosystems of Special

Concern highlights this need for protection without interfering with the risk assessment framework of the

IUCN RLE. This does not prevent the listing of threatened Forest ecosystem types if the IUCN RLE criteria are

met, and additional data on forest condition (using forest resource assessments for example) is a

conservation priority. In future assessments, special ecosystem types in other biomes will be considered

based on factors such as exceptional species diversity and restricted range / endemism.

7.1.6. Ecosystem threat status limitations

The key shortcoming of all of these ecosystem threat status assessments in the terrestrial realm is that we

lack appropriate data on ecosystem condition, land degradation and biotic disruption of ecosystems. This

means that in many regions the baseline ecosystem assessment reported here will underestimate the risk of

collapse. We have reasonable confidence that the ecosystems that are listed as threatened are genuinely at

risk of collapse – but there are many ecosystems that are at risk that which are not on currently listed as

threatened – purely as a result of lack of data. Some (outdated) data is available for the thicket biome, but in

other biomes it is a very challenging problem that will need significant focussed research. One aspect of

degradation that should be possible to map accurately, and therefore use in ecosystem assessment, is

distribution and abundance of alien invasive species. For woody plants that reach high abundances and high

visibility, this certainly seems possible in the near future. Another challenge is that for many of the

ecosystems there is no clear model of ecosystem function against which we can measure biotic disruption or

degradation. This makes calibrating models of ecosystem condition difficult.

A further shortcoming of this assessment is that it relies heavily on land cover data collected in 2013/2014,

making the data over three years old. This is not ideal and, as automated and global scale remote sensing

becomes more accessible, it is hoped that future assessments will not suffer from this long time delay. As

soon as new land cover data become available (scheduled for 2018 release by the Department of

Environmental Affairs) SANBI has set up a system to automatically update the baseline RLE (though there are

many steps for which expert validation are required). The aim is to reduce this time lag to less than one year.

Figure 42. Ecosystem of special concern. Forest ecosystem types that are not considered threatened under the IUCN RLE framework but warrant special protection and monitoring.


112

7.1.7. Ecosystem types with distribution ranges beyond South Africa’s borders

Assessing an ecosystem type across a portion of its global range would result in a partial RLE assessment that

may not reflect the true risk of collapse for the ecosystem. The vegetation map that forms the basis of the

Red List of Ecosystems for South Africa also covers the neighbouring countries of Lesotho and Swaziland. As

a result, ecosystem types that are distributed across these particular international boundaries can be

considered to have been assessed comprehensively. For neighbouring countries such as Namibia, Botswana,

Zimbabwe and Mozambique, no comparable vegetation maps exist, and types that cross these borders

cannot be assessed across their full range at present. For the most part, however, the terrestrial ecosystem

types that occur can only in South Africa and can be considered endemic (406/458 types [89%] are endemic),

and the RLE presented above thus represents an ecosystem-wide assessment for the majority of types.

There are six terrestrial ecosystem types that are listed as Threatened but are likely to occur extensively

outside of South Africa, Lesotho and Swaziland (Table 20). Of these threatened and non-endemic types that

extend beyond the borders of South Africa, Lesotho and Swaziland, three fall into the Savanna biome, two in

the Indian Ocean Coastal Belt and one Forest biome. Efforts are underway to align vegetation maps across

national boundaries in southern Africa, and when this is achieved these “cross border” units will be

comprehensively assessed.

Table 20. Non-endemic ecosystem types included in the Red List of Ecosystems – these types are only partially assessed and their extent and condition outside of South Africa needs to be determined before a final assessment of their status can be made.

Ecosystem type Biome RLE Status

Lebombo Summit Sourveld Savanna Endangered

Lowveld Riverine Forest Forests Vulnerable

Maputaland Coastal Belt Indian Ocean Coastal Belt Endangered

Maputaland Wooded Grassland Indian Ocean Coastal Belt Endangered

Muzi Palm Veld and Wooded Grassland Savanna Critically Endangered

Western Maputaland Clay Bushveld Savanna Endangered

7.2. Ecosystem protection level

7.2.1. Ecosystem protection level calculation method

Ecosystem protection level is an indicator that tracks how well represented an ecosystem type is in the

protected area network. It has been used as headline indicator in national reporting in South Africa since

2005 (Reyers et al. 2007). It is a relatively simple indicator, computed by intersecting the map of ecosystem

types with the map of protected areas. Ecosystem types are then categorised based on the proportion of the

biodiversity target for each ecosystem type that is included in one or more protected areas (Table 21)

(Government of South Africa, 2008).

Table 21. Unprotected, Poorly Protected and Moderately Protected ecosystem types are collectively referred to under-protected ecosystems.

Protection Level % of biodiversity target

Well Protected (WP) ≥ 100% Moderately Protected (MP) 50% - 100%

Under-protected ecosystems Poorly Protected (PP) 5% - 50% Not Protected (NP) < 5%


113

With an average biodiversity target of around 24% of total ecosystem area in South Africa, protection level

thresholds of 5%, 50% and 100% of the biodiversity target equate to average values of about 1%, 12% and

24% of the total ecosystem area respectively. This will of course be a little higher in more biodiverse and

heterogeneous ecosystems (that have biodiversity targets > 24%), and slightly lower in less biodiverse and

more uniform ecosystems (that have targets of <24%). In the computation of the Protection Level indicator,

only the natural remaining extent of each ecosystem type is considered to contribute to the biodiversity

target. This ensures that built-up areas, infrastructure, dams and old croplands that occur inside the

protected areas estate are not included. This is a particularly important step when calculating ecosystem

representation within Protected Environments since these are declared in mixed use landscapes (with active

croplands between the protected natural areas); it is less relevant in National Parks where the vast majority

of the protected areas is in a natural or semi- natural state.

7.2.2. Ecosystem protection level results

Overall, the proportion of South Africa’s total land area included in the protected area network has increased

from 8% in 2010 to 9% in 2018, and, importantly, much of this protected area expansion has happened in

under-protected ecosystem types. The levels of protection that this 9% of land area provides for terrestrial

ecosystem types are shown in Figure 43 below. Just over a quarter of terrestrial ecosystem types are Well

Protected, while 25% are Not Protected.

Figure 44a provides these results by biome, showing that the Indian Ocean Coastal Belt, Nama-Karoo,

Grassland and Albany Thicket biomes have the highest proportion of under-protected ecosystem types.

Forest and Desert have the highest proportion of Well Protected ecosystem types. However, Fynbos, Savanna

and Grassland have by far the highest actual number of under-protected ecosystems, due to their higher

outright number of ecosystem types.

Even within biomes there can be further significant differences between ecosystem types. For example, while

mountain Fynbos ecosystem types tend to be Well Protected, lowland ecosystem types within the biome are

extremely Poorly Protected. Similarly, lowveld Savanna types are Well Protected by the Kruger National Park

and arid Savanna types by Kgalagadi Transfrontier Conservation Area, but the central bushveld Savanna types

(largely in central and western Limpopo) are still Poorly Protected.

Protection levels within provinces follow a similar pattern to biomes, with outright numbers of Well

Protected and under-protected ecosystems closely tracking the numbers of ecosystem types found within

provinces. Unsurprisingly, the Western, Northern and Eastern Cape also contain the highest numbers of

under-protected ecosystem types (Figure 44b). These provinces largely coincide with the spatial extent of

the globally exceptional Cape Floristic Region, with very high numbers of ecosystem types overall. Likely due

to the challenges of representing higher diversity in protected areas, the proportion of under-protected

ecosystem types in each province perfectly tracks the total number of under-protected ecosystem types, so

that the Western Cape still contains the largest percentage of under-protected ecosystem types, followed by

the Northern and Eastern Cape.


114

Figure 43. Protection level map of terrestrial ecosystem types. The inset graph shows the percentage of ecosystem types that fall within each protection category (download spatial data at BGIS).

Figure 44. Protection level per terrestrial biome (a) and per province (b); the percentage of ecosystem types within each protection level category is shown (Appendix C contains a full list of ecosystem types).

7.2.3. Ecosystem protection level trends

It is not possible to directly compare ecosystem protection levels between NBA 2011 and NBA 2018 due to

differences in the underlying map of ecosystem types and to the map of protected areas. To look at trends

in protection level we used the newly compiled protected area layer in combination with the new vegetation



115

map and land cover data to back-cast the protection level status for past timepoints (Figure 45). The

percentage of South Africa’s total land area included in the protected area network has increased to

approximately 9% at present, up from 8% in 201018 and 6% in 1990. Importantly, much of this protected area

expansion has happened in under-protected habitats – meaning that with the increased protected area

estate there has been an increase in the number of Well Protected ecosystem types from 15% Well Protected

in 1990 to 26% Well Protected in 2018. A total of 22 ecosystems have improved in protection level since

2010, of which nine additional ecosystems have moved to Well Protected. Over this same period

(2010–2018) the Albany Thicket and Succulent Karoo biomes had the greatest increases in protected area

estate (3.4% and 2.3% respectively), while the Indian Ocean Coastal Belt and Desert biomes had no significant

additions to their protected area estate. Nonetheless, 25% or 116 of 458 terrestrial ecosystem types are still

considered Not Protected, with less than 5% of the biodiversity target in protected areas, of which 43

ecosystem types or 9.4% of all South African ecosystem types still have absolutely no protection at all.

Section 7.3 contains a brief comparison on threat status and protection levels.

Figure 45. Changes in protection levels for terrestrial ecosystem types since 1960 - using a back casting approach based on the 2018 ecosystem map and protected areas database.

7.2.4. Ecosystem protection level limitations

Ecosystem protection level is a reliable indicator of how well terrestrial ecosystem types are represented in

the protected area estate, but it suffers from the same limitations as ecosystem threat status in that many

highly modified areas are contributing to protection targets (i.e. these areas raise the ecosystem’s protection

level, but do not actually support their representative biodiversity). Improved data on ecosystem condition

would allow for a more nuanced computation of protection level that would better reflect the contribution

of protected areas in different regions.

While not a limitation as such, in the terrestrial realm protection level is a purely representation-based

indicator and does not consider management effectiveness. Efforts are underway to develop a parallel

indicator of management effectiveness, but this approach is also problematic in that effective management

can be very difficult to define and can be different for various components of biodiversity or regions.

18 The NBA 2011 used a protected areas dataset that excluded the legally gazette Private Nature Reserves and as result reported a

lower overall protected areas estate of 6.5% of the mainland.


116

7.3. Protection level vs. threat status for ecosystem types Comparing threat status and protection levels for

terrestrial ecosystems is useful for identifying

ecosystems in particular need of protection. This could,

in turn, provide input into conservation planning such

as protected areas expansion strategies, which are

typically developed at a provincial level. There are

eight Well Protected ecosystems in South Africa that

are threatened (CR, EN or VU) compared to 93

terrestrial ecosystems that are under protected (NP, PP

and MP) and threatened (Table 22). This latter group

are ecosystems types that have both a high risk of

collapse and are under-represented in the current

protected areas network. In most situations, options

for protecting the Critically Endangered types are

limited as they tend to be fragmented and occur on

high agricultural potential land or in and around areas

of high population density. Endangered and Vulnerable

types may have more options for inclusion in protected area expansion strategies. Land use decision making

tools such as bioregional plans often highlight these areas as Critical Biodiversity Areas (see Chapter 11). The

Well Protected ecosystem types that are listed as threatened occur mostly in the Fynbos biomes; these types

are either threatened by processes that can occur within protected areas (e.g. invasive alien species) or are

protected as small patches in agricultural landscapes, which brings into question their viability (Figure 47).

Table 22. Cross tabulation of terrestrial ecosystem threat status (RLE) versus protection level.

RLE ↓ PL→ Not Protected Poorly Protected Moderately Protected Well Protected

Critically Endangered 15 12 5 3

Endangered 14 18 5 2

Vulnerable 11 11 2 3

Least Concern 75 125 47 110

.

Figure 46. Terrestrial ecosystem types which are threatened (CR, EN, VU) and under protected (NP, PP, MP).


117

8. INDIGENOUS SPECIES ASSESSMENTS

Chapter 8: Raimondo, D., Von Staden, L., Van der Colff, D., Child, M., Tolley, K.A., Edge, D., Kirkman, S., Measey, J., Taylor, M., Retief, E., Weeber, J., Roxburgh, L. & Fizzotti, B. 2019. ‘Chapter 8: Indigenous Species Assessments’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

Contributors: Species assessment work is co-ordinated by SANBI’s Threatened Species Unit but is conducted

by groups of species specialists. Each species group has a champion who acts as the key contact person with

whom SANBI co-ordinates the work. Specialist groups are made up of: taxonomists working in collection

institutions, research scientists based either at SANBI or at academic institutions, scientists responsible for

monitoring species from provincial and national conservation agencies, experienced amateurs and

conservation practitioners from NGOS.

8.1. Species threat status (Red List of Species)

8.1.1. Species threat status calculation method

Threat status of South Africa’s indigenous terrestrial species was calculated using the latest version of the

IUCN Red List Categories and Criteria, version 3.1 (IUCN 2012a). The categories are summarised in and the

criteria thresholds are summarised in Table 23. While no modifications were made to the IUCN Categories

and Criteria we did augment the system by adding categories of rarity for South Africa’s highly speciose

groups (plants and butterflies). These additional categories are for range restricted endemic species

occurring where there are no anthropogenic pressures (Figure 47). Such species qualify as Least Concern

under the IUCN system, but are priorities for inclusion in national conservation interventions. South Africa

uses the IUCN Red List system as it is a quantitative, objective system that can be consistently applied across

a range of taxonomic groups. The quantitative criteria are based on scientific studies of populations of a

range of different species and the biological conditions under which they are highly likely to go extinct (Mace

et al. 2008). The quantitative nature of the system demands that assessments are justified by supporting

data. Key data that was collected for each species in order to apply the IUCN Red List Criteria include:

distribution (Extent of Occurrence); area of occupied habitat (Area of Occupancy), population size and

structure, changes in population size over a specified time period, pressures to each species and the impact

that these pressures are having on the population size and the quality of available habitat (see section 6.3

and Box 8) for major input data for assessments and what the key sources of information were used for each

taxon group). While certain Red List parameter data could be calculated using spatially available data on

species occurrences and land use, parameters such as population decline were gathered from expert opinion

by running workshops with expert groups (see Acknowledgements section for list of experts that contributed

to the Red List Process and Appendix A for list of workshops undertaken). All assessments were

independently reviewed by experts that were not involved in the actual assessment, SANBI’s Threatened

Species Unit ensured an overall consistent level of assessment quality and correct application of the IUCN

Red List categories and criteria (Figure 48).


118

Table 23: IUCN 3.1. IUCN Red List Categories and Criteria. Version 3.1, summary of criteria that trigger threat status.


119

Figure 47: Categories for species Red List assessment work, note South Africa uses the IUCN Red List Categories and Criteria, version 3.1 (IUCN, 2012a) but for national monitoring includes subcategories of rarity within the global category of Least Concern. The Rare category is applied to restricted endemics (EOO < 500 km2) that are not known to be declining. Extremely Rare is applied to taxa confined to a single site and declining.


120

Figure 48. Red List assessment process conducted for each terrestrial taxon assessed.

The fact that the IUCN system is objective and scientifically based, as well as its wide use internationally,

means assessments produced for this National Biodiversity Assessment can be consistently compared with

assessments produced by the IUCN globally for the same taxonomic groups and are available for use in

national and international reporting frameworks. South Africa’s assessment are a combination of assessment

that have global scope (Red List Criteria applied to the entire global population of each taxon) and national

scope (Red List Criteria applied to only the portion of the population occurring in South Africa). The scope of

the assessments varied for each taxonomic group (Table 24) and was dependent on the level of data available

to South Africa’s species experts for the portion of the species population occurring outside of South Africa’s

borders.

Table 24. The number and scope of assessments conducted for terrestrial species.

Taxon group Number of terrestrial

indigenous species

Number of Endemics

Assessment scope

Global***

Assessment Scope

National****

Date of most recent assessment

Most recent Red List assessment

Birds 732 38* 612 120 2015 Taylor et al. 2015

Mammals 294 56 56 238 2016 Red List of South African mammals published online https://www.ewt.org.za/reddata

Reptiles 407 209 217 174 2017 Red List of South African reptiles published online http://speciesstatus.sanbi.org/

Amphibians 125** 61* 125 0 2016 Red List of South African frogs published online http://speciesstatus.sanbi.org/

Butterflies 799 418 799 0 2018 Red List of South African butterflies published online http://speciesstatus.sanbi.org/

Plants 20 600** 13 890 13 754 6 616 2017 Red List of South African plants version 2017.1 http://redlist.sanbi.org/

* Endemic counts include Lesotho. ** New species recently described not included here. *** Assessments conducted using IUCN 3.1 Categories and Criteria,

assessments from South Africa will be the same as those reflected on the global IUCN Red List. **** Assessments conducted only on the South African portion of the species range utilising the IUCN Regional Criteria, these are National Red List assessments and are not submitted to the IUCN Red List.

Draft red list assessment

produced

Primary literature consulted to obtain information on each taxon’s distribution, habitat, life-history and key ecological requirements.

Georeferenced occurrence data used to calculate Red List parameters including Extent of Occurrence, Area of Occupancy and number of subpopulations.

Spatial land-cover data and google earth used to determine a preliminary set of pressures for

each taxon.

Taxon experts engaged

Experts verify information gathered during the drafting of assessments.

Experts contribute key additional data on pressures such as utilisation and habitat degradation that cannot be observed in google earth e.g. overgrazing.

Experts decide on red list status for each taxon ensuring all necessary motivation to the support the IUCN Red List Criteria are

provided.

Assessments reviewed & published

Species status reviewed by an independent researcher familiar with both the taxonomic group and the IUCN Red Listing system.

Assessments published, all Red List assessments produced for this NBA are published on the Red List of South African Plants http://redlist.sanbi.org/ or the Red List of South Africa’s Animals

http://speciesstatus.sanbi.org/.


121

8.1.2. Species threat status trends

The trend in species status over time is measured using the globally recognised indicator, the Red List Index

(RLI) developed by (Butchart et al. 2007). The RLI is calculated for specific groups of species based on the

genuine changes in Red List categories over time and indicates trends in the status for each group of species.

The RLI value ranges from 0 to 1. The lower the value the faster the group of species is heading toward

extinction. If the value is 1, all species are least concern and if the value is 0, all species are extinct.

The RLI value for each specific assessment time period is calculated by multiplying the number of species in

each Red List category by a category weight (0 for LC, 1 for NT, 2 for VU, 3 for EN, 4 for CR and 5 for EX). These

products are summed, divided by the maximum possible product (number of species multiplied by the

maximum weight 5), and subtracted from one (Butchart et al. 2007). To accurately determine trends over

the different time periods requires that assessments are back cast and that all information available at the

later time period is taken into account for previous assessment periods. This ensures that changes such as

taxonomic changes and new information do not incorrectly bias the results of the index. Uncertainty is

included in the index by incorporating data deficiency, temporal variability and extrapolation uncertainty.

Red List Indices for each taxonomic group are interpolated linearly for years between data points and

extrapolated linearly (with a slope equal to that between the two closest assessed points) to align them with

years for which Red List Indices for other taxa are available (Butchart et al. 2010).

Repeat assessments have been conducted for all indigenous terrestrial birds, mammals, reptiles, amphibians,

and butterflies for these groups’ taxa where a change in status occurred between the two assessment periods

were identified. The reasons for the change in status were examined to assess whether the change in status

was genuine or not genuine for each taxon, where a change in status was deemed non-genuine, back casting

was applied to retrospectively determine what the actual Red Listing should have been for the first

assessment. This entire process was conducted by species experts during Red List assessment workshops and

the causes of genuine change were captured in South Africa’s Red List animal database.

For plants, South Africa does not have the capacity to conduct repeat assessments for 20 401 species. To

address this, a random sample of 900 terrestrial plant species has been selected for tracking trends following

international best practice for trend analyses for large taxonomic groups (Baillie et al. 2008). The assessment

process of these 900 species was automated as far as possible via the following steps. Point occurrence

records were intersected with a 2x2 km grid (the IUCN’s recommended cell size for Area of Occupancy

[AOO]), and each unique AOO cell was considered a potential location. The population status of each

potential location was assigned according to the percentage of the area of each cell in natural condition for

1990 and 2014 (50-100% cell natural - population status assigned as extant; 20-49% cell natural - population

status assigned as Uncertain; <20% cell natural - population status assigned as Extinct). Extent of Occurrences

(EOO) were calculated using convex hulls around known extant and uncertain point occurrence records for

all species with three or more unique records. For species known from a single locality, an EOO of 10 km2

were assigned, for species with only two distinct localities, EOO was calculated by using a convex hull around

the suitable habitat models. Changes in number of locations, Extent of Occurrence and Area of Occupancy

were assessed against criterion B to detect changes in risk of extinction between 1990 and 2014.

Suitable habitat was modelled for each species (see section 6.3). The extent of loss of suitable habitat was

calculated from land cover data for 1990 and 2014, and the rate of loss was calculated by considering the

difference between the extent of loss between 1990 and 2014. The 1990 and 2014 habitat loss estimates

were fitted to logistic curves, and extrapolated three generations into the past from 1990 and 2014.

Population reductions were then compared to detect trends in Red List status against criterion A2. Genuine


122

decreases in extinction risk were detected in areas where there has been protected area expansion since

1990, but there were not sufficient monitoring data available to determine improvements more broadly.

For species threatened by pressures other than habitat loss including invasive species, utilisation and habitat

degradation the above automated process was not able detect change in status. In a few cases monitoring

by CREW citizen scientists or expert botanists were able to flag recent changes in risk of extinction of sampled

species and these changes were included in the trend analysis. For the majority of species threatened by

unsustainable utilization and habitat degradation, trends in their risk of extinction between 1990 and 2014

could not be assessed, as appropriate monitoring data does not yet exist. This means that the Red List Index

for plants may not be fully reflective of the status and trends in the risk of extinction of South African

indigenous plant species, however, considering that habitat loss is by far the most significant threat to plant

species in South Africa, as well as the cause of most of the historically recorded plant species extinctions, it

is assumed that additional monitoring data will not change the overall trend significantly.

8.1.3. Species threat status and trend results

South Africa has a total of 3 024 threatened terrestrial indigenous taxa, 13% of the 22 667 indigenous

terrestrial taxa assessed to date. There is a trend towards increased risk of extinction in all six taxonomic

groups assessed. South Africa has very high levels of endemism (64% of the species assessed are found

nowhere else) and 19% of these endemics are threatened with extinction (Figure 49). The trend in species

status over time, measured by the Red List Index (RLI), shows that vertebrate groups and plants are declining

in threat status at a similar rate, but butterflies show a sharper RLI decline (Figure 50).

Mammals are the most threatened taxonomic group, with 17% of indigenous taxa threatened (Figure 49).

However, much of the decline was historical and compared to other taxonomic groups they have declined

the least in the last 15 years (Figure 50). Concerted efforts to conserve threatened mammal species by South

African conservation agencies has resulted in ten species becoming less threatened (Box 9). Overall, the

status of South African mammals is still declining, with 13 taxa having moved to a higher category of threat

between 2004 and 2016.

While wildlife abundance continues to decline across most of Africa, South Africa remains a stronghold for mammal

conservation, boasting genuine success stories that often result from co-operation between the public and private

sectors. Both the Cape Mountain Zebra (Equus zebra zebra) and Lion (Panthera leo) are no longer listed as threatened

due to strong population growth on both protected areas and private conservation areas. For the Cape Mountain

Zebra, the population has been increasing steadily from 1985 to 2014, despite being reduced to fewer than 80

individuals in the 1950s. Similarly, the Lion has been stable or increasing over the past 20–30 years. In Kruger National

Park, the population has increased over the past decade, and the population within smaller protected areas and

private conservation areas has increased from 10 to c. 500. Cheetahs (Acinonyx jubatus), which were extirpated from

over 90% of their former distribution range in South Africa, are slowly starting to increase in numbers through careful

metapopulation management. Honey Badgers (Mellivora capensis) have improved in status as a result of reduced

persecution linked to farmers being incentivised via ‘badger friendly’ honey labelling programmes to rather protect

hives from damage than to persecute badgers.

The effectiveness of South African protected areas (both terrestrial and marine) in mitigating threats has been

demonstrated by the improvement of status of Tsessebe (Damaliscus lunatus), Southern Elephant Seal (Mirounga

leonina) and Humpback Whale (Megaptera novaeangliae).

Box 9. Improvement in the threat status of certain mammals


123

The main pressures causing mammals to increase in threat status are direct persecution through poaching

and hunting for bushmeat, crop cultivation, plantation forestry (affecting 46% of taxa), and housing

development (affecting 31% of taxa). Agriculture in the form of crop cultivation and livestock farming is the

pressure that impacts on the highest proportion of taxa of conservation concern (Figure 11). The Savanna

biome has the highest concentration of threatened mammal taxa (Figure 51, Figure 52).

South Africa’s flora shows very high levels of species diversity and endemism: 13 763 of the 20 401 taxa

(67%). Of all groups assessed to date, plants have the absolute highest number of threatened taxa with 2 804

taxa (14%) threatened with extinction (Figure 49), the vast majority of which are endemics (2 722 taxa) (Table

25). A further 1 500 taxa (7% of the flora) are listed under South Africa’s national conservation category of

Rare (Table 25). Approximately 5% of the sample of 900 plant taxa used to calculate the Red List Index

increased in threat status over the 28 year period between 1990 and 2018 (Figure 50, Table 26). The main

pressures causing plant taxa to increase in threat status are competition from invasive plant species (affecting

40% of taxa); crop cultivation (affecting 33% of taxa); urban development (affecting 20% of taxa) and habitat

degradation as a result of livestock overgrazing (affecting 11% of taxa). The ability to detect change in status

of plant species is hindered by lack of monitoring data available on the impacts of overgrazing and medicinal

harvesting, the proportion of plants that have changed status is therefore likely to be underestimated.

Threatened plants are concentrated in the Fynbos biome, with 67% (1893 taxa) of all threatened plant taxa

occurring there (Figure 52, Figure 51). The Fynbos lowlands that have been extensively converted for

cropland agriculture and urban development and have had high concentrations of threatened plants for

many decades the recent rapid spread of invasives into the Cape mountains has resulted in many previously

unthreatened plant species being listed as threatened with extinction for the first time. The emerging plans

to extract water from mountain aquifers is a future pressure to endemic plant restricted to the Cape

mountains that requires close monitoring going forward. The Succulent Karoo and Grassland biomes are also

rich in endemic plants, and with high rates of habitat loss in the Grassland biome and significant degradation

from livestock ranching in the Succulent Karoo there are resulting high numbers of threatened plant taxa

occurring in both biomes (Figure 51, Figure 52). The emerging trend of mass plant mortality linked to the

recent droughts between 2016 and 2018 in the Richtersveld region is driving rapid population declines to

endemic plants in the Desert Biome (see Box 7. in section 5.4.3).


124

Figure 49. The proportion of taxa in each Red List category for six taxonomic groups shown for all terrestrial species and for endemics (smaller circles).

Amphibians are the third most threatened taxonomic group with 13% of all species and 26% of endemics

threatened with extinction (Table 25). A large proportion (50%) of endemic species fall into a category of

conservation concern (Figure 49). The Red List Index (Figure 50) measured indicates that there has been an

overall decrease in the status since 1990. Six amphibian species (4.6%) have become more threatened over

since 1990 as a result of loss of habitat to afforestation and housing development as well as competition

from invasive species. Overall 79% of amphibian taxa of conservation concern are impacted by invasive alien

plants (Figure 11). When compared to the global Red List Index, South Africa’ amphibians are faring better

and are less threatened than amphibians globally (Figure 53). Threatened amphibians are concentrated along

the east coast of KwaZulu-Natal, on the Drakensberg foothills, and in the Cape on the Cape Peninsula, Cape

Hangklip and Agulhas Plain.


125

Table 25. The number of taxa in in each category for all species indigenous to South Africa and those endemic to South Africa shown for each different taxon group comprehensively assessed.

Red List Category

Birds Mammals Reptiles Amphibians Butterflies Plants

All Endemics All Endemics All Endemics All Endemics All Endemics All Endemics

Extinct 0 0 5 3 2 2 0 0 3 3 29 29

Extinct in the Wild 0 0 0 0 0 0 0 0 0 0 7 7

Critically Endangered

(Possibly Extinct) 0 0 1 1 0 0 0 0 5 5 73 73

Critically Endangered 9 0 4 3 2 2 6 6 20 20 399 383

Endangered 23 4 17 7 6 6 9 9 30 30 859 843

Vulnerable 26 6 27 11 11 7 1 1 23 19 1476 1423

Near Threatened 32 6 33 4 13 12 12 10 6 5 504 456

Data Deficient 0 0 9 6 12 12 5 5 2 2 1362 1336

Rare and Extremely

Rare 0 0 0 0 0 0 0 0 54 51 1500 1434

Least Concern 572 22 194 21 345 147 92 31 656 283 14195 7779

Total 662 38 290 56 391 188 125 62 799 418 20401 13763

Threatened (no. & %) 58

9%)

10

(26%)

49

(17%)

22

(39%)

19

(5%)

15

(8%)

16

(13%)

16

(26%)

78

(10%)

74

(18%)

2804

(14%)

2722

(20%)

Taxa of conservation

concern (no. &%)

90

(14%)

16

(42%)

91

(31%)

32

(57%)

45

(11%)

39

(21%)

33

(26%)

31

(50%)

140

(18%)

132

(31%)

6170

(30%)

5948

(43%)

One in four of South Africa’s endemic birds is threatened with extinction (26%) and overall 9% of South

Africa’s terrestrial birds are threatened (Figure 49). Birds became more threatened between 2000 and 2015,

with 27 taxa (4% of the 732 birds assessed) shifting into higher risk categories and only two species improving

in status. The status of Woodwards’ Batis (Batis fratrum) has improved from Near Threatened to now being

listed as Least Concerned as a result of its core range being conserved as part of the consolidation of

isiMangaliso Wetland Park. The Woolly-necked stork (Ciconia episcopus), has experienced a range shift

southwards into the Eastern Cape and Southern KZN and its increasing use of man-made habitats (golf

courses) has resulted in it also being downlisted from Near Threatened to Least Concern. Crop cultivation

(affecting 38% of taxa); plantation forestry (affecting 25% of taxa) and poisoning (affecting 21% of taxa) are

the main pressures that have caused increase in threat status (Table 26). The highest numbers of threatened

birds are concentrated in the north eastern parts of South Africa in the Savanna, Grassland and Indian Ocean

Coastal Belt biomes (Figure 51, Figure 52).

Approximately 5% of South Africa’s reptiles are at risk of extinction (Figure 49). South African reptiles appear

to be faring better than the global average, given that 15% of reptiles have been assessed are listed as

threatened globally (Tolley et al. 2019). There appears to be only a small increase in extinction risk over the

last 25 years, with the Red List Index (RLI) showing a small decline between 1990 and 2018 (Figure 50). Most

of the species now at risk were already at risk in 1990 and that risk has not substantially increased or

decreased in the interim (Figure 50), as most of the habitat loss impacting South Africa’s reptiles took place

prior to 1990. As a result, only fourteen (3.6%) of 391 reptiles assessed in 2018 changed status between 1990

and 2015. The highest concentrations of threatened taxa for reptiles are in northern KwaZulu-Natal, within

the Maputaland Centre of Endemism (Figure 51).


126

To date butterflies are the only terrestrial invertebrate group to have been assessed, of which 10% of taxa

are threatened and 18% listed as of conservation concern (Figure 49). More than 50% of South Africa’s

butterflies’ species are endemics with restricted ranges, making them highly vulnerable to changes in land

use. The RLI for butterfly species (Figure 50) shows the sharpest decline of any terrestrial group, with 13

species becoming more threatened in the short period between 2013 and 2018. Leading causes of decline

are habitat alteration as a result of spreading invasive alien plant species (affecting 46% of taxa); drought

(affecting 38% of taxa); loss of habitat to crop cultivation (affecting 15% of taxa); habitat degradation as a

result of too frequent fire (affecting 15% of taxa) and heavy livestock grazing (affecting 15% of taxa) (Table

26). Threatened butterflies are concentrated in the southern Cape, the Cape Fold Mountains of the South

Western Cape and in the Drakensberg foothills of the Eastern Cape and KwaZulu-Natal (Figure 51).

Figure 50. Trends in status of South Africa’s terrestrial species: reptiles, amphibians, birds, mammals, plants and butterflies based on the Red List Index, the slope of the line indicates the rate at which species groups are becoming more threatened over time, grey shading indicates uncertainty of trends and is most strongly influenced by number of Data Deficient species within a taxonomic group.


127

Table 26. Summary of changes in risk assessment status and the pressures causing species to increase in threat status.

Taxon Group

No of taxa assessed

Back cast assessment

date

Most recent assessment

Genuine improvements (no. taxa & %)

Genuine increase in

threat status (no. taxa & %)

Main pressures to taxa that have increased in threat status between two assessment periods

Reptiles 391 1990 2018 0 10 (2.5%) Housing development (70%); crop cultivation (50%); livestock overgrazing (30%); mining (30%).

Amphibians 125 1990 2016 0 6 (4.8%) Afforestation (33%); housing development (33%); invasive species (33%).

Mammals 336 2004 2016 10 (3%) 13 (3.87%) Direct persecution through poaching and hunting for bush meat (46%); crop cultivation (46%); afforestation (46%); housing development (31%).

Butterflies 799 2013 2018 0 13 (1.63%) Invasive species (46%); drought (38%); crop cultivation (15%); incorrect fire regimes (15%); livestock overgrazing (15%).

Plants 900 * (representative

sample of 20401 taxa)

1990 2018 3 (0.3%) 45 (5%) Invasive species (40%); crop cultivation (33%); urban development (20%); livestock overgrazing (11%).

Birds 732 2000 2015 3 (0.4%) 24 (3%) Crop cultivation (38%); afforestation (25%; persecution through poisoning (21%).

Figure 51. Spatial distribution of threatened species for the six taxonomic groups included in the terrestrial across. (a) Birds, (b) mammals, (c) reptiles, (d) amphibians, (e) plants and (f) butterflies. The legend reflects the number of threatened species per 10km x 10km grid cell. Maps are based on a combination of expert interpreted species distributions, modelled distributions and species range maps.

(a) (b) (c)

(a)

(d) (f) (e)


128

Birds

Mammals

Reptiles

Amphibians

Plants

Butterflies

Figure 52. The number of taxa of conservation concern for each taxonomic group for each biome.


129

Overall the South Africa’s Red List Index for terrestrial species show that all taxon groups have experienced

a decline in threat status over the past 10 years. However, the degree of decline differs for each taxonomic

group. In comparison to the global Red List Indices, which exist for amphibians, mammals and birds, South

Africa’ species taxa are faring better. Mammals, birds and amphibians having higher RLI values than the global

average. South Africa’s birds are however declining in threat status at a faster rate than the global average,

the cause of these declines require further investigation.

Figure 53. Comparison of South Africa's Red List Index values with those produced from globally assessed groups by the IUCN, a) amphibians, b) mammals and c) birds. Dashed line indicates Red List Index for South Africa’s species while the sold line indicates the Global Red List Index for all species in the world. The grey shading indicates uncertainty and is most influenced by levels of data deficiency in the group.

8.1.4. Limitations of species threat status and trends analyses

Significant progress with species assessment has taken place in the past five years. With ongoing field

monitoring and expansion of atlasing projects there has been an improvement in knowledge of species and

the number of data deficient species for all six taxon groups has decreased. Furthermore, there has been

substantial investment in ensuring accurate spatial data for taxa of conservation concern is available. This

has not only benefitted the assessment process but has provided data that can be incorporated into Spatial

Biodiversity Plans and used in Protected Areas Expansion Strategies.

Unfortunately, there is still a bias in our assessments towards vertebrates. The alarming rates of decline being

observed from butterflies indicates that there is a need to track other groups of invertebrates in the country

and shows that trends determined from assessments of vertebrates cannot act as a surrogate for

invertebrate species diversity.

8.2. Protection level for species

8.2.1. Species protection level calculation method

Protection level of species is presented for the first time in this NBA. With no global protection level

assessment system in existence we have developed a methodology specifically for this NBA. Our approach is

a new practical method for tracking progress towards a population persistence target set for each species. It

provides a national level indicator for different taxonomic groups on the effectiveness of a country’s

protected area network to conserve species. As a starting point we propose the definition of a fully protected

species as a species that is adequately represented by viable subpopulations within Protected Areas and

where there is effective mitigation of threats to ensure ecological functioning of the species and the

prevention of population decline.


130

The Protection level indicator has two components. The first measures how well represented a species is

within the protected area network. This component allows the identification of which species require further

protection, where distribution data for species not represented or poorly represented within protected area

network are prioritised for inclusion in spatial planning for protected area expansion. Component two

includes a measure of the effectiveness of each protected area to mitigate threats, and when combined with

protected area representation provides an overall effective protection level measure for each species.

Measuring the effectiveness of the protected area network requires the setting of a population persistence

target for species that represents either the number of individuals, number of viable subpopulations or

amount of suitable habitat required to ensure long term survival. The targets set for each taxonomic groups

is detailed Table 27.

For measuring representation in protected areas we determined what proportion of the species persistence

target occurs with South Africa’s protected area network using the equations shown Table 27 (see section

6.4 for information of which protected areas were included). In order for species to be effectively protected,

a protected area must mitigate against the pressures that cause population decline (e.g. poaching, invasive

alien species, inappropriate fire regimes, and exotic). As such, not all PAs have equal probability of mitigating

threats to species. Protected area effectiveness thus includes an adjustment factor in assessing each

protected areas contribution to the target. As different species respond uniquely to pressures. Networks of

taxon experts provided the effectiveness scores applicable to each species based on their knowledge of each

species’ response to pressures present or absent in each protected area (EPA in Table 27).


131

Table 27. Method for setting targets and measuring the protection level for species.

The categories for protection level are: Well Protected where the species persistence target is met or

exceeded by the protected area network; Moderately Protected where between 50 and 99% of the species

persistence target is met; Poorly Protected where between 5 and 49% of the species persistence target is

met; and Not Protected where less than 5% of the species persistence target is met (Table 28). Protection

level was calculated for terrestrial birds, mammals, reptiles, amphibians, butterflies. Plants were assessed

using a representative sample of 900 taxa. Peripheral taxa, which have less than 5% of their distribution

range occurring in South Africa, were excluded from the analysis.

Taxon type Conservation target

(𝑇𝑎𝑟𝑔𝑒𝑡𝑆𝑝𝑒𝑐𝑖𝑒𝑠)

Equation to calculate representation in PA network

Equation to be used to calculate overall PL that includes the management effectiveness factor

Vertebrates with sufficient MVP studies to extrapolate reference MVP (large mammals, birds)

Area required to support a minimum viable population using taxon specific reference MVPs listed in Trail et al. 2007.

𝑃𝐿𝑅 𝑆𝑝𝑒𝑐𝑖𝑒𝑠

= ∑ 𝑃𝑃𝐴𝑃𝐴

𝑇𝑎𝑟𝑔𝑒𝑡𝑆𝑝𝑒𝑐𝑖𝑒𝑠

𝑃𝐿 𝑆𝑝𝑒𝑐𝑖𝑒𝑠 = ∑ 𝑃𝑃𝐴𝑃𝐴 × 𝐸𝑃𝐴


Vertebrates with insufficient MVP studies to extrapolate reference MVP (reptiles, amphibians, small mammals and plants)

Area required to support 10 000 individuals the threshold in criterion C of the IUCN’s quantitative criteria (Mace et al. 2008)


= ∑ 𝑃𝑃𝐴𝑃𝐴


𝑃𝐿 𝑆𝑝𝑒𝑐𝑖𝑒𝑠 = ∑ 𝑃𝑃𝐴𝑃𝐴 × 𝐸𝑃𝐴


Taxa that experience natural annual fluctuations in population size or where densities are very high (terrestrial invertebrates, FW vertebrates and invertebrates)

10 viable subpopulations*


= ∑ 𝑃𝑃𝐴𝑃𝐴 × 𝑉𝑃𝐴


𝑃𝐿 𝑆𝑝𝑒𝑐𝑖𝑒𝑠 = ∑ 𝑃𝑃𝐴𝑃𝐴 × 𝑉𝑃𝐴 × 𝐸𝑃𝐴


*Note for naturally range restricted and rare taxa where fewer than 10 subpopulations exist, the target is adjusted down to the number of original subpopulations.

Where PPA is the population score for each protected area a species is recorded in. PPA can take the following values, depending on what data is available and following this preference hierarchy:

Estimated or surveyed number of individuals in the protected area

Average density (D) x area of suitable habitat in protected area (APA)

The number of subpopulations present within a Protected Area (only used for species where estimating population size is not possible or where there are natural and regular fluctuations in population size)

VPA is the score of viability of a populations

1 where viability indicated as ‘Viable’

0.1 where viability indicated as ‘Non-viable’

EPA is the effectiveness score of the protected area for the particular population of the species occurring within the protected area. EPA can take one of the following values:

EPA Score

Good Protected area is fully effective in protecting the species against major threats and ensuring the long-term persistence of the population present.

1

Fair Protected area provides some mitigation of major threats to species, but is not completely effective.

0.5

Poor Protected area provides no mitigation of major threats to species – individuals inside the protected area are no better off than those outside.

0.1


132

Table 28. Categories of protection for species.

Percentage of target met Protection Level category

0 – 4.9% Not Protected

5-49% Poorly Protected

50-99% Moderately Protected

100+% Well Protected

For all taxon groups assessed an first initial screening of wide spread and well protected species was

conducted by determining if species occurred as viable populations in more than 10 protected areas.

Peripheral range species with less the 5% of their distribution range or < 2% of the global population occurring

in South Africa were excluded from the analysis. For birds, all vagrants included in the Checklist of Birds in

South Africa as published by BirdLife - South Africa were excluded from analysis see

(http://birdlife.org.za/publications/checklists). Only indigenous taxa were included in the analysis, with all

introduced aliens excluded. GIS spatial intersects were used to calculate progress towards achieving each

target. Experts engaged in the process of determining protection level effectiveness based on local fieldwork.

Where insufficient knowledge exists to assess the effectiveness of a protected area for a particular taxon this

was scored as unknown. These protected areas will be prioritised for future monitoring. For the purposes of

this first time ever assessment it was assumed that protection is sufficiently effective and these were scored

as Good (EPA score 1), this decision was taken after determining that the difference of scoring unknown

reserves poor, fair, or good made less than a 1% difference to the overall level of species that are well

protected. Unknown reserve effectiveness was scored for many small reserves, mostly private that together

constitute a small fraction of the protected areas network and hence contribute little to the overall protection

of populations of species.

An additional protection level analysis was conducted that focused only on assessing if protected area

expansion since 1990 has helped to improve the protection coverage of threatened species. This involved

comparing spatial distribution point occurrence data for threatened species with protected areas and

tracking when each protected area cadastre was declared. Note this analysis is not comparable to the above

protection level index as it does not measure whether a population persistence target is met and does not

take effectiveness of protection into account, it merely represents threatened species presence within

protected area and how this has changed over time.

8.2.2. Species protection level results

South Africa’s protected areas network provides relatively good protection for birds and reptiles with over

85% of their taxa categorised as Well Protected (Figure 54). Protected area expansion has improved the

coverage of threatened birds occurring within protected areas from 80% in 1990 to 94% in 2018 (Figure 55).

Despite this, 53 threatened birds remain under-protected (Figure 56).

Of the 389 reptile species included in the analysis, all non-endemics (100%) and 87% of endemics were

classified as Well Protected (Figure 54). Protected area expansion has improved the coverage of threatened

reptiles occurring within protected areas from 63% to 89%. This is most likely due to the increase in protected

area estate associated with the declaration of the iSimangaliso Wetland Park in 2000, an area of high

concentration of threatened reptiles (Figure 51) and the expansion of protected areas in the arid western

regions of the country. Despite this, seven threatened taxa remain under-protected, and crucially, this

includes the two most threatened reptiles in South Africa – the Critically Endangered Durban Dwarf

http://birdlife.org.za/publications/checklists


133

Burrowing Skink (Scelotes inornatus) and the Critically Endangered Geometric Tortoise (Psammobates

geometricus) (Figure 56) as well as three endangered species (see Box 8).

Figure 54. Protection level for South Africa's indigenous terrestrial taxonomic groups. Analysis conducted for both threatened and non-threatened taxa but excluded peripheral taxa (those with less than 5% of distribution range occurring in South Africa). (a) Shows all analysis for all taxa (b) shows protection level for South African endemics. *Due to the extremely high number of plants occurring in South Africa a representative sample of 900 plants were assessed.

Figure 55. Cumulative coverage of threatened species in protected areas between 1990 and 2018 for six terrestrial taxonomic groups.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

19

90

19

95

20

00

20

05

20

10

20

15

20

18

19

90

19

95

20

00

20

05

20

10

20

15

20

18

19

90

19

95

20

00

20

05

20

10

20

15

20

18

19

90

19

95

20

00

20

05

20

10

20

15

20

18

19

90

19

95

20

00

20

05

20

10

20

15

20

18

19

90

19

95

20

00

20

05

20

10

20

15

20

18

Amphibians Birds Reptiles Plants Mammals Butterflies


134

Figure 56. Protection level for South Africa's indigenous terrestrial taxa disaggregated into three categories: threatened (THR) species [CR, EN, VU]; Near Threatened, Data Deficient and Rare [NT, DD, RARE]; and species of Least Concern (LC). The assessments are comprehensive (covering the whole taxonomic group) except for plants where a representative sample of 900 species was used. Panel (a) plants, birds and butterflies; panel (b) mammals, reptiles and amphibians. Note the logarithmic x-axis for the two panels.


135

Due to South Africa’s extremely high diversity of plant taxa (20 401 taxa), the protection level for plants was

determined using a statistically representative random sample of 900 taxa. Based on this sample, plants have

the highest proportion of under-protected taxa with 17% in the category Not Protected (Figure 54). Of

concern is the fact that 81% of the threatened taxa included in the sample were classified as under-protected

(Figure 56). Interestingly, 18% of widespread least concerned plant taxa included in the sample were also

under-protected. This is due to large parts of South Africa, in particular Bushmanland, the Great Karoo and

north-western Limpopo, having very few protected areas and also low rates of habitat loss. The northern

Namaqualand coast, western border of Bushmanland between Platbakkies and Gamoep and Steytlerville

Karoo have areas with high numbers of Not Protected range-restricted endemic plants. Protected area

expansion in these areas offer good opportunities to improve the representation of plant species in protected

areas.

When determining the effectiveness of protected areas to mitigate against threats, our analysis showed that

6% of plant taxa included in the sample dropped a category of protection, due to the rapid expansion of

invasive alien plant species and inappropriate fire return intervals occurring in protected areas in the Fynbos

biome, or due to the grazing of livestock taking place in certain Grassland and Savanna protected areas (see

Box 9 for case studies on plants and protection level).

Chersobius boulengeri (EN) Well Protected: This tortoise has a large distribution (nearly 60,000 km2) of which approximately 1,400km2 falls within protected areas. Despite this, only 15% of the known localities are considered to have viable populations. The distribution is fragmented, with 50% of the range being degraded, which causes food and shelter to be limited. Despite occurring in some protected areas, the overall loss of ecological integrity has probably caused direct population declines. Indirectly, the landscape no longer supports the larger metapopulation (see glossary), triggering local population declines or extinctions.

Chersobius signatus (EN) Well Protected: This species has a relatively large distribution (approximately 28,000km2) with

1,200km2 falling within protected areas. Some populations have become locally extinct or reduced in size, probably as a result of direct declines associated with increased predation from Pied Crow (Corvus albus) due to this bird’s range expansion. In addition, habitat loss and degradation has fragmented the range of C. signatus, and it is unlikely that the landscape supports the larger metapopulation. This probably has resulted in an overall decline of the species despite its presence in protected areas. Furthermore, Pied Crow are not excluded from protected areas, and as such the species remains at risk to this unprecedented predation.

Scelotes inornatus (CR) Not Protected: This species is a habitat specialist, burrowing in sandy soils of coastal forest. Urban

expansion in the Durban metropolitan area has resulted in a very small number of tiny habitat patches that this species can utilise, none of which are protected areas. At present, the species is known from approximately 10 tiny patches, totalling just 2.9km2. In addition to direct population declines due to this loss of habitat, these small isolated patches lack connectivity resulting in disruption of metapopulation processes.

Bradypodion caffer (EN) Not Protected: This chameleon has a small distribution (89km2) in the remaining forest patches

near Port St. Johns, Eastern Cape. Although it does occur at Silaka Nature Reserve, the forest patch is less than 10km2 and this is not considered a viable population. The species is therefore considered Not Protected. It is a forest specialist and cannot tolerate transformed habitats, but much of the area has been converted for rural homesteads and agriculture. Loss of forest habitat contributes directly to population declines.

Psammobates geometricus (CR) Not Protected: Over 90% of this tortoise’s original distribution has been irreversibly

altered, mostly through widespread conversion of natural habitat for agriculture, contributing directly to a population decline.

Its’ remaining distribution totals just 167km2, over a scattering of very small and isolated populations that are largely

disconnected from each other. These highly fragmented populations are not considered viable over the long-term. Although not

quantified, it is likely that the species has also declined due to predation pressure from the Pied Crow (Corvus albus) and possibly

through the pet trade. None of the known populations occur in protected areas larger than 10km2 so the species is considered

to be Not Protected.

Krystal Tolley – South African National Biodiversity Institute

Box 10. Case studies from the protection level assessment for reptiles.


136

Box 11. Case studies from the protection level assessment for plants

Cheilanthes namaquensis (Least Concern) – Poorly protected

Cheilanthes namaquensis is a widespread fern, occurring from Namaqualand to Matjiesfontein on the edge of the Tanqua Karoo, with an isolated population recorded on the northern Cape Peninsula. It occurs in small, scattered populations, and suitable habitat is limited. Although it has been recorded in three large protected areas – the Namaqua National Park, Tanqua Karoo National Park and Table Mountain National Park, suitable habitat in all three of these reserves is limited and the number of individuals protected is estimated to be small. It also occurs in a few other small protected areas such as Oorlogskloof Nature Reserve on the Bokkeveld Escarpment, but due to this species’ low density, the population target is not being met in existing protected areas.

Cannomois arenicola (Endangered) – Well protected

Cannomois arenicola has a limited distribution range on the Western Cape lowlands between Hopefield and Gordon’s Bay. It is a very long-lived species, and has lost >50% of its habitat to urban and agricultural expansion, but it can be locally dominant in suitable habitat. It has been recorded in only four relatively small protected areas where management effectiveness is challenging due to alien invasive plants, but due to its high abundance, the population target is well exceeded and it is considered well protected.

Alepidea insculpta (Rare) – Well protected

Alepidea insculpta is a range restricted species occurring on high altitude basalt ridges in the central to southern KwaZulu-Natal Drakensberg. This species’ entire known distribution range is well protected in this extensive protected area, and therefore it is classified as Well protected.

Lize von Staden - South African National Biodiversity Institute (SANBI)

The complementary analysis of how protected area expansion has influenced coverage for all of South

Africa’s 2804 threatened plants shows that protection coverage of threatened plant taxa has increased

continuously since 1990 and is currently at 69% of threatened taxa occurring in protected areas (Figure 55).

Since 2010, 62 previously unprotected threatened plants taxa have come under protection. Unfortunately,

during this same period 265 plant taxa were added as threatened to South Africa’s Red List of plants. A total


137

of 869 threatened plant taxa currently have no form of protection. There is therefore a need to accelerate

the expansion of protected areas to sites with high concentrations of threatened plants.

Only 57% of South Africa’s butterfly taxa are Well Protected (Figure 54), with a high proportion (89%) of the

threatened taxa also classified as under-protected (Figure 56). Since 2000, protected area expansion has

brought only one previously unprotected threatened butterfly into protection (Figure 55), and 36 (46%) of

threatened butterfly taxa have no populations occurring in protected areas. This suggests that invertebrates

have not been sufficiently considered in protected area expansion to date. Furthermore, when considering

the effectiveness of protected areas to mitigate pressures that impact butterflies, increases in invasive alien

plant species and livestock grazing within protected areas, often coupled with poor fire management, has

resulted in 49 butterfly taxa (7% of all taxa assessed) dropping a category of protection.

Just over a quarter of all South African amphibians (28%) are under-protected (Figure 54). The situation is

worse for species endemic to South Africa (84 species), of which 44% are under-protected. Most threatened

amphibians (94%) have at least one population occurring within South Africa’s protected area network

(Figure 55). However, protection is not adequate for many species; for example, three South African endemic

frogs: Heleophryne rosei, Capensibufo rosei and Arthroleptella subvoce, which occur exclusively within

protected areas, did not qualify as Well Protected (Box 12). Overall, 9% of amphibian species drop down a

category of protection due to threats within protected areas not being effectively mitigated. Primary drivers

for this are the presence of considerable stands of invasive alien plant species in protected areas and changes

in habitat structure and function due to disrupted natural fire regimes.

Mammals have the lowest levels of protection, with 56% of species assessed as Well Protected (Figure 54).

Of the 47 threatened terrestrial mammals, 42 (89%) are under-protected (Figure 56). Mammals typically

have large home ranges and hence require larger areas to be effectively protected. Simultaneously, high

levels of poaching for bushmeat or illegal wildlife trade mean that protection is not effective for many species.

Eleven mammal taxa (4%) drop to a lower category of protection due to insufficient mitigation of pressures

within protected areas boundaries. There is a need to bring more threatened mammal species into

protection. While representation of threatened mammals within South Africa’s protected area network has

grown from 56% to 61% since 1990 (Figure 55), a number of restricted and threatened endemic small

mammals, such as Golden Moles (Chrysochloridae), are poorly represented in the protected area network.

The results of the protection level analyses for species show that protected area expansion needs to focus

on under-protected and threatened taxa for all taxonomic groups. Protected area expansion in Bushmanland,

Steytlerville Karoo and north-western Limpopo, as well as the Namaqualand coastline where there are still

large areas of intact natural habitat, offer opportunities to enhance the representation of under-protected

taxa.

Our analysis also indicates that protected areas are not meeting ecological requirements for 9% of

amphibian, 6% of plant, 7% of butterfly and 4% of mammal taxa. A mechanism to share data on priority

threatened taxa that are declining within protected areas is currently being development for use by

protected area managers.

Three South African endemics (Heleophryne rosei; Capensibufo rosei; Arthroleptella subvoce), despite occurring

exclusively within protected areas, qualify as under-protected. This reflects the fact that protected areas where these

species occur are not mitigating against pressures and processes causing population decline. Each of these species

also carries a highly threatened Red List status of Critically Endangered signalling the urgent need for protected area

managers to ensure management supports the ecological requirements of these species.

Box 12. Three amphibians are under-protected despite only occurring in protected areas


138

8.2.3. Species protection level limitations in this environment

The protection level assessment presented here is a new indicator for South Africa, developed specifically for

this National Biodiversity Assessment and is novel globally. Experts involved in conducting the assessment

for each group were able to set targets for species and use either actual population count data or modelled

habitat suitability data combined with population density estimates; or a number of viable subpopulations

present to determine how well represented a particular taxon was within the protected area network.

Determining effectiveness of all reserves for each taxon proved more challenging, and experts had to score

the effectiveness of certain protected areas as unknown as described above this had relatively little impact

on the overall scoring but will be an area improved going forward.

Due to the new nature of the index, it is important that the different components of the indicator are tested

to determine the sensitivity of both the actual target set as well as the sensitivity to the different types of

input data used. Two PhD studies are currently underway that are testing the sensitivities of this new index

for a data rich group (mammals) and a data poor group (plants).


139

9. BIOME SUMMARIES

Chapter 9: Skowno, A.L., Raimondo, D.C. & Fizzotti, B. 2019. ‘Chapter 9: Biome Summaries’, in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J. & Fizzotti, B. (eds.). South African National Biodiversity Institute, Pretoria.

9.1. Albany Thicket

Ecosystem Status Ecosystem Pressures Species Pressures

Plants Vertebrates

Threatened Ecosystems (CR, EN, VU)

6 Extent (Km2) 35 250 Livestock farming 73 Hunting 10

Well Protected Ecosystems 8 Natural 91% Mining 50 Non-timber crops

6

Total count of Ecosystems 44 Croplands & old fields 7% Gathering plants 28 Ecosystem modifications

6

Threatened Plants 93 Plantation <1% Tourism areas 14 Livestock farming

5

Threatened Animals 45 Built up 1.4% Wood plantations

5

The Albany Thicket biome is a dense formation of shrubs and trees centred in southeast South Africa. It is an ancient biome that is thought to have been widespread in the Eocene and forms part of the Maputaland-Pondoland-Albany Global Biodiversity Hotspot.

Approximately 9% of the natural habitat of the biome has been lost to anthropogenic land uses – croplands (6.6%) & human settlements (1.6%) - and 45% of the biome is in a moderate to severely degraded state. There are three Critically Endangered and three Vulnerable ecosystem type (out of a total of 44 ecosystem types in the biome). Approximately 18% of the natural habitat of the biome is listed as threatened. Land clearing linked to cropland expansion is the primary driver of habitat loss.

The degradation data used for the assessment were based on studies from 2002 and need to be updated. There are eight Well Protected and 11 Moderately Protected ecosystem types in the biome, and recent improvement in protection levels have been driven by biodiversity stewardship agreements with numerous private game reserves in the Albany region. Habitat degradation as a result of livestock overgrazing is the most severe threat to plant species in the biome, impacting 76 taxa of conservation concern, and is also a dominant pressure for vertebrates. The growth of the wildlife industry within the province is helping to alleviate this pressure, as wildlife in general causes less habitat degradation than cattle. Urban expansion threatens 64 plant taxa and 25 vertebrates with most of these taxa losing habitat due to the urban expansion of the city of Port Elizabeth.


140

9.2. Desert


Plants Vertebrates


1 Extent (Km2) 6260 Livestock farming 73 Hunting 10

Well Protected Ecosystems 6 Natural 99% Mining 50 Non-timber crops

6

Total count of Ecosystems 15 Croplands & old fields <1% Gathering plants 28 Ecosystem modifications

6

Threatened Plants 69 Mining <1% Tourism areas 14 Livestock farming

5

Threatened Animals 11 Built up <1% Wood plantations

5

The Desert biome occupies a small portion of the extreme northwest of the country, forming the southernmost extent of the Namib Desert. The Richtersveld region in particular has the highest botanical diversity and level of endemism of any arid region, and forms part of the Succulent Karoo Global Biodiversity Hotspot.

Less than 2% of the Desert biome has been lost to anthropogenic land use, the major impact being mining. There is no comprehensive land degradation dataset for the biome but various studies indicate that degradation through overstocking of rangelands is widespread. Of the 15 ecosystem types in the biome only one is Endangered, however, given the lack of land degradation data considered in the RLE assessment, this is likely to be an underestimate of threat levels in the biome. Six ecosystem types are Well Protected.

A meta-analysis of the pressures included in the Red List assessment of taxa that occur in the Desert Biome indicate that overgrazing by goats is causing 73 plant taxa to be declining. Open-cast mining along the Orange River Valley is causing ongoing habitat loss and degradation to 50 plant taxa. This region is rich in endemic plants, with many restricted to localised micro habitats such as quartz patches. Dust blown from exposed mine dumps is burying these unique micro habitats and killing the plants adapted to them. Irresponsible off-road driving of mining and construction vehicles is also destroying dwarf succulent plants and their sensitive micro habitats. Succulent collecting is an additional threat impact for at least 28 taxa, while illegal hunting and trapping is the lead pressure to Desert biome vertebrates. Evidence is emerging that climate change (increased mean annual temperature and associated changed in weather patterns) is impacting species diversity in the biome. This is a very recent phenomenon, so it has not yet been possible to complete Red List assessments that reflect this pressure. The high levels of plant mortality and complete death of whole populations of restricted endemic plant species observed during 2018 means that many plants will be up-listed during 2019. Animal taxa are suspected to also suffer large declines in populations as a result of these severe droughts and monitoring of selected restricted animal taxa is recommended.


141

9.3. Forest


Plants Vertebrates


1 Extent (Km2) 4815 Invasives 89 Non-timber crops

41

Well Protected Ecosystems 10 Natural 83% Gathering plants 64 Urban areas 38

Total count of Ecosystems 12 Croplands & old fields 8% Fire 51 Wood plantations

31

Threatened Plants 107 Plantation 7% Livestock farming 46 Hunting 29

Threatened Animals 52 Built up 2%

Indigenous forests in South Africa are scattered along the eastern and southern margins of the country. They typically occur as small (<10ha) patches embedded within larger Fynbos, Grassland, Albany Thicket or Savana biomes, though larger complexes are found in the Knysna area and in the Amathole mountain range (Mucina et al. 2006). The coastal forests of eastern South Africa make up part of the Maputaland-Pondoland-Albany Global Biodiversity Hotspot.

Approximately 18% of the Forest biome has been lost to plantation forestry (11%), croplands (5%) and built-up areas (2%); leaving 82% in natural /near natural state. There was limited loss of Forest habitat in recent years (1990-2014) (<2%).

Of the 12 ecosystem types making up the Forest biome, one is listed as threatened (Vulnerable), covering 1% of the natural remaining habitat of the biome. All the remaining forest ecosystem types are listed as ‘Ecosystems of Special Concern’ and are protected by dedicated legislative tools. In terms of protected areas coverage, all 12 forest types are Well Protected or Moderately Protected, making Forests the best protected biome in South Africa. Despite the low levels of overall transformation of the Forest biome, data from species Red List assessments indicate that extensive degradation of this biome is taking place. Plant taxa restricted to forests are being out-competed by invasive alien plants. Many of South Africa’s most popular medicinal plant species occur in forests and 64 of these are declining and are listed as threatened or Near Threatened as a result of over-harvesting. Similar pressures exist for vertebrate taxa, and collection for muthi and/or hunting for bush meat threaten 29 taxa. The loss of natural forest habitat is impacting vertebrate species, with 41 taxa threatened as a result of crop farming, 38 due to urban and housing developments and 31 due to agroforestry plantations.


142

9.4. Fynbos


Plants Vertebrates


53 Extent (Km2) 81445 Invasives 1665 Urban areas 37

Well Protected Ecosystems 41 Natural 69% Non-timber crops 1378 Non-timber crops

36

Total count of Ecosystems 122 Croplands & old fields 28% Urban areas 858 Invasives 31

Threatened Plants 1893 Plantation 1.2% Fire 814 Hunting 29

Threatened Animals 78 Built up 1.4%

The Fynbos biome is globally recognised as a unique floral kingdom and Global Biodiversity Hotspot and has very high plant species diversity and levels of endemism.

The dominant human impact on biodiversity in the Fynbos biome is from croplands (24% of the biome). Approximately 69% of the biome remains in a natural / near natural state, a large proportion of which is in rugged mountain landscapes with very low agricultural potential. The loss of natural habitat to croplands mainly in the lowland areas continues with 2 026km2 of natural habitat being cleared between 1990 and 2014 (a 4% loss of natural habitat).

Only Indian Ocean Coastal Belt and Grassland show greater rates of decline between 1990 and 2014. The Fynbos biome has 53 threatened ecosystem types (25 Critically Endangered, 18 Endangered & 10 Vulnerable) making up 20% of the natural habitat remaining in the biome. Over a third of the ecosystem types (52) are Well Protected or Moderately Protected, but the most of these are mountain ecosystem types and the lowland ecosystem types remain Poorly Protected or Not Protected. A further significant pressure to the Fynbos biome not picked up from land cover data is the impact of invasive alien species. An extremely high number (1 665) of plant taxa endemic to the Fynbos biome are losing habitat and being out-competed by invasive plant species, while 31 vertebrate taxa, most of these endemic amphibians, are also losing habitat to invasive species. A very large number of plant taxa (1 378) are declining due to loss of habitat to crop cultivation. Recent ongoing transformation in the eastern Overberg has seen a number of plant species being listed for the first time as threatened. Urban development is the dominant threat to vertebrates impacting 37 taxa, while 858 plant taxa of conservation concern are experiencing ongoing declines as a result of development. Too frequent fire return intervals and increasing fuel loads from invasive plants is causing declines to 814 plant taxa of conservation concern. Invertebrates are also negatively impacted. While both Fynbos endemic plants and endemic invertebrates such as Gossamer-winged (Lycaenid) butterflies have evolved to be able to survive fire, if fires are too frequent and too intense this leads to local extinctions of populations.


143

9.5. Grassland


Plants Vertebrates


21 Extent (Km2) 365629 Invasives 195 Wood plantations

56

Well Protected Ecosystems 10 Natural 61% Livestock farming

177 Non-timber crops

55

Total count of Ecosystems 73 Croplands & old fields 31% Wood plantations

172 Livestock farming

50

Threatened Plants 270

Plantation 4% Non-timber crops

113 Urban areas 48

Threatened Animals 105 Built up 3% Hunting 48

The Grassland biome occupies the central plateau of South Africa (Highveld) and lower elevation portions of the Eastern Cape and KZN interior. The majority of the biome is dominated by C4 grasses, with C3 dominated grasslands restricted to montane areas.

It is the second largest biome, covering ¼ of South Africa, of which approximately 61% remains in a natural / near natural state. Although degradation maps do not reliably cover the biome, large portions of the biome have been impacted by poor rangeland management practices.

The dominant land cover impacts in the Grasslands are croplands which resulted in the loss of 30% of the natural habitat of the biome, followed by plantations (5%) and built-up areas (4%). Although mining is relatively wide spread and has a strong negative impact on biodiversity it covers less than 1% of the biome. About 5% of the remaining natural Grassland biome was modified between 1990 and 2014, making Grassland the second most impacted biome during this period. Of the 74 ecosystem types occurring in the biome, 21 are listed as threatened (two Critically Endangered, 18 Endangered, 11 Vulnerable) and cover 21% of the natural remaining habitat of the biome. In terms of protection, 13 of the 74 ecosystem types are Well Protected or Moderately Protected. The high levels of habitat degradation present in the Grassland biome are reflected in the pressures listed for Grassland biome taxa of conservation concern. The dominant pressure for plants is habitat degradation resulting from invasion by alien plant species (195 plant taxa impacted). This is followed by loss of habitat condition due to poor rangeland management, with 177 plant taxa declining due to livestock overgrazing. This loss of habitat structure and function is impacting 50 vertebrate taxa. Invertebrates are also impacted with 36 butterfly taxa threatened in the grassland biome. Invertebrates are impacted as overgrazing results in modification of the microclimate and soil composition resulting in loss of the specialist herbaceous plants which are the invertebrates host plants. Conversion of Grassland to plantations and crop farming are the leading threats to vertebrates, with birds and reptiles particularly severely impacted. Endemic Grassland plant species are also threatened, with 172 plant taxa declining as a result of habitat loss to plantations and 113 plant taxa threatened by loss of habitat to crop farming.


144

9.6. Indian Ocean Coastal Belt


Plants Vertebrates


4 Extent (Km2) 11530 Non-timber crops

83 Non-timber crops

38

Well Protected Ecosystems 0 Natural 36% Livestock farming

81 Wood plantations

34

Total count of Ecosystems 6 Croplands & old fields 34% Invasives 71 Urban areas 30

Threatened Plants 96 Plantation 11% Fire 59 Hunting 29

Threatened Animals 49 Built up 19%

The Indian Ocean Coastal Belt biome forms part of the Maputaland-Pondoland-Albany Global Biodiversity Hotspot and is a heterogeneous complex of forest, mesic savanna and grassland vegetation communities; unique both ecologically and biogeographically.

The Indian Ocean Coastal Belt biome has been very heavily impacted by anthropogenic land uses, and only 36% remains in a natural / near natural state. The biome also has the highest rate of ongoing habitat loss, with 16% of the biome being lost between 1990 and 2014. The dominant human impact in the Indian Ocean Coastal Belt are croplands which resulted in loss of 29% of the natural habitat of the biome, followed by built-up areas (22%) and plantation (13%).

Of the six terrestrial ecosystem types of the Indian Ocean Coastal Belt biome, four are threatened (three Endangered, one Vulnerable), and 62% of the natural vegetation remaining is listed as threatened. Only two of the ecosystem types within the biome are Moderately Protected, none are Well Protected. The impact of high levels of habitat conversion are reflected in the status of species, despite the relatively small size of the biome, 96 plant taxa and 45 vertebrate taxa are threatened with extinction. Loss of habitat to crop cultivation is the dominant pressure to both plants and vertebrates. Further loss of habitat to housing and plantations follow as dominant pressures to vertebrates. Habitat degradation as a result of invasive alien plant species and poor rangeland management through too frequent burning and livestock overgrazing are major pressures to plant taxa. Hunting for bush meat and collection for the muthi trade, a consequence of the high concentration of human settlement within this biome is further threatening 29 vertebrate taxa.


145

9.7. Nama-Karoo


Plants Vertebrates


0 Extent (Km2) 249354 Livestock farming

34 Hunting 31

Well Protected Ecosystems 1 Natural 98% Gathering plants

21 Non-timber crops

25

Total count of Ecosystems 13 Croplands & old fields 1.3% Mining 13 Livestock farming

25

Threatened Plants 28 Plantation <1% Non-timber crops

7 Urban areas 22

Threatened Animals 27 Built up <1% Wood plantations

16

The Nama-Karoo biome lies to the west of the Grassland biome and makes up much of the arid interior of the country. It receives summer rainfall and is dominated by drought tolerant shrubs and C3 grasses.

Less than 2% of the Nama-Karoo biome has been lost to anthropogenic land use, the majority being croplands along the Orange River. There is no comprehensive land degradation dataset for the biome, but various studies indicate that degradation through overstocking of rangelands is extensive. Largely as a result of this lack of land degradation data, there are no threatened ecosystems in the biome, all 13 ecosystems types are listed as Least Concern.

From a protection point of view only two ecosystem types are Well Protected or Moderately Protected. An emerging threat to the biome is the construction of large renewable energy facilities in the last five years. Given its large size, there are relatively few threatened species (only 28 threatened plant taxa and 27 threatened vertebrate taxa) occurring within the Nama-Karoo biome. This is both due to low levels of species endemism in comparison to other biomes as well as low levels of habitat loss. Overstocking and poor rangeland management is a dominant pressure for both plants and vertebrates, with over-utilisation for medicinal use, and hunting and tramping of vertebrate species also contributing to increase in threat status for certain species. Overall however the arid and expansive nature of this biome means that it remains predominantly natural and most species are not declining.


146

9.8. Savanna


Plants Vertebrates




52

Well Protected Ecosystems 25 Natural 81% Urban areas 149 Hunting 52

Total count of Ecosystems 91 Croplands & old fields 15% Invasives 125 Urban areas 46

Threatened Plants 247 Plantation <1% Non-timber crops

119 Livestock farming

44

Threatened Animals 81 Built up 3% Wood plantations

41

The Savanna biome is the largest biome in South Africa, and covers the majority of sub-Saharan Africa. South African savannas span a wide rainfall gradient from the arid Kalahari savannas in the northwest to the mesic Zululand savannas in the east, which form part of the Maputaland-Pondoland-Albany biodiversity hotspot.

The dominant land cover impacts in the Savanna biome are croplands, which resulted in the loss of 14% of the natural habitat of the biome, followed by built-up areas (3.4%) – leaving approximately 81% of the biome in a natural / near natural state. There has been significant recent habitat loss in the biome with 3% of the natural habitat lost between 1990 and 2014, predominantly to (croplands and built-up areas drove this loss).

Of the 91 ecosystem types occurring in the biome, 11 are listed as threatened (two Critically Endangered, six Endangered, three Vulnerable) and cover 3% of the natural remaining habitat of the biome. In terms of protection, 44 of the 74 ecosystem types are Well Protected or Moderately Protected, making Savanna the 2nd best protected biome in South Africa. Large national parks such as Kruger and Kalahari Gemsbok drive this high protection level.

There are 247 threatened plant taxa and 66 threatened vertebrate taxa occurring within the Savanna biome, the main threats to these and other taxa of conservation concern is loss of habitat to crop cultivation (119 plant taxa and 52 vertebrate taxa impacted) and to urban development (149 plant taxa and 46 vertebrate taxa impacted). Large areas of the Savanna biome are used for livestock farming and this land use is the dominant pressure to plants in the biome threatening 165 taxa and also contributing to the extinction risk of 44 vertebrate taxa. Further habitat degradation caused by invasion by alien plants is contributing to 125 plant taxa being listed in a category of conservation concern. High concentrations of human settlement along the boundaries of some of the large Savanna national parks (especially Kruger National Park) is resulting in pressure to mammal species from hunting for bushmeat, traditional medicine and cultural regalia. The past decade has seen a rise in international wildlife trafficking syndicates that are beginning to heavily impact on species desired for overseas markets, including Rhinos (Ceratotherium simum and Diceros bicornis) and Pangolins (Smutsia temminckii).


147

9.9. Succulent Karoo


Plants Vertebrates




20

Well Protected Ecosystems 12 Natural 95% Non-timber crops

213 Hunting 20

Total count of Ecosystems 64 Croplands & old fields 4% Mining 172 Livestock farming

20

Threatened Plants 459 Plantation <1% Gathering plants

117 Urban areas 18

Threatened Animals 25 Built up <1% Mining 10

The Succulent Karoo biome lies in the arid winter rainfall, western parts of the country. It is a globally recognised Biodiversity Hotspot characterised by exceptional succulent plant diversity.

Approximately 5% of the Succulent Karoo biome has been lost to anthropogenic land use, the majority to croplands (3.7%). There is no comprehensive land degradation dataset for the biome but various studies indicate that degradation through overstocking of rangelands is widespread. Largely as a result of this lack of land degradation data, only two ecosystem types out of 64 are listed as threatened (two both Critically Endangered).

From a protection point of view, 26 ecosystem types are Well Protected or Moderately Protected. An emerging threat to the biome is the construction of large renewable energy facilities in the last five years. The Succulent Karoo biome, has the second highest number of threatened plants (459). The impact of degradation through livestock overstocking is reflected in the high number of endemic plant taxa threatened by this land use (342). Many plant taxa endemic to this biome are small succulent shrubs that are highly sensitive to trampling by livestock. Loss of habitat to crop farming severely impacts on endemic plant taxa restricted to ecosystems which have permanent rivers passing through them. Within what is predominantly an arid biome, these areas allow for extensive crop farming to take place along the river banks. Plants taxa endemic to alluvial flats around rivers are particularly severely impacted. Examples include areas of the Knersvlakte occurring in proximity to the Olifants River and many parts of the Little Karoo. Illegal harvesting of succulent plants to support the specialist horticultural trade and illegal collection of reptiles for the pet trade are contributing to the threatened status of 117 plant taxa and 20 vertebrate taxa respectively.


148

10. BENEFITS, TRENDS AND RISKS TO GENETIC DIVERSITY

Chapter 10: Tolley, K.A,, da Silva, J. & Van Vuuren, B. 2019. ‘Chapter 10: Benefits, Trends and Risks to Genetic Diversity’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

10.1. Preface For the first time, a genetic component has been included in the National Biodiversity Assessment (NBA).

Given that the NBA is aimed at assessing the status and trends of South Africa’s biodiversity, the genetic

component is not a review of literature relating to studies that have utilised genetic techniques to quantify

genetic diversity within individual (or groups) of South African taxa. Such studies are truly numerous and

there are already several literature reviews of the body of work (Linder et al. 2010; Lexer et al. 2013; Tolley

et al. 2014; Verboom et al. 2014). Furthermore, individual studies of genetic population structure (or higher

level diversity), while a valuable element of our baseline knowledge, were never meant to address status and

trends of genetic diversity for South Africa. The majority of existing literature relate to uncovering population

or species level differentiation and cover a single (or short) temporal point, providing a snapshot in time.

Therefore, it is not possible to amass the literature to assess trends of genetic diversity over time. Neither

can these studies provide an overall view of the ‘status of genetic diversity’ for South Africa because they are

not within a unifying framework; rather, they have report genetic patterns of various taxa within different

landscapes and across different time periods. Thus, this first addition of a genetic component to the NBA was

set up to highlight these issues, to motivate for a comprehensive framework, and to test the waters regarding

possible indicators. We first motivate why including genes as a fundamental component of biodiversity is

important, and discuss the factors that could pose a risk to maintaining genetic diversity. We then discuss

the need for a genetic monitoring framework that would guide research in South Africa that would speak to

a goal of understanding the status and trends of priority taxa on a national scale. Finally, we propose some

novel approaches for potentially tracking the erosion of genetic diversity on the landscape at a phylogenetic

level.

We recognise that there are many aspects of ‘genetic diversity’ that are not covered by the NBA. There are

additional taxa, other methods, and other objectives that could be developed in the future. We have also

only touched on many of the benefits of and risks to genetic diversity, such as the impact of genetically

modified organisms, or new developments relating to conservation genetics associated with CRISPR gene

editing (e.g. Phelps et al. 2019). Although we advocate for the establishment of a national genetic monitoring

framework, we do not propose the framework here. Rather, we suggest that such a framework is developed

through by a multi-stakeholder engagement, collaboration with global bodies such as the GEOBON Genetics

Working Group, and with careful consideration of all the possible indicators and approaches. Here, we review

some existing indicators and highlight a few new approaches that could be explored in the future.

10.2. Summary Life on earth relates directly to the diversity of genes in space and time. The genomes of organisms encode

the basic physiological, phenological, behavioural, and biological structures that define them, and allows

individuals to survive and persist through time in changing environments. To this end, DNA can best be

described as the foundation of all life on earth. Genetic diversity is recognised as an important component

of biodiversity (together with species diversity and ecosystem diversity). It can be defined as the amount of


149

variation observed in the DNA of distinct individuals, populations or species. The maintenance of this diversity

is of the utmost importance as genetic diversity equates to evolutionary potential and thus allows species or

populations to adapt to an ever-changing environment. Risks to genetic diversity include genetic erosion

through habitat fragmentation reducing population sizes and connectivity, hybridisation and inbreeding,

unsustainable use, and the disruption of co-adapted gene complexes and disease epidemiology through

translocations of individuals, and species extinctions. Genetically modified organisms also present a risk

through the escape of undesirable genes into native populations.

To recognise and minimise genetic erosion, genetic diversity should be monitored over time for a given

species or population. The value of long-term monitoring is well recognised, however, globally, there is a lack

of temporal genetic datasets, as well as a lack of genetic diversity indicators and thresholds, with which data

can be compared (such indicators have been developed, but lack specific genetic input; see Butchart et al.

2010, McGeoch et al. 2010). To date within South Africa, two short-term monitoring studies have been

carried out that explicitly monitor temporal shifts in the genetic diversity of South African taxa. These studies

serve as a baseline and provide valuable insight into ongoing monitoring programmes. To assist future

genetic monitoring programmes and studies, a genetic monitoring framework is required that outlines how

to prioritise species for monitoring, what genetic markers to use, how often populations should be

monitored, and which metrics to include. Moreover, such a framework will not only outline how genetic

diversity can be monitored at a population or species level, but be extended to include ecosystem and at

national levels.

In this chapter we present a case study using reptiles to track genetic diversity at the national level by

interrogating several high level metrics (e.g. over the landscape) as indicators of genetic erosion. The case

study analyses showed that the greatest historical impacts to phylogenetic richness for reptiles are in the

northeast (Limpopo, Mpumalanga, and Gauteng provinces), southwest (Western Cape Province) and the

coastal margin of KwaZulu-Natal. There are several hotspots of elevated genetic erosion in the last few

decades, in particular the uMkhuze or Ndumu region northwest of the iSimangaliso Wetland Park (KwaZulu-

Natal), the Komatipoort area (Mpumalanga), and the Sekhukhune district (Limpopo). Northern Gauteng and

the Soutpansberg area are also hotspots for increasing erosion of phylogenetic richness for reptiles. This case

study highlights the types of indicators that could be used for other taxonomic groups to track genetic

erosion.

Box 13. Key genetic diversity concepts

What is the gene pool?

Within the cells of every living creature resides the molecular building-blocks for life – DNA. The genetic make-up of individuals consists of tens of thousands of genes that each provides a coded set of instructions (or a DNA sequence) that are expressed to form the living organism itself. Every gene has slightly different variants, called alleles, which is why no two individuals are genetically the same (except for identical twins). The set of all alleles (gene variants) across a species constitutes the gene pool. That is, the gene pool contains all the different possibilities for coding all different varieties of individuals of a species. If there are many different alleles, genetic diversity is considered high. In some cases, alleles can be lost from the gene pool, lowering genetic diversity. Such a loss results in fewer types of individuals (less variation) within a species.

What is genetic diversity?

Genetic diversity is the range of all different alleles in a species or population. Species with high overall genetic diversity will have a range of individuals with different characteristics (different phenotypes). The presence of many different phenotypes is desirable because it lowers extinction risk for a species or population. When there are environmental changes, if many different types of individuals are present, there is a higher chance that some individuals will have a phenotype that can cope with the environmental change. If genetic diversity is low for a


150

species, there will be fewer different phenotypes, and this lowers the chance that many individuals will survive an environmental change.

What is genetic erosion?

The loss of genetic diversity over time from the gene pool of a species is termed ‘genetic erosion’. Genetic erosion can occur when population numbers substantially decline due to external environmental factors such as habitat loss or habitat fragmentation. When many individuals are lost from a population, the gene pool becomes reduced because different alleles are lost with the individuals.

Phylogenetic richness as a concept

Species richness refers to the total number of species in an area or across a landscape, and is a useful concept for understanding where areas of high biodiversity occur. Similarly, the phrase ‘phylogenetic richness’ can be used to describe the spatial pattern of phylogenetic diversity. It can be measured by creating a phylogenetic tree for a family, and the overall phylogenetic diversity can then be cross-referenced to the distribution of each species in the phylogeny. This allows for areas of high phylogenetic diversity (i.e. phylogenetic richness) to be mapped spatially, akin to a map of species richness.

10.3. Introduction Genetic diversity is recognised as an important component of biodiversity, together with species diversity

and ecosystem diversity19. Genetic diversity is commonly defined as the amount of variation observed in the

DNA of individuals, populations or species. The maintenance of the diversity is of the utmost importance as

genetic diversity equates to evolutionary potential and resilience (allowing species or populations to adapt

to ever-changing environments). There are a number of factors that could cause genetic erosion by lowering

diversity or shifting the frequency of alleles. The former puts species or populations at risk by lowering their

resilience to environmental change. The latter is risky because populations or species have typically

undergone natural selection for alleles that produce phenotypes that are well-adapted to their natural

environment. Artificially manipulating allelic composition can produce individuals that are less well-adapted,

putting them at risk.

Lower genetic diversity (i.e. genetic erosion) can occur due to habitat fragmentation and loss of connectivity,

which disrupts metapopulation processes and decrease effective population sizes. That is, gene flow is

impeded by unsuitable (e.g. artificial, transformed) habitat, and the small remaining population fragments

then undergo inbreeding, which can result in the loss of alleles. Inbreeding can also occur in any artificial

circumstance where a population has been restricted to an area and/or cut off from other populations (e.g.

game fences). Conversely, genetic hybridisation involves the successful mating of two individuals that are

genetically well-differentiated (genetically distinct), and can occur when animals or plants from one area are

intentionally or unintentionally brought into another. This can result in offspring that have lost important

alleles that are adaptive for a particular environment. These less desirable alleles can be carried further into

a local population through additional mating. The result is that maladapted alleles spread into a population

or species beyond the original hybridisation event (‘introgression’). Both issues have been highlighted for

commercial game breeding and the horticultural industry, as well as the introduction of variants into wild

populations outside their natural range (Jansen Van Vuuren et al. 2019).

Genetically modified organisms (GMOs) also pose a risk to genetic diversity. GMOs typically refer to any living

organism that possesses a novel combination of genetic material generated through the use of modern

biotechnology. That is, they have novel combinations of alleles that have been artificially selected for a

particular trait that is seen as beneficial. These alleles could spread to, or ‘contaminate’, local populations

resulting in hybrids or introgression. This genetic contamination puts local populations at risk because they

19 The NBA 2018 Genetic Report includes a more comprehensive introduction to genetic diversity.


151

could lose alleles that are adaptive for their particular environment, which could lead to loss of traditional

plant varieties or impact negatively on the closely related/wild relatives. South Africa does not have any wild

relatives of our typical genetically modified crops (maize, soybean, cotton), however, traditional varieties of

some genetically modified crops do exist and could be impacted (notably sorghum).

Overall, extrinsic benefits to protecting genetic diversity include genetic rescue, biobanking, applications in

forensic sciences, barcoding of species (known and unknown), bioprospecting, ethnobotany and indigenous

knowledge, and tourism to biodiversity hotspots. Risk factors include those that that increase inbreeding by

reducing population size or decreasing connectivity among populations such as habitat fragmentation; and

those that encourage the transfer of undesirable genes such as hybridization or contamination from

genetically modified organisms, among others. Clearly, protection and management of genetic diversity

underpins the conservation of our entire floral and faunal biodiversity. In spite of these, ecosystems and

species typically form the basis of local, national and global conservation plans as well as strategies for the

protection of biodiversity (e.g. Driver et al., 2012; Schmeller et al., 2015). Therefore, assessment of

biodiversity status and trends classically focuses on threats to species richness and ecosystem function (Vane-

Wright, Humphries and Williams, 1991) rather than focus or include genetic diversity aspects. Although genes

are also recognised as a fundamental component of biodiversity, genes are typically not used to inform

biodiversity planning because of the difficulty in quantifying genetic diversity on the landscape (Scholes et

al., 2012). Despite this, genetic diversity is clearly linked to ecosystem function, evolutionary potential and

species resilience (Hughes et al., 2008; Cardinale et al., 2012) and as such, the inclusion of genetic information

brings a valuable and much needed component to biodiversity assessments.

10.4. Assessing genetic diversity Although measuring trends in genetic diversity could be relatively straightforward when considering

individual populations or species (Hoban et al. 2014; da Silva & Tolley, 2018), this is rarely done. Despite the

ease of such studies, there are a number of factors that make them intractable. First, existing studies are

typically point estimates of genetic diversity (designed as population studies) and are not intended to be

carried out over multiple generations (which would allow an assessment of trends). Furthermore, there is

currently no unifying framework for assessing trends in terms of sampling, relevant markers, and species

choice, which makes comparisons difficult. Perhaps more problematic is that genes typically respond on an

evolutionary timescale whereas biodiversity loss occurs on an ecological or generational timescale. That is,

populations or species might decline or become extinct before the loss of genetic diversity is detected,

making genetic diversity a poor indicator for understanding short-term biodiversity trends. In some cases,

populations or species of concern can show increases in abundance, which might superficially suggest they

are not at risk – and yet their genetic diversity could have been eroded and will take decades, centuries or

even millennia to recover. Finally, the logistics of monitoring large numbers of species is not practical and

there is therefore a need to identify indicator species or approaches that allow for sets of species to be

monitored. Regardless, monitoring changes in genetic diversity over time is essential to understand and track

the effects of genetic erosion.

10.4.1. Monitoring at a species level

Despite the general lack of studies that monitor genetic diversity over time, some examples do exist. For

example, a short-term genetic monitoring study for a Critically Endangered amphibian (Rose’s Mountain

Toadlet: Capensibufo rosei) was set up to test the utility of genetic markers and to propose a framework for

genetic monitoring of priority species (da Silva & Tolley 2018). This species, found only in the Table Mountain

National Park, has undergone an enigmatic decline with the disappearance of several breeding populations


152

(Cressey, Measey & Tolley 2014). Using a suite of microsatellite markers, a short-term genetic monitoring

study was carried out to investigate whether genetic diversity had been eroded, possibly linked to the

population decline. The baseline for genetic diversity was set up in 2011, with a second sampling period in

2015, and allelic richness was used as an indicator (Hoban et al. 2014). When accounting for rarefaction,

allelic richness is essentially unchanged over this short time period of time with no significant differences

across sampling years (da Silva & Tolley 2018). The level of genetic diversity for this species was similar to

that found for other amphibians, suggesting that the loss of local breeding sites has not yet impacted the

genetic diversity of the species overall. However, estimates of genetic diversity are lacking for this species

prior to the decline. If genetic erosion occurred due to the decline, it will not be possible to detect because

of the short time-frame (Hoban et al. 2014). Nevertheless, there is now a baseline from which this species

can be monitored into the future, particularly with regards to loss of unique or rare alleles which would be a

warning sign of decline. Likewise, the plethora of genetic studies for a variety of South African species that

report genetic diversity estimates (even if these were not the main aim of these studies) can be used as

baseline data for future monitoring efforts.

10.4.2. Monitoring at a landscape level

While longer-term genetic monitoring of changes in allelic richness is an important aspect for tracking genetic

erosion, it is limited in scope because it can be practically applied to only a few species and/or populations.

Furthermore, changes in allelic richness may not be apparent on the same time scale on which ecological

changes are occurring. It is therefore important to assess additional potential indicators for tracking genetic

erosion, yet this approach has remained elusive, despite landscape level genetic diversity playing a central

role in creating species richness, as well as underpinning ecosystem function and species resilience (Hughes

et al. 2008; Cardinale et al. 2012). Therefore, changes in genetic diversity should be considered essential to

assess and monitor, given the unprecedented anthropogenic impact on the landscape. Due to the inherent

problems associated with monitoring landscape level genetic diversity (Winter, Devictor & Schweiger 2013),

surrogate metrics were investigated to track genetic diversity over the landscape, using reptiles as a test case

(Tolley & Šmíd 2019).

First, the widely used metric, phylogenetic diversity (PD), is considered an excellent indicator to ascertain

the spatial distribution of genetic diversity at national and global scales across large regions or landscapes

(Forest et al. 2007; Winter, Devictor & Schweiger 2013; Frishkoff et al. 2014; Jetz et al. 2014). For a given

taxonomic group with a comprehensive phylogeny, PD for a geographic region is simply the additive branch

lengths of all taxa in that region, from the tips to root (Faith 1992, 2010). Phylogenetic diversity (PD) is robust

to taxonomic uncertainty, because lineages need not be described species, but are simply distinct tips in the

phylogeny (Mace, Gittleman & Purvis 2003). This metric can be used to identify regions of high genetic

diversity despite an insufficient taxonomic framework. Although seldom employed in actual practice, PD has

been advocated as being useful to guide conservation priorities or to identify areas that have lost genetic

diversity due to anthropogenic impacts (Frishkoff et al. 2014).

Similarly, evolutionary distinctiveness (ED) is a measure of uniqueness within a given phylogeny based on

branch lengths (Vane-Wright, Humphries & Williams 1991) but can also be evaluated spatially (Jetz et al.

2014). Evolutionary distinctiveness (ED) therefore, is useful to identify regions that hold numerous unique

taxa. For example, a global analysis of the Class Aves shows that high ED is not distributed randomly, but has

clusters of unique lineages in isolated regions e.g. Australia, New Zealand and Madagascar (Jetz et al. 2014).

The EDGE metric (Evolutionarily Distinct and Globally Endangered) is an extension of ED that incorporates

phylogenetic uniqueness (long branches in a phylogeny) with level of extinction risk, and can be mapped


153

spatially to examine whether certain regions have a high number of unique but threatened species (Isaac et

al. 2007; Tonini et al. 2016).

Weighted phylogenetic endemism (PE) incorporates both evolutionary history and spatial information,

combining species range size (endemism) with phylogenetic diversity allowing for identification of areas that

that have spatially restricted, highly divergent species (Rosauer et al. 2009; Rosauer & Jetz 2015).

These metrics were estimated for the South African reptiles as proxies for ‘phylogenetic richness’ (Tolley &

Šmíd 2019) and the spatial distribution of the metrics were overlain with spatial distribution of habitat loss

(i.e. national land cover 1990 and 2014). In this case, the genetic metric (e.g. PD) is not tracked over time,

but the ‘pressures’ driving the change are instead tracked. Specifically, land cover has been quantified over

time, and when combined with the genetic metric, a spatial index can be created to show areas that are

sensitive to genetic erosion. The result is a spatial representation of where phylogenetic richness (using any

metric relevant as a proxy) is high and also land cover impacts are high (for 1990 and 2014). Specifically, the

amount of phylogenetic richness contained in areas that are heavily transformed by human activities (e.g.

agriculture, urbanisation, mining) at one timepoint would provide an indication of the threat on areas of high

phylogenetic richness (e.g. high PD), because we can assume that species become locally extinct as their

habitat is lost. This allows for an evaluation of areas that are sensitive to genetic erosion, at our initial

timepoint (1990) and to track the rate of change for this sensitivity by comparing timepoints (1990 versus

2014).

10.5. Findings

10.5.1. The spatial distribution of South Africa’s phylogenetic richness for reptiles

Using reptiles as the test case, phylogenetic richness (using PD as the proxy) was found to be highest in the

north-eastern margin of South Africa (Figure 57). This is primarily due to the area being a contact zone for

temperate or sub-tropical fauna mainly found to the south, and tropical species mainly found to the north.

Phylogenetic diversity (PD) is also relatively high in the southwest of South Africa, with the arid interior

showing the lowest PD for reptiles.


154

Figure 57. Phylogenetic diversity for reptiles from South Africa. Darker shading indicates areas with higher phylogenetic diversity.

10.5.2. Application of an indicator for changes in phylogenetic richness

We intersected the 1990 national land cover with phylogenetic richness (using PD as a proxy) to create a

‘phylogenetic richness index’ (Tolley & Šmíd 2019). This index showed that the most impacted areas are in

the northeast and extreme southwest (Figure 58a). These areas can be considered as having undergone

‘genetic erosion’ as compared to the historical natural state. It should be noted that this map does not

represent true genetic erosion, because some species survive in a partly transformed landscape. However,

these areas should be considered at risk of genetic erosion and highly sensitive to further changes relating to

habitat loss. Most of the areas correspond with high land use or centres of high population density e.g. near

Johannesburg, Tshwane, Cape Town and eThekwini. There are several additional regions that are notable,

including central Limpopo and northern Mpumalanga particularly near Sabi and Mbombela.

Figure 58. Spatial distribution of the areas that show the most genetic erosion to phylogenetic richness for South African reptiles (using phylogenetic diversity as a proxy for phylogenetic richness) at a) 1990 b) 2012. Darker blue shading shows cells with the highest values.

(a) (b)


155

The intersection of PD with 2014 national land cover shows a very similar pattern to the 1990 national land

cover, suggesting that most of the changes to land cover had already occurred by 1990 (Figure 58b). However,

there were some notable areas in which the ‘genetic erosion’ was prominent. Principally, these are found in

southern Limpopo and northern Gauteng, as well as eastern Mpumalanga and KwaZulu-Natal provinces

(Figure 59). Noteworthy, there are six clusters of cells with high phylogenetic richness. Cluster 1 is in the area

of Shoshanguve and Hammanskraal in Gauteng Province. Cluster 2 (Matlerekeng) and Cluster 3 (Sekhukhune

district) are both in Limpopo Province. Cluster 4 is situated south of Komatipoort in Mpumalanga, whereas

Cluster 5 is in northern KwaZulu-Natal in the area near Mkhuze or Ndumu, northwest of the iSimangaliso

Wetland Park. Essentially, these areas show an increasing trend of genetic erosion for reptiles.

Figure 59. Spatial distribution of the areas that show the most genetic erosion (using phylogenetic diversity as a proxy for phylogenetic richness) from 1990 to 2014. Darker blue shading shows cells with the highest values.

The proportion of each specific land use category that contributed to the six clusters was extracted for 2014,

to investigate which type of land use is the greatest pressure to reptile phylogenetic richness (Figure 59). For

Clusters 2 and 3 located in or near Gauteng Province, increasing urban expansion had the greatest affect.

Increasing cultivation shows the greatest impact for Clusters 1 & 4–6, although Cluster 5 in northern

KwaZulu-Natal shows a mixture of land use types that affect phylogenetic richness, primarily urbanisation

and cultivation but also an increased land use from plantations (Figure 60).


156

10.5.3. Additional potential indicators of

genetic change

While PD is considered a good metric for

understanding phylogenetic patterns on the

landscape, a number of other metrics are also

available (Tucker et al. 2016), and the utility of some

was tested for reptiles (Tolley & Šmíd 2019). These

included Evolutionary Distinctiveness (ED), which

accounts for unique species in the phylogeny, as well

as the EDGE metric (Evolutionarily Distinct and

Globally Endangered) which accounts for the unique

species that are also considered threatened (using

their 2018 IUCN Threat status). Of the top 5% ED

scoring species, all are Least Concern species, and one

was Not Evaluated for IUCN as it is considered

peripheral in distribution for South Africa. The top

scoring EDGE species (weighted for threat status) did not correspond with the list of top ED species, although

given that the ED species were all Least Concern, we would expect the two lists to be different. The

northeast region of South Africa was shown to be richest in ED species (Figure 61a) with coastal regions of

KwaZulu-Natal highest in EDGE species richness (Figure 61b).

Figure 61. Spatial distribution of a) evolutionary distinctive reptile species, and b) EDGE reptile species.

Another spatial diversity metric that was assessed was weighted phylogenetic endemism (PE). Like

phylogenetic diversity, PE incorporates both evolutionary history and spatial information, combining species

range size (endemism) with phylogenetic diversity allowing for identification of areas that that have spatially

restricted, highly divergent species (Rosauer et al. 2009; Rosauer & Jetz 2015). Similar to PD, PE is high in the

northeast, although the highest values occur in the northwest in the area of the Richtersveld National Park

(Figure 62). The high values in the northeast, however, are biased because some non-endemic species are

more widespread to the north (outside South Africa) yet have small range sizes in South Africa. That is, they

are not true endemics, although the analysis treats them as such. Despite this, for South Africa, they do have

limited ranges and if considering only South African phylogenetic richness, the pattern can be considered an

acceptable assessment of PE.

Figure 60. The proportion (extent) of each major land cover category for each of the cells within the six clusters that show the highest change.

(a) (b)


157

Figure 62. Phylogenetic endemism for reptiles from South Africa. Darker shading indicates areas with higher phylogenetic endemism.

Phylogenetic endemism is most impacted in the northeast and extreme southeast at both time periods,

similar to phylogenetic diversity (Figure 63). Although PE was highest in the northwest (Figure 64), that area

shows essentially no land use change, as much of the area is within the Richtersveld National Park, or is

otherwise low in population density. The top 5% of cells however, are concentrated in northeastern

KwaZulu-Natal near the northern section of iSimangaliso Wetland Park. This suggests that phylogenetic

endemism for reptiles has undergone ‘genetic erosion’ primarily in this area.

Figure 63. Spatial distribution of the areas that show the most genetic erosion to phylogenetic richness for South African reptiles (using phylogenetic endemism as a proxy for phylogenetic richness) at a) 1990 b) 2014. Darker blue shading shows cells with the highest values. The pattern for the two time periods is very similar.

(a) (b)


158

The cells with the highest values for the change in phylogenetic richness are found in the northeast, similar

to PD (Figure 64). The top 5% of cells however, are concentrated in northeastern KwaZulu-Natal near

northern section of iSimangaliso Wetland Park. This suggests that phylogenetic endemism for reptiles has

undergone ‘genetic erosion’ primarily in this area.

Figure 64. Spatial distribution of the areas that show the most genetic erosion (using phylogenetic endemism as a proxy for phylogenetic richness) from 1990 to 2014. Darker blue shading shows cells with the highest values.

10.5.4. Phylogenetic richness in Protected Areas

To investigate whether phylogenetic richness is preserved in protected areas, PD was intersected with the

South African protected area network. The top 10% of the highest PD values were then mapped within the

protected areas. This allowed us to visually assess which protected areas contain the highest reptile

phylogenetic richness and are therefore essential to conserve into the long term. The results showed that

the protected areas in the northeast of South Africa capture the highest levels of phylogenetic richness

(Figure 65). Indeed, the highest 10% of PD values are captured in protected areas found in Limpopo and

Mpumalanga provinces as well as northern KwaZulu-Natal Province. These protected areas, in particular

Kruger National Park, Blouberg East, Wolkberg, Blyde River Canyon and Tembe Elephant Reserve, should be

regarded as particularly important in conserving the phylogenetic richness of South Africa’s reptiles.


159

Figure 65. Intersect between Protected Areas (grey shading) and phylogenetic diversity (highest 10% of PD values), shaded in a blue gradient.

As an exploration, the phylogenetic richness was intersected with the protected area network. While we do

not expect this change index to reflect within the protected areas (presuming no land cover change within

the protected areas), those protected areas that are in areas of high PD but have high land use change outside

their borders could be identified. The results showed that some important protected areas are likely

influenced by land use change on their borders (Figure 66, Figure 61). These include protected areas in

Limpopo, Gauteng, Mpumalanga and KwaZulu-Natal province.

Figure 66. Intersect between Protected Areas (shown in grey) and highest 50% of values for phylogenetic richness index shaded in blue for a) the northern provinces of Limpopo, Gauteng and Mpumalanga, and b) KwaZulu-Natal Province.

(a) (b)


160

10.6. The Way Forward Currently, a National Biodiversity Monitoring Framework (NBMF) is being developed for South Africa. This

framework will speak to high-level reporting requirements across all levels of biodiversity, drawing from the

CBD’s targets, as well as South Africa’s national biodiversity policies (namely the Biodiversity Act and the

National Biodiversity Strategy and Action Plan [NBSAP]). A critical component of this framework will be

identifying key monitoring indicators, which will be in line with international monitoring standards and

guidelines, such as the Essential Biodiversity Variables being developed by GEOBON

(https://geobon.org/ebvs/what-are-ebvs/). Embedded within this will be a careful evaluation of existing gaps

around these indicators.

A genetic monitoring framework or guidance document will be a component of the NBMF. Ideally, the

framework will outline how genetic diversity can be monitored at a national, ecosystem, species and

population level. Although a national or ecosystem measure of genetic diversity can be hard to contemplate,

if one understands that at the heart of all biodiversity monitoring are population and species-level

assessments, such high-level metrics or indicators are not impossible. We may simply need time and data to

realise or conceptualise what the best indicators could be. At this time, allelic richness for priority species

tracked over time is a consideration for species level monitoring. At the landscape level, indicators are more

difficult to conceptualise, but phylogenetic based indicators that incorporate land cover changes could be a

possibly. Clearly, losses in genetic diversity and phylogenetic richness are expected globally given current

extinction risk levels (Huang, Davies & Gittleman 2012). The lack of a national framework for genetic

monitoring, as well as there being a number of challenges in collecting the necessary data and the scale of

the follow-up analyses make a comprehensive assessment of trends in genetic diversity elusive to date.

Our landscape level approach allows for identification of areas most impacted with regards to phylogenetic

richness, and a means to track the trends given land cover changes. While we have focussed on only one

taxonomic group, this model could be applied across taxonomic groups to better understand broad trends.

For example, groups with nearly complete phylogenies and detailed distribution maps such as birds,

mammals or some plant groups could be analysed readily. By examining congruent patterns of ‘genetic

erosion’ across taxa, a more comprehensive understanding of the status and trends for phylogenetic richness

could be gained. Such an analysis would be useful for informing the prioritisation of conservation efforts. The

method could also be valuable in tracking whether ‘genetic erosion’ might occur at different rates in the

future, or if the areas of greatest impact shift spatially as land use patterns change.

This approach is logistically and financially feasible to apply, as much of the DNA sequence data already exists,

and data gaps can be filled in with relatively little effort. There are reasonably good occurrence records for

many taxonomic groups, which can be used to produce the use of species distribution models to inform the

distribution maps needed for this approach. Essentially, the approach provides an achievable means for

tracking the status and trends to phylogenetic diversity at the landscape level.

The method could also be extended easily to other geographic realms. For example, the marine realm has

been extensively mapped for impacts in South Africa (Sink et al. 2019) and application of this method to some

marine taxonomic groups could be informative as to areas of concern regarding ‘genetic erosion’.

Furthermore, the method could be used to understand whether South Africa’s Critical Biodiversity Areas

(CBAs), Marine Protected Areas (MPAs) or the South African National Protected Area Expansion Strategy will

be instrumental for safeguarding genetic diversity into the future.

An interesting possible extension to using the phylogenetic metrics of biodiversity to track changes over time

across the landscape would be to employ species distribution modelling to project species distributions at


161

future time slices. Similar approaches are commonly applied in estimating species range dynamics and

response to future global climate changes (Burrows et al. 2014; Molinos et al. 2016). A drawback, however,

is that while models account for changes that relate to the climatic variables (e.g. mean annual temperature,

precipitation etc.) they do not account for projections of human-induced changes to habitats, nor account

for a species’ ability to track shifting suitable habitat. This could prove difficult to predict, although rates of

change have been estimated for South Africa (Skowno et al. 2019) and potentially could be extrapolated into

the future.

In some cases, species ranges have well-recorded recent shifts, expansions or contractions. For example,

some avian fauna for South Africa (Hockey et al. 2011; http://sabap2.adu.org.za/) have clearly documented

range shifts that would impact the landscape phylogenetic richness patterns. If historical ranges are known,

and new ranges are documented, this presents and exciting opportunity to ‘back-cast’ changes in landscape

phylogenetic richness as well as monitor changes into the future. In some cases, vegetation shifts have been

documented, particularly where desertification, bush encroachment or climate change has influenced

species assemblages (e.g. Moncrieff et al. 2015; Slingsby et al. 2017). These types of distribution changes

could have a profound impact on phylogenetic richness patterns, and the incorporation of recorded range

shifts into these methods could be used to track the trends of e.g. phylogenetic diversity on the landscape

(even from a time-point in the past to the present day). Such ranges shifts contextualised in a phylogenetic

framework as done here, could be used to document true genetic erosion of the landscape where ranges

have contracted, or perhaps even increases in phylogenetic diversity where species assemblages have

changed. However, detailed data on past and present ranges, as well as species assemblage changes, would

be needed for such an endeavour.

There are a number of very interesting extensions to our proposed methods for examining impacts to

phylogenetic richness on a national or landscape level. With better data on distributions (either static or

shifting distributions) and accompanying phylogenetic information, in combination with detailed maps and

information on land cover changes, it could be possible to track these impacts over time, allowing for

phylogenetic diversity or richness to become an important and informative feature for biodiversity

assessments and planning.

10.6.1. Critical gaps

Genetic monitoring indicators, thresholds and prioritisation of species;

Long-term genetic monitoring datasets for priority species;

Additional taxonomic groups for landscape level analyses;

Testing of additional phylogenetic metrics;

Additional analysis of pressures (land cover types) with other phylogenetic metrics;

Analysis of protection status (e.g. protected areas) with additional phylogenetic metrics;

Analysis of Critical Biodiversity Areas and the National Protected Area Expansion Strategy as

measures to safeguard genetic diversity;

Investigate potential for using recorded range shifts, assemblage shifts, and/or species distribution

modelling to track trends of landscape level ‘genetic erosion’ (and increases in landscape level;

genetic diversity) or to project areas that might undergo genetic erosion in the future; and

Incorporation of landscape level genetic diversity into biodiversity assessments and planning.



162

11. SECTOR ACTIONS AND RESPONSES

Chapter 11: Skowno, A.L., Daniels, F., Driver, A., Midgely, G., Foden, W., Stevens, N., Van Wilgen, B.W., Wilson, J.R., Faulkner, K.T., Mnikathi, Z., Morapi, T., Munyai, T., Rahlao, S., Zengeya, T.A., Poole, C.J. & Pfab, M. 2019. ‘Chapter 11: Responses to Pressures’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

This chapter provides an overview of some of the main biodiversity sector responses to key pressures on

biodiversity. The intension is to link the assessment findings to actions and processes within the biodiversity

sector, it is not a full treatment of responses across all sectors.

The three-way action plan for biodiversity is described in the National Biodiversity Framework as: (i) Avoid

further loss and ensure sustainable resource use (e.g. harvesting of wild populations, rangeland ecosystem

management, catchment management, biodiversity assessment and planning etc.); (ii) Protection of

biodiversity (including protected areas expansion and stewardship programs, in situ conservation, seed

banks etc.); and (iii) Restore ecosystems and promote species recovery and/or reintroductions (including

Ecosystem-based Adaptation, control of biological invasions through pathway management, species

management and area management)

The following responses to pressures on biodiversity are addressed in this section:

Spatial biodiversity planning;

Scientific Authority functions;

Species and ecosystem management plans;

Protected areas and biodiversity stewardship;

Control measures for biological invasions; and

Climate change responses.

11.1. Spatial biodiversity planning The identification of spatially explicit priority areas to inform land-use planning and decision making in South

Africa spans back to the late 1990s. Over the last two decades South Africa has developed a community of

practice around biodiversity planning through the annual Biodiversity Planning Forum and other learning

networks. This community of practice has enabled the development of biodiversity plans that have

standardised spatial biodiversity planning products, i.e. a map of Critical Biodiversity Areas and Ecological

Support Areas.

A Critical Biodiversity Area Map is a spatial plan for ecological sustainability. It identifies a set of biodiversity

priority areas, called Critical Biodiversity Areas (CBAs) and Ecological Support Areas (ESAs), which, together

with Protected Areas, are important for the persistence of a viable representative sample of all ecosystem

types and species as well as the long-term ecological functioning of the landscape as a whole (SANBI 2017b).

These maps are a form of strategic planning for the natural environment, providing a coherent and

systematically identified set of geographic priorities to inform planning, action and decision making in

support of sustainable development.

Critical Biodiversity Areas are areas required to meet biodiversity targets for ecosystems, species and

ecological processes, as identified in a systematic biodiversity plan. Ecological Support Areas are not essential

for meeting biodiversity targets but play an important role in supporting the ecological functioning of Critical

Biodiversity Areas and protected areas and/or in delivering ecosystem services. The primary purpose of a


163

map of Critical Biodiversity Areas and Ecological Support Areas is to guide decision-making about where best

to locate development. The maps are designed for use by a range of sectors, for example to inform land use

planning, environmental authorisations, agricultural authorisations, mining authorisations, water use

licencing, and other decisions that impact on the use and management of natural resources.

Since 2016, every province in South Africa has provincial-wide maps of CBAs and ESAs. The development of

these plans are usually led or commissioned by the provincial conservation authority. Table 29 summarises

current progress on the development of provincial spatial biodiversity plans or maps of CBAs and ESAs. Some

provinces have a longer history of producing spatial biodiversity plans, while other provinces have completed

their first plan more recently.

Table 29. Summary of Provincial maps of CBAs and ESAs produced to date.

Province Name of provincial spatial biodiversity plan

Lead agency First available Most recent update

Eastern Cape Eastern Cape Biodiversity Conservation Plan

Department of Economic Development and Environmental Affairs (DEDEA)

2007 2017

Free State Free State Biodiversity Conservation Plan

Department of Economic Development, Tourism & Environmental Affairs (DEDTEA)

2013 —

Gauteng Gauteng C-Plan (current version 3.3)

Department of Agriculture & Rural Development (GDARD)

2003 2005 (v2), 2011 (v3)

KwaZulu-Natal KZN Biodiversity Conservation Plan

Ezemvelo KZN Wildlife 2002 (v1) 2004 (v2), 2010 (v3)

Limpopo Preliminary Biodiversity Conservation Plan for Limpopo

Department of Economic Development, Environment & Tourism (LEDET)

2011 2013

Mpumalanga Mpumalanga Biodiversity Conservation Plan

Mpumalanga Tourism & Parks Authority (MTPA)

2007 2014

North West North West Biodiversity Conservation Assessment

Department of Economic Development, Environment, Conservation & Tourism (DEDECT)

2009 2015

Northern Cape Northern Cape Biodiversity Sector Plan

Department of Environment & Nature Conservation (DENC)

2011 (only for the Namakwa District)

2017

Western Cape Western Cape Biodiversity Framework

CapeNature 2009 (Fine scale plans for 9 municipalities)

2014 , 2017

Some metropolitan municipalities have developed their own maps of CBAs and ESAs at a finer spatial scale

than the provincial map. Metros that have developed their own spatial biodiversity plans are Nelson Mandela

Bay Municipality and the City of Cape Town. In these cases, the most recent version of the metro’s spatial

biodiversity plan was fed into the provincial map of CBAs and ESAs prior to finalisation. A map of CBAs and

ESAs should be accompanied by land use guidelines linked to the categories on the map, and may be referred

to as a Biodiversity Sector Plan. It may also be formally published as a bioregional plan in terms of the

Biodiversity Act, but need not be. Figure 67 illustrates metros or municipalities that have gazetted their

spatial biodiversity plans as bioregional plans, or are in the review process to submit their spatial biodiversity

plans for gazetting.


164

Figure 67. Map of municipalities (metro, local or district) that have either published a bioregional plan, or are in the review process with the intention to publish a bioregional plan.

Spatial footprint of CBAs and ESAs in the landscape

The footprint of CBAs and ESAs across South Africa varies provincially depending on a range of reasons (Figure

68), including the scale of the data used to identify CBAs and ESAs and the degree of habitat modification

that has taken place within a province. Consequently, SANBI published the Technical Guidelines for CBA Maps

in 2017. This document was developed to ensure an appropriate degree of consistency between CBA maps

in different parts of South Africa, given that provinces lead the development of their own spatial biodiversity

plans.

Currently, the CBA and ESA area in the Eastern Cape, KZN and Limpopo are the highest in terms of their

spatial footprint across the country (Table 30), with the Western Cape and Mpumalanga having the lowest

area identified as CBA. The low area of CBA within provinces should not be equated to low biodiversity value,

provinces like Mpumalanga and the Western Cape have high levels of biodiversity but much of the priority

biodiversity has already been lost to mining, agriculture, urban sprawl, etc.

At a biome level, the proportion of biomes that are CBA and ESA are shown in Table 31. While the Desert and

Forest biomes look like they have a proportionally higher in CBA percentage, these biomes occupy less than

1 percent of the country each. Fynbos, Grassland and Indian Ocean Coastal Belt biomes are under high

development pressure and have 25-30% of their area identified as CBA areas.


165

Figure 68. Map of Critical Biodiversity Areas and Ecological Support Areas for the country.

Table 30. The proportion of Critical Biodiversity Areas and Ecological Support Areas per province. The CBAs together with the Protected Area footprint make up the priority biodiversity within a province.

Province Total (km2) CBAs (km2) ESAs (km2) %CBA %ESA

Eastern Cape 170056 93340 28049 55 16

Free State 130838 15400 68525 12 52

Gauteng 18394 5143 2990 28 16

KwaZulu-Natal 96452 42043 12931 44 13

Limpopo 127948 50193 29435 39 23

Mpumalanga 78115 14534 3768 19 5

North West 105373 30496 28952 29 27

Northern Cape 372950 106448 52654 29 14

Western Cape 129441 28598 16591 22 13


166

Table 31. CBA and ESA percentages per biome.

Biome Biome extent (km2)

Percentage of South

Africa

CBA extent (km2)

CBA as percent of

biome

ESA extent (km2)

ESA as percent of biome

Albany Thicket 35263 3 20277 58 5443 15

Desert 6378 0.5 4323 68 285 4

Forests 4827 0.4 2322 48 1098 23

Fynbos 82171 7 23250 28 13218 16

Grassland 365672 28 101109 28 107890 30

Indian Ocean Coastal Belt 11487 0.9 3161 28 2499 22

Nama Karoo 250662 20 68424 27 38544 15

Savanna 408071 32 100795 25 71253 17

Succulent Karoo 79282 6 33188 42 12869 16

Azonal Vegetation 26244 2 11788 45 6810 26

How do maps of CBAs and ESAs influence land-use planning and decision making?

Critical Biodiversity Areas (CBA) maps are given legal force through the National Environmental Management

Act (Act 107 of 1998), Environmental Impact Assessment Regulations 201420. Listing Notice 3 of the EIA

Regulations specifies geographic areas that trigger environmental authorisation processes, including CBAs

identified in a bioregional plan or in a spatial biodiversity plan that has been adopted by the relevant

authority. Figure 67 shows a map of municipalities or metros that have or are intending to gazette CBA and

ESA maps as bioregional plans. In addition, in 2017 the Northern Cape CBA map was officially adopted by the

Northern Cape Department of Environment and Nature Conservation (NC DENC) and in 2018 the Eastern

Cape started the process to have their CBA and ESA map adopted by the provincial government. Adoption or

gazetting helps ensure that maps of CBAs and ESAs have force in environmental authorisations for a range of

land-use activities. Additionally, CBAs and ESAs are included in the Department of Environmental Affairs’

Environmental Screening Tool as features of very high sensitivity, thus making the development protocol

associated with the application for environmental authorisation stricter than areas that are not identified as

CBAs. This screening tool has additional species related spatial data to guide specialists. This tool is proposed

to become a legal screening instrument in 2019. Critical Biodiversity Areas (CBA) maps are also often a key

informant for Environmental Management Frameworks, which are spatial tools developed in terms of the

Biodiversity Act to proactively identify areas that require varying levels of environmental authorisation.

More recently, maps of CBAs and ESAs have been used widely in national-scale Strategic Environmental

Assessments (SEAs). These SEAs have been in support of rolling out South Africa’s 2030 National

Development Plan (NDP) to eliminate poverty and reduce inequality. The NDP identified 18 Strategic

Integrated Projects (SIPs) to help unlock the South African economy. To date, the maps of CBAs and ESAs

have been used to guide the placement of the Electricity Grid Infrastructure and Phased Gas Pipeline

corridors, influenced the Shale Gas SEA and used as an exclusion area (CBA 1 only) in the identification of

refined focus areas for the phase 2 Wind and Solar (Photovoltaic) SEA.

20 Hereafter referred to as the ‘EIA Regulations’.


167

11.2. Scientific Authority functions The Scientific Authority is established in terms of Section 60 of the Biodiversity Act. The purpose of the

Scientific Authority is to assist with regulating and restricting trade in specimens of TOPS-listed and

CITES-listed species. This is achieved through a scientific and professional review of available information and

consultation with stakeholders when necessary. The

members of the Scientific Authority include one

representative from each of the nine provincial conservation

authorities of South Africa, together with representatives

from the Department of Environmental Affairs, SANBI,

SANParks, and the National Zoological Gardens. SANBI is

responsible for the logistical and administrative functions of

the Scientific Authority, and also plays a scientific

co-ordination role.

Currently there are 187 species and 10 genera of plants and animals listed as TOPS species (Section 56 of the

Biodiversity Act). The undertaking of a restricted activity, as defined in the Biodiversity Act, with a specimen

of any TOPS species would require a permit. The Convention on International Trade in Endangered Species

of Wild Fauna and Flora (CITES), and the domestic legal framework supporting CITES, regulates the granting

of permits or certificates for the international export and import of species listed in Appendices I, II, or III of

CITES (currently 1 309 South African species).

The main functions of the Scientific Authority are to:

Monitor the legal and illegal trade in specimens of TOPS species and CITES species;

Make recommendations to an issuing authority on applications for permits to undertake restricted

activities with TOPS species;

Make non-detriment findings (NDFs) on the impact of international trade on the survival of TOPS and

CITES species;

Advise on the registration of ranching operations, nurseries, captive breeding operations and other

facilities;

Advise whether an operation or facility meets the criteria for producing species considered to be

bred in captivity or artificially propagated;

Advise on amendments to TOPS listings and prohibition of restricted activities;

Advise on the nomenclature of species in trade; and

Assist with identifying species in trade.

Technical highlights of the Scientific Authority (2009 – 2018) include:

An off-take simulator tool was developed to adaptively manage a hunting quota for Cape Mountain

Zebra (Equus zebra zebra).

Twenty-nine complex Non Detriment Findings for priority CITES-listed species were approved.

Leopard (Panthera pardus) hunting quotas were recommended for 2016, 2017 and 2018, and a

national monitoring programme for leopards was established. Monitoring data from the programme

has informed the national annual leopard quota.

Criteria for the captive breeding of White Rhinoceros (Ceratotherium simum simum) in South Africa

as well as guidelines for the implementation of the wild-managed Lion (Panthera leo)

meta-population management plan were developed.

The TOPS list was revised in accordance with science-based listing criteria.

TOPS-listed species are those listed as threatened or protected in terms of section 56 of the Biodiversity Act. CITES-listed species are those included in the Appendices to the Convention on International Trade in Endangered Species of Wild Fauna and Flora.


168

A collaboration between the Scientific Authority and the United Nations Environemnt Programme -

World Conservation Monitoring Centre (UNEP-WCMC) produced the first ever analysis of the CITES

wildlife trade in the SADC region over the period 2005 to 2014.

A collaboration with TRAFFIC for the development of Apps for smartphones and tablets to identify

priority CITES species in trade as well as South African cycad species.

A research project initiated by the Scientific Authority programme and involving the University of the

Witwatersrand, Oxford University and the University of Kent, which aims to analyse and monitor the

lion bone trade in South Africa has been used to inform the annual lion bone quota.

Measures have also been introduced to improve the management of captive-bred Cheetahs

(Acinonyx jubatus) and to ensure that no wild specimens are traded as ‘captive-bred’.

As a result of the support SANBI provided to a Doctoral study on the DNA fingerprinting of the Cape

Parrot (Poicephalus robustus). Tthis species is now recognized by CITES as a separate species from

the Grey-Headed Parrot (Poicephalus fuscicollis), thereby allowing better regulation of both species

in international trade.

11.3. Species management plans Biodiversity Management Plans for species (BMPs) are intended to guide conservation strategies for

individual threatened species, and have been developed for a number of threatened species. These BMPs

include information on population status, trends, pertinent legislation as well as long-term goals, objectives,

actions and indicators of success. Species specific BMPs have been finalised for Black Rhino (Diceros bicornis),

White Rhino (Ceratotherium simum simum), African Lion (Panthera leo), Bearded Vulture (Gypaetus

barbatus), Pelargonium sidoides, Pickersgill's Reed Frog (Hyperolius pickersgilli) and the Albany Cycad

(Encephalartos latifrons). A further three are in draft form: African Penguin (Spheniscus demersus), Cape

Mountain Zebra (Equus zebra zebra) and Clanwilliam Sandfish (Labeo seeberi). Two BMPs covering multiple

species have also been finalised; one covering all shark species in South Africa, and the other covering 15

threatened cycad species.

11.4. Protected areas and biodiversity stewardship Protected area expansion strategies at a provincial and national level help ensure that species and ecosystems

are well represented in the protected area network. The protected area estate increased by 11% between

2010 and 2018, and resulted in an increase in the number of Well Protected terrestrial ecosystem types. This

provides some evidence that recent protected area expansion has been strategic and focussed on the

representation of ecosystem types rather than simple expansion. Biodiversity stewardship programmes

underpinned the majority of the expansion in recent years.

The establishment of protected areas is a key response to pressures on biodiversity. Protected areas are

typically the most secure mechanisms for conserving species and ecosystems, but ecological processes, which

often operate over large areas, are not effectively protected in most protected areas (exceptions include

large protected areas such as the Kruger National Park). Historically, the emphasis in South Africa’s protected

areas establishment was the protection of wildlife resources, charismatic species, mountain catchment areas

and indigenous forests. The preferred mechanism was proclamation of state owned land or purchase and

proclamation by the state. More recently, biodiversity stewardship programmes have expanded, allowing for

the proclamation of protected areas on privately owned land. In the last 8 years over 10 000 km2 has been

added to protected area estate of South Africa, the majority of which was through biodiversity stewardship

agreements.


169

11.5. Control measures for biological invasions

Responses to pressures – Biological Invasions

Historically, South Africa has responded to the threat posed by invasive species by ad hoc, often piecemeal,

legislation. Recently, there has been a more comprehensive sector-specific approach. In 1998, the National

Environment Management Act (NEMA) (Act 107 of 1998) was enacted to provide a framework for

environmental management. In 2004, the National Environmental Management: Biodiversity Act

(NEM:BA, Act no. 10 of 2004) was passed. The NEM:BA is one of the laws built around the NEMA framework,

and is intended to promote the protection and conservation of South Africa’s rich biodiversity.

In 2014, the government published the Alien and Invasive Species Regulations (A&IS Regulations) in terms

of the National Environmental Management: Biodiversity Act (NEM:BA, Act 10 of 2004). These regulations

specify the way in which alien species are to be managed. In addition, the regulations prescribe the process

to be followed if a new alien species is to be imported into the country, and they also list species that are

prohibited from importation. The intent of the regulations is to reduce the risk of importing alien species that

could become invasive and harmful, reduce the number of alien species becoming invasive, limit the extent

of invasions, and reduce the impacts caused by these invasions. This is to be achieved, in particular, by

assigning responsibilities for the control of listed invasive species, and where appropriate to prescribe the

conditions under which species that are both invasive and beneficial can be owned, cultivated, transported

and traded, as well as assign the responsibility to owners to prevent spread of such species.

The regulations also require that research proposals, and research findings should be submitted to the South

African National Biodiversity Institute (SANBI). This includes any ‘research and biological control relating to

any aspect of the invasiveness or potential invasiveness of an alien species or a listed invasive species or the

prevention, eradication or control of such invasive or potentially invasive species’ that is wholly or partly

funded by the state, or conducted in terms of a permit to carry out research on a listed invasive species. The

regulations further require SANBI to report, within three years of the promulgation of the regulations and

every three years thereafter, on the status of listed invasive species and other species that have been

subjected to a risk assessment; and the effectiveness of the regulations and control measures. SANBI is also

expected to carry out research and monitoring necessary to determine status and effectiveness. The first

report was completed (Van Wilgen & Wilson 2018) and submitted to the Minster in March 2018 (see

https://www.sanbi.org/media/the-status-of-biological-invasions-and-their-management-in-south-africa/ ).

There are also several Acts in South Africa, in addition to the National Environmental Management:

Biodiversity Act, that are relevant to the management of biological invasions (

Table 32). The most important of these are under the jurisdiction of the Department of Agriculture, Forestry

and Fisheries (DAFF), and include the Agricultural Pests Act (Act No. 36 of 1983), Animal Diseases Act (Act

No. 35 of 1984), and Animal Health Act (Act no.7 of 2002).

South Africa is also required to give effect to ratified international agreements relating to biodiversity which

are binding on the Republic. The most important of these agreements is the Convention on Biological

Diversity (CBD), which South Africa ratified in November 1995. Article 8(h) of this convention requires each

Contracting Party, as far as possible and as appropriate, to “prevent the introduction of, control or eradicate

those alien species which threaten ecosystems, habitats or species”. Article 19 also requires each contracting

party to take legislative, administrative or policy measures to provide for effective participation in the

Convention. Other relevant Conventions include the International Plant Protection Convention (IPPC), which

https://www.sanbi.org/media/the-status-of-biological-invasions-and-their-management-in-south-africa/


170

requires that signatory countries meet requirements designed to reduce the risks of pests of plants from

either leaving or entering the country (while pests originally referred to animals and fungi;the IPPC definition

has recently been expanded to include plants as pests themselves). From a marine perspective, the UN

Convention on the Law of the Sea obliges parties to prevent, reduce and control the intentional or accidental

introduction of species to the marine environment where they may have significant harmful effects. The

International Convention for the Control and Management of Ship’s Ballast Water and Sediments imposes

obligations to prevent, minimise and ultimately eliminate the transfer of harmful aquatic organisms and

pathogens through the control and management of ship’s ballast water and sediments.

Table 32. Additional legislation in South Africa that is relevant to the regulation and management of biological invasions.

Act Administered

by

Reporting requirements

Agricultural Pests

Act, 1983 (Act No.

36 of 1983)

Department of

Agriculture,

Forestry and

Fisheries

Compulsory notifications of certain pests from land users.

Control measures prescribed for different taxa, or in respect of different areas, different

circumstances, or in other respects as the Minister may think fit.

Permits that have been issued for controlled goods showing the reason for the permit.

Offenses and successful prosecutions.

Animal Diseases

Act, 1984 (Act No.

35 of 1984)

Department of

Agriculture,

Forestry and

Fisheries

Permits for imported controlled animals or other items.

Control measures for controlled animals or other items.

Reports of controlled animal disease.


Animal Health Act,

2002 (Act No. 7 of

2002)

Department of

Agriculture,

Forestry and

Fisheries

Reports of controlled animal disease.

Permits and health certificates for animals, parasites, contaminated or infectious items that

have been imported into the country.


National

Environmental

Management:

Protected Areas Act

(Act 57 of 2003)

The Department

of Environmental

Affairs

Register of alien species in protected areas.

Performance monitoring indicators.


Conservation of Agricultural Resources

Act (Act 43 of 1983)

Department of

Agriculture, Forestry and

Fisheries

Declared weed and invader list.

Weed control schemes and progress reports.

Weeds on any seed, grain, hay or other agricultural product.

Weeds on any animal which is driven on a public road, conveyed

in a vehicle or offered for sale at a livestock auction.

Orders issued for weed destruction, removal or return of the

above-mentioned weeds.

Control plans for invaders and weeds.

Directives for complying with control measures.


171

11.6. Climate change responses

Policy responses

The biodiversity sector was amongst the first in South Africa to begin highlighting climate change

vulnerabilities of nature and people. This led to the prioritisation of ecosystem and biodiversity concerns in

policy documentation by 2000 (e.g. DEAT 2000). Basic scientific and applied work is now either emerging or

well-developed in the fields of integration of ecosystem and biodiversity needs with mitigation options

(DEA 2015c) and actions and Ecosystem-based Adaptation (EbA) response options (Midgley et al. 2012;

DEA 2017).

The National Climate Change Response Policy, released as a white paper in October 2011, and the

recommendations from Phase II of the Long-Term Adaptation Scenarios Flagship Research Programme (LTAS)

laid the groundwork for more effective conservation planning and management at national and subnational

levels. Under the auspices of these reports, eight priority flagship programmes have been established:

The Climate Change Response Public Works Flagship Programme;

The Water Conservation Flagship Programme;

The Renewable Energy Flagship Programme;

The Energy Efficiency and Energy Demand Flagship Programme;

The Transport Flagship Programme;

The Waste Management Flagship Programme;

The Carbon Capture and Sequestration Flagship Programme; and

The Adaptation Research Flagship Programme which helped further develop adaptation responses across five national sectors including water, agriculture and forestry, human health, marine fisheries and biodiversity.

The management of alien and invasive species In South Africa is guided by the Alien and Invasive Species (I&AS) Regulations (2014) of the National Environmental Management: Biodiversity Act (NEM: BA) (Act 10 of 2004). These regulations placed restrictions on the use of listed alien species and regulated how they are to be managed. In addition, the regulations prescribe the process to be followed if a new alien species is to be imported into the country, and list species that are prohibited from importation.

Currently, 559 invasive taxa are listed in terms of the regulations and these include: in different categories:

Category 1a: invasive species which must be control and eradicated. Any form of trade or planting is prohibited

Category 1b: invasive species which must be controlled and were ever possible removed and destroyed. Any form of trade and planting is prohibited.

Category 2: invasive species are the same as category 1b species expect that permits can be issued for their usage.

Category 3: invasive species which may remain in a prescribed area, although they may not be traded or further propagated, and must be controlled if they occur in protected areas or riparian zones.

In terms of the regulations, permits are required for the import of alien species, and these will only be granted if a risk assessment is conducted and the results deemed by the government to be acceptable (See Kumschick et al. 2018). However, 560 taxa have been listed as prohibited, i.e. an import permit cannot be considered for these species.

The regulations, amongst other things, also require the development and adoption of management plans by organs of state; the development of a register of state-funded research projects and results; and the production of a national status report.

Tsungai Zengeya – South African National Biodiversity Institute

Box 14. South Africa`s Alien & Invasive Species Regulations


172

Mitigation

Ecosystem based mitigation was first formally identified in South Africa’s Long Term Mitigation Scenarios

report (Scenario building Team 2007). This process identified two mitigation measures relevant to

ecosystems and biodiversity with potential for short and long term implementation, namely afforestation

and fire control (Marquard, Trollip & Winkler 2011) but ecosystem restoration was not considered. To date,

between 500 000 – 1 000 000 ha of roughly 4 million degraded hectares of Sub Tropical Thicket (Eastern and

Western Cape) has been identified as suitable for restoration, with high potential for carbon sequestration

(DEA 2015c). While the ecosystem restoration approach appears to show benefits for biodiversity and

ecosystem services, including carbon sequestration (DEA 2016), the role of fire control and afforestation

require careful consideration due to potentially adverse impacts on biodiversity and ecosystem services

(Midgley 2018).

The effect of successful international mitigation action on the bioclimate of South African is substantial. While

an increased frequency of warmer and more arid bioclimatic zones are projected in South Africa, this

projection is greatly exacerbated in a high emissions future, where most of South Africa’s climate will undergo

some form of ecosystem transition (Figure 69). With inadequate international mitigation, tropical

temperatures are predicted to engulf most of the northern and eastern borders, as the Northern Cape and

extreme northeast grow considerably more arid. Hot temperatures will also expand northwards from the

coastal belt, eroding most of the stability of high altitude areas that would have persisted under a low

emissions future.

a. b. c.

Figure 69. Projections of future environment zones showing the effects of stringent greenhouse gas mitigation over the next 50 years (b) vs. scenarios where lower reductions are made (c), relative to baseline or historical environmental zones (a). This analysis uses 42 climatic variables to group ecosystems into 18 global environmental zones (Metzger et al. 2013) and predict how those zones may change over the next 50 years. These maps portray the maximum consensus of 10 global climate models (GCM) to improve accuracy and limit uncertainty.

Adaptation


173

Mainstreaming ecological infrastructure considerations is a key priority for adaptation, including at both

national and local scales. For national policy development, an important step is fostering the perspective that

intact ecological infrastructure is an integral part of long-term national development and resilience to climate

change. Locally, decision-makers need to promote building ecosystem & local community resilience via

Ecosystem-based Adaptation for cross-sector benefits (SAEON 2011; SANBI, DEA & GIS 2013).

Ecosystem based adaptation (EbA) is defined as the use of biodiversity as an element of an integrated climate

change response, and the implementation thereof is now taking shape, with key best practices identified

(Midgley et al. 2012) including:

Involvement of key stakeholders in integrated and adaptive planning and implementation;

Focus on development of adaptation measures that are locally contextualised;

Link to national, provincial and local scale ‘enabling’ frameworks;

Consider adaptation within the broader landscape;

Ensure safeguarding against risks and costs;

Consider financial sustainability from the start;

Develop effective monitoring and evaluation;

Track cost effectiveness and resilience outcomes;

Establish learning networks and communities of practice.

Figure 70. Highest priority land areas which, if conserved, would maximise the climate resilience of South Africa’s conservation network. These projections are based on DGVM-infused species distribution models for 13, 206 South African species, maximum consensus for ecosystem transitions according to 10 global climate models (GCMs), and filtered through a land use layer to prioritise intact habitat.

The habitat prioritization maps in Figure 70 above illustrate the highest priority land areas which, if conserved

(formally or informally), could best insulate South Africa’s conservation network from the impacts of climate

change. They are based on the future distributions of 13,206 species including 178 mammals, 567 birds, 55

reptiles and 12,406 plants over the next 50 years. They also account for both ecosystem transitions (Metzger

et al. 2003) [according to the maximum consensus of 10 global climate models (GCMs)] and changes in

vegetation (according to the best available aDGVM data). Highlighted areas contain intact chains of habitat

that will allow species to disperse, tracking suitable habitat through interconnected travel corridors.


174

The highest priority areas, illustrated in dark green, contain the biggest win for biodiversity. Increasing

conservation measures in all areas marked as highest priority would increase South Africa’s protected area

network to meet the 17% Aichi Biodiversity Target (Target 11). The lighter green areas provide important

protections for species (both where they are now, and where species will be in 50 years based on a high

emissions scenario), but will require larger tracts of land to equal the benefit of highest priority areas. Taken

together, increasing conservation efforts in all areas marked in green would minimize climatic risks to

biodiversity in South Africa and expand the conservation network to preserve 30% of terrestrial land.

Priority areas in the west help forge a critical chain of habitat that extends all the way up to the Republic of

Congo. This is one of the highest conservation priorities on the continent, protecting both current migration

corridors and future habitat needed to assist species as they disperse south tracking cooler temperatures.

These western priority lands in South Africa also promote connectivity to the vitally important ribbon of

habitat along most of the coastline. These areas are highlighted for their crucial importance to bird and plant

species in the region. Adding conservation buffers around Kruger National Park and other areas in the

northeast will further reduce risk in these areas of rapid transition, while patches throughout the interior

strengthen connectivity and preserve areas of habitat dissimilarity.

Reducing the climate risk to South Africa’s conservation network will be crucial to maintaining protections in

this megadiverse country, regardless of our climate trajectory. Planning for climate change requires

innovative conservation methods. By preserving both where species are now, and where they will be in the

future, South Africa can reduce climatic impacts on species, ecosystems, and the crucial services they provide.

Research and monitoring

Data collection for long-term research and monitoring of climate impacts, risks, vulnerability and predictions

has increased markedly since the early 2000s. The South African Environmental Observation Network

(SAEON) was established in 2002 to provide a long-term in situ environmental observation platform, an

information management system with the capacity for spatial analytics, and a science education outreach

programme. Through SAEON, the South African Risk and Vulnerability Atlas (SARVA) launched an online

database to provide researchers, policy-makers, NGOs and private sector stakeholders with free access to

maps, reports, case studies and integrated analysis on global change impacts. The SAEON network has also

recently been given the task of establishing the Expanded Freshwater and Terrestrial Environmental

Observation Network (EFTEON), a modular research infrastructure to support the investigation of coupled

social-ecological systems in South Africa. The Department of Environmental Affairs’ Long-Term Adaptation

Scenarios (LTAS) Flagship Research Programme was established in 2012 to develop national and sector-

specific adaptation scenarios under future climate conditions. Phase I (completed June 2013) included

extensive modelling, impacts research and adaptation scoping. Climate trends and projections were analysed

and consensus views were developed for short, medium, and long term time periods. A national assessment

was carried out and the process was repeated for five primary sectors including water, agriculture and

forestry, human health, marine fisheries and biodiversity.

Recommended priorities include:

Development of a coherent national monitoring strategy to detect the impacts of anthropogenic

climate change on biodiversity in South Africa and to test the effectiveness of interventions

carried out to minimise their negative impacts. These may be advanced through the maturing

SAEON and EFTEON programs.

Increasing the scope and quality of assessments of species’ vulnerability to climate change. This

is an essential step in developing effective plans to conserve them (Foden & Young, 2016),


175

although SANBI’s Red List assessments have the facility to include such assessments and data,

few species experts have the capacity to carry out this out. As a result, the national dataset does

not provide a reliable indication of the extent to which climate change is impacting South Africa’s

species (although recent assessments of butterflies and birds may be exceptions). This highlights

the urgent need for development of guidance material and training to build assessors’ capacity

in estimating climate change threat.

Improved climate change vulnerability assessments for protected areas, biomes and

ecosystems. Assessments of savannas should include complex feedbacks between CO2, trees,

C4 grasses, fire & climate (Engelbrecht & Engelbrecht 2016).

Improved understanding of the mechanisms through which climate change impacts biodiversity,

including its interactions between climate change and existing threats, including habitat loss,

invasive species, altered disturbance regime, overharvesting and disease. Botts et al. (2015)

recommend that species-specific range changes of amphibians should be used to investigate

range change drivers on an individual species basis.

Identifying the areas important for species’ long-term persistence, including movement

corridors and climatic refugia.

Improving understanding of species’ inherent sensitivities and adaptive capacities and how

these can be used to develop effective conservation interventions.


176

12. KNOWLEDGE GAPS AND RESEARCH PRIORITIES FOR THE TERRESTRIAL REALM

Chapter 12: Skowno, A.L., Poole, C.J. 2019. ‘Chapter 11: Knowledge gaps and research priorities’ in National Biodiversity Assessment 2018 Technical Report Volume 1: Terrestrial Realm. Skowno, A.L., Raimondo, D.C., Poole, C.J., Fizzotti, B. & Slingsby, J.A. (eds.). South African National Biodiversity Institute, Pretoria.

In this section we focus on knowledge gaps that have been encountered through the process of developing

the NBA 2018 Terrestrial Report. Although the NBA and the data sources available to it have evolved and

grown substantially since its first iteration in 2004, a number of avenues for improvement remain. This

section describes the main limitations of the NBA 2018 Terrestrial Report and outlines potential solutions.

This is followed by a summary of priority research, monitoring and data management needs to improve

future NBAs.

Research, monitoring and data management priorities highlighted in the various technical reports of the

NBA 2018 have been summarised below. Priorities have been clustered into research needs, monitoring

needs and data management needs. Fulfilling these needs clearly supports many other processes that

require similar knowledge foundations for managing and conserving biodiversity, spatial planning or

reporting. The needs are summarised below and a full description of each knowledge gap and its potential

solutions or avenues for improvement are included in (Table 33).

12.1. Research priorities identified from the NBA 2018 Research priorities highlighted in the various technical reports of the NBA 2018 have been collated below.

It is hoped that these will inform formal research strategies such as the National Biodiversity Research &

Evidence Strategy (2015–2025); the SANBI Research and Development Strategy 2019–2030; and research

strategies of institutions with links to the biodiversity sector. Beyond informing these formal strategies, the

information in this section can help to guide research and monitoring project development by providing

clear needs linked to national level assessments and planning.

12.1.1. Research priorities for foundational biodiversity information:

Foundational ecosystem information for improved classification of ecosystem types: The

ground-truthing of ecosystem types remains crucial for the ongoing improvement of their descriptions

and delineations.

Foundational species information for priority taxonomic groups: Most vertebrate and plant groups have

fairly well established research priorities in South Africa, with ongoing work on mapping and modelling

species distributions and active taxonomic research. However, the high levels of Data Deficient taxa for

some taxonomic groups illustrate the need for improved data. Life history, population distribution data,

and population trend data are required for estuarine and marine taxa, and also for highly utilised

species (e.g. medicinal plants etc.). Foundational data for invertebrates are urgently needed,

specifically those with high levels of endemism in South Africa. Initial priorities include work on

important terrestrial invertebrate pollinators.

Taxonomic treatment of poorly known groups: Most invertebrate groups are relatively poorly known,

requiring taxonomic work. To fill the important gaps in our taxonomic knowledge we will need to be

strategic about which taxon groups to focus on. Key priorities include nematodes, mites, beetles, flies,


177

true bugs, small freshwater crustaceans and marine taxa. Modern technologies and approaches such as

DNA barcoding and metabarcoding need to be more widely utilised.

Mapping and assessment of ecological infrastructure: Ecological infrastructure refers to naturally

functioning ecosystems that generate or deliver valuable services to people. The mapping of critical

ecological infrastructure and the assessment of its status is an important research priority going

forward, as it is essential to have a clear understanding of which features of the landscape and

seascape are crucial for delivering services to people, as well as the ecological condition required for

them to fulfil this role. Mapping of selected ecological infrastructure features has taken place in some

parts of the country, but efforts to date have been piecemeal, and methods and approaches remain

experimental. Systematic mapping of critical ecological infrastructure could be integrated into or

complement CBA maps to add value to spatial planning and prioritisation exercises.

12.1.2. Research priorities relating to pressures on biodiversity and ecological condition

Improving ecological condition assessments: Improved ecological condition assessments in all realms is

essential and can be achieved through better mapping of pressures and various forms of ecosystem

degradation. For example, research focussed on collecting data on the distribution and abundance of

invasive alien species will enhance ecological condition data in all realms. Collecting this type of

ecological condition data regularly is also a crucial aspect of national monitoring.

Climate change impacts on biodiversity, including through interaction with other pressures: South

Africa needs a deliberate, coherent strategy for detecting and tracking climate change impacts on

biodiversity. Lack of sufficient data on biological responses to climate change and interacting pressures

reduces the potential to test modelled projections, and thus determine key thresholds with confidence.

Furthermore, the coarse resolution of climate projections makes them biologically less meaningful, and

understanding how these relate to microclimatic niches and interact with different soils and specific

non-climate global change drivers will improve projections of biodiversity impacts. Existing datasets

(e.g. historic and long-term record sets) could be used to establish baselines and track change to date,

as well as identify and prioritise gaps for additional data collection. A coordinated monitoring project is

needed to track climate change impacts on South Africa’s coral communities in both shallow and deep

water.

Impacts of emerging pressures on biodiversity: Studies on the impacts of micro-plastics, herbicides,

pesticides, pharmaceuticals, noise and light on biodiversity are required, as these pressures are poorly

understood and have not been incorporated into ecological condition assessments or ecosystem threat

status assessments. Where drivers and their impacts on biodiversity are poorly understood a

precautionary approach is recommended.

12.1.3. Research priorities for improving and growing the suite of indicators for the NBAs

Effectiveness of intervention measures: Interventions are often implemented, but are often not studied

objectively in terms of their effectiveness. For example tracking whether the delineation of CBAs and

ESAs in spatial biodiversity plans has assisted in reducing developments in these areas.

Incorporation of landscape and seascape level genetic diversity measures into biodiversity

assessments: Most current genetic studies are single point estimates that can be useful baselines

measures for long-term studies that track genetic diversity over time. The indicators proposed in the

NBA 2018 need to be applied to other taxonomic groups and additional metrics need to be tested to


178

further explore the best possible indicators for measuring national-level genetic diversity. A national

genetic monitoring framework is required to provide guidance to researchers.

12.2. Monitoring needs identified from the NBA 2018 The following should be incorporated into the five-year action plan for the National Biodiversity Monitoring

Framework, and the annual plans of research and monitoring institutions:

Long-term, focused monitoring of biodiversity at specific sites, including long-term ecological

research and observation stations is required to enable researchers to tease apart the effects of the

threats climate change and biological invasions have on specific species populations, and to track

these over time to monitor ecosystem functioning.

Regular monitoring for specific species not only provides information about species distribution

and abundance patterns crucial for use in species Red List assessments, but it also gives important

feedback to researchers on where to expand searches for species that are only known from a few

previous records and may also reveal completely new species discoveries.

There are several other monitoring needs mentioned in The status of biological invasions and their

management in South Africa; including monitoring rates of alien species introductions and sites of

high rates of introductions.

Site-based monitoring of the impacts of various pressures on biodiversity (e.g. mining, residential

and commercial development, transport corridors, intensive agriculture) is needed to inform better

understanding of these pressures on ecological condition and species populations.

Detailed monitoring of harvested species (e.g. medicinally used species) is required to support

sustainable management of these crucial resources. Structured and resourced national monitoring

programmes (including citizen scientists) are required. In some cases this could be an opportunity

for indigenous knowledge systems to be consulted as part of an inclusive monitoring approach.

Monitoring of Convention on International Trade in Endangered Species of Wild Fauna and Flora

(CITES) exports, uptake of export quotas, and implementation of non-detriment findings is

required, as is the monitoring of conservation status and utilisation of species listed under the

threatened or protected species (TOPS) regulations to determine if the regulations are effective.

It is vital that existing, established and useful monitoring programmes (such as the ecological condition

monitoring of rivers) receive the support and funding to continue. Establishing new monitoring

programmes is far more difficult than sustaining existing programmes.

12.3. Data management and sharing imperatives identified from the NBA 2018 Effective management of national biodiversity data facilitates data sharing across user groups and sectors.

The principle of open access (i.e. biodiversity data being freely available) and close collaboration between

South Africa’s various biodiversity-related data facilities supports research and monitoring, and ultimately

improves the quality and accuracy of biodiversity assessments, biodiversity planning, and underpins

transparent science-based policy advice and decision making.

South Africa has subscribed to open access to biodiversity data for over a decade. The National Biodiversity

Information System (NBIS), currently under development at SANBI, aims to provide users with a

significantly enhanced ability to search for relevant and linked information, seamlessly across institutions

(e.g. museums, conservation agencies, citizen science projects) as well as across data types (occurrence

records, related ecosystems, publications, images, etc.). To do this, SANBI is investing in replicated versions


179

of the data stores of its partners, which are then conditioned and harmonised into a single national

instance for each data type, that is fully indexed and search-engine optimised. In addition, new visualisation

interfaces and an updated website will provide a superior user experience, making data queries as powerful

and easy as possible.

The following goals for improved data management and sharing emerged from the NBA 2018:

A mechanism is needed to feed information from site-based assessments (such as EIAs) back into

national datasets to add to foundational biodiversity information.

It is important that biodiversity indicators are prepared and released on a more regular basis than

the current NBA intervals (5–7 years). Indicator dashboards are being developed to provide users

with up-to-date information for improved reporting (e.g. SDGs) and streamlined management and

planning.

Several new indicators are emerging internationally, and will need to be incorporated into NBA

data management and sharing processes going forward (e.g. indicators that track the condition of

ecosystems, indicators on Key Biodiversity Areas, indicators linked to the status of ecological

infrastructure, indicators on genetic diversity, indicators on effectiveness of interventions).

Table 33: Summary analysis of knowledge gaps causing limitations to the NBA and priority actions for solutions

Knowledge gap causing limitation to the NBA Priority actions for solutions

Overall

Since anthropogenic climate change is escalating at unprecedented speed, understanding, predicting and minimising its impacts in South Africa are major knowledge gaps. The reliability of models for predicting climate change impacts is improving, but these rely on input data of a high quality and confidence (e.g. species spatial and temporal distribution and weather records). Poor data quality and data gaps lead to low confidence of predictive models, with resulting challenges for decision making.

A cohesive framework and indicators to track biodiversity and ecosystem service impacts as a result of climate change, identify critical thresholds or points of non-return and assess the effectiveness of interventions to minimise these impacts, is essential. Ecosystem change data and dedicated species population monitoring over long timeframes are needed to detect change and inform predictive models. Ensuring that reliable weather station data are available from across South Africa also remains a priority.

There are major gaps in data required to properly measure the indicators developed for the national status report on biological invasions (see chapter 8 of report). The NBA’s terrestrial ecological condition indicators do not yet incorporate biological invasions data.

Spatial data on the abundance and distribution of invasive alien species should be included in ecological condition assessments. More data on the impacts of biological invasions on biodiversity, and the value of management efforts for conservation goals, is needed.

Spatial data on the benefits of biodiversity to people is currently very limited, and there is limited data available on the economic value of biodiversity’s benefits to people.

More quantitative and updated data on the benefits of biodiversity will be very valuable for prioritisation and decision making processes beyond the NBA, and communicating the relevance of biodiversity.

Soil biodiversity is not addressed in the NBA and represents a major gap in our knowledge linked to ecosystem function in natural ecosystems and modified production ecosystems.

Closer collaboration with soil biota experts in the agricultural research sector and focussed research projects aimed at understanding soil biodiversity.

Currently the NBA does not take several emerging pressures into account, as data are not available.

Data on emerging pressures is needed: the impact of herbicides, pesticides and pharmaceuticals in water and soil; impacts of noise and light pollution on species; and impact of micro-plastics on biodiversity.

Species assessments (realm-specific species needs are covered in the realm sections below)

Gaps in taxonomic knowledge are substantial, particularly for invertebrates and for invasive alien species. Taxonomic uncertainties are a major constraint to species assessments and the ability to conduct comprehensive status assessments of groups in all realms.

A systematic process of detailed taxonomic studies on priority groups, including field collections and DNA barcoding, is essential for the enhancement of national species datasets. It is also crucial to build and maintain South African taxonomic knowledge and expertise, especially for understudied taxonomic groups.

Lack of monitoring data to detect changes in species abundance and distribution in response to pressures such as climate change, invasive aliens, biological resource use, etc. limits the ability to determine trends in species status via the Red List Index. Structured monitoring programmes are only in place for birds, butterflies and plants with citizen scientists playing a significant role in the collection of these data.

Monitoring programmes that cover a range of taxa from different realms and that include plants, vertebrates and invertebrates need to be developed and implemented using online citizen science platforms (e.g. iNaturalist).

There is still a bias in species assessments towards vertebrates and to terrestrial taxonomic groups in the current assessment, thereby limiting the utility of the species indicators.

A broader range of invertebrate groups need to be assessed and included in the NBA. Efforts should focus on groups that have a solid taxonomic basis, recent distribution data, high levels of endemism, and that are sensitive to changes in ecological condition or to overharvesting. Some examples likely to be included in the next NBA include: marine and estuarine crabs, and isopods in the genus Tylos; marine invertebrates with high levels of potential threat (e.g. cnidarians, intertidal and subtidal resources); freshwater invertebrates with high endemism that are completely reliant on aquatic systems (e.g. Plecoptera – Stoneflies, Dytiscidae – Water beetles);


180

Knowledge gap causing limitation to the NBA Priority actions for solutions

terrestrial pollinators (selected bees and flies); and groups with high endemism including millipedes, scorpions and sun spiders (Solifugidae).

The protection level indicator for species needs to be further tested and refined and expanded in application to the marine and estuarine realm.

Two PhD studies are currently underway testing the sensitivities of the indicator for a data rich group (mammals) and a data poor group (plants), and these will inform future applications. The index needs to be applied to species in the estuarine and marine realm.

Genetic assessments

Available genetic studies to date are single point estimates and do not focus on tracking genetic diversity over time and are insufficient for monitoring purposes.

Although some studies (with appropriate indicators) could form baseline measures for future monitoring, there is a need for focussed studies that aim to track genetic diversity over time.

Currently, the experimental genetic indicators have only been applied to one taxonomic group (reptiles).

The proposed indicators should be tested across taxonomic groups. Groups with nearly complete phylogenies and detailed distribution maps (e.g. birds, mammals) could be analysed readily.

The current experimental indicators may not be the best possible indicators for measuring and monitoring national-level phylogenetic richness.

There should be testing of additional metrics to further explore the best possible indicators, and additional analyses (e.g. of pressures, of protection) could be included.

There is currently no consensus regarding indicators that are relevant to track genetic diversity for biodiversity assessments.

A genetic monitoring framework is required that outlines how to strategically prioritise taxa for monitoring, identifies appropriate genetic markers and metrics, and provides advice on the frequency of monitoring. The framework would provide guidance to researchers.

Terrestrial realm

Land cover change data is a crucial input layer used for terrestrial, inland aquatic, estuarine ecosystem and species assessments. Currently the gaps between time points (1990 and 2014) are too long to detect recent rapid changes. The data used in NBA is already four years old, biennial data acquisition would be ideal for biodiversity assessments.

Land cover products should be available every 1–4 years, need to be directly comparable between time points and need to utilise common classification schemes. Land cover data should incorporate further drivers of degradation (e.g. invasive species abundance and distribution).

Pressures like overgrazing, modification of fire regimes, bush encroachment and biological invasions are not incorporated into ecological condition estimates for terrestrial ecosystem types. The NBA can only categorise ‘natural/near-natural’ and ‘severely modified and more’ in the terrestrial realm. Other realms use cumulative pressure mapping for ecological condition, which allows for nuanced analyses and more categories (e.g. ‘moderately modified’, ‘critically modified’).

Coordinated national effort is required to measure, model and map ecological condition in the terrestrial realm at a scale suitable for Red List of Ecosystem assessments and for reporting on international indicators. The condition assessment should be repeatable (approximately biennially) to allow for time-series analysis. Local and indigenous knowledge has potential to inform these assessments.

Private and local authority nature reserves, designated under old nature conservation ordinances, are currently included in the protected areas estate and therefore in protection level analyses. But there is uncertainty about their actual contribution to biodiversity conservation.

There is a need to understand private nature conservation efforts in South Africa in terms of biodiversity conservation activities, location of properties and extent of protection. These protected areas need to be investigated, validated and potentially removed from the database.

Protection level is a representation-based indicator for ecosystem types, but it ideally should be complimented by the effectiveness factor for each ecosystem type.

Information to formulate an indicator of protected area effectiveness for each ecosystem type is generally lacking and will require substantial effort and coordinated expert opinion.

There is a lack of current information on the use and status of medicinal plants, which hampers efforts to ensure the sustainable use of this important resource, as well as for trends in the status of the resource to be measured via repeated Red List assessments.

Focussed monitoring of the harvesting and trade in medicinal plants and its resultant impact on wild populations is required to better understand impacts of use. Research on the feasibility of cultivation schemes is essential.


181

13. REFERENCES

Academy of Science of South Africa (ASSAF). 2014. 'First Biennial Report to Cabinet on the State of Climate Change Science and Technology in South Africa.'

Acocks, J. P. H. 1953, ‘Veld types of South Africa’, Memoirs of the Botanical Survey of South Africa, 28, pp. 1–192.

Allsopp, M. H., de Lange, W. J. & Veldtman, R. 2008. ‘Valuing insect pollination services with cost of replacement’, PLoS ONE, 3(9), p. e3128.

Altwegg, R. et al. 2011. ‘Novel methods reveal shifts in migration phenology of barn swallows in South Africa’, Proc. R. Soc. B, p. rspb20111897.

Archer, S. R. et al. 2017. ‘Woody plant encroachment: causes and consequences’, in Rangeland systems. Springer, pp. 25–84.

Armstrong, A. J. et al. 1998. ‘Plantation Forestry in South Africa and its Impact on Biodiversity’, The Southern African Forestry Journal, 182(1), pp. 59–65.

Axelsson, C. R. & Hanan, N. P. 2018. ‘Rates of woody encroachment in African savannas reflect water constraints and fire disturbance’, Journal of Biogeography, 45(6), pp. 1209–1218.

Bac, L. & Tlholoe, G. 2017. Introduction to South Africa’s Tourism Sector. Unpublished report.

Bacher, S. et al. 2018. ‘Socio-economic impact classification of alien taxa (SEICAT)’, in S. Ramula (Ed.). Methods in Ecology and Evolution, pp. 159–168, Wiley/Blackwell.

Baiyegunhi, L. J. S. & Oppong, B. B. 2016. ‘Commercialisation of mopane worm (Imbrasia belina) in rural households in Limpopo Province, South Africa’, Forest Policy and Economics, 62(C), pp. 141–148..

Baillie, J. E. et al. 2008., 'Toward monitoring global biodiversity', Conservation Letters, 1: 18-26.

Barger, N. N. et al. 2018. ‘Chapter 3: Direct and indirect drivers of land degradation and restoration’, in L., Montanarella, R.Scholes, andA. (eds.). The IPBES assessment report on land degradation and restoration. Bonn, Germany, Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem services.

Bartomeus, I. et al. 2013. ‘Biodiversity ensures plant-pollinator phenological synchrony against climate change’, Ecology Letters, 16(11), pp. 1331–1338.

Bates, M. F. et al. 2014. 'The Reptile Atlas and Checklist of South Africa, Lesotho and Swaziland', SANBI, Pretoria.

Bezeng, B. S. et al. 2017. ‘Climate change may reduce the spread of non-native species’, Ecosphere, 8(3). doi: 10.1002/ecs2.1694.

Blackburn, T. M. et al. 2011. ‘A proposed unified framework for biological invasions’, Trends in Ecology & Evolution. Elsevier Current Trends, 26(7), pp. 333–339.

Blackburn, T. M. et al. 2014. ‘A Unified Classification of Alien Species Based on the Magnitude of their Environmental Impacts’, PLoS Biology. Public Library of Science, 12(5), p. e1001850.

Bland, L. M. et al. 2017. Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 1.1. Gland, Switzerland.

Bland, L. M. et al. 2017b. ‘Using multiple lines of evidence to assess the risk of ecosystem collapse’, Proceedings of the Royal Society B: Biological Sciences, 284(1863), p. 20170660.

Bland, L. M. et al. 2018. ‘Developing a standardized definition of ecosystem collapse for risk assessment’, Frontiers in Ecology and the Environment, 16(1), pp. 29–36.

Blaum, N. et al. 2007a. “Land Use Affects Rodent Communities in Kalahari Savannah Rangelands.” African Journal of Ecology 45 (2): 189–195.

Blaum, N. et al. 2007b. ‘Shrub encroachment affects mammalian carnivore abundance and species richness in semiarid rangelands’, acta oecologica, 31(1), pp. 86–92.

Blaum, N. et al. 2009. ‘Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators’, Biodiversity and Conservation, 18(5), pp. 1187–1199.

Boitani, L. et al. 2015. ‘Challenging the Scientific Foundations for an IUCN Red List of Ecosystems’, Conservation Letters, 8(2), pp. 125–131. doi: 10.1111/conl.12111.

Bomhard, B. et al. (2005) ‘Potential impacts of future land use and climate change on the Red List status of the Proteaceae in the Cape Floristic Region, South Africa’, Global Change Biology, 11(9), pp. 1452–1468.

Bond, W. J. & Midgley, G. F. (2003a) ‘What controls South African vegetation—climate or fire?’, South African Journal of Botany.

Bond, W. J. et al. 2003b. ‘The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas’, Global Change Biology. Wiley/Blackwell (10.1111), 9(7), pp. 973–982.

Botha, A. J. et al. 2017. ‘CMS Multi-species Action Plan to conserve African-Eurasian Vultures.’ Coordinating Unit of UNEP/Raptors MoU; Abu Dhabi.

Botha, H. et al. 2011. ‘The decline of the Nile crocodile population in Loskop Dam, Olifants River, South Africa’, Water SA. Water Research Commission (WRC), 37(1). doi: 10.4314/wsa.v37i1.64109.


182

Bothma, J. du P. & du Toit, J. G. (eds.). 2015. 'Game Ranch Management'. 6th edn. Pretoria, Van Schaik Publishers.

Botts, E. 2015. 'Case study: Local government and civil society: Climate change response in Alfred Nzo District Municipality'.

Botts, E. A. et al. 2015. ‘Observed range dynamics of S outh A frican amphibians under conditions of global change’, Austral Ecology, 40(3), pp. 309–317.

Botts, E.A., et al. (in review) 'Integration of South Africa’s threatened ecosystems into conservation planning and environmental policy'. Biological Conservation.

Bourne, A. et al. 2015. A Climate Change Vulnerability Assessment for the Namakwa District, South Africa: The 2015 Revision. Cape Town, South Africa, Conservation South Africa.

Broennimann, O. et al. 2006. ‘Do geographic distribution, niche property and life form explain plants’ vulnerability to global change?’, Global Change Biology. Wiley/Blackwell (10.1111), 12(6), pp. 1079–1093.

Brooks, T.M. et al. 2015. ‘Harnessing biodiversity and conservation knowledge products to track the Aichi Targets and Sustainable Development Goals’, Biodiversity, 16.

Burrows, M. T. et al. 2014. ‘Geographical limits to species-range shifts are suggested by climate velocity’, Nature, 507(7493), pp. 492–495. doi: 10.1038/nature12976.

Bush, A. et al. 2012. ‘Determining vulnerability of stream communities to climate change at the landscape scale’, Freshwater Biology, 57, pp. 1689–1701.

Bussière, E. M. S. et al. 2015. ‘Patterns of bird migration phenology in South Africa suggest northern hemisphere climate as the most consistent driver of change’, Global Change Biology, 21(6), pp. 2179–2190.

Butchart, S. H. M. et al. 2007. ‘Improvements to the Red List Index’, PLoS ONE, 2(1), p. e140.

Butchart, S. H. M. et al. 2010. ‘Global Biodiversity: Indicators of Recent Declines’, Science, 328(5982), pp. 1164–1168. doi: 10.1126/science.1187512.

Cardinale, B. J. et al. 2012. ‘Biodiversity loss and its impact on humanity’, Nature, 486(7401), pp. 59–67. doi: 10.1038/nature11148.Access.

Carvalheiro, L. G. et al. 2010. ‘Pollination services decline with distance from natural habitat even in biodiversity-rich areas’, Journal of Applied Ecology, 47(4), pp. 810–820.

Carvalheiro, L. G. et al. 2011. ‘Natural and within-farmland biodiversity enhances crop productivity’, Ecology Letters, 14(3), pp. 251–259. doi: 10.1111/j.1461-0248.2010.01579.x.

Challinor, A. et al. 2007. ‘Assessing the vulnerability of food crop systems in Africa to climate change’, Climatic Change, 83(3), pp. 381–399.

Chalmandrier, L. et al. 2013. ‘Effects of time since fire on birds in a plant diversity hotspot’, Acta Oecologica, 49, pp. 99–106.

Child, M. F. et al. (eds.). 2016. ‘The 2016 Red List of Mammals of South Africa, Swaziland and Lesotho.’ South African National Biodiversity Institute and Endangered Wildlife Trust, South Africa.

Child, M. F. et al. 2017. 'Mammal Red List 2016: Introduction and methodology'. South Africa: South African National Biodiversity Institute and Endangered Wildlife Trust.

Christenhusz, M. J. M. & Byng, J. W. 2016. ‘The Number of known Plants Spesies in the Word and its Annual Increase’, Phytotaxa, 261(May), pp. 201–217.

Cloete, P. C. et al. 2015. Game ranch profitability in South Africa. Pretoria, South Africa, Caxton.

Collatz, G. J. et al.. 1998. ‘Effects of climate and atmospheric CO 2 partial pressure on the global distribution of C 4 grasses: present, past, and future’, Oecologia, 114(4), pp. 441–454.

Cowling, R. M. et al. 2005. ‘On the origin of southern African subtropical thicket vegetation’, South African Journal of Botany. Elsevier, 71(1), pp. 1–23.

Craigie, I. D. et al. 2010. ‘Large mammal population declines in Africa’s protected areas’, Biological Conservation, 143(9), pp. 2221–2228.

Cressey, E. R. et al. 2014. ‘Fading out of view: the enigmatic decline of Rose’s mountain toad Capensibufo rosei’, Oryx, (April), pp. 1–8.

Cunningham, A. B. 1988. 'An investifation of the herbal medicine trade in Natal/KwaZulu'. INR Invetigatorial Report 29. Pietermaritzburg, South Africa.

da Silva, J. M. &Tolley, K. A. 2018. ‘Conservation genetics of an endemic and threatened amphibian (Capensibufo rosei): a leap towards establishing a genetic monitoring framework’, Conservation Genetics, 19, pp. 349–363.

DAFF .2016. 'National Strategic Action Plan for the Conservation and Sustainable Use of Crop Wild Relatives in South Africa' Department of Agriculture, Forestry and Fisheries. Unpublished report. Pretoria, South Africa.

DAFF .2018. ‘Trends in the Agricultural Sector 2017’. Department of Agriculture, Forestry and Fisheries.Pretoria, South Africa:

Davis-Reddy, C. 2018. 'Assessing vegetation dynamics in response to climate variability and change across sub-Saharan Africa'. PhD thesis, Stellenbosch University.

Davis-Reddy, C. L. & Vincent, K. (2017) Climate risk and vulnerability: A handbook for southern Africa. 2nd edn. Pretoria, South Africa: CSIR.


183

Dayaram, A. et al. 2017. ‘Vegetation Map of South Africa, Lesotho and Swaziland 2009 and 2012: A description of changes from 2006’, Bothalia-African Biodiversity and Conservation, 47(1), pp. 1–10.

de Lange, W. J. et al. 2013. ‘Valuation of pollinator forage services provided by Eucalyptus cladocalyx’, Journal of Environmental Management, 125, pp. 12–18.

de Waal, J. H. et al. 2017. ‘Extreme 1-day rainfall distributions: Analysing: Change in the Western Cape’, South African Journal of Science, 113(7–8), pp. 1–8.

DEA.(2012a. Chapter 10: Air quality, in 2nd South Africa Environment Outlook Report. Pretoria: Department of Environmental Affairs, Pretoria, South Africa:pp. 181–207.

DEA .2012b. Chapter 13: Waste management, in 2nd South Africa Environment Outlook Report. Pretoria: Department of Environmental Affairs, Pretoria, South Africa: pp. 277–304.

DEA.2013. Long-Term Adaptation Scenarios Flagship Research Programme (LTAS) for South Africa. Department of Environmental Affairs, Pretoria, Souith Africa.

DEA et al. 2013. Mining and biodiversity guideline: Mainstreaming biodiversity into the mining sector. Pretoria, South Africa: Department of Environmental Affairs, Department of Mineral Resources, Chamber of Mines, South African Mining and Biodiversity Forum, South African National Biodiversity Institute, p. 100.

DEA. 2015a. Biodiversity economy strategy. Government Gazette. Pretoria: Department of Environmental Affairs. Pretoria, South Africa.

DEA .2015b. The scope and extent of the utilisation of indigenous biological resources by bioprospecting industries in South Africa. Department of Environmental Affairs. Pretoria, South Africa

DEA .2016. 'Climate change adaptation plans for South African biomes'. Edited by J. R. M. Kharika et al. Pretoria, South Africa: Department of Environmental Affairs. Available at: https://www.gov.za/sites/default/files/climatechangeadaptation_plansforsouthafricanbiomes_reporta.pdf .

DEA .2017. ‘Guidelines for ecosystem-based adaptation (EbA) in South Africa’.

DEA. 2018. South Africa State of Waste. A report on the state of the environmen'. Pretoria, South Africa: Department of Environmental Affairs. Available at: www.environment.gov.za.

DEAT.2000. South Africa’s Initial National Communication: Under the United Nations Framework Convention On Climate Change.

Desmet, P. & Cowling, R. 2004. ‘Using the species-area relationship to set baseline targets for conservation’, Ecology and Society, 9(2), p. 11.

Dietemann, V. et al. 2009. ‘Is there a need for conservation of Honey Bees in Africa?’, Apidologie, 40(3), pp. 285–295.

Ditlhogo, M. et al. 1996. ‘Interactions between the Mopane Catepillar, Imbrasia belina, and its host Colophospermum mopane in Botswana’, Management of Mopane in southern Africa, pp. 26–29.

Dold, T. & Cocks, M. 2012. Voices from the forest: celebrating nature and culture in Xhosaland. Jacana Media,.Auckland Park.

Drewes, S. E. 2012. “Natural Products Research in South Africa: 1890-2010', South African journal of science. Vol 108: pp.5-6

Driver, A. et al. 2004. South African National Spatial Biodiversity Assessment 2004: Summary Report. Pretoria: South African National Biodiversity Institute. Available at: www.nbi.ac.za .

Driver, A. et al. 2005. National Spatial Biodiversity Assessment 2004: priorities for biodiversity conservation in South Africa. Pretoria: Strelitzia 17, South African National Biodiversity Institute.

Driver, A. et al. 2012. National Biodiversity Assessment 2011: An assessment of South Africa’s biodiversity and ecosystems. Synthesis Report. Pretoria: South African National Biodiversity Institute and Department of Environmental Affairs.

du Toit, J. C. O. 2010. ‘An analysis of long-term daily rainfall data from Grootfontein, 1916 to 2008’, Grootfontein Agric, 10(2).

du Toit, J. C. O. & O’Connor, T. G. 2014. ‘Changes in rainfall pattern in the eastern Karoo, South Africa, over the past 123 years’, Water SA, 40(3), pp. 453–460.

du Toit, J. C. et al. 2015a. ‘Photographic evidence of fire-induced shifts from dwarf-shrub-to grass-dominated vegetation in Nama-Karoo’, South African Journal of Botany, 101, pp. 148–152.

du Toit, J. C. et al. 2015b. ‘Fire effects on vegetation in a grassy dwarf shrubland at a site in the eastern Karoo, South Africa’, African Journal of Range & Forage Science, 32(1), pp. 13–20.

du Toit, J. C. & O’Connor, T. G. 2017. ‘Minimum temperatures and frost at Grootfontein in the eastern Karoo, South Africa, over 98 years’, Transactions of the Royal Society of South Africa, 72(1), pp. 39–46.

Econex. 2016. ‘Comments on select aspects of the NHI White Paper’. Occasional Note Econex. Available at: https://econex.co.za/wp-content/uploads/2016/06/ECONEX_Occasional-Note_June-2016.pdf .

Egan, B. A. 2013. Culturally and economically significant insects in the Blouberg Region, Limpopo Province, University of Limpopo, South Africa..

Eilers, E. J. et al. 2011. ‘Contribution of pollinator-mediated crops to nutrients in the human food supply’, PLoS ONE, 6(6), p. e21363.

Ekesi, S. et al. 2016. ‘Taxonomy, ecology, and management of native and exotic Fruit Fly species in Africa’, Annual Review of Entomology, 61(1), pp. 219–238.


184

EM-DAT. 2016. The international disaster database.

Engelbrecht, C. J. & Engelbrecht, F. A. 2016. ‘Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals’, Theoretical and Applied Climatology, 123(1–2), pp. 247–261.

Engelbrecht, F. A. et al. 2009. ‘Dynamics of the Conformal-Cubic Atmospheric Model projected climate-change signal over southern Africa’, International Journal of Climatology. Wiley-Blackwell, 29(7), pp. 1013–1033.

Engelbrecht, F. A. et al. 2016. ‘Chapter 3: Climate Change over South Africa: From trends and projected changes to vulnerability assessments and the status quo of national adaptation strategies’, in South Africa’s 3rd National Communication to UNFCCC, Department of Environmental Affairs Pretoria.

Erasmus, B. et al. 2002. ‘Vulnerability of South African animal taxa to climate change’, Global Change Biology, pp. 679–693.

Faith, D. P. 1992. ‘Conservation evaluation and phylogenetic diversity’, Biological Conservation, 61(1), pp. 1–10.

Faith, D. P. 2010. ‘Quantifying Biodiversity : a Phylogenetic Perspective’, 16(1), pp. 248–252.

Fill, J. M. et al. 2017. ‘An assessment of the effectiveness of a long-term ecosystem restoration project in a fynbos shrubland catchment in South Africa’, Journal of Environmental Management, 185, pp. 1–10.

Foden, W. et al. 2007. ‘A changing climate is eroding the geographical range of the Namib Desert tree Aloe through population declines and dispersal lags’, Diversity and Distributions, 13, pp. 645–653. doi: 10.1111/j.1472-4642.2007.00391.x.

Foden, W. B. and Young, B. E. 2016. IUCN SSC Guidelines for Assessing Species ’ Vulnerability to Climate Change. Version 1.0. Occasional Paper of the IUCN Species Survival Commission No. 59. Cambridge, UK: IUCN Species Survival Commission.

Foden, W. B. et al. 2018. ‘Climate change vulnerability assessment of species’, WIRES Climate Change, pp. 1–36.

Forest, F. et al. 2007. ‘Preserving the evolutionary potential of floras in biodiversity hotspots’, Nature, 445(7129), pp. 757–760.

Forman, R. T. T. & Alexander, L. E. 1998. ‘Roads and their major ecological effects’, Annual Review of Ecology and Systematics, 29(1), pp. 207–231.

Frishkoff, L. et al. 2014. ‘Loss of avian phylogenetic diversity in neotropical agricultural systems’, Science, 345(6202), pp. 1343–1346.

Gbetibouo, G. & Ringler, C. 2009. ‘Mapping South African Farming Sector Vulnerability to Climate Change and Variability’, in Amsterdam Conference on the Human Dimensions of Global Environmental Change ‘Earth System Governance: People, Places and the Planet’, Theme: Adaptiveness of Earth System Governance, Amsterdam.

Geijzendorffer, I. R. et al. 2015. ‘Bridging the gap between biodiversity data and policy reporting needs: An Essential Biodiversity Variables perspective’, Journal of Applied Ecology, 53(5), pp. 1341–1350.

GeoTerraImage. 2015. Technical Report: 2013/2014 South African National Land Cover Dataset version 5. Pretoria.

GeoTerraImage. 2016. Technical Report: 1990 South African National Land Cover Dataset version 5.2. Pretoria.

Gotzmann, I. 2002. 'Vegetationsökologie und Vegetationsdynamik im Richtersveld (Republik Südafrika)'. Dissertation, Universität Köln, Germany.

Government of South Africa. 2008. National Protected Area Expansion Strategy for South Africa 2008.

Greve, M. et al. 2017. ‘Terrestrial invasions on sub-Antarctic Marion and Prince Edward Islands’, Bothalia, 47(2), p. 2143.

Guo, D. et al. 2017. ‘Climate Change Impacts on Dwarf Succulents in Namibia as a Result of Changes in Fog and Relative Humidity’, Journal of Water Resource and Hydraulic Engineering, 6(3), pp. 57–63.

Guo, D., Zietsman, G. & Hockey, P. A. R. 2016. ‘Climate Change Impacts on the Common Swift in South Africa’, International Journal of Environmental Science and Development, 7(4), pp. 306–311.

Hanke, W. et al. 2014).‘The impact of livestock grazing on plant diversity: an analysis across dryland ecosystems and scales in southern Africa’, Ecological Applications, 24(5), pp. 1188–1203.

Harris, L. et al. 2019. Advancing land-sea integration for ecologically meaningful coastal conservation and management. Biological Conservation, 237, 81-89

Hassan, R. M. et al. 2005. Ecosystems and Human Well-Being: Current State and Trends: Findings of the Condition and Trends Working Group. Island Press (Ecosystems and Human Well-being).

Hawkins, C. L. et al. 2015. ‘Framework and guidelines for implementing the proposed IUCN Environmental Impact Classification for Alien Taxa (EICAT)’, R. Duncan (ed.) in Diversity and Distributions, pp. 1360–1363

Henderson, L. & Wilson, J. R. U. 2017. ‘Changes in the composition and distribution of alien plants in South Africa: An update from the Southern African Plant Invaders Atlas’, Bothalia, 47(2), pp. 1–26.

Hewitson, B. C. & Crane, R. G. (2006) ‘Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa’, International Journal of Climatology, 26(10), pp. 1315–1337.

Hoban, S. et al. 2014. ‘Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion’, Evolutionary Applications, 7(9), pp. 984–998.

Hockey, P. A. R. et al. 2011. ‘Interrogating recent range changes in South African birds: Confounding signals from land use and climate change


185

present a challenge for attribution’, Diversity and Distributions, 17(2), pp. 254–261.

Hockey, P. & Midgley, G. F. 2009. ‘Avian range changes and climate change: a cautionary tale from the Cape Peninsula’, Ostrich, 80(1), pp. 29–34.

Hoffman, M. T. et al. 2018. ‘Long-term changes in land use, land cover and vegetation in the Karoo drylands of South Africa: Implications for degredation monitoring’, African Journal of Range & Forage Science. Vol 35(3&4): pp 209-221.

Hoffman, T. & Ashwell, A. (2001) Nature divided: land degradation in South Africa. University of Cape Town Press.

Holness, S. et al. 2018. 'Priority areas for the in situ conservation of crop wild relatives in South Africa', Plant Genetic Resources, pp. 1–13.

Hope, A. et al. 2014. ‘Evaluating drought response of Southern Cape Indigenous ForestsSA’, International Journal of Remote Sensing, 35(13), pp. 4852–4864.

Hope, R. A. et al. 2009. ‘Experimental analysis of adoption of domestic Mopane Worm farming technology in Zimbabwe’, Development Southern Africa, 26(1), pp. 29–46.

Hoveka, L. N. et al. 2016. ‘Effects of climate change on the future distributions of the top five freshwater invasive plants in South Africa’, South African Journal of Botany, 102, pp. 33–38.

Huang, S. et al. 2012. ‘How global extinctions impact regional biodiversity in mammals’, Biology Letters, 23(8), pp. 222–225.

Hughes, A. R. et al. (2008) ‘Ecological consequences of genetic diversity’, Ecology Letters, 11(6), pp. 609–623.

Human, H. et al. 2011. ‘The Honeybee disease American foulbrood — An African perspective’, African Entomology, 19(2), pp. 551–557.

IPCC. 1996. ‘IPCC Second Assessment: Climate Change 1995’.

IPCC . 2007. ‘Climate Change 2001: Synthesis Report’, Assessment, (September), pp. 24–29.

IPCC . 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change in C.B. Field et al. (eds.). Cambridge University Press, Cambridge.

IPCC.2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.

IIsaac, N. J. B. et al. 2007. ‘Mammals on the EDGE: Conservation priorities based on threat and phylogeny’, PLoS ONE, 2(3).

IUCN. 2012a).IUCN Red List categories and criteria: Version 3.1. Second Edition. Gland, Switzerland and Cambridge, UK: IUCN. Available at: www.iucn.org/publications .

IUCN. 2012b. Guidelines for application of IUCN Red List criteria at regional and national levels: Version 4.0. Gland, Switzerland and Cambridge, UK: IUCN. Available at: www.iucn.org/publications .

IUCN. 2018. 'The IUCN Red List of Threatened Species. Version 2018-1' viewed 5 July 2018, from http://www.iucnredlist.org .

Jack, S. et al. 2014. ‘Blow me down: A new perspective on Aloe dichotoma mortality from windthrow’, BMC Ecology, 14(1), p. 7.

Jetz, W. et al. 2014. ‘Global Distribution and Conservation of Evolutionary Distinctness in Birds’, Current Biology. 24(9), pp. 919–930. doi: 10.1016/j.cub.2014.03.011.

Jordaan, D. 2009. ‘Bankruptbush (Slangbos)–A silent threat to grasslands?’, Grassroots Newsl. Grassl. Soc. South. Africa, 9, pp. 40–42.

Jürgens, N., Gotzmann, I. & Cowling, R.M. 1999. 'Remarkable medium-term dynamics of leaf succulent Mesembryanthemaceae shrubs in the winter-rainfall desert of northwestern Namaqualand, South Africa', Plant Ecology 142: 87-96.

Jürgens N, Strohbach B, Lages F, Schmiedel U, Finckh M, Sichone P, Hahn L-M,& Zigelski P. 2018. 'Biodiversity observation – an overview of the current state and first results of biodiversity monitoring studies' in R. Revermann R, K.M Krewenka, U. Schmiedel,J.M. Olwoch, J. Helmschrot & N. Jürgens (eds.). Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions. Göttingen & Windhoek, Klaus Hess Publishers. p. 382-396.

Keith, D. A. et al. 2013. ‘Scientific foundations for an IUCN Red List of Ecosystems’, PLoS ONE, 8(5), p. e62111.

Keith, D. A. et al. 2015. ‘The IUCN Red List of Ecosystems: Motivations, Challenges, and Applications’, Conservation Letters, 8(3), pp. 214–226.

Kotzé, J.D.F. et al. 2010. National Invasive Alien Plant Survey. Report Number: GW/A/2010/21. Agricultural Research Council: Institute for Soil, Climate and Water, Pretoria.

Kraaij, T. et al. 2018. ‘An assessment of climate, weather, and fuel factors influencing a large, destructive wildfire in the Knysna region, South Africa’, Fire Ecology. Nature Publishing Group, 14(2), p. 4.

Krook, K., Bond, W. J. & Hockey, P. A. 2007. ‘The effect of grassland shifts on the avifauna of a South African savanna’, Ostrich-Journal of African Ornithology, 78(2), pp. 271–279.

Kruger, A. C. & Nxumalo, M. 2017. ‘Surface temperature trends from homogenized time series in South Africa: 1931–2015’, International Journal of Climatology, 37(5), pp. 2364–2377.

Kruger, A. C. & Sekele, S. S. 2013. ‘Trends in extreme temperature indices in South Africa: 1962-2009’, International Journal of Climatology, 33(3), pp. 661–676.


186

Kumschick, S. et al. 2018. Framework and guidelines for conducting risk analyses for alien species. Preprints. doi:10.20944/preprints201811.0551.v1

Lawler, J. J. 2009. ‘Climate change adaptation strategies for resource management and conservation planning’, Annals of the New York Academy of Sciences, p. 1162:79-98.

Le Maitre, D. C. et al. 2016. ‘Estimates of the impacts of invasive alien plants on water flows in South Africa’, Water SA. Water Research Commission (WRC), 42(4), p. 659.

Le Maitre, D. C. et al. 2000. ‘The impact of invading alien plants on surface water resources in South Africa: A preliminary assessment’, 26(3), p. 397.

Lee, A. T. K. & Barnard, P. 2016. ‘Endemic birds of the Fynbos biome: a conservation assessment and impacts of climate change’, Bird Conservation International. © BirdLife International, 26(01), pp. 52–68.

Lee, C. & Murray, N. 2017. ‘redlistr: Tools for the IUCN Red List of Ecosystems and Species. R Package version 1.0.1’.

Lexer, C. et al. 2013. ‘Next generation’biogeography: towards understanding the drivers of species diversification and persistence', Journal of Biogeography, 40(6), 1013-1022.

Linder, H. P. et al. 2010. 'Biotic diversity in the Southern African winter-rainfall region', Current opinion in environmental sustainability, 2(1-2), 109-116.

Lindsey, P. A. et al. 2009. ‘The importance of conservancies for enhancing the value of game ranch land for large mammal conservation in southern Africa’, Journal of Zoology, 277(2), pp. 99–105.

Little, I. T. et al. (2015) ‘Impacts of fire and grazing management on South Africa’s moist highland grasslands: A case study of the Steenkampsberg Plateau, Mpumalanga, South Africa’, Bothalia, 45(1), pp. 1–15.

Liu, Y., Stanturf, J. & Goodrick, S. (2010) ‘Trends in global wildfire potential in a changing climate’, Forest Ecology and Management, 259, pp. 685–697.

Lloyd, J. W. et al. 2002. Patterns of transformation and degradation in the Thicket Biome, South Africa. Terrestrial Ecology Research Unit, University of Port Elizabeth.

Loftie-Eaton, M. 2014. 'Geographic range dynamics of South Africa’s bird species', PhD thesis, Citeseer.

Mace, G. M. et al. 2008. ‘Quantification of extinction risk: IUCN’s system for classifying threatened species’, Conservation Biology, 22(6), pp. 1424–1442.

Mace, G. M., Gittleman, J. L. & Purvis, A. 2003. ‘Preserving the tree of life’, Science, 300(5626), pp. 1707–1709.

MacKellar, N. et al. 2014. ‘Observed and modelled trends in rainfall and temperature for South Africa: 1960-2010’, South African Journal of Science. Academy of Science of South Africa, 110(7–8), p. 13 pages.

Makhado, R. et al. 2014. ‘A review of the significance of mopane products to rural people’s livelihoods in southern Africa’, Transactions of the Royal Society of South Africa. Taylor & Francis, 69(2), pp. 117–122.

Mander, M. 1998. ‘Marketing of indigenous medicinal plants in South Africa - A case study in Kwazulu-Natal’. Rome: Food and Agriculture Organization of the United Nations (FAO).

Mander, M. et al. 2007. ‘Economics of the traditional medicine trade in South Africa’, South African Health Review 2007. Edited by S. Harrison, R. Bhana, and A. Ntuli. Durban: Health Systems Trust.

Marker, L. & Dickman, A. (2004) ‘Human aspects of cheetah conservation: lessons learned from the Namibian farmlands’, Human Dimensions of Wildlife, 9(4), pp. 297–305.

Marquard, A. et al. 2011. ‘Opportunities for and Costs of Mitigation in South African Economy’.

Martin, R. O. et al. 2014. ‘Phenological shifts assist colonisation of a novel environment in a range-expanding raptor’, Oikos, 123(12), pp. 1457–1468.

Masters, G. & Norgrove, L. 2010. ‘Climate change and invasive alien species’, UK: CABI Working Paper, 1.

Masubelele, M. L. et al. 2014. ‘A 50 year study shows grass cover has increased in shrublands of semi-arid South Africa’, Journal of Arid Environments, 104, pp. 43–51.

Masubelele, M. L. et al. 2015. ‘A repeat photograph analysis of long-term vegetation change in semi-arid South Africa in response to land use and climate’, Journal of Vegetation Science, 26(5), pp. 1013–1023.

Masubelele, M. L. et al. 2015. ‘Biome stability and long-term vegetation change in the semi-arid, south-eastern interior of South Africa: A synthesis of repeat photo-monitoring studies’, South African Journal of Botany, 101, pp. 139–147.

Maxted, N. et al. 2006. ‘Towards a definition of a crop wild relative’, Biodiversity and Conservation. Kluwer Academic Publishers, 15(8), pp. 2673–2685.

Maze, K. 2015. Biodiversity for Development South Africa's landscape approach to conserving. doi: 10.13140/RG.2.1.1696.2409.

McCleery, R. et al. 2018. ‘Animal diversity declines with broad-scale homogenization of canopy cover in African savannas’, Biological Conservation, 226, pp. 54–62.


187

McClelland, G. T. W. et al. 2018. ‘Climate change leads to increasing population density and impacts of a key island invader’, Ecological Applications, 28(1), pp. 212–224.

Mecenero, S. et al. (eds.). 2013. Conservation assessment of butterflies of South Africa, Lesotho and Swaziland: Red List and atlas. Johannesburg and Cape Town, Safronics (Ptu) Ltd and Animal Demography Unit.

Meik, J. M. et al. 2002. ‘Effects of bush encroachment on an assemblage of diurnal lizard species in central Namibia’, Biological Conservation, 106(1), pp. 29–36.

Meissner, H. H., Scholtz, M. M. & Palmer, A. R. 2013. ‘Sustainability of the South African livestock sector towards 2050 Part 1: Worth and impact of the sector’, South African Journal of Animal Sciences, 43(3), pp. 282–297.

Metzger, MJ, Brus, DJ, Bunce, RGH, Carey, PD, Gonçalves, J, Honrado, JP, Jongman, RHG, Trabucco, A. & Zomer, R. 2013. Environmental stratifications as the basis for national, European and global ecological monitoring. Ecological Indicators, Vol 33: 26-35.

Midgley, G. F. & Van Der Heyden, F. 1999. ‘Form and function in perennial plants’, The Karoo: ecological patterns and processes, pp. 91–106, Cambridge University Press, Cambridge.

Midgley, G. F. et al. 2002. ‘Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot’, Global Ecology and Biogeography. Wiley/Blackwell (10.1111), 11(6), pp. 445–451.

Midgley, G. F. et al. 2003. ‘Developing regional and species-level assessments of climate change impacts on biodiversity in the Cape Floristic Region’, Biological Conservation. Elsevier, 112(1–2), pp. 87–97. doi: 10.1016/S0006-3207(02)00414-7.

Midgley, G. F. et al. 2012. Biodiversity, climate change and sustainable development - harnessing synergies and celebrating successes.

Midgley, G. F. & Bond, W. J. 2015. ‘Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change’, Nature Climate Change. Nature Publishing Group, 5(9), pp. 823–829. doi: 10.1038/nclimate2753.

Midgley, G. 2018. ‘Narrowing pathways to a sustainable future’, Science, 360(6390), pp. 714–715. doi: 10.1126/science.aat6671.

Milton, S. J. & Dean, W. R. J. 1995. ‘South Africa’s arid and semiarid rangelands: Why are they changing and can they be restored?’, Environmental Monitoring and Assessment, 37(1–3), pp. 245–264. doi: 10.1007/BF00546893.

Mittermeier, R. A. et al. 2011. ‘Global Biodiversity Conservaiton: The Critical Role of Hotspots’, in F. Zachos andJ. Habel (eds.). Biodiversity Hotspots, pp. 3–22, Berlin Heidelberg,Springer-Verlag. doi: 10.1007/978-3-642-20992-5.

Mittermeier, R. A. et al. (eds.). 1997.Megadiversity. Earth’s biologically wealthiest nations. CEMEX, Mexico City.

Mokhatla, M. M. et al. 2015. ‘Assessing the effects of climate change on distributions of Cape Floristic Region amphibians’, South African Journal of Science, 111(11–12), pp. 1–7.

Molinos, J. G. et al. 2016. ‘Climate velocity and the future global redistribution of marine biodiversity’, Nature Climate Change, 6(1), pp. 83–88.

Moncrieff, G. R. et al. 2015. ‘Understanding global change impacts on South African biomes using Dynamic Vegetation Models’, South African Journal of Botany.

Mucina, L. & Rutherford, M. C. (eds.). 2006.The vegetation of South Africa, Lesotho and Swaziland. Pretoria: Strelitzia 19, South African National Biodiversity Institute.

Musil, C.F. et al. 2005. 'Lethal effects of experimental warming approximating a future climate scenario on southern African quartz-field succulents: a pilot study', The new phytologist, Vol.165(2), p.539-547

Nakic enovic , N. et al. 2000. Special report on emissions scenarios (SRES), A special report of Working Group III of the Intergovernmental Panel on

Climate Change. Cambridge University Press, Cambridge, United Kingdom.

New, M. et al. 2006. ‘Evidence of trends in daily climate extremes over southern and west Africa’, Journal of Geophysical Research. Wiley-Blackwell, 111(D14), p. D14102.

Nicholson, E. et al. 2012. ‘Making robust policy decisions using global biodiversity indicators’, PLoS ONE, 7(7), p. e41128.

Nicholson, E. et al. 2015. ‘Towards consistency, rigour and compatibility of risk assessments for ecosystems and ecological communities’, Austral Ecology, 40(4), pp. 347–363.

Nicholson, E., et al. 2009. ‘Assessing the threat status of ecological communities’, Conservation Biology, 23(2), pp. 259–274.

Noss, R. F. 1990. ‘Essay Indicators for Monitoring Approach Biodiversity : Hierarchical’, Conservation Biology, 4(4), pp. 355–364.

O’Connor, T. G. et al. 2010. ‘Which grazing management practices are most appropriate for maintaining biodiversity in South African grassland?’, African Journal of Range & Forage Science. Taylor & Francis, 27(2), pp. 67–76.

O’Connor, T. G. & Kuyler, P. 2009. ‘Impact of land use on the biodiversity integrity of the moist sub-biome of the grassland biome, South Africa’, Journal of Environmental Management, 90(1), pp. 384–395. doi: https://doi.org/10.1016/j.jenvman.2007.10.012.

O’Connor, T. G. et al. 2014. ‘Bush encroachment in southern Africa: changes and causes’, African Journal of Range & Forage Science, 31(2), pp. 67–88.

Olowoyo, J. L. et al. 2015. ‘Trace metals in soil and plants around a cement factory in Pretoria, South Africa’, Polish Journal of Environmental Studies, 24(5), pp. 2087–2093.


188

Otto, H. 2014. Butterflies of the Kruger National Park and surrounds. Penguin Random House South Africa.

Pekel, J.-F. et al. 2016. ‘High-resolution mapping of global surface water and its long-term changes’, Nature, 540(7633), pp. 418–422.

Péron, G. & Altwegg, R. 2015. ‘Twenty-five years of change in southern African passerine diversity: nonclimatic factors of change’, Global change biology, 21(9), pp. 3347–3355.

Phelps, M. P., Seeb, L. W., & Seeb, J. E. 2019. 'Transforming ecology and conservation biology through genome editing' Conservation Biology. Online Early.

Phiri, E. E. et al. 2009. ‘Spatial variation in structural damage to a keystone plant species in the sub-Antarctic: interactions between Azorella selago and invasive house mice’, Antarctic Science, 21(3), pp. 189–196.

Prince, S. et al. 2018. ‘Chapter 4: Status and trends of land degradation and restoration and associated changes in biodiversity and ecosystem fundtions’, in L. Montanarella, R. Scholes and A. Brainich (eds.). The IPBES assessment report on land degradation and restoration , pp. 315–426, Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem services, Bonn, Germany.

Pringle, K. L. 2001. ‘Biological control of tetranychid mites in South African apple orchards’, in R.B. Halliday (eds.). Acarology: Proceedings of the 10th International Congress, pp. 429–435, CSIRO Publishing, Melbourne.

Pringle, K. L. & Heunis, J. 2006. Biological control of phytophagous mites in apple orchards in the Elgin area of South Africa using the predatory mite, Neoseiulus californicus (McGregor) (Mesostigmata: Phytoseiidae): A benefit-cost analysis, African Entomology.

R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna:.

Rasthe, T. 2017. 'The utilization and management of selected listed- threatened or protected species in the limpopo Province, South Africa', MSc thesis, University of Limpopo.

Reyers, B. et al. 2007. ‘Developing products for conservation decision-making: lessons from a spatial biodiversity assessment for South Africa’, Diversity and Distributions, 13(5), pp. 608–619.

RMRD. 2016. Research and development plan for the large stock and small stock meat industries in South Africa.

Rodríguez, J. P., Rodríguez-Clark, K. M., & Baillie, J. E. M., et al. 2011. ‘Establishing IUCN Red List Criteria for Threatened Ecosystems’, Conservation Biology, 25(1), pp. 21–29.

Rosauer, D. et al. 2009. ‘Phylogenetic endemism: A new approach for identifying geographical concentrations of evolutionary history’, Molecular Ecology, 18(19), pp. 4061–4072.

Rosauer, D. F. & Jetz, W. 2015. ‘Phylogenetic endemism in terrestrial mammals’, Global Ecology and Biogeography, 24(2), pp. 168–179.

Rouget, M. et al. 2003. ‘Current patterns of habitat transformation and future threats to biodiversity in terrestrial ecosystems of the Cape Floristic Region, South Africa’, Biological Conservation, 112(1–2), pp. 63–85.

Rowland, J. A. et al. 2018. ‘Selecting and applying indicators of ecosystem collapse for risk assessments’, Conservation Biology, pp. 1–35.

Rowland, J. A. et al. (in review) ‘Ecosystem indices to support global biodiversity conservation'. Conservation Letters.

RSA. 2011. National list of ecosystems that are threatened and in need of protection, National Environmental Management: Biodiversity Act (Act 10 of 2004). Government Gazette 34809.

Rutherford, M. & Westfall, R. 1994. Biomes of southern Africa: an objective categorization. 2nd Editio. Pretoria, South Africa: National Botanical Institute.

Rutherford, M. C. et al. 1999a. “South African Country Study on Climate Change.” Pretoria, South Africa, Terrestrial Plant Diversity Section, Vulnerability and Adaptation, Department of Environmental Affairs and Tourism, 264–268.

Rutherford, M. C. et al. 1999. ‘Climate change in conservation areas of South Africa and its potential impact on floristic composition: a first assessment’. Wiley/Blackwell (10.1111), 5(6), pp. 253–262.

Rutherford, M. C. et al. 2012. ‘Plant diversity consequences of a herbivore-driven biome switch from G rassland to N ama-K aroo shrub steppe in South Africa ’, Applied Vegetation Science, 15(1), pp. 14–25.

SAEON. 2011.'Full Technical Report: Climate Change Impacts on and Adaptation Responses for the Marine Fisheries Sector under Future Climate Change Scenarios.'

SANBI, DEA & GIS. 2013. ‘Climate Change and Biodiversity.'

SANBI .2017a. ‘Red List of South African Plants South African National Biodiversity Institute' viewed 5 October 2018, from http://redlist.sanbi.org/

SANBI. 2017b. 'Technical Guidelines for CBA Maps: guidelines for developing a map of Critical Biodiversity Areas and Ecological Support Areas using systematic biodiversity planning. 1st Beta Version', (eds.). A. Driver, S. Holness F. Daniels. South African National Biodiversity Institute. Pretoria, South Africa.

SANBI. 2019. National Biodiversity Assessment 2018 Supplementary Material: Compendium of Benefits of Biodiversity. South African National Biodiversity Institute, Pretoria.

Scenario building Team. 2007. Long term mitigation scenarios: Scenario Document. Pretoria, South Africa.


189

Scheffers, B. R. et al. 2016. ‘The broad footprint of climate change from genes to biomes to people’, Science, 354(aaf7671).

Scheiter, S. et al. 2012. ‘Fire and fire-adapted vegetation promoted C4 expansion in the late Miocene’, New Phytologist. Wiley/Blackwell (10.1111), 195(3), pp. 653–666.

Scheiter, S. & Higgins, S. I. 2009. ‘Impacts of climate change on the vegetation of Africa: An adaptive dynamic vegetation modelling approach’, Global Change Biology, 15(9), pp. 2224–2246.

Schmeller, D. S. et al. 2015. ‘Towards a global terrestrial species monitoring program’, Journal for Nature Conservation, 25, pp. 51–57.

Scholes, R. J. et al. 2012. ‘Building a global observing system for biodiversity’, Current Opinion in Environmental Sustainability, 4(1), pp. 139–146

Scholtz, M. et al. 2013. ‘A South African perspective on livestock production in relation to greenhouse gases and water usage’, South African Journal of Animal Science, 43(3), p. 247.

Schultz, P. A. 2007. ‘Does bush encroachment impact foraging success of the critically endangered Namibian population of the Cape Vulture Gyps coprotheres?’

Seymour, C. L. & Dean, W. R. J. 2010. ‘The influence of changes in habitat structure on the species composition of bird assemblages in the southern Kalahari’, Austral Ecology, 35(5), pp. 581–592.

Sirami, C. et al. 2009. ‘The impact of shrub encroachment on savanna bird diversity from local to regional scale’, Diversity and Distributions, 15(6), pp. 948–957

Sirami, C. & Monadjem, A. 2012. ‘Changes in bird communities in Swaziland savannas between 1998 and 2008 owing to shrub encroachment’, Diversity and Distributions, 18(4), pp. 390–400.

Skowno, A. L. et al. 2017. ‘Woodland expansion in South African grassy biomes based on satellite observations (1990–2013): general patterns and potential drivers’, Global Change Biology, 23(6), pp. 2358–2369.

Skowno, A. L. 2018. Terrestrial habitat modification change map (1990-2014) for South Africa: a national scale, two timepoint, land cover derived, map of terrestrial habitat modification - National Biodiversity Assessment 2018 Technical Report. South African National Biodiversity Institute Pretoria.

Skowno, A.L. et al. 2018. Terrestrial Ecosystem Threat Status Assessment: Briefing Document for Provincial Authorities. National Biodiversity Assessment 2018 Technical Report. South African National Biodiversity Institute, Pretoria.

Skowno, A. L. & Bond, W. J. 2003. ‘Bird community composition in an actively managed savanna reserve, importance of vegetation structure and vegetation composition’, Biodiversity & Conservation, 12(11), pp. 2279–2294.

Slingsby, J. A. et al. 2017. ‘Intensifying postfire weather and biological invasion drive species loss in a Mediterranean-type biodiversity hotspot’, PNAS, 114(18), pp. 4679–4702.

Smit, I. P. & Prins, H. H. 2015. ‘Predicting the effects of woody encroachment on mammal communities, grazing biomass and fire frequency in African savannas’, PloS one, 10(9), p. e0137857.

Smith, V. et al. 2002. ‘The diet and impact of house mice on a sub-Antarctic island’, Polar Biology. Springer-Verlag, 25(9), pp. 703–715.

Smith, V. R. 2002. ‘Climate Change in the sub-Antarctic: An Illustration from Marion Island’, Climatic Change. Kluwer Academic Publishers, 52(3), pp. 345–357.

Sofaer, H. R. et al. 2018. ‘Misleading prioritizations from modelling range shifts under climate change’, Global Ecology and Biogeography, 27(6), pp. 658–666. doi: 10.1111/geb.12726.

Spierenburg, M. & Brooks, S. 2014. ‘Private game farming and its social consequences in post-apartheid South Africa: contestations over wildlife, property and agrarian futures’, Journal of Contemporary African Studies, 32(2), pp. 151–172. doi: 10.1080/09637494.2014.937164.

Stack, J. et al. 2003. ‘Mopane Worm utilisation and rural livelihoods in Southern Africa’, in International Conference on Rural Livelihoods, Forests and Biodiversity. Bonn, Germany. Available at: http://www.cifor.org/publications/corporate/cd-roms/bonn-proc/pdfs/papers/T2_FINAL_Stack.pdf.

Stats South Africa. 2017. General Household Survey, 2016. Available at: http://www.statssa.gov.za/?p=9922 .

Stats SA.2017. Labour Market Dynamics in South Africa 2017. Report No. 02-11-02, Statistics South Africa, Pretoria.

Stats SA.2018. Tourism Satellite Account for South Africa, final 2015 and provisional 2016 and 2017. Report No. 04-05-07, Statistics South Africa, Pretoria.

Stevens, N. 2015. Change is in the air: ecological trends and their drivers in South Africa. South African Environmental Observation Network.

Stevens, N. et al. 2016. ‘Woody encroachment over 70 years in South African savannahs: overgrazing, global change or extinction aftershock?’, Phil. Trans. R. Soc. B, 371(1703), p. 20150437.

Stevens, N. et al. 2017. ‘Savanna woody encroachment is widespread across three continents’, Global Change Biology, 23(1), pp. 235–244.

Stewart, W.I. & Jorgensen, P.J. 2016. Updating of Systematic Biodiversity Plan and development and publication of Bioregional Plan for the Nelson Mandela Bay Municipality: NMBM 2015 Landcover. SRK Consulting, South Africa.

Tanentzap, A. J. et al. 2017. ‘Better practices for reporting on conservation’, Conservation Letters, 10(1), pp. 146–152.

Taylor, M. R. & Peacock, F. 2018. State of South Africa’s Bird Report 2018. BirdLife South Africa, Johannesburg, South Africa.


190

Taylor, M. R. et al. 2015. The 2015 Eskom Red Data Book of Birds of South Africa, Lesotho and Swaziland. Johannesburg, South Africa.

Taylor, W. A. et al. 2015. An assessment of the economic, social and conservation value of the wildlife ranching industry and its potential to support the green economy in South Africa. Johannesburg.

Teffo, L. S. et al. 2007. ‘Preliminary data on the nutritional composition of the edible stink-bug, Encosternum delegorguei Spinola, consumed in Limpopo province, South Africa’, South African Journal of Science, 103, pp. 434–436. Available at https://repository.up.ac.za/bitstream/handle/2263/8842/Teffo_Preliminary_2008.pdf?sequence=1.

Thomas, B. 2013. ‘Sustainable harvesting and trading of mopane worms ( Imbrasia belina ) in Northern Namibia: an experience from the Uukwaluudhi area’, International Journal of Environmental Studies. Routledge, 70(4), pp. 494–502.

Thomas, C. D. et al. 2011. ‘A framework for assessing threats and benefits to species responding to climate change’, Methods in Ecology and Evolution, 2(2), pp. 125–142.

Thompson, M. W. et al. 2009. ‘Mapping grazing-induced degradation in a semi-arid environment: A rapid and cost effective approach for assessment and monitoring’, Environmental Management, 43(4), pp. 585–596.

Thompson, M. W. et al. 2009. ‘Mapping grazing-induced degradation in a semi-arid environment: A rapid and cost effective approach for assessment and monitoring’, Environmental Management, 43(4), pp. 585–596.

Thuiller, W. et al. 2006. ‘Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions’, Global Change Biology, 12(3), pp. 424–440.

Tilman, D. and Downing, J. A. 1994. ‘Biodiversity and stability in grasslands’, Nature, 367(6461), pp. 363–365.

Tingley, M. W. et al. 2009. ‘Birds track their Grinnellian niche through a century of climate change.’, Proceedings of the National Academy of Sciences, 106, pp. 19637–19643.

Tittensor, D. P. et al. 2014. ‘A mid-term analysis of progress toward international biodiversity targets’, Science, 346(6206), pp. 241–244.

Tolley, K.A. et al. 2014. 'The shifting landscape of genes since the Pliocene: terrestrial phylogeography in the Greater Cape Floristic Region' in N. Allsopp, J.F. Colville and T. Verboom T. (eds.), Ecology and Evolution of Fynbos Understanding Megadiversity, pp. 142-163, Oxford University Press.

Tolley, K.A. et al. 2019. 'No safe haven: Protection levels show imperilled South African reptiles not sufficiently safe-guarded despite low average extinction risk', Biological Conservation, 233, Pages 61-72.

Tolley, K. A. et al. (no date) Extinction risk and protection levels for South African Reptiles.

Tonini, J. F. R. et al. 2016. ‘Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status’, Biological Conservation, 204, pp. 23–31.

Tucker, C. M. et al. 2016. ‘A guide to phylogenetic metrics for conservation, community ecology and macroecology’, Biological Reviews, 92, pp. 698–715.

Vane-Wright, R.I. et al. 1991. ‘What to protect?-Systematics and the agony of choice’, Biological Conservation, 55(3), pp. 235–254.

Venter, Z. S. et al. 2018. ‘Drivers of woody plant encroachment over Africa’, Nature communications, 9(1), p. 2272.

Verboom, G. A., Linder, H. P., Forest, F., Hoffmann, V., Bergh, N. G., & Cowling, R. M. 2014. 'Cenozoic assembly of the Greater Cape flora' Fynbos: Ecology, evolution and conservation of a megadiverse region, 93-118.

Versfeld, D. B., Le Maitre, D. C. & Champan, R. A. 1998. Alien invading plants and water resources in South Africa. TT 99/98. Pretoria, South Africa, Water Research Commission.

Welcome, A. K. & van Wyk, B.-E. 2019. An inventory and analysis of the food plants of southern Africa. South African Journal of Botany. Vol. 122, p. 136-179.

Wharton, R. A. 1989. ‘Classical biological control of fruit infesting Tephritidae’, in A.S. Robinson and A.S. Hooper (eds.). Fruit flies: Their biology, natural enemies and control, pp. 303–311, Elsevier, Amsterdam.

Whiting, M. J. et al. 2011. ‘Animals traded for traditional medicine at the Faraday market in South Africa: species diversity and conservation implications’, Journal of Zoology, 284, pp. 84–96.

Van der Linde, J. A. et al. 2012. ‘Die-off of giant Euphorbia trees in South Africa: Symptoms and relationships to climate’, South African Journal of Botany, 83, pp. 172–185.

van Deventer, H. et al. 2018. ‘Review of available data for a South African Inventory of Inland Aquatic Ecosystems (SAIIAE)’, Water SA, 44(2).

Van Ruijven, J. & Berendse, F. 2010. ‘Diversity enhances community recovery, but not resistance, after drought’, Journal of Ecology, 98(1), pp. 81–86.

Van Wilgen, B. W. et al. 2008. ‘A biome-scale assessment of the impact of invasive alien plants on ecosystem services in South Africa’, Journal of Environmental Management, 89(4), pp. 336–349. doi: 10.1016/j.jenvman.2007.06.015.

Van Wilgen, B. W. et al. 2011. ‘National-scale strategic approaches for managing introduced plants: insights from Australian acacias in South Africa’, Diversity and Distributions. Wiley/Blackwell (10.1111), 17(5), pp. 1060–1075.


191

Van Wilgen, B.W. et al. 2012. An assessment of the effectiveness of a large, national-scale invasive alien plant control strategy in South Africa. Biological Conservation, 148, 28–38.

Van Wilgen, N. J. & Herbst, M. 2016. Taking stock of parks in a changing world: The SANParks Global Environmental Change Assessment. South African National Parks,Cape Town South Africa.

Van Wilgen, B.W. et al. 2016. 'Historical costs and projected future scenarios for the management of invasive alien plants in protected areas in the Cape Floristic Region' Biological Conservation, 200, 168–177.

Van Wilgen, B. W. & Wannenburgh, A. 2016. ‘Co-facilitating invasive species control, water conservation and poverty relief: achievements and challenges in South Africa’s Working for Water programme’, Current Opinion in Environmental Sustainability. Elsevier, 19, pp. 7–17.

Van Wilgen, B.W., Fill, J.M., Govender, N. & Foxcroft, L.C. 2017. 'An assessment of the evolution, costs and effectiveness of alien plant control operations in Kruger National Park, South Africa', NeoBiota, 35, 35–59. Van Wilgen, B. W. & Wilson, J. R. U. (eds.). 2018. The status of biological invasions and their management in South Africa in 2017. South African National Biodiversity Institute, Kirstenbosch and DST-NRF Centre of Excellence for Invasion Biology, Stellenbosch.

Van Zyl, E. & Avenant, P. 2018. ‘Bankrupt bush: a serious threat to South Africa’s central grassland’, FarmBiz, 4(2), pp. 40–43.

Williams, V. L. et al. 2014. ‘Risks to Birds Traded for African Traditional Medicine: A Quantitative Assessment’, PLoS ONE. Edited by D. L. Roberts. Public Library of Science, 9(8), p. e105397.

Williams, V. L. et al. 2013. ‘Red Listed medicinal plants of South Africa: Status, trends, and assessment challenges’, South African Journal of Botany. Elsevier, 86, pp. 23–35.

Wilson, A. M. et al. 2015. ‘Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa’, Proceedings of the National Academy of Sciences, 112(29), pp. 9058–9063.

Wilson, J.R.U. et al. 2018. 'Indicators for monitoring biological invasions at a national level', Journal of Applied Ecology 55: 2612-2620.

Winter, M. et al. 2013. ‘Phylogenetic diversity and nature conservation: Where are we?’, Trends in Ecology and Evolution. Elsevier Ltd, 28(4), pp. 199–204.

Witteveen, M. et al. 2016. ‘Anthropogenic debris in the nests of kelp gulls in South Africa’, Marine Pollution Bulletin2.

Wong, T. T. Y. et al. 1992. ‘Suppression of a Mediterranean Fruit Fly (Diptera: Tephritidae) population with concurrent parasitoid and sterile fly releases in Kula, Maui, Hawaii’, Journal of Economic Entomology, 85(5), pp.

World Resources Institute. 2010. South Africa : Ecosystem-Based Planning for Climate Change.

Zengeya, T. et al. 2017. ‘Managing conflict-generating invasive species in South Africa: Challenges and trade-offs’, Bothalia, 47(2): a2160.


192

14. LIST OF APPENDICES

A List of meetings held for development of this report

B Land cover changes per biome (km2) between 1750 and 1990 and 2014

15. LIST OF ANNEXURES (SEPARATE DOCUMENTS AND DATASETS)

The following are available as annexures to this terrestrial report on http://bgis.sanbi.org/Projects/Detail/221

Name of supplementary material

Integrated ecosystem type map across realms

Vegetation Map 2018

Red List of Ecosystems database

Technical report on land cover change

Species Status Website

Plant Red List Website

Species Protection Level Tables



193

16. LIST OF ACRONYMS, ABBREVIATIONS, INITIALISMS AND SYMBOLS

CBA Critical Biodiversity Area

CBD Convention on Biological Diversity

CITES Convention on the International Trade in Endangered Species

CR Critically Endangered

CSIR Council for Scientific and Industrial Research

DAFF Department of Agriculture, Forestry and Fisheries (former government department)

DALRRD Department of Agriculture, Land Reform and Rural Development (formed by merging DAFF and Department of Rural Development and Land Reform in June 2019)

DD Data Deficient

DEA Department of Environmental Affairs (Former government department)

DEFF Department of Environment, Forestry and Fisheries (formed by merging DAFF and DEA in June 2019)

DNA Deoxyribonucleic acid

DSI Department of Environment, Forestry and Fisheries (formed by merging DAFF and DEA in June 2019)

DST Department of Science and Technology (Former government department)

DWS Department of Water and Sanitation

EIA Environmental impact assessment

EN Endangered

FBIP Foundational Biodiversity Information Programme

FEPA Freshwater Ecosystem Priority Areas

GIS Geographic Information Systems

IPBES The Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services

IUCN International Union for the Conservation of Nature

KBA Key Biodiversity Area

KZN KwaZulu-Natal

LC Least Concern

MPA Marine protected area

NBA National Biodiversity Assessment

NBF National Biodiversity Framework

NBSAP National Biodiversity Strategy and Action Plan

NDP National Development Plan

NE Not Evaluated

NEMBA / NEM:BA National Environmental Management: Biodiversity Act (10 of 2004) / Biodiversity Act

NFEPA National Freshwater Ecosystem Priority Areas

NPAES National Protected Area Expansion Strategy

NRF National Research Foundation

NSBA National Spatial Biodiversity Assessment

NT Near Threatened

PA Protected area

PD Phylogenetic diversity

PEIs Prince Edward Islands – consisting of Marion and the smaller Prince Edward Island and their surrounding seas

SAEON South African Environmental Observation Network

SANBI South African National Biodiversity Institute

SDF Spatial Development Framework

SEA Strategic Environmental Assessment

SPLUMA Spatial Planning and Land Use Management Act (16 of 2013)

SWSA Strategic Water Source Areas

ToCC Taxon of Conservation Concern

TOPS regulations Threatened or protected species regulations under NEM:BA

UNCCD United Nations Convention to Combat Desertification

UNE United Nations Environment

VU Vulnerable

WCMC World Conservation Monitoring Centre

WRC Water Research Commission


194

17. GLOSSARY OF TERMS

Also see the Lexicon of Biodiversity Planning in South Africa, which provides standard definitions of key concepts and frequently used terms.

Benefits of biodiversity: a general term meant to encompass terminology in popular use for various purposes, such as ‘ecosystem services’, ‘ecosystem goods’, ‘ecological infrastructure’, and ‘nature’s contributions to people’. The NBA 2018 authors felt that ‘benefits’ is a term that is currently understood well in South Africa by multiple audiences. The work on the term ‘nature’s contributions to people’ (defined as: All the benefits (and occasionally losses or detriments) that humanity obtains from nature), underway through the Intergovernmental Platform on Biodiversity and Ecosystem Services, is fully acknowledged and efforts to find inclusionary terminology that encompasses the diverse world views on the human-nature relationship and further opportunities to incorporate non-monetary values into our discourse are welcomed.

Biodiversity assets: Species, ecosystems and other biodiversity-related resources that generate ecosystem services, support livelihoods, and provide a foundation for economic growth, social development and human wellbeing.

Biodiversity Management Plan: A plan aimed at ensuring the long‐term survival in nature of an indigenous species, a migratory species or an

ecosystem, published in terms of the Biodiversity Act. Norms and standards to guide the development of Biodiversity Management Plans for Species have been developed. At the time of writing, norms and standards for Biodiversity Management Plans for Ecosystems were in the process of being developed.

Biodiversity planning: Spatial planning to identify geographic areas of importance for biodiversity. Also see Systematic biodiversity planning.

Biodiversity priority areas: Features in the landscape or seascape that are important for conserving a representative sample of ecosystems and species, for maintaining ecological processes, or for the provision of ecosystem services. They include the following categories, most of which are identified based on systematic biodiversity planning principles and methods: protected areas, Critically Endangered and Endangered ecosystems, Critical Biodiversity Areas and Ecological Support Areas, Freshwater Ecosystem Priority Areas, high water yield areas, flagship free-flowing rivers, priority estuaries, focus areas for land-based protected area expansion, and focus areas for offshore protection. Marine ecosystem priority areas and coastal ecosystem priority areas have yet to be identified but will be included in future. The different categories are not mutually exclusive and in some cases overlap, often because a particular area or site is important for more than one reason. They should be seen as complementary, with overlaps reinforcing the importance of an area.

Biodiversity stewardship: a model for expanding the protected area network in which conservation authorities enter into contract agreements with private and communal landowners to place land that is of high biodiversity value under formal protection. Different categories of agreement confer varying degrees of protection on the land and hold different benefits for landowners. The landowner retains title to the land, and the primary responsibility for management remains with the landowner, with technical advice and assistance provided by the conservation authority.

Biodiversity target: The minimum proportion of each ecosystem type that needs to be kept in a natural or near natural state in the long term in order to maintain viable representative samples of all ecosystem types and the majority of species associated with those ecosystem types.

Biodiversity thresholds: A series of thresholds used to assess ecosystem threat status, expressed as a percentage of the original extent of an ecosystem type. The first threshold, for Critically Endangered ecosystems, is equal to the biodiversity target; the second threshold, for Endangered ecosystems, is equal to the biodiversity target plus 15%; and the third threshold, for Vulnerable ecosystems, is usually set at 60%. Also see Ecosystem threat status.

Biodiversity: The diversity of genes, species and ecosystems on Earth, and the ecological and evolutionary processes that maintain this diversity.

Biome: An ecological unit of wide extent, characterised by complexes of plant communities and associated animal communities and ecosystems, and determined mainly by climatic factors and soil types. A biome may extend over large, more or less continuous expanses or land surface, or may exist in small discontinuous patches.

Bioregional plan (published in terms of the Biodiversity Act): A map of Critical Biodiversity Areas and Ecological Support Areas, for a municipality or group of municipalities, accompanied by contextual information, land- and resource-use guidelines and supporting GIS data. The map should be produced using the principles and methods of systematic biodiversity planning, in accordance with the Guideline for Bioregional Plans. A bioregional plan represents the biodiversity sector’s input into planning and decision making in a range of other sectors. The development of the plan is usually led by the relevant provincial conservation authority or provincial environmental affairs department. A bioregional plan that has not yet been published in the Government Gazette in terms of the Biodiversity Act is referred to as a biodiversity sector plan.

Coast: The coast or coastal zone was determined ecologically, by identifying terrestrial and marine ecosystem types with strong coastal affinities. In addition, all estuarine ecosystem types were considered coastal. It is recognised that this is different to the definition of coastal zone in the Integrated Coastal Management Act which uses fixed buffer distances from the high water mark.

Collapsed (CO) (Red List category): An ecosystem type is Collapsed when it is virtually certain that its defining biotic or abiotic features are lost, and the characteristic native biota are no longer sustained.

Conservation area: Areas of land not formally protected by law but informally protected by the current owners and users and managed at least partly for biodiversity conservation. Because there is no long-term security associated with conservation areas, they are not considered a strong form of protection. Also see Protected area.

Conservation planning—see Biodiversity planning.

Critical Biodiversity Area: Areas required to meet biodiversity targets for ecosystems, species or ecological processes, as identified in a systematic biodiversity plan. May be terrestrial or aquatic.

http://biodiversityadvisor.sanbi.org/wp-content/uploads/2016/06/2016_06_02-Lexicon.pdf


195

Critically Endangered (CR) (IUCN Red List category): Applied to both species/taxa and ecosystems: A species is Critically Endangered when the best available evidence indicates that it meets at least one of the five IUCN criteria for Critically Endangered, indicating that the species is facing an extremely high risk of extinction. Critically Endangered ecosystem types are considered to be at an extremely high risk of collapse. Most of the ecosystem type has been severely or moderately modified from its natural state. The ecosystem type is likely to have lost much of its natural structure and functioning, and species associated with the ecosystem may have been lost. Critically endangered species are those considered to be at extremely high risk of extinction.

Data Deficient (DD) (Red List category): An ecosystem type or species is Data Deficient when there is inadequate information to make a direct, or indirect, assessment of its risk of extinction (species) or risk of collapse (ecosystems). Listing ecosystems or species in this category indicates that their situation has been reviewed, but that more information is required to determine their risk status.

Degradation: the many human-caused processes that drive the decline or loss in biodiversity, ecosystem functions or ecosystem services in any terrestrial and associated aquatic ecosystems.

Ecological infrastructure: The stock of ecosystems and species, or natural capital, that provides a flow of essential ecosystem services to human communities. Networks of ecological infrastructure may takes the form of large tracts of natural land or ocean, or small remaining patches or corridors embedded in production landscapes. If ecological infrastructure is degraded or lost, the flow of ecosystem services will diminish. Ecological infrastructure is just as important as built infrastructure for providing vital services that underpin social and economic activity.

Ecological Support Area: An area that is not essential for meeting biodiversity targets but plays an important role in supporting the ecological functioning of one or more Critical Biodiversity Areas or in delivering ecosystem services. May be terrestrial or aquatic.

Ecosystem protection level: Indicator of the extent to which ecosystems are adequately protected or under-protected. Ecosystem types are categorised as Well Protected, Moderately Protected, Poorly Protected, or Not Protected, based on the proportion of the biodiversity target for each ecosystem type that is included within one or more protected areas. Not Protected, Poorly Protected or Moderately Protected ecosystem types are collectively referred to as under-protected ecosystems.

Ecosystem services: the benefits that people obtain from ecosystems, including provisioning services (such as food and water), regulating services (such as flood control), cultural services (such as recreational benefits), and supporting services (such as nutrient cycling, carbon storage) that maintain the conditions for life on Earth. Ecosystem services are the flows of value to human society that result from a healthy stock of ecological infrastructure. If ecological infrastructure is degraded or lost, the flow of ecosystem services will diminish. See also benefits of biodiversity.

Ecosystem threat status: Indicator of how threatened ecosystems are, in other words the degree to which ecosystems are still intact or alternatively losing vital aspects of their structure, function or composition. Ecosystem types are categorised as Critically Endangered, Endangered, Vulnerable, Near Threatened or Least Concern, based on the proportion of the original extent of each ecosystem type that remains in good ecological condition relative to a series of biodiversity thresholds. Critically Endangered, Endangered and Vulnerable ecosystems are collectively referred to threatened ecosystems, and may be listed as such in terms of the Biodiversity Act.

Ecosystem type: An ecosystem unit that has been identified and delineated as part of a hierarchical classification system, based on biotic and/or abiotic factors. Factors used to map and classify ecosystems differ in different environments. Ecosystem types can be defined as, for example, vegetation types, river ecosystem types, wetland ecosystem types, estuary ecosystem types, or marine or coastal habitat types. Ecosystems of the same type are likely to share broadly similar ecological characteristics and functioning. Also see National ecosystem classification system.

Ecosystem-based Adaptation (to climate change): The use of biodiversity and ecosystem services as part of an overall adaptation strategy to help people adapt to the adverse effects of climate change. Includes managing, conserving and restoring ecosystems to buffer humans from the impacts of climate change, rather than relying only on engineered solutions. Combines socio-economic benefits, climate change adaptation, and biodiversity and ecosystem conservation, contributing to all three of these outcomes simultaneously.

Endangered (EN) (Red List category): Applied to both species/taxa and ecosystems: A species is Endangered when the best available evidence indicates that it meets at least one of the five IUCN criteria for Endangered, indicating that the species is facing a very high risk of extinction. Endangered ecosystem types are considered to be at a very high risk of collapse. Endangered species are those considered to be at very high risk of extinction.

Estuarine functional zone: The open water area of an estuary together with the associated floodplain, incorporating estuarine habitat (such as sand and mudflats, salt marshes, rock and plant communities) and key physical and biological processes that are essential for estuarine ecological functioning.

Invasion debt: the potential increase in the biological invasion problem that a given region will face over a particular time frame in the absence of any strategic interventions. It is composed of the number of new species that will be introduced (introduction debt), the number of species that will become invasive (species-based invasion debt); the increase in area affected by invasions (area-based invasion debt); and the increase in the negative impacts caused by introduced species (impact-based invasion debt).

Least Concern (LC) (Red List category): An ecosystem type that has experienced little or no loss of natural habitat or deterioration in condition or a species considered at low risk of extinction. Widespread and abundant species are typically classified in this category.

Mainstem river: A quaternary mainstem, or a river that passes through a quaternary catchment into a neighbouring quaternary catchment. In situations where no river passes through a quaternary catchment, the longest river in the quaternary catchment is the main river. Also see Tributaries.

Metapopulation: A metapopulation is the set of discrete local populations that are connected through immigration. Shrinking local populations due to habitat loss and fragmentation isolates these populations and reduces immigration between them. This loss of connectivity disrupts the metapopulation, resulting in tiny isolated populations that lack resilience to stochastic events and increases extinction risk.


196

Moderately Protected (MP): An ecosystem type or species that has between 50 and 100% of its biodiversity target included in one or more protected areas.

National ecosystem classification system: A hierarchical system for mapping and classifying ecosystem types in the terrestrial, river, wetland, estuarine, coastal and marine realm. South Africa has a well-established classification system for terrestrial ecosystems in the form of vegetation mapping, and much progress has been made in mapping and classifying aquatic ecosystems as part of the NBA 2011. Factors used to map and classify ecosystems differ in different environments, but in all cases ecosystems of the same type are expected to share broadly similar ecological characteristics and functioning. The national ecosystem classification system provides an essential scientific foundation for ecosystem-level assessment, planning, monitoring and management. Also see Ecosystem type.

Near Threatened (NT) (Red List category): An ecosystem type or species is Near Threatened when it has been evaluated against the IUCN criteria but does not qualify for CR, EN or VU, but it is close to qualifying for or is likely to qualify for a threatened category in the near future.

Not Evaluated (NE) (Red List category): An ecosystem type or species is Not Evaluated when it is has not been assessed against any of the IUCN criteria for assessing the threat status of species or ecosystems.

Not Protected (NP): An ecosystem type or species that has less than 5% of its biodiversity target included in one or more protected areas

Poorly Protected (PP): An ecosystem type or species which has between five percent and 50% of its biodiversity target included in one or more protected areas.

Present Ecological State: A set of categories for describing the ecological condition of rivers, wetlands and estuaries, developed by the Department of Water Affairs. Assessment of Present Ecological State takes into account a range of factors including flow, inundation, water quality, stream bed condition, introduced instream biota, and riparian or stream bank condition. The categories range from A (natural or unmodified) through to F (critically or extremely modified), with clear descriptions linked to each category.

Protected area target: A quantitative goal for how much of an ecosystem type should be included in the protected area network by a certain date. The National Protected Area Expansion Strategy 2008 sets five-year and twenty-year protected area targets for each terrestrial ecosystem type, based on a portion of its biodiversity target. Protected area targets are revised every five years.

Protected area: An area of land or sea that is formally protected by law and managed mainly for biodiversity conservation. This is a narrower definition than the IUCN definition, which includes areas that are not legally protected and that would be defined in South Africa as conservation areas rather than protected areas. Also see Conservation area.

Spatial biodiversity plan: A plan that identifies one or more categories of biodiversity priority area, using the principles and methods of systematic biodiversity planning. South Africa has a suite of spatial biodiversity plans at national and sub-national level, which together should inform land-use planning, environmental impact assessment, water resource management, and protected area expansion.

Systematic biodiversity planning: A scientific method for identifying geographic areas of biodiversity importance. It involves: mapping biodiversity features (such as ecosystems, species, spatial components of ecological processes); mapping a range of information related to these biodiversity features and their ecological condition; setting quantitative targets for biodiversity features; analysing the information using software linked to GIS; and developing maps that show spatial biodiversity priorities. The configuration of priority areas is designed to be spatially efficient (i.e. to meet biodiversity targets in the smallest area possible) and to avoid conflict with other land and water resource uses where possible.

Taxa of Conservation Concern (ToCC) are species and subspecies that are important for South Africa’s conservation decision-making processes. They include all taxa that are assessed according the IUCN Red List criteria as Critically Endangered (CR) Endangered (EN), Vulnerable (VU), Data Deficient (DD) or Near Threatened (NT). They also include range restricted taxa (Extent of Occurrence < 500 km2) that are classified according to South Africa’s national criteria as Rare. Detailed information on the pressures impacting these taxa has been captured during the Red List assessment processes. Throughout the NBA reference to the impact of a particular pressure on a taxonomic groups is determined from the proportion of taxa of conservation concern impacted by that pressure.

Taxon (plural taxa) is any unit used in the science of biological classification, or taxonomy. Some species have been split into sub-species and/or varieties and assessed at these levels. Consequently, if a taxonomic group includes sub-species or varieties, the summary statistics use the term ‘taxa’. If a group contains only species then the term ‘species’ is used in the summary statistics.

Threatened ecosystem: An ecosystem that has been classified as Critically Endangered, Endangered or Vulnerable, based on an analysis of ecosystem threat status. A threatened ecosystem has lost or is losing vital aspects of its structure, function or composition. The Biodiversity Act allows the Minister of Environmental Affairs or a provincial MEC for Environmental Affairs to publish a list of threatened ecosystems. To date, threatened ecosystems have been listed only in the terrestrial environment. In cases where no list has yet been published by the Minister, such as for all aquatic ecosystems, the ecosystem threat status assessment in the NBA can be used as an interim list in planning and decision making. Also see Ecosystem threat status.

Threatened species: A species that has been classified as Critically Endangered, Endangered or Vulnerable, based on a conservation assessment (Red List), using a standard set of criteria developed by the IUCN for determining the likelihood of a species becoming extinct. A threatened species faces a high risk of extinction in the near future.

Vulnerable (VU) (Red List category): Applied to both species/taxa and ecosystems: A species is Vulnerable when the best available evidence indicates that it meets at least one of the five IUCN criteria for Vulnerable, indicating that the species is facing a high risk of extinction. An ecosystem type is Vulnerable when the best available evidence indicates that it meets any of the criteria A to E for VU, and is then considered to be at a high risk of collapse.

Well Protected (WP): An ecosystem type or species that has its full biodiversity target included in one or more protected areas.

Years to ecosystem Collapse (YtC): combines the recent rate of loss with the current (2014) extent of natural habitat and estimates the number of years until the ecosystem extent declines to zero.


197

APPENDICES

APPENDIX A: List of meetings held for development of this report The following formal meetings were held for the development of this technical report, and it should be

noted that many other small gatherings of technical experts occurred. Meeting reports are available on

request.

Meeting date Venue Nature of meeting Participants

22-23 September 2015

SANBI Pretoria Species Component Meeting

Adrian Armstrong, Andrew Skowno, Brian Colahan, Carol Poole, Craig Whittington-Jones, Daan Buijs, Dave Edge, Dean Ricketts, Deshni Pillay, Dewidine Van Der Colff, Domitilla Raimondo, Errol Moeng, Fiona MacKay, Gordon O'Brien, Graham Alexander, Harriet Davies-Mostert, Heidi van Deventer, Hermien Roux, John Measey, Karin Steenkamp, Krystal Tolley, Lara Van Niekerk, Lize v/d Merwe, Lize von Staden, Martin Taylor, Mbulelo Xalu, Michele Pfab, Michelle Hammer, Nacelle Collins, Namhla Mbona, Nhlanganiso Biyela, Petro Marais, Quinton Joshua Reinier Terblanche, Res Altwegg, Silvia Kirkman, Smiso Bengu, Stephen Lamberth, Tasneem Variawa, Theresa Sethusa, Tommie Steyn, Tony Rebelo.

20 October 2015

SANBI Kirstenbosch

National Vegetation Map Committee

As per committee member list in Acknowledgements.

12 October 2016

Willows Country Lodge, Pretoria

Terrestrial reference

group meeting* at

Provincial & Metro Biodiversity Planning Working Group 2016

Mervyn Lotter, Boyd Escott, Phil Desmet, Linda Harris, Andrew Skowno, Tsamaelo Malebu, Kedibone Lamula, Vincent Egan, Kagiso Mangwale, Ray Schaller, Enrico Oosthuysen, Fahiema Daniels, Warrick Stewart, Gen Pence, Norma Malajti, Jeff Manuel, Mandy Driver, Tammy Smith, Stephen Holness, Don Kirkwood, Derek Berliner, Domitilla Raimondo, Lize Von Staden.

9 November 2016

SANBI Kirstenbosch



22-23 March 2017

SANBI Kirstenbosch

Measuring Protection Level for South Africa Species

Lize von Staden, Martine Jordaan, Francois Roux, Ian Little, Daniel Marnewick, Ernst Retief, Silvia Kirkman, Matthew Child, Reuhl Lombard, Lizanne Roxburgh, Jeanne Tarrant, Mohlamatsane Mokhatla, Krystal Tolley, Andrew Turner, Jessica da Silva, Andrew Skowno, Domitilla Raimondo, Rupert Koopman, Ismail Ebrahim, Dewidine Van der Colff.

18-21 September 2017

SANBI Kirstenbosch

Freshwater Fish Protection Level Workshop

Albert Chakona, Dewidine Van der Colff, Francois Roux, Martine Jordaan, Skumbuzo Kubeka, Natalie Hayward.

16-18 October 2017

Willows Country Lodge, Pretoria

Provincial & Metro Biodiversity Planning Working Group

Mandy Driver, Jeff Manuel, Fahiema Daniels, Tsamaelo Malebu, Andrew Skowno, Tammy Smith, Domitilla Raimondo, Mthobisi Nzimande,, Abigail Bahidwa, Sagwata Manyika, Shonisani Netshishivhe, Kristal Maze, Boyd Escott, Mervyn Lotter, Gen Pence, Enrico Oosthuysen, Kagiso Mangwale, Linda Harris, Heidi Van Deventer, Stephen Holness, Emily Botts, Don Kirkwood, Phil Desmet, Warrick Stewart, Greer Hawley, Amanda Britz, Franz Scheepers, Pam Kershaw, Abulele Adams, Luanita Snyman-van der Walt, Lize Von Staden.

1 November 2017

SANBI Kirstenbosch



30 October – 1 November 2018

Lombardy Boutique Hotel, Pretoria East

Provincial & Metro Biodiversity Planning Working Group [Attendance listed for special session on threat status assessment for terrestrial ecosystems held 31/10/2018]

Abigail Bahindwah, Alana Duffell-Canham, Andrew Skowno, Anisha Dayaram, Boyd Escott, Debbie Jewitt, Deshni Pillay, Enrico Oosthuysen, Errol Tukiso Moeng, Fahiema Daniels, Fusi Kraai, Genevieve Pence, Greer Hawley, Heidi Van Deventer, Jeffrey Manuel, Johan Bester, Kagiso Mangwale, Karen Steenkamp, Kedibone Lamula, Kerry Sink, Lara Van Niekerk, Linda Harris, Lize von Staden, Mandy Driver, Marc Leroy, Marthán Theart, Mathabo Phoka, Mervyn Lötter, Miyelani Ngobeni, Moagi Keretetse, Mthobisi Nzimande, Nacelle Collins, Nancy Job, Nontokozo Mahlalel, Norma Malatji, Peter Cloete, Philip Desmet, Ray Schaller, Rolivhuwa Nemakonde, Stephan Veldsman, Tammy Smith, Tamsyn Livingston, Tilla Raimondo, Tinyiko Malungani, Tsamaelo Malebu.

12 November 2018

SANBI Kirstenbosch



* Note: At this meeting, it was confirmed that there would be a session at each annual Provincial & Metro Biodiversity Planning Working Group

meeting that relates to the terrestrial report, and therefore no separate meetings would be needed going forward.


198

APPENDIX B: Land cover changes per biome (km2) between 1750 and 1990 and

2014

Albany Thicket Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Indian Ocean CB Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Natural 35250 32450 32126 -3124 -2800 -324 Natural 11530 4825 4148 -7382 -6705 -677

Artificial waterbody 0 142 148 148 142 6 Artificial waterbody 0 16 18 18 16 2

Built up 0 490 502 502 490 12 Built up 0 2471 2181 2181 2471 -290

Cropland 0 1583 1644 1644 1583 61 Cropland 0 2048 2646 2646 2048 597

Erosion Erosion

Mine 0 24 9 9 24 -15 Mine 0 6 7 7 6 1

Plantation 0 46 49 49 46 3 Plantation 0 1328 1237 1237 1328 -91

Secondary natural 0 515 774 774 515 259 Secondary natural 0 835 1293 1293 835 458

Desert Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Nama-Karoo Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Natural 6260 6179 6166 -93 -80 -13 Natural 249354 245220 244526 -4828 -4134 -694


Built up 0 4 6 6 4 1 Built up 0 157 172 172 157 15

Cropland 0 8 6 6 8 -1 Cropland 0 1946 2227 2227 1946 281

Erosion Erosion 0 274 339 339 274 65

Mine 0 66 63 63 66 -2 Mine 0 157 152 152 157 -5

Plantation 0 0 0 0 0 0 Plantation 0 14 18 18 14 4


Forest Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Savanna Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Natural 4544 3838 3754 -790 -706 -84 Natural 394159 327852 319094 -75065 -66307 -8758


Built up 0 68 75 75 68 7 Built up 0 11739 12558 12558 11739 819


Erosion Erosion 0 617 921 921 617 304

Mine 0 1 5 5 1 5 Mine 0 1067 1133 1133 1067 66

Plantation 0 458 315 315 458 -143 Plantation 0 3083 2720 2720 3083 -363


Fynbos Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Succulent Karoo Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Natural 81444 57891 55865 -25579 -23553 -2027 Natural 78203 74907 74608 -3595 -3296 -299


Built up 0 1049 1115 1115 1049 66 Built up 0 86 91 91 86 5


Erosion Erosion

Mine 0 30 28 28 30 -1 Mine 0 388 393 393 388 5

Plantation 0 1378 958 958 1378 -421 Plantation 0 3 2 2 3 -1


Grassland Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Azonal Vegetation Extent 1750

(Reference)

Extent

1990

Extent

2014

Change

(1750-2014)

Change

(1750-1990)

Change

(1990-2014)

Natural 330861 209239 198057 -132804 -121622 -11182 Natural 26082 21779 21303 -4778 -4302 -476


Built up 0 11047 10698 10698 11047 -349 Built up 0 149 135 135 149 -13


Erosion 0 388 685 685 388 297 Erosion 0 8 10 10 8 2

Mine 0 1011 1278 1278 1011 267 Mine 0 72 72 72 72 0

Plantation 0 12712 13230 13230 12712 518 Plantation 0 65 50 50 65 -14



199

APPENDIX C: Terrestrial Ecosystem Threat Status and Protection Level

Vegetation type (ecosystem type)

Biome Original Extent (km2)

% Natural

Threat status NBA 2018

RLE Basis Protection Level

2018 Endemism

Aggeneys Gravel Vygieveld Succulent

Karoo 372.004 99.7

Least Concern

Not Protected Endemic

Agter-Sederberg Shrubland Succulent

Karoo 928.614 97.5

Least Concern

Poorly Protected Endemic

Agulhas Limestone Fynbos Fynbos 294.009 91.8 Critically

Endangered B1thrsp_inv Poorly Protected Endemic

Agulhas Sand Fynbos Fynbos 246.829 51.6 Critically

Endangered B1thrsp_inv

Moderately Protected

Endemic

Albany Alluvial Vegetation Azonal

Vegetation 653.865 46.1 Endangered B1 Poorly Protected Endemic

Albany Arid Thicket Albany Thicket

14.614 99.9 Least

Concern Poorly Protected Endemic

Albany Bontveld Albany Thicket

53.761 95.8 Least


Albany Broken Veld Nama-Karoo 742.643 93.0 Least

Concern


Endemic

Albany Mesic Thicket Albany Thicket

729.212 80.1 Least

Concern


Endemic

Albany Valley Thicket Albany Thicket

1175.651 88.6 Least

Concern


Endemic

Albertinia Sand Fynbos Fynbos 517.614 54.6 Least


Alexander Bay Coastal Duneveld Desert 17.078 12.3 Endangered A3 Not Protected Endemism uncertain

Algoa Sandstone Fynbos Fynbos 345.623 43.1 Critically

Endangered B1 Poorly Protected Endemic

Aliwal North Dry Grassland Grassland 7163.265 80.3 Least

Concern Not Protected Endemic

Amathole Mistbelt Grassland Grassland 158.302 97.6 Least


Amathole Montane Grassland Grassland 5027.012 85.4 Least


Amersfoort Highveld Clay Grassland

Grassland 3927.052 56.5 Least


Andesite Mountain Bushveld Savanna 2017.807 73.2 Least

Concern


Endemic

Anenous Plateau Shrubland Succulent

Karoo 241.782 83.3

Least Concern


Atlantis Sand Fynbos Fynbos 689.053 52.0 Endangered B1thrsp_inv, B1thrsp_ovgr

Poorly Protected Endemic

Auob Duneveld Savanna 2898.159 100.0 Least

Concern Well Protected

Likely not endemic

Barberton Montane Grassland Grassland 1291.377 63.4 Least


Likely endemic to ZA, LS, eS

Barberton Serpentine Sourveld Savanna 109.492 67.5 Least

Concern Well Protected Endemic

Basotho Montane Shrubland Grassland 3469.845 71.4 Least

Concern Poorly Protected

Likely endemic to ZA, LS, eS

Baviaans Valley Thicket Albany Thicket

1077.525 98.8 Least


Baviaanskloof Shale Renosterveld

Fynbos 118.717 100.0 Least


Bedford Dry Grassland Grassland 1433.768 98.4 Least


Besemkaree Koppies Shrubland Grassland 9677.822 95.7 Least


Bethelsdorp Bontveld Albany Thicket

35.528 59.3 Vulnerable A3CITY Not Protected Endemic

Bhisho Thornveld Savanna 7762.397 63.5 Least


Bloemfontein Dry Grassland Grassland 4949.574 53.7 Least


Bloemfontein Karroid Shrubland Grassland 80.504 85.8 Least

Concern


Endemic

Blombos Strandveld Fynbos 20.774 98.4 Least

Concern


Endemic

Blouputs Karroid Thornveld Nama-Karoo 607.464 99.2 Least


Likely not endemic

Bokkeveld Sandstone Fynbos Fynbos 1014.175 79.8 Least


Boland Granite Fynbos Fynbos 523.968 56.8 Endangered B1thrsp_inv Well Protected Endemic

Breede Alluvium Fynbos Fynbos 501.672 38.3 Endangered B1,B2,B1thrsp_inv Poorly Protected Endemic


200



% Natural



2018 Endemism

Breede Alluvium Renosterveld Fynbos 497.693 39.8 Endangered B1 Not Protected Endemic

Breede Quartzite Fynbos Fynbos 97.849 94.0 Least


Breede Sand Fynbos Fynbos 97.698 46.6 Vulnerable A3,A3WC Poorly Protected Endemic

Breede Shale Fynbos Fynbos 318.148 66.8 Endangered B1thrsp_inv Moderately Protected

Endemic

Breede Shale Renosterveld Fynbos 1049.908 60.7 Endangered B1thrsp_inv Poorly Protected Endemic

Buffels Mesic Thicket Albany Thicket

386.340 81.7 Least


Buffels Valley Thicket Albany Thicket

215.462 45.4 Critically

Endangered B1 Not Protected Endemic

Bushmanland Arid Grassland Nama-Karoo 41251.694 99.6 Least

Concern Not Protected

Likely not endemic

Bushmanland Basin Shrubland Nama-Karoo 41250.821 99.5 Least


Bushmanland Inselberg Shrubland

Succulent Karoo

817.646 99.8 Least


Likely not endemic

Bushmanland Sandy Grassland Nama-Karoo 2677.220 99.9 Least


Bushmanland Vloere Azonal

Vegetation 5177.068 93.6

Least Concern


Canca Limestone Fynbos Fynbos 781.328 80.8 Least


Cape Flats Dune Strandveld Fynbos 398.641 56.1 Endangered B1,B2,B1thrsp_inv Moderately Protected

Endemic

Cape Flats Sand Fynbos Fynbos 556.967 23.8 Critically

Endangered B1,B1thrsp_inv,B1th

rsp_ovgr Not Protected Endemic

Cape Lowland Alluvial Vegetation

Azonal Vegetation

350.889 37.1 Endangered B1 Poorly Protected Endemic

Cape Seashore Vegetation Azonal


Least Concern

Well Protected Endemic

Cape Winelands Shale Fynbos Fynbos 83.992 46.2 Vulnerable A3 Well Protected Endemic

Carletonville Dolomite Grassland



Endemism uncertain

Cathedral Mopane Bushveld Savanna 277.067 100.0 Least


Endemism uncertain

Cederberg Sandstone Fynbos Fynbos 2523.629 89.5 Least


Endemism uncertain

Central Coastal Shale Band Vegetation



Endemism uncertain

Central Free State Grassland Grassland 16012.837 67.2 Least


Endemism uncertain

Central Inland Shale Band Vegetation



Endemism uncertain

Central Knersvlakte Vygieveld Succulent

Karoo 129.893 99.6

Least Concern

Well Protected Endemism uncertain

Central Mountain Shale Renosterveld



Endemism uncertain

Central Richtersveld Mountain Shrubland

Succulent Karoo

1200.447 100.0 Least


Endemism uncertain

Central Ruens Shale Renosterveld

Fynbos 2027.527 11.9 Critically

Endangered A3WC Not Protected

Endemism uncertain

Central Sandy Bushveld Savanna 17255.231 65.0 Least


Endemism uncertain

Ceres Shale Renosterveld Fynbos 491.732 47.3 Vulnerable A3,A3WC Poorly Protected Endemism uncertain

Citrusdal Shale Renosterveld Fynbos 47.005 28.2 Critically

Endangered B1 Not Protected

Endemism uncertain

Citrusdal Vygieveld Succulent

Karoo 185.223 74.7

Least Concern

Poorly Protected Endemism uncertain

Crocodile Gorge Mountain Bushveld

Savanna 539.891 80.9 Least

Concern


Endemism uncertain

Crossroads Grassland Thicket Albany Thicket

311.136 87.0 Least

Concern


Endemism uncertain

De Hoop Limestone Fynbos Fynbos 690.268 96.1 Least

Concern


Endemism uncertain

Delagoa Lowveld Savanna 2722.055 72.6 Least

Concern


Endemism uncertain

Die Plate Succulent Shrubland Succulent

Karoo 127.573 100.0

Least Concern

Not Protected Endemism uncertain

Doringrivier Quartzite Karoo Succulent

Karoo 540.776 84.3

Least Concern


Doubledrift Karroid Thicket Albany Thicket

2975.919 87.9 Least


Endemism uncertain


201



% Natural



2018 Endemism

Drakensberg Afroalpine Heathland



Endemism uncertain

Drakensberg Foothill Moist Grassland



Endemism uncertain

Drakensberg-Amathole Afromontane Fynbos



Endemism uncertain

Dry Coast Hinterland Grassland Grassland 3024.248 47.2 Vulnerable A3 Not Protected Endemism uncertain

Dwaalboom Thornveld Savanna 9663.246 79.8 Least

Concern


Endemism uncertain

Dwarsberg-Swartruggens Mountain Bushveld



Endemism uncertain

East Griqualand Grassland Grassland 8728.251 55.8 Least


Endemism uncertain

Eastern Coastal Shale Band Vegetation

Fynbos 78.041 40.0 Endangered B1B2 Poorly Protected Endemism uncertain

Eastern Free State Clay Grassland

Grassland 15063.858 41.2 Vulnerable A3B1 Not Protected Endemism uncertain

Eastern Free State Sandy Grassland


Concern A3 Poorly Protected

Endemism uncertain

Eastern Gariep Plains Desert Desert 1217.978 98.8 Least


Endemism uncertain

Eastern Gariep Rocky Desert Desert 2094.701 99.8 Least


Endemism uncertain

Eastern Gwarrieveld Albany Thicket

2128.964 99.4 Least


Endemism uncertain

Eastern Highveld Grassland Grassland 12772.492 32.9 Vulnerable A3,A3CITY,A3MPL,B

1 Poorly Protected

Endemism uncertain

Eastern Inland Shale Band Vegetation



Endemism uncertain

Eastern Little Karoo Succulent

Karoo 1578.461 88.5

Least Concern


Eastern Lower Karoo Nama-Karoo 8321.272 98.5 Least


Endemism uncertain

Eastern Ruens Shale Renosterveld

Fynbos 2762.698 16.5 Endangered A2b,A3,A3WC,B1,B1thrsp_inv,B1thrsp_o

vgr Not Protected

Endemism uncertain

Eastern Upper Karoo Nama-Karoo 49834.231 96.7 Least


Endemism uncertain

Eastern Valley Bushveld Savanna 10185.443 70.3 Least


Endemism uncertain

Eenriet Plains Succulent Shrubland

Succulent Karoo

260.779 99.9 Least


Endemism uncertain

Egoli Granite Grassland Grassland 1093.176 22.4 Critically

Endangered B1 Poorly Protected

Endemism uncertain

Elands Forest Thicket Albany Thicket

40.466 76.2 Least


Endemism uncertain

Elgin Shale Fynbos Fynbos 279.464 29.6 Critically

Endangered B1,B1thrsp_inv Poorly Protected

Endemism uncertain

Elim Ferricrete Fynbos Fynbos 693.883 37.3 Endangered B1thrsp_inv,B1thrsp

_ovgr Poorly Protected

Endemism uncertain

Escarpment Arid Thicket Albany Thicket

1239.923 99.5 Least

Concern


Endemism uncertain

Escarpment Mesic Thicket Albany Thicket

1030.526 88.7 Least


Endemism uncertain

Escarpment Valley Thicket Albany Thicket

784.628 98.2 Least


Endemism uncertain

Fish Arid Thicket Albany Thicket

674.019 93.4 Least


Endemism uncertain

Fish Mesic Thicket Albany Thicket

241.373 87.8 Least


Endemism uncertain

Fish Valley Thicket Albany Thicket

3596.306 96.4 Least

Concern


Endemism uncertain

Frankfort Highveld Grassland Grassland 9891.598 56.7 Least


Endemism uncertain

Fynbos Riparian Vegetation Azonal


Least Concern


Gabbro Grassy Bushveld Savanna 760.249 99.7 Least


Endemism uncertain

Gamka Arid Thicket Albany Thicket

491.873 98.8 Least


Endemism uncertain

Gamka Karoo Nama-Karoo 20205.896 99.6 Least


Endemism uncertain

Gamka Valley Thicket Albany Thicket

167.539 97.3 Least


Endemism uncertain

Garden Route Granite Fynbos Fynbos 498.338 40.7 Critically


Endemism uncertain


202



% Natural



2018 Endemism

Garden Route Shale Fynbos Fynbos 564.741 47.9 Vulnerable A3,A3WC Poorly Protected Endemism uncertain

Gauteng Shale Mountain Bushveld



Endemism uncertain

Geluk Grassland Thicket Albany Thicket

30.155 62.1 Least


Endemism uncertain

Ghaap Plateau Vaalbosveld Savanna 15482.871 97.7 Least


Endemism uncertain

Goariep Mountain Succulent Shrubland

Succulent Karoo

170.770 100.0 Least


Endemism uncertain

Gold Reef Mountain Bushveld Savanna 2030.916 80.6 Least

Concern


Endemism uncertain

Gordonia Duneveld Savanna 37063.404 99.9 Least

Concern


Endemism uncertain

Gordonia Kameeldoring Bushveld



Endemism uncertain

Gordonia Plains Shrubland Savanna 7918.573 99.9 Least

Concern


Endemism uncertain

Goukamma Dune Thicket Albany Thicket

91.779 73.1 Least


Endemism uncertain

Gouritz Valley Thicket Albany Thicket

176.853 63.9 Least


Endemism uncertain

Graafwater Sandstone Fynbos Fynbos 1343.112 72.6 Least


Endemism uncertain

Grahamstown Grassland Thicket Albany Thicket

1290.842 67.4 Least


Endemism uncertain

Granite Lowveld Savanna 19839.118 76.4 Least


Endemism uncertain

Grassridge Bontveld Albany Thicket

245.847 90.5 Least

Concern


Endemism uncertain

Gravelotte Rocky Bushveld Savanna 323.496 89.3 Least


Endemism uncertain

Greyton Shale Fynbos Fynbos 266.632 58.6 Least


Endemism uncertain

Groot Brak Dune Strandveld Fynbos 28.188 48.1 Vulnerable A3WC,D3WC Poorly Protected Endemism uncertain

Grootrivier Quartzite Fynbos Fynbos 388.877 99.9 Least


Endemism uncertain

Hamburg Dune Thicket Albany Thicket

701.664 68.4 Least


Endemism uncertain

Hangklip Sand Fynbos Fynbos 88.682 63.6 Critically



Endemism uncertain

Hantam Karoo Succulent

Karoo 7632.556 96.2

Least Concern


Hantam Plateau Dolerite Renosterveld



Endemism uncertain

Hartenbos Dune Thicket Albany Thicket

650.692 83.4 Least


Endemism uncertain

Hawequas Sandstone Fynbos Fynbos 1050.636 96.4 Least


Endemism uncertain

Helskloof Canyon Desert Desert 8.226 100.0 Least


Endemism uncertain

Highveld Alluvial Vegetation Azonal


Least Concern


Hopefield Sand Fynbos Fynbos 1009.591 65.3 Least


Endemism uncertain

Humansdorp Shale Renosterveld

Fynbos 372.049 43.4 Vulnerable A3,A3CITY Not Protected Endemism uncertain

Income Sandy Grassland Grassland 4653.668 50.5 Endangered B1 Not Protected Endemism uncertain

Ironwood Dry Forest Forests 81.679 99.5 Least


Endemism uncertain

Ithala Quartzite Sourveld Grassland 1545.487 81.6 Least


Endemism uncertain

Kaalrug Mountain Bushveld Savanna 468.382 79.5 Least

Concern


Endemism uncertain

Kahams Mountain Desert Desert 592.257 100.0 Least


Endemism uncertain

Kalahari Karroid Shrubland Nama-Karoo 8634.152 99.4 Least


Endemism uncertain

Kamiesberg Granite Fynbos Fynbos 64.816 98.9 Least


Endemism uncertain

Kamiesberg Mountains Shrubland

Succulent Karoo

396.873 98.8 Least


Endemism uncertain

Kango Conglomerate Fynbos Fynbos 404.931 98.0 Least


Endemism uncertain


203



% Natural



2018 Endemism

Kango Limestone Renosterveld Fynbos 501.700 87.6 Least


Endemism uncertain

KaNgwane Montane Grassland Grassland 9654.181 57.7 Least


Endemism uncertain

Karoo Escarpment Grassland Grassland 8370.620 97.5 Least

Concern


Endemism uncertain

Kasouga Dune Thicket Albany Thicket

307.541 69.1 Least

Concern


Endemism uncertain

Kathu Bushveld Savanna 7452.600 98.3 Least


Endemism uncertain

Kimberley Thornveld Savanna 19593.222 73.6 Least


Endemism uncertain

Klawer Sandy Shrubland Succulent

Karoo 201.118 57.4


B1 Not Protected Endemism uncertain

Klerksdorp Thornveld Grassland 3932.268 58.2 Least


Endemism uncertain

Knersvlakte Dolomite Vygieveld Succulent

Karoo 59.843 96.6

Least Concern


Endemism uncertain

Knersvlakte Quartz Vygieveld Succulent

Karoo 1320.877 98.7

Least Concern


Knersvlakte Shale Vygieveld Succulent

Karoo 983.579 99.8

Least Concern


Knysna Sand Fynbos Fynbos 152.123 22.8 Critically


Endemism uncertain

Kobee Succulent Shrubland Succulent

Karoo 142.748 98.4

Least Concern


Koedoesberge-Moordenaars Karoo

Succulent Karoo

4714.483 99.4 Least


Endemism uncertain

Koedoeskloof Karroid Thicket Albany Thicket

59.853 90.0 Least


Endemism uncertain

Kogelberg Sandstone Fynbos Fynbos 914.229 84.3 Critically

Endangered B1thrsp_inv Well Protected

Endemism uncertain

Koranna-Langeberg Mountain Bushveld



Endemism uncertain

Kosiesberg Succulent Shrubland Succulent

Karoo 612.174 100.0

Least Concern


Kouebokkeveld Alluvium Fynbos Fynbos 180.076 34.1 Critically


Endemism uncertain

Kouebokkeveld Shale Fynbos Fynbos 428.023 49.7 Vulnerable A3,A3WC Moderately Protected

Endemism uncertain

Kouga Grassy Sandstone Fynbos Fynbos 4052.129 91.8 Least


Endemism uncertain

Kouga Sandstone Fynbos Fynbos 2402.919 90.8 Least


Endemism uncertain

Kuruman Mountain Bushveld Savanna 4361.656 98.3 Least


Endemism uncertain

Kuruman Thornveld Savanna 5801.209 96.3 Least


Endemism uncertain

Kuruman Vaalbosveld Savanna 3948.003 95.4 Least


Endemism uncertain

Kwaggarug Mountain Desert Desert 107.793 99.7 Least


Endemism uncertain

KwaZulu-Natal Coastal Belt Grassland


4141.562 19.5 Endangered A2b,A3,A3KZN,B1 Not Protected Endemism uncertain

KwaZulu-Natal Coastal Belt Thornveld


1121.134 39.3 Vulnerable A3 Not Protected Endemism uncertain

KwaZulu-Natal Highland Thornveld



Endemism uncertain

KwaZulu-Natal Hinterland Thornveld



Endemism uncertain

KwaZulu-Natal Sandstone Sourveld

Savanna 1812.744 15.9 Endangered A3,A3KZN,B1 Not Protected Endemism uncertain

Lambert's Bay Strandveld Fynbos 354.279 70.2 Least


Endemism uncertain

Langebaan Dune Strandveld Fynbos 341.858 87.0 Least


Endemism uncertain

Langkloof Shale Renosterveld Fynbos 207.125 35.4 Endangered A3WC,B1,B2 Not Protected Endemism uncertain

Lebombo Summit Sourveld Savanna 134.580 34.0 Endangered B1,B2 Not Protected Endemism uncertain

Legogote Sour Bushveld Savanna 3562.423 33.5 Endangered B1 Poorly Protected Endemism uncertain

Leipoldtville Sand Fynbos Fynbos 2055.637 39.8 Endangered B1 Not Protected Endemism uncertain

Lekkersing Succulent Shrubland Succulent

Karoo 836.299 99.1

Least Concern


Endemism uncertain


204



% Natural



2018 Endemism

Leolo Summit Sourveld Grassland 20.344 94.3 Least


Endemism uncertain

Lesotho Highland Basalt Grassland



Endemism uncertain

Limpopo Ridge Bushveld Savanna 2779.635 97.1 Least


Endemism uncertain

Limpopo Sweet Bushveld Savanna 12011.976 90.1 Least


Endemism uncertain

Little Karoo Quartz Vygieveld Succulent

Karoo 240.075 95.7

Least Concern


Loerie Conglomerate Fynbos Fynbos 211.190 86.8 Least


Endemism uncertain

Long Tom Pass Montane Grassland



Endemism uncertain

Loskop Mountain Bushveld Savanna 2066.330 93.8 Least

Concern


Endemism uncertain

Loskop Thornveld Savanna 759.944 59.4 Least


Endemism uncertain

Lourensford Alluvium Fynbos Fynbos 35.851 22.3 Critically

Endangered

A2b,A3CITY,A3WC,B1,B1thrsp_inv,B1thr

sp_ovgr Poorly Protected

Endemism uncertain

Low Escarpment Moist Grassland



Endemism uncertain

Lower Gariep Alluvial Vegetation

Azonal Vegetation

867.843 65.1 Least


Endemism uncertain

Lower Gariep Broken Veld Nama-Karoo 4671.129 99.5 Least


Endemism uncertain

Lowveld Riverine Forest Forests 176.561 76.5 Vulnerable B2 Well Protected Endemism uncertain

Lowveld Rugged Mopaneveld Savanna 3154.107 78.2 Least


Endemism uncertain

Lydenburg Thornveld Grassland 1551.104 78.6 Least


Endemism uncertain

Mabela Sandy Grassland Grassland 492.917 34.4 Vulnerable A3,A3KZN Not Protected Endemism uncertain

Madikwe Dolomite Bushveld Savanna 974.020 97.6 Least


Endemism uncertain

Mafikeng Bushveld Savanna 14382.755 62.3 Least


Endemism uncertain

Makatini Clay Thicket Savanna 335.085 82.1 Least


Endemism uncertain

Makhado Sweet Bushveld Savanna 10110.947 64.1 Least


Endemism uncertain

Makuleke Sandy Bushveld Savanna 2090.616 76.8 Least


Endemism uncertain

Malelane Mountain Bushveld Savanna 630.569 95.6 Least


Endemism uncertain

Mamabolo Mountain Bushveld Savanna 666.895 90.2 Least


Endemism uncertain

Mangrove Forest Forests 43.020 86.9 Least


Endemism uncertain

Maputaland Coastal Belt Indian Ocean Coastal Belt

2354.791 38.9 Endangered B1 Moderately Protected

Endemism uncertain

Maputaland Pallid Sandy Bushveld


Concern


Endemism uncertain

Maputaland Wooded Grassland Indian Ocean Coastal Belt

1122.137 38.8 Endangered B1 Moderately Protected

Endemism uncertain

Marikana Thornveld Savanna 2528.697 38.3 Endangered B1 Poorly Protected Endemism uncertain

Matjiesfontein Quartzite Fynbos Fynbos 1268.230 98.7 Least


Endemism uncertain

Matjiesfontein Shale Fynbos Fynbos 106.526 95.5 Least


Endemism uncertain

Matjiesfontein Shale Renosterveld



Endemism uncertain

Midlands Mistbelt Grassland Grassland 6972.234 31.9 Vulnerable A2b,A3,A3KZN,B1 Poorly Protected Endemism uncertain

Moist Coast Hinterland Grassland

Grassland 6280.582 38.4 Vulnerable A3,A3KZN Not Protected Endemism uncertain

Molopo Bushveld Savanna 22765.889 96.0 Least


Endemism uncertain

Mons Ruber Fynbos Thicket Albany Thicket

286.192 93.9 Least


Endemism uncertain

Montagu Shale Fynbos Fynbos 186.774 78.9 Least


Endemism uncertain

Montagu Shale Renosterveld Fynbos 1607.910 82.1 Least


Endemism uncertain


205



% Natural



2018 Endemism

Mooi River Highland Grassland Grassland 2862.426 61.0 Endangered B1 Poorly Protected Endemism uncertain

Moot Plains Bushveld Savanna 2900.816 67.8 Least


Endemism uncertain

Mopane Basalt Shrubland Savanna 2804.723 99.8 Least


Endemism uncertain

Mopane Gabbro Shrubland Savanna 310.427 99.9 Least


Endemism uncertain

Mossel Bay Shale Renosterveld Fynbos 866.494 40.3 Critically


Endemism uncertain

Motherwell Karroid Thicket Albany Thicket



Endemism uncertain

Mthatha Moist Grassland Grassland 5281.553 42.8 Vulnerable A3 Not Protected Endemism uncertain

Muscadel Riviere Azonal

Vegetation 407.258 40.5 Endangered A3WC Not Protected

Endemism uncertain

Musina Mopane Bushveld Savanna 8796.294 91.9 Least

Concern


Endemism uncertain

Muzi Palm Veld and Wooded Grassland

Savanna 703.526 76.5 Critically


Endemism uncertain

Namaqualand Arid Grassland Succulent

Karoo 287.013 100.0

Least Concern


Namaqualand Blomveld Succulent

Karoo 3108.345 93.5

Least Concern


Namaqualand Coastal Duneveld Succulent

Karoo 868.068 86.7

Least Concern


Endemism uncertain

Namaqualand Granite Renosterveld



Endemism uncertain

Namaqualand Heuweltjie Strandveld

Succulent Karoo

838.943 77.6 Least


Endemism uncertain

Namaqualand Heuweltjieveld Succulent

Karoo 5040.679 91.4

Least Concern


Namaqualand Inland Duneveld Succulent

Karoo 917.494 97.2

Least Concern


Namaqualand Klipkoppe Shrubland

Succulent Karoo

7581.836 97.1 Least


Endemism uncertain

Namaqualand Riviere Azonal


Least Concern


Namaqualand Sand Fynbos Fynbos 1301.002 85.6 Least


Endemism uncertain

Namaqualand Seashore Vegetation

Azonal Vegetation

13.209 82.0 Least


Endemism uncertain

Namaqualand Shale Shrubland Succulent

Karoo 539.348 99.2

Least Concern


Namaqualand Spinescent Grassland

Succulent Karoo

469.909 90.8 Least


Endemism uncertain

Namaqualand Strandveld Succulent

Karoo 3151.636 81.6

Least Concern


Namib Lichen Fields Desert 1.533 79.7 Least


Endemism uncertain

Namib Seashore Vegetation Azonal



A3 Not Protected Endemism uncertain

Nanaga Savanna Thicket Albany Thicket

696.995 62.7 Least

Concern


Endemism uncertain

Nardouw Sandstone Fynbos Fynbos 547.971 66.8 Critically


Endemism uncertain

Ngongoni Veld Savanna 803.107 42.8 Vulnerable A3 Not Protected Endemism uncertain

Nieuwoudtville Shale Renosterveld



Endemism uncertain

Nieuwoudtville-Roggeveld Dolerite Renosterveld



Endemism uncertain

Noms Mountain Desert Desert 335.950 99.9 Least


Endemism uncertain

Norite Koppies Bushveld Savanna 260.101 86.4 Least


Endemism uncertain

North Hex Sandstone Fynbos Fynbos 394.054 94.7 Least


Endemism uncertain

North Kammanassie Sandstone Fynbos



Endemism uncertain

North Langeberg Sandstone Fynbos



Endemism uncertain

North Outeniqua Sandstone Fynbos



Endemism uncertain

North Rooiberg Sandstone Fynbos



Endemism uncertain


206



% Natural



2018 Endemism

North Sonderend Sandstone Fynbos



Endemism uncertain

North Swartberg Sandstone Fynbos



Endemism uncertain

Northern Afrotemperate Forest Forests 194.035 84.1 Least


Endemism uncertain

Northern Coastal Forest Forests 679.268 76.9 Least


Endemism uncertain

Northern Drakensberg Highland Grassland



Endemism uncertain

Northern Escarpment Afromontane Fynbos



Endemism uncertain

Northern Escarpment Dolomite Grassland

Grassland 939.152 39.1 Vulnerable A3 Poorly Protected Endemism uncertain

Northern Escarpment Quartzite Sourveld


Concern


Endemism uncertain

Northern Free State Shrubland Grassland 30.042 92.6 Least


Endemism uncertain

Northern Inland Shale Band Vegetation



Endemism uncertain

Northern Knersvlakte Vygieveld Succulent

Karoo 1673.852 99.8

Least Concern


Endemism uncertain

Northern KwaZulu-Natal Moist Grassland



Endemism uncertain

Northern Lebombo Bushveld Savanna 1335.534 99.9 Least


Endemism uncertain

Northern Mistbelt Forest Forests 389.291 74.4 Least


Endemism uncertain

Northern Nababiepsberge Mountain Desert

Desert 247.018 99.4 Least


Endemism uncertain

Northern Richtersveld Scorpionstailveld

Succulent Karoo

327.146 100.0 Least


Endemism uncertain

Northern Richtersveld Yellow Duneveld

Succulent Karoo

536.073 98.6 Least


Endemism uncertain

Northern Upper Karoo Nama-Karoo 42273.635 94.6 Least


Endemism uncertain

Northern Zululand Mistbelt Grassland

Grassland 539.231 55.4 Endangered B1 Poorly Protected Endemism uncertain

Northern Zululand Sourveld Savanna 5173.727 72.2 Least


Endemism uncertain

Nossob Bushveld Savanna 762.458 100.0 Least


Endemism uncertain

Nwambyia-Pumbe Sandy Bushveld



Endemism uncertain

Ohrigstad Mountain Bushveld Savanna 1999.976 88.5 Least

Concern


Endemism uncertain

Olifants Sandstone Fynbos Fynbos 498.273 94.7 Least


Endemism uncertain

Olifantshoek Plains Thornveld Savanna 8517.830 99.2 Least


Endemism uncertain

Oograbies Plains Sandy Grassland

Succulent Karoo

123.312 99.9 Least


Endemism uncertain

Oudshoorn Karroid Thicket Albany Thicket

571.931 97.2 Least


Endemism uncertain

Overberg Dune Strandveld Fynbos 347.541 94.2 Endangered B1thrsp_inv Well Protected Endemism uncertain

Overberg Sandstone Fynbos Fynbos 1179.680 90.4 Least


Endemism uncertain

Paulpietersburg Moist Grassland


Peninsula Granite Fynbos Fynbos 91.831 37.4 Critically



Endemism uncertain

Peninsula Sandstone Fynbos Fynbos 219.454 93.4 Critically


Endemism uncertain

Peninsula Shale Fynbos Fynbos 12.626 47.7 Vulnerable A3,A3WC Well Protected Endemism uncertain

Peninsula Shale Renosterveld Fynbos 25.285 14.6 Critically

Endangered B1thrsp_inv Poorly Protected

Endemism uncertain

Phalaborwa-Timbavati Mopaneveld



Endemism uncertain

Piketberg Quartz Succulent Shrubland

Succulent Karoo


Endangered B1,B2 Not Protected

Endemism uncertain

Piketberg Sandstone Fynbos Fynbos 422.884 89.3 Least


Endemism uncertain

Pilanesberg Mountain Bushveld Savanna 434.979 96.4 Least


Endemism uncertain


207



% Natural



2018 Endemism

Platbakkies Succulent Shrubland Succulent

Karoo 653.334 99.9

Least Concern


Polokwane Plateau Bushveld Savanna 4445.303 59.0 Least


Endemism uncertain

Pondoland-Ugu Sandstone Coastal Sourveld


1299.902 51.3 Least


Endemism uncertain

Postmasburg Thornveld Savanna 929.210 96.3 Least


Endemism uncertain

Potberg Ferricrete Fynbos Fynbos 40.572 49.3 Vulnerable A3 Poorly Protected Endemism uncertain

Potberg Sandstone Fynbos Fynbos 107.487 94.0 Least


Endemism uncertain

Poung Dolomite Mountain Bushveld


Concern


Endemism uncertain

Pretoriuskop Sour Bushveld Savanna 942.897 67.7 Least


Endemism uncertain

Prince Albert Succulent Karoo Succulent

Karoo 2555.192 98.8

Least Concern


Queenstown Thornveld Grassland 3606.332 84.0 Least


Endemism uncertain

Rand Highveld Grassland Grassland 10306.379 44.8 Vulnerable A3,A3CITY Poorly Protected Endemism uncertain

Richtersberg Mountain Desert Desert 361.424 100.0 Least


Endemism uncertain

Richtersveld Coastal Duneveld Succulent

Karoo 508.064 68.4

Least Concern


Richtersveld Red Duneveld Succulent

Karoo 566.065 100.0

Least Concern


Richtersveld Sandy Coastal Scorpionstailveld

Succulent Karoo

449.088 98.7 Least


Endemism uncertain

Richtersveld Sheet Wash Desert Desert 160.035 99.8 Least


Endemism uncertain

Riethuis-Wallekraal Quartz Vygieveld

Succulent Karoo

136.426 98.5 Least


Endemism uncertain

Robertson Granite Fynbos Fynbos 16.994 84.1 Least


Endemism uncertain

Robertson Granite Renosterveld Fynbos 19.229 98.3 Least


Endemism uncertain

Robertson Karoo Succulent

Karoo 653.417 78.4

Least Concern


Roggeveld Karoo Succulent

Karoo 5357.985 97.9

Least Concern


Roggeveld Shale Renosterveld Fynbos 3217.460 98.1 Least


Endemism uncertain

Roodeberg Bushveld Savanna 6496.387 79.9 Least


Endemism uncertain

Rooiberg Quartz Vygieveld Succulent

Karoo 129.248 99.8

Least Concern


Rosyntjieberg Succulent Shrubland

Succulent Karoo

50.560 100.0 Least


Endemism uncertain

Ruens Silcrete Renosterveld Fynbos 209.723 15.3 Endangered A2b,A3,A3WC,B1,B2,B1thrsp_inv,B1thrs

p_ovgr Not Protected

Endemism uncertain

Saldanha Flats Strandveld Fynbos 1643.401 37.5 Endangered B1 Poorly Protected Endemism uncertain

Saldanha Granite Strandveld Fynbos 298.558 28.5 Critically

Endangered B1,B1thrsp_inv,B1th

rsp_ovgr Poorly Protected

Endemism uncertain

Saldanha Limestone Strandveld Fynbos 61.537 81.9 Critically

Endangered B1thrsp_ovgr


Endemism uncertain

Saltaire Karroid Thicket Albany Thicket

910.470 97.9 Least


Endemism uncertain

Sand Forest Forests 265.188 92.4 Least


Endemism uncertain

Sardinia Forest Thicket Albany Thicket

25.252 64.0 Vulnerable A3CITY Not Protected Endemism uncertain

Scarp Forest Forests 1029.633 92.1 Least

Concern


Endemism uncertain

Schmidtsdrif Thornveld Savanna 5038.916 82.1 Least


Endemism uncertain

Schweizer-Reneke Bushveld Savanna 2027.525 49.5 Vulnerable A3 Poorly Protected Endemism uncertain

Sekhukhune Montane Grassland Grassland 1380.850 63.0 Least


Endemism uncertain

Sekhukhune Mountain Bushveld Savanna 2316.162 79.2 Least


Endemism uncertain

Sekhukhune Plains Bushveld Savanna 2522.840 48.1 Endangered B1 Poorly Protected Endemism uncertain


208



% Natural



2018 Endemism

Senqu Montane Shrubland Grassland 3737.092 75.0 Least


Endemism uncertain

South Eastern Coastal Thornveld



Endemism uncertain

South Hex Sandstone Fynbos Fynbos 319.737 99.0 Least


Endemism uncertain

South Kammanassie Sandstone Fynbos



Endemism uncertain

South Langeberg Sandstone Fynbos



Endemism uncertain

South Outeniqua Sandstone Fynbos



Endemism uncertain

South Rooiberg Sandstone Fynbos



Endemism uncertain

South Sonderend Sandstone Fynbos



Endemism uncertain

South Swartberg Sandstone Fynbos



Endemism uncertain

Southern Afrotemperate Forest Forests 775.316 80.2 Least


Endemism uncertain

Southern Cape Dune Fynbos Fynbos 81.320 82.1 Least


Endemism uncertain

Southern Coastal Forest Forests 185.250 81.9 Least


Endemism uncertain

Southern Drakensberg Highland Grassland



Endemism uncertain

Southern Kalahari Mekgacha Azonal


Least Concern


Endemism uncertain

Southern Karoo Riviere Azonal


Least Concern


Southern KwaZulu-Natal Moist Grassland


Southern Lebombo Bushveld Savanna 2583.968 87.7 Least


Endemism uncertain

Southern Mistbelt Forest Forests 1062.140 83.4 Least

Concern


Endemism uncertain

Southern Nababiepsberge Mountain Desert

Desert 343.206 100.0 Least


Endemism uncertain

Southern Namaqualand Quartzite Klipkoppe Shrubland

Succulent Karoo

996.957 91.5 Least


Endemism uncertain

Southern Richtersveld Inselberg Shrubland

Succulent Karoo

365.563 100.0 Least


Endemism uncertain

Southern Richtersveld Scorpionstailveld

Succulent Karoo

722.640 100.0 Least


Endemism uncertain

Southern Richtersveld Yellow Duneveld

Succulent Karoo

331.374 93.0 Least

Concern


Endemism uncertain

Soutpansberg Mountain Bushveld



Endemism uncertain

Soutpansberg Summit Sourveld Grassland 93.879 97.9 Least


Endemism uncertain

Soweto Highveld Grassland Grassland 14573.719 40.7 Vulnerable A3,A3CITY,A3MPL Not Protected Endemism uncertain

Springbokvlakte Thornveld Savanna 8928.416 45.6 Vulnerable A3,A3CITY,A3MPL Poorly Protected Endemism uncertain

St Francis Dune Thicket Albany Thicket

264.387 85.7 Least


Endemism uncertain

Steenkampsberg Montane Grassland



Endemism uncertain

Stella Bushveld Savanna 3221.170 59.6 Least


Endemism uncertain

Steytlerville Karoo Succulent

Karoo 793.296 97.6

Least Concern


Stinkfonteinberge Eastern Apron Shrubland

Succulent Karoo

65.881 100.0 Least


Endemism uncertain

Stinkfonteinberge Quartzite Fynbos



Endemism uncertain

Stormberg Plateau Grassland Grassland 2966.321 81.7 Least


Endemism uncertain

Strydpoort Summit Sourveld Grassland 268.043 97.8 Least


Endemism uncertain

Subtropical Alluvial Vegetation Azonal


Least Concern


Subtropical Dune Thicket Azonal


Least Concern


Subtropical Seashore Vegetation

Azonal Vegetation

27.978 94.7 Least


Endemism uncertain


209



% Natural



2018 Endemism

Sundays Arid Thicket Albany Thicket

5647.184 98.3 Vulnerable D3STEP Moderately Protected

Endemism uncertain

Sundays Mesic Thicket Albany Thicket

580.198 89.8 Least


Endemism uncertain

Sundays Valley Thicket Albany Thicket

1963.477 88.1 Least

Concern


Endemism uncertain

Suurberg Quartzite Fynbos Fynbos 683.383 98.3 Least

Concern


Endemism uncertain

Suurberg Shale Fynbos Fynbos 283.318 98.3 Least


Endemism uncertain

Swamp Forest Forests 100.241 73.5 Least


Endemism uncertain

Swartberg Altimontane Sandstone Fynbos



Endemism uncertain

Swartberg Shale Fynbos Fynbos 75.132 86.7 Least


Endemism uncertain

Swartberg Shale Renosterveld Fynbos 276.370 96.1 Least


Endemism uncertain

Swartland Alluvium Fynbos Fynbos 477.264 34.4 Endangered A3WC,B1,B1thrsp_i

nv,B1thrsp_ovgr Poorly Protected

Endemism uncertain

Swartland Alluvium Renosterveld

Fynbos 63.038 62.3 Vulnerable A3WC Not Protected Endemism uncertain

Swartland Granite Renosterveld Fynbos 951.312 20.5 Endangered A3,A3CITY,A3WC,B1,B1thrsp_inv,B1thrs

p_ovgr Not Protected

Endemism uncertain

Swartland Shale Renosterveld Fynbos 4963.739 12.4 Critically


Endemism uncertain

Swartland Silcrete Renosterveld Fynbos 101.066 15.9 Critically


Endemism uncertain

Swartruggens Quartzite Fynbos Fynbos 1646.141 98.5 Least

Concern


Endemism uncertain

Swartruggens Quartzite Karoo Succulent

Karoo 559.390 99.6

Least Concern


Endemism uncertain

Swaziland Sour Bushveld Savanna 4460.339 77.1 Least


Endemism uncertain

Swellendam Silcrete Fynbos Fynbos 868.551 48.1 Endangered B1 Poorly Protected Endemism uncertain

Tanqua Escarpment Shrubland Succulent

Karoo 1318.329 99.7

Least Concern


Endemism uncertain

Tanqua Karoo Succulent

Karoo 6988.281 99.3

Least Concern


Endemism uncertain

Tanqua Wash Riviere Azonal


Least Concern


Endemism uncertain

Tarkastad Montane Shrubland Grassland 4242.272 98.5 Least


Endemism uncertain

Tatasberg Mountain Succulent Shrubland

Succulent Karoo

3.268 100.0 Least


Endemism uncertain

Tembe Sandy Bushveld Savanna 1124.371 78.8 Least

Concern


Endemism uncertain

Thorndale Forest Thicket Albany Thicket

43.608 70.3 Least


Endemism uncertain

Thukela Thornveld Savanna 2215.724 74.6 Least


Endemism uncertain

Thukela Valley Bushveld Savanna 2706.622 73.9 Least


Endemism uncertain

Transkei Coastal Belt Indian Ocean Coastal Belt

1652.026 56.3 Least


Endemism uncertain

Tsakane Clay Grassland Grassland 1313.282 36.7 Endangered B1 Poorly Protected Endemism uncertain

Tsende Mopaneveld Savanna 5315.204 89.2 Least


Endemism uncertain

Tshokwane-Hlane Basalt Lowveld



Endemism uncertain

Tsitsikamma Sandstone Fynbos Fynbos 2296.517 69.5 Least


Endemism uncertain

Tsomo Grassland Grassland 6137.240 62.0 Least


Endemism uncertain

Tzaneen Sour Bushveld Savanna 3399.514 53.3 Least


Endemism uncertain

uKhahlamba Basalt Grassland Grassland 1346.909 99.9 Least


Endemism uncertain

Umdaus Mountains Succulent Shrubland

Succulent Karoo

432.813 99.9 Least


Endemism uncertain

Umtiza Forest Thicket Albany Thicket


Endangered B1


Endemism uncertain

Uniondale Shale Renosterveld Fynbos 1347.791 83.2 Least


Endemism uncertain


210



% Natural



2018 Endemism

Upper Annisvlakte Succulent Shrubland

Succulent Karoo

192.119 99.8 Least

Concern


Endemism uncertain

Upper Gariep Alluvial Vegetation

Azonal Vegetation

1787.227 71.8 Least


Endemism uncertain

Upper Karoo Hardeveld Nama-Karoo 11734.335 99.8 Least


Endemism uncertain

Vaal Reefs Dolomite Sinkhole Woodland



Endemism uncertain

Vaalbos Rocky Shrubland Savanna 1457.806 97.5 Least


Endemism uncertain

Vaal-Vet Sandy Grassland Grassland 22843.821 28.9 Endangered A3 Not Protected Endemism uncertain

Vanrhynsdorp Gannabosveld Succulent

Karoo 988.532 85.1

Least Concern


Vanrhynsdorp Shale Renosterveld



Endemism uncertain

Vanstadens Forest Thicket Albany Thicket

187.736 98.6 Least


Endemism uncertain

VhaVenda Miombo Savanna 0.336 94.1 Least


Endemism uncertain

Vredefort Dome Granite Grassland

Grassland 921.572 47.6 Vulnerable A3 Not Protected Endemism uncertain

Vyftienmyl se Berge Succulent Shrubland

Succulent Karoo

18.369 100.0 Least


Endemism uncertain

Wakkerstroom Montane Grassland



Endemism uncertain

Waterberg Mountain Bushveld Savanna 8823.541 93.0 Least

Concern


Endemism uncertain

Waterberg-Magaliesberg Summit Sourveld



Endemism uncertain

Western Altimontane Sandstone Fynbos



Endemism uncertain

Western Bushmanland Klipveld Succulent

Karoo 101.793 100.0

Least Concern


Western Coastal Shale Band Vegetation



Endemism uncertain

Western Free State Clay Grassland



Endemism uncertain

Western Gariep Hills Desert Desert 418.271 93.2 Least


Endemism uncertain

Western Gariep Lowland Desert Desert 217.013 92.5 Least


Endemism uncertain

Western Gariep Plains Desert Desert 139.931 90.7 Least


Endemism uncertain

Western Gwarrieveld Albany Thicket

760.354 98.2 Least


Endemism uncertain

Western Highveld Sandy Grassland

Grassland 8592.237 18.7 Endangered A3,B1 Not Protected Endemism uncertain

Western Little Karoo Succulent

Karoo 4109.033 96.3

Least Concern


Endemism uncertain

Western Maputaland Clay Bushveld

Savanna 1644.970 42.3 Endangered B1 Moderately Protected

Endemism uncertain

Western Maputaland Sandy Bushveld



Endemism uncertain

Western Ruens Shale Renosterveld


Endangered B1B1thrsp_inv,B1th

rsp_ovgr Not Protected

Endemism uncertain

Western Sandy Bushveld Savanna 6494.142 92.8 Least


Endemism uncertain

Western Upper Karoo Nama-Karoo 17149.405 99.2 Least


Endemism uncertain

Willowmore Gwarrieveld Albany Thicket

2252.310 98.9 Least


Endemism uncertain

Winburg Grassy Shrubland Grassland 1571.957 83.5 Least


Endemism uncertain

Winterhoek Sandstone Fynbos Fynbos 1135.958 93.3 Least


Endemism uncertain

Wolkberg Dolomite Grassland Grassland 260.586 93.8 Least


Endemism uncertain

Woodbush Granite Grassland Grassland 430.592 27.3 Critically


Endemism uncertain

Xhariep Karroid Grassland Grassland 13392.485 93.0 Least


Endemism uncertain

Zastron Moist Grassland Grassland 4268.064 64.7 Least


Endemism uncertain

Zeerust Thornveld Savanna 4128.105 69.9 Least


Endemism uncertain


211



% Natural



2018 Endemism

Zululand Coastal Thornveld Savanna 694.618 28.3 Critically


Endemism uncertain

Zululand Lowveld Savanna 8564.749 68.0 Least

Concern


Endemism uncertain

Documents

National Biodiversity Assessment 2018opus.sanbi.org/jspui/bitstream/20.500.12143/6370/4/... · National Biodiversity Institute (SANBI) in terms of allocating some staff time and funding