Quantification of Human Impacts on Fish Assemblages in the Upper Volta Catchment, Burkina Faso

University of Natural Resources and Life Sciences, Vienna Universität für Bodenkultur Wien

Department of Water, Atmosphere and Environment Department Wasser‐Atmosphäre‐Umwelt

Institute of Hydrobiology and Aquatic Ecosystem Management Institut für Hydrobiologie und Gewässermanagement

Quantification of human impacts on fish assemblages in the Upper Volta catchment, Burkina Faso

A thesis submitted to the University of Natural Resources and Life Sciences, Vienna, Austria for the award of the „Master of Science“ (MSc) composed by Sebastian Stranzl

ACADEMIC SUPERVISORS Ao.Univ.Prof. Dipl.‐Ing. Dr.nat.techn. Stefan SCHMUTZ Dipl.‐Ing. Dr. nat.techn. Andreas H. MELCHER

University of Natural Resources and Life Sciences, Vienna, Austria Department of Water, Atmosphere and Environment Institute of Hydrobiology and Aquatic Ecosystem Management

Dr. Adama OUEDA

Université de Ouagadougou, Ouagadougou, Burkina Faso Laboratoire de Biologie et Ecologie Animale Vienna, May 2014

Acknowledgements I want to thank the OEAD for funding SUSFISH (Sustainable Management of Water and Fish Resources in Burkina Faso), a great APPEAR (Austrian Partnership Programme in Higher Education & Research for Development) program, in the frame of which I’ve been writing this thesis. Financially I’ve got a great support for my data collection in Burkina Faso from (1) the scholarship for short‐term scientific research work abroad (KUWI), managed by the center for international relations (ZIB) and funded by the University of Natural Resources and Applied Life Sciences, Vienna; and (2) the from the Burkinabe SUSFISH partners who organized our accommodation. Thanks to the local SUSFISH coordinator in Burkina Faso, Dr. Raymond Ouedraogo (Ministry of Research and Education, Ouagadougou) for support in all matters and friendship in Burkina Faso. The work could not have been made without the great cooperation between BOKU and the University of Ouagadougou and the organization talent of Dr. Adama Oueda. Many thanks to my supervisors Prof. Stefan Schmutz and Dr. Andreas Melcher. Dr. Melcher also coordinates the SUSFISH program and friendly supported me in all matters of writing this thesis. Prof. Moog ensured good mood during his stay in Burkina Faso and helped me in the final phase of my thesis. Thanks to Werner Macho for introducing me into and helping me with Quantum GIS. I want to highlight the cooperation and support of all our Burkinabe partners and colleagues who made this trip unforgettable. Tobias Musschoot, Gert Boden and Dr. Emmanuel Vreven were helping us with species determination. I want to thank my family for all the support they gave me all through my life. I also want to thank Theresa Theuretzbacher for her patience and motivation. Cooperation The fieldwork in Burkina Faso was conducted together with (1) the Austrian Applied Limnology students Paul Meulenbroek (fish) Daniel Trauner, and Thomas Koblinger (invertebrates), (2) with Burkinabe LAEB (Laboratory of Animal Ecology and Biology) students and (3) local fishermen, especially Noufou Bonkoungou aka DumDum who accompanied us on every field trip. I want to thank them for friendship and cooperation. Additionally to field work also species determination, analyses and writing of the introduction and methods was done in close cooperation with Paul Meulenbroek and Mano Komandan. Parts of this work (especially the chapters introduction and methods) were initially composed together with Paul Meulenbroek and Mano Komandan.

2 Abstract

Fish is an important protein source for the population of Burkina Faso. However, overfishing, fragmentation, loss of habitat, agriculture and other human pressures decline the fish population’s diversity and biomass in the Burkinabe water bodies. Biological indices are essential for the assessment of water and ecosystem health and sustainable management to assure food security and healthy water bodies.

The goal of this thesis is to characterize fish assemblages in the sampling area, to find out about human pressures on the fish community, to implement a fish, habitat and pressures database and to find species and metrics which react on pressures.

The six sampling areas in the Nakambe catchment between the reservoir of Loumbila, North of Ouagadougou and the border of Ghana are exposed to pressure intensities. Altogether, 37 sites and 137 habitats were sampled.

Each habitat was fished electrically and with a cast net. On site, adjacent landuse, stressors and physicochemical parameters were noted. With GIS, land use was refined, and migration barriers were identified.

Most common families are Cichlidae and Cyprinidae, which together make out more than 50% of all caught individuals, whereas Anabantidae and Citharinidae are heavily endangered.

The tested fish metrics react on the pressure intensity. This means, with increasing pressure, the fish stock decreases. Especially the relative abundance of Mormyridae show a distinct drop with the pressure intensity, while Cichlidae and Cyprinidae increase. Other authors (Hugueny et al. 1996; Anne, Lelek, and Tobias 1994) show the same trends.

We also found some sentinel taxa like Auchenoglanis and Hydrocynus, which were only caught in low‐pressure sites.

This work is a basis for a fish based assessment method in Burkina Faso, which will be implemented by Burkinabe students in the frame of the SUSFISH project, helping managers to protect their waters in West Africa.

Keywords: Freshwater, Fish, Indicator taxa, Human impacts, Cast net, Electric fishing, Burkina Faso, West Africa

3 Abstract

Fische sind eine wichtige Eiweißquelle für Burkina Fasos Bevölkerung. Überfischung, Fragmentierung, Lebensraumverlust, Landwirtschaft und andere menschliche Einflüsse führten zu einer Abnahme der Fischbiomasse und ‐biodiversität in Burkina Fasos Gewässern. Biologische Bewertungsmethoden sind wichtig für die Erhebung der Qualität von Wasser und Ökosystemen um die Ernährungssicherheit und gesunde Gewässer zu gewährleisten.

Das Ziel dieser Arbeit ist eine Charakterisierung der Fischzusammensetzung im Untersuchungsgebiet und die Identifizierung von Stressoren und Zeigerarten, sowie die Implementierung einer Fisch, Habitat und Belastungsdatenbank.

Die sechs Probengebiete im Nakambe Einzugsgebiet zwischen Loumbila nördlich von Ouagadougou und der ghanaischen Grenze sind unterschiedlichen Belastungsintensitäten ausgesetzt. Es wurden 37 Probestellen und 137 Habitate beprobt.

Jedes Habitat wurde elektrisch und mit einem Wurfnetz befischt. Vor Ort wurden die angrenzende Landnutzung, Stressoren und physikalisch‐chemische Parameter erhoben. Mithilfe von GIS wurden die Landnutzung und das Vorkommen von Migrationsbarrieren überprüft.

Die häufigsten Familien waren Cichlidae und Cyprinidae die gemeinsam über 50% der gefangenen Individuen ausmachen. Anabantidae und Citharinidae sind stark gefährdet.

Die getesteten Fisch‐metrics reagieren auf die Belastungsintensität, mit zunehmender Belastung nimmt auch der Fischbestand ab. Besonders Mormyridae haben eine starke Abnahme mit der Belastungsintensität, während Cichlidae und Cyprinidae zunehmen. Andere Autoren (Hugueny et al. 1996; Anne, Lelek, and Tobias 1994) zeigen dieselben Trends.

Wir fanden auch einige Zeigertaxa wie Auchenoglanis und Hydrocynus die nur in wenig belasteten Probestellen gefangen wurden.

Diese Arbeit kann eine Basis für einen Fischindex für Burkina Faso darstellen, der im Rahmen des SUSFISH Projekts von Studierenden aus Burkina Faso erstellt wird um das Management der Gewässer in West Afrika zu erleichtern.

Stichwörter: Süßwasser, Fisch, Indikator Taxa, menschliche Belastungen, Wurfnetz, Elektrofischen, Burkina Faso, West Afrika

4 5 Table of content

1. INTRODUCTION ...... 8

BURKINA FASO ...... 9 CATCHMENTS, HYDROLOGY ...... 9 ANTHROPOGENIC PRESSURES IN BURKINA FASO ...... 12 FISH AS BIOINDICATORS ...... 12 SPECIFIC AFRICAN PROBLEMS ...... 13 THE SUSFISH PROJECT ...... 13

2. METHODS ...... 16

STUDY AREA, SAMPLING SITE SELECTION AND WORKFLOW ...... 16 FIELD SAMPLING – ENVIRONMENTAL DATA ...... 20 FIELD SAMPLING: FISH DATA ...... 24 SPECIES DETERMINATION ...... 25 DATA HANDLING AND STATISTICAL ANALYSIS ...... 25 METRIC CALCULATIONS ...... 29

3. RESULTS ...... 32

ENVIRONMENTAL PARAMETERS ...... 32 PRESSURES ...... 35 PRESSURES PER SAMPLING AREA ...... 37 FISH ...... 39 FISHES PER SAMPLING AREA ...... 41 RELATION OF FISH AND ENVIRONMENT ...... 45 LENGTH‐WEIGHT REGRESSIONS ...... 51

4. DISCUSSION ...... 55

ENVIRONMENTAL PARAMETERS ...... 55 PRESSURES ...... 56 PRESSURES PER SAMPLING AREA ...... 58 FISH ...... 59 FISHES PER SAMPLING AREA ...... 60 RELATION OF FISH AND ENVIRONMENT ...... 61 LENGTH‐WEIGHT REGRESSION ...... 64

5. CONCLUSION ...... 65

OUTLOOK ...... 66

6. REFERENCES ...... 67

LIST OF FIGURES ...... 73

6 LIST OF TABLES ...... 75

LIST OF ABBREVIATIONS ...... 76

7. APPENDIX ...... 77

ENVIRONMENTAL PARAMETERS ...... 77 PRESSURES ...... 81 SPECIES PROFILES ...... 82 LENGTH FREQUENCY GRAPHS ...... 86 ASSESSMENT SHEETS ...... 90

1. Introduction Freshwater is scarce in Burkina Faso (UNDP 2013), and water bodies are stressed by the needs of the society. As in the rest of the world, the most desired river goods are fresh water, hydroelectric power, and fish (Brismar 2002). Fresh water bodies are degraded worldwide as a result of human activities. Often it is a result of poor management that makes this resource rare or hardly available. Overexploitation, water pollution, flow modification, habitat degradation, fragmentation and exotic species invasion threaten the water security and the freshwater biodiversity (Baron et al. 2002; Dudgeon et al. 2006; Stendera et al. 2012; Vörösmarty et al. 2010). Frequently aquatic ecosystems are exposed to multiple human pressures (Baron et al. 2002). For instance Schinegger et al. (2012) found in a study across 14 European countries that 85% of all sampling sites were affected by connectivity pressures, 59% by poor water quality, 41% by hydrological pressures and 39% by morphological pressures. Similar results are seen by other authors (Degerman et al. 2007; Tockner, Uehlinger, and Robinson 2009).

Aarts, Van Den Brink, and Nienhuis (2004) state that the chemical water quality in rivers in the EU and USA has improved in the last decades but that due to habitat loss and connectivity problems the fish fauna has not recovered accordingly. Often occurring pressures on water bodies in Africa are (1) water shortage, water abstraction, (2) pollution (suspended solids, solid wastes, organic), (3) damming, (4) deforestation and (5) overfishing (Payet and Obura 2004; Twumasi and Merem 2007; Amisigo 2005).

The remarkable population growth in Africa is accompanied by urbanization, increased industrial and agricultural activities. These changes brought a severe increase in the volume of consumed water and of discharges and a wider and wider variation of pollutant types flowing into the rivers, with adverse effects on the water quality and river biota (Biney et al. 1987). Water abstraction can reach excessive extends as seen in the case of river Niger which has lost 40% to 60% of its annual discharge since 1944 (Twumasi and Merem 2007) or the shrinking of lake Tchad to a 20th of its size since 1963 (Huq, Reid, and Murray 2014). Pollution sources are amongst others untreated sewage, chemical discharges, petroleum leaks, dumping from mines and pits, and agricultural chemicals washed in from fields. Additionally water hyacinth (Eichornia crassipes) can deteriorate water quality: decaying weed mats can cause eutrophication (Serageldin 2000).

De Fraiture et al. (2014) describe the agricultural usage along Burkinabe reservoirs: In many cases the producers along the reservoir banks are more productive than the downstream farmers. However the uncontrolled proliferation with pumps for the upstream cultivation creates some environmental problems like over abstraction, reservoir degradation and pollution with agricultural chemicals. Hyrkäs and Pernholm (2007) observed that the poor agronomic practices around reservoirs in Ouagadougou with large amounts of fertilizer and

8 pesticides used improperly in vegetable production, make polluted runoff very likely. Additionally, pollution from horticulture is exceeded at many reservoirs.

Huq, Reid, and Murray (2014) propose forest covers as a good indicator for watershed degeneration because of its importance for maintaining water quality and moderating the water flow. The Volta Basin has lost almost 97% of its original forest cover (Revenga et al. 1998; Amisigo 2005).

Declines in biodiversity are much greater in freshwater than in terrestrial ecosystems: freshwater ecosystems are among the most endangered ecosystems in the World (Sala et al. 2000). Dudgeon et al. (2006) pointed out how rare these ecosystems are: only 0.01% of the World’s total water and only about 0.8% of the Earth’s surface is freshwater, however, this small fraction of water supports about 6% of all described species.

Burkina Faso Burkina Faso is a Sub‐Sahelian landlocked country in the central part of West Africa (Ouedraogo 2010). It is one of the poorest and least developed countries in the world, with the fifth last ranking in the human development index (UNDP 2013), showing the urgent need for sustainable development.

This is on the one hand challenged by natural conditions like long dry seasons with chronic water scarcity and frequent floods and droughts. On the other hand there are several socioeconomic constrains: The current population of 17.5 million people has grown six fold in the past hundred years and is still growing fast (UN DESA 2012). Moreover almost half of the people is at poverty level, only one third of the children complete primary school (INSD 2009) and more than 40% of the five year old children suffer from chronic malnutrition. As a consequence food security is a central goal in the national development policies and strategies (DGPSA 2007).

Catchments, hydrology Burkina Faso is drained by three large river basins. The largest and most important is the Volta basin with over 120,000 km² (64% of the Nation’s area) followed by the Niger (30%), and the Comoé (6%). The Volta basin has three major rivers in Burkina, the Mouhoun, Nakambe and Nazinon (former Black‐, Red‐, and White Volta) which finally all flow into Lake Volta in Ghana (Ouedraogo 2010). Figure 1.1 shows the main catchments and the most important rivers in Burkina Faso.

Figure 1.1: Important rivers and catchments in Burkina Faso. Adapted from Badiel (2014); Cecchi et al. (2009). Burkina Faso has a tropical climate with two seasons. In the rainy season, which lasts approximately from May/June to September, the country receives between 500 (north) and 1000 mm (south) of rainfall per year (Figure 1.2). In the dry season the high temperature (up to 45°C) results in massive water loss of about 2,000 mm per year due to high evaporation rates (Baijot, Moreau, and Bouda 1994; Manson and Knight 2011; Ouedraogo 2010).

Figure 1.2: Climatic regions of Burkina Faso and climate charts for Dori, Ouagadougou and Bobo‐ Dioulasso. Adapted from Yahmed (2005); climate data from klimadiagramme (2014).

10 As a reaction to severe droughts more than 1400 reservoirs of 1 to 25,000 ha surface area were built since 1950 which are mainly used for agriculture, livestock farming and fisheries (Ouedraogo 2010), making Burkina Faso to a leading country in water resources development in Africa. Figure 1.3 shows all reservoirs in the (DGRE 2001) census. 86,6% of all built reservoirs are smaller than 1 million cubic meter (Mm³), whereas the two largest ones (Bagre and Kompienga) contribute for more than 60% of the countrywide capacity (Cecchi et al. 2009). The construction of reservoirs increased fishery landings by 15 times since 1950, employing more than 30000 fishermen and several thousand women processing and selling the fish (Ouedraogo 2010). Therefore, fish has become an important protein source (FAO‐ MAFAP 2011). On the other hand, reservoirs are responsible for the spread of water‐borne and water‐related diseases (Boelee 2009; UNEP 2010).

Figure 1.3: Map of reservoirs in Burkina Faso. Units in million cubic meter (Mm³). Source: Cecchi et al. (2009), adapted. The need to further expand water provisions has driven plans for multi‐million dollar dam constructions near wetlands in the southwest of Burkina Faso in the Samandéni and Ouessa regions. The planned series of dams is expected to cost approximately US$150 million and combined could deliver five billion cubic meters of water (UNOCHA 2010).

Anthropogenic pressures in Burkina Faso Human pressures and loss of habitat led to a decline of the fish population in terms of total population, biodiversity and average fish size in Burkina Faso (Melcher, Ouedraogo, and Schmutz 2011). High water demands and poor management led to an overuse of surface waters. The rising water demand combined with elevated siltation rates due to change in land use deplete reservoir volumes to a point where some reservoirs could disappear within the next 25 years (Melcher 2011; Ouedraogo 2010). Especially in urban areas pollution and eutrophication threatens water bodies (Haro et al. 2013; Ouedraogo 2010) and channelization and flood protection alter the morphology. In areas with livestock and agricultural activities water is abstracted from the water bodies while sediments, nutrients, herbicides and pesticides are washed into the water (Cecchi et al. 2007; Hyrkäs and Pernholm 2007; Ouedraogo 2010). Sediment input can be partially explained by a 97% loss of the original forest cover in the Volta catchment (Amisigo 2005).

According to DGIRH (2001) and Boelee (2009), more than 90% of the annual discharge of the Nakambe basin is held by dams. Many reservoirs are not passable for fish due to lacking or poorly planned fish ladders (Ouedraogo 2010). Moreover, large reservoirs have severe downstream effects like altered flow and sediment regime, water parameters, erosion and disturbed river biota (Welcomme 1989). Laë (1994); Welcomme (1989); Petts (2007) and Marmulla (2001) reported massive decreased recruitment and fish catches downstream of large dams in Africa due to missing flood pulses. Additionally, overfishing affects the fish communities (Ouedraogo 2010).

Fish as bioindicators Native fish species are well adapted to their environment to an extend that some species use the entire river continuum from the headwaters to the estuaries within their life cycle (Welcomme 1989). Because fish cannot easily migrate between aquatic systems they have to adapt to changing conditions or die. Therefore they are potential indicators of environmental changes and trends in general aquatic biodiversity since they interact with other aquatic organisms via predation, nutrient input and mechanical effects (Lévêque 2006).

Karr (1981) lists reasons to use fish as bioindicators: (1) Fish ecology is well known, (2) Fish assemblages consist of different trophic groups and can therefore be a good indicator for the surrounding environment, (3) fishes are relatively easy to identify, (4) fish occur in almost all aquatic environments (except for the most impacted), (5) fish are popular and can raise public awareness to water pollution. Some, but not all items are fulfilled in a satisfactory manner in Burkina Faso. Species keys exist (Lévêque 2006; Román 1966) and fish diets are known for some species (Froese and Pauly 2013; Lauzanne 1988; Oueda et al. 2008). However, little knowledge exists about the sensitivity or tolerance of species regarding human pressures. Still, a fish index can become well established in Africa’s societies since fish is an important food source (Hugueny et al. 1996).

Specific African problems The lack of reliable data in Africa concerning fish ecology, and in general is well known as mentioned by many authors (Ouedraogo 2010; Tito de Morais 2007). Environmental parameters and guilds for the fish species occurring in Burkina Faso are scarce, and the IUCN red list status is not available for one sixth of the species in this research (IUCN 2013). Fish species for the country vary between the data sources and names have changed over the years, which makes determination complicated. Therefore, with the help of the IUCN West and Central Africa, three fish lists for Burkina Faso (Roman 1966; Paugy et al. 2003; Ouedraogo 2010) were compared, and a list of potential fish species was compiled by Meulenbroek (2013).

The SUSFISH project This work was written in the frame of the SUSFISH project. The APPEAR (Austrian Partnership Programme in Higher Education and Research for Development) project SUSFISH is funded by the ADA (Austrian Development Agency), implemented by the OEAD (Austrian Agency for International Cooperation in Education and Research ) and coordinated by Dr. Andreas Melcher from the University of Natural Resources and Life Sciences in Vienna.

Figure 1.4: Organization chart and work packages. Source: Melcher (2011), adapted.

13 The scientific project partners are the BOKU (University of Natural Resources and Life Sciences in Vienna), the University of Ouagadougou, the Direction Générale des Ressources Halieutiques, the IUCN West and Central Africa, the Université Polytechnique de Bobo‐ Dioulasso, the University of Vienna, the IIASA (International Institute for Applied Systems Analysis) and the Institut Français d'Autriche.

This master study covers topics of work packages (WP) 2 (Basic tools – environment and biodiversity) and WP 3 (Quality of waters – risk assessment) (Figure 1.4). The packages have the focus on species diversity and conservation status (WP 2), and on fish assemblages and water quality parameters (WP 3). These work packages include three master theses and two dissertations: Kabore (in prep.) and Koblinger and Trauner (2013) focus on benthic invertebrates, Meulenbroek (2013), and Mano (in prep.) and this thesis deal with fishes.

The SUSFISH project is a program for higher education with the overall goal to build capacity, monitor and manage sustainable fisheries.

The objectives of the SUSFISH project are

(1) Build capacity to study, monitor and manage sustainable fisheries (overall goal) (2) to develop water management and assessment methods based on fish, appropriate for Burkina Faso; (3) to identify, evaluate, and prepare existing data for fish, environment and pressures for a national database; (4) to analyze the relationships between pressures (incl. overfishing, land use, continuity) and the dynamics in fish assemblages and in water quality. (5) to develop ecological awareness by using appropriate case studies to demonstrate the importance of ecological services and biodiversity to the nation’s food security and health care. (6) to support the implementation and dissemination of project results by (a) integration of the project results in the education policies and on‐going national programmes, (b) workshops and international conferences.

The overall goal of this thesis are

(1) to implement a fish, habitat and pressure database, (2) to identify main human pressures in the Nakambe catchment, (3) to determine reactions of the fish community to these pressures, and (4) to find species sensitive to pressures

14 Research questions of this study:

(1) Are Nakambe fish communities influenced by human pressures? (2) Which typical human pressures affecting fish can be found in Burkina Faso? (3) How to select representative sampling sites for a monitoring campaign? (4) Is there an appropriate method for fish length‐weight estimation? (5) What are the most sensitive and vulnerable fish taxa in Burkina Faso?

This thesis was done in cooperation with the PhD thesis of a Burkinabe student (Mano in prep.) using fish communities for assessment of the ecological status of aquatic ecosystems in Burkina Faso, and can therefore be an important contribution to conservation and food security in Burkina Faso.

Figure 1.5: The SUSFISH sampling team, December 2012. Source: Trauner (2012)

15 2. Methods This chapter gives an overview about the study area, used data sources, field assessment development, data management and analysis.

Study area, sampling site selection and workflow The study area is located in the Nakambe catchment in Burkina Faso between the reservoir of Loumbila, north of Ouagadougou, and the border of Ghana in the south. The sampling took place between October and December 2012 in the first period of the dry season. Sampling areas were selected visually by means of GIS and expert judgment of our supervisors, local fishermen, the Ministry of Environment and the University of Ouagadougou. Decision criteria were water availability, accessibility, different human stressors and spatial variability, security and travelling costs. Due to the war in Mali (2012/2013) and for security reasons we could not sample in the north of the country. Each sampling area was subdivided into different sites. A site is the entity of all nearby and accessible habitats. Figure 2.1 gives an overview of the sampling sites and sampling areas. Figure 2.2 shows detailed maps of the sampling areas.

Figure 2.1: Burkina Faso, overview of the main sampling areas (black circles) and sampling sites (blue dots). Modified from Google Earth (2013).

Figure 2.2: The areas of Kougri (A), Koubri (B), Bagre (C) and Nazinga (D) with the sampling sites marked as black dots. Modified from Google Earth (2013). Kougri is located at the river Nakambe in the east of Ouagadougou. Figure 2.2 A shows the Nakambe, flowing from the north to the south. In the southwest the Massili meets the Nariale which then flows into the Nakambe from the east.

Loumbila is a reservoir in the river Massili northwest of Figure 2.2 A and faces moderate pressures.

The area of Koubri (Figure 2.2 B) consists of 15 Reservoirs and belongs to the mentioned Nariale catchment (Ouedraogo 2010; Melcher, Ouedraogo, and Schmutz 2011). For analysis Koubri was separated into an impacted upstream section (Koubri) and a less impacted free flowing (downstream) section (Koubri_free). Koubri_free consists of a long‐time broken reservoir where dam repairing constructions just ended, when we sampled there, another broken reservoir (Peele) and the free‐ flowing stretch to the Nakambe.

Bagre (Figure 2.2 C) is the biggest Reservoir in Burkina Faso and was constructed to reinforce irrigation and for providing hydroelectricity. This large shallow reservoir was built in 1994 by damming the Nakambe River (Villanueva, Ouedraogo, and Moreau 2006).

The protected game ranch of Nazinga (Figure 2.2 D) is located in the south of Burkina Faso, close to the border of Ghana and was created 1979. It is characterized by low population density, no economic activities such as agriculture, livestock breeding or wood usage. There is one natural pond and additionally 11 small reservoirs were built to provide wildlife with water (Melcher, Ouedraogo, and Schmutz 2011; Ouedraogo 2010).

Table 2.1 lists the sampling sites, sampling dates, the number of fished habitats, GPS location and elevation. Figure 2.3 shows the workflow from data collection to statistical analysis.

Figure 2.3: Data management flow chart

Table 2.1: Study site names, date (2012), number of fished habitats, GPS‐Coordinates and elevation. Only sites with at least two sampled habitats were considered in the analysis. Sampling areas in bold. Sampling Location Date Fished habitats Longitude Latitude Elevation (m) Bagre (9) 31 Bagre‐Bangako 29.11. 2 ‐0,554605 11,461552 232 Djerma/Boussouma 27.11. 5 ‐0,862597 11,675352 248 Fungu 30.11. 4 ‐0,731184 11,497439 237 Lengho 28.11. 3 ‐0,742808 11,622711 236 Nakambe 29.11. 2 ‐0,515558 11,409616 212 Niagho 28.11. 3 ‐0,777061 11,757274 224 Béguédo 27.11. 4 ‐0,725718 11,769889 232 Béguédo 2 27.11. 4 ‐0,752621 11,807014 244 Zangoula 29.11. 4 ‐0,557837 11,562785 240 Koubri (8) 36 Naba Zana 21.10 8 ‐1,351469 12,204276 280 Noungou 29.10 5 ‐1,304137 12,204209 269 Pedga 30.10 5 ‐1,341553 12,180401 291 PK25 05.11. 2 ‐1,402519 12,192986 279 Naba Zana 31.10 3 ‐1,392333 12,187168 281 Tolguin 06.11. 3 ‐1,322209 12,229311 280 Tyokin 06.11. 3 ‐1,396337 12,235285 291 Wendbila 07.11 7 ‐1,415616 12,151827 292 Koubri_free flowing (4) 15 Arzoum Baongo 20.10 7 ‐1,29724 12,221255 280 Peele 12.11. 4 ‐1,190097 12,249763 268 Segda 22.10 4 ‐1,284121 12,223419 268 Pitioko 13.11. 6 ‐1,116612 12,26906 265 Kougri (5) 23 Kougri 19.11. 5 ‐1,080785 12,378996 259 Nakambe petit Barrage 14.11. 5 ‐1,089082 12,245099 251 Nakambe under Nariale 14.11. 3 ‐1,098804 12,256668 250 Nakambe/Masili 20.11. 4 ‐1,09619 12,268317 256 Ziga 21.11. 3 ‐1,076134 12,492104 269 Loumbila (1) 11 Loumbila 18.10. 11 ‐1,397884 12,493584 285 Nazinga (4) 22 Bodjero 12.12. 6 ‐1,504391 11,091481 269 Kouzougou 14.12. 3 ‐1,531004 11,154303 266 Naguio 11.12. 8 ‐1,583241 11,128345 274 Talango 13.12. 5 ‐1,528114 11,188797 270 Total (31) 137

19 Field sampling – environmental data The field assessment sheet was developed by adjusting existing assessment sheets (Barbour et al. 1999; BMLFUW 2010; EFI+ Consortium 2011) to the conditions and requirements in Burkina Faso. After two initial runs it was adapted to fit to the conditions and to elevate the available and necessary Burkinabe habitat and pressure characteristics. The pressures were defined by a consortium of experts (Dr. Oueda, University of Ouagadougou; Dr. Ouedraogo, Ministry of Environment, Burkina Faso; Dr. Melcher, BOKU). The final habitat and pressure assessment sheet is attached in the Appendix (Table 7.4, Table 7.5). A summary of all variables and parameters in the field assessment sheet is shown in Table 2.2.

Table 2.2: Summary of variables and parameters of the field protocol for sampling fish and environmental data.

20 At each habitat we used measuring tape and a Zeiss laser distance meter to measure the river width and depth at randomly selected transects. Flow velocity was measured using the Global Water Flow Probe FP111. Width, depth and velocity were measured at seven randomly selected points. The number of measurements is empirically chosen as the smallest statistically relevant quantity (Parasiewicz 2007).

Figure 2.4. Selection of sampled habitat parameters (velocity in m/s, width and depth in m, shading in %, presence‐absence of in‐stream structures). Source: Meulenbroek (2013).

The degree of surface covered with shading was estimated and noted. Presence‐absence of in‐stream structures like Xylal, rocks, water plants, trees, reed and out‐washed bank were recorded. Figure 2.4 illustrates these recorded data. The substrate distribution was estimated and dedicated to the different classifications (Pelal <6um, Psammal 6um‐2mm, Akal 2‐20mm, Mikrolithal 20‐63mm, Mesolithal 63‐200mm, Macrolithal 200‐400mm, Megalithal >400mm, Primary rock and Concrete) according to Austrian ÖNORM 6232 (ÖNORM 1997).

Basic physicochemical parameters were measured with a WTW Multi 340i Gear namely pH, oxygen (O₂), temperature and conductivity.

21 In addition for each habitat adjacent land use and obvious stressors were noted on‐site according to our categories. For selected sites we took water samples and sent them immediately to a laboratory in Ouagadougou (Laboratoire AINA Suarl, Ouagadougou) for chemical analysis. In total, eight water samples were analyzed: two in Nazinga, two in Kougri, one in Bagre and three in Koubri.

Back home via GIS a buffer of 1 km around each sampling site was created and land use and pressures were controlled and refined. Figure 2.5 illustrates a landuse buffer for the sampling site of Kougri.

Strahler stream orders (Strahler 1952), catchment areas and a distance matrix between the sampling sites were calculated with ESRI ArcGis within the country borders of Burkina Faso.

Figure 2.5: Buffered sampling site of Kougri. Adapted from Google Earth (2013) with (QGis 2011).

22 Table 2.3: A selection of pressures found in Burkina Faso. (Source: Meulenbroek 2012, Stranzl 2012)

Impoundment Residual flow Hydrograph modification (Source: Volta River Authority 2013)

Cross section Barriers upstream/downstream Eutrophication

Pollution Fishermen throwing a NET, all Agriculture white dots are buoys of GN

Rice A farmer using pesticide for his Livestock vegetables

23 Field sampling: Fish data Fish were sampled in all water bodies within one sampling site using mainly two types of equipment: electric fishing (EF) and cast net (NET). Additional at some sites a gill net (GN) was used, but not considered in this analysis because no additional species were caught.

For EF the backpack‐generator ELT60‐IIH from Hans Grassl (Grassl 2012) was used. The generator has 1.3 kW and can be switched between 300 V and 500 V. Due to low conductivity all except of one habitat were fished with 500 V. The anode ring has a diameter of 30 cm with a net of 5 mm mesh size in the center. Each habitat was fished in one run by at least three people, one carrying and operating the generator, one landing the fish and a third for security reasons and carrying a bucket to empty the net. Elapsed time was recorded and fished area was estimated to make the results comparable. Small water bodies were fished completely, while big ones were point sampled. EF was always performed by wading (Brousseau, Randall, and Clark 2005; Peter and Erb 1996; Reid 2011; Schmutz et al. 2001).

Figure 2.6: Electrofishing (left) (Trauner 2012), Cast net fishing (right) (Koblinger 2012)

Two professional local fishermen were recruited to conduct the ‘traditional’ NET fishing method. Two different kinds of nets with 10/25 mm mesh size and a diameter of 4.3/4.5 m were used. The number of throws was noted for comparison. Most of the time the fishermen were wading, for some deeper areas they used a boat (Compare Edo 2011). There is a video available explaining how to throw the NET by Noufou Bonkoungou, one of the fisherman with 40 years’ experience (SUSFISH 2014). For qualitative sampling a GN was used by local fishermen. It had a mesh size of 50 mm and was placed for two to five hours. When possible, EF and NET were applied to the same habitats.

24 Species determination Fish were kept in separate buckets for determination. The total length was recorded for each individual at the nearest 0.1 cm using a fish measuring board. At the first four sites we additionally measured the weight of each individual to the nearest 0.1g to have a comparison to the data of Ouedraogo (2010).

Based on the list of potential fish species Meulenbroek (2013) compiled a hotkey for determination in the field by means of literature research in cooperation with IUCN (Afrique centrale et de l’ouest), and the determination key by (Paugy, Lévêque, and Teugels 2003). All animals that were questionable or not possible to determine to species level were collected and preserved in 70% alcohol for further analyses. Some species were determined together with experts (Tobias Musschoot, Gert Boden and Dr. Emmanuel Vreven) from the Musée royal de l'Afrique central, Tervuren Belgique by Mano in prep. Some species were left on genus level for the analysis.

Data handling and statistical analysis All abiotic and biotic data, including the raw data of Ouedraogo (2010) and species metrics from Froese and Pauly (2013) was connected via MS Access (Figure 2.7). Statistical analyses were achieved using IBM SPSS statistics, MS Excel and PAST (Hammer, Harper, and Ryan 2001). Only sites with at least two habitats were considered in the analysis.

Pressures were categorized according to Schinegger et al. (2012) into hydrological pressures (Hyd), morphological pressures (Morph), water quality pressures (Wq) and connectivity pressures (Conn). Additionally, fishing and agriculture were considered as two own pressure categories. All pressures were ranked from 0 (low pressure) to 1 (medium pressure) and 2 (high pressure) based on expert decision in accordance with the Burkinabe and Austrian supervisors. Pressures, pressure types and judgment criteria are listed in Table 2.4. Pictures of some pressures are showed in Table 2.3.

Connectivity pressures were measured on a segment scale: small rivers (catchment area <100 km²) 2km (1km up‐ and downstream), medium sized rivers (100‐1000 km²) 10km and for large rivers (>1000 km²) 20 km.

Figure 2.7: A screenshot of the database.

26 Table 2.4: List of pressures, pressure types and effects considered for analysis according to Ouedraogo (2010) and Schinegger et al. (2012). Hyd= hydrological pressure, Morph= morphological pressure, Conn= Connectivity pressure, Wq= water quality pressure. Pressure Type Description (expert judgment) Effects Impoundment Hyd Velocity alteration due to Loss of fluvial habitat, altered 1 (Imp) impoundment substrate and channel form (Orthofotos, on site) Hydropeaking Hyd Site affected by hydropeaking Stranding and desiccation1 (feedback of local people) Residual flow (Res Hyd Site affected by water abstraction Decrease in maintenance flow, flow) (adjacent pumps and rice fields, geomorphic and water quality downstream of reservoirs) impacts1 Reservoir flushing Hyd Fauna affected by upstream Increased suspended sediment (Res flush) reservoir flushing flow1 (was set yes for dams with bottom outlet) Hydrograph Hyd Seasonal hydrograph Changes in channel morphology modification (Hyd modification (upstream large and physical habitat composition1 mod) dam or series of dams) Channelization Morph Alteration of the channel plan Reduced habitat heterogeneity, form (on site observations, riverbed degradation1 orthofotos) Cross section Morph Alteration of the cross section Habitat degradation1 (Cross sect) (on site observations) Instream habitat Morph Alteration of instream habitat Habitat degradation1 (In hab) conditions (on site observations) Embankment Morph Artificial embankment (on site Disrupts lateral connectivity, loss observations) of riverbank habitats1 Flood protection Morph Presence of dykes for flood Altered riparian and floodplain (Flood prot) protection (on site observations, habitat1 orthofotos) Barriers upstream Conn Barriers on segment level Habitat fragmentation1 upstream (buffer on orthofotos) Barriers Conn Barriers on segment level Habitat fragmentation1 downstream downstream (buffer on orthofotos) pH Modification Wq (Very high/ low pH, big pH Physiological stress for fish1 (pH mod) fluctuation between habitats, washing observed) Eutrophication Wq Artificial eutrophication Algal blooms, oxygen depletion1 (Eut) (nutrient input, intensive plant growth) Pollution Wq Is pollution observed, can diffuse Physiological stress for fish1 pollution be expected (trash, pesticides observed) Fishing Fish Alteration of fish community due Changes in fish community2 to overfishing (observations, feedback of local people) Agriculture Agri Site affected by agriculture within Nutrient input, pollution, a buffer of 1 km sedimentation2 1 (Schinegger et al. 2012), 2 (Ouedraogo 2010)

27 Pressure type indices were calculated for each pressure type adapted from Schinegger et al. (2012): The average of single pressure parameter values of class 1 and 2 was calculated to avoid that 0 values compensate for 1 or 2 values.

(1) Hydrological pressure index (HPI)=

(2) Morphological pressure index (MPI) =

(3) Connectivity pressure index (CPI) =

(4) Water quality pressure index (WPI)=

(5) Agricultural pressure index (API)=

Whereas m=n(xi>0)

Fishing pressure index: intensity of fishing pressures on site

For example a site with strong pollution (Pollution=2), medium pH modification (pH mod=1) and no Eutrophication (Eut=0) will receive a WPI value of 1.5 instead of 1.0 like in classical average of all values.

Affected pressure groups (affected groups) were calculated, ranging from 0 (none of the pressure groups Hyd, Morph, Conn, Wq, Fish, Agri) to 6 (all pressure groups). In a next step, a global pressure index (GPI) was calculated as in Schinegger et al. (2012):

GPI= ∗

The GPI can range from 0 (no pressure) to 12 (maximal pressure intensity). For visualization and some analysis the GPI was categorized into “Low” (GPI 0‐4), “Medium” (4‐8) and “High” (8‐12) pressure intensity.

28 Cross tabulation was used to describe the data. Correlations and regressions were used to test the metrics. To test the categories for independence I used the t‐test and the non‐ parametric U‐test (Mann‐Whitney test to compare pairs) and the H‐test (Kruskal‐Wallis test to compare multiple groups). For visualization mainly scatter plots and box‐whisker plots were used.

For the illustration of the population structure, length frequency diagrams were produced, showing the relation between length classes and relative frequency.

Metric calculations All frequency and relative frequency metrics were calculated from electrofishing data in order to compare a single standardized method. These metrics are: Proportion of intolerant species and proportion of tolerant species , proportion of dwarf species (fish with a maximum TL of 150mm according to Froese and Pauly (2013), proportion of species.

The number of taxa (species, genera, families) is calculated from all fish caught in one sampling site by means of NET and EF.

Tolerant and intolerant species were defined by literature research and expert judgment (Austrian and Burkinabe hydrobiologists, experts from the Burkinabe ministry of environment). Figure 2.8 and Figure 2.9 show some tolerant and intolerant species.

For the biomass and density estimations, length‐weight regressions based on the fish raw data of Ouedraogo (2010) were calculated. The length‐weight equation W=aLb is described in Ricker (1987), W is the weight (g), L is the total length (cm), a is an empirically determined constant and b is the slope. The slope generally lies between 2.5 and 3.5 and often tends towards 3.0 (Abobi and Ekau 2013). Different regressions (power, exponential growth, S) were tested to find the most appropriate one.

Power function (Potenzfunktion): W=aLb

Growth function (Exponentialfunktion): W=abL

Additionally, available length‐weight regressions from Froese and Pauly (2013) were compared. Finally the calculated power‐function as proposed by Ricker (1987) was used for further calculations. Based on the length‐weight regressions, the biomass in kg/ha and g/min was calculated for each EF habitat and then averaged for each site.

Barbus macrops (3.04) Clarias gariepinus (3.5)

Oreochromis niloticus (2.02) Sarotherodon galilaeus (2.23)

Tilapia zillii (2.52) Figure 2.8: Pictures of tolerant fish species (trophic level from Froese and Pauly 2013) (Source: Meulenbroek 2012, Stranzl 2012). A complete list is in Table 7.2.

Alestes baremoze (3.05)

Auchenoglanis occidentalis (2.9)

Bagrus bajad (3.39), Bagrus docmak (4.08) Citharinus citharus (2)

Ctenopoma kingsleyae (na) Heterobranchus bidorsalis (na)

Hyperopisus bebe (3.6) Hydrocynus forskali (4)

Pollimyrus isidori (2.61) Labeo coubie (2.04) Figure 2.9: Pictures of intolerant fishes (trophic level from Froese and Pauly 2013) (Source: Meulenbroek 2012, Stranzl 2012)

31 3. Results

Environmental parameters The catchment area ranges from eight to 35,625 km² with a median of 864 km². Both, the smallest and largest value occur in the sampling area Bagre. Kougri has the largest catchment areas and Strahler orders followed by Nazinga. Koubri_free has a wide range in these parameters. 32% of all sites have a catchment area smaller than 100 km², 26% have an area smaller than 1,000 km², 20% smaller than 10,000 km² ant the rest has an area larger than 10,000 km².

Temperature has a median of about 28°C in Nazinga and Bagre and about 33°C in all other sampling areas. Temperature correlates with the latitude (Pearson correlation= 0.651, 0.05 significance level) and slightly with the altitude (0.360, not significant). Figure 3.2 shows a linear regression of latitude and water temperature.

Figure 3.1 shows physicochemical and environmental parameters in each sampling area. Conductivity is generally low with less than 100 µS/cm. The sampling areas Koubri_free and Bagre and one site in Kougri (Nakambe/Massili) have elevated conductivity values. Looking at the scatter plot reveals that all habitats with high conductivity in Koubri_free come are located in two sites: Pitioko (Confluent Massili/Nariale) and Arzoum Baongo.

The oxygen saturation is higher in Nazinga, Kougri and Koubri_free as in Loumbila, Bagre and Koubri. Also the range is wider in the latter three sampling areas.

The widest pH range was measured in Koubri. Some sites in Koubri_free and Kougri have elevated pH values (Arzoum Baongo, Nakambe/Massili).

All environmental and physicochemical and chemical parameters are listed in Table 3.1 and Table 3.2.

Figure 3.1: Physicochemical parameters of the sampling areas.

Figure 3.2: Correlation of water temperature and latitude.

33 Table 3.1: Chemical parameters of the largest sampling areas conducted by Laboratoire AINA Suarl, Ouagadougou. Range, median and standard deviation (SD). Sampling area Parameters Nazinga Kougri Bagre Koubri Susp. solids Range 82.4 ‐ 86.2 46.3 ‐ 57.3 na 55.1 ‐ 64.2 (mg/l) Median (SD) 84.3 (2.7) 51.8 (7.8) na 58.0 (4.6) Ca²+ (mg/l) Range 8.5 ‐ 14.1 7.8 ‐ 8.4 14.5 ‐ 14.5 6.0 ‐ 10.0 Median (SD) 11.3 (4.0) 8.1 (0.4) 14.5 (0.0) 8.3 (2.0) Mg²+ (mg/l) Range 11.3 ‐ 11.4 2.9 ‐ 7.5 11.6 ‐ 11.6 2.4 ‐ 6.0 Median (SD) 11.4 (0.1) 5.2 (3.3) 11.6 (0.0) 4.6 (1.8) Turbidity (NTU) Range 36.4 ‐ 68.5 27.5 ‐ 168.0 80.7 ‐ 80.7 58.0 ‐ 345.0 Median (SD) 52.5 (22.7) 97.8 (99.3) 80.7 (0.0) 84.6 (158.6) Na+ (mg/l) Range 9.7 ‐ 12.2 0.7 ‐ 8.3 22.4 ‐ 22.4 1.2 ‐ 4.3 Median (SD) 11.0 (1.8) 4.5 (5.4) 22.4 (0.0) 4.0 (1.7) K+ (mg/l) Range 4.7 ‐ 6.1 1.5 ‐ 6.2 3.0 ‐ 3.0 0.0 ‐ 0.2 Median (SD) 5.4 (1.0) 3.9 (3.3) 3.0 (0.0) 0.1 (0.1) ‐ HCO3 (mg/l) Range 64.8 ‐ 65.9 35.7 ‐ 46 82.2 ‐ 82.2 30.3 ‐ 83.0 Median (SD) 65.4 (0.8) 40.9 (7.3) 82.2 (0.0) 45.8 (27.1) Cl‐ (mg/l) Range 0.7 ‐ 0.7 0.4 ‐ 0.5 0.6 ‐ 0.6 0.4 ‐ 2.0 Median (SD) 0.7 (0.0) 0.4 (0.1) 0.6 (0.0) 0.5 (0.9) Fe (mg/l) Range 0.7 ‐ 2.8 2.5 ‐ 2.6 1.4 ‐ 1.4 0.5 ‐ 2.5 Median (SD) 1.8 (1.5) 2.5 (0.0) 1.4 (0.0) 1.0 (1.0) + NH4 (mg/l) Range 0.2 ‐ 0.7 0.3 ‐ 0.3 0.8 ‐ 0.8 0 ‐ 2.4 Median (SD) 0.4 (0.4) 0.3 (0.0) 0.8 (0.0) 0.1 (1.4) ‐ NO3 (mg/l) Range 1.8 ‐ 2.6 0.4 ‐ 2.2 1.8 ‐ 1.8 0.4 ‐ 3.0 Median (SD) 2.2 (0.6) 1.3 (1.2) 1.8 (0.0) 1.3 (1.3) ‐ SO4² (mg/l) Range 2.0 ‐ 2.0 2.0 ‐ 2.0 2.0 ‐ 2.0 2.0 ‐ 6.0 Median (SD) 2.0 (0.0) 2.0 (0.0) 2.0 (0.0) 4.0 (2.0) ‐ PO4³ (mg/l) Range 0.4 ‐ 0.6 0.5 ‐ 0.7 0.2 ‐ 0.2 0.0 ‐ 0.5 Median (SD) 0.5 (0.1) 0.6 (0.2) 0.2 (0.0) 0.3 (0.2) Total P (mg/l) Range 0.1 ‐ 0.2 0.2 ‐ 0.2 0.1 ‐ 0.1 0.0 ‐ 0.2 Median (SD) 0.2 (0.0) 0.2 (0.0) 0.1 (0.0) 0.1 (0.1)

Table 3.2: Environmental and physicochemical variables in the sampling areas. Range, median and standard deviation (SD) Sampling area Parameters Nazinga Kougri Koubri_free Loumbila Bagre Koubri Catchment Range 1231 ‐ 6161 20724 ‐ 25820 22 ‐ 4383 2029 ‐ 2029 8 ‐ 35628 15 ‐ 802 area (km²) Median (SD) 4629 (2110) 21400 (2326) 932 (1827) 2029 (0) 37 (12038) 155 (287) Strahler order Range 4 - 5 6 - 6 2 - 5 4 - 4 1 - 6 1 - 4 Median (SD) 5 (1) 6 (0) 4 (1) 4 (0) 1 (2) 2 (1) Water depth Range 0.0 ‐ 3.0 0.1 ‐ 11.0 0.1 ‐ 8.0 0.1 ‐ 2.0 0.1 ‐ 2.0 0.0 ‐ 2.2 (m) Median (SD) 0.5 (0.5) 0.5 (1.8) 0.9 (2.4) 0.8 (0.6) 0.5 (0.4) 0.4 (0.4) Velocity Range 0.0 ‐ 0.9 0.0 ‐ 1.3 0.0 ‐ 0.1 0.0 ‐ 0.6 0.0 ‐ 0.7 0.0 ‐ 0.3 (m/s) Median (SD) 0.0 (0.2) 0.0 (0.1) 0.0 (0) 0.0 (0.1) 0.0 (0.2) 0.0 (0) Wetted width Range 1.0 ‐ 35.0 1.0 ‐ 142.0 0.1 ‐ 50.0 1.0 ‐ 17.0 0.2 ‐ 250.0 0.1 ‐ 70.0 (m) Median (SD) 6.0 (7.2) 25.0 (31.3) 7.5 (8.3) 10.0 (7.1) 7.0 (49.6) 3.5 (13.4) Conductivity Range 67.2 ‐ 93.3 46.3 ‐ 255.0 66.5 ‐ 271.0 36.9 ‐ 48.2 61.8 ‐ 219.0 42.5 ‐ 213.0 (µS/cm) Median (SD) 68.6 (10.0) 85.0 (40.5) 134.0 (85.4) 48.2 (3.8) 114.6 (60.3) 72.0 (41.3) pH Range 7.6 ‐ 8.6 7.5 ‐ 9.8 7.2 ‐ 9.8 7.7 ‐ 8 7.9 ‐ 9.0 6.4 ‐ 10.1 Median (SD) 7.6 (0.4) 7.8 (0.5) 7.4 (1) 7.7 (0.1) 8.2 (0.5) 7.0 (0.9) Temperature Range 23.3 ‐ 30.2 28.2 ‐ 35 28 ‐ 35.1 32.6 ‐ 33.1 24 ‐ 31 29.2 ‐ 35.1 (°C) Median (SD) 25.3 (2.3) 32.0 (1.7) 31.2 (2.2) 32.6 (0.2) 26.0 (2.7) 31.6 (1.7) Oxygen (mg/l) Range 3.7 ‐ 7.3 4.4 ‐ 9.5 3.2 ‐ 10.3 4.1 ‐ 4.9 3.7 ‐ 10.1 1.2 ‐ 8.8 Median (SD) 6.6 (1.1) 5.6 (1.3) 5.2 (2.2) 4.1 (0.3) 4.5 (2.5) 5.5 (1.9) Oxygen (%) Range 64 ‐ 90 53 ‐ 114 43 ‐ 158 48 ‐ 76 46 ‐ 131 18 ‐ 127 Median (SD) 88 (8.2) 80 (18.7) 82 (33.2) 48 (9.3) 53 (35.2) 75 (28.1)

34 Pressures Fishing occurs in all sites although some sites are exposed to lower fishing pressure either due to licenses as in Nazinga or bad accessibility as in Bagre (Lengho).

More than 90% of all sites experience hydrological pressures: The most frequent pressures are hydrograph modification (87%) and residual flow (68%) whereas hydropeaking and reservoir flushing only occur in one site (Nakambe hydropeaking). Local people reported frequent water level fluctuations downstream of the Bagre dam.

Agricultural activities are present in 87% of all sites, rice cultivation occurs fewer than the other pressures.

74% of the sites are affected by water quality pressures, eutrophication is the most frequent pressure.

Figure 3.3: Percentages of sites affected by pressure categories. A little more than half of the sites have continuity pressures, dams upstream occur more often than dams downstream. On catchment scale, all sites have continuity obstacles: the dams of Akosombo (lake Volta) and Kpong in Ghana block the whole Volta and do not have fish migration facilities.

Morphological pressures are present in 47% of the sites. Cross section and in stream habitat modification are the most frequent.

Table 3.3 shows the relative frequencies of the pressure categories (presence/absence). All sites are affected by fishing, 28 sites by hydrology, 27 sites by agriculture, 23 sites by water quality, 17 by connectivity and 14 sites by morphological pressures. Figure 3.3 shows the percentage of sites affected by the pressure categories. Figure 7.3 in the appendix shows the frequencies of all pressures per pressure type.

Table 3.3: Relative frequency of pressures. (presence/absence) Pressure Pressure Relative Type frequency Impoundment 48% Hydropeaking 6% Residual flow 68% Hydrology Reservoir flushing 6% Hydrograph 87% modification Channelisation 19% Cross section 32% Morphology Instream habitat 29% Embankment 3% Flood protection 0% pH Modification 48% Water Euthrophication 71% quality Pollution 61% Landuse rice 71% Landuse agriculture 87% Agriculture Landuse vegetables 87% Landuse livestock 87% Barriers upstream 55% Connectivity Barriers downstream 13% Fishing Fishing 100%

Table 3.4: Pearson correlation between the pressure indices.

Hydrological Morphological Water quality Connectivity Fishing Agricultural pressure pressure pressure pressure pressure pressure index index index index index index

Hydrological ** ** * ** pressure index 1 .674 .629 .094 .442 .716 Morphological ** ** * pressure index .674 1 .489 .096 .004 .408 Water quality ** ** ** ** pressure index .629 .489 1 .037 .471 .682 Connectivity pressure index .094 .096 .037 1 .172 .263 Fishing * ** ** pressure index .442 .004 .471 .172 1 .711 Agricultural ** * ** ** pressure index .716 .408 .682 .263 .711 1 **significant at 0..01 level * significant at 0.05 level

36 Pressures per sampling area The cumulative sum of pressure indices gives a good overview over the pressure intensity of the sampling areas (Figure 3.4). Table 3.5 shows the frequency of GPI categories per sampling area. Table 3.5: Number of sites per GPI category The pressure intensities for each Sampling area Low Medium High pressure category and sampling area Nazinga 4 can be seen in Figure 3.5. Kougri 5 Hydrological pressure index is highest Koubri_free 2 1 1 in Bagre with a constant HPI value of Loumbila 1 2 and lowest in Nazinga (Median at 0). Bagre 7 2 Koubri 5 3 Morphology is generally in a good Total 11 14 6 condition, with only two sites in Bagre having a MPI value worse than 1.

The water quality index is best for Nazinga and Loumbila, all other sampling areas have a median of about 1. The free flowing section of Kougri (between Ziga and Bagre) and the reservoir of Bagre reach the best connectivity indices. Koubri_free still has dams within the river segment in the upstream sites and therefore receives a bad CPI index. Nazinga has dams but some of them are passable and some (maybe) selective passable. The areas Loumbila and Koubri receive the highest CPI values. Agricultural pressures are high in all sites except for the protected area of Nazinga. Fishing has a pressure index of two for all sites except for the area of Nazinga and one remote site in Bagre which have a fishing pressure index of one.

Figure 3.4: Cumulative sum of pressure indices per sampling area.

37 Figure 3.5: Pressure intensities per category for the sampling areas. 0=low pressure, 2= high pressure.

38 Fish In 32 sites a total number of 18335 individuals were caught. Altogether 61 fish species in 36 genera and 16 families were determined.

Figure 3.6 gives an overview over the whole fish composition. Dominating families are Cyprinidae and Cichlidae which together contribute to more than half of all caught fishes. Mormyridae and Alestidae together count for almost a third of the composition. Schilbeidae, Clariidae and Mochokidae reach about 5% each. All other families occur in lower abundances of less than 1%.

Figure 3.6: Relative abundance of families of all sampled fish. (Rare families <0,5%) Table 3.6 shows the number of genera and species within the families. A detailed summary of all species is in the appendix (Table 7.2, Table 7.3). The diversity of Alestidae, Cyprinidae and Mormyridae together contributes for almost 50% of all specimen and genera.

A list of rare and vulnerable species is shown in Table 3.7. Of 61 found species, 20 occur in 3 or less sites with a total abundance of less than 50. Of these, 7 species occurred in only one protected site.

Table 3.6: List of the caught families, number of genera and number of species for all sites and abundance for all sites with EF and NET. Family Number of genera Number of species N EF N NET % caught with EF Alestidae 5 11 1641 850 66% Anabantidae 1 1 2 2 50% Bagridae 1 2 28 118 19% Centropomidae 1 1 19 98 16% Cichlidae 4 5 1631 2612 38% Citharinidae 1 1 0 3 0% Clariidae 2 3 662 295 69% Claroteidae 2 3 16 88 15% Cyprinidae 4 12 3117 1386 69% Distichodontidae 1 1 4 5 44% Malapteruridae 1 1 0 2 0% Mochokidae 1 5 478 318 60% Mormyridae 8 10 306 2352 12% Polypteridae 1 1 7 4 64% Protopteridae 1 1 1 0 100% Schilbeidae 2 3 77 920 8% Total: 16 36 61 7989 9053

Seven species were only caught with EF: Barbus ablabes, Barbus baudoni, Barbus hypsolepis, Chrysichthys auratus, Leptocypris sp., Mormyrus hasselquistii and Protopterus annectens annectens.

Likewise, 11 species were exclusively caught with NET: Briemomyrus niger, Brycinus leuscicus, Citharinus citharus, Heterobranchus longifilis, Hydrocynus brevis, Hydrocynus forskali, Malapterurus electricus, Mormyrops anguilloides, Mormyrus mactropthalmus, Synodontis claria and Synodontis membranaceus.

Table 3.7: List of vulnerable species: less than 50 caught individuals in 3 or less sites. Rare species Caught in 1 site only Caught in protected areas only Brycinus leuciscus Hydrocynus brevis x x Hydrocynus forskali x Ctenopoma kingsleyae x Citharinus citharus x Heterobranchus longifilis x x Chrysichthys auratus x Barbus hypsolepis Barbus leonensis Barbus pobeguini Distichodus rostratus Malapterurus electricus Synodontis clarias x x Synodontis ocellifer Brienomyrus niger x x Hippopotamyrus pictus x x Mormyrops anguilloides x x Mormyrus hasselquistii x x Mormyrus macrophthalmus x Protopterus annectens x 40 Fishes per sampling area The most frequent fish are Barbus macrops and Barbus sp. with an average of 10% across all sampling areas. Only in Kougri, Koubri_free the frequency is a bit lower.

Brycinus nurse, Tilapia zillii and Clarias sp. have an average frequency of about 8% but have occurrences of >20% in some sampling areas.

Nazinga has 36% Cyprinids, mostly Barbus (26%) and Labeo (7%). A third of the fishes is Alestidae, 14% Rhabdalestes and 12% Brycinus. Synodontis contributes 11% and there is 10% of Mormyrids (7% Petrocephalus) and Cichlids. Clarias contributes 3%.

Kougri is dominated by Cyprinids (66%): 32% Barbus, 21% Chelaethiops, 10% Leptocypris and 4% Labeo. Cichlidae share 17% and Alestidae 10%. We caught only 1% Clarias and almost no Mormyrids.

Koubri_free has a share of almost 48% Cichlids. Micralestes has a share of 15%, Clarias and Barbus 10% and Synodontis of 8%. Mormyridae share 1.5% of the community.

Loumbila is dominated by Barbus (39%), Cichlids together make a third of the community. There is 12% of Synodontis, and only 4% of Clarias and Mormyridae

In Bagre, Barbus and Brycinus dominate the community with 28% and 24%. Cichlidae have a share of 15%, Mormyridae of 1%.

In Koubri the genus Barbus has a share of 25% and the cichlids together make out a third of the community. Clarias has a share of 22%. 5% in Koubri are Mormyrids, half of them Hyperopisus.

Figure 3.7 compares the TL of all fishes caught with NET and EF. With NET slightly bigger fish were caught. With NET the median of TL is about twice as big in Nazinga as in all other areas. EF shows decreasing TL from Nazinga to Bagre, Koubri has higher values again.

The biomass in g/min is decreasing from Nazinga to Koubri except for Kougri which has the lowest median of all areas. Kougri also has the lowest biomass in kg/ha (Figure 3.8). Also the number of taxa (Figure 3.9) is decreasing from Nazinga to Koubri except for Kougri.

The share of tolerant species is lowest in Nazinga (57.2%) and highest in Koubri (88.3%) (Table 3.10). Vice versa the sensitive species have the highest share in Nazinga (19.9%) too. However the lowest share of sensitive species is in Koubri_free with only 3.4% followed by Koubri with 7%.

Dwarf species have an average share of 42.1%. In Koubri_free there are only 17.7% and in Loumbila and Koubri only about a third. Kougri has a share of 57.5% dwarf species.

41 Table 3.8: Relative frequencies of fishes (%) in the sampling areas, caught with EF (species in brackets were only caught with NET). Highlight of species with a frequency >10% (bold) and occurring exclusively in one sampling area (cursive). X for presence in NET samplings. Last row: Number of species and exclusive species only occurring in one area for EF and NET. Species with EF (with NET) Total Nazinga Kougri Koubri_free Loumbila Bagre Koubri Barbus macrops 11.8 10.4% 7.1% 8.7% 12.8% 17.6% 16.1% Barbus sp. 10.1% 11.7% 20.5% 1.8% 8.2% 1.0% Brycinus nurse 8.7% 9.5% 2.7% 2.4% 4.9% 22.2% 0.1% Tilapia zillii 8.6% 4.3% 6.3% 25.6% 19.1% 2.1% 14.9% Clarias sp. 8.3% 3.2% 1.7% 10.4% 3.8% 5.6% 39.0% Rhabdalestes septentrionalis 6.9% 13.6% 6.9% X 6.5% 0.2% Chelaethiops bibie 6.6% 3.0% 21.1% 0.2% 2.8% 3.8% Oreochromis niloticus 4.9% 2.8% 3.0% 12.0% 1.7% 5.6% 5.7% Sarotherodon galilaeus 4.5% 2.2% 3.1% 5.8% 4.5% 7.4% 6.1% Synodontis schall 3.9% 10.4% 2.2% 2.2% 1.4% 0.7% 0.2% Micralestes occidentalis 2.9% X 15.1% 4.4% X Petrocephalus bovei 2.6% 7.4% 0.1% 0.5% 3.5% 0.8% 1.0% Leptocypris sp. 2.5% 9.1% 2.0% Labeo coubie 2.4% 4.4% 3.9% 0.2% 1.0% Synodontis sp. 2.0% 0.9% 1.3% 5.6% 10.4% 0.7% 2.3% Hemichromis bimaculatus 2.0% 0.6% 4.1% 4.4% 1.0% 2.6% Barbus ablabes 1.9% 4.1% 26.0% Barbus baudoni 1.2% 4.0% 0.8% Leptocypris niloticus 1.1% 0.1% 1.2% 3.7% Schilbe intermedius 0.9% 2.6% X 0.9% 0.7% X 0.2% Brycinus luteus 0.9% 1.6% 0.1% 1.0% 1.2% Labeo senegalensis 0.8% 2.5% 1.0% 0.1% Barbus hypsolepis 0.6% 2.6% 0.1% Brycinus longipinnis 0.5% 0.5% X 0.9% 1.0% Pollimyrus isidori 0.5% 1.0% 0.1% 0.5% 0.5% X Hemichromis fasciatus 0.5% 0.8% 6.6% Alestes baremoze 0.4% 0.7% 0.1% 0.1% 0.3% 0.9% Marcusenius senegalensis 0.4% 0.6% X 0.4% 1.0% 0.1% 1.1% Bagrus docmak 0.3% 1.1% X 0.1% Lates niloticus 0.2% 0.4% 0.1% X 0.9% Alestes sp. 0.2% 0.7% 0.7% Hyperopisus bebe 0.2% X X X 0.1% 1.5% Alestes dentex 0.2% 0.2% X 0.6% X Barbus leonensis 0.2% 1.5% Chrysichthys nigrodigitatus 0.1% 0.6% Mormyrus rume 0.1% 0.3% X 0.1% X X 0.3% Bagrus Bajad 0.1% 0.1% 0.3% 0.1% Polypterus senegalus senegalus 0.1% 0.2% 0.2% 0.1% Parailia pellucida 0.1% 0.1% 0.3% 0.1% Auchenoglanis occidentalis 0.1% 0.1% X 0.3% 0.1% Distichodus rostratus 0.1% X X 0.2% Hippopotamyrus pictus 0.1% Mormyrus hasselquistii 0.1% Synodontis ocellifer 0.3% Chrysichthys auratus 0.2% Ctenopoma kingsleyae 0.1% Barbus pobeguini X 0.1% X Heterobranchus bidorsalis X Protopterus annectens annectens 0.1% Schilbe mystus X 0.1% (Brienomyrus niger) X X (Brycinus leuciscus) X X X (Citharinus citharus) X X (Heterobranchus longifilis) X X (Hydrocynus brevis) X X (Hydrocynus forskali) X X (Malapterurus electricus) X X (Mormyrops anguilloides) X X (Mormyrus macrophthalmus) X X (Synodontis clarias) X X (Synodontis membranaceus) X X X X Number of species (exclusive) 61 44 (12) 32 (0) 35 (2) 20 (0) 37 (0) 30 (4)

42 Table 3.9: Relative frequencies of genera (%) in the sampling areas, caught with EF. Highlight of species with a frequency >10% (bold) and occurring exclusively in one sampling area (cursive). Genera Total Nazinga Kougri Koubri_free Loumbila Bagre Koubri Barbus 19.4% 10.5% 26.5% 17.6% 16.6% 26.1% 28.5% Marcusenius 9.5% 24.3% 0.9% 1.8% 13.9% 1.0% 2.4% Sarotherodon 8.6% 2.6% 12.6% 7.1% 23.7% 7.5% 15.7% Oreochromis 8.3% 4.7% 3.1% 22.2% 4.6% 6.7% 7.2% Brycinus 8.0% 6.5% 9.9% 4.4% 8.7% 22.8% 0.7% Tilapia 6.8% 2.1% 3.6% 16.1% 11.9% 2.0% 12.4% Clarias 5.4% 4.9% 1.5% 4.0% 1.9% 5.1% 15.4% Schilbe 5.4% 6.2% 6.3% 8.4% 3.6% 0.7% 2.5% Synodontis 4.7% 6.7% 4.0% 4.7% 5.4% 1.7% 3.2% Chelaethiops 3.7% 1.2% 14.0% 0.2% 0.0% 2.5% 2.6% Rhabdalestes 3.5% 5.5% 4.4% 0.3% 0.0% 5.8% 1.2% Petrocephalus 2.0% 5.2% 0.0% 0.7% 1.6% 0.8% 0.4% Pollimyrus 2.0% 5.1% 0.5% 0.5% 0.0% 1.1% 0.0% Micralestes 1.9% 0.0% 0.1% 5.2% 0.0% 3.9% 2.8% Leptocypris 1.7% 0.1% 5.7% 0.0% 0.0% 5.1% 0.0% Labeo 1.6% 3.2% 2.3% 0.2% 0.4% 1.0% 0.0% Hemichromis 1.2% 0.8% 2.3% 1.4% 3.4% 0.0% 1.2% Alestes 1.2% 1.0% 0.3% 2.0% 1.9% 2.2% 1.0% Mormyrus 1.0% 1.8% 0.5% 0.4% 2.1% 0.6% 0.6% Hyperopisus 1.0% 2.4% 0.2% 0.1% 0.0% 0.2% 1.1% Bagrus 0.9% 1.3% 1.0% 0.7% 0.0% 1.0% 0.0% Lates 0.7% 1.7% 0.0% 0.2% 0.1% 0.0% 0.8% Parailia 0.5% 0.0% 0.4% 1.9% 0.0% 0.1% 0.0% Auchenoglanis 0.3% 1.0% 0.0% 0.0% 0.1% 0.1% 0.0% Chrysichthys 0.3% 0.0% 0.0% 0.0% 0.0% 2.2% 0.1% Heterobranchus 0.2% 0.6% 0.0% 0.0% 0.0% 0.0% 0.0% Polypterus 0.1% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% Distichodus 0.1% 0.0% 0.0% 0.0% 0.0% 0.4% 0.0% Hippopotamyrus 0.1% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% Hydrocynus 0.1% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% Brienomyrus 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% Ctenopoma 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% Citharinus 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% Malapterurus 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% Mormyrops 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Protopterus 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Number of genera 36 31 22 26 16 24 22 (exclusive) (7) (0) (1) (0) (0) (1)

Table 3.10: Share of tolerant, intolerant and dwarf species in the sampling areas. Species metrics Total Nazinga Kougri Koubri_free Loumbila Bagre Koubri Tolerant 67.54% 57.22% 70.73% 78.19% 69.20% 60.81% 88.25% Sensitive 10.54% 19.86% 9.40% 3.41% 8.93% 7.34% 6.98% Indifferent/unknown 21.93% 22.92% 19.87% 18.40% 21.88% 31.85% 4.77% Dwarf species 42.11% 44.54% 57.48% 17.66% 29.02% 49.57% 33.73%

43 Figure 3.7: TL of all fish in the sampling areas. Left: NET, right: EF

Figure 3.8: Biomass in g/min (left) and kg/ha (right) per sampling area

Figure 3.9: Number of taxa per sampling area

44 Relation of fish and environment Large order‐streams have a slightly higher diversity than their tributaries (Figure 3.10). There was no clear correlation between chemical parameters (median of all habitats per site) and species diversity per site. There is a clear reaction to human pressures. Table 3.11 summarizes genera whose relative abundance significantly (significance level=0.05) correlate with the pressure indices. Auchenoglanis, Ctenopoma and Hydrocynus occur in almost all pressure indices.

Figure 3.10: Diversity in low‐ and high order streams

Table 3.11: Summary of genera correlating significantly (0.05) with the pressure indices. Pressure index Correlating genera (Pearson correlation) HPI Auchenoglanis (‐0.452), Ctenopoma (‐0.611), Heterobranchus (‐0.478), Hydrocynus (‐0.602), Polypterus (‐0.468), Schilbe (‐0.460) MPI Chrysichthys (0.426), Schilbe (‐0.502) CPI Brycinus (‐0.556), Distichodus (‐0.437), Hemichromis (0.384), Tilapia (0.545) WPI Auchenoglanis (‐0.370), Citharinus (‐0.388), Ctenopoma (‐0.459), Hydrocynus (‐0.428), Malapterurus (‐0.379), Marcusenius (‐0.367) API Auchenoglanis (‐0.516), Citharinus (‐0.616), Ctenopoma (‐0.727), Heterobranchus (‐0.493), Hydrocynus (‐0.678), Marcusenius (‐0.593), Polypterus (‐0.370) Fishing Alestes (‐0.483), Auchenoglanis (‐0.454), Citharinus (‐0.527), Ctenopoma (‐0.622), Heterobranchus (‐0.414), Hydrocynus (‐0.580), Marcusenius (‐0.423) GPI Citharinus (‐0.451), Ctenopoma (‐0.552), Heterobranchus (‐0.399), Hydrocynus (‐0.523), Marcusenius (‐0.396), Schilbe (‐0.439), Tilapia (0.469)

45 The global pressure index is highly correlating with the number of taxa, each significant at a 0.01 level. Correlation at family level is ‐0.710, at genus level ‐0.740 and at species level ‐0.792.

A linear regression of GPI and number of species per site is shown in Figure 4.4. The outlier site Talango is a river which was almost dried out. Nakambe below Nariale confluent and Nakambe barrage are nearby the confluent of Nariale and Nakambe. Most sites have a GPI rank between 6 and 8.

Low Medium High Low Medium High

Low Medium High

Figure 3.11: Relation of number of taxa and GPI. In Figure 3.11 the relation of number of taxa and GPI is drawn. The median of number of species drops from 25 species at low pressure sites to 10 at high pressure sites. Accordingly, number of genera drops from 20 to 10 and number of families from 9 to 5. For all three graphs the number of taxa is significantly different in each GPI category (Kruskal‐Wallis test, significance level 0.01).

46 Low Medium High Low Medium High

Figure 3.12: TL of all fish (left) and TL of large fish caught with NET (right) per GPI category.

Figure 3.12 compares the TL of all fish and the TL of large fish across the GPI categories. For both the Median is significantly different (Kruskal‐Wallis test, significance level 0.05). With the NET we caught significantly larger fishes (U‐test, significance level=0.05) especially in the low‐pressure sites. However, the Median does not show a big difference between medium and low pressure sites.

Low Medium High Low Medium High

Figure 3.13: Biomass in kg/ha (left) and g/min (right) per GPI category

minute

er p

Individuals

Figure 3.14: Individuals per minute EF per GPI category. Biomass has a big drop towards high pressure sites (Figure 3.13). The trend is stronger with biomass g/min than with kg/ha. Also the number of individuals has a distinct decrease with pressure intensity (Figure 3.14). The share of sensitive species decreases with pressure intensity while the share of tolerant species increases (Figure 3.15).

Figure 3.15: Share of sensitive species (left) and tolerant species (right) per GPI category. The total length of all fishes is significantly different between medium and high fishing pressure (t‐test and U‐test, Significance=0.001). In Figure 3.16 you can see the decrease in total length.

48 Medium (remote High or managed) (unmanaged)

Figure 3.16: Total length of all fishes caught with NET for medium and high fishing pressure intensity.

Table 3.12: Percentage of selected economically valuable fish reaching maturity per fishing pressure, caught with NET (Maturity lengths from Froese and Pauly 2013). The figure illustrates the TL of Tilapia zillii for sites with medium and high fishing pressure, and the maturity length according to fishbase. % of adult fish Species Medium High Bagrus Bajad 1% 0% Brycinus nurse 87% 37% Citharinus citharus 33% 0%

Clarias sp. 24% 12% Maturity length: 70 mm Lates niloticus 1% 0% Oreochromis niloticus 53% 0% Petrocephalus bovei 92% 17% Sarotherodon galilaeus 2% 3% Schilbe intermedius 96% 20% Synodontis schall 10% 0% Tilapia zillii 100% 49%

Figure 3.17: Boxplot of trophic levels per GPI category. The trophic level has a distinct drop at high pressure sites. The median is at about 3.0 at low‐ and medium pressure sites and at about 2.5 at high‐pressure sites (Figure 3.17).

Figure 3.18: Relative abundance of families per GPI category for EF. Rare families: <1% of total abundance.

50 Length‐weight regressions Based on the raw data of Ouedraogo (2010) length‐weight regressions according to the method of Ricker (1987) were calculated. Additionally the observed values were compared to the exponential function because for some fishes the proposed power function underestimates the weight of large specimen (see the examples in Table 3.13).

For Lates niloticus and Sarotherodon galilaeus the power function fits well. For Clarias Gariapinus and Tilapia zillii the power function underestimates, while the exponential function seems to fit better. For Barbus macrops and Sarotherodon galilaeus both do not fit very well. Generally the power function underestimates while the exponential function overestimates. The exponential function gives also better results for fishes with small sampling size.

The underestimation for the mentioned fishes is also apparent in the log‐transformed length‐ and weight values proposed by Ricker (1987) for easier curve fitting.

Anyhow, correlation of measured values was high with all three methods with a correlation coefficient (Pearson correlation) of 0.986 for exponential regression, 0.984 for power regression and 0.997 for the regression based on fishbase parameters, with a significance of 0.01 each. Table 3.14 compares biomass calculations based on estimations and measured values.

51 Table 3.13: Length‐weight regressions for some selected fishes.

52 Table 3.12 (continued)

Table 3.14: Biomass calculations for habitats fished with EF (kg/ha) based on exponential function, power function and fishbase estimation and measured values Biomass (kg/ha) Habitat exponential power fishbase measured 01.02 16.5 13.4 17.7 13.6 01.05 26.8 32.6 44.6 28.5 01.06 29.4 37.8 31 29.9 02.01 17.6 16.1 36.0 30.9 02.02 37.5 50.4 42.3 38.0 02.03 30.3 36.0 75.8 63.2 02.04 70.8 85.0 94.4 76.9 02.05 100.6 109.6 103.7 86.4 03.01 75.5 71.6 85.4 89.5 03.02 115.8 73.9 105.1 98.0 03.07 103.7 78.2 123.7 106.5 03.09 188.2 180.4 213.5 196.2 03.11 313.2 368.5 430.6 362.6

4. Discussion

Environmental parameters The study analyses the reaction of fish communities under different pressures. To characterize the environmental situation the following parameters/variables were used: Catchment area, Strahler order, wetted width, water depth, temperature, conductivity, oxygen saturation and pH.

All sites were analyzed amongst each other in order to have a larger data set. However there are large differences between the sampling areas regarding physical parameters. The catchment area varies with a factor of 500 (Table 3.2) between the small tributaries around the Bagre reservoir and the sites along the Nakambe river, likewise the Strahler order varies between 1 and 6. Big differences are also in the wetted width and the depth, nonetheless because these have large seasonal variations in these intermittent water bodies. Regarding the temperature – which basically is correlated with the latitude, the sampling areas form two groups: the cooler areas in the South of Burkina Faso (Temperature median of about 28°C) and the warmer ones further north (Temp. median of about 33°C). The positive correlation between decreasing temperature and decreasing altitude can be explained by the flow direction towards the South. Furthermore, the Southern sites partially are located in a different climatic region (Figure 1.2).

Apart from these parameters, there are also two more catchments to be considered for a nationwide biotic index. In these catchments also the biota varies a lot (Mano in prep.), therefore a river typology will be necessary.

Still, the sampled sites have a lot in common. The conductivity is generally very low (<100 µScm‐1) which is comparable to other studies in West Africa (Abdul‐Razak, Asiedu, and Entsua‐Mensah 2009; Welcomme 1974). Outliers were identified at construction sites and sites with high pressure intensity with values >200 µScm‐1. Koblinger and Trauner (2013) reported conductivity values up to 400 µScm‐1 in polluted channels and the reservoir N°2 in Ouagadougou, both highly and multiple impacted sites. Meulenbroek (2013) found a significant decrease of taxa in habitats with a conductivity of >120 µScm‐1 for water bodies in the Nakambe catchment. Therefore, elevated conductivity values could serve as a metric for the water quality.

Oxygen saturation is around 100% and varies less in the low pressure areas than in the impacted areas. Therefore it could serve as a potential metric in a multi metric index (MMI). Only sites downstream of the Nariale/Massili confluent have outliers in the low‐pressure sites (Figure 4.1). It is possible that this river stretch is still affected by the wastewater effluents of Ouagadougou, carrying a higher nutrient load than expected (see e.g. (Haro et al. 2013)) which causes a higher oxygen demand. Meulenbroek (2013) showed a drop in diversity at habitats with oxygen saturations >120%, which we measured in all areas except

55 for Nazinga. Low oxygen saturations <50% were only measured in impacted sites (except for one disconnected pool in Pitioko). Nazinga has the highest O2 saturation median value followed by Koubri_free and Kougri (all >80%). The more impacted sites have a median between 50 and 75% O2 saturation.

The pH‐value is generally between seven and eight, except for the sites Bodjero in Nazinga, Nakambe/Massili in Kougri, Arzoum Baongo in Koubri_free, Niagho in Bagre and Naba Zana in Koubri.

Although Nazinga is located in a protected game park and the least deforested area, it has the highest value and median of suspended solids. Most nutrients also have a higher median in Nazinga than in the other sampling areas. Turbidity, on the other hand has the lowest median in Nazinga.

Figure 4.1: Oxygen saturation per GPI category.

Pressures This study could describe some major pressures in Burkina Faso. Compared to Europe, the importance of some pressures varies distinct. While overfishing is not mentioned as a main pressure in European‐scale studies on running water (Degerman et al. 2007; Schinegger et al. 2012), this is a serious problem in Burkina Faso. All sampled Burkinabe sites were exposed to fishing activities. In Nazinga fishermen need to buy a license in order to fish. There is a season closed for fishing. Anyhow, camps of professional fishermen sell huge amounts of fish to nearby cities and Ouagadougou. Therefore Nazinga and a few remote sites were judged as medium fishing pressure sites, all others were judged as high pressure sites.

Almost all sites (89%) are exposed to hydrograph modifications and/or residual flow, which is a lot compared to Europe, where about 41% are affected by hydrological pressures (Schinegger et al. 2012). This is mainly due to the high reservoir density in the sampling areas.

56 Mahe et al. (2002) calculated a reservoir volume in 1994 of 170 Mm³ compared to 315 Mm³ mean annual discharge, with most of the stored water being consumed. Figure 4.2 illustrates the flow alterations after the construction of the Bagre reservoir. The maximum daily runoff in this period was delayed from early August to early September due to the increase in storage capacity (Mahe et al. 2002). At the same time, despite an additional decrease in precipitation, river flows have increased due to changes in land cover.

Figure 4.2: Changes in average flow before (fat, black) and after the construction of Bagre reservoir (blue). (Source: Volta River Authority 2013) Agricultural pressures were present in 87% of our sites, which can be related to the 76% of cultivated land in the Nakambe catchment: For 1995, Mahe et al. (2002) calculated that of the Nakambe catchment only 13% of the area remained as natural vegetation, 76% is cultivated land and 11% is bare soil. They conclude, the water holding capacity has decreased 1/3 in 30 years.

While across Europe about 59% of the waterbodies are affected by water quality pressures (Schinegger et al. 2012), we found more than 70% of the sites being affected in Burkina. Especially in areas with high population density or high agricultural activities, water quality is frequently affected and eutrophication occurs.

Schinegger et al. (2012) found 35% of Europe’s sites have a bad connectivity. In our sites, more than 50% of all sites are affected by connectivity pressures, which is likely with more than 450 dams in the Nakambe catchment (World Bank 2001). However, at catchment level, all sites are disrupted by the Akosombo (lake Volta) dam and another dam (Pwalugu dam, see Volta River Authority 2013) is planned in the free flowing stretch between lake Volta and the Bagre reservoir.

Stream morphology is intact in more than half of the sites and the pressures are moderate compared to other occurring pressures at the same sites. The areas with high population density (Koubri, Bagre) have the highest share of morphological pressures. Flood protection

57 in form of embankments are hardly found and as Meulenbroek (2013) showed, most of the sites are rich in in‐stream structures. Compared to Europe, where about 40% of all sites have a bad morphological index according to Schinegger et al. (2012), Burkina is in a good state here.

Because many pressures occur at the same time, there is a strong correlation between some pressure types: e.g. Agriculture is mostly located along or downstream of reservoirs (de Fraiture et al. 2014), pollutants are washed into the water (Hyrkäs and Pernholm 2007) farmers seasonally go fishing (Ouedraogo 2010) and therefore correlates with fishing, hydrology, water quality, and morphology.

Pressures per sampling area Bruner et al. (2001) showed that protected areas are an effective way of preventing land degradation and biodiversity‐loss. This is also true for Nazinga: The park has only small reservoirs which can hold small amounts of water for game watering and some of the dams are fish passable (Ouedraogo 2010). Morphology and adjacent land use are natural and the water quality is good. The only pressures occurring are fishing (with closed seasons) and connectivity, resulting in the highest diversity.

The other sampling areas face all pressure categories. The biggest share of pressures indices is fishing and agriculture followed by hydrology. Due to the high reservoir density Koubri has the highest values in connectivity pressure.

Bagre receives good connectivity values although the sites are along is the largest reservoir amongst the sampled areas. This is due to the chosen connectivity definition: most of the sampled sites were small tributaries with a catchment <10 km2 which had a considered river stretch of 2 km and therefore still had a good connectivity within their buffers. On the other hand some sites were already considered as impounded and therefore reached a bad hydrology judgment.

58 Fish In total 61 fish species, 36 genera and 16 families were recorded. These represent 50% of the known fish fauna in the whole Volta catchment according to Román (1966).

The most diverse families are Cyprinidae, Alestidae and Mormyridae (12, 11 and 10 species). Together they contribute for almost 50% of all specimen and genera. A study in northern Ghana at the Nakambe 200 km South of Burkina (Obodai and Laweh 2009) found only 3 Mormyridae and 2 Cyprinidae species and mentions Mochokidae, “Characidae” (proper family: Alestidae) and Cichlidae as the families with most species (6 species each, vs 5, 11 and 5 in this study).

Cyprinidae and Cichlidae together also contribute to more than 50% of all individuals. Obodai and Laweh (2009) also found a high share of Cichlidae, but far less Cyprinidae. The high abundance of Cichlids can be explained with their high reproduction potential (Hepher and Pruginin 1981) and their tolerance to a wide range of environmental conditions (El‐ Sayed 2006). Other families can be regarded as locally endangered. Of more than 18,000 individuals 12 species were caught less than 10 times in three or less sites (Hydrocynus brevis, Hydrocynus forskali, Ctenopoma kingsleyae, Citharinus citharus, Heterobrancus longifilis, Chrysichthys auratus, Barbus pobeguini, Synodontis clarias, Synodontis occellifer, Brienomyrus niger, Mormyrops anguilloides, Mormyrus hasselquistii and Mormyrus macrophtalmus). Ten of these were only caught in protected areas. All these species are least concerned or not available on the IUCN red list presented by (Froese and Pauly 2013).

Obodai and Laweh (2009) on the other hand found the in our study rare species Citharinus citharus as the fifth‐most frequent species and Hydrocynus brevis, H. foskalii and Mormyrus macrophtalmus among the 20 most‐frequent ones in Northern Ghana. These differences can most likely be explained by different fishing methods, Obodai and Laweh (2009) used fish traps and gill nets, a two months later sampling period and the location lies more than 200 km further South. Additionally, no information about human pressures and land use is given.

The applied catching methods are selective for some families: More than 80% of all Bagridae, Centropomidae, Claroteidae, Malapteruridae, Mormyridae and Schilbidae individuals were caught with cast net (NET). On the other hand, more than 60% of Alestidae, Clariidae, Cyprinidae, Mochokidae, Polypteridae and Protopteridae individuals were caught with electric fishing (EF).

The same picture can be drawn at species level: seven specimens caught with EF were not caught with NET and 11 specimens the other way round.

Penczak et al. (1998) also showed significant differences between the applied fishing methods and proposes to use results from EF only as an “index of density” instead as for and index of diversity. Additionally other fishing methods should be used complementary if an index of diversity is needed.

59 This highlights the importance of using different sampling methods to get a broader overview over the fish community as proposed by Meulenbroek (2013) and Melcher, Ouedraogo, and Schmutz (2011). Both methods are hard to apply in the deep sections of the Nakambe river which makes an additional method or a consideration in the river typology necessary (Schmutz et al. 2001).

Fishes per sampling area Nazinga differs from all other areas. It has the highest diversity, the largest fish in median and the highest biomass per hectare, as well as the highest share in sensitive species. The TL of all fishes together was almost double as high in Nazinga as in the other areas, indicating the success of a managed fishery and of closed seasons (Shin et al. 2005).

In Nazinga, 44 species were caught, among them were 12 exclusive species. In the other areas between 30 and 37 species were caught, except for Loumbila where we caught only 20 species. This difference becomes even more distinct if one considers the sampling efforts. It was almost double as high in the impacted sampling areas (8 and 9 sampled sites in Koubri and Bagre vs. 5 sites in Kougri, 4 sites in Nazinga and Koubri_free). Loumbila had the lowest sampling effort, only one site was sampled.

Although Kougri was considered of being rather non impacted, it has the lowest biomass. This is most likely a methodological error, because EF catching efficiency was very limited (or not possible) in many deep habitats of the Nakambe and weight was calculated from EF data (see Schmutz et al. 2001). Because we could not sample deep habitats, there is also the highest share of dwarf species and a little share of bottom‐feeders, like Mormyrids. Welcomme and De Merona (1988) highlighted the high share of small species in shallow habitats in West African rivers due to their advantages in micro‐habitats. On the other hand there is the highest share of sensitive species in Kougri after Nazinga, indicating a good ecological status.

The sampling area of Koubri_free (right next to the Koubri area) has twice as much species per site as Koubri. However, the total length of fishes is larger in Koubri. One reason could be that Koubri_free was already starting to dry out and larger migratory fishes probably moved back down into the Nakambe (Welcomme and De Merona 1988). The stagnant water situation of this sampling area could be another explanation for the typical lake community caught in this area (Cichlids and Barbus dominating).

Koubri has a little α‐diversity, while all other areas have between 20 and 30 specimen per site, Koubri has only 12 (median). It has also the lowest number of species of all areas with comparable sampling effort, indicating the high pressures lasting on the area.

60 Relation of fish and environment While for some metrics used for fish indices in Europe (Segurado et al. 2008; FAME CONSORTIUM 2004) data is limited in Burkina Faso, some can definitely be applied. It is apparent that the species react to the defined pressure categories and that diversity decreases drastically with the pressure intensity. Figure 4.4 shows a linear regression between the GPI and the number of species.

Taxa richness, total density (g/min, individuals/min) and share of intolerant species react very distinct on the tested pressure classification and could serve as core metrics. The trophic level drops from medium to high pressures from 3 to 2.5. The total length (TL) from net fishing and the percentage of adult fish (especially of these sold on the market) can serve as a metric for the fishing pressure (Table 3.12). The total share of small/large species does not have a clear reaction and probably needs to be observed on a taxa level.

Other potential metrics include: general water quality tolerance (Mormyridae are very sensitive (Hugueny et al. 1996), Cichlidae are quite tolerant (El‐Sayed 2006)), adult trophic guild (Froese and Pauly (2013) or use Mormyrids as insectivorous representatives (Hugueny et al. 1996)), the feeding habitat is known for some species. Other metrics like spawning habitats, migration behavior and tolerance to chemicals, pH, etc. might need further research.

Except for Hemichromis and Tilapia all genera have a negative correlation between the pressure indices and relative abundance of genera, indicating that these two are generalists. The share of Cichlids and Barbus increases with pressure intensity. These taxa are mentioned as to be quite tolerant and to be frequent in reservoir communities by various authors (Hugueny et al. 1996; Anne, Lelek, and Tobias 1994). Some genera like Auchenoglanis or Hydrocynus correlate with many pressure groups, showing that they are very sensitive and maybe indicating the correlation amongst the pressure groups (Table 3.4).

Adams (1985) reports the loss of Lates niloticus, Hydrocynus sp., Gymnarchus niloticus, Auchenoglanis sp. and Bagrus sp. downstream of reservoirs in Nigeria. We also caught Lates more frequently in free flowing stretches. Hydrocynus was found only in Nazinga, indicating that it is generally sensitive. We could find Auchenoglanis in Nazinga, Bagre (in tributaries to the reservoir) but also downstream of the reservoir of Loumbila. Bagrus was caught in all areas except for Nazinga and Loumbila.

There might be evidence of a lost species: Gymnarchus niloticus was found in PK25 (Nagbangré) by Baijot, Moreau, and Bouda (1994). Since then, we and Ouedraogo (2010) have not found it there, or in any other of the study areas.

The study shows a variation in spatial distribution of fish species richness. The median of species richness decreased to 40% compared to the low pressure sites (GPI). However, if connectivity is intact and species can still migrate to the appropriate habitats for their life stages, this can compensate some other pressures (Figure 4.3). About 50% of the caught

61 species are reported as potamodromous by Froese and Pauly (2013). This way, connectivity could serve as a core metric in a MMI. Since morphology is still widely intact in Burkina Faso, restoring and preserving connectivity could be an important contribution to protect the Burkinabe fish communities. Zitek, Schmutz, and Jungwirth (2008) conclude that restoration of connectivity is most successful in morphologically intact rivers e.g. in the Danube catchment. However, there is little knowledge about an appropriate fish migration facility design for Burkinas water bodies, and further research is necessary.

Figure 4.3: Species richness for (medium and high) impacted sites (GPI) with low and medium & high connectivity pressures. Correlation between GPI and number of species/number of genera shows not much difference (‐0.79 vs. ‐0.74). Because species are hard to determine and most fishermen differ between fishes on genus level (own observations and Ouedraogo (2010)), it could be an idea to create a future fish index for Burkina Faso based on genera to make it wider applicable.

Biomass in kg/ha shows a slight decrease with pressure intensity. However biomass in g/min EF shows a much clearer drop (Figure 3.13). Still, the values vary a lot. Especially in Kougri had a very low biomass because the deeper areas couldn’t be fished with EF.

Furthermore, the share of sensitive species decreases significantly while the tolerant species increase with pressure intensity. This lets us conclude that our assumption of tolerant/sensitive species was quite appropriate.

62 Figure 3.16 shows a significant difference of TL of all fishes caught with NET in medium and high fishing pressure sites. The cast net is more selective for large fish (depending on the mesh size) and one of the main fishing tools in Burkina Faso (Ouedraogo 2010). This way TL of fish caught with NET could be a good metric to indicate fishing pressures.

Figure 4.4: Linear regression of GPI and the number of species. Figure 3.18 shows the relative abundance of families in the pressure categories. While cichlids increase with pressure, Mormyridae and Mochokidae decrease. In a study by Hugueny et al. (1996) Mormyridae were almost extinct at impacted sites and were proposed as intolerant species for future IBI in Africa. Alestidae are similar in low and medium pressure sites but have a drop at high pressure sites. Clariidae are frequent in medium pressure sites but less frequent in low‐ and high pressure sites. Anne, Lelek, and Tobias (1994) experienced a considerable reduction in the abundance of the genus Citharinus in reservoir‐environments, and a substitution by cichlids and pelagic species like cyprinids. We could only find Citharinus in the protected area of Nazinga in very low numbers.

Further research is necessary regarding a “lowest anchor point”: a site with extremely bad conditions, e.g. waste channels in Ouagadougou, or the outlet of the Ouagadougou wastewater treatment plant (compare the studies of Koblinger and Trauner (2013) based on benthic invertebrates).

Length‐weight regression Based on the available data of Ouedraogo (2010) weight estimations for biomass calculations were performed. For some fishes the usually used method according to Ricker (1987) underestimates the measured values in the dataset. Ricker (1987) proposes to form size classes with the same random number of fishes placed in each class, which we haven’t done. Anyhow, looking at the log‐transformed graphs (Table 3.13), the highest classes would still have a median above the curve.

If an exponential function is applied instead, the curve fits better for some of the “problem” fishes whilst overestimating others which were fitting well with the power function. Also for some species with a low sample size, the exponential function seems to fit better to the measured values (Table 3.13, Mormyrops anguilloides, although the regression is not reliable in this case).

The reasons for the underestimations could be other growth curves of these fishes, problems with the estimations (e.g. the fact that the division into classes was not performed) or problems with the dataset (see e.g. the log‐transformed weight curves of S. gallilaeus and T. zillii, Table 3.13).

Because hardly any caught fishes were of a size to be affected by the underestimations and correlation with fishes we measured ourselves was high, the weight estimation according to Ricker (1987) was selected for further analysis.

5. Conclusion This was the first time to analyze land use and other human impacts with fish in West Africa on a large scale in a big part of the upper Nakambe catchment. The North South distance between the sampling sites was more than 160 km. This study revealed, that most waters of Burkina Faso are exposed to multiple human pressures, whereas overfishing, hydrological changes, agricultural pressures and water quality are the most frequent.

The results of Nazinga highlight the importance of protected areas as a refuge habitat for sensitive and rare species, and as a genetic reserve for future restoration measures.

The differences of total length at managed, and non‐managed fishing sites show the importance of a sustainable fisheries management. A licensing system for fishermen and closed seasons with better controls can assure a long‐term higher fish yield and improve the food security.

Water quality is still a big problem in Burkina Faso. Urban areas urgently need better waste water treatment. Water quality in agricultural areas could be improve a lot with re‐ vegetation of riparian buffer forests along the water bodies. This could also mitigate the siltation of reservoirs.

Some taxa react distinctly on the pressure intensity. As shown in this study and proposed by several authors, the sensitive Mormyridae, Polypterus, Brycinus, Hydrocynus, Heterobranchus, Synodontis, Labeo, Citharinus, Lates niloticus and Heterotis niloticus could serve as indicator taxa in waters as in Burkina Faso (Ouedraogo 2010; Tawari‐Fufeyin and Ekaye 2007). Additionally, Auchenoglanis, Ctenopoma and Schilbe show distinct reactions in this study. Other species like Tilapia zillii are tolerant to human pressures and increase significantly with pressure intensities.

Besides the services a reservoir can provide there is also the danger of diseases. Stagnant waters provide habitats for organisms and vectors resulting in the proliferation of diseases like Malaria and Bilharzia, which are both present in Burkina Faso (UNEP 2010).

A lot of problems come along with the creation of dams, e.g. siltation and reservoir volume loss (Ouedraogo 2010), downstream effects on biota, migration obstacles, the changes in hydrological and sediment regime and the impacts on rain fed agriculture (e.g. Marmulla 2001). Facing all these problems, it is questionable if the effort of developing new dams shouldn’t be focused on alternative solutions: e.g. Martin and van de Giesen (2005) showed that only 5% of the average annual groundwater recharge in large parts of the Burkinabe Nakambe basin is exploited at present. They conclude that from a geo‐scientific view, further development of groundwater abstraction would be sustainable and desirable.

65 Outlook Further research is required regarding a lowest anchor point (sites with a maximum pressure intensity), how population density corresponds with pressures and biota, a fish‐ and pressure gradient along the Massili river (with focus on the wastewater of Ouagadougou) and the effects of stocking on the fish population. For the mitigation of migration obstacles, a design for a fish migration aid, appropriate for the local fish population is urgently needed.

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72 List of Figures Figure 1.1: Important rivers and catchments in Burkina Faso. Adapted from (Badiel 2014; Cecchi et al. 2009)...... 10 Figure 1.2: Climatic regions of Burkina Faso and climate charts for Dori, Ouagadougou and Bobo‐Dioulasso. Adapted from (Yahmed 2005); climate data from (klimadiagramme 2014)...... 10 Figure 1.3: Map of reservoirs in Burkina Faso. Units in million cubic meter (Mm³). Adapted from (Cecchi et al. 2009) ...... 11 Figure 1.4: Organization chart and work packages. (Melcher 2011) ...... 13 Figure 1.5: The SUSFISH sampling team, December 2012 (Trauner 2012) ...... 15 Figure 2.1: Burkina Faso, overview of the main sampling areas (black circles) and sampling sites (blue dots). Modified from Google Earth (2013)...... 16 Figure 2.2: The areas of Kougri (A), Koubri (B), Bagre (C) and Nazinga (D) with the sampling sites marked as black dots. Modified from Google Earth (2013)...... 17 Figure 2.3: Data management flow chart ...... 18 Figure 2.4. Selection of sampled habitat parameters (velocity in m/s, width and depth in m, shading in %, presence‐absence of in‐stream structures) (Meulenbroek 2013)...... 21 Figure 2.5: Buffered sampling site of Kougri. Adapted from Google Earth with (QGis 2011)...... 22 Figure 2.6: Electrofishing (left) (Trauner 2012), Cast net fishing (right) (Koblinger 2012) ...... 24 Figure 2.7: A screenshot of the database...... 26 Figure 2.8: Pictures of tolerant fish species (trophic level from Froese and Pauly 2013) (Meulenbroek 2012, Stranzl 2012). A complete list is in Table 7.2...... 30 Figure 2.9: Pictures of intolerant fishes (trophic level from Froese and Pauly 2013) (Meulenbroek 2012, Stranzl 2012)...... 31 Figure 3.1: Physicochemical parameters of the sampling areas...... 33 Figure 3.2: Correlation of water temperature and latitude...... 33 Figure 3.3: Percentages of sites affected by pressure categories...... 35 Figure 3.4: Cumulative sum of pressure indices per sampling area...... 37 Figure 3.5: Pressure intensities per category for the sampling areas. 0=low pressure, 2= high pressure...... 38 Figure 3.6: Relative abundance of families of all sampled fish. (Rare families <0,5%) ...... 39 Figure 3.7: TL of all fish in the sampling areas. Left: NET, right: EF ...... 44 Figure 3.8: Biomass in g/min (left) and kg/ha (right) per sampling area ...... 44 Figure 3.9: Number of taxa per sampling area ...... 44 Figure 3.10: Diversity in low‐ and high order streams ...... 45 Figure 3.11: Relation of number of taxa and GPI...... 46 Figure 3.12: TL of all fish (left) and TL of large fish caught with NET (right) per GPI category...... 47 Figure 3.13: Biomass in kg/ha (left) and g/min (right) per GPI category ...... 47 Figure 3.14: Individuals per minute EF per GPI category...... 48 Figure 3.15: Share of sensitive species (left) and tolerant species (right) per GPI category...... 48

73 Figure 3.16: Total length of all fishes caught with NET for medium and high fishing pressure intensity...... 49 Figure 3.17: Boxplot of trophic levels per GPI category...... 50 Figure 3.18: Relative abundance of families per GPI category for EF. Rare families: <1% of total abundance...... 50 Figure 4.1: Oxygen saturation per GPI category...... 56 Figure 4.2: Changes in average flow before (fat, black) and after the construction of Bagre reservoir (blue). (Source: Volta River Authority 2013) ...... 57 Figure 4.3: Species richness for (medium and high) impacted sites (GPI) with low and medium & high connectivity pressures...... 62 Figure 4.4: Linear regression of GPI and the number of species...... 63 Figure 7.1: Catchment area, Strahler orders Conductivity and pH of the sampling areas...... 77 Figure 7.2: Conductivity categories for sampled river segments in the area of Loumbila (N‐W), Ziga (N‐E), Kougri and Koubri...... 78 Figure 7.3: Relative frequencies of pressures per pressure type...... 81

74 List of Tables Table 2.1: Study site names, date (2012), number of fished habitats, GPS‐Coordinates and elevation. Only sites with at least two sampled habitats were considered in the analysis. Sampling areas in bold...... 19 Table 2.2: Summary of variables and parameters of the field protocol for sampling fish and environmental data...... 20 Table 2.3: A selection of pressures found in Burkina Faso. (Meulenbroek 2012, Stranzl 2012) ... 23 Table 2.4: List of pressures, pressure types and effects considered for analysis according to Ouedraogo (2010) and Schinegger et al. (2012). Hyd= hydrological pressure, Morph= morphological pressure, Conn= Connectivity pressure, Wq= water quality pressure...... 27 Table 3.1: Chemical parameters of the largest sampling areas conducted by Laboratoire AINA Suarl, Ouagadougou. Range, median and standard deviation (SD)...... 34 Table 3.2: Environmental and physicochemical variables in the sampling areas. Range, median and standard deviation (SD) ...... 34 Table 3.3: Relative frequency of pressures. (presence/absence) ...... 36 Table 3.4: Pearson correlation between the pressure indices...... 36 Table 3.5: Number of sites per GPI category ...... 37 Table 3.6: List of the caught families, number of genera and number of species for all sites and abundance for all sites with EF and NET...... 40 Table 3.7: List of vulnerable species: less than 50 caught individuals in 3 or less sites...... 40 Table 3.8: Relative frequencies of fishes (%) in the sampling areas, caught with EF (species in brackets were only caught with NET). Highlight of species with a frequency >10% (bold) and occurring exclusively in one sampling area (cursive). X for presence in NET samplings. Last row: Number of species and exclusive species only occurring in one area for EF and NET...... 42 Table 3.9: Relative frequencies of genera (%) in the sampling areas, caught with EF ...... 43 Table 3.10: Share of tolerant, intolerant and dwarf species in the sampling areas...... 43 Table 3.11: Summary of genera correlating significantly (0.05) with the pressure indices...... 45 Table 3.12: Percentage of selected economically valuable fish reaching maturity per fishing pressure, caught with NET (Maturity lengths from Froese and Pauly 2013)...... 49 Table 3.13: Length‐weight regressions for some selected fishes...... 52 Table 3.14: Biomass calculations for habitats fished with EF (kg/ha) based on exponential function, power function and fishbase estimation and measured values ...... 54 Table 7.1: Air‐distance (km) between the sampling sites...... 79 Table 7.2: Species descriptions. Maximal length (L max) in cm, Maximal weigth (W max) in grams, IUCN concern (lc‐least concerned, na‐ not available), trophic level, Pigmy species (max length <150mm), data source: Froese and Pauly 2013 and sensitive and tolerant species...... 82 Table 7.3: Summary of all caught families, genera and species and the number of sites they were caught...... 84 Table 7.4: Habitat assessment sheet 1 ...... 90 Table 7.5: Habitat assessment sheet 2 ...... 91

List of abbreviations Expression Abbreviation Agricultural pressure index API Cast net NET Connectivity pressure index CPI Connectivity pressures Conn Cross section Cross sect Electrofishing EF Eutrophication Euth Flood protection Flood prot Gill net GN Global pressure index GPI Hydrograph modification Hyd mod Hydrological pressure index HPI Hydrological pressures Hyd Impoundment Imp In stream habitat In hab Least concerned lc Maximum length L max Maximum weight W max Million cubic meter Mm³ Morphological pressure index MPI Morphological pressures Morph Multi metric index MMI Not available na pH modification pH Mod Reservoir flushing Res flush Residual flow Res flow Total length TL Water quality pressure index WPI Water quality pressures Wq

7. Appendix

Environmental parameters This chapter shows boxplots of environmental parameters which were not shown in the results.

Figure 7.1: Catchment area, Strahler orders Conductivity and pH of the sampling areas.

77 Figure 7.2 shows median conductivity values for the areas Koubri, Koubri_free, Loumbila and Kougri. The sections in Ziga and Loumbila have a much lower conductivity than most other sections.

Figure 7.2: Conductivity categories for sampled river segments in the area of Loumbila (N‐W), Ziga (N‐E), Kougri and Koubri.

Table 7.1 shows the air‐distance between the sampling sites. The greatest distance is between Ziga in the North and Bodjero in Nazinga. Distances are rounded to km.

Table 7.1: Air-distance (km) between the sampling sites. Site Site name 3 4 5 6 7 8 9 11 12 13 14 15 17 18 3 Loumbila 32 32 32 33 35 34 33 28 32 38 35 42 43 4 Arzoum Baongo 32 6 1 2 7 11 12 11 3 15 12 22 23 5 Naba Zana 32 6 8 5 3 5 6 6 3 9 18 28 29 6 Segda 32 1 8 3 8 13 13 12 4 16 11 21 21 7 Noungou 33 2 5 3 5 10 11 11 3 14 13 23 24 8 Poedogo 35 7 3 8 5 6 6 8 5 8 18 28 29 9 pond Naba Zana 34 11 5 13 10 6 1 5 8 5 23 33 34 inlet 11 PK25 33 12 6 13 11 6 1 5 9 5 24 34 35 12 Toyoko 28 11 6 12 11 8 5 5 8 9 22 33 33 13 Tanvi 32 3 3 4 3 5 8 9 8 12 15 25 26 14 Wedbila 38 15 9 16 14 8 5 5 9 12 27 37 37 15 Peele 35 12 18 11 13 18 23 24 22 15 27 10 11 17 Nakambe below 42 22 28 21 23 28 33 34 33 25 37 10 2 Nariale conﬂuent 18 Nakambe barrage 43 23 29 21 24 29 34 35 33 26 37 11 2 19 Kougri 37 29 35 28 31 36 40 41 38 32 44 19 14 15 20 Nakambe/Masili 41 23 29 21 24 29 34 34 33 25 37 10 1 3 21 Ziga 35 38 43 37 40 45 48 48 45 40 52 29 26 27 22 Beguedo 108 80 83 79 79 81 86 87 89 82 86 73 67 66 23 Beguedo 2 103 75 79 74 74 76 82 82 85 77 82 68 62 61 24 Djerma/Boussouma 106 76 78 75 74 75 80 81 84 77 79 72 68 67 26 Niagho 106 76 80 76 76 77 82 83 86 78 82 71 65 64 27 Lengho 120 89 92 89 88 90 94 95 98 91 94 85 80 78 28 Zangoula 138 109 112 108 108 109 114 115 118 110 114 103 97 95 30 Bagre-Bangako 146 117 120 116 116 117 122 123 126 118 121 111 106 104 Spillway 31 Nakambe 154 124 127 123 123 124 129 130 133 125 128 118 113 112 hydropeaking 32 Fungu 131 101 103 100 99 100 105 106 109 102 104 96 92 91 40 Naguio 152 125 121 125 123 119 119 119 124 123 114 131 135 135 41 Bodjero 155 126 124 127 124 121 121 122 127 125 117 132 136 135 42 Talango 145 117 114 117 115 111 111 112 117 116 107 123 127 126 43 Kouzougou 149 121 118 121 119 115 115 116 120 120 111 127 131 130

Table 7.1: Air‐distance (km) between the sampling sites. (continued) Site 19 20 21 22 23 24 26 27 28 30 31 32 40 41 42 43 3 41 35 108 103 106 106 120 138 146 154 131 152 155 145 149 37 4 29 23 38 80 75 76 76 89 109 117 124 101 125 126 117 121 5 35 29 43 83 79 78 80 92 112 120 127 103 121 124 114 118 6 28 21 37 79 74 75 76 89 108 116 123 100 125 127 117 121 7 31 24 40 79 74 74 76 88 108 116 123 99 123 124 115 119 8 36 29 45 81 76 75 77 90 109 117 124 100 119 121 111 115 9 40 34 48 86 82 80 82 94 114 122 129 105 119 121 111 115 11 41 34 48 87 82 81 83 95 115 123 130 106 119 122 112 116 12 38 33 45 89 85 84 86 98 118 126 133 109 124 127 117 120 13 32 25 40 82 77 77 78 91 110 118 125 102 123 125 116 120 14 44 37 52 86 82 79 82 94 114 121 128 104 114 117 107 111 15 19 10 29 73 68 72 71 85 103 111 118 96 131 132 123 127 17 14 1 26 67 62 68 65 80 97 106 113 92 135 136 127 131 18 15 3 27 66 61 67 64 78 95 104 112 91 135 135 126 130 19 12 12 78 73 80 76 91 107 116 124 104 149 149 140 144 20 12 25 68 63 69 66 81 98 107 114 93 137 137 128 132 21 12 25 88 83 92 87 103 117 127 134 115 160 161 152 156 22 78 68 88 5 19 6 16 29 39 46 29 117 113 109 111 23 73 63 83 5 19 6 20 34 44 51 34 117 113 109 111 24 80 69 92 19 19 13 15 36 43 49 25 99 95 90 93 26 76 66 87 6 6 13 15 32 41 48 28 112 108 103 106 27 91 81 103 16 20 15 15 21 28 34 13 106 101 98 100 28 107 98 117 29 34 36 32 21 10 18 20 122 115 114 115 30 116 107 127 39 44 43 41 28 10 7 21 119 112 112 113 31 124 114 134 46 51 49 48 34 18 7 26 121 113 113 114 32 104 93 115 29 34 25 28 13 20 21 26 102 95 94 95 40 149 137 160 117 117 99 112 106 122 119 121 102 10 9 6 41 149 137 161 113 113 95 108 101 115 112 113 95 10 11 7 42 140 128 152 109 109 90 103 98 114 112 113 94 9 11 4 43 144 132 156 111 111 93 106 100 115 113 114 95 6 7 4

Pressures Figure 7.3 shows the relative frequencies (% of sites affected) for each pressure, split up into pressure types.

Figure 7.3: Relative frequencies of pressures per pressure type.

81 Species profiles Table 7.2 gives a short description of the species, with data from Froese and Pauly (2013). Table 7.3 gives an overview of the taxonomy of the species, and the number of catches.

Table 7.2: Species descriptions. Maximal length (L max) in cm, Maximal weigth (W max) in grams, IUCN concern (lc‐least concerned, na‐ not available), trophic level, Pigmy species (max length <150mm), data source: Froese and Pauly (2013) and sensitive and tolerant species. Species Genus L max W max IUCN trophic Dwarf species Sensi Toler (cm) (g) Concern level (150 mm) tive ant Alestes baremoze Alestes 43 500 lc 3.05 0 1 0 Alestes dentex Alestes 55 600 lc 2.93 0 1 0 Auchenoglanis Auchenoglanis 70 na lc 2.9 0 1 0 occidentalis Bagrus bajad Bagrus 112 12500 lc 3.39 0 1 0 Bagrus docmak Bagrus 127 35000 lc 4.08 0 1 0 Barbus ablabes Barbus 11 na lc 2.45 1 0 1 Barbus macrops Barbus 9.8 8 lc 3.04 1 0 1 Brienomyrus niger Brienomyrus 13 na lc 2.94 1 0 0 Brycinus Brycinus 53 2000 lc 2.75 0 0 0 macrolepidotus Brycinus nurse Brycinus 25 200 lc 2.81 0 0 0 Chelaethiops bibie Chelaethiops 5.5 na lc 2.25 1 0 0 Chrysichthys Chrysichthys 65 na lc 3.17 0 0 0 nigrodigitatus Citharinus citharus Citharinus 58 7000 na 2 0 1 0 Clarias gariepinus Clarias 170 60000 na 3.5 0 0 1 Ctenopoma Ctenopoma 24.5 125 lc na 0 1 0 kingsleyae Ctenopoma Ctenopoma 14 na lc 3.16 1 1 0 petherici Distichodus Distichodus 76 6300 lc 2.58 0 1 0 rostratus Hemichromis Hemichromis 26.5 300 lc 3.18 0 0 0 fasciatus Hemichromis Hemichromis 11.9 na lc 3.04 1 0 0 letourneauxi Heterobranchus Heterobranchus 150 30000 lc na 0 1 0 bidorsalis Heterobranchus Heterobranchus 150 55000 lc 3.72 0 1 0 longifilis Heterotis niloticus Heterotis 100 10200 lc 2.93 0 0 0 Hippopotamyrus Hippopotamyru 25 na lc na 0 0 0 paugyi s Hippopotamyrus Hippopotamyru 30 100 lc na 0 0 0 pictus s Hydrocynus forskali Hydrocynus 78 15500 lc 4 0 1 0 Hydrocynus Hydrocynus 105 28000 lc 3.93 0 1 0 vittatus Hyperopisus bebe Hyperopisus 51 1000 lc 3.6 0 1 0

82 Table 7.2 (continued)

Labeo coubie Labeo 75 5000 lc 2.04 0 1 0

Labeo niloticus Labeo 47 na lc 2.53 0 1 0

Labeo senegalensis Labeo 65 3800 na na 0 1 0

Lates niloticus Lates 200 200000 lc 3.95 0 0 0

Malapterurus Malapterurus 122 20000 lc 2.93 0 0 0 electricus Marcusenius Marcusenius 32.1 200 lc 2.79 0 1 0 senegalensis Micralestes Micralestes 6 na na 3.26 1 0 1 elongates Mormyrops Mormyrops 150 15000 lc 3.77 0 0 0 anguilloides Mormyrus Mormyrus 50 1100 lc na 0 0 0 Hasselquisti Mormyrus rume Mormyrus 100 5300 na 2.48 0 0 0

Oreochromis Oreochromis 60 4300 na 2.02 0 0 1 niloticus Petrocephalus Petrocephalus 11 13 na 3.11 1 0 0 bovei Pollimyrus isidori Pollimyrus 9 7 na 2.61 1 1 0

Polypterus Polypterus 72 3300 na 3.77 0 0 0 endlicheri Polyterus Polypterus 79 207 na 3.44 0 0 0 senegalus Sarotherodon Sarotherodon 34 1600 na 2.23 0 0 1 galilaeus Schilbe intermedius Schilbe 60.5 na lc 3.6 0 1 0

Schilbe mystus Schilbe 40 250 lc 3.28 0 1 0

Siluranodon auritus Siluranodon 17.5 na lc 2.86 0 0 0

Synodontis clarias Synodontis 36 na lc 2.96 0 0 0

Synodontis Synodontis 22 na near threathened 0 0 0 comoensis Synodontis Synodontis 27.5 na lc na 0 0 0 ﬁlamentosus Synodontis Synodontis 50 na lc 3.11 0 0 0 membranaceus Synodontis Synodontis 26 na lc na 0 0 0 punctifer Synodontis schall Synodontis 49 500 lc 2.92 0 0 0

Synodontis velifer Synodontis 23.8 na lc na 0 0 0

Synodontis Synodontis 50 na lc na 0 0 0 vermiculata Tilapia zillii Tilapia 50 300 na 2.52 0 0 1

83 Table 7.3: Summary of all caught families, genera and species and the number of sites they were caught. Family (N=16) Genus (N=36) Species (N=61) Number sites (N=38) Alestidae Alestes Alestes baremoze 15 Alestes dentex 6 Alestes sp. 2 Brycinus Brycinus leuciscus 2 Brycinus longipinnis 8 Brycinus luteus 12 Brycinus nurse 27 Hydrocynus Hydrocynus brevis 1 Hydrocynus forskali 2 Micralestes Micralestes occidentalis 5 Micralestes pabrensis 1 Rhabdalestes Rhabdalestes septentrionalis 19 Anabantidae Ctenopoma Ctenopoma kingsleyae 3 Bagridae Bagrus Bagrus bajad 17 Bagrus docmak 6 Centropomidae Lates Lates niloticus 13 Cichlidae Hemichromis Hemichromis bimaculatus 18 Hemichromis fasciatus 5 Oreochromis Oreochromis niloticus 35 Sarotherodon Sarotherodon galilaeus 34 Tilapia Tilapia zillii 35 Citharinidae Citharinus Citharinus citharus 2 Clariidae Clarias Clarias sp. 34 Heterobranchus Heterobranchus bidorsalis 3 Heterobranchus longifilis 1 Claroteidae Auchenoglanis Auchenoglanis occidentalis 7 Chrysichthys Chrysichthys auratus 1 Chrysichthys nigrodigitatus 7 Cyprinidae Barbus Barbus ablabes 6 Barbus baudoni 4 Barbus hypsolepis 2 Barbus leonensis 2 Barbus macrops 36 Barbus pobeguini 4 Barbus sp. 30 Chelaethiops Chelaethiops bibie 25 Labeo Labeo coubie 13 Labeo senegalensis 6 Leptocypris Leptocypris niloticus 12 Leptocypris sp. 8

84 Table 7.3 (continued) Distichodontidae Distichodus Distichodus rostratus 9 Malapteruridae Malapterurus Malapterurus electricus 2 Mochokidae Synodontis Synodontis clarias 1 Synodontis membranaceus 7 Synodontis ocellifer 2 Synodontis schall 21 Synodontis sp. 24 Mormyridae Brienomyrus Brienomyrus niger 1 Hippopotamyrus Hippopotamyrus pictus 1 Hyperopisus Hyperopisus bebe 13 Marcusenius Marcusenius senegalensis 24 Mormyrops Mormyrops anguilloides 1 Mormyrus Mormyrus hasselquistii 1 Mormyrus macrophthalmus 2 Mormyrus rume 22 Petrocephalus Petrocephalus bovei 14 Pollimyrus Pollimyrus isidori 15 Polypteridae Polypterus Polypterus senegalus senegalus 5 Protopteridae Protopterus Protopterus annectens 1 Schilbidae Parailia Parailia pellucida 10 Schilbe Schilbe intermedius 22 Schilbe mystus 3

85 Length frequency graphs The length frequency graphs are drawn for the sampling area of Nazinga which is regarded as reference area due to the low pressure intensity.

89 Assessment sheets Table 7.4: Habitat assessment sheet 1

Table 7.5: Habitat assessment sheet 2