MODELLING THE MIGRATION OF GREVY’S ZEBRA IN FUNCTION …endeleo.vgt.vito.be/Documents/thesis_eline_hostens.pdf · MODELLING THE MIGRATION OF GREVY’S ZEBRA IN FUNCTION OF HABITAT

Faculteit Bio-ingenieurswetenschappen

Academiejaar 2008-2009

MODELLING THE MIGRATION OF GREVY’SZEBRA IN FUNCTION OF HABITAT TYPE USING

REMOTE SENSING

Eline HOSTENS

Promotor: Prof. Dr. ir. Robert R. DE WULF

Masterproef voorgedragen tot het behalen van de graad vanBIO-INGENIEUR IN HET BOS- EN NATUURBEHEER

Faculteit Bio-ingenieurswetenschappen

Academiejaar 2008-2009

MODELLING THE MIGRATION OF GREVY’SZEBRA IN FUNCTION OF HABITAT TYPE USING

REMOTE SENSING

Eline HOSTENS

Promotor: Prof. Dr. ir. Robert R. DE WULF

Masterproef voorgedragen tot het behalen van de graad vanBIO-INGENIEUR IN HET BOS- EN NATUURBEHEER

De auteur en de promotor geven de toelating deze masterproef voor consultatie beschikbaar te stellenen delen ervan te kopieren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen vanhet auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te vermeldenbij het aanhalen van resultaten uit deze scriptie.

The author and promotor give the permission to use this thesis for consultation and to copy parts of itfor personal use. Every other use is subjected to the copyright laws, more specifically the source mustbe exensively specified when using results from this thesis.

The promotor: The author:

Prof. dr. ir. R. De Wulf Eline Hostens

Foreword

The making of a thesis is quite a challenge. I would never have been able to do this without the helpof a lot of people. Here I would like to take the opportunity to thank all the people who contributed tothe success of this work.

First let me express my sincere thanks to my supervisor prof. dr. ir. Robert R. de Wulf who gave methe opportunity to make this thesis about a passion of mine, i.e. animals. I would also like to thankToon Westra for the support during the year. I could always go to him for advice about practical workor for any other questions.I am grateful to Northern Rangelands Trust for the collection of ground truth data and the deliveryof GPS tracking data and especially to Juliet King fot the coordination. I’d also like to thank ElseSwinnen of VITO for preparing the SPOT-Vegetation ten-day composites.I would like to show my appreciation to Kenny Devos and Els Verdonck who read and improved mythesis. I would like to thank my father Ivan Hostens, who has always helped me where possible duringmy studies, for reading this work and for a lot of other problems and jobs he has taken for his account.

A word of gratitude goes to all the people who made my student days one of the best periods of mylife so far. All the new friends I made in Gent, all the people of my year and especially my collegue-roomers to whom I could always go to have a good chat and for support. I am really going to missthem.

My parents as well deserve appreciation as they made a great effort to give me the opportunity tostudy and explore my possibilities. Therefore I will always be grateful to them. I would also like tothank them for supporting me during the more difficult times and for their trust in me.

Handzame, mei 2009Eline Hostens

List of Abbreviations

2-D : two-dimensional3-D : three-dimensionalAVHRR : Advanced Very High Resolution RadiometerCITES : Convention on International Trade in Endangered SpeciesEVI : Enhanced Vegetation IndexGPS : Global Positioning SystemLAI : Leaf Area IndexLCCS : Land Cover Classification SystemLiDAR : airborne lasersMCP : Minimum Convex PolygonMIR : Mid Infra RedNASA : National Aeronautics and Space AdministrationNDVI : Normalized Difference Vegetation IndexNIR : Near Infra RedNN : Artificial Neural NetworksNOAA : National Oceanic and Atmospheric AdministrationNRT : Northern Rangelands TrustPAs : Protected AreasPC : Principal ComponentPCA : Principal Component AnalysisPDOP : Positional Dilution Of PrecisionPTT : Platform Transmitter TerminalsSA : Selective AvailabilitySAR : Synthetic Aperture RadarSSC : Species Survival CommisionTLU : Tropical Livestock UnitUNEP : United Nations Environment ProgramVHF : Very High Frequency

Contents

1 Introduction 1

2 Grevy’s Zebra (Equus grevyi) 3

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Social structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Habitat and diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4 Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

5 Predators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

6 Threats and conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Study area 11

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 Livestock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5 Conservancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.2 Conservation of Grevy’s zebras . . . . . . . . . . . . . . . . . . . . . . . . 17

II

Contents

4 Wildlife telemetry 18

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2 Very-High-Frequency (VHF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 Satellite tracking: Argos system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Global Positioning System (GPS) tracking . . . . . . . . . . . . . . . . . . . . . . . 22

4.1 Operation of the system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3 Obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.4 Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Examples of studies using GPS telemetry . . . . . . . . . . . . . . . . . . . 28

5 Wildlife tracking and remote sensing 29

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2 Habitat maps and habitat suitability mapping . . . . . . . . . . . . . . . . . . . . . 29

2.1 Habitat maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2 Habitat suitability maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3 Spatial heterogeneity assessment based on primary productivity . . . . . . . . . . . 31

4 Temporal heterogeneity assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5 Heterogeneity assessment based on landscape structural properties . . . . . . . . . . 33

6 Heterogeneity assessment based on plant chemical constituents . . . . . . . . . . . . 34

6 Data and methods 35

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2 Satellite images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

III

Contents

2.2 Landsat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.3 MODIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.4 SPOT-Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3 Tracking data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4 Vector data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.1 Ground truth data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2 Artificial Neural Networks (NN) . . . . . . . . . . . . . . . . . . . . . . . . 44

5.3 Classification methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.4 Accuracy assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6 Analysis of Grevy’s zebra tracking data . . . . . . . . . . . . . . . . . . . . . . . . 48

7 Analysis of Grevy’s zebras’ migration . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.2 Correlation of the zebras’ migration with biomass . . . . . . . . . . . . . . . 49

7.2.1 Linking NDVI and tracking datasets . . . . . . . . . . . . . . . . 49

7.2.2 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.3 Correlation between zebra presence and water . . . . . . . . . . . . . . . . . 50

7.4 Correlation between zebra presence and livestock . . . . . . . . . . . . . . . 51

7.5 Correlation between zebra presence and towns . . . . . . . . . . . . . . . . 51

7.6 Habitat preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.7 Integration of all factors influencing the migration . . . . . . . . . . . . . . 53

7 Results and discussion 54

1 Habitat classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

1.1 Landsat-based habitat classification . . . . . . . . . . . . . . . . . . . . . . 54

IV

Contents

1.2 MODIS-based habitat classification . . . . . . . . . . . . . . . . . . . . . . 56

1.3 Analysis of the result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2 Analysis of tracking data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3 Correlation between tracking data and biomass . . . . . . . . . . . . . . . . . . . . 69

4 Correlation between tracking data and water . . . . . . . . . . . . . . . . . . . . . . 74

5 Correlation between tracking data and livestock . . . . . . . . . . . . . . . . . . . . 75

6 Correlation between tracking data and towns . . . . . . . . . . . . . . . . . . . . . . 77

7 Habitat preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

7.2 Habitat preference tested on the MODIS classification . . . . . . . . . . . . 80

7.2.1 First level comparison: testing for non-random use . . . . . . . . . 80

7.2.2 First level comparison: ranking of the habitat types in order of pref-erence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

7.2.3 Second level comparison: testing for non-random use . . . . . . . 83

7.3 Habitat preference tested on Africover . . . . . . . . . . . . . . . . . . . . . 84

7.3.1 First level comparison: testing for non-random use . . . . . . . . . 85

7.3.2 First level comparison: ranking of the habitat types in order of pref-erence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

7.3.3 First level comparison: integration over all sixteen zebras . . . . . 88

7.3.4 Second level comparison: testing for non-random use . . . . . . . 88

7.3.5 Second level comparison: integration over all sixteen zebras . . . . 88

8 Integration of all factors influencing the occurrence . . . . . . . . . . . . . . . . . . 89

8 Conclusion 94

V

Contents

9 Nederlandse samenvatting 97

1 Inleiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

2 Literatuurstudie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

2.1 Grevy’s zebra (Equus grevyi) . . . . . . . . . . . . . . . . . . . . . . . . . . 97

2.2 Studiegebied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

2.3 Wildlife telemetrie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

2.4 Tracking van wild en teledetectie . . . . . . . . . . . . . . . . . . . . . . . 101

3 Data en methoden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

3.1 Satellietbeelden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

3.2 Tracking data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

3.3 Classificatie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

3.4 Analyse van de Grevy’s zebra’s tracking data en migratie . . . . . . . . . . . 103

4 Resultaten en discussie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

4.1 Classificatie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

4.2 Analyse van de Grevy’s zebras tracking data en migratie . . . . . . . . . . . 105

4.2.1 Correlatie tussen tracking data en biomassa . . . . . . . . . . . . . 105

4.2.2 Correlatie tussen tracking data en aanwezigheid van water, vee endorpen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

4.2.3 Habitatpreferentie . . . . . . . . . . . . . . . . . . . . . . . . . . 106

4.2.4 Integratie van alle factoren . . . . . . . . . . . . . . . . . . . . . 107

Reference 108

A Ground truth collection form 115

B Classes of the Africover classification of the study area 117

VI

Contents

C Boxplots for the different seasons 119

D Habitatpreference based on made classification 121

E Habitatpreference based on the Africover reclass classification 123

F Histograms for the different seasons 125

VII

Chapter 1

Introduction

The Grevy’s zebra (Equus grevyi) is listed on the IUCN red list as an endangered species that can onlybe found in the eastern part of Ethiopia and the northern part of Kenya. There has been a fast decline ofthe remaining population in the past decades. The major threat for this species is introduced livestockthat compete for grazing. As cattle is mostly kept nearby water, Grevy’s zebras are sometimes forcedto drink at night, when they are more vulnerable to predation. As the zebras can travel large distances,much of their home range is located outside protected areas. They are mostly found in the arid andsemi-arid rangelands.

In this thesis migration of Grevy’s zebras is modelled in function of habitat type and plant biomassusing remote sensing. As the Grevy’s zebra is a threatened species, it is very important to monitortheir movement and to increase the knowledge about their behaviour. The more is known about theuse of resources and migration, the more efforts can be made to preserve them. There are two majorobjectives in this thesis. The first is to perform a habitat classification of the study area with the aimof determining the habitat use of the Grevy’s zebras. The second objective is the modelling of themigration of the Grevy’s zebras.

The habitat classification of the study area will be based on Landsat and MODIS satellite images.There will be searched for the best method of classifying the study area. Habitat classes will bederived from ground truth data and the classification will be conducted with the Maximum Likelihoodclassifier and with Neural Networks. An Africover map, a rough habitat map of Africa is alreadyavailable for the study area, but there will be tried to make a more detailed map. Additionally, attemptswill be made to make a ranking of the habitat preference of Grevy’s zebras based on the habitatclassification and the Africover classification.

The objective of the modelling of the Grevy’s zebras’ migration will be divided into some sub-objectives: the correlation of the tracking data with biomass, with available water, with livestockpresence and with the presence of towns. Data about the migration and location of the Grevy’s zebras

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CHAPTER 1. Introduction

is obtained by applying GPS-collars to sixteen zebras. There are several factors influencing the ani-mals’ movement and these will be investigated separately. The most important influence is probablythe availability of food sources. As Grevy’s zebras are herbivores, biomass can be used as an indicatorfor the available amount of food. This will be modelled using the Normalised Difference VegetationIndex (NDVI) as a proxy. SPOT-Vegetation NDVI images will be applied to derive time series ofNDVI for the study area. To model the influence of water availability, a map of the distance to thenearest water source is being used. The impact of livestock will also be examined. The influence oftowns will also be examined by calculating the distance to the nearest town and finding the relation-ship between this distance and zebra occurence. Finally all these factors will be merged together tomake a prediction of the areas within the study area that are best suitable for the Grevy’s zebras.

First, there is a brief overview of the literature. The species Grevy’s zebras will be discussed as well asthe study area. To understand the tracking technique, GPS tracking is handled. To make a comparisonwith the other possibilities, VHF and satellite tracking will also be discussed. Last, a link will bemade between animal movement and remote sensing.

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Chapter 2

Grevy’s Zebra (Equus grevyi)

1 Introduction

Zebras are still numerous and widespread in Africa. There are three species: The plains zebra (Equusburchelli Gray), the Grevy’s zebra (Equus grevyi Oustalet) and the mountain zebra (Equus zebra L.).The Grevy’s zebra is listed on the IUCN red list as an endangered species that can only be found in theeastern part of Ethiopia and the northern part of Kenya. The Grevy’s zebra is the biggest species of thewild equids. It can easily be distinguished from other zebra species by its larger size, big rounded ears,narrow, evenly divided stripes, a white unmarked belly, and a brown spot on the nose (Rubenstein,2004). They are about 250–275 cm long and have a shoulder height of about 140–160 cm. Femalesweigh about 350–400 kg, males 380–450 kg (ARKive, Images of life on Earth, read 07/2008).

Figure 2.1: A Grevy’s zebra (Gardner, read 08/2008)

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CHAPTER 2. Grevy’s Zebra (Equus grevyi)

Table 2.1: Taxonomic classification of the Grevy’s zebra.Kingdom AnimaliaPhylum ChordataClass MammaliaOrder PerissodactylaFamily EquidaeGenus EquusSpecies Equus grevyi

2 Social structure

Social structures of all species, like group size, spatial dispersion, and mating systems, are shapedby the environment. The major force leading to sociality for zebras was probably the need to protectagainst predation. Of all predatory attacks on zebras by lions, 35% are successful when zebras aresolitary, whereas only 22% when zebras live in moderate-sized groups. Social relationships may alsobe influenced by the fulfilling of other needs, such as acquiring food, water, and mates (Rubenstein,1986).

The social structure of the Grevy’s zebra is different from the other zebra species as it is a much openersociety (ARKive, Images of life on Earth, read 07/2008). They are loosely social animals, which canbe found in the most distinct groupings. There are groups of mostly young stallions without territory,who live in bachelor groups; there are groups of mares with or without foals; and there are also mixedgroups of stallions and mares. The herd composition varies constantly as the members do not have anindividual connection with each other. The formation of mixed big flocks is connected to the seasonalmigration. The most stable relationships are those of a stallion to his territory and of a mare to herfoal (Grzimek, 1972).

The female’s movements and association choices are primarily thought to be dependent on water andforage distribution. There’s a difference in dietary needs, both quantitative and qualitative, and suscep-tibility to predation between lactating and non-lactating females (Sundaresan et al., 2007b). Havinga good condition is important for survival, embryo development, and the raising of young to inde-pendence. Non-lactating females put efforts in acquiring large quantities of high-quality vegetation,while lactating females both want to acquire food and access to predator-free sources of water. If highquality food and safe water coincide, then the different reproductive classes can be found together.Otherwise, they are distributed in different areas (Rubenstein, 1986). Grevy’s zebras live in arid areaswith scarce water. Only lactating females need to drink every day. When a foal is killed, the mothersgo to sites more distant from water and with more plentiful vegetation (Rubenstein, 2004). These

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different needs prevent them to form stable bonds among each other. Grevy’s zebra’s females rangebetween 10 to 15 kilometres per day (Rubenstein, 1986). Competition for vegetation is rare amongfemales. Regardless of abundance, they avoid interfering with each other as they try to consume asmuch food as quickly as possible by adjusting their spacing (Rubenstein, 2004).

About 10% of a population’s mature stallions (ARKive, Images of life on Earth, read 07/2008) have aterritory of 2.5–10.5 km2, which is huge in comparison to other herbivores’ territories. The strongestmales claim prime watering and grazing areas. These factors attract other zebras to the territory. Theterritory stallion tolerates other stallions if they don’t approach receptive mares; the resident male hasthe exclusivity to mate in his territory. If they do approach the mares, they will be attacked and chasedaway from the mare about 30–100m. They are rarely chased away from the territory. The attackedstallion admits to the dominance of the territory owner and will not defend himself. Stallions without aterritory will fight each other to mate with a mare. The territories are located along recognition pointsin the landscape. The main marking of the territory is by the presence of the owner. The sound andsmell signals, which indicate the borders, are presumably of subordinate role. These piles of manureseemingly help the animal orientate in its terrain. The piles are several square meters in size and about40cm high (Grzimek, 1972).

3 Habitat and diet

Today, Grevy’s zebra can only be found in the northern parts of Kenya and in the south of Ethiopia.This is due to a rapid decline in their population. They used to roam in semi arid shrublands and plainsof Somalia, Ethiopia, Eritrea, Djibouti, and Kenya (African Wildlife Foundation, read 07/2008). Theyare presumed extinct in Somalia since its last sighting in 1973 (figure2.2) (ARKive, Images of life onEarth, read 07/2008).

Grevy’s zebras live in a more arid semi-desert habitat compared to the other zebra species (Youth,Howard, 2004). These habitats include arid grasslands and dusty acacia savannas. The bushed grass-land habitats have woody vegetation that is dominated by Acacia species. The grasses are primarilyof the genera Themeda, Cynodon and Pennisetum (Sundaresan et al., 2007a).

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Figure 2.2: Historical and current range of Grevy’s zebras (Grevy’s zebra Trust, read 08/2008)

Grevy’s zebras nearly always coexist with people. Therefore they have a trade-off between loca-tions with good quality vegetation and proximity to human activities. According to Sundaresan et al.(2007a) forage quantity and quality, and habitat openness are vegetation features important to zebras.The ability to detect predators is affected by the visibility, which in turn is affected by the bush den-sity. Grevy’s zebras may avoid areas close to humans and their livestock, due to direct disturbance orbecause of indirect competition with domestic ungulates for forage (Sundaresan et al., 2007a).

Eating is a major occupancy for the Grevy’s zebras. They spend nearly two-thirds of their day on it(Saint Louis Zoo, read 07/2008). They are predominately grazers. Forbs, shrubs, and trees are alter-native foods if grasses are scarce. Leaves can constitute up to 30% of their diet (Smithsonian NationalZoological Park, read 07/2008). They can digest many types and parts of plants that cattle cannot(African Wildlife Foundation, read 07/2008). They are also beneficial to other wild grazers becausethey clear off the tops of coarse grasses that are difficult for other herbivores to digest. Zebras also eatcoarse grasses that grow on marginal lands where cattle do not dwell (Seaworld Adventure Parks, read07/2008). Zebras ferment vegetation after digestion in the stomach. Therefore food processing is notslowed down as in ruminant grazers (Rubenstein, 2004). The contact with the absorptive surfaces ofthe intestine is limited. To survive they must therefore consume large quantities of vegetation, whichcan be of low quality. Zebra foraging is consequently only limited by the time they can devote tofeeding (Rubenstein, 1994).

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Grevy’s zebras are mostly observed in areas of short, green grass. It can be expected that they seekout areas with high-quality forage. However, lactating females and bachelors use areas with greenerbut shorter grass, seeking higher-quality forage at the cost of reduced quantity. The specific nutrientdemands of lactation may drive the choice for the females with foals. For the bachelors there can beseveral possible explanations. The presence of lactating females may attract them, as these femalescome into predictable oestrus. They may require particular micronutrients, more abundant in growinggrass, because many are still growing. Or bachelors may be avoiding territorial males who can harassthem. Lactating females are also more often seen in dense woody vegetation, which is strange as theseareas are thought to be unsafe as they provide cover for lions and given the fact that foals are veryvulnerable to predation. The use of denser bush by lactating females suggests a trade-off between therisk of predation and other benefits of these areas, such as proximity to water or high-quality forage.The bachelors’ greater use of medium bush area can be due to their avoidance of territorial males.Non-lactating females and territorial males may pursuit a strategy of gaining weight by using areaswith lower-quality, higher bulk forage (Sundaresan et al., 2007a).

Water is also indispensable and a key to Grevy’s zebras’ survival and reproductive success. Theanimals must always be within fairly easy reach of water holes or rivers. If water is available theywill drink daily, but as an adaptation to living in semi-desert, they can go without water for 2–5 days.The rain is the primary source of these water sources, and it also transforms the land around them.After the rain, the dusty plains are transformed into fertile pastures, peaking the zebras access to waterand their breeding. As lactating females are forced to drink every day, they stay close to permanentwater sources and the groups of mothers and foals often travel together. Zebras prefer drinking duringthe day, when they can easier see danger coming. During the daytime, some of the water sources areshielded off because cattle is grazing. Then, the zebras are forced to drink at night, after herders andtheir livestock left. This implies a greater risk of being caught by predators (Youth, Howard, 2004).

4 Breeding

The Grevy’s zebra females wander through the territories of up to four males in one day. They matewith several males with which they associate; they are polyandrous. The females with newborn foals,remain near permanent sources of water in one male’s territory and mate exclusively with this male;they are monandrous. The males copulate twice as frequently with polyandrous females then whenconsorting with relatively sedentary monandrous females (Ginsberg & Rubenstein, 1990).

Grevy’s zebras mate year round, with a gestation period of 13 months. A mare gives birth to only onefoal. The peak birth and mating periods are from July through August and October through November.The breeding starts at an age of three for the females and six for the males and usually follows a twoyear interval (African Wildlife Foundation, read 07/2008). When there’s a shortage of food or water,the interval can become once every three years. In longer dry periods, the breeding ceases because

7


the females go out of oestrus as their bodies adjust more to a state of survival than one of readiness tomate (Youth, Howard, 2004).

The newborn foals have a long hair crest down the back and belly, and their stripes are more brownish(African Wildlife Foundation, read 07/2008). They are able to stand a mere six minutes after birth, andrun after 45 minutes, (ARKive, Images of life on Earth, read 07/2008) which is necessary because thefoals are specifically vulnerable to predators. They start with the learning of the mother’s individualstripe pattern and smell before the mother lets any other zebra get close. The foal follows the motherevery step and they spend time together playing, nuzzling, and nursing (African Wildlife Foundation,read 07/2008). The foals remain dependent on their mother’s milk until six to eight months of ageand the young zebra stays about 2–3 years with its mother (ARKive, Images of life on Earth, read07/2008).

5 Predators

The main predator of all zebra species is the lion (Panthera leo L.). The zebras are most attackedduring the night at waterholes (Grzimek, 1972). Lion activity peaks at night. The darkness providesthem adequate concealment to hunt in open field, thereby shifting their habitat use from woodlandto grassland. It is therefore more dangerous to be in open areas for zebras at night time becausethen lions are more likely to be present. Zebras can minimize the risk of an attack by reducing thenumber of encounters with lions, for instance by looking up more bushy habitats. However, theirdigestion system of hindgut fermentation forces a zebra to graze frequently throughout the day andnight. Grazing occupies about 60% of their time. They prefer grassland, so moving to a safe placeand waiting out the lions is not always an option (Fischhoff et al., 2007).

By associating with other ungulates, the Grevy’s zebras obtain an advantage to predators. Wildebeest(Connochaetes taurinus Burchell), beisa oryx (Oryx gazelle beisa L.), eland (Taurotragus oryx Pallas),and plains zebras are some of the species with which they sometimes graze and travel (Youth, Howard,2004).

6 Threats and conservation

The Grevy’s zebra is classified as an endangered species on the IUCN Red List 2008 (IUCN/SSC, read07/2008). They are also listed on Appendix I of the Convention on International Trade in EndangeredSpecies (CITES), which effectively bans international trade in the species (ARKive, Images of lifeon Earth, read 07/2008). In the late 1970s, the remaining population was estimated at about 15000.Recent estimates are 2000 remaining wild individuals in Kenya and about 120–250 in three isolated

8


Ethiopian populations. The species is considered extinct in Somalia (Saint Louis Zoo, read 07/2008).The species occurs in several protected reserves throughout much of its current range (ARKive, Im-ages of life on Earth, read 07/2008).

The first major threat to the Grevy’s zebra is introduced livestock that compete for grazing. Cowschew the plants to the ground which results in a considerable environmental degradation due to thehighly erosive soil and fragile vegetation (Youth, Howard, 2004). The large cattle are mostly kept ingrasslands nearby water, thereby making the water unreachable for the zebras during daytime, andforcing them to drink during the night, when they are more vulnerable to predation. This is one of thereasons why the population in Kenya declined with about 70% between 1977 and 1988 (IUCN/SSC,read 07/2008). The traditional water sources are sometimes dried up due to the intensive irrigation infarm areas upstream. Some herders block the zebra’s access to water by fencing it with thorn coveredacacia limbs. These are all reasons why there is a constant decline in the water reserves for the Grevy’szebras (Saint Louis Zoo, read 07/2008). Non-lactating females avoid livestock more than any otherage or sex class. As livestock exploit the best grazing sites and females, in need of replenishing theirbody reserves after giving birth, try to avoid these areas, this could lengthen the inter-birth interval,and slow down the population growth (Rubenstein, 2004).

Another threat is the hunting by poachers for zebra skins. High prices were paid for the zebra fur. Thehunt is the reason why the species is threatened in Ethiopia. The species is extinct in Somalia becauseof hunting and wars. Thanks to CITES, this trade is now banned (African Wildlife Foundation, read07/2008). The species was declared protected by the Kenyan and Ethiopian governments about 20years ago. Despite the laws, the animals are still hunted for food and are used in traditional medicine(Youth, Howard, 2004).

In some Kenyan reserves, Grevy’s zebras can drink and feed in cattle and gun free refuges. Unfortu-nately, these protected areas can only meet their needs for part of the year. The zebras keep spendingmuch of their time on unprotected lands. Less than 0.5% of Grevy’s zebras’ range falls within pro-tected areas, according to the IUCN Species Survival Commision’s (SSC) action plan. Even in theseareas the animals encounter stress, caused by tour vehicles ignoring the rules and driving off-road,disturbing the zebras and other wildlife, causing erosion, and destroying fragile vegetation. Zebrassometimes stay away from waterholes during the day because tourists use them for swimming pools(Youth, Howard, 2004).

Another serious problem can be due to the plains zebras. Whenever they outnumber the Grevy’s ze-bras, they significantly lower the feeding rate of the Grevy’s zebras. The replenishment of previouslypoached wildlife populations within National Parks is one of the goals of the Kenyan government.This can be achieved by translocations from densely to sparsely populated areas. The removal ofplains zebras from areas where Grevy’s zebras are abundant, but where their numbers are not increas-ing, may help reduce competition and increase Grevy’s zebra birth rates (Rubenstein, 2004).

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The fact of habitat loss is the most serious threat today in the already restricted area where the Grevy’szebra lives. Grasslands are still cleared to make way for agriculture (African Wildlife Foundation,read 07/2008).

However, there are also positive initiatives. The Grevy’s zebra is kept in zoos and breeding pro-grammes have been started to preserve the species. Scientist and local communities in Africa are alsoworking together to stop the decline and try to multiply the number of Grevy’s left (Saint Louis Zoo,read 07/2008). Zoos play vital roles as they educate people about the Grevy’s zebra’s situation andprovide opportunities to observe the animals (Youth, Howard, 2004).

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Chapter 3

Study area

1 Introduction

The Republic of Kenya is situated on the east coast of Africa, on the equator. Kenya has severalphysical features, from low lying arid and semi-arid lands to a coastal belt, plateaus, highlands andthe lake basin around Lake Victoria. The Great Rift Valley, which extends for 8700km from the DeadSea in Jordan to Beira in Mozambique, bisects the country. Mount Kenya, rising to a height of 5199m,is the second highest snow capped mountain in Africa after Mount Kilimanjaro.Kenya has a diverse population of an estimated 34 million people of 42 ethnic groups. The capital cityis Nairobi (Government of Kenya, read 11/2008). Kenya contains 8 provinces (figure 3.1(a)), namelyCentral, Coast, Eastern, Nairobi, North Eastern, Nyanza, Rift Valley and Western (Kenya-space, read11/2008). The study area is the area where all tracking data of the zebras was collected. It is located inthe centre of Kenya (figure 3.1), between latitudes 0.3◦ and 2◦ North and longitudes 36.99◦ and 38.1◦

East. It is located in parts of 6 districts: Laikipia District, Isiolo District, Samburu District, MarsabitDistrict, Meru District and Nyambene District.

Kenya mainly consists of savanna and grassland ecosystems (39%), as well as bushland and woodlandecosystems (36%). Agroecosystems cover 19% of the country, forests make up 1.7% and urban landtakes only 0.2%. There is a small percentage of areas that are naturally devoid of vegetation, baregrounds (World Resources Institute et al., 2007).

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CHAPTER 3. Study area

(a) Provinces of Kenya (b) Districts within the study area

Figure 3.1: Location of the study area (International Livestock Research Instistute, read 2009)

Kenya has a very rich biodiversity. The country is home to 6500 plant species, more than 260 ofwhich are found nowhere else in the world. Kenya has second place among African countries inspecies richness for birds (1000 species) and mammals (350 species). As Kenya is on the boundarybetween Africa’s northern and southern savannas, more species of large mammals are concentratedin its rangelands than in almost any other African country (World Resources Institute et al., 2007).Rangelands are all the habitats suitable for grazing livestock or wildlife (Dictionary, read 04/2009).

The rangelands sustain livestock and wildlife. The wildlife species are an important income for thecountry through tourism. The wildlife and livestock census of 1994-96 showed that rangelands weredominated by livestock, with about 84% of all grazing animals in that area consisting of cattle, sheepor goat. There was a decline of 61% of all large grazing wildlife species in the rangelands between1977-78 and 1994-96. The main reasons for this decline were the competition for land and water withhumans and their livestock, as well as illegal hunting. It has been shown that livestock near waterpoints push wildlife away from water. In almost all the rangeland districts, water demand is greater bylivestock than by wildlife. Only in a few areas near or within protected areas, the water consumptionby wildlife is larger than the water consumption by livestock. It has been shown that the density ofhuman settlements has an impact on wildlife densities as well. The lower the human densities are, thehigher the wildlife densities (World Resources Institute et al., 2007).

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2 Climate

In Kenya there is a tropical climate with moderate temperatures averaging about 22°C throughout theyear. The coast is hot and humid, the inland is temperate and the north and northeast parts of thecountry are dry (Government of Kenya, read 11/2008). The mean annual temperatures in LaikipiaDistrict range between 16°C and 26°C. The mean temperature in Samburu District is 29°C, the one inIsiolo District is about 27°C. So the study area is on average in the hotter parts of the country (Ministryof state for the Development of Northern Kenya and other arid lands, read 11/2008).

For a country on the equator, the annual rainfall in Kenya is low with an annual average of about630mm per year. This is very unevenly distributed over the land and varies greatly between the years.In each decade over the past 30 years, there have been regularly major droughts and floods. Distinctseasonal patterns can also be discriminated. There are two rainy seasons: the short rains are fromOctober to December and the long rains from March to June, with the hottest period being Januaryto March. This high variability of rainfall throughout the seasons, between years, and across spacehas influenced the distribution of plants, animals and humans within the country (World ResourcesInstitute et al., 2007).The northern and eastern parts of the country get about 200–400mm, while the western part borderinglake Victoria, and central Kenya close to the high mountain ranges receive more than 1600mm. InLaikipia District, there is an annual rainfall between 400–750mm, in Samburu District between 250and 1250mm in the higher parts, Isiolo district has an average annual rainfall of about 580mm, andMarsabit District between 200–1000mm. So the study area is located in the drier parts of the countrywith only higher rainfall averages on the more elevated parts (Ministry of state for the Developmentof Northern Kenya and other arid lands, read 11/2008).Kenya consists of more than 80% arid and semi-arid land. Only about 15% of the country receivesenough rain to grow non-drought resistant crops, 13% has marginal rainfall sufficient to grow selecteddrought resistant crops, while the other 72% has no agronomic useful growing season. On these lattergrounds, no agriculture is possible without irrigation. When no irrigation is applied, the land consistsof a mixture of grasses, shrubs and trees, with water availability and soil type determining the exactspatial patterns of the plant communities (World Resources Institute et al., 2007).

3 Livestock

There has been an increased grazing pressure on the semi-arid rangelands of northern Kenya duringthe last decades (Cornelius & Schultka, 1997). The semi-arid savannas in the Isiolo and SamburuDistricts used to be pastures for cattle during the rainy season. In the dry season, the herds moved towetter grazing refugia on the Laikipia plateau and on the Wamba mountains. During the past decades,a successive change of the grazing schemes was observed to a year round grazing of cattle. Grasses are

13


the main component of cattle’s diet, even during the dry season. The rangelands of northern Kenya,have very limited biomass of valuable standing hay, and there is a quick deterioration of the foragequality of the herb layer after the rains. As the rainfall is extremely unreliable, the forage supply variesgreatly between the years. As this region has so much limitations and uncertainties, year round cattlegrazing is not a suitable land use here (Schultka & Cornelius, 1997). The consequence of this year-round grazing is a deterioration of the natural pastures. The overgrazing is often accompanied by andecrease of perennials in favour of annuals. The vegetational degradation also causes a replacementof indigenous flora by invaders (Cornelius & Schultka, 1997).However, the rangelands possess a huge potential for food production. Besides grasses and forbs, thereis the available biomass of dwarf shrubs, shrubs and trees. These plant forms can provide forage forsmaller livestock (sheep, goats, . . . ), donkeys and camels. When a mixture of grazers, browsers andintermediate types of feeders is used, the rangelands are best utilized and risks of climatic uncertaintiesand prolonged droughts are less severe.Livestock can have an influence on vegetation patterns. One example is the encroachment of Acaciaspecies which results in thickets. This encroachment into thickets is a widespread problem in Africansavanna that is mainly attributed to overstocking. There are different origins for thickets, some occuron soils eroded by heavy trampling like Acacia reficiens and Acacia horrida thickets, others are limitedto eutrophicated sites like juveniles of Acacia tortilis. As soil erosion is irreversible, the first thicketsare very hard to restore (Schultka & Cornelius, 1997). Grasses and herbs are suppressed by theseimpenetrable thickets. Overgrazing is believed to be the cause for woody plant encroachment dueto changed grass-tree competitive interactions. Another reason can be the loss of fuel leading toa disrupted fire regime (Wiegand et al., 2006). This increase in woody plant abundance over thepast century in rangeland results in a decline in the suitability of rangeland for cattle production.Native ungulates, especially elephants, can play an important role in reducing and even reversingshrub encroachment (Augustine & McNaughton, 2004).

4 Vegetation

In northern Kenya the savannas receive low and erratic rainfall that is coupled with a high evaporativedemand. Between the rainy seasons long dry spells occur, with plant opportunities limited by a shortrainy season, normally lasting about 60 days. Plants that establish and quickly mature have a goodchance of surviving to the next generation (Keya, 1997). The study site mostly consists of savannas,communities composed of more or less continuous herbaceous layers and of a discontinuous shrub-arborescent layer. Water is collected from different pedological horizons by grasses and trees. Grassesuse the shallow water rather than the deeper water and this allows the coexistence between the treesand grasses (Akpo, 1997). Savanna grasses’ growth is largely confined to the wet season. They have arapid growth response to the first rains as that is the moment where nutrients become available throughdecomposition of litter accumulated during the dry season. Woody plant species grow throughout the

14


year with a top growth during the wet season. Woody trees and shrubs, contrary to herbs, produce newleaves before the first rains, possibly triggered by photoperiodicity, temperature and moisture (de Bieet al., 1998).

A lot of trees within the study area have a multi-stem growth form. Some contrasting growth formsof trees that occur regularly are the dense umbrella-shaped canopy of Acacia tortilis and the open,irregular canopy of Commiphora Africana. There is also a common occurrence of the evergreen treeBoscia coriacea. Some nearly closed woody vegetation along rivers and channels contain trees as themost conspicuous life form, but are dominated by shrub life forms. The most characteristic speciesof these gallery woods are Grewia bicolor and Cordia crenata. Acacia xanthophloea is a tall growingtree confined to the banks of some permanent rivers. This tree overgrows A. tortilis by about 4m.

Some characteristic shrub species are Acacia mellifera, Grewia villosa, Caucanthus albidus, Cadabafarinosa, Grewia tenax and Cordia sinensis. Some patches are more composed of thickets, which canbe formed by the shrubs Acacia horrida or Acacia reficiens.Some locations contain a well developed understorey of dwarf shrubs with some dominating speciesbeing Lippia carviodora, Vernonia cinerascens and Sericocomopsis pallida. All these (dwarf) shrubspecies are indigenous to the semi-arid lowlands of northern Kenya. A much occurring dwarf shrubis Indigofera spinosa, a species of the semi-desert grassland and drier Acacia-Commiphora bushland.Some others are Hibiscus micranthus, Indigofera volkensii and Pavonia patens which are characteris-tic of dry savannas (Schultka & Cornelius, 1997).Sometimes there are vegetation patches around shrubs. These originate from a positive response ofplants to an increased infiltration, a reduced soil moisture evaporation and the protection from herbi-vores created by these shrubs. So within these patches, there is a concentration of cycling resources,with a limited movement of water, nutrients and propagules outward into the inter-shrub areas (King,2008).

The herbaceous layer consists of grasses and forbs. Some species that occur with high frequencies aswidespread weeds on arable fields are Ipomoea plebeia, Oxygonum sinuatum, Ocimum americanumand Pupalia lappacea. Some annual grasses that are species typical of disturbed habitats like heavygrazed pastures, arable fields and roadsides are Tragus berteronianus and Setaria verticillata. Thereare also some forbs typical of disturbed habitats, they are occurring in arable fields or in the vicinityof settlements like Digera muricata, Cyathula orthacantha, Tribulus cistoides, Achyranthes aspera,Leucas urticifolia, Commelina benghalensis and Erucastrum arabicum. Sporobolus nervosus is a sa-vanna grass species, Chrysophogon plumulosus and Oropetium minimum are perennials with the latteralso being adapted to more arid conditions. Some annual grasses are Cyperus blysmoides, Tetrapogoncenchriformis, a typical plant of semi-deserts and the pioneer Setaria acromelaena. Some indigenoussavanna pioneer forbs are Blepharis linnarifolia, Ipomoea cordofana and Farsetia stenoptera. Somespecies live in the bed of dried channels and rivers like the annual forb Mollugo cervinia, the annualgrass Eragrostis aspera and the perennal grass Cynodon plectostachyus that experiences seasonal

15


flooding (Cornelius & Schultka, 1997).

5 Conservancies

5.1 Introduction

A large part of the study area consists of conservancies, community-led conservation initiatives. Con-servancies contribute to the protection of specific biodiversity, they provide green corridors for themovement of game, or they can be protected habitats where rare and endangered species occur. Theregistration of a conservancy does not involve a change in land use, there are for instance many farmsthat are part of conservancies. The only requirement is that the land is managed by good environmen-tal practices (conservancies.co.za, read 11/2008).The conservancy areas in the study area are managed by the local communities with the support ofa local institution, the Northern Rangelands Trust. The membership of community conservancies isexpanding, the area is already about 600000 hectares, and home to approximately 60000 pastoralistsof different ethnic origin. The goal of the Northern Rangelands Trust is to solve the local problems bycreating long-lasting local solutions, and by this, leading the community to development and preserv-ing the resident wildlife. The growing recognition of the value of wildlife as an alternative incomestrategy and contributor to development for the community at large, is one of the main reasons forconservancy establishment. The wildlife value is clear in the demarcation of core conservation areaswithin conservancies, which are livestock free and focused on the development of wildlife and tourism(NRT, read 11/2008). The conservancies are: Il Ngwesi, Kalama, Lekurruki, Meibae, Melako, Na-munyak, Sera, West Gate, Ruko, Naibunga, Ltungai, and Newlew.

Figure 3.2: Conservancies (NRT, read 11/2008)

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5.2 Conservation of Grevy’s zebras

The community-owned rangelands of northern Kenya are one of the few places left in Africa wherewildlife can move freely across a vast area without fences, that is protected by communities (NRT,read 11/2008). Large areas of land are secured, allowing a continued migration of wildlife throughtheir natural range, with complementary protection, monitoring and management of wildlife andits rangeland. The communities have already undertaken several actions to protect the endangeredGrevy’s zebras.

An anthrax outbreak in Wamba area in December 2005, disproportionately affected Grevy’s zebras.After a test period on a small group of animals, a broader vaccination was successfully conductedon approximately 620 Grevy’s zebras. This vaccination was led by the Kenya Wildlife Service inassociation with the Lewa Wildlife Conservancy and Northern Rangelands Trust. They also had theassistance from County Councils and communities. The main targets were the populations most atrisk from the disease. To reduce the level of environmental contamination, a mass vaccination of over50000 head of livestock was performed.

Since May 2003, there is a Grevy’s Zebra Scout Programme, which employs women and men ofthe communities to collect data on the distribution and abundance of Grevy’s zebras. The NorthernRangelands Trust coordinate the programme. It receives funding support from Saint Louis Zoo andtechnical support from Dr. D. Rubenstein of Princeton University. The collected information providesa better understanding of the ecological pressure on this species in areas of high livestock densityand guides the community conservation programs so that community members themselves have theopportunity to make recommendations on ways to reduce competition between Grevy’s zebra andlivestock.

Wildlife and telemetry experts have been able to develop advanced tracking systems for Grevy’s ze-bras. Collars for Grevy’s zebra were developed and deployed by the Northern Rangelands Trust, LewaWildlife Conservancy and Save The Elephants in June 2006. The collars measure GPS position ev-ery hour. This information is critical in helping the communities manage their conservation areas tobenefit Grevy’s zebra (NRT, read 11/2008). In this work, the data collected by these collars will beused.

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Chapter 4

Wildlife telemetry

1 Introduction

According to the dictionary, the definition of telemetry is: ”The science and technology of automaticmeasurement and transmission of data from remote sources by wire, radio, or other means to receivingstations for recording and analysis” (Dictionary, read 04/2009). Telemetry will be discussed here asit is used to monitor the migration of the Grevy’s zebras.

The term wildlife telemetry is generally associated with the study of animal movements with the useof radio tags. Radio-tracking is like wildlife telemetry but without the transmittance of data on thestatus of the animal. When using wildlife telemetry, the disturbance of the normal behaviour of theanimal being studied should be avoided. The basic idea of wildlife telemetry is to study living animalsin their natural environment.

There are several ways to collect measurements by remote means. There is always the need forinterception of energy radiated by the animal or reflected from the animal. Wildlife telemetry has touse a transmission mode not detectable by the animal, to avoid the disturbance of their communicationwith one another or their sensing of the environment (Priede & Swift, 1992).

In the past, data were often obtained from field surveys using direct observation of the animals. Tran-sect surveys were conducted where animals in the vicinity of a set of sampling lines or points wererecorded. The problem with these methods was that it yielded relatively few sightings, particularlyfor rare species living in inaccessible environments. By using the advances in communication andinformation technology, radio- and satellite-telemetry became available and increased the amount ofdata on animal movement, with a focus on the individual animal (Aerts et al., 2008). Nowadays, thereis also the possibility of GPS tracking. Other basic information like survival, mortality, migrationperiods, home range, and territoriality can also be achieved using telemetry methods. In addition, thisinformation can be related to other individuals: which animals share their home range, which ones

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CHAPTER 4. Wildlife telemetry

avoid each other, which are the likely partners,. . . Telemetry can also be helpful to locate the animalsfor direct observation. The data obtained from telemetry studies are usually coupled with remote sens-ing data and GIS technology. More about the link between tracking data and remote sensing will behandled in chapter 5. In the following sections all three of the telemetry technologies, VHF-tracking,satellite tracking and GPS tracking, will be discussed (Priede & Swift, 1992). GPS-tracking was themethod used in this thesis to collect data about the migration of Grevy’s zebras.

2 Very-High-Frequency (VHF)

Ground based very-high-frequency (VHF) radio tracking became possible in the 1960s, and it allowsthe scientist to monitor species movements and home ranges over 50 to 300 km2. VHF tracking canrecord species location to within a couple of meters and can be undertaken in areas with dense canopycover, which is an advantage over satellite tracking (Gillespie, 2001). This classical radio trackingtechnique uses very-high-frequencies; this is the wavelengths between 1m and 10m. The animalshave a radio transmitter in a collar or a tag attached, and with the use of a hand-held directionalantenna, a receiver and headphones, a field researcher is able to track them. An animal’s location isdetermined from a series of bearings, which is determined by listening for peaks or nulls in the signallevel, and it is usually confirmed by visual sighting (Priede & Swift, 1992).

The choice for VHF band has several reasons. VHF is the highest frequency at which simple crystaloscillators can be used to generate the carrier frequency directly. The transmitters can therefore bemade with less than ten components and can weigh less than 1g. There are several transmitters varyingin size, complexity and performance. The antenna dimensions are also advantageous. As the antennasize is directly proportional to wavelength, the VHF frequencies give a reasonable practical portabledirectional antenna. To achieve optimal performance, it is important that the transmitter is carefullymatched to the application. The transmitters typically emit a 20 ms long pulse every 0.5–1 seconds tominimize power consumption. To distinguish between different individuals, different pulse rates andfrequencies are used, but the combinations are limited in the narrow bands allocated in most countriesfor biotelemetry.

There is one major problem with VHF tracking, which is unfortunately often ignored, and that isthe poor location precision of the technique. A visual confirmation of the animal wipes out thisproblem. The simple, relatively cheap equipment and the own manufacture of transmitters are themajor advantages. However it is still a very labour intensive method and the use of it in routineinvestigations can not always be justified as it has a huge demand for resources. Another disadvantageis that the information is only gained when researchers are actively receiving signals, although theradio is constantly transmitting. The result is small sample sizes with only a few locations per day.With this conventional system it is difficult for a person to collect more than three locations for 20animals per day. To collect more locations, more people are needed or fewer animals can be tracked.

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More people will increase the errors due to different operators. Instead, it is common to take bearingsintensively over a short period of time. The loss of dispersing individuals during non-telemetry timescan obstruct further data collection. Typically only one person takes all bearings which results in alower accuracy as animals move between the bearings. If the operator is too close, he can cause adisturbance of the animals (Priede & Swift, 1992).

VHF telemetry is not as commonly used anymore as there are easier and more efficient methods avail-able. Four recent studies that use VHF telemetry are given as an example. In Belgium, a study wasconducted on red deer (Cervus elaphus L.) to investigate their habitat use. The location of the taggedanimals was recorded once a week (Licoppe, 2006). In Norway, a study was conducted on ringedseals (Pusa hispida Schreber) to examine their haul-out behaviour. In addition to visual counts, someseals were VHF-tagged and their haul-out behaviour was monitored via an automatic recording station(Carlens et al., 2006). In Utah and Idaho (USA), pumas (Puma concolor L.) were VHF tracked toestimate their preying behaviour (Laundre, 2008). In Northern Chile, Humboldt penguins (Spheniscushumboldti Meyen) were VHF tracked to determine their at-sea behaviour (Culik et al., 1998).

The use of radio telemetry is generally restricted to a limited area or to species with a limited rangeof movement. Unless observers are able to stay within several kilometres of the animals, it is ratherdifficult to apply it to study long-distance migrants. The receiving of signals and following of animalsoften becomes constraining in hilly terrains or dense vegetation, challenging the use of this technology(Javed et al., 2003).

3 Satellite tracking: Argos system

A major challenge in satellite tracking is the requirement of a radio signal, coming from a smalltransmitter package on the animal, which is powerful enough to be received on board a spacecraft.The use of high altitude geostationary orbits was therefore excluded and receivers were located onpolar-orbiting remote-sensing satellites. There is currently only one operational system namely theUS/French Argos system which began service in 1978. The program is the result of a far-reaching co-operation between the Centre National d’Etudes Spatiales (CNES, France), the National Aeronauticsand Space Administration (NASA, USA) and the National Oceanic and Atmospheric Administration(NOAA, USA). The receivers are carried on board the NOAA series of satellites, which are space-crafts in circular, polar orbits at 850km altitude. These satellites are scheduled to provide a completeglobal coverage to the Argos system, so that it can collect locations of fixed and moving platformsworldwide. The transmittance at 401.650 MHz by the Argos platform transmitter terminals (PTTs),makes conveniently small antennas and very high transmission rates possible (Priede & Swift, 1992).Service Argos estimates the PTT’s location from Doppler shifts in frequency. The Doppler effect isthe change in frequency of the electromagnetic wave caused by the motion of the transmitter and thereceiver relative to each other. The frequency of the signal measured by the satellite receiver is higher

20


than the actual transmitted frequency when the satellite approaches the transmitter, and lower whenthe satellite moves away. These location measurements are then relayed to ground stations in USAand France from where users can directly retrieve data from Service Argos’ website or via electronicmail. Argos provides a range of location accuracies. The most accurate location, class 3 (LC3), has anestimated range of radius of 150m. LC2 has a radius range of 350m, LC1 of 1000m, and LC0 of morethan 1000m. Argos also provides a signal quality index with each location. After PTT purchase, thePTTs need to be registered with Service Argos and an agreement has to be signed (Javed et al., 2003).On this agreement form there is information about the programme, the objectives, the requirements ofthe system, the duration of the program, . . .

A spacecraft’s pass over a given position lasts 10–12 minutes on average and the Argos PTTs transmitmessages every 90 seconds. Data collection is possible from a single message, but the location of ananimal is determined using two messages. For tracking very mobile species, there is the possibility torequest a shorter repetition period of 60 seconds between messages.

Several thousand PTTs are in operation in the world today. Researchers and manufacturers in satellite-based tracking face major problems with the development of PTT technology, including environmen-tal capability, matching the PTT to the animal, the PTT power supply and sensor data. The productionof hardware that is suitable for the animal and withstands its natural environment is a significant partof the effort. PTTs must be resistant to corrosion and sea water, total leak-tight, resistant to impactand resistant to pressure. The suitability of a PTT for an animal is dependent on several aspects likeweight, shape and size, and attachment method (Priede & Swift, 1992). Currently the PTTs manu-factured can weigh less than 50g (Telonics, read 10/2008). The PTTs include solar panels or lithiumbatteries. A PTT must not disturb an animal’s aerodynamics or hydrodynamics and must not modifyits temperature regulation. There are several attachment methods available including subdermal an-choring, boding inside fur, and PTTs inside collars, harnesses, and harpoons attached to float. Thechance that the animal can be freed at the end of the programme should be maximized as a result ofthe design of the attachment method. As long-term animal tracking programmes are now possiblewith the use of satellite telemetry, the production of reliable equipment and the use of long-life powersupplies is encouraged. PTTs can also be used for other purposes besides animal tracking. They canprovide data on a range of behavioural and physiological characteristics, for example the monitoringof animal activity over short and long periods, number, duration and depth of dives in marine animals,water temperature, air temperature and barometric pressure . . .

There are two ways to collect location data namely continuous monitoring, where each movement ofan animal is noted, or interval sampling. To collect behavioural data, or to track animals in terrains thatare difficult to reach is only practical by using continuous monitoring. To analyse the data, a softwarepackage is used which is able to calculate some summary statistics for each monitoring session witha particular animal and some proportions of fixes in particular categories (Priede & Swift, 1992).

The locations recorded by the receivers in the NOAA satellites are in the form of latitude and lon-

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gitude. With the use of habitat information gathered via remote sensing and the tracking data, it ispossible to develop a more complete picture of the animal’s long-distance movements, an aspect of itsecology especially important for conservation of species and their habitats (Javed et al., 2003).

The satellite tracking method has its own advantages. Once a transmitter is attached, the researcherdoes not need to undertake extensive field triangulation. It is also easier to study wide-ranging speciesthat regularly cross international boundaries (Gillespie, 2001). In relation to VHF radio- tracking, theArgos system is highly affordable and competitive if full programme costs are taken into account.These costs include satellite-based wildlife tracking and Argos data distribution, journey and staffcosts and other travel expenses, the capital equipment needed for fieldwork and the associated logisticsburden, and the purchase of hardware such as receivers (Priede & Swift, 1992).

There are a lot of studies where satellite tracking is used. Only a small number of them will begiven as an example. In Mexico, humpback whales (Megaptera novaeangliae Borowski) were satel-lite tagged to follow their migratory movements and surfacing rates (Lagerquist et al., 2008). InMongolia, Mongolian gazelles (Procapra gutturosa Pallas) were satellite tracked to compare theirmigration to seasonal ranges with biomass via NDVI (Ito et al., 2006). In West Greenland, satellitetracking of caribou (Rangifer tarandus L.) was a valuable tool to identify critical caribou areas insummer. That makes it possible to change tourism and mineral exploitation to have a minimal impacton caribou population (Tamstorf et al., 2005). A study using satellite tracking of leatherback turtles(Dermochelys coriacea Vandelli) across the North Atlantic ocean, showed that some turtles are notforaging at particular hotspots but have a pattern of near continuous travelling (Hays et al., 2006). InSweden, satellite tracking of common buzzard (Buteo buteo L.) revealed their short-distance migrationpattern (Strandberg et al., 2009).

4 Global Positioning System (GPS) tracking

4.1 Operation of the system

The relatively new tool, Global Positioning System (GPS) collars, can solve a lot of problems asso-ciated with traditional radio-telemetric techniques (Johnson et al., 2002). This tool became widelyavailable to wildlife biologists in the mid 1990s (Blake et al., 2001). The determination of the loca-tion by GPS is based on a measurement of the distance between satellite and receiver. As the positionof the satellites is known, the location can be derived from the time the radio waves take to get fromthe satellite to the receiver. GPS has the advantage of monitoring the most precise locations with thefewest biases and has the possibility of relocating animals frequently, up to once per second. GPSalso works 24 hours, so data is even collected during the night, and during any weather condition(Johnson et al., 2002). Like traditional radio-collars, GPS collars require the capture of the animaland the fitting of the collar. The collars are normally pre-programmed to collect data according to

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specified schedules (Coelho et al., 2007). The collar can be located due to a traditional VHF receiverand with a UHF modem link it is possible to communicate between the collar and a remote laptopcomputer. This link allows data download, RAM memory clearing, and reprogramming of the collar(Blake et al., 2001).

The collected data consists of the wearer’s identity, time of day, date and coordinates, de PDOPvalue (Positional Dilution Of Precision) and if the acquired signal is two-dimensional (2-D) or three-dimensional (3-D) (Coelho et al., 2007). 2-D fixes are recorded when the GPS unit simultaneouslycontacts three satellites, and 3-D fixes are those recorded when the GPS contacts four or more satellites(Lewis et al., 2007). So, researchers obtain substantially larger spatial location datasets with greaterprecision and significant cost savings per location. This brings also the challenge of managing andanalyzing huge volumes of data constantly updated. A standard, complete and cost-effective softwaresystem to fully support management, modelling and analysis of GPS collar data is not yet available tothe scientific community (Cagnacci & Urbano, 2008). This technology should if possible be combinedwith field research. The cost of GPS collars is more the price of conventional VHF radio-collars, butGPS tracking makes it possible to simultaneously collect spatial data on many individuals (Coelhoet al., 2007).

Using GPS data, several attributes of free-ranging animals can be calculated. Location can be esti-mated from a single GPS fix, speed can be calculated from a minimum of two points and by usingthree points, turning angle calculations become possible. These estimates are based on straight linedistances between location points. In the intervening period between a long fix interval, there is uncer-tainty about an animal’s activity. These long fix intervals underestimate the actual distance travelledby the animal. Only the minimum speed and minimum distance travelled can be calculated betweentwo consecutive pairs of location fixes. In reality, animals take less direct routes to the second pointand therefore could travel faster and this higher speed enables them to access a wider variety of re-sources. With increasing time between GPS fixes, there is an increasing prediction error (Swain et al.,2008).

GPS gives the possibility of obtaining worldwide locations at 1-second intervals throughout the 24hour cycle, regardless of weather and roughness of terrain. The major advantages of GPS for wildlifebiologists is its simple use, cheapness, lightweight and it can give instantaneous locational informationin real time.

4.2 Accuracy

Until May 2000, the accuracy of GPS locations was downgraded by the process called SelectiveAvailability (SA) intentionally imposed by the US department of Defence. Before this date, onlyuncorrected or post-processed differential GPS data could be used. In the future it will still be possible

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that SA is reactivated. Uncorrected GPS data had a location error between 20m and 80m and post-processed differential GPS data had an error between 4m and 8m (Hulbert & French, 2001). Inthe differential method, post-treatment corrects distances to individual satellites in the GPS collarwith data obtained simultaneously by a GPS reference base station (Adrados et al., 2002). Both thereceiver and the reference station record errors in time and hence distance between the GPS receiverand from all visible satellites. After the post-treatment, locations are accurately determined. With thedisablement of SA, the accuracy of GPS locations is considered to be less than 1m. This accuracy waspreviously not possible without the use of complex or expensive equipment.

Data accuracy can also be improved using more satellites to calculate the location. A fourfold im-provement in accuracy can be achieved between locations obtained from four satellites and thosecalculated using data from five or more satellites. Some errors can also be caused by the receiveritself, topographically induced errors or errors due to ionospheric and tropospheric delays. The objec-tives of the study and data requirements of the hypothesis being tested will determine whether furthercomplex tasks should be performed on the data to remove these errors and obtain an even better ac-curacy. It is important that the accuracy of the GPS-derived locations is better than the resolution ofthe map used for tracking. Before each study, it is important to test the GPS device for instrumenterrors and to test for errors specific to each site before deploying this technique on animal collars orfor mapping.

As a consequence of suboptimal satellite geometry, the accuracy of many locations can degrade be-yond that quoted by the manufacturer. Purchasers of GPS collars have the option to employ differ-ential correction, which can increase precision (Hulbert & French, 2001). Differential correction canremove other sources of error besides SA, namely satellite clock errors, ionospheric and troposphericobstruction (Adrados et al., 2002). However, differential correction can have many unforeseen draw-backs that can add to project costs, or reduce immediate usefulness of the data. With the disablementof SA, the use of it is substantially reduced. So, differential correction is not necessary for all projectsas it requires a large amount of computing time, more memory per location, more frequent retrievalof data, greater power demands, and therefore results in a reduction of the collar’s field life (Johnsonet al., 2002). The choice to use differential correction will be determined by the scale needed in thestudy. It opens new approaches to wildlife research as it allows the study of animal-habitat relation-ships at a very fine spatio-temporal scale, never achieved before with other techniques (Adrados et al.,2002).

4.3 Obstructions

The GPS collars have to be small enough for an animal not to be hindered (Sprague et al., 2004). Therecommended weight of a collar has to be lower or equal to a limit of 5% of the body weight (Coelhoet al., 2007). In using GPS collars, there is always the trade-off between the weight and functions

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of the collar. Functions that require electricity and large battery must be reduced to acquire a lighterGPS collar. These collars may limit the satellite search time, record fewer positions, and need tobe recovered for data download after detaching automatically. As batteries, antennas, and electricalefficiency keep improving, it will be possible to get better acquisition rates and increasing number ofpositions recorded. It may even become available to have data download and detachment on radiocommand for smaller GPS collars.

Satellite signals can be blocked by canopy enabling the GPS device to calculate a location. Althougheven in forests, sufficient opportunities exist for the GPS device to receive satellite signals to calculatethe position. Animals sometimes are in clearings or under deciduous trees without leaves, wherethere is a very good reception of the satellites. Some animals are able to climb in trees, where at thatheight they have a good reception (Sprague et al., 2004). If the receiver is not capable of obtainingsignals from a minimum of three satellites, it cannot calculate a location. Before using this technology,researchers should perform an assessment of the GPS device’s performance across the habitat typesanimals are expected to use. Generally, the reception of satellite signals will be degraded by largediameter, dense and tall vegetation, and steep topography. Consequently there can be a large variationin location acquisition rates within and among study areas (Johnson et al., 2002). GPS receivers canbe confused by multi-path effects, where multiple signals are bounced off of tree trunks or movingcanopy in windy conditions.

The topography plays an important role in acquiring contact with satellites as well, hills for instancecan block the sky (Sprague et al., 2004). The orientation of a hill on which an animal is present canalso play a role in location determination. It is however safe for the researcher to assume that largebiases into GPS radio-telemetry data will not be imposed by orientation alone (D’Eon & Delparte,2005).

Animal behaviour can also play a significant role. In contrast to stationary collars, movement of thecollars on free-ranging animals may result in much lower GPS location performance. The positionof the GPS antenna attached to the collar has also an effect on the collar performance. In an openflat terrain, there is no difference in performance between a vertical or horizontal antenna position,possibly because there are more than enough satellites available to calculate the location in the openarea. However, in under closed canopy, horizontal positions, for instance on laying animals, exhibitincreased location time and location error, decreased fix rate, number of satellites available, and 3-Dproportion. So there is a negative effect of the horizontal antenna position in forests on the locationperformance, which suggests that the vegetation makes it difficult to collect information from satelliteswith a horizontal antenna. If the number of satellites available reduces, it becomes impossible for theGPS to choose an ideal constellation distribution in order to calculate a more accurate location oreven impossible to acquire a location at all. An antenna on a collar worn by an animal can easilyshift to horizontal position when the animal is feeding, sleeping or resting resulting in a decrease infix rate (Jiang et al., 2007). Attention has to be paid for biases introduced by this effect of antenna

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position. For example, the activity of an animal digging while foraging may potentially translate intosignificantly lower fix rates than at other times, and would result in proportionally fewer recordedlocations for this habitat type. The researcher could therefore draw the conclusion that these locationsare used infrequently or even be avoided, when in fact the opposite is true (D’Eon & Delparte, 2005).

The location time is affected by all habitat features except for available sky. This suggests that batterylongevity will be shortened by all of the obstacles that limit the number of satellites available or thatinterfere with the connection between the satellite and the GPS collar. Poor dispersion of satelliteswill result in a poor triangulation and thus in low-quality location data (Jiang et al., 2007).

In some studies, the collars can be subjected to extreme variations in temperature over short periods oftime, rapid changes in humidity, and complete immersion in water. Some collars, operating under ex-treme conditions, can face premature failure. This failure can cause extra costs by other factors. First,the malfunction is sometimes not immediately diagnosed due to the infrequent animal relocations.If a collar failure is detected, there is some additional time needed to arrange a recapture operation,which then can be delayed due to poor weather or unsuitable terrain. Once recovered, collars haveto be diagnosed, repaired and returned by the manufacturer. So collar malfunctions, together withorganisation, logistics and weather delays, contribute to a significant loss of potential data. To ensurethe collection of enough data, there should always be a minimum number of collars in the field. Thiscan be assured by keeping a pre-determined number of collars in reserve to replace the ones who fail.

Three manufacturers of GPS collars (Lotek Engineering, Newmarket, Ontario, Canada; Televilt In-ternational AB, Lindesber, Sweden; Telonics, Inc., Mesa, Arizona, USA) make the remote retrievalof data and diagnostics capable. The remotely programming with new location and communicationschedules, for instance where sampling strategies need to be adjusted in accordance with unpredictableanimal behaviour, is possible with the GPS collars from Lotek Engineering. Depending on the speciesand study duration, these features can be very useful. The user-collar communication is not necessarywhen animal capture can be performed year-round and is inexpensive, or when information about an-imal movement is required only for short periods. If study lengths exceed collar memory and animalsare difficult to capture or where animals periodically move large distances and are difficult to relocate,remote data retrieval is recommended (Johnson et al., 2002).

The ability to record fixes at a high frequency, even though GPS data is extremely accurate, resultsin increased noise/signal ratios when there is little movement or when speeds are low. It is best toanalyse GPS data where movements are at least twice the minimum resolution per sampling intervalto obtain a respectable signal/noise ratio (Ryan et al., 2004).

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4.4 Errors

The detailed information acquired with GPS can be used to evaluate wildlife movement, space use andresource selection with a high degree of precision and accuracy. Nevertheless, there are two types oferrors that can bias analysed data on GPS locations, namely missed location fixes and location error,the difference between an objects actual or true position and that estimated by a GPS fix.

First there is the unsuccessful fix acquisition, which leads to missing location data. Stationary GPScollars have fix rates ranging from 68–100%, with most collars above 85%, but sometimes rates aslow as 13% (Lewis et al., 2007). Missing locations equate to a loss of information, which implicates areduced efficiency and potential biases. As failed location attempts do not occur randomly but system-atically, bias is likely to occur in GPS telemetry studies. The conditions that can affect GPS locationacquisition are canopy type, percentage canopy cover, tree density, tree height, and tree basal area,which all can be strengthened by the interaction with a mountainous study area (Frair et al., 2004).GPS collar data may therefore be biased towards acquiring satellite fixes in more open habitats withfavourable topography. Another major factor affecting GPS collar fix rates is animal behaviour. Col-lars that attempt location fixes at shorter trials have also higher fix rates, so collar location acquisitionschedules also influences fix rates. Development and application of correction factors can be appliedto GPS location data sets to counter biases associated with missed locations.

The second error type is inherent in all telemetry systems: location error. Dependent on the magni-tude of location error and the degree of landscape heterogeneity, this can lead to misclassification ofhabitats used by the animal. Habitat components, like canopy cover and terrain obstructions, largelyinfluence location error. Atmospheric conditions can also contribute to this. The 3-D fixes are gen-erally more accurate. With increasing canopy cover, 2-D fixes increase as a result of satellite signalobstruction.For each location, there is a PDOP value recorded. This is a measurement of satellite geometry. LowerPDOP values indicate a wider satellite spacing able to minimize triangulation error and thus a moreaccurate location estimate. Screening of location data is sometimes used to reduce location error.There are several screening mechanisms, for instance, screening out of 2-D fixes, removing data withhigh PDOP values. . . However, this screening can also lead to significant reduction of location dataand introduce additional biases into analyses of animal locations. The seemingly most effective dataapproach could be the screening out of 2-D fixes at a specific PDOP cut-off; this can be a suitablecompromise between reducing large location errors and minimizing data reduction. By evaluatingthe proportion of locations with relatively large errors, an appropriate PDOP threshold value for datascreening can be chosen. Screening should be used with precaution not to potentially introduce bi-ases that affect estimates of habitat and space use. These extra biases can be caused by eliminatinglocations associated with habitats that induce greater errors. Sites with high terrain obstruction, highcanopy cover or a combination of both have most missing fixes. In addition, these sites could havegreater location error because collars receive location signals from fewer satellites that exhibit poorer

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satellite configuration. The PDOP values and number of 2-D fixes will therefore increase (Lewis et al.,2007).

4.5 Examples of studies using GPS telemetry

There is an increasing amount of studies using the GPS technique to follow animal movement. InBrazil, wild maned wolves (Chrysocyon brachyurus Illiger) were GPS tracked during full and newmoon nights to discover the effect of the full moon on the activity of the predator (Sabato et al.,2006). In Kenya, African elephants (Loxodonta Africana Blumenbach) were GPS collared to inves-tigate their movements and use of corridors in relation to protected areas (Douglas-Hamilton et al.,2005). In Greece, loggerhead sea turtles (Caretta caretta L.) were GPS tracked to examine their useof microhabitat as the reason of their reproductive success at a margin of their range (Schofield et al.,2009). In Wales (UK), Manx shearwaters (Puffinus puffinus Brunnich) were GPS tracked to monitortheir foraging movements with a GPS device weighing 17g (Guilford et al., 2008). On Europa Is-land, in the Mozambique Channel, red-footed boobies’ (Sula sula L.) flight pattern and the way theyforage over tropical waters was examined using GPS packages (Weimerskirch et al., 2005). In Swe-den, moose (Alces alces L.) and roe deer (Capreolus capreolus L.) were GPS tracked to examine theeffectiveness of a highway overpass (Olsson et al., 2008).

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Chapter 5

Wildlife tracking and remote sensing

1 Introduction

The most important methodology for plant diversity assessment is the direct mapping of species andassociations, based on characteristic spectral reflectance features of plant species or plant communi-ties. Faunal species, which are mobile, complicate the assessment of species occurrence and richness.This is especially the case for migrants, which can move long distances occupying a wide range ofhabitats. For these non-sessile animals approaches are based upon proxies and surrogates (Leyequienet al., 2007). Animals’ basic needs, forage, water, and shelter, mostly vary spatially and temporally,which makes their movements not randomly, but distributed in relation to the variation of their needs.Remote sensing techniques can provide the opportunity to map these basic needs. The physiognomiclandscape features, shelter and shade, can be derived directly from spectral information on variousimagery bands. For forage distribution, several indices can be applied, like the widely used Nor-malised Difference Vegetation Index (NDVI) (van Bommel et al., 2006). In the following sectionssome methodologies are discussed to map the distribution of animals.

2 Habitat maps and habitat suitability mapping

2.1 Habitat maps

Land cover is the observed physical description of the earth’s surface, and is the attribute most com-monly mapped with remote sensing methods. This first data layer is then combined with additionalinformation to derive more useful spatial products. These land cover maps are usually not enough toreveal underlying mechanisms and the dynamics of complex natural landscapes or to improve predic-tions of species distributions. The more useful habitat maps can be derived indirectly from land covermaps or they can be modelled through integration with other environmental factors.

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CHAPTER 5. Wildlife tracking and remote sensing

Habitat mapping can be conducted on various spatial scales. The labour-intensive manual interpreta-tion of aerial photographs is limited in range, and frequently used as a complement to field surveys.It is used in studies of species with limited ranges and in the analysis of relatively small areas. Theskilled interpreters, who perform these analyses, generate detailed, high-quality information.Digital processing of high spatial resolution imagery, like Landsat-7 (30m), medium spatial resolu-tion imagery, like MODIS (250m), and low spatial resolution imagery, like SPOT-Vegetation (1km)is possible for larger study areas (McDermid et al., 2005). The use of time series of satellite data caneven improve this habitat mapping. The changes over time of vegetation structure, productivity, andphenology are as important for some species perception of habitat quality as temperature and precipi-tation (Bradley & Fleishman, 2008).A habitat classification of a larger area is based on training data collected during field surveys. Thistraining data are being digitized and the spectral properties of the different habitat classes are de-termined. The result is a classification of the area of interest into discrete habitat types. A majordrawback of this technique is the assumption that the conditions at the training locations may be ex-trapolated over a larger area. To make sure that no biased result is obtained, these assumptions needto be tested carefully.

2.2 Habitat suitability maps

Habitat suitability is a widely used remotely sensed proxy for species distribution and richness. Itrelies on the fact that each animal has its own environment in which it lives and grows. A habitat mapis created with the use of airborne or satellite data, biophysical, geophysical, and meteorological datain combination with the knowledge of habitat preferences and requirements of the species of interest.Data on species distribution, habitat use or characteristics, can be collected by field surveys or byanalysing the movements of collared individuals. These findings can be extrapolated to cover a largeregion of interest and to estimate habitat suitability.

This discrete classification approach is not always sufficient for ecological purposes. A lot of speciesrequire the micro-heterogeneity of areas, and many herbivore species use more than one distinct veg-etation type (Leyequien et al., 2007). The quality of the habitat is also very important, for instance,the structural complexity of vegetation and the relative proportion of cover in the understory, shrublayer, and canopy (Bradley & Fleishman, 2008). Some non-herbivore species on the other hand showlittle direct association with a habitat or vegetation type, and many species, regardless of the degree ofhabitat specificity, do not occupy the full extent of their preferred habitat type. To make a successfulcorrelation of animal occurrence and remotely sensed habitat data, the animals need to be well studiedand their habitat preference well documented. Other species have a changing habitat preference withgeographical position, which restricts the predictive value of the animal-habitat relationship. Somepredicted distributions are wrong due to the socio-biology of the species, for example an interspecificcompetition or anthropogenic influences can force them to use other less suitable vegetation classes.

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The limited accuracy in some studies using this approach can be caused by applying proxies at inap-propriate spatial, spectral, and temporal resolutions. So this technique should always be applied withprecaution.

3 Spatial heterogeneity assessment based on primary productivity

Spatial heterogeneity is a key component in the explanation of species richness. Environmental het-erogene ecosystems have more different niches in comparison to simple ecosystems and can thussupport more species. It is determined by factors like temporal and spatial variation in the biological,physical, and chemical features of the environment. The species distribution and local abundanceof individuals is thought to be influenced by the spatial and temporal varying plant productivity andbiomass of ecosystems (Leyequien et al., 2007).

There are several vegetation indices used in remote sensing to represent the presence and conditionof green vegetation. These vegetation indices are mathematical combinations of the red (R) andNear-Infrared (NIR) bands of several sensors. The most commonly used vegetation index is theNormalised Difference Vegetation Index (NDVI): NDVI=(NIR-R)/(NIR+R) (Lillesand et al., 2004). Itis a proxy for photosynthetic activity as it is based on the strong absorption of the incident radiationby chlorophyll in the red, and the contrasting high reflectance by plant cells in the NIR spectral region(Mutanga & Skidmore, 2004b). As NDVI seems to be a suitable indicator for vegetation parameterslike biomass and aboveground primary productivity, it is often correlated to faunal species occurrenceand diversity (Leyequien et al., 2007).High NDVI values indicate vegetated areas, as these have a relatively high NIR reflectance and low redreflectance. Clouds, water, and snow have negative values as these areas have larger red reflectancethan NIR reflectance. Rock and bare soil areas show similar reflectances in both NIR and red and thushave NDVI values near zero. An advantage of NDVI is that it helps compensate for extraneous factorslike changing illumination conditions, surface slope, aspect, . . . (Lillesand et al., 2004). Caution has tobe taken with the use of NDVI in semi-arid areas, because of soil interference and darkening effects.If the study area consists of a single soil type with only some sparse parts of other material, the NDVIcan be used without big negative effects (Verlinden & Masogo, 1997). There are also some factorsinfluencing NDVI observations that are unrelated to vegetation conditions, these are for instance thevariability in incident solar radiation, radiometric response characteristics of the sensor, atmosphericeffects and off-nadir viewing effects (Lillesand et al., 2004).

NDVI is correlated to vegetation biomass and dynamics in various ecosystems worldwide. It hasbeen used to monitor vegetation, estimate primary production and detect environmental change. It isdetermined by the composition of species within the plant community, the vegetation form, growthand structure, the vertical and horizontal vegetation density, and by the reflection, absorption and

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transmission within and on the surface of the vegetation or ground, and by the atmosphere, clouds andatmospheric contaminants (Pettorelli et al., 2006).

In this approach, animal occurrence and diversity is related to terrestrial features by means of an eco-logical, trophic link. This means that herbivore animals can be related to the vegetation that theyconsume. If additional environmental variables, like landscape diversity, evapotranspiration, land sur-face temperature, rainfall, altitude,. . . are included, together with primary productivity, considerablevariation in species richness can be explained. However, scale or resolution is the main factor influ-encing the accuracy of predictions of species richness using primary productivity indicators (NDVI).It is more difficult to correlate NDVI with the distribution of less abundant species as these might notoccupy all suitable habitats. This biomass-based approach is only successful for herbivorous speciesthat are sensitive to differences in vegetation characteristics across an area (Leyequien et al., 2007).The use of NDVI always has to be used with elaborate ground thruthing. For instance, high NDVIvalues in heavily grazed areas probably indicate high bush cover rather than green grass (Verlinden &Masogo, 1997).

The main weakness of NDVI is its asymptotical approach to a saturation level above a certain biomassdensity and leaf area index (LAI) (Gao et al., 2000). The technique has therefore a limited value inassessing biomass during, for example, the peak of seasons. This problem can be overcome by usingnarrow band vegetation indices in areas with dense vegetation. These indices are calculated usingnarrow bands in the whole electromagnetic spectrum (350-2500nm) (Mutanga & Skidmore, 2004b).More recent remote sensing products and techniques, like the Enhanced Vegetation Index (EVI) fromthe MODIS product suite, can overcome this saturation problem. The goal of the EVI is to opti-mize the vegetation signal with improved sensitivity in high biomass regions and improved vegetationmonitoring through a decoupling of the canopy background signal and a reduction in atmosphere in-fluences. The EVI has a higher range in values in high biomass regions, making it possible to detectmore variation in these areas. However, the NDVI has a higher range in values over semi-arid regions,making it possible to detect more variation in biomass in these areas. For intermediate regions, bothindices show an identical range in values (Huete et al., 2002).

4 Temporal heterogeneity assessment

Seasonal climatic variations cause differences in plant species growth and establishment, leading tochanges in species composition and distributions. As a result there are changes in the spatial distribu-tion of plant phenology and growth. When looking at the land cover data of multiple years, a visioncan be made of the influence of climate variability on ecosystems. Ecosystems are the most importantfeature in biodiversity assessment and multitemporal satellite data can have the potential to describeinteractions among seasonal, annual and long-term climate variability to understand species diver-sity. As many animal species are very mobile over time, multi-temporal data can also provide a more

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complete view of their occurrence and distribution unlike single-date studies that do not cover theircomplete range of habitats. With the establishment of the Advanced Very High Resolution Radiome-ter (AVHRR) meteorological satellite series in 1980, continuous data to study ecoclimatic dynamicsbecame available (Leyequien et al., 2007).

5 Heterogeneity assessment based on landscape structural properties

Many species select their habitat based on structural properties of the habitat instead of species as-semblage. It is stated that, in general, the more vertically diverse a forest is, the more diverse its biotais. It is possible to estimate the structural properties and assess their heterogeneity with the use ofremote sensing (Nelson et al., 2005). Most common remote sensing techniques for relating landscapestructural properties to animal diversity, are Synthetic Aperture Radar (SAR) and the height mea-suring technologie of airborne lasers (i.e. airborne LiDAR). Both are active remote sensing systems(Lillesand et al., 2004). These tools are used to map vegetation height and its variability, percentcanopy cover, field boundary height, fractional vegetation cover, and aboveground biomass (Nelsonet al., 2005; Hinsley et al., 2002).

Radar uses microwave energy while LiDAR sensors use pulses of laser light. Radar measures thestrength and origin of echoes or reflections received from objects within the system’s field of view.LiDAR measures the time of pulse return, which is then processed to calculate the variable distancesbetween the sensor and the surfaces present on the ground. LiDAR has not only the possibility todiscriminate features as forest canopy and bare ground but also surfaces in between. An advantage ofLiDAR is that the data is georeferenced which makes it compatible with GIS applications (Lillesandet al., 2004). There is extreme potential for high resolution mapping of wildlife habitats by combiningthese techniques, that measure vegetation structural types, and information obtained from other remotesensing techniques, like multispectral satellite images (Hinsley et al., 2002; Imhoff et al., 1997).

These techniques are mostly used in forest ecosystems. Several examples illustrate the use of thesetechniques. In Delaware, LiDAR was used to identify and locate forested sites potentially supportiveof populations of the Delmarva fox squirrel (Sciurus niger cinereus L.) (Nelson et al., 2005). InAustralia, vegetation heterogeneity mapped with the use of SAR and aerial photography were relatedto field studies of bird abundances (Imhoff et al., 1997). In England, the quality of woodland forGreat Tits (Parus major L.) and Blue Tits (Parus caeruleus L.) was estimated with the use of airborneLiDAR (Hinsley et al., 2002).

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6 Heterogeneity assessment based on plant chemical constituents

This last approach uses plant chemical constituents to define habitat heterogeneity and eventually as-sess and predict species richness. Animal species are attracted to a habitat by the spatial and structuralcomposition, but also by the forage quality that an animal perceives in that habitat. For example, thespatial distribution of many wildlife species in the African savannas is influenced by the variation ingrass quality (Leyequien et al., 2007). High productivity areas can sometimes be limited for herbi-vores in plant chemical composition. There is a decrease in forage quality as grass matures by theaccumulation of structural tissues and their fibre content decreases as well, reducing their digestibil-ity. It may therefore be important for broad-scale satellite-based habitat models for wild ungulatesto consider the forage quality-quantity trade-offs (Mueller et al., 2008). A canopy quality estimationon a large scale appears thus relevant to understand wildlife diversity. Broadband satellites such asLandsat TM or SPOT are not spectrally detailed enough to detect or estimate the concentration ofchemical constituents. Imaging spectrometers, on the contrary, can detect and quantify canopy bio-chemical components by measuring canopy reflectance in narrow and contiguous spectral bands ina wide wavelength range. The many subtle absorption features of the spectrometer data allows theidentification of a wide range of plant compounds and their concentration. The relationship betweenspectral properties and foliar chemicals have been examined from dried and fresh leaves, to entirecanopies. The estimation of biochemicals of entire canopies brings along complicating factors, likethe masking effect of leaf water absorption, the complexity of the canopy architecture, variation inleaf internal structure and directional, atmospheric and background effects. Several methods, includ-ing band ratios and difference indices, have been developed to maximize sensitivity to the vegetationcharacteristics, while minimizing confounding factors.

A number of studies have shown the potential of this technique in understanding the movement anddistribution of wildlife, particularly in areas where herbivorous wildlife is known to be limited bynutrients. In Australia, chemical constituents of leaves of four Eucalyptus species were investigatedto predict herbivory by greater gliders (Petauroides volans Kerr) and common ringtail possums (Pseu-docheirus peregrinus Boddaert) (McIlwee et al., 2001). In South-Africa, the different levels of nitro-gen concentration in grass was mapped with the use of imaging spectroscopy and neural networks(Mutanga & Skidmore, 2004a). In the future, monitoring of seasonal changes in foliar nutrient con-centration as well as extending the method to predict other macro nutrients and secondary compoundsin both grass and tree canopies may be possible. The major constraint is the little contribution offoliar chemicals to the canopy optical properties. Currently, It is a prerequisite to further investigatethe spectral features of attractants and repellents of forage and their influence on faunal species dis-tributions to successfully upscale these findings to large areas for monitoring and conserving faunalspecies (Leyequien et al., 2007).

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Chapter 6

Data and methods

1 Introduction

First the different satellite images will be discussed, followed by the tracking data and ground truthdata. Later in this chapter, the classification methods and statistical analysis are mentioned. Most ofthe tasks conducted on satellite images were performed using the program Idrisi Andes and in somecases Arview 3.1. The statistical analysis is conducted with the help of S-Plus 8.

2 Satellite images

2.1 Introduction

As different satellites have different properties, three types of satellite images will be used. Landsat-7 and MODIS images are used to create a classification of the study area. The Landsat-7 imageswere chosen for their high spatial resolution to select the training data. As the MODIS images havea coarser spatial resolution, they contain more mixed pixels, which makes it more difficult to selectappropriate training data. However, MODIS images were chosen for their high temporal resolution(every 2 days a global coverage), which makes it possible to obtain time series. These time seriesmake it possible to monitor the vegetation phenology over the year, so that the different vegetationclasses can easier be distinguished. Landsat and MODIS data are distributed by the Land ProcessesDistributed Active Archive Center (LP DAAC), located at the U.S. Geological Survey (USGS) EarthResources Observation and Science (EROS) Center (lpdaac.usgs.gov). SPOT-Vegetation images wereused to find a relationship between zebras and biomass, with the Normalised Difference VegetationIndex (NDVI) as the biomass indicator.

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CHAPTER 6. Data and methods

2.2 Landsat

Two landsat-7 images were downloaded from USGS Global Visualisation Viewer (GloVis) (USGS,read 12/2008). These images were acquired on February 21th, 2000, with the Enhanced ThematicMapper Plus (ETM+) sensor on board Landsat-7. The first image corresponds with path 168 and row59 in World Reference System (WRS), the second image with path 168 and row 60. The WRS is anotation system for Landsat data, which divides the world in a global grid of 233 paths by 248 rows.It enables a user to choose a scene by specifying the path and row number (figure6.1).

Figure 6.1: WRS path/row numbering scheme (NASA, read 2009)

The reference system of the Landsat images is UTM-37N in meters. The reference datum and ref-erence ellipsoid are WGS84. The ETM+ collects 15m resolution panchromatic data and six bandsof data in the visible, near-Infrared (NIR) and mid-Infrared (MIR) spectral regions at a resolution of30m. The seventh, thermal band has a resolution of 60m (table 6.1).

In the first image, bands 1–5 have 8713 columns and 7573 rows, in the second image 8741 columnsand 7599 rows. For all the different bands, the two images were mosaicked together and an area wasextracted on which the classification was performed. This extracted area is smaller than the study areaas first different classification methods are tested. The image of the extracted area has 3266 columnsand 4330 rows.(figure 6.2)

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Table 6.1: Bandwidth and resolution of the different ETM+ bandsBandwidth Name Resolution

(1) 0.45 to 0.52 Blue-Green 30(2) 0.52 to 0.60 Green 30(3) 0.63 to 0.69 red 30(4) 0.76 to 0.90 NIR 30(5) 1.55 to 1.75 MIR 30(6) 10.4 to 12.5 Thermal-IR 60(7) 2.08 to 2.35 MIR 30

PAN 0.50 to 0.90 Panchromatic 15

Figure 6.2: Two Landsat images and the extracted area with the coordinates in UTM-37N at each corner.

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2.3 MODIS

The MODIS images were downloaded from the NASA Warehouse Inventory Search Tool (WIST)(NASA, read 01/2009). In the WIST tool MODIS Terra, Vegetation Indices 16-Day L3 Global 250mSIN grid (short name: MOD13Q1) was selected. These are 16 day composites. Eighteen images weredownloaded from the year 2008, with the images from half March until the end of May missing (table6.2).

Table 6.2: Start and end date of the 16 day periods of the different imagesImage Start date End date Image Start date End date

1 19 Dec 2007 03 Jan 2008 10 12 Aug 2008 27 Aug 20082 17 Jan 2008 01 Feb 2008 11 28 Aug 2008 12 Sep 20083 02 Feb 2008 17 Feb 2008 12 13 Sep 2008 28 Sep 20084 18 Feb 2008 04 Mar 2008 13 29 Sep 2008 14 Oct 20085 24 May 2008 08 Jun 2008 14 15 Oct 2008 30 Oct 20086 09 Jun 2008 24 Jun 2008 15 31 Oct 2008 15 Nov 20087 25 Jun 2008 10 Jul 2008 16 16 Nov 2008 01 Dec 20088 11 Jul 2008 26 Jul 2008 17 02 Dec 2008 17 Dec 20089 27 Jul 2008 11 Aug 2008 18 18 Dec 2008 02 Jan 2009

Each MOD13Q product contains 6 bands. There are four composited surface reflectance bands: red(band 1) , NIR (band 2), blue (band 3), and MIR (band 7) (table 6.3). The other two bands are vegeta-tion indices: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index(EVI). These vegetation indices give an indication of the biomass present on the ground. The EVIminimizes canopy background variations and optimizes sensitivity in dense vegetation conditions.It also includes the blue band to reduce atmosphere influences caused by smoke and sub-pixel thinclouds (Huete et al., 2002).

Table 6.3: Bandwidth and spatial resolution of the different surface reflectance bands downloadedBand Bandwidth Resolution

(1) red 620-670 nm 250(2) NIR 841-876 nm 250(3) blue 459-479 nm 500(7) MIR 2105-2155 nm 500

The images were downloaded in HDF-EOS format with a sinusoidal projection. The coordinate sys-tem was converted to UTM-37N with reference datum WGS84, and the study area was extracted. The

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images of the study area were 527x821 pixels. (figure6.3)

Figure 6.3: MODIS image of the study area with the coordinates in UTM-37N of the corners

2.4 SPOT-Vegetation

The SPOT-Vegetation NDVI images were delivered by VITO (Vlaamse Instelling voor TechnologischOnderzoek, Flemish institution for technological research). There are 36 images for the years 2006and 2007, and 34 images for the year 2008. There are 3 images per month, this is for days 1–10, days11–20, and day 21 till the end of the month. It consists of synthesis products over 10 day periods.These images are obtained from the compilation of daily atmospherically corrected images of tenconsecutive days taken by the SPOT-Vegetation sensor on board SPOT-5. The resulting value for eachpixel corresponds to the value of the date with maximum NDVI for that pixel, so the synthesis is thuscomposed of pixels with values from different dates (SPOT-Vegetation, read 03/2009). The NDVIvalues of these SPOT-Vegetation images are rescaled between 0 and 250 using a linear model withintersect -0.08 and slope 0.004. Some additional values are assigned to the missing pixels, namely251 to a missing pixel, 252 to a cloud pixel, 253 to a snow pixel, 254 to a sea pixel, and 255 to aback pixel. The reference system is UTM-37S with Arc1960 the reference datum. These images wereconverted to the latitude/longitude reference system and the WGS84 reference datum. The spatialresolution is 1km and each image has 205 x 232 pixels. (figure 6.4)

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Figure 6.4: SPOT-Vegetation image with the coordinates in Latitude/Longitude (Degrees) of the corners

3 Tracking data

The tracking data of the Grevy’s zebras were collected using GPS collars. This was part of the ’Savethe Elephants Animal Tracking Project’. The data were delivered by the Northern Rangelands Trust(NRT). Data are available from the period June 2006 till August 2008, but the period of data collectionand the amount of data are different for each animal (figure 6.5). The reason why a collar stopsmeasuring locations is mostly due to an equipment failure or sometimes due to the dead of the animal.In total, sixteen Grevy’s zebras have been collared.

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Figure 6.5: Period of data collection for each zebra

For each zebra there is a database file containing the information collected with the GPS collar (table6.4). From these database files, point vector files were created with the use of ArcView 3.1, and thenimported into Idrisi Andes. In figure 6.6, the vector files of four zebras are shown, with a colour paletteindicating the movement: Change in colour over time. For the background image, a SPOT-VegetationNDVI image was used with a greenscale as colour palette.

Table 6.4: The information contained in the database file for each fixed locationInformation Description

objectID Number of the location fixcollarID Number of the collarFix time Date when the location measurement was obtained

Download time Date when the location measurement was downloaded from the collarLocation coordinates in latitude and longitude

Height above sea level in metresTemperature in degrees Celsius

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Figure 6.6: Tracking data shapefiles for 4 zebras, with a change in colour indicative of the change in locationover time

4 Vector data

Vector data on the district boundaries, major rivers, roads and towns, protected areas, water bodiesand waterpoints in Kenya were downloaded from the World Resources Institute (International Live-stock Research Instistute, read 2009). Vector data indicating the livestock density from 1990 wascollected as well. NRT provided vector files containing the conservancies. An Africover land covermap of Kenya was used for comparison with the land cover classification map produced in this study.Africover is an initiative of the Food and Agriculture Organization of the United Nations (FAO). Thisland cover map has classes based on the FAO/UNEP (United Nations Environment Program) interna-tional standard Land Cover Classification System (LCCS).

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5 Classification

5.1 Ground truth data

Classification of satellite images requires ground truth data to assist in the interpretation of the dif-ferent land cover classes in the image and for the selection of training data. Ground truth data wasprovided by NRT. For each sampled vegetation point, the GPS location was measured, a vegetationdescription was performed by filling in a form, and a photograph was acquired. The form is shown inAppendix A. In figure 6.7 some examples of photographs taken by NRT are shown. Vegetation de-scription consisted of estimating the percent cover in the herbaceous, shrub and tree layer. In the form,the percent of trees, shrubs and herbaceous was indicated as closed (C: 70%–100% cover, crownsoverlapping, touching, or very slightly separated), open (O: 20%–70% cover, crowns not touching,distance between crowns up to twice the average crown diameter), sparse (S: 2%–20% cover, distancebetween crowns more than twice the average crown diameter), or absent (A). For shrubs, it was indi-cated whether the average height was more or less then half a meter. For herbaceous, the compositionwas indicated as forbs (F: >75% cover of forbs), grasses (G: >75% cover of grasses), or mixed (M:forbs cover less then 75% and grasses cover less then 75%). In total, 65 GPS locations were measured.

(a) Shrubland class (b) Woodland more than 70% trees class

(c) Herbaceous class

Figure 6.7: Examples of photographs taken from different vegetation classes

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5.2 Artificial Neural Networks (NN)

Artificial Neural Networks (NN) will be discussed in this section as it will be used to perform classi-fications. NN are based on biological nervous systems’ information processing. They are composedof a large number of interconnected artificial neurons with several inputs and a single output. Thenetwork consists of one input layer, some hidden layers and an output layer. It is able to process infor-mation and analyze patterns in data that are too difficult to distinguish for humans and other computertechniques.

Figure 6.8: Artificial neural network with the three layers: input, hidden and output

When the neuron is in training mode, it is trained to associate outputs with input patterns. In the usingmode, the neuron fires, this means is activated, when it recognizes the input pattern. If not, a firingrule is used to determine whether it should fire or not. These rules account for the high flexibility ofNN. So the network tries to identify the input pattern and match the associated output pattern with it.When an input is not known, it is given an output of an input pattern that is least different from it.

In more sophisticated neurons, the connections between the neurons have weights so that every inputhas a different effect on the output. The network is trained for a specific application by a learningprocess which involves adjustments to the connections between the neurons, to the weights. Theweights are adjusted so that the error between the desired and actual output is reduced. To control thisprocess, the network calculates how the error changes as each weight is increased or decreased.

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Figure 6.9: Neuron with several weighted inputs and a single output (Stergiou & Siganos, read 04/2009)

There are two learning methods. There is supervised learning where the network is given the inputand matched output, the network learns to infer the relationship between the two. If the network isthen properly trained, it has learned to model the function that relates the input variables to the outputvariables. It can then be used to make predictions for inputs with unknown outputs. Unsupervisedlearning is based on local information. The network organizes the data presented by itself and detectsemergent collective properties. When the network is given an input of which there is no matchingoutput, the network assigns the output of the input that is most closely related.

There are three categories of transfer functions. In linear units, the output activity is proportional tothe total weighted output. In threshold units, the input is multiplied with the weight, this gives theweighted input, and if the sum of these exceeds a pre-set threshold value, the neuron is activated. Insigmoid units, the output varies continuously but not linearly as the input changes. When the neuronfires, the activation signal is passed through an activation function to produce the output of the neuron.

In a feed-forward NN, signals can only travel one way, from input to output. In feedback networks,signals can travel both ways by introducing feedback loops in the network. These are very powerfulnetworks but they can get extremely complicated. A great advantage of NN is that users don’t needto understand the internal mechanism of the task and they are very well suited for real time systemsbecause of their fast response and computational times which are due to their parallel architecture.

There are several parameters in a NN. The learning rate determines by how much the weights arechanged at each step. In the used algorithm, the learning rate is 0.01. The momentum is 0.1 andallows the change to the weights to persist for a number of adjustment cycles. The number of cells inthe hidden layer is 10 and the maximum number of cycles the network is run is 1000. After every 5cycles, the error on the test set is calculated. The activation function of the used network is the tangenthyperbolic (tanh). The training fraction is 0.5, meaning that half of the data is used as training set andhalf is used as test set (Statsoft, read 04/2009; Stergiou & Siganos, read 04/2009).

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5.3 Classification methods

Several methods (Maximum Likelihood and NN with different inputs) were used to perform a classi-fication of the study area. First training data were digitized on the Landsat images using the groundtruth data. On the ground truth form the direction in which the photographs were taken is mentioned,so the training pixels were also selected on that side of the GPS location point. Initially, water wasalso included in the trainingdata, but as the Landsat image is from the dry season, hardly no water isvisible, so water was excluded from the classification. In total more than 2000 pixels per class wereselected. On these pixels several classification methods were tested.

A supervised Maximum Likelihood classification, was performed using the signatures of the trainingpixels. The signature statistics are extracted over all available bands (bands 1–5) for each informa-tional class. This classification method is based on the probability density function associated with aparticular training site signature. Each pixel is assigned to the most likely class based on a comparisonof the probabilities that the pixel belongs to each class (Idrisi Andes Help). As no knowledge existsabout the prior probabilities with which each class can occur, equal prior probabilities are used.

As it is better to use independent validation or testpixels for the calculation of the accuracy of theclassification, classifications using half of the training pixels were performed. Half of the trainingpixels were selected to calculate the signatures, the other half is being used as test pixels. The trainingpixels were split using the program randompixelselection (Frieke Van Coillie).

Secondly, NN was used to make a classification. The NN program used is called pixelclass (ToonWestra). The program needs some network parameters contained in a parameter file, the trainingpixelsand a file containing all information that can be used to base the classification on. The output is aclassification in raster file. The program selects a trainingset of pixels and a testset of pixels. Thetrainingset is used to train the network and to make the classification while the testset is used to testthe classification’s accuracy. As input, all the available bands from the Landsat image were used.

The temporal information contained in the MODIS time series can contribute to a better differenti-ation between the different classes. The trainingpixels from the Landsat images were reused on theMODIS images, but as the spatial resolution of the two satellites is different, the trainingpixels fromthe Landsat images needed to be enlarged on the MODIS images. In total about 100 pixels per classwere selected. The maximum likelihood classification was used again, in the same manner as abovementioned. In the NN method, several combinations of input images were evaluated. The followingcombinations of input images were evaluated:

• Spectral bands from all images: to make a classification based on the difference in spectralreflectances over the year between the vegetation classes.

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• All NDVI images: to make a classification based on the differences in biomass over the yearbetween the different habitat classes.

• First three components from the Principal Component Analysis (PCA) of the NDVI imageswith first three components form the PCA of the EVI images: When there are a lot of data, partof the information is surplus as it is correlated with other variables. PCA is used to transformdata in such a way that new variables are created that are not correlated. The eigenvaluesand eigenvectors of the original covariance matrix are being calculated. Every eigenvalue andassociated eigenvector describes a principal component, with the eigenvector being the directionof the new component and the eigenvalue a measure of the amount of information contained inthat principle component (Lillesand et al., 2004). Here, only the first three components areused, as these already contain most of the variation in the information. By using PCA, thevariation between different pixels is maximized in the components and this may make it easierto distinguish between the different habitat classes.

• All spectral bands from all images, together with the first three components from the PCA ofthe NDVI images and the first three components from the PCA of the EVI images: combinationbetween the differences in spectral reflectances over the year and the maximized variations inbiomass created by PCA of NDVI and EVI.

• All spectral bands from all images, together with all NDVI images and all EVI images: all thespectral reflectances over the year and all the biomass changes over the year described by thevegetation indices NDVI and EVI.

5.4 Accuracy assessment

An error matrix can be calculated based on the training data, from which several accuracies can bederived. The Kappa values already give a first indication of the accuracy of the classification result.They indicate the amount of ’true’ agreement of the percentage of correct values in the error matrixby taken out the percentage of correct values due to a ’chance’ agreement. It can be calculated asfollows:

kappa =N∑r

i=1 xii −∑r

i=1(xi+ ∗ x+i)N2 −

∑ri=1(xi+ ∗ x+i)

where r=number of rows in the errorxii=number of observations in row i and column i (on the main diagonal)xi+=total of observations in row ix+i=total of observations in column iN=total number of observations included in matrix

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The nondiagonal elements in the columns represent errors of omission, those in the rows errors ofcommission. The overall accuracy is determined by the quotient of the total number of correctlyclassified pixels and the total number of reference pixels. The producer’s accuracies are those resultingfrom the quotient of the number of correctly classified pixels for each category and the number ofpixels of that category in the ground truth data. This measurement indicates how well the trainingpixels of the given habitat type are classified. The user’s accuracies are those resulting from thequotient of the number of correctly classified pixels in each category and the total number of pixelsthat were classified in that category. This indicates the probability that a pixel classified into a givencategory actually represents that category on the ground (Lillesand et al., 2004). The classificationwith the highest accuracy was also compared with Africover, by means of a comparison matrix.

6 Analysis of Grevy’s zebra tracking data

First some general information was extracted from the tracking data to get a view of the animalsfollowed. For each animal, the home range, distance moved and number of fixes within protectedareas (PAs) was calculated. The home range was calculated using the Minimum Convex Polygon(MCP) method. This simply draws a polygon around all the fixes and thus tends to exaggerate thetotal home range area. However, MCP is still widely in use. As the variation in number of fixes andtime period of data collection between the different animals is great, this will affect the home rangesize and therefore the MCPs cannot strictly be compared.As the Grevy’s zebra is a threatened species, it is important to know how much time a zebra spendswithin PAs, where they are better protected. The percentage of fixes for each zebra falling within PAswas determined. PAs include community conservancies, National Reserves and National Parks.In Arcview the total distance moved by each animal and the mean distance moved per day can becalculated. As the period of data collection plays an important role in the total distance moved, thisis only calculated for interest. Contrarely, the mean distance moved per day is comparable betweenanimals and is indicative of how mobile each animal was.

7 Analysis of Grevy’s zebras’ migration

7.1 Introduction

The main objective of this thesis is to model the migration of Grevy’s zebras. There are many factorsinfluencing the movement of the animals. First vegetation biomass will be investigated. Grevy’szebras are herbivores so the vegetation distribution and biomass will probably play an important rolein their migration. As animals cannot survive without water, this source will also be investigated as aninfluence on their behaviour. The presence of livestock will be taken into account, as livestock is an

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important competitor for resources. Based on the obtained land cover classification map and on theAfricover map, the habitat preference of the Grevy’s zebras will be investigated.

7.2 Correlation of the zebras’ migration with biomass

The aim of this part is to seek for a relationship between the migration and the available biomass,using NDVI as the indicator.

7.2.1 Linking NDVI and tracking datasets

The objective was to obtain a dataset containing the NDVI values for the regions where zebra presencehas been tracked. NDVI values of locations with no zebras present will also be determined as this isnecessary to make a comparison between the NDVI values of the preferred and other areas.

First the vector file containing the tracking data was split into subsets in Arcview, using a script (ToonWestra). For each ten day period within the NDVI time series, a vector file per zebra was created withthe location points recorded during that ten day period. So for every zebra, three vectorfiles per monthwere obtained: the file for day 1–10, the file for day 11–20 and the file from day 21 till the end ofthe month. All of these vectorfiles were then converted to rasterfiles in Idrisi Andes, with the samepixelsize as the SPOT-Vegetation NDVI images. Every pixels has the value of the amount of GPSlocation points that it contains. This is done to make the images compatible with the program for theextraction of the NDVI values and amount of zebra location points.Secondly for every zebra a mask was created: per zebra all the pixels were selected where the zebraoccurred at least once during the study period. This mask will be used as the zebra’s range. In evereyten day period, the NDVI values of all the pixels of the range are extracted. As a comparison needs tobe made between the NDVI values of the pixels where the zebra is present and pixels where the zebrais absent at that time, both values need to be known. When all the NDVI values of the pixels fromthe range are extracted for every ten day period, many NDVI values are known from pixels that arenot being used by zebras at that time. However, all the pixels within the range are accessible to thezebras, so no NDVI values are obtained from pixels that are inaccessible and thus impossible to useby the zebras.

For every ten day period, there is a rasterfile containing the location points for the zebra in that periodand a SPOT-Vegetation image with the NDVI values. Next, for every 10-day period, the SPOT-Vegetation NDVI value and the corresponding amount of zebra location points is determined for allpixels within the zebras’ home range. As the mask contains all the pixels where the zebra is present atleast once in the entire period, a lot of pixels do not have any zebra location points in a ten day period.This data extraction is done with the program zebra-extract (Toon Westra).As a result, an excel file is obtained per zebra containing the period (year-month-period, for example

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200662: year 2006, June, from day 11–20), the NDVI and the amount of zebras present per pixel. Thisfile will be reduced for the statistical analysis. All sixteen zebras will be merged together, per period.For every period all the pixels with zebras present will be selected. The amount of zebra locationpoints for each NDVI value will be determined, and this NDVI value will then be listed that amountof times. This is done so that for each NDVI value the preference of use will be accounted. After thisis done for all the NDVI values in that ten day period, the total amount of records is determined. Thenan equal amount of non-zebra NDVI values will be selected at random. It is better for the statisticalanalysis that there is an equal amount of data in the two groups that need to be compared. At the end,a dataset is obtained with three columns: the period, the NDVI and a last column indicating if it is azebra present (1) or zebra absent (0) record.

7.2.2 Statistical analysis

The statistical analysis was performed in S-Plus 8. The test variable is always the NDVI and zebrapresent/absent is the grouping variable. The tests were done for several combinations of periods. Firstall periods together, this is all the data of the entire study period together. To get some better ideaof the preferred NDVI values, the rainy and dry seasons were tested separately. Many statistical testsrequire that the data are normal. The data are tested for normality with the Kolmogorov-Smirnov test.In this test the null hypothesis is that the distribution is normal. Attention should be paid to the centrallimit theorem. This says that a sample of more than 30 observations has an average that approachesquite good the asymptotical normal distribution. Practically this means that a p-value of a parametrictest close to the nominal significance level should be handled with caution, in other cases, the smalldeviation of normality does not affect the result. As the equity of variances is important too, the nexttest consists of the Levene test. This test is a homogeneity-of-variances test that is not dependent onthe assumption that the data need to be from a normal distribution. As the data contain more than 30observations it is allowed to test parametrically. To compare the averages of the two groups, zebrapresent and zebra absent, a Student’s t-test was performed. As a control the non-parametric test wasalso done, namely the Wilcoxon rank test. Almost all the t-tests were done one-sided, there was testedwhether the average NDVI of the pixels with zebras present was higher than the average NDVI of thepixels with zebras absent. Only for the first and second rainy seasons other tests were performed. Forthe first rainy season, there was tested whether the average NDVI of the pixels with zebras present waslower than the average NDVI of the pixels with zebras absent. The test for the second rainy seasonwas done two-sided.

7.3 Correlation between zebra presence and water

To search for a correlation between the tracking of the zebras and the availability of water, the distanceto water is used. Two shapefiles are used as the sources for water. The shapefile of waterbodies

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contains the lakes and permanent rivers. As water is only limited in the dry season no temporal riversare included in the analysis, as these are mostly non-existent during the dry season. The other shapefilecontains the waterpoints in Northern Kenya. The shapefiles with waterbodies and waterpoints inNorthern Kenya are merged together and a raster file is created giving a continuous scale of thedistance from water in every pixel. Next, the amount of zebra location points at every distance fromwater is determined. This output is being redistributed in intervals of half a kilometre and put in agraph together with the area of each distance class.

7.4 Correlation between zebra presence and livestock

To search for a correlation between zebra tracking and livestock, the shapefile containing data aboutlivestock density in 1990 is used. The amount of zebra location points in each livestock density classis determined and put out graphically.

7.5 Correlation between zebra presence and towns

To search for a correlation between the tracking of the zebras and the presence of towns, the distanceto the nearest town is used as indicator. A shapefile containing all the towns in the study area wasused to create a map indicating the distance to the nearest town in kilometres. Then the amount ofzebra location points at every kilometre from the nearest town was determined. This output was putin a graph together with the area of each distance class within the study area.

7.6 Habitat preference

To assess the habitat preference of Grevy’s zebras, the tracking data of all 16 zebras is used togetherwith the habitat classification of the study area and the Africover classification. The method used isbased on the article of Aebischer et al. (1993). The comparison of utilized and available habitat isperformed on two levels: home range composition versus total study area, and proportional habitatuse based on GPS locations versus home range composition. The habitat use of an animal is theproportion of the animal’s path contained within each habitat. The tracking data approximates thispath, so the proportion of GPS locations in each habitat estimates the use of each habitat. The homerange of an animal is the area in which its path is located during a given period. The area within thehome range occupied by each habitat type can be expressed as a proportion of the total home rangearea. Based on its widespread use, the home range is estimated using the Minimum Convex Polygon(MCP) method. In Arcview the extension ’animal movement’ is used to do this. ’Extract’ in IdrisiAndes was used to calculate the habitat composition of the total study area and of each animal’s homerange. It was also used to determine the number of GPS locations from each animal within each

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habitat type. The percentage of each habitat type in the total study area and the MCP’s is calculated,as is the percentage of GPS locations from each animal in each habitat type.

In the ideal case, all habitat types are available and all are used by each animal. In practice, not allthe habitats may be utilized by the animals according to the tracking data. If the habitat is not presentin the MCP, or no GPS data falls within the habitat, a percentage of zero usage for this habitat isobtained. The zero percentage of utilized habitat implies that the use is so low that it was not detected.As a zero numerator or denominator in log-ratio transformation is invalid, a small positive value willbe substituted, here 0.01%.

First it is checked whether the habitat selection is random or not. The available (total study area) andutilized (MCP home range) habitat compositions are transformed to log-ratios yA and yU using theproportion of woodland (<70% trees) as the denominator. According to the article Aebischer et al.(1993), for any component xj of a composition, the log-ratio transformation y=ln(xi/xj) rendersthe yi linearly independent. If there is a random use of the habitat types, yU equals yA or the pairwise differences d=yU -yA between matching log-ratios for utilized and available habitat follows amultivariate normal distribution such that d=0. So after the log-ratio transformation, the differenced= yA-yU is calculated. A residual matrix R2 is created, this is the matrix of raw sums of squares andcross-products calculated from d. R1 a matrix of mean-corrected sums of squares and cross-productsis also calculated from d. This is used to calculate Λ=|R2|/|R1| and the quantity -N*ln(Λ) is then χ2

distributed. This gives an idea whether the habitat use is random or non-random.

When habitat use is non-random, the second step is to rank the habitat types in order of preference.A preferred habitat type is one that is used more than expected from its availability. The concept ofpreference allows the ranking of habitat types from least preferred to most preferred. This rankingcan be achieved by comparisons based on the pair wise differences d. When di>0, habitat i is usedmore than expected relatively to habitat j, or habitat j is used less than expected relatively to habitati. When di>0 for all i, habitat j is used less than expected relatively to all other habitat types, it isthe relatively least used habitat type. So the habitat types are ranked by calculating the matrix (d1,. . . ,dD) as illustrated in table 6.5, for each zebra. The matrix columns are indexed by the habitat typeused as denominator in the log-ratio, and the rows by the numerator. This is an antisymmetric matrix,and because of this and the independence property of log-ratios, each element is independent of theothers in the same row or column. The number of positive elements in each row ranks the habitats inorder of increasing relative use, with 0 the worst and D-1 the best. To combine all 16 zebras, the meanand standard error of the elements at each position is calculated. The ratio mean/standard error givesa t-value. As the non-random use was already checked, the significance level stays 5% rather than forinstance Bonferroni levels. It is important to know that the ranking of the sample of the population issubject to error, and the pattern of t-values can be used to asses which ranks give a reliable order andwhich ones are interchangeable.

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Table 6.5: Matrix used to establish habitat rankings. The number of positive values ranks the habitats in in-creasing order of preference

Habitat types Habitat types (denominator) Positive values(numerator) 1 . . . D (total)

1 . . . ln(xU1/xUD)-ln(xA1/xAD) r12 ln(xU2/xU1)-ln(xA2/xA1) . . . ln(xU2/xUD)-ln(xA2/xAD) r2. . . . .. . . . .. . . . .D ln(xUD/xU1)-ln(xAD/xA1) . . . . rn

7.7 Integration of all factors influencing the migration

Until now several factors having an influence on the migration of Grevy’s zebra were treated as dis-tinct features. In reality a complex interaction between all these factors and others determines themigration pattern. The aim of this part is to search whether it is possible to predict which areas inthe study area are best suitable for Grevy’s zebras. The different factors influencing their movementand occurence will first be investigated separately. These results will then be combined to produce ageneral suitability map for Grevy’s Zebras for the entire study area.

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Chapter 7

Results and discussion

1 Habitat classification

Several habitat classifications of the study area were performed in order to investigate the relationshipbetween the habitat and the zebra tracking data: which habitats do they prefer, which habitats arebeing avoided. First a Landsat image from the dry season of 2000 was used as input for the habitatclassification. Next, a time series of eighteen 16-day composite MODIS images from the year 2008were applied, as these might reveal more distinction between the different classes based on the differ-ences in plant behaviour throughout the year. Two classification techniques were tested: the MaximumLikelihood Classifier and Neural Networks (NN). There were six habitat classes distinguished:

• Herbaceous: cover of the herbaceous layer is more than 50% with a shrub and tree cover lowerthan 50%

• Low vegetation cover: vegetation cover lower than 20%

• Shrubland: cover of shrubs more than 50%

• Woodland (<70% trees): cover of trees between 50–70%

• Woodland (>70% trees): cover of trees more than 70%

• Forests: closed tree cover, could easily be distinguished based on their spectral properties

1.1 Landsat-based habitat classification

Nine classifications were based on the Landsat image from february 2000, using the spectral bands1–5. Six of them were performed with the Maximum Likelihood Classifier (table 7.1). The class

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CHAPTER 7. Results and discussion

crops was excluded from the classification as there was no ground truth of cropland present lyingwithin the study area. The intention to include cropland in the classification was based upon the pres-ence of cropland within the study area on the Africover classification. Classification 2–5 were donewith the class water included. Water was excluded as the image was from the dry season and notenough trainingdata was available for this class. Only one permanent river could be distinguished.The first classification result revealed that most of the area was classified as woodland. To give abetter idea of habitat variability, the woodland class was therefore split into two groups based on thevegetation description in the ground truth data form. This subdivision was based on percentage oftree cover. Woodland 1 indicates the class woodland with more than 70% tree cover (closed wood-land) and woodland 2 indicates the class woodland with less than 70% tree cover (open woodland).Classifications 7, 8 and 9 were performed using NN. For classification 7, the same training pixelswere used as in classification 6. The training pixels were adjusted between classification 7 and 8 totry to obtain a better classification result. This was done by selecting a bigger region at every groundtruth point. In the final trainingset, 2000 pixels per class were selected. Classification 9 was based onthe same trainingpixels as classification 8, but only half of the training pixels were used to make theclassification. The other half was used as testset.

Table 7.1: Classifications made on the Landsat image

number classification method classes present Kappa value

1 Maximum Likelihood herbaceous, low veg. cover, shrubland, woodland, forest 39.93%6 Maximum Likelihood idem 5 minus class water and with more training pixels 54.02%7 NN herbaceous, low veg. cover, shrubland, woodland1, 70.51%

forest, woodland28 NN idem 7 70.91%9 NN idem 7 63.36%

As only one image was available from the dry season, no good result was obtained. The result with thebest Kappa value, classification 8 can be seen in figure 7.1. The only habitat class that could easily bedistinguished from the others using Maximum Likelihood was the forest class. The other classes, allsubclasses of savanna, gave no good result. In Stuart et al. (2006) it is also stated that a classificationbased on Landsat data using conventional Maximum Likelihood Classification is only suitable forextracting the overall boundaries of savannas with associated vegetation types (like forests), but thatit is not able to make a reliable map of the distribution of vegetation formations within savanna areas.NN gives better classification results, but there are still quite some misclassifications. The class bestmapped is again the forest class.

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Figure 7.1: The Landsat classification with the best Kappa value

1.2 MODIS-based habitat classification

Habitat classifications were also performed based on MODIS time series using Maximum Likelihoodand NN classification techniques. The training set derived from the Landsat image was first used,but was then adjusted and enlarged. This training set was used again as the Landsat image showeda lot more details than the MODIS images and so it was easier to indicate the training sites on theLandsat image. These training sites had to be enlarged on the MODIS images as MODIS images havea coarser spatial resolution and a lot of the training sites from the Landsat image didn’t even coverone MODIS pixel. There were several combinations of input images used for the classification.

1. All spectral bands from all 18 MODIS images

2. All 18 NDVI images

3. First three components from the PCA of NDVI and the PCA of EVI

4. All spectral bands of all images and the first three components from the two PCAs

5. All spectral bands from all images with all NDVI images and all EVI images

The Principal Components of the NDVI and EVI of these MODIS images were used to reduce theamount of images for classification. As the first three components of the PCA contain most infor-mation and explain the greatest variation between areas, these were used (for loadings see table 7.2).In the PCA of the EVI images, the first component contained 75.93% of the variation, the second

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component 6.90% and the third component 5%. By using the first three components of the PCA ofthe EVI, 87.83% of all the variation contained in the EVI images is used for classification. In the PCAof the NDVI images, the first component contained 84.69% of the variation, the second component5% and the third component 3.02%. By using the first three components of the PCA of the NDVI,92.71% of all the variation contained in the NDVI images is used for classification. From table 7.2it can be observed that all images contribute highly positive to the first Principal Component (PC1).Each factor has a lot less contribution to PC2 and PC3 and some have positive while others have anegative contribution.

Table 7.2: Loadings from the PCA of the EVI and NDVI imagesEVI NDVI

Image PC1 PC2 PC3 Image PC1 PC2 PC3M1EVI 0.883 -0.168 -0.229 M1NDVI 0.921 -0.152 -0.193M2EVI 0.870 -0.212 -0.336 M2NDVI 0.925 -0.188 -0.254M3EVI 0.881 -0.195 -0.339 M3NDVI 0.915 -0.196 -0.290M4EVI 0.862 -0.038 -0.370 M4NDVI 0.940 -0.015 -0.246M5EVI 0.903 0.052 -0.096 M6NDVI 0.950 0.071 -0.125M6EVI 0.922 0.137 -0.136 M5NDVI 0.941 -0.015 -0.109M7EVI 0.928 0.168 -0.133 M7NDVI 0.954 0.141 -0.118M8EVI 0.877 0.348 -0.022 M8NDVI 0.928 0.225 -0.017M9EVI 0.837 0.478 0.084 M9NDVI 0.894 0.403 0.086M10EVI 0.816 0.513 0.085 M10NDVI 0.894 0.403 0.067M11EVI 0.852 0.461 0.039 M11NDVI 0.914 0.365 0.014M12EVI 0.813 0.495 0.125 M12NDVI 0.894 0.392 0.073M13EVI 0.848 0.341 0.102 M13NDVI 0.921 0.273 0.047M14EVI 0.843 -0.176 0.217 M14NDVI 0.868 -0.238 0.205M15EVI 0.859 -0.186 0.316 M15NDVI 0.913 -0.211 0.221M16EVI 0.845 -0.317 0.295 M16NDVI 0.915 -0.215 0.224M17EVI 0.897 -0.126 0.177 M17NDVI 0.935 -0.064 0.148M18EVI 0.922 -0.022 0.002 M18NDVI 0.959 0.010 0.023

The classifications performed on the entire study area, based on MODIS images are listed in table 7.3,as well as the Kappa values obtained with the training set used as test set. From all the classificationsconducted on the MODIS images, the first 4 classifications were performed using the original Landsatimage training sites. As already mentioned, these contained too little training pixels on the MODISimages to obtain good results. There were high Kappa values obtained for these classifications, butthis can be explained by the fact that only a small amount of pixels from the training set were usedto test the accuracy. The training pixels were enlarged several times to obtain better classification

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results. This was done by selecting the entire pixel instead of only a small part. The final training setcontained 200 pixels per class. From classification 24 onward the entire study area was used insteadof a smaller sample to obtain a good classification.Classification 30 was completely the same as classification 28. The NN was ran a second time anda different output was obtained. In the last three classifications (28, 29 and 30) an independent testset too was used to calculate the Kappa value. This resulted in kappa values of 84.41%, 83.45% and82.34% respectively. Therefore, the best classification result for the study area was classification 28,obtained using NN and all spectral bands from all images, all NDVI images and all EVI images (figure7.2). In further discussion this classification will be referred to as the ’MODIS classification’. Theerror matrix using the training data as test data is given in table 7.4. The accuracies are given in table7.5.The classification based on the PCA did not give a better result compared to when all NDVI and allEVI images were used, due to the fact that NN were able to process all the available information. Itwas not necessary to reduce the amount of information to speed up the processing as the amount oftime needed to make a classification was limited.

Table 7.3: Classifications made on the MODIS imagesNumber Classification method Used images Trainingset used Kappa value

24 NN All spectral S1 87.01%25 NN All spectral + PCA S1 86.59%26 NN All spectral, NDVI and EVI S1 87.18%27 NN All spectral, NDVI and EVI S2 87.13%28 NN All spectral, NDVI and EVI S3 90.39%29 NN All spectral, NDVI and EVI S4 90.61%30 NN All spectral, NDVI and EVI S3 88.79%

Table 7.4: Error matrix of the MODIS classification with all trainingdata used as testdata1 2 3 4 5 6 Total ErrorC

1 213 15 11 8 0 4 251 0.15142 11 130 9 4 0 5 159 0.18243 10 6 115 4 0 7 142 0.19014 6 1 7 403 1 14 432 0.06715 1 0 0 3 1141 2 1147 0.00526 12 2 1 4 0 108 127 0.1496

Total 253 154 143 426 1142 140 2258ErrorO 0.1581 0.1558 0.1958 0.0540 0.0009 0.2286 0.0655

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Table 7.5: Accuracies obtained from the error matrix for the MODIS classificationClass Accuracy

Kappa value 84.41%Overall accuracy 93.45%

producer’s accuracy herbaceous 84.19%low veg. cover 84.41%

shrubland 80.42%woodland1 94.60%woodland2 77.14%

forest 99.91%user’s accuracy herbaceous 84.86%

low veg. cover 81.76%shrubland 80.99%woodland1 93.29%woodland2 85.04%

forest 99.48%

1.3 Analysis of the result

As already mentioned, the overall Kappa of the MODIS classification obtained after calculation of theError Matrix using all the training data as input, is 90.39%. The Kappa obtained using an independenttest set is 84.41%. However, these Kappa values are not an ultimate indicator of a good classificationresult. This high value means that the classification strategy employed works well in the trainingareas. The accuracies based on training data are a bit too optimistic, especially when derived fromlimited data sets (Lillesand et al., 2004). As the Kappa obtained with the independent test set is alsoquite good, the classification result may be a good indicator of reality. However, the test set was rathersmall because the total amount of ground truth data was small. This means that the Kappa value onlygives an indication of the classification on a small part of the study area. So an absolute decisionwhether a good classification result was obtained or not is rather difficult to make as the Kappa valueshave only a limited value to make a decision of accuracy.The area of the different classes within the study area, extracted from the MODIS classification arelisted in table 7.6. The herbaceous class is the largest, followed by woodland (more than 70% trees).Forest covers the smallest area within the study area.

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Figure 7.2: MODIS classification: classification result with the highest accuracy

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Table 7.6: Area of the different classes within the study areaclass area (km2)

herbaceous 6863.38low vegetation cover 3387.01shrubland 3257.84woodland1 5461.98forest 1586.12woodland2 2717.07

When the classification is further investigated it is clear that the result probably shows some differ-ences with reality. This further investigation can be done by comparing the classification with theAfricover classification. Africover is only a rough classification of Africa, so there are misclassifica-tions on Africover as well. But it can be used as an indicator to compare with the MODIS classifi-cation. The study area was extracted from Africover and reclassed into bigger groups resembling theselected habitat types. Table 7.7 gives the reclassification scheme. The class names and class numbersof the Africover classification can be found in Appendix B.

Table 7.7: reclassification scheme to compare Africover with the made classificationclass in the made classification class numbers of Africover

classes not able to match the MODIS classification (0) 1, 2, 20, 231 and 232herbaceous (1) 125, 126, 131, 132, 133, 162 and 163low vegetation cover (2) 10, 127 and 134shrubland (3) 121, 122 and 124woodland1 (4) 114, 115, 116 and 145forest (5) 112 and 113woodland2 (6) 117 and 118

In the comparison matrix (table 7.8), made with Africover and the MODIS classification, it can beseen that a lot of pixels are classified differently. Only the elements on the major diagonal of the errormatrix are those that are classified into the same land cover categories. The calculated accuraciescan be found in table 7.9. The Africover herbaceous class is a very large class, as it includes allclasses with the main vegetation type herbeaceous. So this is probably an overestimation of the classherbaceous, which can explain the huge amount of Africover herbaceous pixels that are classified intoother habitat groups in the MODIS classification. However, there are still a lot of pixels classifiedas herbaceous that do not fall within the herbaceous class of Africover. The shrubland class on theMODIS classification covers only a small amount of the shrubland pixels on Africover, only the low

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vegetation cover class has less Africover shrubland pixels. Only the forest class of Africover fallsrelatively well within the forest class of the MODIS classification. But here again a lot of forest pixelson the MODIS classification are non-forest on Africover. So in general it can be stated that there isnot a good resemblance between the MODIS classification and Africover.

Table 7.8: Error matrix of the MODIS classification and Africover. In the columns are the Africover pixels andon the rows the MODIS classification pixels.

1 2 3 4 5 6 Total ErrorC

1 119422 1253 2388 2385 146 944 126538 0.05622 60540 64 456 1145 32 115 62352 0.99903 54723 1044 1405 1500 47 401 59120 0.97624 86753 314 5167 4857 469 1178 98738 0.95085 5535 214 8374 4358 9483 429 28393 0.66606 38530 749 6230 2158 55 949 48671 0.9805

Total 365503 3638 24020 16403 10232 4016 423812ErrorO 0.6733 0.9824 0.9415 0.7039 0.0732 0.7637 0.6787

Table 7.9: Accuracies obtained from the error matrixClass Accuracy

Kappa value 5.95%Overall accuracy 32.13%

producer’s accuracy herbaceous 32.67%low veg. cover 1.76%

shrubland 5.85%woodland1 29.61%woodland2 23.63%

forest 92.68%user’s accuracy herbaceous 94.38%

low veg. cover 0.10%shrubland 2.38%woodland1 4.92%woodland2 1.95%

forest 33.40%

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1.4 Discussion

There is great uncertainty about the accuracy of the classification and whether a good classificationresult was obtained or not. There was only a small amount of ground truth data and already a greatuncertainty existed about the definition of the classes. The MODIS classification was presented to theNorthern Rangelands Trust to check with the reality and no comments were received.

Savanna ecosystems are very difficult to classify into subtypes. Even the distinction of these subtypeson the ground can be challenging, and they have quite similar reflectance spectra (Stuart et al., 2006).In savannas there are a lot of subtypes where the vegetation consists of various plant forms, for in-stance combinations of shrubs and grasses, woodland with a understorey of grasses and forbs, andeven a combination of the three main vegetation types: trees, shrubs and herbs. In this study, only sixhabitat classes were distinguished so these are certainly classes composed of combinations of plantforms. It should be better to make more distinction between all of these combination types, basedon different cover percentages, but then a lot more ground truth data should be collected. As therewere only 65 data points, there could only be a limited amount of classes, covering distinct vegetationtypes. There will always be a certain amount of mixture of herbs and shrubs, but with more groundtruth data, more distinctions could be made and a better classification result could be obtained. Themethod of collection is also very important to obtain accurate ground truth data. Stuart et al. (2006)mention that it is also important to locate homogeneuos areas that are larger than the spatial resolu-tion of the satellite images used. Then accurate ground truth data is obtained and complete pixelscan be selected as training data. For instance, when Landsat images are used, homogeneous areas ofabout 30m diameter should be selected. As the MODIS images have a spatial resolution of 250 m,the number of homogeneous pixels reduces considerably. Many pixels will contain several classes(mixed pixels), which make the classification process more difficult. It might be possible to obtaina more accurate classification using Landsat images when more ground truth data are collected inhomogeneous areas.

The low classification accuracy might also be partially explained by errors during ground data col-lection. The ground data collection included estimation of ground cover for the herbaceous, shruband tree layer. Human misjudgements in estimation of ground cover could have induced classifica-tion mistakes. If the ground data is collected by several persons, vegetation cover might be estimateddifferently by each person. The photographs acquired for each sampled point were sometimes mis-leading, as some were taken in bird perspective, only showing a small piece of the area. There wasalso only one photograph per GPS location, showing the vegetation in only one direction. It couldhave been possible that some photos were taken on the edge of vegetation classes inducing locationpoints to be classified as one class while they were on the edge of different classes. On the Landsatimage the training pixels were selected at the side of the location point in which the photograph wastaken. The Landsat training pixels were enlarged on the MODIS images to cover complete pixels.As the MODIS pixels are already mixed pixels the mistakes of taken a photograph on the edge of a

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vegetation type and classifying the entire pixel as that class is probably neglible. In general, it wouldhave been better that a photograph was taken in each direction with a horizontal angle.

It could be possible to obtain better classification results by using other classification techniques.For instance, finer resolution imagery like IKONOS (1m spatial resolution) imagery might be used.However, the cost of acquiring the IKONOS data covering large study areas as in this thesis will betoo high. Another possibility is the combination of optical and radar satellite images. By combiningthese two data types, vegetation classes could be distinguished based on their spectral differencesand texture differences (measured by radar). Haack & Bechdol (2000) investigated the use of ShuttleImaging Radar and optical Landsat Thematic Mapper (TM) satellite images for mapping savanna andwoodland vegetation in eastern Africa. The results indicated that there is a high potential in combiningoptical and radar data for mapping the basic land cover patterns. The radar data by itself had goodclassification accuracies, but the combinations of radar and optical data improved the classificationresult.

As a conclusion it should be mentioned that it is extremely difficult to make a classification based onground truth data collected by others without the own knowledge of the study area. To obtain a goodclassification of the study area more data should be collected and other classification techniques couldbe applied: combination radar and optical imagery, more Landsat images of different dates . . .

2 Analysis of tracking data

In this section, some general characteristics of the movement of the Grevy’s zebras are extracted fromthe tracking data. The proportion of tracking data within protected areas (PAs) is also investigated.First the location of the tracking data within the study area was analysed (Figure 7.3 and 7.4). Thereare two major hotspots for the tracked zebras within the study area, one in the Nort-Eastern part aroundLaisamis and the other in the South-West from Wamba over Barsalinga till the South at Archers Postand near Isiolo. In between these two hotspots no zebra location data points were recorded.In figure 7.3, the distribution is shown of the zebras: Hiroya, Kobosa, Dableya, Martha, Johnna, Njeri,Belinda, Lepere, Liz and Silurian2. In figure 7.4, the tracking data are shown of the zebras: Rose,Petra, Jeff, Samburu, Loijuk and Samburu2. The zebras Hiroya, Kobosa and Dableya are located inthe North-Eastern part of the study area, near the town Laisamis. Liz, Petra and Lepere have smallerhome ranges located in the Western part of the study area, west of the town Wamba and north of thetown Barsalinga. North of Barsalinga part of the tracking data of Loijuk is located as well, but she alsoranges more south-east, passing East from Barsalinga till the western part of Archers Post. Belindaand Johnna range from Archers Post till Barsalinga. The home ranges of Martha, Jeff and Rose aresituated near Archers Post. Njeri has some location point East from Barsalinga but also a smalleramount of location points are located at the West side of the town. Silurian2’s home range is locatedin the Southern part of the study area, West from Isiolo. Most location data points of Samburu are

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located in the surrounding of Archers Post, with some data extending to the West in the direction ofBarsalinga. Samburu2 has location data in the South-Western part and in the North-Eastern part of thestudy area. It seems that this zebra has two home ranges. It is very unlikely for a zebra to be locatedon such a large home range without any location data inbetween the two hotspots. It seems that thedata of two zebras were accidently merged together into one dataset. For the further investigationsSamburu2 will be handled as one zebra with all the given location points. As all location points fromall zebras are always merged together for most analysis, this will not have any effect on the result.Only for the analysis of speed and distance Samburu2 is left out.

Figure 7.3: The location within the study area of the home ranges of Belinda, Dableya, Hiroya, Johnna, Kobosa,Lepere, Liz, Martha, Njeri and Silurian2.

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Figure 7.4: The location within the study area of the home ranges of Jeff, Loijuk, Petra, Rose, Samburu andSamburu2.

Secondly, some general characteristics were determined in Arcview: total distance moved, meandistance moved between fixes, minimum speed per day, the maximum speed per day, the mean dailyspeed and the Minimum Convex Polygon (MCP) area. In table 7.10 the results are shown for allsixteen zebras. Njeri showed the highest mean movement between fixes (930m). Jeff and Silurian2show also high mean movement rates between fixes, especially in comparison to their MCP area. Thismeans that these zebras do not undertake large scale movements, but move very extensively withintheir home range. The average over all zebras of the mean distance between fixes is 500m. Samburuhas a negative minimum speed, which is probabely due to the fact that data of some days are missing.Samburu disregarded, the minimum speed ranges from 5.13 m/day for Belinda till 23.09 m/day forNjeri. This low value of 5.13 m/day can be explained by the fact that sometimes measurements oflocation are limited to two observations per day. If these are recorded on a relatively short interval,the distance travelled that day is very low. The value for Njeri of 92 km/day as maximum speedis completely unrealistic. This is probably caused by some missing values. The maximum speedotherwise ranges from 0.85 km/day for Silurian2 till 13.41 km/day for Samburu. The average meandaily speed for all zebras is about 10 km/day, ranging from 15.22 km/day for Dableya till 7.37 km/dayfor Loijuk and hiroya. These are realistic values as in literature the average is set between 10 and 15km/day (Rubenstein, 1986).

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Table 7.10: Analysis of tracking data: the number of bearings per zebra, the distances travelled, the speedanalysis and MCP areas

ZEBRA Total distance Mean distance Min speed Max speed Mean daily MCP Area(km) (m) (m/day) (km/day) speed (km/day) (km2)

belinda 5072.45 475.35 5.13 12.67 7.47 1508.24dableya 3881.43 599.54 5.76 5.58 15.22 2815.41hiroya 700.00 568.64 11.00 3.42 7.37 1256.38

jeff 577.67 651.26 8.06 9.26 11.54 114.34johnna 1963.87 464.71 5.96 6.21 11.76 1340.22kobosa 827.53 586.07 5.50 7.71 15.06 935.24lepere 1631.03 360.77 7.38 3.19 9.27 201.80

liz 1483.11 436.46 7.02 3.62 9.96 319.74loijuk 4512.06 411.35 6.17 6.63 7.37 1607.91martha 3029.20 309.10 5.58 4.80 8.16 297.51njeri 757.66 929.64 23.09 92.23 9.13 1003.09petra 1153.35 363.95 6.30 5.51 9.38 159.60rose 76.50 382.50 21.90 1.05 10.93 36.13

samburu 2087.19 551.87 -0.74 13.41 8.96 1370.43silurian2 95.81 573.68 8.38 0.85 13.69 45.53

In table 7.11 the analysis of tracking data within PAs is given. PAs include National Reserves, ForestReserves and community conservancies. The National Reserves located within the study area areShaba, Samburu, Losai and Buffalo Springs. The Forest Reserves in the study area are MatthewsRange, Ngaia, Ndotos Range and Mukogodo. The community conservancies within the study areaare Melako, Sera, Namunyak, Kalama, West Gate, Meibae, Naibunga, Lekurruki, Il Ngwesi and asmall part of Lewa (figure 7.5).From table 7.11 it is clear that still half of the time zebras move outside of PAs. The conservanciesplay an important role in the conservation, as some animals do stay within these protected areas all ofthe time (Lepere, Liz and Petra). They account for 54% of the total amount of zebra location pointswithin protected areas. Only 4.67% of the location points of all zebras is located within NationalReserves or Forest Reserves. Five out of the sixteen collared zebras spent more than 90% of their timewithin PAs; five spent between 50 and 90% of their time within PAs. The remaining six animals spentless than 30% of their time within PAs.

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Table 7.11: Percentage of bearings in PAsZEBRA % in reserves % in conservancies % in PAsbelinda 0.45 21.58 22.03dableya 3.10 0.54 3.61hiroya 4.63 2.60 5.28

jeff 36.94 52.48 89.41johnna 5.39 0.12 5.51kobosa 26.82 2.90 27.25lepere 0 100 100

liz 0 100 100loijuk 6.36 92.70 99.06martha 0 82.63 82.63njeri 0 95.45 95.45petra 0 100 100rose 68.66 0 68.66

samburu 30.66 37.19 67.86samburu2 4.12 52.36 56.37silurian2 0 0 0

Total 4.67 54.01 58.57

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Figure 7.5: The location of the protected areas within the study area.

3 Correlation between tracking data and biomass

The most important factor influencing zebra migration is probably the biomass distribution. As zebrasare herbivores, biomass is directly linked with their food resources. The proxy for biomass used isthe NDVI, which is determined from SPOT-Vegetation NDVI ten-day composites. The NDVI valuesare determined for each pixel within each zebra’s range for every ten day period. The range is thepixels at least once used by the zebra during the study period. All pixels where zebras are absentduring a certain period can be reached and used by the zebras as they do this at other times. So thereare values obtained for pixels where zebras are present and pixels where zebras are absent during thatperiod. The goal is to determine which areas are being used by zebras on specific times based onNDVI values. The principal idea is to compare the NDVI values of pixels where zebras are presentand pixels where zebras are absent. The names of the ten day periods are always year/month/ten-dayperiod of that month, for example 200662 is the second period of June in the year 2006.

To form a general idea of the difference in NDVI values between zebra present and zebra absent pixels,the averages for every ten day period for the zebra absent and zebra present data were calculated andput in figure 7.6. The figure clearly shows the difference between the rainy seasons (peaks) and the

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dry seasons. In almost all cases the average NDVI value of the pixels with zebras present is higherthan the average NDVI value of the pixels with zebras absent. Only in the first rainy season, fromNovember 2006 till February 2007, the average of zebra present pixels is lower. This rainy seasonwas a very wet one, and the values lie well above the other rainy season values.

Figure 7.6: Graph showing the average NDVI value for every image (ten day period) for the data with zebrapresent and the data with zebra absent

The tests were done on several testsets: one global testset, over the entire study period and one testsetfor every season. For all the testsets, normality was never present, but as the dataset is always muchbigger than 30 measurements, it is allowed to use the limit theorem, and the tests can be conductedparametric. To compare the averages between the NDVI values in pixels with zebras present and theNDVI values in pixels with zebras absent, two sample t-tests were conducted. When the variances areequal, this was marked in the t-test. As a control the non-parametric test, the Wilcoxon rank test, wasalso done but as could be expected this always gave the same result. S-Plus did have some difficultiescalculating the exact p-values, probably because of the size of the dataset. Almost all tests gave ap-value of zero. To check whether this was no mistake, some tests were done in SPSS and R as well,but these programs gave a p-value of zero too. So it can be assumed that the output of a p-value ofzero in S-Plus means an extremely small p-value and a rejection of the null hypothesis.

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The first statistical test was performed on the entire dataset, including all the rain seasons and allthe dry seasons. The t-test was conducted one-sided, in other words it is tested that the average ofthe pixels with zebra present is larger than the average of the pixels with no zebras. The result wassignificant, so zebras use areas with on average larger NDVI values within their home range. Theaverage NDVI over the entire study period for pixels with zebras is 0.296; the average NDVI forpixels without zebras is 0.272. The boxplot (figure 7.7) shows that there is a lot of overlap betweenthe two groups and that there are a lot of outliers, especially in the larger NDVI values. The factthat the result is significant although there is only a small difference between the two averages can beexplained by the huge amount of data available. The dataset converges to infinite. The characteristicsof the two groups are given in the table 7.12. The distribution of the NDVI values used by zebras canbe seen in the histogram shown in figure 7.8. Here it can be seen that the zebras select NDVI valuesbetween 39 (0.076) and 195 (0.7), with the core amount of date between 52 (0.128) and 143 (0.492).The data do not follow a normal distribution but rather a right-skewed normal distribution, where theright tail is longer and heavier than the left one.

Figure 7.7: Boxplot showing the distribution of the NDVI values for pixels with zebras (1) and for pixelswithout zebras (0)

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Table 7.12: Summary statistics for the entire datasetZebras absent Zebras present

Min: 20 Min: 261st Qu.: 66 1st Qu.: 73Mean: 88.38 Mean: 93.82Median: 80 Median: 863rd Qu.: 102 3rd Qu.: 114Max: 227 Max: 220Total N: 117347 Total N: 117444Variance: 973 Variance: 720Std Dev.: 31.2 Std Dev.: 26.83SE Mean: 9.10e-002 SE Mean: 7.83e-002

Figure 7.8: Histogram indicating the amount of NDVI values within each interval, for all the pixels used byzebras over the entire study period.

To get some better idea of the use of areas with specific NDVI values, the dry and wet seasons weretested separately. To split the dataset in subsets indicative of the seasons, the figure 7.6 was used tohave an idea of when the NDVI values increased or decreased. The seasons chosen here probably do

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not coincide with the actual rainy and dry seasons as the reaction of biomass on the rain or drought cansometimes be a bit delayed. It is however best to use the NDVI as the indicator to choose the seasonsrather than the actual rain pattern, as food is delivered by biomass and thus this is the indicator for thezebra migration.Table 7.13 shows the derived periods for the different seasons together with the NDVI averages forthe zebra present and zebra absent data. The values between brackets are the rescaled NDVI values.For the dry seasons, all t-tests were performed one-sided, in other words it was tested that the averageNDVI of the pixels with zebras present is larger than the average NDVI of the pixels with zebrasabsent. The first rainy season was tested vice versa, it was tested that the pixels with zebras presenthave a smaller NDVI average than those with zebras absent. The second rainy season was tested two-sided as the figure gave no clear idea about whether larger or smaller NDVI values were preferred. Forthe other rainy seasons the same test was performed as for the dry seasons. Except for the second rainyseason (p-value = 0.8757), all the tests were significant. So in general, zebras choose larger NDVIvalues than in the surroundings. A possible explanation for the selection of smaller NDVI values inthe first rainy season can be that the higher NDVI values after this very wet season are in regions withmore woody biomass. As zebras prefer forbs and grasses they still choose these habitats and not thewoody vegetation with the higher biomass and NDVI values. In the boxplots (Appendix C) can beseen that the range of values is big and that there is quite some overlap between the NDVI values ofareas where zebras are present and NDVI values of areas where zebras are absent. This makes it ratherdifficult to make a selection of the NDVI values chosen by zebras.

Table 7.13: Periods and results for the different seasonsPeriod Starting period Ending period zebra absent zebra present

average average

dry 1 200662 2006103 0.188 (67) 0.208 (72)wet 1 2006111 200722 0.416 (124) 0.412 (123)dry 2 200723 200742 0.232 (78) 0.236 (79)wet 2 200743 200761 0.32 (100) 0.32 (100)dry 3 200762 2007111 0.204 (71) 0.244 (81)wet 3 2007112 200822 0.26 (85) 0.336 (104)dry 4 200823 200833 0.196 (69) 0.232 (78)wet 4 200841 200853 0.28 (90) 0.364 (111)dry 5 200861 200881 0.188 (67) 0.212 (73)

Derived from these results it’s difficult to predict zebra presence based on NDVI. The range has toomuch overlap to select the preference NDVI of Grevy’s zebras. There should also be some knowledgeabout the habitat type corresponding to the NDVI values as shown for the first rainy season wherelower NDVI values were selected by the zebras.

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4 Correlation between tracking data and water

A distance to water map was created to test the relationship between zebra movement and availabilityof water. As the distance to water is an indicator for the time they have to spent to get to water, zebraswill always have to be in areas where they can reach water in time to drink. First a map (figure 7.9)was created indicating the distance in kilometres to the nearest water body. The available water is inwaterpoints, permanent rivers or lakes.

Figure 7.9: Map showing the distance to water for the study area

After the extraction of the distance from water for all the zebra location points the different distanceswere aggregated into classes of 0.5km. This was done to obtain a more continuous graph insteadof location points every meter (figure 7.10). The area present in the different distance classes wasalso calculated, so that a comparison is possible between the distributaion of the distance to waterclasses that occur in the study area and the distribution of the distance to water classes preferred bythe Grevy’s zebras.

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Figure 7.10: Graph showing the amount of zebra location points in relation to the distance to water and thearea covered by each distance class

The graph has a peak in the range 0–7km. The amount of zebras increases from the distance 0-3.5km,and then decreases rapidly. At a distance of about 10–18km the amount of zebra location points isalmost zero. If the distance is too large, zebras will not occur as they need water to survive. In thisstudy, all zebras are in relative close proximity to water as they can go without water for about 2–5days and can travel about 10–15km per day. Very close to water, the amount of zebras is lower thanin the 2.5–4.5 km distance range, probably because of the high chance of predation near waterpointsor the interference of livestock. In comparison with the available area, the zebras show less usage ofthe areas closer to water and a faster decline in usage after the peak. The peak shows more usage ofthese distance classes in comparison to the available amount.

5 Correlation between tracking data and livestock

Based on the map of the livestock density in the study area (figure 7.11), the number of zebras presentin the different livestock density areas is extracted. The extracted values are aggregated in classes of5 units per square kilometres. These values are then put in a graph (figure 7.12) using the middleof the classes as x-value. On the map, the livestock density is expressed as Tropical Livestock Units(TLU). This is a common unit used in the tropics, in which different kinds of livestock (cattle, smallruminants etc) can be compared. One TLU is equal to an animal weight of 250kg. For instance onecow equals 0.7 TLU, one camel accounts for 1.8 TLUs, and 14 goats or sheep are needed to make up

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one TLU. Even wildlife species can be expressed as TLU. One elephant for example is equivalent to7 TLUs, one buffalo to 2.5 TLUs and one wildebeest to 0.9 TLU (World Resources Institute et al.,2007).

Figure 7.11: Map of the livestock density in the study area in units livestock per square kilometre

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Figure 7.12: Graph of the amount of zebra GPS data per livestock density

In figure 7.12 it is easy to see that the amount of zebras present decreases with an increasing amountof livestock present. This is logical as livestock is a direct competitor of food and water. Sometimesthe areas where a lot of livestock are present are sealed off from the surroundings so that zebras andother wildlife cannot enter these areas, so they cannot be present there. An exponential curve is fitthrough the data but is not a very good indicator for the smaller livestock values, where the amount ofzebra location points increases more rapidly.

6 Correlation between tracking data and towns

A map was created indicating the distance to the nearest town (figure 7.13). This was used to testthe effect of towns on the presence of Grevy’s zebras. The amount of zebra location points perkilometre was extracted and the area covered by the different distance classes within the study areawas determined. Both the amount of zebra location points and the area were put in a graph (figure7.14). In the graph there is a first peak at about 3km from the nearest town, then a second peakat about 8km of the nearest town. After the second peak, the amount of zebras declines to becomeapproximately zero at about 33km from the nearest town. When compared to the amount of areaavailable in the distance classes, the zebra graph shows an earlier peak and a faster decline. Grevy’szebras do not occur in very close proximity to towns, but have a peak from 3–13km from the nearest

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town. They may stay within relative close proximity of humans as these might be located on thebest grazing grounds. A lot of people are dependent upon livestock so they might live near the bestpastures. As zebras occupy the same habitat, they can be found in relative close proximity of towns.Towns are also mostly nearby water, which is a possible explanation for the shape of the graph as well.It seems as that other factors have much more influence on the occurence and migration of Grevy’szebras than the distance to towns.

Figure 7.13: Map showing the distance to the nearest town within the study area

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Figure 7.14: Graph showing the amount of zebra location points in relation to the distance to the nearest townand the area covered by each distance class

7 Habitat preference

7.1 Introduction

In this section the habitat preference of the zebras will be examined. This will be based on twoclassifications: the MODIS classification and a reclass of Africover. The calculation of the habitatpreference is divided in different steps. First it is tested whether there is a non-random use of theavailable habitats. If this is not the case, zebras use the habitats in the same amount as could beexpected from the availability of the habitats. A ranking of preferred habitats can only be made whenthe habitat use is non-random. Secondly, a comparison will be made between the available habitatand the used habitat. This can be performed on two levels, the first level is the comparison betweenthe amount of each habitat in the study area and the amount of each habitat within each animals’home range. The second level comparison is that of the amount of each habitat in each animals’ homerange and the number of GPS locations recorded within each habitat. Preference ranking is performedfor each zebra separately, so for each zebra a different ranking is made. To have a general idea of thepreference of habitats for Grevy’s zebras, results from all sixteen zebras were integrated by calculatingthe mean and standard error of all log-ratio differences between the available and utilised habitat.

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7.2 Habitat preference tested on the MODIS classification

First the habitat compositions in the total study area and in each animal’s MCP were calculated, andthe percentage of GPS locations from each zebra in each habitat was determined using extract in IdrisiAndes. The table showing these values can be found in appendix D. The missing habitat types weretreated by changing a 0% use of available habitat in a 0.01% use of that habitat. These proportionswere then transformed into log-ratios, using the proportion of woodland (<70% trees) as denominator.The choice of the denominator is arbitrarely because it is only used to determine whether there is anon-random use or not.

7.2.1 First level comparison: testing for non-random use

The first level comparison between the utilized and available habitat is that of home range compositionversus total study area. The difference matrix d, the difference between log-ratios of available habitatand log-ratios of utilized habitat, was calculated. R1, the matrix of mean-corrected sums of squaresand cross-products, and R2, the matrix of raw sums of squares and cross-products, were extractedfrom d and used to calculate Λ = |R1|/|R2|.

Λ =

∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣

48.728 67.379 35.005 47.168 23.351

67.379 103.496 46.710 66.627 34.049

35.005 46.710 30.208 30.191 19.555

47.168 66.627 30.191 76.713 19.864

23.351 34.049 19.555 19.864 61.633

∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣

56.247 76.809 40.680 49.507 −14.568

76.809 115.322 53.827 69.561 −13.507

40.680 53.827 34.491 31.957 −9.063

49.507 69.561 31.957 77.440 8.066

−14.568 −13.507 −9.063 8.066 252.869

∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣So -N*ln(Λ) = -16*ln(0.1655) = 28.78 this yields a p-value of 0.00003 < 0.05 when compared to achi-squared distribution with 5 degrees of freedom. There can be concluded that there is a significantnon-random use of the available habitat types.

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7.2.2 First level comparison: ranking of the habitat types in order of preference

The second part is the ranking of the habitat types in order of use, or preference. Per zebra, a matrixwas set up like the one in chapter Materials and methods, table 6.5. In table 7.14 the preferenceranking is given for the zebra Belinda as an example. This is done in the same way for the otherzebras as well. In table 7.15 habitat preference ranking for all zebras is summarized. The habitatsare ranked from 0–5, where the habitat with index 0 is the least preferred and the one with index 5the most preferred. There are some differences in preference amongst the different zebras. This canbe explained by the fact that not every zebra is present in the same area of the study area. As thisdiffers, the composition of the habitats can also differ so their preference for other habitats can bedue to the fact that other habitats occur more. The habitat forest is always least preferred. The lowvegetation cover habitat is most preferred for 7 zebras, the others prefer woodland 2. In figure 7.15the proportion of each habitat type in each zebra’s MCP is represented graphically. Herbaceous isalmost always present for about 20% of the home range. The habitat with low vegetation cover canreach up to 40% of some zebra’s home ranges. For the zebras living in the Northern part of the studyarea, woodland 2 is absent from their home ranges, in the others it can make up as much as 20%.

Table 7.14: Preference ranking of habitat types for Belinda

Belinda herbaceous low veg. cover shrubland woodland1 forest woodland2 rank

Herbaceous 0.339 -0.162 -0.063 1.910 -0.318 2sparse -0.339 -0.501 -0.401 1.571 -0.656 1

shrubland 0.162 0.501 0.100 2.072 -0.155 4woodland1 0.063 0.401 -0.100 1.972 -0.255 3

forest -1.910 -1.571 -2.072 -1.972 -2.227 0woodland2 0.318 0.656 0.155 0.255 2.227 5

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Table 7.15: Preference ranking of habitat types per zebrazebra herbaceous low veg. cover shrubland woodland1 forest woodland2

Belinda 2 1 4 3 0 5Dableya 4 5 2 3 0 1Hiroya 4 5 2 3 0 1

Jeff 2 1 4 3 0 5Johnna 2 1 3 4 0 5Kobosa 4 5 3 2 0 1Lepere 4 5 2 3 0 1

Liz 2 4 1 3 0 5Loijuk 1 4 2 3 0 5Martha 5 1 4 3 0 2Njeri 2 4 3 5 0 1Petra 4 5 2 3 0 1Rose 4 5 2 1 0 3

Samburu 3 1 4 2 0 5Samburu2 3 5 2 4 0 1Silurian2 3 2 5 0 1 4

Figure 7.15: Percentage of habitat use based on MCP for each zebra

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To make a preference ranking of the Grevy’s zebras as a species, all sixteen zebras were integrated.At each position in the matrix, the mean and standard error over all 16 zebras was calculated. Thesignificance of the ratio was evaluated with t-values compared to t-distributions with 15 degrees offreedom. From these t-tests the interchangeability in preference of the habitats could be determined.Only the forest habitat was significantly less preferred in comparison to the others. For the five otherhabitat types, the ranking was not significant.

7.2.3 Second level comparison: testing for non-random use

The second level comparison is that of the habitat use based on GPS locations versus home rangecomposition. This time, the habitat forest is left out, as this is practically absent in all zebra locationdata points and very low in area in the MCPs. So the further analysis is done for the five remaininghabitats. The difference matrix d, difference between log-ratios of home range and log-ratios oftracking data was calculated. R1 and R2 were extracted again from d and used to calculate Λ.

Λ =

∣∣∣∣∣∣∣∣∣∣∣∣

27.714 29.714 16.050 23.045

29.714 47.398 19.530 23.038

16.050 19.530 15.900 20.025

23.045 23.038 20.025 35.046

∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣∣

37.373 37.327 23.235 30.563

37.327 53.400 25.194 28.964

23.235 25.194 21.246 25.618

30.563 28.964 25.618 40.897

∣∣∣∣∣∣∣∣∣∣∣∣The calculation of -N*ln(Λ)= -16*ln(0.6317)= 7.35 resulted in a p-value of 0.1186 when compared toa chi-squared distribution with 4 degrees of freedom. As this is larger than 0.05 there is a significantrandom use of the available habitat types, meaning that the zebras use the habitat in the same amountas would be expected from the habitat availability. The reason for this random use of habitats canbe that the classification does not really resemble reality or it can be that Grevy’s zebras show nosignificant preference for the available habitat types. As there is a random use of habitats, it has nopoint to rank the habitat types in order of preference. Only the summary of the percentage of locationpoints per zebra in each habitat type is represented graphically in figure 7.16. For each zebra, exceptJeff, about 20% of their location point falls within herbaceous (for Njeri up to more than 60%). Thehabitat class with low vegetation cover can contain more than 60% of some zebras’ location points.Only Jeff has no location points in this class. This could be explained by the fact that Jeff is the onlymale animal with and can have a territory. Male Grevy’s zebras choose territories wich are attractive

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to females, so territories with higher amount of vegetation.

Figure 7.16: Percentage of habitat use based on tracking data for each zebra

It is not possible to draw a conclusion from this part. It is not possible to make a preference rankingof the available habitats on the MODIS classification due to the fact that the MODIS classificationdoes not show a good resemblance to reality or due to the fact that the Grevy’s zebras do not showany habitat preference. The habitat preference of the Grevy’s zebras should be further investigated onother classifications, for instance on the Africover classification (See next subsection).

7.3 Habitat preference tested on Africover

As no habitat preference was concluded from the MODIS classification, the test was also conductedon the Africover classification. Africover was first reclassed into larger groups so that the amount ofclasses reduced. The reclassification scheme is given in the table 7.16 and the result in figure 7.17.From this classification, the amount of location points per class and per zebra was determined as wasthe area of each habitat in the study area and the different MCPs (appendix E). As there were nolocation points in the classes forest, closed shrubs and crops, these classes were left out. The zeropercentages in the remaining classes were changed to 0.01%.

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Table 7.16: Reclassification scheme for the Africover classificationclass number class name classes from Africover

1 settlements class 1 and 22 bare class 103 water class204 forest class 112 and 1135 open woody class 114,115,116,1456 very open woody class 117 and 1187 closed shrubs class 121 and 1228 open-sparse shrubs class 124,125,126, 1279 herbaceous and shrubs class 131,132,16210 herbaceous class 133, 134, 16311 crops class 231, 232

7.3.1 First level comparison: testing for non-random use

The proportions were transformed into log-ratios, using the proportion of class herbaceous as denom-inator. To compare the utilized (home range) with the available (study area) habitat, the differencematrix d was calculated and R1 and R2 were extracted. The Λ was calculated in the same way asabove and -N*ln(Λ) equaled to 36.15 with a p-value of 0.00001 when compared to a chi squared dis-tribution with 7 degrees of freedom. So there is a significant non-random use of the available habitattypes.

7.3.2 First level comparison: ranking of the habitat types in order of preference

For each zebra a matrix like in chapter Materials and methods, table 6.5, was made and the habitattypes were ranked in order of preference. The result for all the zebras can be found in table 7.17. Thereis again a difference between the different zebras. The least preferred habitat is very open woody (6),as this habitat has 6 rank zero values and 6 rank one values. The most preferred habitat is herbaceousand shrubs (9) with 6 rank seven values and 5 rank six values. In figure 7.18 the percentage of eachhabitat in the MCP is given per zebra. The habitat classes open-sparse shrubs (8), herbaceous andshrubs (9) and herbacous (10) are most abundant. Especially class 9, which can be found in almost100% of the MCP of some zebras (Lepere, Petra).

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Figure 7.17: Result from the reclass of the Africover classification into larger groups.

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Table 7.17: Preference ranking of habitat types per zebrazebra 1 2 3 5 6 8 9 10

Belinda 4 7 3 1 0 6 5 2dableya 4 7 1 3 0 2 5 6hiroya 4 2 1 3 0 5 6 7

jeff 5 2 1 6 0 3 7 4johnna 7 1 0 5 4 3 6 2kobosa 4 2 0 3 1 5 6 7lepere 6 2 5 3 1 4 7 0

liz 6 2 4 3 1 5 7 0loijuk 4 2 5 3 0 6 7 1martha 5 3 2 4 1 7 6 0njeri 3 1 2 6 7 4 5 0petra 6 5 4 2 1 3 7 0rose 7 3 2 0 1 4 5 6

samburu 7 3 0 1 4 5 6 2samburu2 6 7 5 1 0 3 4 2silurian2 6 4 3 1 2 5 7 0

Figure 7.18: Percentage of habitat use based on MCP for each zebra

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7.3.3 First level comparison: integration over all sixteen zebras

At each position in the matrix, the mean and standard error over all 16 zebras was calculated. Thesignificance of the ratio was evaluated with t-values compared to t-distributions with 15 degrees offreedom. Classes 1 and 9 are the most preferred. The preference between these classes is not signif-icantly different. However, both classes have a significantly higher preference compared to all otherclasses. The third preferred habitat type is class 8, which is significantly less preferred than 1 and9 and significantly more preferred than the others. The outcome of the ranking for the other habitattypes is 5–2–10–3–6 with habitat type 5 being most preferred. These last habitat types however areinterchangeable. The relationships that are not significant according to the t-tests are: 2 versus 3, 2versus 5, 2 versus 10, 3 versus 6, 3 versus 10, 5 versus 10, and 6 versus 10.

7.3.4 Second level comparison: testing for non-random use

Next, a comparison can be made between the GPS locations and the home range composition. Againa difference matrix d was calculated, being the difference between the log-ratios of home range andlog-ratios of tracking data. R1 and R2 were extracted and used to calculate Λ= -16*ln|0.2246| =23.89. This results in a p-value of 0.0012 when compared to a chi squared distribution with 7 degreesof freedom. So there is a significant non-random use within the home range of the different habitattypes. It was however not possible to make a ranking of the habitat types per zebra as a lot of habitattypes showed an equal proportion in the MCP and in the GPS data, which resulted in a differencevalue of zero.

7.3.5 Second level comparison: integration over all sixteen zebras

However, when the mean and standard error of the log-ratio differences is calculated over all 16zebras, there were no zero values and a ranking could be made. The ranking made was (from mostpreferred to least preferred): 1–10–9–6–2–3–8–5. However only a small number of relationships aresignificant, namely 1 versus 2, 1 versus 5, 1 versus 6, 1 versus 8, 5 versus 9, 5 versus 10, 6 versus 8, 8versus 9, and 8 versus 10. So it is rather difficult to make a significant ranking of the preferred habitattypes. When only the habitats 5 (open woody), 8 (open-sparse shrubs), 9 (herbaceous and shrubs) and10 (herbaceous) are taken into account, it is possible to make a ranking. These habitats are chosen asthey compose most of the areas in the study area and in the MCPs. Habitat 5 and 8 are significantlyless preferred than habitat types 9 and 10. Habitat type 5 and 8, and habitat type 9 and 10 are notsignificantly more or less preferred from each other. In figure 7.19 the percentage of GPS data in eachhabitat type are shown per zebra. Habitat class 9 is the main habitat type where zebra location pointsoccur for almost all zebras. Only Rose has a dominant use of the class herbaceous (10). Dableya hasan almost equal amount of location points in classes 9 and 10.

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Figure 7.19: Percentage of habitat use based on tracking data for each zebra

As a conclusion it can be stated that Grevy’s zebras prefer habitat types with herbaceous as main covertype. This can be in mixture with shrubs as well. This outcome could be expected from literaturewhere the Grevy’s zebras diet is said to consist mainly of grasses and forbs, the primary componentsof herbaceous habitat.

8 Integration of all factors influencing the occurrence

In this section, all the factors influencing the Grevy’s zebras’ migration, are being integrated to deter-mine the parts within the study area that are most suitable for Grevy’s zebras. All the areas that arenot being used by the Grevy’s zebras were extracted, based upon the obtained results. Then the otherareas are divided into several preference classes based upon their distance to the nearest water pointand their NDVI value.

As could be seen in the section about habitat preference (section 7), Grevy’s zebras avoid foresthabitat. So the forest habitat areas are extracted from the MODIS classification and considered asnon-suitable Grevy’s zebra area.Based on figure 7.10, the map of the distance to the nearest water point was divided into four classes.A distance more than 20km was indicated as non-suitable area. The edge of 20km is rather low, aszebras can be much further away from water, but in this study, the amount of zebra GPS points dropsto zero at a location 18km of the nearest water point. Next, a value of one was assigned to the areaswith a distance from water of 11–20km, a value of two was assigned to the distance classes 0–2km and

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6–11km. The peak of zebra values, the areas between 2–6km of the nearest water point were givena value of 3. So the higher the value, the more suitable for Grevy’s zebras. Water is only a limitedfactor during the dry seasons, thus the effect of water is only of importance during these seasons. Inthe rainy seasons, a lot more water is being available and the distribution of water has no longer aninfluence on the distribution of Grevy’s zebras. However, the dry seasons are the most limited forsurvival of the zebras, making it therefore important to base the indication of best suitable areas uponthese periods.From figure 7.12 can be observed that the amount of zebras reaches an extremely low value at alivestock density of 20TLU/km2. This livestock density is chosen to select the areas not suitable forthe Grevy’s zebras: the areas with a livestock density above 20TLU per square kilometre.

For every season (seasons are again defined as the ones in table 7.13), the histogram is made for allNDVI values of the locations where zebras were present during that season. These histograms can befound in appendix F. For each season a lower and upper boundary was selected. These boundarieswere not chosen at the absolute edges as some very high or very low NDVI values whith hardly noobservations were left out. The cut-off value was different for every season, as it was dependenton the total amount of observations. For all the dry seasons together and for all the rainy seasonstogether, the average was calculated of the upper and lower boundaries and a range was obtained forthe dry and rainy seasons within which almost all observations were found. The boundaries for everyseason and the overall range can be found in table 7.18. An average SPOT-Vegetation NDVI imagewas created for the dry seasons. The average NDVI value for each pixel over all the ten day periodswithin the five dry seasons was therefore calculated. The same was calculated for the rainy seasonswith an average SPOT-Vegetation NDVI image for the rainy seasons as a result. On these averagedSPOT-Vegetation NDVI images, the areas are extracted that did not fall within the determined NDVIranges. The area that is indicated as non-used on both SPOT-Vegetation images was then taken intoaccount as non-suitable for Grevy’s zebras.

Table 7.18: The selected upper- and lower NDVI boundaries for each season and the extracted averages asNDVI ranges for the dry and rainy seasons

Dry seasons Lower boundary Upper boundary Rainy seasons Lower boundary Upper boundary

dry 1 54 94 wet 1 75 174dry 2 54 108 wet 2 61 145dry 3 43 133 wet 3 59 158dry 4 64 84 wet 4 91 136dry 5 62 90Average 55 102 Average 72 153

It was also determined in which areas zebras occurred most based on the histograms of the NDVI

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values in all five dry seasons and all four rainy seasons. These histograms can be found in figure 7.20.The NDVI ranges extracted for the dry seasons was 61–88, this is the range where the intervals havemore than 10000 observations and for the rainy seasons 91–143, where the intervals have more than7000 observations. This difference in treshold value is due to the fact that a different distribution isobserved between the dry and rainy seasons. These core areas obtained a value of 2, while the otherareas were given a value of 1. The two images, of the dry and of the rainy seasons, were multiplied.An image was obtained which had areas with values 1, 2 and 4. This image was reclassified by re-placing the value 4 with a value of 3.

(a) Histogram of all five dry seasons

(b) Histogram of all four rainy seasons

Figure 7.20: Histograms of both the dry and rainy seasons, indicating the distribution of the NDVI values ofthe zebra present pixels

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Then in a final step, all images were merged together. All areas not suitable for the Grevy’s zebraswere assembled. These areas were indicated on the image of the distance classes and the image of theNDVI classes as being an area with value zero. These last two images were then summed up. Thefinal result was an image indicating areas with values between 0–5 (figure 7.21). The areas with value5 are supposed to be most used by the Grevy’s zebras, while the areas with value zero are supposedto be avoided. To test this result, the amount of zebra location points within each class was extracted.The area of the different classes was also calculated. To get an idea of the usage of the areas by thezebras, the percentage of zebra point and the percentage of the study area was calculated for eachclass and the ratio determined. If the ratio is more than 1, the zebras use this class more than expectedfrom the availability of the class. A ratio below 1 means that the class is less used than expected fromthe availability. The results can be seen in table 7.19. It can be seen that class 5, being the expectedbest class is used about 2.4 times more than would be expected from its area. So this class is definitelypreferred by the zebras. The other classes are all used less than would be expected from their area.

Figure 7.21: Areas suitable for the Grevy’s zebras

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Table 7.19: Results of the analysis of the integration mapclass zebra area %zebra %area ratio

0 7000 5650 5.91 24.12 0.241 4088 1046 3.45 4.47 0.772 4692 1632 3.96 6.97 0.573 7860 3385 6.63 14.45 0.464 23642 5851 19.95 24.98 0.805 71225 5860 60.10 25.02 2.40

Total 118507 23424

The integration of all these factors does not give an exclusive idea of where the Grevy’s zebras wouldoccur. There are besides the factors examined here also other factors influencing the occurence ofGrevy’s zebras. For instance predators have a high influence on their prey. When lions are present,Grevy’s zebras will try to avoid these areas, sometimes by departing to other less suitable areas (Fis-chhoff et al., 2007). Another factor that has a high influence on zebra occurrence is the reproductivestate of the females. Lactating females have other nutritive needs than non-lactating females. Theyalso have to be in closer proximity to water, as they have to drink every day (Rubenstein, 1986). Com-petition with other ungulates can also affect Grevy’s zebras area use. For instance, plains zebras canoutnumber the Grevy’s zebras in good grazing areas, forcing the Grevy’s zebras to use less appropri-ate areas (Rubenstein, 2004). To integrate all the factors influencing Grevy’s zebras occurence andmigration, a lot more data should be obtained, not only about the Grevy’s zebra, but also about otherungulates and predator species.

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Chapter 8

Conclusion

As the Grevy’s zebra is a threatened species, it is important to know as much as possible about theirhabitat use and migration pattern. This thesis had two main objectives: the creation of a habitatclassification and the analysis of the Grevy’s zebras migration. The habitat classification was basedon Landsat and MODIS images. Both Maximum Likelihood and Neural Networks were used toconduct the classification. To analyse the migration, data obtained from the GPS-tracking of sixteenGrevy’s zebras was used. Several factors with a possible influence on the migration were examined:distribution of biomass, water, livestock and towns. The final step was to make an integration of allthese factors to predict the areas within the study area that are most suitable for Grevy’s zebras.

The first objective of this thesis was to make a habitat classification of the study area. The use ofLandsat satellite images was abandoned as no good result was obtained using these images. Insteadtime series of MODIS images were used which enhanced the distinction between different classesproviding information on the plant phenology. The Maximum Likelihood classification method onlymade a good separation of the forest class from the other habitat classes. Using the Neural Networksclassification technique, a better distinction between the different savanna sub-classes was obtained.The best classification result was obtained with NN using all MODIS spectral images, all NDVIimages and all EVI images as input. However, there might still be some distinctions between theclassification result and reality. The reason for this is the small amount of ground truth data points andthe collection method.

The second objective was to model the migration of the Grevy’s zebras. The most important factorinfluencing the migration of the Grevy’s zebras was the available biomass as food source. NDVI wasused as a proxy for available biomass. The Grevy’s zebras almost always used areas with significantlyhigher NDVI values than in the surroundings. Only during the first rainy season they preferred areaswith significantly lower NDVI values and in the second rainy season there was no significant differ-ence between the NDVI values in pixels where zebras were absent or present. The fact that in thefirst rainy season areas with lower NDVI values were chosen can be explained by the very wet rainy

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season.

The other factors influencing Grevy’s zebra migration are proximity to water and livestock density.The zebras mostly prefer areas between 0–15km of water. They are most present within the range of2.5–4.5km from the nearest water point. Areas very close to water are less preferred as there is morecompetition in these areas with other wildlife and livestock. In this study, all zebras were always inrelatively close proximity to water, as they can go without water for 2–5 days and can travel between10–15km per day.When comparing the tracking data and livestock density it was found that Grevy’s zebras avoid areaswith high livestock density. This can be explained by the direct competition between zebras andlivestock for water and food. The relationship of the Grevy’s zebras and the distance to the nearesttown resembles the relationship between the zebras and the distance to the nearest water point. Theirmigration and occurrence is probably not very affected by the towns in the study area.

Based on the MODIS and Africover classification, a habitat preference ranking for the Grevy’s zebraswas performed. First it was tested whether there was a random use of habitat or not. In the case ofa random use, the zebras use the available habitat in proportion to the area of each habitat type. Incase of a non-random use of habitats, a ranking was made per zebra of which habitat they preferred.Finally, the result of all sixteen zebras was integrated to obtain an overall habitat preference rankingfor all Grevy’s zebras tracked in the study area. From the preference ranking based on the MODISclassification, it could only be concluded that Grevy’s zebras avoid forest habitat. Between the otherhabitat types no significant distinction in preference could be made. A possible explanation is that theclassification does not correspond with reality very well.From the preference ranking based on the Africover classification could be concluded that in the firstlevel comparison, between the composition of the study area and that of the home ranges of eachanimal, there is a significant preference of the habitats settlements and shrubs & herbaceous, followedby a preference for open-sparse shrubs. All other habitat types could not be ranked in a significantorder. For the second level comparison, this is between the home range compositions and the GPSdata, there is a significant preference of the habitat types herbaceous & shrubs, and herbaceous. Thenext habitat types in the preference ranking are open woody and open-sparse shrubs. The other habitattypes could be left out as most of the MCPs were composed of these four habitat types.

Finally an integration of all the factors influencing the migration was made based on the obtainedresults. The areas not suitable for Grevy’s zebras were determined. For the other areas the influenceof the distance to the nearest water point and of the NDVI was taken into account to divide these areasinto different preference classes. The result showed an 2.4 times more usage of the most suitable areasby the Grevy’s zebras than would be expected from the area of this class. However, there are a lotmore factors influencing the occurrence and migration of the Grevy’s zebras. For instance, there isan influence of predators, other ungulates and reproductive state of the Grevy’s zebra females. Dataabout all these influences and maybe even more should be collected and taken into account to get a

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better idea of the areas preferred and used by Grevy’s zebras.

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Chapter 9

Nederlandse samenvatting

1 Inleiding

Deze masterproef handelt over de migratie van Grevy’s zebra’s (Equus grevyi) in functie van habitattype en vegetatie biomassa, gebruik makend van teledetectie. Aangezien de Grevy’s zebra een uiterstbedreigde diersoort is, is het belangrijk om hun bewegingen te kennen en om zoveel mogelijk te wetenover hun gedrag. Hoe meer geweten is over hun gebruik van voedsel, water, beschutting . . . , hoe meerinspanning kan geleverd worden om de soort te behouden. Deze masterproef heeft dan ook tweeobjectieven. Ten eerste zal getracht worden een habitatclassificatie op te stellen van het studiegebied,zodat het habitatgebruik van Grevy’s zebra’s kan onderzocht worden. Het tweede objectief is demodellering van de migratie van Grevy’s zebra’s. Dit laatste wordt onderverdeeld in sub-objectieven.Er worden verschillende factoren onderzocht die mogelijks een invloed hebben op de migratie zoalsbiomassa, water, vee en de aanwezigheid van dorpen.

2 Literatuurstudie

2.1 Grevy’s zebra (Equus grevyi)

De Grevy’s zebra is een uiterst bedreigde diersoort die enkel nog voorkomt in het noorden van Keniaen het oosten van Ethiopie. Het is de grootste zebra soort en kan gemakkelijk onderscheiden wordenvan de andere soorten door de grote ronde oren, nauwe gelijk verdeelde strepen, een witte buik en eenbruine vlek op de neus.De sociale structuur van de Grevy’s zebra is eveneens verschillend van de andere zebra soorten. Zeleven in een veel opener gemeenschap, waarbij zo’n 10% van de mannetjes territoria hebben. Hunleefgebied ligt gelokaliseerd in ariede gebieden met schaars water. Alleen lacterende vrouwtjes dienen

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CHAPTER 9. Nederlandse samenvatting

iedere dag te drinken, de anderen kunnen 2–5 dagen zonder water. Hun verplaatsing bedraagt gemid-deld 10–15km per dag.De kwaliteit en kwantiteit van het voedsel en de openheid van de vegetatie zijn belangrijke kenmerkenvoor Grevy’s zebra’s. Ze brengen ongeveer twee derden van hun tijd door al etend. Het zijn grazers,die ook wel eens kruiden, struiken en bomen consumeren wanneer gras schaars is. Bladeren kunnentot 30% van hun dieet uitmaken. Ze mijden meestal erg gesloten vegetatie, omdat de kans op een con-frontatie met predatoren zoals bijvoorbeeld leeuwen er groter is. Zebra’s verkiezen ook om overdagte drinken, omdat dan eveneens de kans lager is op een confrontatie. Er zijn echter waterplassen dieoverdag afgeschermd worden voor het wild, zodat het vee er ongestoord kan grazen. Dan worden dezebra’s gedwongen om ’s nachts te drinken wanneer het predatierisico veel groter is.De overblijvende Grevy’s zebrapopulatie werd in 1970 geschat op 15000 individuen, recente schat-tingen zijn 2000 resterende individuen in Kenia en ongeveer 120–250 in Ethiopie. De eerste grotebedreiging vormt het vee die voor competitie zorgt voor voedsel en water. Koeien kunnen onder anderezorgen voor een degradatie van het milieu door toegenomen erosie en een fragielere vegetatie. Eenandere reden van de afname van de soort zijn stropers, maar dankzij CITES is de handel in Grevy’szebra producten nu verboden. In reservaten kunnen zebra’s drinken en eten in vee- en wapenvrijezones, maar deze gebieden bedekken slechts 0.5% van hun home ranges volgens het IUCN/SSC actieplan. De steppezebra kan ook voor competitie zorgen. Het ernstigste probleem is het habitatverliesvan de reeds gelimiteerde oppervlakte waar de Grevy’s zebra voorkomt. Er zijn gelukkig ook posi-tieve zaken, er zijn reeds kweekprogramma’s opgestart en wetenschappers en locale gemeenschappenwerken samen om de achteruitgang van de soort te stoppen en het aantal terug op te krikken.

2.2 Studiegebied

De Republiek Kenia is gesitueerd aan de oostkust van Afrika. Kenia bestaat hoofdzakelijk uit savanneen grasland ecosystemen (39%) en bushland en woodland ecosystemen (36%). Landbouw bedekt19% van het land, bossen 1.7% en stedelijk gebied slechts 0.2%. Het studiegebied ligt centraal in hetland tussen 0.3◦ and 2◦ Noord en 36.99◦ en 38.1◦ Oost. Het is gelegen in 6 verschillende districten:Laikipia, Isiolo, Samburu, Marsabit, Meru en Nyambene.

Kenia heeft een tropisch klimaat met gemiddelde jaartemperaturen rond de 22°C. De kust is warmen vochtig, het binnenland is gematigd en het noorden en noordoosten van het land is droog. Degemiddelde neerslag is erg laag voor een land op de evenaar, slechts een gemiddelde van 630mm perjaar. Dit is zeer onevenredig verdeeld over het land en varieert sterk tussen de jaren. Er kunnen ooktwee regenseizoenen onderscheiden worden: de korte regens van oktober tot december en de langeregens van maart tot juni. Kenia bestaat voor meer dan 80% uit ariede en semi-ariede gebieden.

Het studiegebied bestaat grotendeels uit savanne ecosystemen opgebouwd uit een min of meer con-tinue kruidlaag en een discontinue struiken boomlaag. De meest voorkomende soorten in de struik-

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en boomlaag zijn Acacia soorten. De afgelopen jaren is er in de semi-ariede rangelands een toegenomengraasdruk waargenomen. Het gevolg van deze overbegrazing is een achteruitgang van de natuurlijkegraslanden. Er is een overgang vastgesteld van overblijvende planten naar eenjarigen en een vervang-ing van de inheemse flora door exoten. Vee kan ook een effect hebben op het vegetatiepatroon, bijvoor-beeld de verstruiking naar struwelen met hoofdzakelijk Acacia soorten. Dit is een veelvoorkomendprobleem in alle Afrikaanse savannes.

Een groot deel van het studiegebied bestaat uit conservancies, gemeenschapsgeleide initiatieven. Zekunnen overal voorkomen waar het land beheerd wordt volgens goede milieupraktijken. Ze dragenbij tot de bescherming van specifieke biodiversiteit, ze zorgen voor groene corridors voor de be-weging van wild of ze kunnen beschermde gebieden zijn waarin zeldzame en bedreigde diersoortenvoorkomen. De conservancies in het studiegebied worden gesteund door een lokale organisatie,de Northern Rangelands Trust. Er wordt gezocht naar oplossingen voor lokale problemen met eenlangdurige lokale oplossing. Dit leidt tot de ontwikkeling en bescherming van het aanwezige wild.De gemeenschappen hebben reeds enkele acties ondernomen om de Grevy’s zebra’s te beschermen.De Grevy’s zebra’s werden gevaccineerd tijdens een anthrax uitbraak, er is een Grevy’s zebra scoutprogramma opgestart waarin lokale mensen data verzamelen over de distributie en aantallen van deGrevy’s zebra’s en er werd een tracking project opgezet met GPS halsbanden om de Grevy’s zebra’ste volgen. De data hiervan werd ook voor deze masterproef aangewend.

2.3 Wildlife telemetrie

Telemetrie is de wetenschap en technologie om automatisch metingen uit te voeren en de data van opeen afstand te verzenden met behulp van draad, radio of nog andere manieren, naar ontvangststationsvoor opslag en analyse. Er zijn drie belangrijke telemetrie methodes: VHF-tracking, satelliet trackingen GPS tracking.

De VHF-tracking techniek gebruikt heel hoge frequenties, dit zijn de golflengtes tussen 1 en 10m. Dedieren dragen een zender in een halsband en met behulp van een draagbare antenne, een ontvanger enkoptelefoon is een onderzoeker in staat het dier te volgen. Uit het signaal kunnen pieken en nullenafgeleid worden en uit deze serie kan de locatie bepaald worden. Dit wordt dan meestal bevestigddoor een visuele waarneming, omdat de locatie precisie anders erg laag is. Een ander nadeel is dateen onderzoeker actief moet bezig zijn met het ontvangen van signalen terwijl de zender constantsignalen uitzendt. Het resultaat hiervan is een kleine steekproef met slechts een paar locaties per dag.Het gebruik van VHF is meestal gelimiteerd tot soorten met een beperkt oppervlaktegebruik of eenbeperkte beweging.

Bij de satelliet tracking techniek is er momenteel slechts 1 operationeel systeem, namelijk het VS/FransArgos systeem. De ontvangers bevinden zich aan boord de NOAA series van satellieten. Dit zijnruimtetuigen in een circulair, polaire orbit op 850km hoogte. De locatie wordt berekend aan de hand

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van een Doppler shift in frequentie. Er kan ook extra informatie geleverd worden naast de locatievan het dier, namelijk een hele reeks van gedrag en fysiologische karakteristieken, bijvoorbeeld deactiviteit over korte of langere periodes; aantal, duur en diepte van een duik bij mariene dieren, watertemperatuur, luchttemperatuur en barometrische druk, . . . Met deze methode is het makkelijker omdieren te bestuderen die over een grote oppervlakte bewegen en regelmatig internationale grenzenkruisen.

Het laatste systeem, de GPS tracking werd toegepast in dit onderzoek om de Grevy’s zebra’s te vol-gen. De locatie wordt bepaald door het meten van de afstand tussen satelliet en ontvanger. The positievan de satelliet is hierbij gekend en vanuit de tijd die de radiogolven nodig hadden om tot de ont-vanger te komen kan de locatie bepaald worden. GPS berekent de meest precieze locatie. Het zoueen theoretische precisie hebben van minder dan een meter. Het grote voordeel van GPS is dat hetoveral kan gebruikt worden, dat er locatie metingen kunnen gebeuren tot een keer per seconde en hetwerkt 24u per dag. De data bevat informatie over de eigenaar, tijd van de dag, coordinaten, de PDOPwaarde en of het signaal 2D (GPS heeft contact met 3 satellieten) of 3D (GPS heeft contact met 4 ofmeer satellieten) is.Tot mei 2000 werd de accuraatheid van GPS locaties gedegradeerd door het proces van selectievebeschikbaarheid opzettelijk opgelegd door het Amerikaanse Ministerie van Defensie. Voor deze da-tum konden alleen ongecorrigeerde of nabehandelde differentiele GPS data gebruikt worden. Ongecor-rigeerde GPS data hebben een locatie fout van 20–80m, nabehandelde differentiele GPS data een foutvan 4–8m. Deze nabehandeling houdt een correctie in gebaseerd op de simultane locatie meting vande ontvanger en een referentie grondstation. Aangezien beiden dezelfde fouten registreren en de lo-catie van het grondstation gekend is kan de fout worden verbeterd.Obstructies, zoals gesloten kroonlaag kunnen ervoor zorgen dat het GPS toestel niet in staat is eenlocatie te berekenen. Dit kan zijn omdat er niet genoeg satellieten binnen het bereik liggen. De to-pografie van het terrein speelt hierin ook een belangrijke rol, heuvels kunnen bijvoorbeeld het signaalblokkeren. Het gedrag van het dier zelf kan ook een invloed uitoefenen. Wanneer de dieren bewe-gen zal een lagere precisie gehaald worden dan wanneer ze stil staan. De antenna kan ook door destand van het dier een horizontale positie aannemen met een hogere locatie fout als gevolg in geslotenvegetatie.Er zijn twee soorten fouten die kunnen optreden. Er zijn ten eerste de gemiste metingen, die leiden totontbrekende data. Stationaire halsbanden hebben een fix rate van 68–100% met de meeste boven de85%. Deze gemiste locaties gebeuren echter niet random, waardoor bias hoogstwaarschijnlijk is. Decondities die dit beınvloeden zijn kroonlaag type, kroonlaag bedekking, boomdensiteit, boomhoogteen basale oppervlakte. Een heuvelachtig studiegebied kan dit alles nog eens versterken. Dus de datakan gebiased zijn naar meer open habitat. Het tweede type fout is de locatie fout. De PDOP-waardeis een meting van de satelliet geometrie, waarbij lagere PDOP waarden bredere satelliet spatieringvoorstellen die de triangulatie fout kunnen minimaliseren en betere resultaten opleveren. De datakunnen gescreend worden om de ergste fouten te verwijderen alvorens verdere berekeningen worden

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uitgevoerd.

2.4 Tracking van wild en teledetectie

Plantendiversiteit gebaseerd op de spectrale karakteristieken van de verschillende plantensoorten ofgemeenschappen kan rechtstreeks in kaart worden gebracht. Diersoorten, die meestal mobiel zijn,maken de zaak wat ingewikkelder. Hun diversiteit en verdeling dient meestal in kaart gebracht teworden door gebruik te maken van benaderingen.

Landbedekking is de geobserveerde fysische beschrijving van het aardoppervlak en is het attribuut diemeestal gekarteerd wordt met behulp van teledetectie. Deze laag wordt dan meestal gecombineerd metadditionele informatie zodat habitatkaarten kunnen ontstaan. Habitatgeschiktheid is een veelgebruiktebenadering voor de modellering van soortendiversiteit en rijkdom. Dit kan bekomen worden doorsatellietbeelden of luchtfoto’s, biofysische, geofysische en meteorologische data te combineren metde kennis van habitatpreferentie en eisen van een bepaalde diersoort. Data over de verspreiding van desoort, hun habitatgebruik of karakteristieken kunnen verzameld worden door veldonderzoek of doorhet analyseren van de bewegingen van individuen die gevolgd worden via wildlife tracking. Dit kandan geextrapoleerd worden naar grotere gebieden.

Ruimtelijke heterogeniteit is een sleutelcomponent in het verklaren van soortenrijkdom. Hoe hetero-gener ecosystemen zijn, hoe meer niches ze bevatten en hoe meer soorten ze dus kunnen onderhouden.De distributie van soorten wordt beınvloed door ruimtelijke en temporele variatie in plantproductiviteiten biomassa van ecosystemen. Er worden verschillende vegetatie indices gebruikt in de teledetectieom de aanwezigheid en toestand van vegetatie te meten. De meest gebruikte is de Normalised Differ-ence Vegetation Index (NDVI). Hoge NDVI waarden duiden op plantrijke gebieden. Wolken, water ensneeuw hebben negatieve waarden terwijl stenen en naakte grond waarden hebben rond de nul. NDVIwordt gebruikt om vegetatie te modelleren, primaire productie te schatten en milieuveranderingen tedetecteren. Bij deze benadering wordt het voorkomen van bepaalde diersoorten gerelateerd aan ter-restrische features door middel van een ecologische, trofische link. Herbivoren worden gerelateerdaan het voedsel dat ze consumeren.

Seizoensgebonden klimaatsveranderingen kunnen verschillen veroorzaken in platensoorten, hun groeien vestiging. Dit leidt tot veranderingen in soortensamenstelling en distributie. Wanneer de landge-bruikdata van meerdere jaren wordt geıntegreerd, dan kan een visie gevormd worden over de invloedvan klimaat op de variabiliteit binnen ecosystemen. Ook doordat veel soorten mobiel zijn in de tijd,kunnen multitemporele data een completer beeld geven van hun voorkomen en distributie.

Er zijn ook veel soorten die hun habitat selecteren op basis van structurele kenmerken in plaats vansoortensamenstelling. Structurele kenmerken kunnen ingeschat worden met gebruik van teledetectie.Hiervoor worden actieve sensors gebruikt, namelijk LiDAR en radar. Radar gebruikt microgolf

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energie terwijl LiDAR pulsen van laser licht gebruikt.

Habitatheterogeniteit kan tenslotte ook beschreven worden aan de hand van de chemische bestandde-len van de plant. Voedselkwaliteit is een belangrijke factor bij het aantrekken van bepaalde soorten.Beeldvormende spectrometers kunnen biochemische componenten detecteren en kwantificeren doorhet meten van de plantreflectie in de nauwe en aaneengesloten spectrale banden van een breed golf-lengten bereik.

3 Data en methoden

3.1 Satellietbeelden

Er werden drie soorten satellietbeelden gebruikt voor dit onderzoek. Landsat en MODIS beeldenwerden gebruikt om een habitatclassificatie te maken, terwijl SPOT-Vegetation NDVI beelden ge-bruikt werden om de migratie van de zebra’s in functie van biomassa te analyseren.

Twee Landsat-7 beelden gemaakt met de ETM+ sensor op 21 februari 2000 werden gedownload vande USGS Global Visualisation Viewer (GloVis). Deze beelden hebben een ruimtelijke resolutie van30m. Voor de classificatie werden ze hoofdzakelijk gebruikt om de trainingdata op aan te duiden.Achttien MODIS beelden van het jaar 2008 werden gedownload van de NASA Warehouse InventorySearch Tool (WIST). Dit zijn 16 dagen composieten met een resolutie van 250m . Naast de spec-trale banden rood, NIR, blauw en MIR, zijn ook twee vegetatie indices beschikbaar, namelijk NDVIbeelden en EVI beelden.De SPOT-Vegetation NDVI beelden werden bekomen via VITO (Vlaamse Instelling voor Technolo-gisch Onderzoek). Er zijn 36 beelden beschikbaar voor het jaar 2006 en 2007 en 34 beelden voor hetjaar 2008. Dit zijn tien-dagen composieten die bekomen werden door het compileren van dagelijksatmosferisch gecorrigeerde beelden van tien opeenvolgende dagen. De resulterende waarde per pixelis de maximum NDVI voor die pixel gedurende die tien dagen. De NDVI waarden werden lineairgetransformeerd naar waarden tussen 0 en 250.

3.2 Tracking data

Zestien Grevy’s zebra’s werden gevolgd via GPS-tracking. De data werd geleverd door de NorthernRangelands Trust in Kenia. Data is beschikbaar van de periode juni 2006 tot augustus 2008, metduidelijke verschillen in hoeveelheid data en periode van verzameling tussen de verschillende dieren.De reden waarom een halsband stopt met data verzameling kan een apparatuurbreuk of de dood vanhet dier zijn.

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3.3 Classificatie

Het Northern Rangelands Trust zorgde eveneens voor ground truth data voor de classificatie, bestaandeuit een formulier met specificaties en een foto. Gebaseerd op deze data werden zes klassen onder-scheiden: herbaceous, lage vegetatiebedekking, shrubland, woodland met meer en minder dan 70%boombedekking en bos.

Artificiele neurale netwerken kunnen gebruikt worden om een classificatie uit te voeren. Het netwerkwordt eerst getraind. Tijdens dit trainen leert het bepaalde input patronen te combineren met deovereenkomstige output. Wanneer dan onbekende informatie aan het netwerk wordt voorgeschoteld,wordt aan de hand van dezelfde regels een output gecreeerd. Aan de inputs kunnen verschillendegewichten toegekend worden, zodat bepaalde factoren een grotere invloed uitoefenen op het uitein-delijke resultaat dan anderen.

Voor de classificatie werd gestart met Landsat beelden, waarop de training sites werden aangeduid.Omdat dit Landsat beeld geen goed resultaat gaf, werd overgeschakeld op MODIS beelden. Doorde hogere temporele resolutie, werd getracht het onderscheid tussen de verschillende vegetatievor-men te maken op hun verschillende fenologie. Er werden classificaties uitgevoerd met de MaximumLikelihood classifier en met neurale netwerken.

3.4 Analyse van de Grevy’s zebra’s tracking data en migratie

Eerst werd gekeken naar de locatie van de verschillende zebra’s binnen het studiegebied evenals naarhun gemiddelde snelheden en de oppervlakte van hun home range. Er werd ook gekeken naar dehoeveelheid locaties die binnen beschermde gebieden zoals reservaten of conservancies vielen.

Aangezien er verschillende factoren zijn die de migratie van Grevy’s zebra’s beınvloeden, werdgekeken naar de afzonderlijke invloed van deze factoren en getracht deze ook gezamenlijk te inte-greren zodat een uitspraak kon gedaan worden over de geschikte gebieden. Een eerste belangrijkefactor is plantbiomassa. Aangezien zebra’s herbivoren zijn is er een directe link tussen biomassa envoedsel. De NDVI werd hierbij gebruikt als indicator voor biomassa. Per zebra werd een range afge-bakend als zijnde elke pixel waarin de zebra minstens eenmaal voorkomt tijdens de studieperiode.Voor elke tien dagen periode werd voor elke pixel binnen deze range de NDVI waarde bepaald enhoeveel zebra locatie punten er in die periode voorkwamen. Er werden dus heel veel NDVI waardenbekomen waar op dat moment geen zebra’s voorkwamen. Dit is noodzakelijk om een vergelijking temaken tussen de NDVI waarden van de verkozen gebieden en de andere NDVI waarden. Aan de handvan t-testen werd gecontroleerd of er een significant verschil was tussen de beide groepen. Deze testenwerden uitgevoerd op de volledige dataset die alle regen- en alle droge seizoenen omvat en eveneensop alle seizoenen afzonderlijk.

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Een tweede factor die een belangrijke invloed uitoefent op de zebra migratie is de aanwezigheid vanwater. Hierbij werd de afstand tot het dichtstbijzijnde waterpunt gebruikt als indicator. Een derdefactor is de aanwezigheid van vee, aangezien deze een rechtstreekse concurrent is voor voedsel enwater. Er werd ook nagezien of de aanwezigheid van dorpen een invloed heeft op de zebra’s, dit werduitgevoerd door de afstand tot het dichtstbijzijnde dorp te berekenen.

De habitatpreferentie van de Grevy’s zebra’s werd ook bepaald. Eerst werd getest worden of hunhabitatgebruik random is of niet. Indien hun habitatgebruik random is, gebruiken ze elke habitatin proportie van de oppervlakte. Bij een non-random gebruik kan een preferentie rangschikkingopgesteld worden. Dit werd eerst gedaan voor elke zebra afzonderlijk. Daarna werd geıntegreerdover alle 16 zebra’s. Aan de hand van t-testen werd dan bepaald welke rangschikking significant isof welke habitats verwisseld konden worden. Deze habitat preferentie test werd uitgevoerd op degemaakte classificatie en op een reclass van Africover.

Als allerlaatste werd getracht de verschillende factoren die een invloed hebben op de migratie te inte-greren. Voor de verschillende factoren werd gekeken welke gebieden geschikt waren voor de zebra’sen welke niet. Al de ongeschikte gebieden werden samengebracht en voor de overgebleven gebiedenwerd een indeling gemaakt op basis van de afstand tot water en de NDVI waarden. Het resultaat werdgecontroleerd door de hoeveelheid zebra GPS-punten te bepalen in elke geschiktheidsklasse.

4 Resultaten en discussie

4.1 Classificatie

Het doel van deze habitatclassificatie was een link te onderzoeken tussen habitat en zebra-voorkomen.Eerst werden classificaties uitgevoerd op een Landsat beeld uit het droge seizoen van 2000. Op hetLandsat beeld werden de training-data gedigitaliseerd. De klasse water werd uitgesloten omdat hetbeeld van het droge seizoen was en er niet genoeg training pixels konden aangeduid worden. Erwerden classificaties uitgevoerd gebruik makende van de Maximum Likelihood classifier en metNeurale Netwerken. Het Landsat beeld alleen gaf echter geen goed resultaat. Enkel de klasse boskon gemakkelijk onderscheiden worden van de rest.

Er werd overgeschakeld op het gebruik van achttien MODIS 16-dagen composiet beelden uit het jaar2008. Het gebruik van een tijdserie maakt het mogelijk verschillende habitats te onderscheiden opbasis van hun fenologie. De meeste classificaties werden uitgevoerd met Neurale Netwerken, omdatdit betere resultaten opleverde dan Maximum Likelihood. Er werd gebruik gemaakt van verschillendecombinaties van input beelden:

1. Alle spectrale banden van alle 18 MODIS beelden

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2. Alle 18 NDVI beelden

3. Eerste drie componenten van de Principale componenten analyse van de NDVI en van de EVI

4. Alle spectrale banden van alle beelden en de eerste drie componenten van de twee PCAs

5. Alle spectrale banden van alle beelden met alle NDVI en alle EVI beelden

Het beste resultaat werd bekomen met NN en als input alle spectrale banden van alle beelden met alleNDVI en alle EVI beelden. De kappa-waarde van dit resultaat bedroeg 90.39% wanneer de volledigetrainingset als testset werd gebruikt en 84.41% bij gebruik van een onafhankelijke testset. Aan de handvan deze waarden kan geen eenduidige conclusie getrokken worden omtrent het resultaat. Door hetbeperkt aantal referentiepunten geeft de kappa-waarde slechts een indicatie van het classificatieresul-taat over een kleine oppervlakte van het studiegebied. Het bekomen resultaat, de MODIS classificatiegenaamd, werd ook vergeleken met Africover. Hieruit blijkt dat er heel wat verschillen zijn tussenbeide. Africover is echter slechts een grove classificatie, gemaakt op het niveau van Afrika, zodat hierwaarschijnlijk ook misclassificaties aanwezig zijn.

Het is dus heel moeilijk een uitspraak te doen over de kwaliteit van het resultaat. Een betere classificatiezou eventueel bekomen kunnen worden door het gebruik van meer referentiedata. Eigen terreinkenniszou hierbij zeker een pluspunt zijn. Fouten kunnen ook zijn opgetreden doordat de data hier doorverschillende personen werd verzameld. De inschatting van de kruid-, struiken boombedekkingkan verschillend zijn voor verschillende personen. Zo kan het gebeuren dat gebieden met eenzelfdebedekking toch als verschillende habitats geclassificeerd werden.

4.2 Analyse van de Grevy’s zebras tracking data en migratie

4.2.1 Correlatie tussen tracking data en biomassa

Eerst werd de relatie onderzocht tussen de Grevy’s zebra tracking en de aanwezige biomassa aande hand van SPOT-Vegetation NDVI beelden. Er werd een dataset opgesteld met per datum NDVIwaarden voor alle punten waar zebra’s aanwezig zijn op dat moment en een gelijk aantal ad randombepaalde NDVI waarden uit de overvloed aan waarden vanuit de range waar op dat moment geenzebra GPS punt gelokaliseerd was. Er was een dataset bestaande uit alle data, dus voor alle regen- enalle droge seizoenen en er was een dataset per seizoen. Op deze datasets werden t-testen uitgevoerd.Er werd telkens, behalve voor de dataset van het eerste en tweede regenseizoen, getest of de gemid-delde NDVI van pixels met zebra’s aanwezig hoger was dan de gemiddelde NDVI van pixels zonderzebra’s. Voor het eerste regenseizoen werd net het omgekeerde getest, namelijk of de gemiddeldeNDVI van pixels met zebra’s aanwezig lager was dan de gemiddelde NDVI van pixels zonder zebra’s.Voor het tweede regenseizoen werd tweezijdig getest. De manier van testen en de afbakening van

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de seizoenen werd bepaald uit de grafiek waarop alle gemiddeldes per tien dagen periode staan vooralle pixels met zebra’s aanwezig en voor alle pixels zonder zebra’s. Alle testen, behalve deze voorhet tweede regenseizoen, waren significant. Dus algemeen gesteld verkiezen Grevy’s zebra’s hogereNDVI waarden. Wanneer de boxplots bekeken werden, werd vastgesteld dat er een grote overlap is inwaarden tussen beide groepen. Dat de testen toch significant zijn kan verklaard worden door het feitdat de dataset convergeert naar oneindig. Het is dus heel moeilijk om te beslissen welke waarden deGrevy’s zebra’s nu juist zullen gebruiken. Het feit dat het eerste regenseizoen omgekeerd significantis kan verklaard worden door het erg natte regenseizoen. Hierdoor komen de hogere NDVI waardenwaarschijnlijk overeen met houtige gewassen die minder verkozen worden als voedselbron.

4.2.2 Correlatie tussen tracking data en aanwezigheid van water, vee en dorpen

Wanneer de afstand tot water werd vergeleken met de aanwezigheid van de Grevy’s zebra’s, konbesloten worden dat de zebra’s zich hoofdzakelijk bevinden tussen 0–10km afstand van het dichtst-bijzijnde waterpunt. Vanaf een afstand van 18km valt het aantal aanwezige zebra’s bijna op nul. Hetaantal zebra’s neemt toe tussen 0 en 3.5km om daarna snel af te nemen. In deze studie bevonden deGrevy’s zebra’s zich relatief dicht bij water aangezien ze gemakkelijk 2–5 dagen zonder water kunnenen gemiddeld 10–15km per dag kunnen afleggen.Bij een toename van de vee dichtheid neemt de hoeveelheid zebra’s sterk af. Dit kan verklaard wordendoor het feit dat vee rechtstreeks in competitie treedt met de zebra’s voor voedsel en water.De relatie tussen de aanwezigheid van Grevy’s zebra’s en dorpen was gelijkaardig aan de relatiemet water. Er is dus geen uitgesproken effect van de dorpen op de zebra’s, andere factoren zullenwaarschijnlijk belangrijker zijn in het bepalen van de migratie.

4.2.3 Habitatpreferentie

Er werd ook getest of de Grevy’s zebra’s een uitgesproken habitatpreferentie vertonen. Dit werd getestop de MODIS classificatie en op Africover. Er werd een preferentie volgorde opgesteld van de ver-schillende habitats per zebra wanneer het habitatgebruik non-random was. Er werd ook geıntegreerdover de verschillende zebra’s zodat een algemeen besluit kon getrokken worden voor alle Grevy’szebra’s in het studie gebied. Indien er een random habitatgebruik is, gebruiken de zebra’s de habitatsin proportie tot hun oppervlakte. De habitatpreferentie werd getest op twee verschillende niveaus. Devergelijking op het eerste niveau gebeurde tussen de samenstelling van het studiegebied en de samen-stelling van de verschillende home ranges. De vergelijking op het tweede niveau was dan tussende samenstelling van de home ranges en de verdeling van de GPS metingen over de verschillendehabitats.Uit de MODIS classificatie kon geen significante habitat preferentie besloten worden. Er werd alleenaangetoond dat de Grevy’s zebra’s boshabitat significant minder gebruiken dan de andere habitat-

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vormen. Uit de Africover classificatie kon na integratie over alle zebra’s op het eerste niveau beslotenworden dat de klassen dorpen en kruiden met struiken significant meest geprefereerd werden in dehome ranges. Daarna werd de klasse open-schaarse struiken verkozen. Tussen de andere habitats kongeen significante volgorde opgesteld worden. Wanneer naar het tweede niveau werd gekeken blevener vier habitats over, degene die het grootste deel van de home ranges uitmaakten. Er kon beslotenworden dat de Grevy’s zebra’s de klassen kruiden met struiken en kruiden meest prefereerden, bovende klassen open houtig en open-schaarse struiken. Onderling zijn deze twee klasses telkens uitwissel-baar.

4.2.4 Integratie van alle factoren

Door de verschillende factoren te combineren werd een kaartje gecreeerd waarop alle gebieden aange-duid staan die volgens de bekomen resultaten minder geschikt zijn voor de Grevy’s zebra’s en welkegebieden juist heel geschikt zijn. Wanneer dit resultaat werd vergeleken met de locatie van de GPSpunten bleek dat de beste klasse 2.4 keer meer data punten bevatte dan van de oppervlakte zouverwacht worden. Dit gebied wordt dus wel degelijk geprefereerd. De andere gebieden werden alle-maal minder gebruikt dan van de oppervlakte zou verwacht worden.Om echter een volledige uitspraak te kunnen doen over de geschikte gebieden voor de Grevy’s zebra’sdienen veel meer factoren gekend te zijn. De aanwezigheid van predatoren heeft eveneens een invloedop het voorkomen van de zebra’s. Andere factoren die mogelijks een invloed hebben zijn competitiemet andere grote grazers zoals bijvoorbeeld de steppezebra en ook de voorplantingstoestand van devrouwelijke Grevy’s zebra’s speelt een belangrijke rol. Lacterende vrouwtjes hebben andere voedsel-behoeftes dan niet-lacterende wijfjes en ze gebruiken dan ook andere gebieden. Er dient dus nog heelwat onderzoek te gebeuren om een echte voorspelling te maken van het voorkomen en de migratie vande Grevy’s zebra’s.

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114

Appendix A

Ground truth collection form

115

CHAPTER A. Ground truth collection form

Date:

GPS

point #

Direction

in which

picture is

taken

Vegetation description (circle the estimated

cover/ height/ composition using guidelines

below)

Specify cover if no

natural vegetation is

present (for example

settlement, rock,

bare soil, …)

% cover

of TREES

% cover of

SHURBS +

average height

% cover

HERBACEOUS

+ composition

C O

S A

C O S A C O S A

>0.5 m <0.5 m F G M

C O

S A

C O S A C O S A

>0.5 m <0.5 m F G M

C O

S A

C O S A C O S A

>0.5 m <0.5 m F G M

C O

S A

C O S A C O S A

>0.5 m <0.5 m F G M

C O

S A

C O S A C O S A

>0.5 m <0.5 m F G M

Guidelines

% Cover Herbaceous composition

C = Closed (70% - 100% cover, crowns overlapping,

touching, or very slightly separated)

O = Open (20% - 70% cover, crowns not touching,

distance between crowns up to twice the average crown

diameter)

S = Sparse (2 % - 20 % cover distance between crowns

more than twice the average crown diameter)

A = Absent

F = Forbs (> 75 % cover of forbs)

G = Grasses (> 75 % cover of grasses)

M = Mixed (forbs cover less than 75% and grasses cover

less than 75 %)

116

Appendix B

Classes of the Africover classification ofthe study area

117

CHAPTER B. Classes of the Africover classification of the study areaTa

ble

B.1

:Afr

icov

ercl

assi

ficat

ion

clas

ses

clas

snu

mbe

rcl

ass

nam

ecl

ass

num

ber

clas

sna

me

1bu

iltup

124

open

shru

bs+

herb

aceo

us2

refu

gee/

rura

lset

tlem

ent

125

very

open

shru

bs+

herb

aceo

us+

spar

setr

ees

10ba

re12

6ve

ryop

ensh

rubs

+he

rbac

eous

20w

ater

bodi

es12

7sp

arse

shru

bs+

herb

aceo

us11

2cl

osed

woo

dy+t

rees

131

herb

aceo

us+

tree

s+

shru

bs11

3cl

osed

woo

dy+

shru

bs13

2he

rbac

eous

+sh

rubs

114

open

woo

dy+

shru

bs13

3cl

osed

toop

enhe

rbac

eous

115

open

woo

dy+

herb

acea

ous

134

spar

sehe

rbac

eous

116

open

tree

s+

herb

acea

ous

+sh

rubs

145

open

woo

dy-fl

oode

d11

7ve

ryop

entr

ees

+sh

rubs

162

herb

aceo

us+

shru

bs-fl

oode

d11

8ve

ryop

entr

ees

+sh

rubs

+he

rbac

eous

163

herb

aceo

us-fl

oode

d12

1cl

osed

shru

bs+

tree

s23

1he

rbac

eous

crop

s-R

F12

2cl

osed

shru

bs23

2m

aize

-RF

118

Appendix C

Boxplots for the different seasons

(a) Boxplot of first dry season

(b) Boxplot of first wet season (c) Boxplot of second dry season

119

CHAPTER C. Boxplots for the different seasons

(d) Boxplot of second wet season (e) Boxplot of third dry season

(f) Boxplot of third wet season (g) Boxplot of fourth dry season

(h) Boxplot of fourth wet season (i) Boxplot of fifth dry season

120

Appendix D

Habitatpreference based on madeclassification

Table D.1: Percentage of each habitat type in the MCP of each zebra% MCP

zebra herbaceous sparse veg shrubland woodland1 forest woodland2




samburu 27.05 9.40 16.18 21.38 0.60 25.38samburu2 26.86 19.04 12.71 26.65 5.19 9.54silurian2 24.61 7.02 52.68 0.12 0.36 15.22

121

CHAPTER D. Habitatpreference based on made classification

Table D.2: Percentage of tracking data in each habitat type per zebra% tracking data

zebra herbaceous sparse veg shrubland woodland1 forest woodland2




samburu 45.02 15.60 6.56 9.41 0.03 23.39samburu2 27.15 25.08 12.06 30.16 0.11 5.44silurian2 19.05 5.36 41.67 0.00 0.01 33.93

122

Appendix E

Habitatpreference based on the Africoverreclass classification

Table E.1: Percentage of each habitat type in the MCP of each zebra1 2 3 5 6 8 9 10

Belinda 0.01 0.29 0.18 0.43 0.01 55.34 41.77 1.63dableya 0.01 1.05 0.01 1.73 0.01 19.24 42.12 35.86hiroya 0.01 0.01 0.01 0.79 0.01 23.72 47.09 28.41

jeff 0.01 0.01 0.01 3.52 0.01 10.90 81.81 3.76johnna 0.30 0.01 0.01 5.89 1.44 31.93 56.49 3.95kobosa 0.01 0.01 0.01 0.78 0.01 42.01 40.62 16.58lepere 0.01 0.01 0.11 0.21 0.01 2.96 96.72 0.01

liz 0.01 0.01 0.22 0.74 0.01 14.74 84.30 0.01loijuk 0.01 0.01 0.48 0.91 0.01 31.73 66.70 0.17martha 0.01 0.01 0.01 1.28 0.01 60.63 38.09 0.01njeri 0.01 0.01 0.28 13.32 3.46 24.45 58.50 0.01petra 0.01 0.01 0.03 0.10 0.01 1.35 98.52 0.01rose 10.67 0.01 0.01 0.01 0.01 9.48 34.37 45.48

samburu 0.31 0.20 0.05 1.14 0.96 47.60 43.77 5.78samburu2 0.03 0.30 1.08 2.83 0.45 46.55 36.38 9.94silurian2 0.01 0.01 0.01 0.01 0.01 8.92 91.08 0.01

123

CHAPTER E. Habitatpreference based on the Africover reclass classification

Table E.2: Percentage of tracking data in each habitat type per zebra1 2 3 5 6 8 9 10

Belinda 0.01 0.01 0.01 0.21 0.01 41.63 47.72 10.43dableya 0.01 1.39 0.01 0.08 0.01 15.95 37.10 45.48hiroya 0.01 0.01 0.01 0.06 0.01 10.31 61.91 27.72

jeff 0.01 0.01 0.01 1.35 0.01 8.90 87.61 2.14johnna 1.89 0.01 0.01 11.59 0.43 11.95 61.84 12.30kobosa 0.01 0.01 0.07 0.01 0.01 42.96 35.03 21.94lepere 0.01 0.01 0.04 0.13 0.01 0.29 99.54 0.01

liz 0.01 0.01 0.09 0.18 0.01 1.03 98.71 0.01loijuk 0.01 0.01 0.05 0.55 0.01 25.68 73.72 0.01martha 0.01 0.01 0.01 0.57 0.01 7.07 92.36 0.01njeri 0.01 0.01 0.25 2.83 0.74 26.29 69.90 0.01petra 0.01 0.01 0.09 0.09 0.01 0.28 99.53 0.01rose 9.95 0.01 0.01 0.01 0.01 4.48 1.49 84.08

samburu 2.25 0.01 0.08 0.45 0.24 9.70 69.05 18.24samburu2 0.16 0.14 0.02 1.70 0.05 21.94 67.42 8.58silurian2 0.01 0.01 0.01 0.01 0.01 3.57 96.43 0.01

124

Appendix F

Histograms for the different seasons

(j) Histogram of first dry season

(k) Histogram of first wet season (l) Histogram of second dry season

125

CHAPTER F. Histograms for the different seasons

(m) Histogram of second wet season (n) Histogram of third dry season

(o) Histogram of third wet season (p) Histogram of fourth dry season

(q) Histogram of fourth wet season (r) Histogram of fifth dry season

126