MICROHABITAT PREFERENCE OF BENTHIC …

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MICROHABITAT PREFERENCE OF BENTHIC

MACROINVERTEBRATES IN THE MOUNTAINOUS

GURA RIVER, KENYA

Master of Science Thesis

by

Beryl Atieno Omollo

This thesis is submitted in partial fulfillment of the requirements for the joint academic

degree of Master of Science in Limnology and Wetland Management jointly awarded by the

University of Natural Resources and Life Sciences, Vienna, Austria

UNESCO-IHE Institute for water Education, Delft, the Netherlands

Egerton University, Njoro, Kenya

University of Natural Resources and Life Sciences (BOKU), Vienna, Austria

April 2019

Supervisors:

Assoc. Prof. Dr. Wolfram Graf (BOKU, University of Natural Resources and Life Sciences)

Prof. Charles M’Erimba (Egerton University, Department of Biological sciences)

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Acknowledgements

There are many individuals who worked very hard and smart towards the success of this thesis

and to whom I am very grateful. A very special thanks to my supervisors, Assoc. Prof. Dr.

Wolfram Graf and Prof. Charles M’Erimba for their inspiring discussions, continuous

guidance, their presence during sampling in Kenya and helpful comments regarding this

research.

I am forever indebted to the Austrian Development cooperation (ADC) through IPGL office

for the financial support of this research and for the scholarship opportunity that enabled me to

join the MSc programme and achieve my dreams and passion for freshwater ecosystems. My

gratitude to the whole IPGL office in BOKU for making sure everything went well.

Special thanks for assistance go to the Department of Water, Atmosphere and Environment of

the Institute of Hydrology and Aquatic Ecosystem Management (IHG). DI Patrick Leitner and

Zoltan Leonardo, B.Sc. provided excellent assistance on the statistical analysis. Many thanks

to Thomas Bechter, MSc who provided further GIS analysis for this study. I greatly appreciate

Martin Seebacher for his support on the computer related issues.

Moreover, I wish to thank Egerton University, Department of Biological Sciences for the

permission to use the laboratory during my fieldwork. Special thanks to the LWM coordinator

Prof. Nzula Kitaka for the wonderful study time and research in Kenya. Many thanks to Mr.

Eddison for organizing all the logistics like transport to the study site. A hearty thanks to

Doreen and Joshua who made our fieldwork experience cool. Thanks to Robert who assisted

me in the field and sorting macroinvertebrates from organic matter in the laboratory. Many

thanks to Mr. Mungai for helping me with BOD5 laboratory analysis. Thanks to Prof. S.T.

Kariuki for his support in the identification of plant species found along Gura River.

Finally, and most importantly, I wish to express my warmest gratitude to my family who

supported me in so many ways. I thank my husband Job and daughter Alade for their love

and patience with me when I had to study away from home. I am truly blessed to have

wonderful parents (Omollo and Kerina) who always believe in, appreciate and support me

reach my dreams. My sisters, Dona and Jemmy for making sure my daughter had mothers

love while I was away. My family at large has always been my source of strength throughout

my academic life.

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Abstract

Scientific work addressing microhabitat preferences of benthic macroinvertebrates in streams

in Kenya remain poorly documented. The objective of this study was to analyze microhabitat

preferences based on the distribution of macroinvertebrates and to assess the possible

relationship between microhabitat types and main functional feeding guilds in Gura River. At

each sampling site, substrate specific sampling of 74 sampling units was conducted and abiotic

parameters such as current velocity, water depth and substrate size were determined from 17th

to 20th October 2018. An NMS analysis was performed to evaluate the effects of water velocity,

depth and substrates on the taxa. The highest community richness and abundance were found

in substrates where velocity ranged from 0.5 to 0.9 ms-1 and Ephemeroptera was the most

abundant taxa (48%) in Gura River. Results from habitat suitability curves revealed that

Baetidae and Heptageniidae showed no specialized current velocity preference whereas

Hydropsychidae types showed different current velocity preferences. NMS ordination showed

that substrate type and current velocity were the most important parameters for the distribution

of taxa whereas depth was not a significant factor. Indicator species analysis revealed

significant indicators for substrate and current preferences for 19 taxa of Ephemeroptera,

Diptera, Oligochaeta, Mollusca, Trichoptera, Hemiptera and Odonata. Regarding functional

feeding guilds, gathering collectors and predators dominated substrates that were exposed to

slow current velocities (0.01-0.3 m/s) whereas substrates found in moderate to high current

velocities (0.5->0.9 m/s) supported a high number of filterers and scrapers. This study clearly

showed the need for lower taxonomic resolution and that the different habitat requirements of

macroinvertebrate taxa in terms of velocity and substratum type can be used to assess the

ecological conditions of macroinvertebrates assemblage.

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Table of Contents

List of Figures ............................................................................................................................. v

List of Tables ............................................................................................................................. vi

List of Abbreviations ................................................................................................................ vii

1.0 Introduction .......................................................................................................................... 1

1.2 Stream microhabitats ........................................................................................................... 2

1.3 Specific objectives ............................................................................................................... 5

1.4 Hypotheses ......................................................................................................................... 5

2.0 Materials and Methods ................................................................................................................... 6

2.1 Study area...................................................................................................................................... 6

2.2 Sampling design and procedure .................................................................................................... 8

2.3 Field work and sampling ............................................................................................................. 17

2.4 Statistical analysis ....................................................................................................................... 20

3.0 Results ......................................................................................................................................... 22

3.1 Physical chemical parameters ................................................................................................. 22

3.2 Macroinvertebrates composition ................................................................................................. 22

3.3: Microhabitat Preferences ........................................................................................................... 30

3.4 Indicator Species Analysis .......................................................................................................... 39

3.5: Functional feeding guilds ........................................................................................................... 44

4.0 Discussion....................................................................................................................................... 46

5.0 References ...................................................................................................................................... 52

6.0 Appendix ........................................................................................................................................ 58

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List of Figures

Figure 2.1: Gura River study site map………………………………………………6

Figure 2.2: Agricultural activities in lower sections of Gura River………………... 7

Figure 2.3: Photos of the sampling sites………………………………………….9-12

Figure 2.4: Photos of different microhabitat types…………………………………14

Figure 2.5: Laboratory photos……………………………………………………...19

Figure 2.6: Fieldwork and laboratory flowchart…………………………………...19

Figure 3.1: Boxplots of abundance and richness of taxa across substrates.……….23

Figure 3.2: Boxplots of abundance of selected taxa…………………………25 & 26

Figure 3.3: Boxplots of abundance of Hydropsychidae types…………………….28

Figure 3.4: Boxplots of abundance for each velocity class……………………….29

Figure 3.5: Biplots of microhabitat preferences………………………………….30

Figure 3.6: Joint plot of NMDS for each microhabitat preference……………….31

Figure 3.7: NMDS for substrate preferences of selected macroinvertebrates.32&34

Figure 3.8: Water velocity and depth analysis…………………………………….34

Figure 3.9: Habitat suitability curves of selected taxa………………………35 & 36

Figure 3.10: Habitat suitability curves for Hydropsychidae types………………...37

Figure 3.11: Boxplots for current preferences of Hydropsychidae types………….38

Figure 3.12: Hierarchical cluster analysis across substrates…………………….…39

Figure 3.10: Relative abundance of Functional feeding guilds…………………….45

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List of Tables

Table 2.1: Description of the sampling sites……………………………………….8

Table 2.2: Stream habitat classification………………………………………. ….13

Table 2.3: Sampling design…………………………………………………….14- 16

Table 2.4: Current velocity classes……………………………….……………….17

Table 3.1: Mean values of physical chemical parameters………………………….22

Table 3.2: Abundance of macroinvertebrates across microhabitats…………23 & 24

Table 3.3: Indicator Species Analysis………………………………………...40 - 44

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List of Abbreviations

APHA American Public Health Association

BMI Benthic Macroinvertebrates

BOD Biological Oxygen Demand

CPOM Course Particulate Organic Matter

DO Dissolved Oxygen

EC Electrical Conductivity

EPT Ephemeroptera, Plecoptera and Trichoptera

FFGs Functional Feeding Groups

FPOM Fine Particulate Organic Matter

HCA Hierarchical Cluster Analysis

ISA Indicator Species Analysis

IV Indicator Value

MHS Multihabitat sampling

NMS Non-metric Multidimensional scaling

NTU Nephelometric Turbidity Units

SRP Soluble Reactive Phosphorus

T Temperature

TP Total Phosphorus

YSI Yellow Spring Instruments

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1.0 Introduction

Water is a key natural resource which is vital for the survival of all ecosystems worldwide.

However, less than 1% of earth’s water resources are accessible to humans as fresh water, in

the form of either surface or ground water (Carpenter et al., 2011). In Kenya for example,

inland water bodies occupy about 11,227 km2, the bulk of which is in Lake Victoria and Lake

Turkana. Most of the waters originates from its five water towers: Mau forest complex,

Aberdares range, Mount Kenya, Mount Elgon and the Cherengani hills (Nyingi et al., 2013).

Aquatic ecosystems, especially rivers and streams offer tremendous biological, ecological and

economical ecosystem services. For example, economically, they facilitate food production

through irrigation, hydropower generation, drinking water supply, livestock watering, transport

and sanitary services among others and ecologically they provide habitats for a variety of

aquatic biota hence contributing to freshwater biodiversity.

However, in spite of their integral economic and ecological role, freshwater ecosystems have

undergone faster degradation than most other ecosystems. This is as result of multiple stressors

which in most cases are human induced (Allan and Arbor, 2014). Rivers in Kenya are currently

undergoing various human-induced stressors such as: water pollution caused by domestic

effluents, industrial effluents, agricultural effluents and solid waste disposal into the rivers;

excessive water abstraction; climate change and indirect effects from land use change

(intensive use of agricultural land, deforestation and change of vegetation along the river banks

from natural to eucalyptus). Activities such as bathing, laundry washing affect stream habitats

and its biotic features (Mathooko, 2001).

Changes in the landscape that affect the functioning of rivers are reflected in the composition

of the resident biota (Harding et al., 1999). Macroinvertebrates have been used worldwide to

evaluate river ecosystems (Cairns and Pratt, 1993; Oyediran et al., 2017) because of their

general sensitivity towards pollutants and habitat degradation leading to continued growth on

the information about their responses to changes in the environmental quality.

Climate change i.e. decreased precipitation and increased temperatures is a worldwide concern

and its impact on the biodiversity of aquatic insects through the physical impacts on their

habitats has been investigated in some studies. According to Bonada et al. (2007), “climate

change have a stronger implications for the local taxa than for the trait composition of streams

macroinvertebrates communities” in the Mediterranean and temperate regions. Researching on

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European caddisflies, Hering et al. (2009) showed that due to “regional differences there was

a South- North gradient in the European Trichoptera as a result of continental ice cover during

Pleistocene period.” The effect of climate change on the sediment transport has been

highlighted by (Ashmore and Church (2001) that it is due to climate wetting than to climate

warming.

Sedimentation due to erosion caused by deforestation in the river catchments is another cause

of alarm. As a result, there is continued deposition of fine-grained substrates. Fine substrate

can completely change microhabitat composition of benthic macroinvertebrates from

heterogenous to homogenous community assemblage (Graf, 2005). This is because fine

sediment deposition clogs or fills up the patches of coarser substrates. A study by Kasangaki

et al. (2008) in Uganda showed that clearing of forested areas to create space for land for

cultivation leads to increased levels of temperature, total suspended solids, total dissolved

solids and conductivity.

Similarly, in Kenya a study by Ongwenyi et al. (1993) showed that soil erosion and

sedimentation problems are as a result of expansion of agriculture in the beginning of the 20th

century with the highest rates observed in areas with steep slopes. The increased supply of fine

sediments into the river channel negatively affects the instream habitats and the associated

biological communities (Allan and Arbor, 2014) with the most impact being on their functional

and structural characteristics.

The diversity of benthic macroinvertebrates is closely associated to their microhabitats. Stream

habitats are defined in terms of their physical structure, organic content, stability and

heterogeneity. The availability of appropriate microhabitats enables BMIs to hide from

predators and to acquire food resources. According to Schröder et al. (2013), the substratum

type/ size and substratum composition (hydrological and physical-chemical factors) are the

most specific parameters influencing macroinvertebrate assemblage in a stream. Some studies

in different parts of the world have focused on substratum specific benthic invertebrate

assemblages (Pardo and Armitage, 1997; Schröder et al., 2013; Aschalew and Moog, 2015 &

Vilenica et al., 2018) and also addressed in “grey” literature by Solomon (2014).

1.2 Stream microhabitats

Microhabitat utilization by benthic macroinvertebrates is an essential part of ecology of lotic

environments. Microhabitats are the major drivers of benthic macroinvertebrate richness,

abundance and ecological process (Warfe, 2012). Apart from the physical and chemical

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characteristics of water, microhabitat heterogeneity also can influence the composition of BMI,

with individual species a times being associated with a particular microhabitat (Bauernfeind

and Moog, 2000 ; Leitner et al., 2015). The abiotic conditions of these microhabitats can

influence the survival and reproduction of aquatic species. These conditions include dissolved

oxygen, turbidity, light and temperature. According to Miliša et al. (2006), microhabitat

preferences are closely related to substrate type, water velocity and depth. Bunn and Arthington

(2002) revealed that flow was the major determinant of the physical habitat of streams that

determines the distribution of benthic macroinvertebrates. Another study also found out that

aquatic macroinvertebrates distribution is influenced by eco-hydrological variables and

processes including hydrological dynamics, hydraulics, in channel processes and

environmental changes (e.g. discharge, depth, velocity, Froude number, pH, temperature,

substrate type and conductivity) that occur in the river (Masikini et al., 2018).

The microhabitats are associated with specific flow velocities. However, some studies have

shown that species occupy a wide range of habitats (Ditsche-kuru, 2009; Kubosova et al.,

2010). Invertebrates have inherent need for current for feeding purposes or because of their

respiratory requirements. Water current has an influence on the ecological distribution as well

as macroinvertebrate behavioral and morphological attributes (Lamouroux et al., 2004).

Benthic macroinvertebrates have developed specialized devices in order to attach themselves

to the substrate to withstand the forces of flow. Macroinvertebrates with flattened body forms

colonize the surface of larger stones, where there are strong currents.

Benthic macroinvertebrates (BMIs) are some of the most diverse and abundant organisms in

the streams and rivers of the world. Macroinvertebrates play significant roles in the stream

ecosystem functioning however, they differ in the ways or rates at which they perform a distinct

ecosystem service. Based on feeding behavior, five different functional feeding guilds (FFG)

are identified: shredders, collector-gatherers, collector-filterers, scraper-grazers and predators

(Ramírez and Gutiérrez-Fonseca, 2014), primarily the scrapers and shredders are useful in

organic matter decomposition. Decomposition of organic matter is a process through which

dissolved organic carbon, inorganic carbon and nutrients are released to the environment,

thereby facilitating the process of photosynthesis and ensures dead organic matter do not build

up.

They transform allochthonous material into body tissue utilized by higher trophic level

organisms (aquatic food webs) whereas, some graze on the periphyton (may prevent bloom in

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some areas). Shredders convert coarse particulate organic matter (CPOM) into fine particulate

organic matter (FPOM) and are therefore frequently observed in forested headwaters. FPOM

is a food source to collector gatherers and collector filterers whereas predators feed on the live

prey. Hence, they play a key role in the natural flow of energy and nutrients in the ecosystem.

Leaf packs are a primary food source and serve as habitat for many aquatic insects due to high

heterogeneity and a rich periphytic flora. Woody debris is used as food by xylophagous

invertebrates, and it provides substrate for the growth of biofilm which many invertebrates feed

upon.

In a study involving mayflies in Big Darby creek, Ohio; it was found that microhabitat

preferences of mayflies can be based on their feeding strategies during their larval stages.

Grazers and scrapers prefer macrophytes and/or inorganic sediments coated in diatom-rich

biofilms, but shredders and gatherers/collectors occur in substrates containing decomposing

coarse and fine particulate organic matter (Lamp and Britt, 1981).

Streams and rivers in central Kenya are increasingly impacted by anthropogenic activities

resulting in a rapid decline of aquatic biodiversity and habitat degradation (Mathooko, 2001;

M’Erimba et al., 2014). These anthropogenic activities require adequate management strategies

and monitoring as well as evaluation of the current ecological, bio-physical and hydrological

status of the river ecosystem.

Research on BMI as bioindicators of water quality in rivers and streams in Kenya has increased

in the recent past (Masese et al., 2009; Minaya et al., 2013; M’Erimba et al., 2014). Though

microhabitat preferences remain poorly characterized. This study focused on the benthic

macroinvertebrates’ taxonomy, microhabitat preferences and the functional feeding groups in

Gura River, Kenya. The overall objective of this study was to provide general/ baseline

information on macroinvertebrates microhabitat preferences that could be used to strengthen

management strategies for Gura River. The data collected will provide baseline information

for assessing correlations between macroinvertebrate species and their environment thereby

providing adequate foundation for conservation of aquatic habitats and BMI biodiversity in

Gura River.

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1.3 Specific objectives

i. Documentation of diversity, composition and distribution of benthic

macroinvertebrates in Gura River.

ii. To analyze microhabitat preferences based on the distribution of macroinvertebrate

community in Gura River.

iii. To assess possible relationship between microhabitat types and main functional feeding

groups in Gura River.

1.4 Hypotheses

HO1: Macroinvertebrates abundance, diversity and composition can be differentiated based on

environmental factors such as substratum, depth, and velocity.

HO2: There is a difference in macroinvertebrates functional feeding groups along transects

regarding flow velocities and substrates

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2.0 Materials and Methods

2.1 Study area

The Gura River basin, 00o 31’ 00” S and 36o 55’ 00” E, with an estimated area of 430 km2 is

part of the upper catchment of the Tana River watershed (Figure 2.1). The river is

approximately 60 km long from its source in the Aberdares forest to its confluence with the

Sagana River. The Gura River, with Magura as the main tributary, starts from a first order to a

third order stream (Strahler system). The altitude of the catchment varies between 2977 to 1547

m above the sea level.

Figure 2.1: Map of the study area indicating sampling sites in Gura River, Kenya. The direction

of the water flow in the Gura River is from west to east

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Hydrology

The Gura River sub basin is characterized by a bi-modal rainfall pattern, with long rains

normally 1200 – 1600 mm from March to May and the short rains between 500 – 1500 mm

from October to December. There is a great variability in rainfall patterns in Central Kenya

highlands (Usaid, 2017). Aberdares ranges mean maximum temperature is 25.80 0C and mean

minimum temperature is 10.30 0C. The lowest temperatures are experienced in July and August

(KFS, 2010).

Land use

The study area in the upper section is mainly forested, there is a hydropower plant after the

forested sections and smaller towns are found in the lower sections. Land use patterns includes

natural vegetation (forest, grassland and wetlands), rain-fed and irrigated agriculture (tea,

coffee, bananas, maize and cereals) and rangeland. Agriculture is the mainstay of economy of

the local community (Figure 2.2). The riparian communities are fully dependent on Gura River

water for linen washing, cattle watering, bathing, irrigation and drinking.

Figure 2.2: Agricultural activities such as vegetable farming (left figure) and tea farming

(right figure) in the lower sections of Gura River

Catchment geology

Kenya has a geological structure which is divided into two: Neoarchean rocks found in the

west of the country and metamorphic rocks in the northern central part of the country (Geertsma

et al., 2011). The study area (Upper Tana Catchment) has two main geological structures:

volcanic rocks of the Cenozoic origin and metamorphic rocks of the Mozambique belt. Though,

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in some parts around major Lakes in the Upper Tana, patches of the Precambrian intrusive

rocks can be found (Geertsma et al., 2011).

2.2 Sampling design and procedures

Sampling site selection

Ideal reference sites i.e. with undisturbed flora and fauna composition in a river or stream is

highly significant in establishing comparisons within and between running waters in the same

eco region. However, with the intensive anthropogenic activities in the catchments of Kenyan

rivers, it is difficult to find such reference sites. Some part of the up-stream sections of Gura

River is in the Aberdares National park, the area is protected hence can act as a reference site

with good ecological data.

Sampling sites selection was done prior to the actual sampling campaign. This was done by

putting into consideration road accessibility, topography, hydrology and land use type and

visibility of the microhabitats in the sampled stream area. For this reason, sampling site design

included seven study sites evenly distributed along the stretch of Gura River. The codes of the

sampling sites were given according to the river and its position from the source. For example,

G1 means the sampling site was in the Gura River and was the most upstream site and for the

case of S1, it is in the Sagana River and it was its upstream site before joining with Gura River.

Table 2.1 : Description of the sampling sites

Site

name

Site

code

Coordinates Altitude

(m)

Distance

from source

(km)

Channel

slope (%)

Dominant substrate type

Latitude Longitude

Magura G1 0.4895 0S 36.42 0E 2982 6.58 2.65 Mesolithal, microlithal

Kigumo G2 0.4909 0S 36.50 0E 2035 21.64 3.70 Megalithal, macrolithal

Gitwiga G3 0.4962 0S 36.52 0E 1914 26.56 2.25 Macrolithal, mesolithal

Kagere G4 0.4965 0S 36.56 0E 1760 32.72 1.15 Macrolithal, mesolithal

Tambaya G5 0.5220 0S 37.00 0E 1611 43.04 0.50 Mesolithal, microlithal

Gura DS G6 0.5166 0S 37.04 0E 1557 50.30 0.25 Macrolithal, mesolithal

Sagana

US

S1 0.5190 0S 37.04 0E 1520 65.41 0.45 Megalithal, macrolithal

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Sampling sites description

Magura: G1

This sampling site had a width of 6.4 m and composed of coarse and finer substrate type

(mesolithal, microlithal and Psammal). The river banks are low with a water depth that ranges

between (20 to 25 cm). The stream runs through the Aberdares forest and channel form was

natural with minimal modifications, though it had occasional disturbance from the elephants

thereby increasing the level of organic pollution in the water. This site showed a high number

of Chironomus observed during a quick identification in the field.

The canopy cover was estimated as being ≤ 50% and the surrounding vegetation species

according to Prof. S.T. Kariuki (Egerton University) were, Pteridophyte, Hypericum

revolutum, Alchemilia rothii, Agrocharis melanantha, Haplosciadium abyssinicum and

Acanthus eminens. The sampling was done after the bridge (Figure 2.3a) below.

Figure 2.3a: River Magura, sampling site G1

Kigumo: G2

This sampling site was immediately after the Aberdares forest reserve (Figure 2.3b). It had a

stream width of 15.8 m and water depth ranging between (26 to 64 cm). The dominant

substrates were megalithal and macrolithal. The riparian vegetation was dense with a cover

approximately 70 to 80%. Some of the dominant species were Pteridium aquilium, Rumex

abyssinicus, Piper capense and Vernonia lasiopus. The valley form was steeper on both sides

(V-shaped) and river channel natural with no modifications. There were lots of Elmidae

specimen observed during a quick field identification.

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Figure 2.3b: Kigumo sampling site, G2

Gitwiga: G3

This was a stream of width 16.3 m and water depth varying between 22 to 50 cm. The stream

runs through agricultural land where farming was undertaken at small scale and there were also

surrounding rural homesteads near the stream (Figure 2.3c). The canopy cover was

approximately ≤ 40% though on the left side of the stream most trees are cut down. The

vegetation species were Caesalpinia decapetala and Lantana camara and lots of Eucalyptus at

the left bank of the river. The bed sediment mainly consisted macrolithal, mesolithal and

microlithal. There was a lot CPOM in the stream from the upstream sections.

Figure 2.3c: Gitwiga sampling site, G3

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Kagere: G5

This was stream of 20.1 m width and a depth of 15 to 50 cm and stream bed substratum

composed of course substrates. The canopy cover was approximately 60%. (Figure 2.3d) The

dominant vegetation species observed: Pteris catoptera, Fimbristilis sp., Echinochloa

pyramidalis and Ocimum grattissimum. There was disturbance to the stream due to livestock

watering and a lot of laundry done at the river banks. Observed also was agriculture up to the

riparian land.

Figure 2.3d: Kagere sampling site

Tambaya: G6

This sampling site had a width of 23.3 m and a variable width ranging between 13 to 71cm.

The stream runs through agricultural lands. The canopy cover was approximated to be < 50%

at 100 m upstream the sampling point (Figure 2.3e). Dominant vegetation species were Croton

macrostachyus, Sesbania sesban, Bridelia micrantha and Aframomum angustifolium. The bed

sediment was mainly composed of mesolithal, microlithal and lots of silt and sand

(sedimentation). Anthropogenic influences observed were diffuse pollution from agricultural

fields and animal grazing.

Figure 2.3e: Tambaya sampling site, G5

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Gura above the confluence (DS): G7

This was the last sampling site in Gura River just before it joins Sagana River in the

downstream. The river width was 19.2 m and a water depth ranging from 40 to 60 cm. The

canopy cover was approximated below 50% and the dominant vegetation were Maesa

lanceotate, Rhus vulgaris and Lantana camara. This stream runs through agricultural field

hence there could possible diffuse pollution (Figure 2.3f).

Figure 2.3f: Gura above confluence (DS) sampling site, G6

Sagana before confluence: S1

The sampling site had a width of 30 m and a water depth ranging between 48 to 54 cm. The

canopy cover was roughly below 50% with scattered vegetation along the river bank. Some of

the dominant vegetation species included Polygonum pulchtum, Sesbania sesban and

Hypoestis verticillarus. Anthropogenic influences observed was a likely diffuse pollution from

the agricultural fields to the river. There was also large-scale water abstraction for small scale

electricity supply. The stream was influenced by human through frequent crossing to different

sides of the stream (Figure 2.3g).

Figure 2.3g: Sagana sampling site, S1

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Sampling design

Substrate size classification (Table 2.2) was carried out before sampling, all the major

microhabitats (above 5%) within a representative 100 m reach were considered for

macroinvertebrate sampling. The habitats were defined using the classification Moog (1999)

which is adapted by Graf et al. (2017).

Table 2.2: Stream habitat classification adapted from Moog (1999)

Mineral habitat Particle size class

Megalithal >40 cm; large cobbles, boulder and blocks, bedrock

Macrolithal 20cm -40 cm; coarse blocks, head sized cobbles with variable

percentage of cobbles, gravel and sand

Mesolithal 6cm -20cm fist to hand sized cobbles with variable percentage of

gravel and sand

Microlithal 2cm-6cm coarse gravel size of pigeon egg to child fist with

variable percentages of Medium to fine gravel

Akal 0.2cm-2cm fine to medium sized gravel

Psammal 6µm-2mm sand

Psammolpelal Mixture of sand with mud

Pelal 6µm mud /organic mud and sludge

Argyllal Silt; loam, clay (inorganic)

Technolithal Artificial substrates e.g. riprap, stone plastering with or without

interstices, concrete with or without seam

Organic habitat

CPOM Deposits of particulate organic matter, coarse particulate organic

matter like fallen leaves

Submerged macrophytes Totally immersed macrophytes, including water mosses, water

ferns and algae

FPOM Deposition of particulate organic matter, Fine particulate organic

matter

Woody debris Fallen dead trees and remains of large branches

14

Figure 2.4: Photos showing different microhabitats; a) macrolithal, b) mesolithal, c)

microlithal and d) Psammal; source Ecology of benthic invertebrates’ lecture notes.

Sampling campaign was carried out from 17th to 20th October 2018 (base-flow conditions). All

the major microhabitats (psammal, microlithal, mesolithal, macrolithal and macrophytes) that

exceeded 5% coverage were sampled, thus 74 standardized substrate specific samples collected

(Table 2.3). A substrate specific sample is a single application of the MHS net sampler with a

sampling area of 25 x 25 cm and a mesh size of 500 μm.

At each sampling unit/ substrate type current velocity was measured using a Marsh-Mc Birney

portable flow meter model 2000, and velocity values assigned to a certain current velocity class

(Table 2.4). In addition, water depth (at 0.6 x depth from the surface) and distance to shore

were taken. For manual of substrate specific sampling see Appendix 1).

Table 2.3: Number of samples per microhabitat and site, current velocity and water depth.

Site name Substrate Current

velocity (m/s)

Water depth

(cm)

Date

Magura Psammal 0.05 12 19/10/2018

Magura Psammal 0.15 18 19/10/2018

Magura Psammal 0.08 16 19/10/2018

Magura Mesolithal 0.27 5 19/10/2018

a b

d c

15






Magura Macrolithal 0.48 14 19/10/2018




Kigumo Mesolithal 0.32 35 18/10/2018



Kigumo Macrolithal 0.70 42 18/10/2018










Gitwiga Microlithal 0.1 10 18/10/2018




Gitwiga Macrolithal 0.31 16 18/10/2018







Kagere Mesolithal 0.10 32 20/10/2018



16

Kagere Macrolithal 0.87 28 20/10/2018

Kagere Macrolithal 0.43 32 20/10/2018

Kagere Microlithal 0.21 15 20/10/2018



Tambaya Microlithal 0.21 5 20/10/2018





Tambaya Mesolithal 0.82 30 20/10/2018



Gura DS Mesolithal 0.64 50 17/10/2018





Gura DS Macrolithal 0.53 43 17/10/2018



Gura DS Macrophytes 0.30 29 17/10/2018



Sagana US Mesolithal 0.63 33 17/10/2018




Sagana US Macrolithal 0.66 20 17/10/2018



Sagana US Macrophytes 0.0 47 17/10/2018



17

Table 2.4: Current velocity classes for Gura River fieldwork (17th – 20th October 2018)

Current velocity (m/s) Velocity class Description

0.01 – 0.1 1 No visible flow, or pool

0.1 – 0.3 2 Very slow flow, mostly near the shore

0.3 – 0.5 3 Slow visible current

0.5 – 0.7 4 Moderate current

0.7 – 0.9 5 Fast current

>0.9 6 Very fast current

2.3 Field work and sampling

Physical and chemical measurements

Water samples from the sampling sites were taken prior to disturbances caused by

measurements of the in-situ parameters. One-liter container (pre-acid washed and labelled) was

filled with water from each sampling site and stored in ice box. The water samples were taken

for analysis of total phosphorus in the laboratory. A second water sample was taken for BOD

analysis using a 300 milliliter BOD water bottles, the samples immediately fixed with Winkler

reagents, tightly closed and wrapped with aluminum foil. All the analysis was carried out in

accordance to water quality standards provided by American Public Health Association

(APHA, 2005).

A third water sample was taken to measure turbidity. Portable turbidity meter Hach 2100Q,

which gives readings in Nephelometric Turbidity Units (NTU units); range is from 0 to 1000

NTU. The readings were taken three times then the average used for each sampling site.

After the above procedures, other physical chemical parameters were measured at each site

using a handheld multimeter probe (YSI Professional Plus). Before the measurements, the

hand-held meters were calibrated with DO saturation measurement. The parameters measured

after stabilization were: PH, conductivity (µs), water temperature (oC) and DO (mg/l).

Macroinvertebrate sampling

Benthic macroinvertebrates sampling was done using a hand net or a kick net of 500 µm Multi-

habitat Sampling (MHS) net and a 25 cm by 25cm frame area. This involved walking through

the water with the net, dragging and kicking the macrophyte vegetation and benthic substratum

to dislodge any attached macroinvertebrates. Where the collection of substrate material seemed

impossible (for instance for larger boulders and stones), the substratum was sampled by

18

manually scrubbing an equally sized area of 25 × 25 cm of the substratum surface. Each sample

was treated separately to assess consistency of each site in BMI composition and 20 sampling

units not pooled to 1 MHS sample, it was separated per substrate type.

Sampling was done from downstream to upstream against the flow direction to avoid prior

disturbance of areas to be sampled. Roots were shaken vigorously whereas bigger leaves were

thoroughly washed and inspected for attached organisms. For megalithal or macrolithal, a

brush was used to clean and remove macroinvertebrates gently. Macroinvertebrates that

adhered to the net and the substrate were picked using forceps and thereafter the net was washed

carefully to get all the animals sampled. Samples were placed in pre-labelled plastic

containers/bags containing necessary information (site, microhabitat, velocity, depth and date).

The sample was fixed with 4% buffered formaldehyde and transported to the laboratory for

sorting and identification.

Laboratory work

Total phosphorus and BOD analysis

Total phosphorus (TP) was determined using ascorbic acid method (APHA, 1999). The

concentration of TP was determined from known concentrations of TP standard solutions.

Biochemical Oxygen Demand (5- days at 200 C) was analysed using oxygen electrode method,

where oxygen concentration was measured by oxygen electrode immediately at the start of the

experiment and after 5 days. Initial oxygen concentration was measured at the site using a

multimeter probe. After 5 days, the samples were titrated with 0.025M Sodium thiosulphate

(APHA, 1999). The concentration of O2 in the sample was calculated in mgL-1 and the

difference with in-situ measurement value was taken as change in dissolved oxygen which is

referred as the biological oxygen demand. The amount of dissolved oxygen was calculated

using the formula below.

O2 conc. (mg L-1) = (ml titrant) x (molarity of thiosulphate) (8000) x ((vol. titrated) x (ml

of bottle – 1)/ (vol. of bottle))-1

Macroinvertebrates sorting and Identification

The preserved samples were repeatedly washed under gently flowing tap water through a series

of sieves of different mesh size. Washed samples were thereafter put on white trays for sorting

BMI from the debris. Sorted macroinvertebrates were identified using a dissecting microscope.

19

Identification was done to the family level (Gerber and Gabriel, 2002; Stals and Moor, 2007)

and special attention paid to family Hydropsychidae by further examining seven different types

based on front clypeus anterior margin. Identified BMI were enumerated, transferred to

individual vials and preserved in 70% ethanol. Afterwards the samples were labelled

accordingly and taken to BOKU, Austria for further identification.

Figure 2.5: The figure on the left show researcher sorting macroinvertebrates from sediments

and figure on the right shows the identification of macroinvertebrates under microscope.

Study site

pre-visit

Preliminary

sampling site

design

Field work

Lab work

Sampling sites

selection

Inspection 100m

upstream

Field

protocol

Water samples

collection

Physical-

chemical

measurements

Hydro-morphological

measurements

Lab analysis

of samples

Interpretation of

results

Sampling 90 unit

samples from 8 sites Microhabitat

assessment

Preservation in

ethanol 70%

Total taxa, Abundance,

diversity, %EPT etc.

Macro invertebrates

identification (Family level)

20

Figure 2.2: Flowchart of the sampling design

2.4 Statistical analysis

Physical chemical parameters were expressed as means ± SE for each sampling site.

Hydrological and physical-chemical parameters were studied in relation to macroinvertebrate

assemblages. Each taxon was enumerated, and abundance expressed as individuals m-2 to allow

for comparison of different sized samples. To allocate for microhabitat preferences, only

macroinvertebrates with more than 10 times occurrence were considered. Values for velocities

were grouped into classes of a class width of 0.2 ms-1. To enable modelling of

macroinvertebrate habitats, taxa specific suitability curves were generated according to the

flow velocity values of their physical habitats. The habitat suitability curves were based on the

habitat suitability index that represents the capacity of a given habitat to support a species. For

the purposes of this study, I took the maximum abundance of a species and assumed that it

represents the maximum suitability of 1.0 (100%), all other values calculated relative to this

maximum value and then the curve was fit by connecting the values. Habitat suitability curves

were generated using the R statistical software (version 3.5.0).

Non- metric multidimensional scaling (Kruskal, 1964)) ordination based on a Bray-Curtis

similarity matrix was used to examine variability in macroinvertebrates assemblage

composition among microhabitats. Data were log transformed prior to analysis to weight down

the influence of mass occurrence in a single sample (reduced kurtosis/ heteroscedacity). A

biplot was run to see the relationship of taxa with the three environmental variables, velocity,

depth and substrate. Hierarchical cluster analysis (HCA) based on the macroinvertebrate

abundance was used to group assemblage across different substrates. Bray- Curtis (Sorensen)

was used as a distance measure following Bray and Curtis (1957).

After clustering, Indicator Species Analysis (ISA) was calculated to find out macroinvertebrate

species most related to different substrate types according to Dufrêne and Legendre (1997).

This analysis is based on the specificity and fidelity measured for each taxon in an assemblage,

with indicator values ranging from 0 to 100% and reaching a maximum when all individuals

of a taxon are recorded in only a single microhabitat type (high specificity) and when the taxon

is present in all samples of that microhabitat type (high fidelity). Species indicative of a

microhabitat have high and significant percentage IV (>60%) and were considered symmetrical

or best indicators and those with IV >40 as ‘good’ indicators. The indicator values were tested

21

for statistical significance using Monte Carlo tests with 4,999 permutations. NMDS, HCA and

ISA were performed with PC-ORD 5.33.

The functional feeding group composition of macroinvertebrates in different microhabitats was

classified using according to Cummins et al. (2005). The functional feeding group of each

individual species is presented as a proportion of the assemblage. Benthic macroinvertebrates

which do not exclusively feed on a single food resource, were assigned as indifferent feeding

type. Using the given points and percentage of each species within the assemblage, the

functional feeding group composition of macroinvertebrate assemblages at each microhabitat

type was calculated. (credit for data analysis goes to DI Patrick and Leonardo who assisted me

much in this).

22

3.0 Results

3.1 Physical chemical parameters

Mean values obtained per sampling station for physical chemical parameters are presented in

Table 3.1. Temperature, dissolved oxygen (%) and pH were lowest in Magura 13.6±0.3 0C,

101±0.3 % and 6.8±0.0 respectively and highest values 25.0±0.4 0C, 108.2±0.0 % and 8.6±0.0

respectively measured at Gura River before its confluence with Sagana. There was a slight

difference in dissolved oxygen concentration.

There was a major difference in turbidity with the lowest value observed at Kagere 11.5±0.9

NTU and highest value of 51.9±1.4 NTU observed at Sagana before its confluence with Gura

River. Electrical conductivity was highest in Sagana US 164.1±1.2 µS/cm and lowest in

Magura 17.7±0.0 µS/cm. Nutrient analysis showed that total phosphorus was lowest in Magura

2.8±0.1 µg/l and highest in Kagere 7.6±0.2 µg/l. On the other hand, BOD5 was lowest value

was observed at Gitwiga 1.0±0.2 mg/l and highest in Tambaya 2.7±0.1 mg/l.

Table 3.1: Mean values (± SD) of physical chemical parameters for the sampling sites on

Gura River, 16 - 20 October 2018

Site T (°C) DO

(mg/l)

DO (%) pH Turbidity

(NTU)

EC

(µS/cm)

TP

(µg/l)

BOD5

(mg/l)

Magura 13.6±0.3 7.4±0.1 101.7±0.3 6.8±0.0 12.2±0.6 17.7±0.0 2.8±0.1 2.5± 0.1

Kigumo 15.7±0.3 8.1±0.1 103.5±0.3 7.4±0.0 27.1±1.1 23.5±0.2 3.1±0.1 2.0±0.2

Gitwiga 20.6±1.4 7.3±0.3 102.0±1.6 7.5±0.0 20.1±0.9 32.6±0.2 4.0±0.2 1.0±0.2

Powerplant 17.2±0.1 8.0±0.0 103.8±0.0 7.5±0.0 22.1±3.5 27.7±0.1 3.5±0.1 1.6±0.2

Kagere 16.8±0.3 8.3±0.1 104.8±0.3 7.7±0.0 11.5±0.9 34.5±0.1 7.6±0.2 1.4±0.1

Tambaya 21.1±0.4 7.9±0.1 106.7±0.2 7.8±0.1 32.0±3.5 50.9±0.4 2.3±0.1 2.7±0.1

Gura DS 25.0±0.4 7.4±0.1 108.2±0.0 8.6±0.0 12.2±0.7 97.2±0.7 3.4±0.1 1.8±0.1

Sagana US 24.3±0.2 7.5±0.0 107.4±0.2 8.1±0.0 51.9±1.4 164.1±1.2 6.6±0.3 2.4±0.1

3.2 Macroinvertebrates composition

A total of 10,073 BMI individuals within 35 taxa belonging to 12 orders were identified from

the 7 sampling sites (Appendix 2). Macrolithal had the highest number of individuals (3,758

ind. /m2) and the highest number of taxa (27) whereas psammal had the lowest number of taxa

(8). Chironomus sp. Contributed to (71%) of individuals found in psammal. Baetidae had the

highest abundance of 48%, 30%, 30% and 33% of all the collected taxa in microlithal,

mesolithal, macrolithal and macrophytes respectively. Prosopistomatidae occurred only in the

23

macrolithal and Dicercomyzidae had its highest abundance in mesolithal. FFG is the functional

feeding guild and for blank spaces shows that it has not been classified (Table 3.2).

Table 3.2: Abundance (N, Individuals m-2) and abundance % of macroinvertebrates across

various microhabitats of Gura River (17 to 20th October 2018)

Taxa Psammal

N %

Microlithal

N %

Mesolithal

N %

Macrolithal

N %

Macrophytes

N %

FFG

Ephemeroptera

Caenidae 6 0.6 26 1.5 20 0.6 24 0.6 3 0.6 GC

Baetidae 14 1.4 802 47.5 941 30.1 1,142 30.4 161 33.4 GC

Heptageniidae 4 0.4 295 17.5 399 12.8 622 16.6 6 1.2 SC

Tricorythidae 0 0 90 5.3 49 1.6 115 3.1 7 1.5 GC

Leptophlebiidae 0 0 19 1.1 28 0.9 20 0.5 3 0.6 GC

Prosopistomatidae 0 0 0 0 0 0 5 0.13 0 0

Dicercomyzidae 0 0 5 0.3 36 1.2 3 0.08 0 0

Oligoneuridae 0 0 9 0.5 0 0 19 0.5 0 0 FC

Trichoptera

Hydropsychidae 0 0 150 8.9 788 25.2 848 22.6 19 3.9 FC

Philopotamidae 0 0 44 2.6 18 0.5 116 3.1 0 0 FC

Lepidostomatidae 0 0 11 0.7 11 0.4 26 0.7 3 0.6 SH

Diptera

Chironomus 723 71.0 0 0 0 0 0 0 0 0 GC

Chironomidae 0 0 22 1.3 160 5.1 51 1.4 7 1.5

Muscidae 0 0 5 0.3 2 0.06 4 0.1 0 0 P

Simuliidae 0 0 55 3.3 74 2.4 184 4.9 2 0.4 FC

Tipulidae 0 0 29 1.71 55 1.8 16 0.4 0 0 SH

Tabanidae 0 0 2 0.1 4 0.1 0 0 0 0 P

Bivalvia

Sphaeriidae 107 10.5 2 0.1 14 0.5 2 0.05 0 0 FC

Corbiculidae 0 0 0 0 0 0 0 0 6 1.2 FC

Coleoptera

Dytiscidae 0 0 0 0 0 0 1 0.02 2 0.4 P

Elmidae 0 0 33 2.0 153 4.9 100 2.7 42 8.7 SC

Psephenidae 0 0 7 0.4 31 1.0 12 0.3 2 0.4 P

Scirtidae 9 0.9 39 2.3 199 6.4 320 8.5 4 0.8 SC

Decapoda

Potamonautidae 0 0 14 0.8 13 0.4 19 0.5 3 0.6 SC

Hemiptera

Corixidae 10 1.0 0 0 0 0 0 0 0 0 SC

Gerridae 0 0 0 0 0 0 1 0.02 16 3.3 P

Naucoridae 0 0 0 0 0 0 0 0 38 7.9 P

Veliidae 0 0 0 0 2 0.06 0 0 0 0 P

Lepidoptera

Pyralidae 0 0 0 0 20 0.6 7 0.2 4 0.8 SH

Odonata

Aeshnidae 0 0 4 0.2 0 0 3 0.08 0 0 P

Coenagrionidae 0 0 0 0 9 0.3 0 0 132 27.4 P

24

Libellulidae 0 0 2 0.1 15 0.5 3 0.08 12 2.5 P

Oligochaeta

Tubificidae 158 15.5 22 1.3 21 0.7 0 0 0 0 GC

Plecoptera

Perlidae (Neoperla) 0 0 3 0.2 9 0.3 34 0.9 0 0 P

Turbellaria

Planaria 0 0 41 2.4 35 1.12 53 1.4 0 0 P

Totals (ind.m-2) 1,019 1,690 3,124 3758 482

Number of taxa 8 25 26 27 20

Regarding substrates size and BMIs, the abundance and richness of taxa across different

microhabitats showed a great variation. The mean abundance was highest in psammal (340 ±

66 ind m-2) and lowest in the macrophytes (80 ± 17 ind m-2), though the difference in mean

abundance among course substrates was not much. The higher abundance in the psammal is

due to the dominance by Chironomus sp. The total taxa richness reached 35 taxa, microlithal

had the highest mean number of taxa whereas psammal had the lowest taxa number (Figure

3.1).

3

12

2330

6

10

20

30

Psammal Microlithal Mesolithal Macrolithal Macrophytes

Substrate

Nu

mb

er

Of

Taxa

Number Of Taxa

3

1223

30

6

0

200

400

600


Substrate

Abun

da

nce/m

²

Total Abundance

A

B

25

Figure 3.1: Boxplots of taxonomic groups (A) mean abundance and (B) total taxa for each

substrate; total number of sampling units are displayed above each boxplot.

The mean abundance of Baetidae differed across different substrates types. Microlithal showed

the highest abundance of Baetidae compared to the other coarse substrates. Caenidae,

Tricorythidae and Leptophlebiidae highest abundances were also recorded in the microlithal

than the in the other coarse substrates (mesolithal and macrolithal). Hydropsychidae were

mostly found in the macrolithal than in the mesolithal. Heptageniidae had its highest abundance

in the macrolithal whereas Elmidae were recorded highest in the macrophytes than in the coarse

substrates (Figure 3.2). For all the comparisons in Figure 3.2 below, psammal and macrophytes

were not considered for comparisons across substrates because of the smaller number of

sampling units apart from Elmidae boxplots.

2

12

21 296

0

50

100

150


Substrate

Abu

nd

an

ce

/Sa

mp

ling

Un

it B

ae

tid

ae

Abundance/Sampling Unit Baetidae

3 7

8

11

1

0.0

2.5

5.0

7.5

10.0


Substrate

Abun

da

nce

/Sa

mp

ling

Unit C

ae

nid

ae

Abundance/Sampling Unit Caenidae

26

1

1118

23

2

0

25

50

75

100


Substrate

Abun

da

nce

/Sam

plin

g U

nit

He

pta

ge

niid

ae

Abundance/Sampling Unit Heptageniidae

3

5 5 1

0.0

2.5

5.0

7.5

10.0


Substrate

Abun

da

nce

/Sam

plin

g U

nit

Le

pto

ph

lebiid

ae

Abundance/Sampling Unit Leptophlebiidae

4

7 9 1

0

10

20

30


Substrate

Abu

nd

an

ce/S

am

plin

g U

nit

Tric

ory

thid

ae

Abundance/Sampling Unit Tricorythidae

27

Figure 3.2: Boxplots of mean abundances of selected benthic invertebrates of Gura River, 17th

to 20th October 2018; the total number of sampling units displayed on top of each boxplot.

To allow for lower taxonomic resolution, Hydropsychidae were thereafter divided into

different types based on the head anatomy (front clypeus anterior margin and coloration) as the

distinguishing factor (see appendix 3). The results showed variation in the abundances based

on the substrate. The groups were: A, B, D, G, J, K & L. Overall, type L was only found in the

microlithal. Type A was found in all the microhabitats but had its highest abundance in the

mesolithal and lowest abundance in macrolithal. Type B showed the highest abundance in

mesolithal to macrolithal whereas type J had the highest abundance in macrolithal (Figure 3.3

and appendix 4).

5

12

15

3

0

5

10

15

20

25


Substrate

Abun

da

nce

/Sam

plin

g U

nit

Elm

ida

e

Abundance/Sampling Unit Elmidae

11

19 25

3

0

25

50

75

100


Substrate

Abu

nda

nce/S

am

plin

g U

nit H

ydro

psych

ida

e

Abundance/Sampling Unit Hydropsychidae

28

Figure 3.3: Boxplots of abundances of Hydropsychidae types across microhabitats; total

number of the sampling units displayed on top of each boxplot.

1

617

0

20

40

60

Microlithal Mesolithal Macrolithal

Substrate

Abun

da

nce/H

yd

rop

sychid

ae T

yp

e A

Abundance/Hydropsychidae Type A for each substrate type

2

11

0

20

40

60


Substrate

Abun

da

nce/H

yd

rop

sychid

ae T

yp

e B

Abundance/Hydropsychidae Type B for each substrate type

1

6

6

0

20

40

60


Substrate

Abun

da

nce

/Hyd

rop

sychid

ae

Type

J

Abundance/Hydropsychidae Type J for each substrate type

29

Flow velocity: The highest community richness and abundance were found in those substrates

where velocity ranged from 0.5 to 0.9 m/s. Mean abundance was highest in the moderate flow

class of (0.5 – 0.7 m/s) and lowest in very fast flowing water (>0.9 m/s). Number of taxa was

highest in fast water current class of (0.7 – 0.9 m/s) and lowest in very fast current >0.9 m/s

(Figure 3.4). Therefore, velocity class >0.9 m/s had low taxa abundance and richness.

Figure 3.4: Boxplots of the abundance of benthic invertebrates (left) and total taxa (right) for

each velocity class of Gura River (17th - 20th October 2018). The number of sampling units

displayed on top of each boxplot.

816 16

21 11

2

0

200

400

600

0.01−0.1 0.1−0.3 0.3−0.5 0.5−0.7 0.7−0.9 >0.9

Velocity Class [m/s]

Abun

da

nce/m

²

Total Abundance

816

16

21

11

2

5

10

15

20

0.01−0.1 0.1−0.3 0.3−0.5 0.5−0.7 0.7−0.9 >0.9

Velocity Class [m/s]

Nu

mb

er

Of

Taxa

Number Of Taxa

30

3.3: Microhabitat Preferences

To show the relationship between environmental variables (substrate, current velocity and

water depth) and benthic invertebrates, NMDS ordination was run. The results showed that

macroinvertebrates were significantly influenced by substrate, velocity and depth, the substrate

being the most important variable. Water current and depth exhibited small arrows (Figure 3.5),

depicting a weak relationship between them and taxa distribution. Therefore, the distribution

of taxa in Gura River is highly dependent on the substrate types compared to other observed

environmental variables.

Figure 3.5: Joint plot of NMDS analysis for macroinvertebrates samples of river Gura (n=74);

vectors (r2= 0.5); Environmental variables (arrows); stress 18.07 for a 2-dimensional solution.

Legend: 1=Psammal, 2=Microlithal, 3=Mesolithal, 4=Macrolithal & 5=Macrophytes

Further NMDS analysis presented as a joint plot (cut- off value r2 = 0.5) showed that

macroinvertebrates grouped according to substrate type. Samples belonging to coarse

substrates macrolithal, mesolithal and microlithal grouped together displayed on the right side.

Psammal substrate is clustered on the left bottom of the joint plot and macrophyte substrate

clustered separately (Figure 3.6). This shows a shift in taxa composition with a decreasing

substrate size (Figure 3.6a). Macroinvertebrates did not group based on the water depth and

flow velocity (Figure 3.6 b & c), for flow velocity this could be due to heavy rains experienced

during the sampling period thereby making current velocity uniform and perhaps the

macroinvertebrates of Gura River are well adapted to its fast- flowing waters. One sample is a

single unit in the diagrams.

31

-3,0 -2,0 -1,0 0,0 1,0

-1,5

-0,5

0,5

1,5

nms

Axis 1

Axis

2

Substrat

12345

-3,0 -2,0 -1,0 0,0 1,0

-1,5

-0,5

0,5

1,5

nms

Axis 1

Axis

2

Depth_Cl

01020304050

-3,0 -2,0 -1,0 0,0 1,0

-1,5

-0,5

0,5

1,5

nms

Axis 1

Axis

2

Vel_Cl

0102030405060708090

a

b

c

32

Figure 3.6: Joint plot of the NMDS analysis for the macroinvertebrate samples of Gura River

(n = 74), stress: 12.68 for 3-dimensional solution; a) substrate preferences and b) depth

preferences and c) velocity preferences. Legend: 1=Psammal, 2=Microlithal, 3=Mesolithal,

4=Macrolithal & 5=Macrophytes

Substrate preferences

Specific substrate preference for each taxon was analyzed using NMDS simple scatter plots. In

the diagrams (Figure 3.7), the size of the symbols is proportional to microhabitat preferences.

Baetidae had a wide range of microhabitat preference; mesolithal, macrolithal and to some

extent macrophytes. Caenidae had preference for psammal, microlithal and mesolithal.

Heptageniidae, Scirtidae, Psephenidae and Hydropsychidae showed a higher preference for

mesolithal and macrolithal. Tricorythidae, Dicercomyzidae and Lepidostomatidae did not

show a clear microhabitat preference, this could be due to their low numbers observed during

sampling. Leptophlebiidae had a clear preference for course substrates to fine and macrophyte

substrates. Elmidae showed a preference for mesolithal whereas Potamonautidae and Perlidae

showed a clear preference for macrolithal (Figure 3.7).

Baetidae Caenidae

Heptageniidae Tricorythidae

33

Leptophlebiidae Dicercomyzidae

Oligoneuridae Scirtidae

Elmidae Perlidae

Psephenidae

Philopotamidae

34

Figure 3.7: NMDS ordination for substrate preferences of some benthic macroinvertebrates.

Legend: 1=Psammal, 2=Microlithal, 3=Mesolithal, 4=Macrolithal & 5=Macrophytes

Water velocity and depth analysis

Water depth and velocity differed across microhabitats in the Gura River catchment. Depth

was lower in microlithal microhabitats compared to those with macrophytes, psammal,

mesolithal and macrolithal. Velocity was higher in mesolithal and macrolithal. Hence, higher

water velocity also had higher water depth (Figure 3.8).

Figure 3.8: Water velocity and depth (mean ± SD) in different microhabitats in Gura River.

Velocity preferences

To identify velocity preference for each taxon, habitat suitability curves were analyzed.

Results from the analyses showed distinct preference for stream flow velocities by all the

benthic macroinvertebrates analyzed (Figure 3.9). For example, Perlidae preferred velocity

class of (0.3 – 0.9m/s), Tricorythidae had preference for (0.3 – 0.7m/s). Leptophlebiidae,

Lepidostomatidae Hydropsychidae

35

Simuliidae and Oligochaeta showed preference for a range of (0.1 – 0.7m/s) and

Lepidostomatidae preferred velocity class (0.3 – 0.7m/s). Caenidae preferred a velocity class

(0.5- 0.7m/s). Conversely, Baetidae, Heptageniidae and Hydropsychidae showed no

specialized flow velocity preference.

36

Figure 3.9: Habitat suitability curves for selected macroinvertebrates of Gura River, (17th –

20th October 2018).

Hydropsychidae types and their velocity preferences

Lower taxonomic identification of the Hydropsychidae showed distinct variation in flow

velocity preferences. Flow velocity as a grouping factor, type A is ubiquitously across all

velocity classes but mostly preferred (0.3 -0.7m/s). Type B preferred a current class of (0.7 –

0.9m/s), Types G, J & D had preference for (0.5 – 0.7m/s) whereas type K mostly preferred

(0.3 -0.9m/s) as shown in (figure 3.10). The curves clearly show the importance of lower

taxonomic resolution.

37

Figure 3.10: Habitat suitability curves for Hydropsychidae types based on the flow velocity

38

Using boxplots to show velocity preferences when more than one number of a certain type is

present also showed variation in velocity preferences. This could be as a result of competition

among individuals of the same species. For example, type A when the taxa number was zero,

its mean velocity was 0.3m/s but in the presence of another taxon, the mean velocity preference

was recorded to be 0.7m/s. For type B again in the presence of other taxa of the same type,

there was a range in velocity preference; In zero taxa number approximately 0.58m/s, when

one taxa was added the mean velocity was 0.63m/s and in the presence of second taxa, mean

velocity dropped down to approximately 0.38 m/s (Figure 3.11).

Figure 3.11: Boxplots of preferred current range per Hydropsychidae-types (A-L) under

presence of different numbers of coexisting Hydropsychidae taxa (Taxa number)

39

Hierarchical Cluster Analysis (HCA)

Cluster analysis performed on the log(x+1) transformed abundances of macroinvertebrate

resulted into two main groups/ clusters of which one was divided into two main clusters while

the second one consisted of four sub clusters (Figure 3.12). Communities from Psammal

substrates are clustered together, whereas those from coarse substrates (microlithal, mesolithal

and macrolithal) also clustered together and the same case for macrophytes.

Figure 3.12: cluster analysis of benthic macroinvertebrates across substrate types. Legend:

1=Psammal, 2=Microlithal, 3=Mesolithal, 4=Macrolithal & 5=Macrophytes.

3.4 Indicator Species Analysis

Indicator species analysis identified benthic macroinvertebrates that serve as indicators for

different substrate types. The indicator species values ranged from 4.1 to 100 for all taxa based

on the substrate type. Table 3.3 shows the results and the significant indicators were:

Chironomus (p= 0.0002), Tubificidae (p= 0.002), Sphaeriidae (p= 0.0012) and Caenidae (p =

0.0388) were found to be indicator species of fine substrates. Tubificidae (p= 0.059),

cl

Distance (Objective Function)

Information Remaining (%)

8,8E-03

100

3E+00

75

6E+00

50

9,1E+00

25

1,2E+01

0

1105121115181108161327051348163431xx23323523463752671465222965272765302973633375482213061214452413211332.11034411234952543103243272263634773803332070732451052210542562075251963635063705063645073523074623164634374662063665164534364504474543073894413451213512214481414251642211544433275.04724463853733252180552561053703253823042652144872843101524084024143024343024123124532824704252572134448320806242838340512343116245450247142246237348421348626

Substrat

1 2 3 4 5

40

Hydropsychidae (p= 0.0728) and Philopotamidae (p= 0.0594) were indicators for coarse

substrates (microlithal, mesolithal and macrolithal respectively). Whereas Coenagrionidae (p=

0.0008) and Naucoridae (p= 0.0002) were indicators for macrophytes. In general, Chironomus

was the best indicator (IV = 100, p= 0.0002).

Table 3.3: Indicator Species Analysis values for benthic macroinvertebrate species by substrate

type. Significant values (p ≤ 0.05) in bold.

Taxa Substrate type

Value (IV) Mean S. Dev p *

Chironomus Psammal 1 100 9.3 7.47 0.0002

Tubificidae

1 66.7 8,7 7.65 0.002

Sphaeriidae

1 65.3 11.3 8.29 0.0012

Caenidae 1 38.7 19.8 7.62 0.0388

Baetidae Microlithal 2 33.3 27.8 4.79 0.1224

Heptageniidae

2 31.6 25.9 6.2 0.1618

Tubificidae

2 30,4 14.2 8.87 0.059

Planaria

2 19.6 17.7 9.32 0.3103

Tricorythidae

2 15.2 18 8.81 0.5405

Potamonautidae

2 12 16 8.81 0.6209

Muscidae

2 10.6 12.1 8.02 0.4371

Oligoneuridae

2 9.3 12.4 8.14 0.5315

Leptophlebiidae

2 9.1 15.4 8.88 0.791

Aeshnidae

2 6 8.7 7.47 0.6451

Tabanidae 2 4.1 8.5 7.35 0.7445

Hydropsychidae Mesolithal 3 37.4 27.4 6.24 0.0728

Tipulidae

3 20.1 18.1 9.07 0.2825

Dicercomyzidae

3 19.6 12.9 8.25 0.149

Chironomidae

3 19.2 2.7 10.61 0.5529

Psephenidae

3 18 15.8 8.78 0.2669

Pyralidae

3 14 13.6 8.53 0.3719

Elmidae

3 12.8 10.3 7.6 0.2909

Veliidae 3 4.3 6.9 6.81 0.6073

41

Philopotamidae Macrolithal 4 34.1 17.3 9.19 0.0594

Simuliidae

4 27.1 23.3 9.06 0.236

Scirtidae

4 24.9 25.7 10.3 0.4161

Perlidae

4 23.4 15 8.25 0.1212

Lepidostomatidae

4 13.3 15.9 9.39 0.4915

Prosopistomatidae

4 6.7 8.6 7.26 0.5727

Coenagrionidae Macrophytes 5 81.9 13.7 8.99 0.0008

Naucoridae

5 66.7 10.6 8.08 0.0002

Elmidae

5 20.8 21.6 7.89 0.4179

Corbiculidae

5 16.7 6.7 6.58 0.1172

Corixidae

5 16.7 6.7 6.58 0.1172

Shrimp

5 16.7 6.7 6.58 0.1172

Gerridae

5 16.4 8.1 6.86 0.0996

Dytiscidae

5 13.9 8.8 7.73 0.1804

Libellulidae 5 12.1 12.2 7.98 0.3313

Table 3.4 shows the results for analysis based on the velocity class. Chironomus (p= 0.0668),

Libellulidae (p= 0.07), Sphaeriidae (p= 0.0156) and Tubificidae (p= 0.0334) were indicators

for velocity class (0 - 0.1 m/s). Simuliidae (p= 0.0956) showed indication for velocity class

(0.3 – 0.5 m/s). For velocity class (0.5 – 0.7 m/s), Elmidae (p= 0.0682), Hydropsychidae (p=

0.0012) and Muscidae (p= 0.0508) were the indicators. Oligoneuridae (p= 0.0296) and

Philopotamidae (p= 0.0262) were the indicators for velocity class 0.7 – 0.9m/s.

42

Table 3.4: Indicator Species Analysis for benthic macroinvertebrate species by velocity class.

Significant p- values indicated in bold. Key: velocity class 1= (0.0-0.1m/s), 2= (0.1-0.3 m/s),

3= (0.3-0.5 m/s), 4= (0.5-0.7 m/s) and 5= (0.7-0.9 m/s).

Velocity

Class Value (IV) Mean S. Dev p *

Aeshnidae 1 6.1 6.7 3.58 0.4879

Baetidae 5 27.3 25.8 3.16 0.2777

Caenidae 1 21.2 16.4 4.42 0.1342

Chironomidae 2 33.7 22.8 8.66 0.1258

Chironomus 1 17.5 8.6 4.41 0.0668

Coenagrionidae 1 7.5 12.7 6.36 0.7932

Corbiculidae 1 9.1 6.8 2.21 0.2705

Corixidae 1 9.1 6.8 2.21 0.2705

Dicercomyzidae 5 11.3 9.4 4.7 0.2531

Dytiscidae 1 5.4 6.7 3.62 0.5787

Elmidae 4 26.6 18.5 4.96 0.0682

Elmidae 2 9.5 8.7 4.8 0.3417

Gerridae 1 8.6 7.6 3.58 0.3913

Heptageniidae 5 30.2 23.3 4.15 0.0692

Hydropsychidae 4 42.3 24.3 4.2 0.0012

Lepidostomatidae 5 9.4 13.1 5.79 0.7041

Leptophlebiidae 2 10.6 12 5.01 0.5281

Libellulidae 1 18.4 9.7 5.19 0.07

Muscidae 4 17.5 8.3 4.46 0.0508

Naucoridae 2 10.8 7.5 4.4 0.1774

Oligochaeta 2 6.9 10.7 4.81 0.7886

Oligoneuridae 5 20.2 8.9 4.62 0.0296

Perlidae 5 6.6 11.7 4.9 0.9198

Philopotamidae 5 29.4 14.4 5.99 0.0262

Planaria 3 12.4 14.4 5.51 0.5679

Potamonautidae 4 5.3 12.5 5.02 0.9974

Prosopistomatidae 1 5.6 6.7 3.6 0.4927

Psephenidae 5 14.2 12.4 5.12 0.2729

Pyralidae 4 8.5 10.4 4.98 0.5621

Scirtidae 3 15.4 21.9 6.37 0.8804

Shrimp 1 9.1 6.8 2.21 0.2705

Simuliidae 3 28.5 20.4 5.8 0.0956

Sphaeriidae 1 24.9 9.6 5.22 0.0156

Tabanidae 5 9.3 6.8 3.72 0.1554

Tipulidae 5 14.6 14.9 5.71 0.4263

Tricorythidae 4 11.8 14.6 5.25 0.6555

Tubificidae 1 18.2 6.7 3.66 0.0334

Veliidae 4 4.3 6.7 2.21 1

43

Indicator Species Analysis per sampling site showed the following results (Table 3.5). For site

1, Chironomus (p= 0.013), Elmidae (p= 0.0074), Scirtidae (p= 0.0006), Simuliidae (p= 0.0298)

and Sphaeriidae (p= 0.0306) were the indicator species. Perlidae (p= 0.0008) was an indicator

species for site 2. Oligoneuridae (p= 0.0004) was an indicator species for site 3. For site 4,

Baetidae (p= 0.0002), Potamonautidae (p= 0.0022) and Tricorythidae (p= 0.0328) were the

indicator species. Dicercomyzidae (p= 0.0027), Heptageniidae (p= 0.003), Muscidae (p=

0.0134), Oligochaeta (p= 0.0372), Psephenidae (p= 0.0012), Tabanidae (p= 0.022) and

Tipulidae (p= 0.002) showed indication for site 5. Whereas, Coenagrionidae (p= 0.096) and

Elmidae (p= 0.047) were indicators for site 7.

Table 3.5: Indicator Species Analysis values for benthic macroinvertebrate species per site.

Significant p- values are in bold.

Taxa Site Value (IV) Mean S. Dev p *

--------- -------- -------- - ---- ------ -------

Aeshnidae 4 7.7 7.6 4.66 0.2122

Baetidae 4 35.5 20.2 2.71 0.0002

Caenidae 4 15.5 14.2 4.08 0.2991

Chironomidae 1 9.8 23.4 9.35 0.9382

Chironomus 1 25 10.9 4.91 0.013

Coenagrionidae 7 34.3 14.3 7.24 0.0096

Corbiculidae 7 10 9.5 1.75 0.3527

Corixidae 7 10 9.5 1.75 0.3527

Dicercomyzidae 5 33.6 9.6 4.92 0.0024

Dytiscidae 7 5.7 7.6 4.6 0.5475

Elmidae 7 24.4 15.8 4.27 0.047

Elmidae 1 33.3 10.3 5.5 0.0074

Gerridae 7 9.3 10 3.87 0.3519

Heptageniidae 5 32.6 18.8 3.55 0.003

Hydropsychidae 6 22.2 19.7 3.59 0.2184

Lepidostomatidae 1 18.4 13.2 6.05 0.1964

Leptophlebiidae 5 19.1 11.4 4.94 0.075

Libellulidae 4 7.6 10.1 5.43 0.5943

Muscidae 5 24.2 8.7 4.75 0.0134

Naucoridae 7 14.2 8.2 4.87 0.1172

Oligochaeta 5 21.6 10.6 5.03 0.0372

Oligoneuridae 3 42.1 9.2 4.86 0.0004

Perlidae 2 41.6 11.1 4.86 0.0008

Philopotamidae 3 22.7 13.8 6.09 0.0846

Planaria 5 18.4 13.7 5.68 0.162

Potamonautidae 4 33.2 11.6 4.69 0.0022

Prosopistomatidae 4 5.6 8.1 4.41 0.6511

Psephenidae 5 39.9 11.7 4.91 0.0012

Pyralidae 6 15.8 10.3 4.85 0.1208

44

Scirtidae 1 49.6 19.6 6.12 0.0006

Shrimp 7 10 9.5 1.75 0.3527

Simuliidae 1 31.3 18.1 5.62 0.0298

Sphaeriidae 1 24.5 11 6.02 0.0306

Tabanidae 5 25 8.1 4.5 0.022

Tipulidae 5 40 13.9 5.48 0.002

Tricorythidae 4 24.4 13.3 4.91 0.0328

Tubificidae 1 16.7 7.9 4.68 0.113

Veliidae 7 10 9.5 1.74 0.3573

3.5: Functional feeding guilds

In Gura River, macroinvertebrates were dominated by gathering collectors (43.78%), filtering

collectors (24.24%) and scrapers (22.53%) following closely, predators had (4.28%), shredders

(2.30%) and the indifferent feeding guilds occupying (2.48%). The fine substrates (psammal)

had the highest proportion of gathering collectors compared to other microhabitats.

Macrophytes within the streams facilitates the sedimentation of fine particles due to reduced

flow velocities and may therefore act as habitats for gatherers and collectors. Filtering

collectors and scrapers was highest in macrolithal and mesolithal. Predators had a higher

proportion in macrophytes compared to the rest of the substrates. Shredders and indifferent

percentage across substrates were recorded in low proportion (Figure 3.13a).

Along with substrates, current velocity is an important parameter that determines the FFG of

benthic invertebrates. Gathering collectors and predators preferred low water current velocity

(0.01 – 0.1 m/s) whereas filtering collectors had its highest proportion in moderate water

velocity class (0.5 – 0.7m/s). Shredders were completely missing in low water velocity (0.01 –

0.1m/s) and in very fast flowing water (> 0.9m/s) and scrapers was recorded highest in very

fast flowing water (> 0.9m/s) figure 3.11b).

45

Figure 3.13: Relative abundance of functional feeding groups of BMIs and their distribution

in a) substrates and b) in each velocity class in Gura River catchment. Legend: FC Filterers;

GC gatherers; SC scrapers; SH shredders; P predators; I Indifferent

a

b

46

4.0 Discussion

Physical chemical variables

The spatial differences in mean temperature values showed a significantly lower temperatures

at the most upstream sites (Magura and Kigumo) is due to the good riparian cover at these sites.

Riparian vegetation regulates the amount of solar radiation reaching the water surface (Allan

and Arbor, 2014). The slight differences in the level of dissolved oxygen could be as a result

of mixing of water due to high flow velocity nature of Gura River. Dissolved oxygen (DO)

availability is an important factor influencing the composition and distribution of freshwater

macroinvertebrates. Aquatic macroinvertebrates have a diverse range of structural and

behavioral respiratory adaptations (Eriksen ,1963), this clearly shows that different taxa differ

in their oxygen requirements and tolerance to hypoxia.

Biological oxygen demand concentration varied across different sites. It was highest in Magura

and Tambaya, this could as a result of anthropogenic activities. For example, in Magura site

there was a wild life watering point in the stream thereby causing organic pollution. Organic

pollution increases the consumption of oxygen in water thus leading to higher BOD levels in

water. And in Tambaya there was an accumulation of sediments and other wastes that could

have led to higher BOD concentrations being that in the previous day before sampling Tambaya

there was a huge runoff. Masese et al. (2009) showed that increased run off transports

sediments and organic matter into the river.

Macroinvertebrates composition

Ephemeroptera was the dominant order in the study area (Appendix 2) accounting for 48.44%

of all the taxa abundance. Similar results have been obtained in highland tropical streams for

example, Mathooko and Mavuti (1992) while investigating Mount Kenya streams, found the

benthic communities were dominated by Baetis sp. (Ephemeroptera: Baetidae). Head water

stream like Magura was dominated by Diptera (Appendix 2): Chironomus sp. and Oligochaeta,

this can be attributed to organic pollution caused by wildlife excreta (Moreyra and Padovesi-

Fonseca, 2015). Chironomus sp. had 71% abundance of the total taxa collected in psammal

substrate (Table 3.2) and they were the best indicator species 100% (Table 3.3). Gao et al.

(2014) showed that most anthropogenically impacted sites in a stream were dominated by sand

and silt and were dominated by the most tolerant taxa such as Chironomidae and Oligochaeta.

Psammal had the lowest number of taxa and a higher mean abundance (Figure 3.1), this could

be because organisms usually found in fine sediment areas prefer slow flowing streams (Figure

47

3.8) where current does not interfere with their movements and food acquisition and some of

them several Chironomus have hemoglobin for respiration. The mean abundance of Elmidae

was highest in the macrophytes (Figure 3.2). Similar studies on the microhabitat preferences

of beetles (Sarr et al., 2013) showed that certain species of Elmidae were associated with moss

as compared to cobbles. A unique/ rare occurrence of Prosopistomatidae and Dicercomyzidae

(Table 3.1 and Appendix 5 for the photos) was also recorded in Gura River. Dicercomyzidae

occurred on all the coarse substrates though with a higher mean abundance on the mesolithal

whereas Prosopistomatidae only occurred once in macrolithal at Kigumo site G2 (Appendix 2)

this site was less disturbed in the forest. The occurrence of this rare taxon was recorded by

Yam (2015) where the nymphs of Prosopistomatidae were collected at riffle habitats dominated

by gravel and pebbles with moderate to high current velocity (26.7 – 65.1cm/s) in an upstream

undisturbed sites of Baishih River in Taiwan.

Macroinvertebrates microhabitat preferences

From the results, substrate types and current velocity are important for the distribution of

benthic invertebrates (Figure 3.5). These results are consistent with many other studies showing

that substrate type and current velocity influences the distribution of taxa in rivers and streams

(Pardo and Armitage, 1997; Rempel et al., 2000; Korte, 2010; Vilenica et al., 2018). NMDS

analyses on specific substrate preferences for selected invertebrate taxa (Figure 3.6) showed

that most of the taxa in Gura River had a broader range of substrate preferences. This has been

observed by Schröder et al. (2013) where broad substratum preferences and distribution

patterns for many species of benthic invertebrates were recorded.

The fine substrate usually considered as unstable and disturbed is known to support poor

species richness (Darrow and Pruess, 1989), this was confirmed with the species richness in

the psammal substrate (Figure 3.1). The results of the NMDS analysis also proved this by

grouping species of psammal substrates, coarse substrates and macrophytes separately (Figure

3.6a). NMDS showed a shift in taxa composition with the decreasing substrate sizes, this

finding was also demonstrated in the work of Leitner et al. (2015). Overall NMDS results

indicated higher preference for substrate size rather than flow velocity and water depth, this

could be because Gura River being a fast-flowing stream, the benthic invertebrates have well

adapted to the flow conditions and the sampling took place during a rainy season hence uniform

current velocity. Depth appeared not to have a strong relationship with the taxa distribution in

this study.

48

Caenidae preferred psammal substrates (Figure 3.7) which is associated with poor habitat

conditions and low current velocity, this can be explained by the presence of a gill cover in

Caenidae. Similar results also observed by Solomon (2014). Hydropsychidae types showed

distinct preference for specific microhabitats. For instance, type J preferred velocity class (0.5-

0.7m/s) whereas type B preferred (0.5 – 0.9m/s). This corroborates the assertion that each

taxon has a specific habitat requirement and tolerance levels of flow (Dewson et al., 2007).

And such results also reveal the importance of lower taxonomic resolution for benthic

invertebrates.

Water velocity is of great importance in the microhabitat selection by benthic invertebrates, it

directly influences the distribution of BMI and controls the distribution of substrates, food

and influences oxygen concentration (Lauzon and Harper, 2008). Thus, the highest benthic

invertebrates’ abundances in Gura River catchment were recorded at microhabitats such as

macrolithal and mesolithal which were associated with higher water velocity and higher water

depths. Based on the data, indicator species analysis showed that indicator species colonizing

coarse substrates (Table 3.3) were Oligochaeta, Hydropsychidae and Philopotamidae. This is

in line with a similar study by Leitner et al. (2015), they found out that coarse substrates were

significantly indicated by Oligochaeta and Hydropsychidae. Solomon (2014) showed that

Chironomus sp. was the indicator species in fine substrates and the overall best indicator

species, this is the same with the finding in Gura River. As previously recorded for other aquatic

insects (Masikini et al., 2018), benthic macroinvertebrates composition and structure in a

stream is influenced by microhabitat characteristics (i.e. water velocity, water depth, substrate

type), this has been confirmed in this study in Gura River.

Functional feeding guilds

Functional feeding guild composition analysis (Figure 3.13) showed evidence of divergent

macroinvertebrate communities in streams of varying flow velocities and substrate size. Gura

River was dominated by gatherers and scrapers/grazers, this is not surprising due to its

morphology which is mainly characterized by high water velocity and presence of coarse

substrates (macrolithal and mesolithal) with trapped organic matter and attached periphyton.

Vilenica et al. (2018) in a study in Plitvice lakes showed that mayflies’ assemblage was

dominated by grazers and gatherers due to the morphology of the Karst. Again, gatherers

presence on the macrophytes is due to accumulation of organic particles on the macrophytes.

49

Similar study in highland streams in Kenya (M’Erimba et al., 2014) showed that gathering

collectors was the dominant functional feeding guild.

High proportion of filtering collectors in the coarse substrates (macrolithal and mesolithal)

could be due to the accumulation of organic particles on the barrier substrates (Vilenica et al.,

2018). Filter feeders such as Simuliidae preferred coarse substrates and macrophytes which

provides suitable physical characteristics for the family. This clearly shows that family’s

trophic preferences are related to specific substrates. The low number of shredders across all

microhabitats likely indicate problems with the riparian litter supply. Different riparian plants

species produce litter that varies significantly in its decomposition time that renders the litter

suitable as a shredder food resource (Cummins and Klug, 2003). Another explanation could

simply be a general lack of riparian cover to supply enough litter inputs, most of the sampling

sites in Gura River had scattered riparian vegetation and most of the vegetation was mainly

eucalyptus for example, Gitwiga sampling site. According to Graça et al. (2005) and Basaguren

et al. (2014) Eucalyptus-plantations negatively affect the biology of streams because they

produce litter of low quality, lower nutrient input than some deciduous forests and also the

litter they produce is degraded by detritivores at a low rate. Furthermore, lack of shredders in

the Psammal substrates could be as a result of under estimation caused by very low sampling

units (3). The results on the shredder analysis conform to the general belief that shredders are

underrepresented in the tropics (Cummins et al., 2005; Dudgeon & Wu, 2016).

Regarding family’s current preferences and associated functional feeding guilds. Substrates

(e.g., macrophytes and psammal) with slow current velocity (0.01 – 0.3m/s) support a

community of shredders and gathering collectors whereas in moderate to high current velocity

(0.5 - >0.9 m/s) filtering collectors and scrapers dominated the substrates. Similar findings

were recorded in Lamouroux et al. (2004) where they revealed that substrates found in lentic

zones of a stream (e.g., fine/coarse organic material, sand/mud) had burrowers, deposit feeders

and shredders whereas substrates of lotic stream zones (e.g., stones/rocks and macrophytes)

support active and passive filterers. Another study by Schröder et al. (2013) found that coarse

and mineral organic substrates exposed to high current supported rheophilic and rheobiont

species whereas limnophilic species prefer shallow lentic zones.

50

Summary and Conclusions

This thesis investigated the ecological requirements of benthic macroinvertebrates of Gura

River, Kenya. Data on the structure and functional requirements of BMI in terms of the

microhabitat preference is still missing in Gura River and Kenya in general. On this perceived

need, three objectives were developed;

For the first objective; Documentation of diversity and distribution of benthic

macroinvertebrates in Gura River. The results gave a good indication of the distribution patterns

of the families in the twelve orders under investigation. The high number and richness of

Ephemeroptera and Trichoptera were found in coarse substrates and moderate to fast current

velocities. These environmental conditions as seen are improving the habitat diversity for

sensitive taxa which are good indicators of a healthy riverine ecosystem. It also highlighted the

distribution of some rare families such as Prosopistomatidae and Dicercomyzidae.

The second objective; To analyze microhabitat preferences based on the distribution of

macroinvertebrate community. The hypothesis tested in this section was that the

macroinvertebrate abundance, diversity and composition can be differentiated based on

environmental factors such as substratum, depth, and velocity. The results indicated that:

Substrate type and current velocity were the most important variables for the distribution of

taxa in Gura River. Water depth was not a significant factor in determining the distribution of

benthic invertebrates under consideration. Most taxa preferred moderate current velocities and

some taxa such as Heptageniidae and Hydropsychidae showed a clear preference only for

macrolithal and mesolithal whereas Baetidae showed a broad range of habitat from coarse

substrates to macrophytes. However, the macroinvertebrate assemblage structure can be

differentiated based on a combination of environmental factors and the null hypothesis that the

macroinvertebrate assemblage structure cannot be differentiated based on environmental

factors is therefore rejected. The information obtained in this section provides a first step in

setting microhabitat requirements for selected families of Hydropsychidae, Baetidae Scirtidae,

Elmidae and Heptageniidae and need for obtaining more data on these families. As substratum

preferences differed between closely related species, preferences should always be given at the

species level as opposed to coarser taxonomic units.

The last hypothesis tested was: The functional feeding guilds of taxa in Gura River will differ

based on the current velocities and substrate types. The results showed a distinct variation in

51

FFG composition in different current classes and substrate types. Therefore, the null hypothesis

that FFG cannot be differentiated based on current velocity and substrate type is rejected.

In conclusion, the next step in evaluating microhabitat preference of benthic invertebrates in

streams and rivers in Kenya should focus more on understanding the inter-correlation between

environmental parameters other than substrate, current and feeding strategies to evaluate the

importance of a single parameter for the biota. In addition, future research should focus on

lower taxonomic resolution to obtain useful ecological information; niche concept works best

on species level, although this thesis showed that ecological allocations are also possible at

family level. Since BMI are widely used as bioindicators of freshwater ecosystems in Kenya

(M’Erimba et al., 2014; Minaya et al., 2013), data on benthic invertebrates ecology, i.e.

microhabitat preferences presented here, represent the necessary background for further

research and conservation practices in Gura River. Finally, this thesis contributes to exploring

riverine life of Gura River and the observations add to the literature on microhabitat preferences

of benthic macroinvertebrates.

52

5.0 References

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Limnologica, 52, 11–19.

Ashmore and Church . (2001). The impact of climate change on rivers and river processes in

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Basaguren, A., Ferreira, V., Larrañaga, A., Graça, M. A. S., Gulis, V., Pozo, J., & Elosegi, A.

(2014). The effects of eucalypt plantations on plant litter decomposition and

macroinvertebrate communities in Iberian streams. Forest Ecology and Management, 335,

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6.0 Appendix

Appendix 1: Manual for substrate sampling

River: Code/Name: / Date/Time

Coordinates: Conductivity: pH O2: Temp:

Site No.

Unit-

No. Substrate

Depth

[cm]

Current velocity [m/s]

Distance from

shore [m]

Notes

Near

bottom

In 40% of

depth

Water-

surface

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Appendix 2: Taxa distribution

Table 3.2: Macroinvertebrate taxa recorded at the seven sampling sites of the Gura River,

17th to 20th October 2018.

Order Family G1

(n=13)

G2

(n=13)

G3

(n=11)

G4

(n=8)

G5

(n=8)

G6

(n=11)

S1

(n=10)

Bivalvia Corbiculidae 0 0 0 0 0 0 7

Sphaeriidae 121 0 2 0 0 0 0

Coleoptera Dytiscidae 0 1 0 0 0 0 1

Elmidae 58 40 25 14 50 44 92

59

Psephenidae 0 0 0 14 27 5 7

Scirtidae 325 138 77 24 0 6 2

Decapoda Potamonautidae 0 5 6 26 7 2 5

Diptera Chironomidae 139 30 12 12 28 13 10

Chironomus 723 0 0 0 0 0 0

Muscidae 1 0 0 0 7 2 2

Simuliidae 151 62 54 42 11 2 19

Tabanidae 0 0 0 0 5 0 0

Tipulidae 11 6 2 14 61 7 0

Ephemeroptera Baetidae 413 227 381 1,105 376 274 279

Caenidae 6 6 15 20 14 7 13

Dicercomyzidae 0 3 0 0 28 13 0

Heptageniidae 5 425 210 210 398 47 42

Leptophlebiidae 10 0 2 26 20 7 4

Oligoneuridae 0 0 28 4 0 0 0

Prosopistomatidae 0 3 0 2 0 0 0

Tricorythidae 15 6 38 130 13 16 45

Hemiptera Corixidae 0 0 0 0 0 0 10

Gerridae 0 1 0 0 0 0 16

Naucoridae 0 0 0 0 0 12 26

Veliidae 0 0 0 0 0 0 2

Lepidoptera Pyralidae 1 0 0 0 7 15 9

Odonata Aeshnidae 0 3 0 4 0 0 0

Coenagrionidae 1 0 0 0 0 20 119

Libellulidae 0 1 3 10 5 0 12

Oligochaeta Tubificidae 171 0 6 0 18 0 6

Plecoptera Perlidae 0 31 6 0 7 2 0

Trichoptera Hydropsychidae 238 60 183 246 169 508 401

Lepidostomatidae 18 4 0 10 9 7 3

Philopotamidae 0 55 57 8 0 10 7

Turbellaria Planaria 36 9 18 2 36 12 18

Total (ind/m2) 2,435 1,102 1,116 1,926 1,294 1,049 1,151

Taxa no. 20 22 20 21 22 23 27

Legend: G1= Magura site; G2 = Kigumo site; G3 = Gitwiga site; G4 = Kagere site; G5 =

Tambaya site; G6 = Gura site before its confluence with Sagana River and S1 = Sagana above

the confluence.

60

Appendix 3: Hydropsychidae types (A, B, D, G& J), source: BOKU

A A

B B

D D

61

Appendix 4: Hydropsychidae types distribution

G G

J J

62

Appendix 5: Photos of the rare taxa, source: BOKU

3 3

0

20

40

60


Substrate

Abun

da

nce/H

yd

rop

sych

ida

e T

yp

e D

Abundance/Hydropsychidae Type D for each substrate type

19 6

0

20

40

60


Substrate

Abu

nda

nce/H

yd

rop

sychid

ae T

yp

e G

Abundance/Hydropsychidae Type G for each substrate type

1 64

0

20

40

60


Substrate

Abun

da

nce/H

yd

rop

sych

ida

e T

yp

e K

Abundance/Hydropsychidae Type K for each substrate type

Dicercomyzidae

Prosopistomatidae

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