Post on 19-Feb-2022
i
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)
ii
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.
iii
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.
iv
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
v
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
vi
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
vii
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
1
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
2
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
3
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
4
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.
5
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
6
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
7
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,
8
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
9
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.
10
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
11
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
12
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
13
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 Mesolithal 0.21 13 19/10/2018
Magura Mesolithal 0.48 16 19/10/2018
Magura Mesolithal 0.06 12 19/10/2018
Magura Mesolithal 0.45 12 19/10/2018
Magura Mesolithal 0.51 22 19/10/2018
Magura Macrolithal 0.48 14 19/10/2018
Magura Macrolithal 0.48 14 19/10/2018
Magura Macrolithal 0.45 24 19/10/2018
Magura Macrolithal 0.25 16 19/10/2018
Kigumo Mesolithal 0.32 35 18/10/2018
Kigumo Mesolithal 0.46 37 18/10/2018
Kigumo Mesolithal 0.46 38 18/10/2018
Kigumo Macrolithal 0.70 42 18/10/2018
Kigumo Macrolithal 0.54 50 18/10/2018
Kigumo Macrolithal 0.08 40 18/10/2018
Kigumo Macrolithal 0.28 38 18/10/2018
Kigumo Macrolithal 0.62 37 18/10/2018
Kigumo Macrolithal 0.34 30 18/10/2018
Kigumo Macrolithal 0.14 30 18/10/2018
Kigumo Macrolithal 0.71 42 18/10/2018
Kigumo Macrolithal 0.53 28 18/10/2018
Kigumo Macrolithal 1.12 31 18/10/2018
Gitwiga Microlithal 0.1 10 18/10/2018
Gitwiga Microlithal 0.07 7 18/10/2018
Gitwiga Microlithal 0.08 6 18/10/2018
Gitwiga Microlithal 0.45 10 18/10/2018
Gitwiga Macrolithal 0.31 16 18/10/2018
Gitwiga Macrolithal 0.41 12 18/10/2018
Gitwiga Macrolithal 0.86 26 18/10/2018
Gitwiga Macrolithal 0.95 25 18/10/2018
Gitwiga Macrolithal 0.84 21 18/10/2018
Gitwiga Macrolithal 0.05 21 18/10/2018
Gitwiga Macrolithal 0.44 8 18/10/2018
Kagere Mesolithal 0.10 32 20/10/2018
Kagere Mesolithal 0.27 22 20/10/2018
Kagere Mesolithal 0.10 15 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
Kagere Microlithal 0.56 20 20/10/2018
Kagere Microlithal 0.65 21 20/10/2018
Tambaya Microlithal 0.21 5 20/10/2018
Tambaya Microlithal 0.18 5 20/10/2018
Tambaya Microlithal 0.57 21 20/10/2018
Tambaya Microlithal 0.67 14 20/10/2018
Tambaya Microlithal 0.56 10 20/10/2018
Tambaya Mesolithal 0.82 30 20/10/2018
Tambaya Mesolithal 0.70 32 20/10/2018
Tambaya Mesolithal 0.73 32 20/10/2018
Gura DS Mesolithal 0.64 50 17/10/2018
Gura DS Mesolithal 0.70 50 17/10/2018
Gura DS Mesolithal 0.63 50 17/10/2018
Gura DS Mesolithal 0.66 51 17/10/2018
Gura DS Mesolithal 0.63 47 17/10/2018
Gura DS Macrolithal 0.53 43 17/10/2018
Gura DS Macrolithal 0.63 43 17/10/2018
Gura DS Macrolithal 0.50 44 17/10/2018
Gura DS Macrophytes 0.30 29 17/10/2018
Gura DS Macrophytes 0.22 29 17/10/2018
Gura DS Macrophytes 0.27 27 17/10/2018
Sagana US Mesolithal 0.63 33 17/10/2018
Sagana US Mesolithal 0.52 30 17/10/2018
Sagana US Mesolithal 0.89 44 17/10/2018
Sagana US Mesolithal 0.80 33 17/10/2018
Sagana US Macrolithal 0.66 20 17/10/2018
Sagana US Macrolithal 0.62 31 17/10/2018
Sagana US Macrolithal 0.54 30 17/10/2018
Sagana US Macrophytes 0.0 47 17/10/2018
Sagana US Macrophytes 0.25 19 17/10/2018
Sagana US Macrophytes 0.48 22 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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Psammal Microlithal Mesolithal Macrolithal Macrophytes
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
Microlithal Mesolithal Macrolithal
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
Microlithal Mesolithal Macrolithal
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
Allan, D. J., & Arbor, A. (2014). The Influence of Land Use on Stream Ecosystems. Annual
Review of Ecology and Systematics, 35, 257–284.
Aschalew, L., & Moog, O. (2015). Benthic macroinvertebrates based new biotic score
“ETHbios” for assessing ecological conditions of highland streams and rivers in Ethiopia.
Limnologica, 52, 11–19.
Ashmore and Church . (2001). The impact of climate change on rivers and river processes in
Canada. Bulletin of the Geological Survey of Canada (Vol. 1523).
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,
129–138.
Bauernfeind, E., & Moog, O. (2000). Mayflies (Insecta: Ephemeroptera) and the assessment of
ecological integrity : a methodological approach. Hydrobiologia, 422/423(1902), 71–83.
Bonada, N., Dolédec, S., & Statzner, B. (2007). Taxonomic and biological trait differences of
stream macroinvertebrate communities between mediterranean and temperate regions:
Implications for future climatic scenarios. Global Change Biology, 13(8), 1658–1671.
Bray, J. R., & Curtis, J. T. (2006). An Ordination of the Upland Forest Communities of
Southern Wisconsin. Ecological Monographs, 27(4), 325–349.
Bunn, S. E., & Arthington, A. H. (2002). Basic principles and ecological consequences of
altered flow regimes for aquatic biodiversity. Environmental Management, 30(4), 492–
507.
Cairns, J., & Pratt, J. R. (1993). A history of biological monitoring using benthic
macroinvertebrates. Freshwater Biomonitoring and Benthic Macroinvertebrates, 10–27.
Carpenter, S. R., Stanley, E. H., & Vander Zanden, M. J. (2011). State of the World’s
Freshwater Ecosystems: Physical, Chemical, and Biological Changes. Annu. Rev.
Environ. Resour.36, 75–99.
Cummins, K. W., & Klug, M. J. (2003). Feeding Ecology of Stream Invertebrates. Annual
Review of Ecology and Systematics, 10(1), 147–172.
53
Cummins, K. W., Merritt, R. W., & Andrade, P. C. N. (2005). The use of invertebrate
functional groups to characterize ecosystem attributes in selected streams and rivers in
south Brazil. Studies on Neotropical Fauna and Environment, 40(1), 69–89.
Darrow, P. O., & Pruess, K. P. (1989). Effects of substrate on density of aquatic insects in a
southeast Nebraska (USA) stream. Transactions Of The Nebraska Academy Of Sciences,
17, 19–22.
Dewson, Z. S., James, A. B. W., & Death, R. G. (2007). A review of the consequences of
decreased flow for instream habitat and macroinvertebrates. Journal of the North
American Benthological Society, 26(3), 401–415.
Ditsche-kuru, P. (2009). Influence of the surface roughness of hard substrates on the attachment
of selected running water macrozoobenthos.
Dudgeon, D., & Wu, K. K. Y. (2016). Leaf litter in a tropical stream: food or substrate for
macroinvertebrates? Fundamental and Applied Limnology, 146(1), 65–82.
Dufrêne, M., & Legendre, P. (1997). Species assemblages and indicator species: The need for
a flexible asymmetrical approach. Ecological Monographs, 67(3), 345–366.
Eriksen, B. Y. C. H. (1963). Respiratory regulation in Ephemera simulans walker and
hexagenia limb. Journal of Experimental Biology, 455–467.
Gao, X., Niu, C., Chen, Y., & Yin, X. (2014). Spatial heterogeneity of stream environmental
conditions and macroinvertebrates community in an agriculture dominated watershed and
management implications for a large river (the Liao River, China) basin. Environmental
Monitoring and Assessment, 186(4), 2375–2391.
Geertsma, R., Wilschut, L. I., & Kauffman, J. H. (2011). Baseline Review of the Upper Tana ,
Kenya. Agriculture Ecosystems & Environment - Agr Ecosyst Environ, (2009).
Gerber, A., & Gabriel, M. (2002). Aquatic Invertebrates of South African Rivers - Illustrations.
Graça, M. A. S., Pozo, J., Canhoto, C., & Elosegi, A. (2005). Effects of Eucalyptus Plantations
on Detritus, Decomposers, and Detritivores in Streams. The Scientific World Journal, 2,
1173–1185.
Graf, W. L. (2005). Geomorphology and American dams: The scientific, social, and economic
context. Geomorphology, 71(1–2), 3–26.
54
Hering, D., Schmidt-Kloiber, A., Murphy, J., Lücke, S., Zamora-Muñoz, C., López-Rodríguez,
M. J., Huber, T., Graf, W. (2009). Potential impact of climate change on aquatic insects:
A sensitivity analysis for European caddisflies (Trichoptera) based on distribution patterns
and ecological preferences. Aquatic Sciences, 71(1), 3–14.
Graf, W., Hartmann. A., Leitner, P., Schmidt-Kloiber, A., Shwarzinger, I. (2017). Sustainable
Highland Rivers Management in Ethiopia Manual on Multi-Habitat-Sampling of Benthic
Invertebrates from wadeable rivers in Ethiopia.
Harding, J. S., Young, R. G., Hayes, J. W., Shearer, K. A., & Stark, J. D. (1999). Changes in
agricultural intensity and river health along a river continuum. Freshwater Biology, 42(2),
345–357.
Kasangaki, A., Chapman, L. J., & Balirwa, J. (2008). Land use and the ecology of benthic
macroinvertebrate assemblages of high-altitude rainforest streams in Uganda. Freshwater
Biology, 53(4), 681–697.
KFS. (2010). Aberdare Forest Reserve Management Plan 2010 - 2019.
Korte, T. (2010). Current and substrate preferences of benthic invertebrates in the rivers of the
Hindu Kush-Himalayan region as indicators of hydromorphological degradation.
Hydrobiologia, 651(1), 77–91
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric
hypothesis. Psychometrika, 29(1), 1–27.
Kubosova, K., Brabec, K., Jarkovsky, J., & Syrovatka, V. (2010). Selection of indicative taxa
for river habitats: A case study on benthic macroinvertebrates using indicator species
analysis and the random forest methods. Hydrobiologia, 651(1), 101–114.
Lamouroux, N., Dolédec, S., & Gayraud, S. (2005). Biological traits of stream
macroinvertebrate communities: effects of microhabitat, reach, and basin filters. Journal
of the North American Benthological Society, 23(3), 449–466.
Lamp, W. O., & Britt, N. W. (1981). Resource partitioning by two species of stream mayflies
(Ephemeroptera: Heptageniidae). The Great Lakes Entomologist, 14(3), 151–157.
Lauzon, M., & Harper, P. P. (2008). Life history and production of the stream-dwelling mayfly
Habrophlebia vibrans Needham (Ephemeroptera; Leptophlebiidae) . Canadian Journal of
Zoology, 64(9), 2038–2045.
55
Leitner, P., Hauer, C., Ofenböck, T., Pletterbauer, F., Schmidt-Kloiber, A., & Graf, W. (2015).
Fine sediment deposition affects biodiversity and density of benthic macroinvertebrates:
A case study in the freshwater pearl mussel river Waldaist (Upper Austria). Limnologica,
50, 54–57.
Masese. F. O., Muchiri. M., Raburu P.O. (2009). Macroinvertebrate assemblages as biological
indicators of water quality in Macroinvertebrate assemblages as biological indicators of
water quality in the Moiben River , Kenya, 14–26.
M’Erimba, M. C. M., Mathooko, J. M., Karanja, H. T., & Mbaka, J. G. (2014). Monitoring
water and habitat quality in six rivers draining the Mt . Kenya and Aberdare Catchments
using Macroinvertebrates and Qualitative Habitat Scoring, 14(2073), 81–104.
Masikini, R., Tunu, L., & Chicharo, L. (2018). Ecohydrology & Hydrobiology Evaluation of
ecohydrological variables in relation to spatial and temporal variability of
macroinvertebrate assemblages along the Zigi River – Tanzania. Integrative Medicine
Research, 18(2), 130–141.
Mathooko, J. M. (2001). Disturbance of a Kenya rift valley stream by the daily activities of
local people and their livestock. Hydrobiologia, 458, 131–139.
Mathooko, J. M., & Mavuti, K. M. (1992). Composition and seasonality of benthic
invertebrates, and drift in the Naro Moru River, Kenya. Hydrobiologia, 232(1), 47–56.
Miliša, M., Habdija, I., Primc-Habdija, B., Radanović, I., & Kepčija, R. M. (2006). The role of
flow velocity in the vertical distribution of particulate organic matter on moss-covered
travertine barriers of the Plitvice Lakes (Croatia). Hydrobiologia, 553(1), 231–243.
Minaya, V., McClain, M. E., Moog, O., Omengo, F., & Singer, G. A. (2013). Scale-dependent
effects of rural activities on benthic macroinvertebrates and physico-chemical
characteristics in headwater streams of the Mara River, Kenya. Ecological Indicators, 32,
116–122.
Moreyra, A. K., & Padovesi-Fonseca, C. (2015). Environmental effects and urban impacts on
aquatic macroinvertebrates in a stream of central Brazilian Cerrado. Sustainable Water
Resources Management, 1(2), 125–136.
Nyingi, D. W., Gichuki, N., & Ogada, M. O. (2013). Freshwater Ecology of Kenyan Highlands
and Lowlands. Developments in Earth Surface Processes (1st ed., Vol. 16). Elsevier B.V.
56
Ongwenyi, G. S., Kithiia, S. M., & Denga, F. 0. (1993). An overview of soil erosion and
sedimentation problems in Kenya. Sediment Problems: Strategies for Monitoring
Prediction and Control, (217), 216–221.
Oyediran, A. G., Ebeniro, L. A., Ayodele, O. P., Olaoti, K. S., & Nwoga, P. T. (2017).
Abundance and distribution of macro-benthic invertebrates as bio-indicators of water
quality in Ikwo River , Ishiagu .. International Journal of Fauna and Biological Studies,
4(2), 43–46.
Pardo, I., & Armitage, P. D. (1997). Species assemblages as descriptors of mesohabitats.
Hydrobiologia, 344(1), 111–128.
Ramírez, A., & Gutiérrez-Fonseca, P. E. (2014). Functional feeding groups of aquatic insect
families in Latin America: A critical analysis and review of existing literature. Revista de
Biologia Tropical, 62, 155–167.
Rempel, L. L., Richardson, J. S., & Healey, M. C. (2000). Macroinvertebrate community
structure along gradients of hydraulic and sedimentary conditions in a large gravel-bed
river. Freshwater Biology, 45(1), 57–73.
Sarr, A. B., Benetti, C. J., Fernández-Díaz, M., & Garrido, J. (2013). The microhabitat
preferences of water beetles in four rivers in Ourense province, Northwest Spain.
Limnetica, 32(1), 1–10.
Schröder, M., Kiesel, J., Schattmann, A., Jähnig, S. C., Lorenz, A. W., Kramm, S., … Hering,
D. (2013). Substratum associations of benthic invertebrates in lowland and mountain
streams. Ecological Indicators, 30, 178–189.
Stals, R., and Moor, I.J. (2007). Guides to Freshwater Invertebrates of Southern Africa. Water
Research Commission.
Solomon. A. (2014). Ecological impacts of siltation, the case of three Ethiopian Rivers.
Usaid. (2017). Climate Risk Profile, (June).
Vilenica, M., Brigić, A., Sartori, M., & Mihaljević, Z. (2018). Microhabitat selection and
distribution of functional feeding groups of mayfly larvae (Ephemeroptera) in lotic karst
habitats. Knowledge & Management of Aquatic Ecosystems, (419), 17.
Warfe, D. M. (2012). Habitat complexity : approaches and future directions, 1–17.
57
Yam, R. S. W. (2015). First record of the genus Prosopistoma Latreille, 1833 (Ephemeroptera,
Prosopistomatidae) in Taiwan. ZooKeys, 156(473), 147–156.
58
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
Microlithal Mesolithal Macrolithal
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
Microlithal Mesolithal Macrolithal
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
Microlithal Mesolithal Macrolithal
Substrate
Abun
da
nce/H
yd
rop
sych
ida
e T
yp
e K
Abundance/Hydropsychidae Type K for each substrate type
Dicercomyzidae
Prosopistomatidae