DISTRIBUTION, GROWTH AND MORTALITY OF altianalis OF RIVER KUJA, BASIN, .

SUNDA KEMUNTO CHRISTINE

BSC. (UNIVERSITY OF ELDORET)

A THESIS SUBMITTED TO THE SCHOOL OF POST-GRADUATE STUDIES IN

PARTIAL FULLFILLMENT OF THE REQUIREMENTS OF THE MASTERS DEGREE

OF THE SCHOOL OF AGRICULTURE AND NATURAL RESOURCE

MANAGEMENT, DEPARTMENT OF AQUATIC AND FISHERY SCIENCES,

KISII UNIVERSITY

SEPTEMBER 2018

DECLARATION AND RECOMMENDATION

DECLARATION BY THE CANDIDATE I declare that this thesis is my original work and has not been presented anywhere for a degree in any university or any other award.

SUNDA KEMUNTO CHRISTINE ......

MAN/25/50006/15 Signature Date

RECOMMENDATION BY THE SUPERVISORS

This thesis has been submitted for examination with our approval as University supervisors

PROF. ALBERT GETABU ......

Research and Extension office Signature Date

Kisii University

PROF. JAMES NJIRU ......

Kenya Marine & Fisheries Signature Date

Research Institute, (KMFRI)

ii

PLAGIARISM DECLARATION

DECLARATION BY STUDENT

i. I declare I have read and understood Kisii University Postgraduate Examination Rules

and Regulations, and other documents concerning academic dishonesty.

ii. I do understand that ignorance of these rules and regulations is not an excuse for a

violation of the said rules.

iii. If I have any questions or doubts, I realize that it is my responsibility to keep seeking

an answer until I understand.

iv. I understand I must do my own work.

v. I also understand that if I commit any act of academic dishonesty like plagiarism, my

thesis/project can be assigned a fail grade (“F”)

vi. I further understand I may be suspended or expelled from the University for

Academic Dishonesty.

Name: SUNDA KEMUNTO CHRISTINE Signature______

Reg. No: MAN/25/50006/15 Date______

iii

DECLARATION BY SUPERVISOR (S)

i. I/we declare that this thesis has been submitted to plagiarism detection service.

ii. The thesis contains less than 20% of plagiarized work.

iii. I/we hereby give consent for marking.

1. Prof. Albert Getabu Signature______

Research and Extension Office Date______

2. Prof. James Njiru Signature______

Kenya Marine & Fisheries Res. Inst. Date______

iv

DECLARATION OF NUMBER OF WORDS FOR MASTERS THESIS

Name of Candidate: Sunda Kemunto Christine ADM NO: MAN/25/50006/15

Faculty: Agriculture and Natural Resource Management

Department: Aquatic and Fishery Science

Thesis Title: Distribution, growth and mortality of Labeobarbus altianalis of River Kuja, Lake Victoria Basin Kenya

I confirm that the word length of: 1) the thesis, including footnotes, is ……21,393…..the appendices are …………………498……………………… I also declare the electronic version is identical to the final, hard bound copy of the thesis and corresponds with those on which the examiners based their recommendation for the award of the degree. Signed: …………………………………… Date:…………………… Sunda Kemunto Christine

I confirm that the thesis submitted by the above-named candidate complies with the relevant word length specified in the School of Postgraduate and Commission of University Education regulations for the Masters Degrees. Signed:...... Email……………………… Tel………………….. Date:…………… Prof. Getabu

Signed:...... Email………………...…….. Tel………………….. Date:…………… Prof. Njiru

v

COPY RIGHT

All rights are reserved. No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the author or Kisii University on that behalf.

© 2018, Sunda Kemunto Christine

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DEDICATION

This work is dedicated to my loving father, Mr. David Sunda who has always desired for my academic growth and has devoted himself in everything to achieve this.

vii

ACKNOWLEDGEMENT

The successful study has been achieved with a dose of gratitude and I thank the Almighty God for His providence and the various people to whom I owe so much. First, I would like to thank my supervisors Prof. Getabu (Associate Professor Kisii University) and Prof. Njiru (Director KEMFRI) for their guidance, advice and mentoring throughout the entire Master’s Degree Program. I sincerely thank Prof. Njiru for personally organizing for the data collection through KEMFRI Kisumu staff. I am indebted to Mr. Auwondo, Mr. Ongore, Mr. Achiya, Dr. Aura, Dr. Nyamweya and the entire team that came from Kisumu monthly so as to help in sampling River Kuja. I am grateful to Prof. Getabu for finding time to accompany us to River Kuja for sampling.

I would like to thank my colleagues Enock and Emmy for being available for discussion during the entire Master’s program. I am grateful for their encouragement, their resources in sampling and ideas shared during the challenging moments. I am grateful to the German Academic Exchange Service (DAAD) for funding my Master’s degree through a scholarship. I truly appreciate the schools fees aid, stipend and research fund offered during the program. This achievement wouldn’t be possible without their assistance. May God bless Deutscher Akademischer Austauschdienst (DAAD) for supporting many students.

I would like to express gratitude to my parents and siblings for their support, encouragement and prayers. I wish to appreciate my spiritual parents for their prayers and special thanks to Dr. Nyaga and family for standing with me throughout the way. Finally i thank my husband Mr. Njoroge, for the continuous encouragement and being there for the family when I was absent during data collection.

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ABSTRACT

This study was conducted with the major objective of investigating the distribution, growth and mortality of Labeobarbus altianalis in River Kuja which is one of the major rivers draining into Lake Victoria. So far, limited research has been undertaken on the biology and of the in the Lake Victoria basin, particularly in the riverine environments. A total of 1486 fish specimens were collected monthly at six sampling sites along River Kuja from November 2016 to August 2017 using an electrofishing equipment. The length and weight of the fish were measured then separated into males and females to obtain sex ratio and dissected for sexual maturity examination. Monthly length-frequency distributions of L. altianalis were analyzed using the FiSAT computer software to estimate growth parameters, recruitment pattern, mortality, exploitation rate and longevity of the species. This data was augmented with information on the physical and chemical characteristics of the sampling point along the river for use in explaining the distribution pattern of the species. The asymptotic length (L∞) and growth coefficient (K) were estimated as 60.00 cm TL and 0.40 yr-1 respectively using the ELEFAN method, while the Powel Wetheral method provided an L∞ of 61.29 cm TL and a ratio of total mortality to growth coefficient (Z/K) of 2.80. The length at 50% maturity for the female and male were 38.33 cm and 33.33 cm TL respectively. The exploitation rate, growth performance index and potential longevity were 0.61, 3.16 and 7.5 years respectively. The total, natural and fishing mortality were 1.83 yr-1, 0.71 yr-1 and 1.12 yr-1 respectively. The fish recruits twice a year November to January and April to July. The results indicate presence of L. altianalis in the Kuja River though 81.03% constitute fish that are sexually immature. The species is naturally recruiting and is able to grow to an age of reproduction. The knowledge gained will be useful for management purposes since it acts as inputs for formulating the models used for sustainable management. The management will ensure the growth of the immature fish and conserve the L. altianalis populations of River Kuja.

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TABLE OF CONTENTS

DECLARATION AND RECOMMENDATION ...... ii PLAGIARISM DECLARATION...... iii DECLARATION OF NUMBER OF WORDS FOR MASTERS THESIS ...... v COPY RIGHT...... vi DEDICATION ...... vii ACKNOWLEDGEMENT ...... viii ABSTRACT...... ix TABLE OF CONTENTS...... x LIST OF TABLES ...... xiv LIST OF FIGURES ...... xvi LIST OF APPENDICES ...... xvii LIST OF ABBREVIATION ...... xviii CHAPTER ONE ...... 1 1.0 INTRODUCTION ...... 1 1.1 BACKGROUND OF THE STUDY ...... 1 1.1.1 Lake Victoria Basin Drainage System ...... 1 1.1.2 History of decline of Labeobarbus altianalis in the Lake Fisheries ...... 2 1.1.3 Labeobarbus altianalis ...... 2 1.2 Problem Statement ...... 5 1.3 Justification ...... 5 1.4 Assumptions ...... 6 1.5 Objectives ...... 7 1.5.1 General Objective ...... 7 1.5.2 Specific objectives ...... 7 1.6 Hypothesis ...... 7 CHAPTER TWO ...... 8 2.0 LITERATURE REVIEW ...... 8 2.1 Labeobarbus ...... 8 2.2 and Distribution ...... 9

x

2.3 Ecology ...... 9 2.4 Food and feeding of Labeobarbus altianalis ...... 11 2.5 Growth of Labeobarbus altianalis ...... 11 2.6 Mortality ...... 13 CHAPTER THREE ...... 15 3.0 MATERIALS AND METHODS...... 15 3.1 Study area ...... 15 3.1.1 Sampling sites description ...... 16 3.2 Methodology ...... 19 3.3 Water quality ...... 19 3.4 Water velocity ...... 19 3.5 Collection of abundance and length frequency data ...... 19 3.6 Data processing and analysis ...... 21 3.7 Length at 50% maturity ...... 24 3.8 Relationship of water quality and abundance ...... 24 3.9 Length weight...... 24 3.10 Relative condition factor ...... 25 CHAPTER FOUR...... 26 4.0 RESULTS ...... 26 4.1 Water quality ...... 26 4.1.1 pH ...... 26 4.1.2 Temperature ...... 27 4.1.3 Conductivity ...... 27 4.1.4 Dissolved Oxygen ...... 28 4.1.5 Total Phosphates ...... 28 4.1.6 Total Nitrates ...... 29 4.1.7 Total Dissolved Solids ...... 29 4.1.8 Turbidity ...... 30 4.1.9 Speed ...... 30 4.1.10 Correlation between physicochemical parameters and abundance (Appendix 1) ...... 31 4.2 Distribution and abundance of Labeobarbus altianalis ...... 33 4.2.1 Monthly temporal distribution of Labeobarbus altianalis from River Kuja ...... 34

xi

4.3 Mean length and mean weight of Labeobarbus altianalis ...... 35 4.4 Length frequency distribution...... 36 4.5 Length Weight relationship ...... 38 4.6 Relative condition factor ...... 39 4.7 Growth parameters of Labeobarbus altianalis ...... 41 4.7.1 Powel Wetherall plot ...... 41 4.7.2 ELEFAN Analysis ...... 41 4.7 Recruitment of Labeobarbus altianalis ...... 42 4.8 Estimation of Mortality and the exploitation rate ...... 43

4.10 Growth performance index (∅′) and potential longevity (tmax) ...... 44 4.13 Sex ratios ...... 44 4.14 Maturity ...... 45 CHAPTER FIVE ...... 49 5.0 DISCUSSION ...... 49 5.1 Water Quality (Physicochemical parameters) ...... 49 5.1.1 pH ...... 49 5.1.2 Temperature ...... 49 5.1.3 Conductivity ...... 50 5.1.4 Dissolved Oxygen ...... 51 5.1.5 Phosphates ...... 51 5.1.6 Total Nitrates ...... 52 5.1.7 Total dissolved solids (TDS) ...... 52 5.1.8 Turbidity ...... 53 5.1.9 Stream velocity ...... 54 5.2 Relationship between physicochemical parameters and fish abundance...... 54 5.2.1 pH ...... 54 5.2.2 Temperature ...... 55 5.2.3 Conductivity ...... 55 5.2.4 Dissolved Oxygen ...... 55 5.2.5 Total Phosphates ...... 56 5.2.6 Total Nitrates ...... 56 5.2.7 Total Dissolved Solids ...... 56 5.2.8 Turbidity ...... 57 xii

5.2.9 Stream velocity ...... 57 5.2.10 Labeobarbus altianalis fish abundance in River Kuja ...... 57 5.3 Distribution and abundance...... 58 5.4 Mean length of fish ...... 59 5.5 Length frequency ...... 59 5.6 Length Weight relationship ...... 60 5.7 Condition factor ...... 61 5.8 Population growth...... 62 5.9 Mortality ...... 63 5.10 Exploitation rate ...... 64 5.11 Growth Performance Index (∅′) ...... 65 5.12 Potential longevity ...... 65 5.13 Recruitment pattern ...... 65 5.14 Sex ratio ...... 66 5.15 Maturity ...... 67 CHAPTER SIX ...... 68 6.0 CONCLUSION AND RECOMMENDATION...... 68 6.1 CONCLUSION ...... 68 6.2 RECOMMENDATION ...... 70 REFERENCE...... 71 APPENDICES ...... 88

xiii

LIST OF TABLES

Page Table 1. Sexual maturity stages characteristics …………………………………….. 21

Table 2. pH levels at the six sampling sites along River Kuja ……………………… 26

Table 3. The temperature of the six sampling sites along River Kuja ………………. 27

Table 4. Conductivity measures of the six sampling sites along River Kuja ………… 27

Table 5. Dissolved Oxygen measures of the six sampling sites along River Kuja…… 28

Table 6. Total Phosphates concentration of the six sampling sites along River Kuja.… 28

Table 7. Total Nitrates concentration of the six sampling sites along River Kuja…..…..29

Table 8. Total Dissolved Solids of the six sampling sites along River Kuja ……...…….29

Table 9. Turbidity levels of the six sampling sites along River Kuja …………..…….... 30

Table 10. Velocity of water of the six sampling sites along River Kuja ……….……….30

Table 11. Distribution of Labeobarbus altianalis (numbers) at the six sampling stations along

River Kuja during the period November 2016 to August 2017 ………….……..……… 33

Table 12. Labeobarbus altianalis abundance during the dry and wet season ….….…… 34

Table 13. Temporal distribution of Labeobarbus altianalis of River Kuja during the period

November 2016 to August 2017 ……………………………..….……………………… 35

Table 14. Mean length of Labeobarbus altianalis at the six sampling sites along River Kuja

…………………………………………………………………..……………….………. 36

xiv

Table 15. Percentage frequency distribution of Labeobarbus altianalis per length class in

River Kuja during the period November 2016 to August 2017 ……………………… 38

Table 16. Correlation between log TL (cm) and log W (g) of Labeobarbus altianalis population from sampling sites along River Kuja…………………………………..… 39

Table 17. Ratio of females to males Labeobarbus altianalis sampled at various sites along

River Kuja………………………………………………………………………..…….. 45

Table 18. Maturity ratio of male to female Labeobarbus altianalis sampled at various sites along River Kuja………………………………………………………………………… 46

xv

LIST OF FIGURES

Page Figure 1. Labeobarbus altianalis …………………………………………………… 3

Figure 2. River Kuja drainage basin with the sampled stations …………………….. 16

Figure 3. Length Frequency distribution of Labeobarbus altianalis sampled in River Kuja

……………………………….……………………………………………………..… 37

Figure 4. Monthly changes in relative condition factor in Labeobarbus altianalis along River

Kuja ………………………………………………………………………………….. 40

Figure 5. Estimation of L∞ and Z/K using the Modified Wetherall method for Labeobarbus altianalis in River Kuja ……………………………….……………………………………. 41

Figure 6. Monthly length frequency distribution of Labeobarbus altianalis fitted with growth curves ……..……………………………………………………………………………. 42

Figure 7. Recruitment pattern of Labeobarbus altianalis of R. Kuja during the months of

Nov 2016 to August 2017 ……………………………………………………………… 43

Figure 8. Length converted catch curve for Labeobarbus altianalis in River Kuja……. 44

Figure 9. Mean length at sexual maturity of female Labeobarbus altianalis in River Kuja

………………………………………………………………………………………..… 47

Figure 10. Mean length at sexual maturity of male Labeobarbus altianalis in River Kuja

……………………………………………………………………………….…..….….. 47

xvi

LIST OF APPENDICES

Page

Appendix 1.0 Pearson correlation table between physicochemical parameters and fish abundance ……………..……………………………………………………………….. 89

Appendix 2.0 Pearson correlation table between physicochemical parameters and fish abundance ……………..……………………………………………….……………….. 90

Appendix 3.0 Descriptive statistics and estimated parameters of the length – weight relations for Labeobarbus altianalis in various sampling stations along River Kuja

………………………………………....…………..…………………….……………… 91

Appendix 4.0 Relative recruitment values …………………………………………….. 92

xvii

LIST OF ABBREVIATION

ANOVA – Analysis of Variance

FISAT – FAO-ICLARM Stock Assessment Tool

FAO – Food and Agriculture Organization

LVB - Lake Victoria Basin

GDP – Gross Domestic Product

LVFO – Lake Victoria Organization

IUCN – International Union for Conservation of Nature

ELEFAN – Electronic Length Frequency Analysis

VBGF - von Bertalanffy growth function

xviii

CHAPTER ONE

1.0 INTRODUCTION

This chapter presents the background, statement of the problem, justification, objectives and hypothesis of the research on the distribution, growth and mortality of Labeobarbus altianalis of River Kuja.

1.1 BACKGROUND OF THE STUDY

1.1.1 Lake Victoria Basin Drainage System

The general elevation of the Kenyan catchment area within the Lake Victoria Basin is about

1100-1800 m above sea level (Okungu, Njoka, Abuodha & Hecky, 2005). The Lake Victoria

Basin drainage system is comprised of five major rivers namely River Nzoia, River Kuja,

River Nyando, River Yala, and River Sondu. (Juma, Wang & Li, 2014). These rivers drain water to the lake at different discharge percentages: River Nzoia discharged 39%, River Kuja disharges 20%, River Sondu disharges 14%, River Yala discharges 13% and River Nyando discharges 6% (Okungu et al., 2005).

The rivers within the Lake Victoria Basin are major contributors to the lake’s water budget and aquatic biodiversity. They are a source of employment, breeding ground for many fish species, source of water, food, transport and source of hydropower to the local communities.

Riverine fisheries are considered of low importance by national governments due to their low contribution to the export economy (Mwangi, Ombogo, Amadi, Baker & Mugulu, 2012).

The rivers along the Lake Victoria Basin drainage system largely originate from the highlands and are polluted through the release of domestic and industrial wastes from both

1 urban and rural centers distributed within their catchments (Okeyo, Raburu, Masese &

Omari, 2012).

Degradation through various anthropogenic activities (Manyala & Ochumba, 1990) has caused a decline in the fishing opportunity, loss of biodiversity and a great impact on the people depending on riverine fisheries as a source of revenue. Detrimental activities that pollute the rivers eventually affect people’s livelihoods all over the basin.

1.1.2 History of decline of Labeobarbus altianalis in the Lake Fisheries

The introduction of exotic fish like perch has distorted Lake Victoria’s food web assembly and further led to the diversity decline of native fish species like L. altianalis

(Okungu et al., 2005). Other factors like overfishing, obliteration of fish and pollution have affected the fisheries status of Lake Victoria and rivers draining into it. The native species have declined due to overfishing, predation, competition and environmental degradation. Lake Victoria’s fishery is currently dominated by Rastrineobola argentea, Lates niloticus and Oreochromis niloticus (Njiru, Mkumbo & van der Knaap 2010).

The Lake Victoria Basin resources have been utilized in an unsustainable way hence the current scenario of a biodiversity decline and seeming disappearance of native species

(LVEMP, 2004). The reduction in fish species of Lake Victoria has been documented as one of the greatest biodiversity losses in an ecosystem caused by man (Witte et al., 1999; Njiru,

Mkumbo & van der Knaap 2010).

1.1.3 Labeobarbus altianalis The ripon barbel (Labeobarbus altianalis) belongs to the following group of taxa; kingdom:

Animalia, phylum: , subphylum: Vertebrata, infraphylum: Gnathostoma, superclass: Osteichytes, class: , subclass: Actinopterygii, order:

2

Cypriniformes, family: , genus: Labeobarbus, species: L. altianalis (Ntakimazi,

2006).

Figure 1: Labeobarbus altianalis

The ripon barbel has three dorsal spines, nine to eleven dorsal soft rays, five to six anal soft rays and two to three anal spines. It has a short anterior barbel that spreads to the middle of the eye and a posterior barbel that spreads past the anterior eye. It has well developed lips and may have an outgrowth on the snout (Fish Base Team & Geelhand, 2016).

The cyprinid family comprises the most abundant species of the riverine fisheries and are majorly located at the middle and lower reaches of the river (Lucas & Batley, 1996). L. altianalis inhabits Lake Victoria and its drainage basin, Lake Edward, Lake George, Lake

Kyoga, Lake Kivu and Akagera River (Ntakimazi, 2006). It’s an important fish for food and sports fishing and its local names are Fwani in Dholuo and Kasinja in Swahili (Chemoiwa &

Jepleting, 2018). In Kenya, L. altianalis is restricted to the Lake Victoria and its drainage basin. It is a potamodromous fish that migrates upstream to spawn (Chemoiwa et al., 2013).

It inhabits shallow waters of rivers and lakes, as well as fast-flowing waters and feeds on fish, , plant material and mollusk (Balirwa, 1978; Chemoiwa & Jepleting, 2018).

3

It’s a stomach less riverine fish with a maximum recorded total length of 90 cm (Ntakimazi,

2006). It forms a major subsistence fishing activity in the riverine communities. It is recorded in the International Union for Conservation of Nature (IUCN) red list of as a least concerned species due to its widespread attributes (Ntakimazi, 2006; Chemoiwa &

Jepleting, 2018).

There has been a decline in the L. altianalis catch rates in Lake Victoria where it is recorded to have reduced to the lowermost levels of 0.5 to 0.2 kghr-1 (Chande & Mhitu, 2005;

Ntakimazi 2006) in the 1970s, and to a further to 0.06 kghr-1 in in 2002 (Chande & Mhitu,

2005). Currently, the species is not listed in the catches of other landed fish species at the landing sites of the Kenyan sector of Lake Victoria.

The species survives both under lacustrine and riverine habitats (Balirwa, 1978). Buonerba

(2010) realized that the L. altianalis juveniles are more abundant in the riverine habitats, while the adults occupy both lacustrine and riverine habitats.

The fish faces threats like siltation and water turbidity as a consequence of erosion and farming activities, eutrophication as a result of anthropogenic activities like mining, agriculture, fishing pressure and loss of migratory routes in the rivers (Ondhoro et al., 2016).

Little information exists on the biology and ecology of L. altianalis in the Lake Victoria basin since this fish species is currently used mainly for subsistence, there is need to ensure that there is sustainability of its catches. However information on the population dynamics and other fisheries related content is scanty. There is therefore need to conduct studies on the population dynamics of the species in particular on distribution, growth, mortality and

4 exploitation of the species to obtain information that can be used for the conservation and management of the species.

1.2 Problem Statement

The diversity decline of native species in the Lake Victoria catches drives the need to study

River Kuja as one of the rivers where the Labeobarbus altianalis migrates to spawn. Little information exists on the biology and ecology of L. altianalis in the Lake Victoria basin since this fish species is currently used mainly for subsistence, there is need to ensure that there is sustainability of its catches. The existence of healthy confined populations of the species in the rivers provides new motivation for conservation of the species. There is need to identify and recognize specific refuges so that they can be protected as conservation units (Chemoiwa et al., 2013) more so simply because the species is no longer encountered in the catches from

Lake Victoria. The biology and the ecology of the L. altianalis is poorly understood; in particular few studies have been conducted on population dynamics and stock assessment in the Lake Victoria basin. There is need to conduct a study on growth, recruitment and mortality of this species. There is limited research on the population structure and recruitment success of L. altianalis in River Kuja. There is need for scientific evidence so as to understand the survival and recruitment of the species in the Kuja River.

1.3 Justification

There is limited research on the population structure and recruitment success of Labeobarbus altianalis in River Kuja. There is a knowledge gap on whether the fish is naturally recruiting or is stocked and also whether it is able to grow to an age where they can reproduce. The knowledge on growth and mortality will help provide management advice that will lead to sustainable harvesting and utilization of this species. Information gained on various

5 population parameters like the asymptotic length (L∝), growth coefficient (K), mortality

(total, natural and fishing) rates and exploitation level (E) are necessary for planning and management of River Kuja.

The availability of L. altianalis in River Kuja and knowledge on its population dynamics will act as inputs in formulating models that will be useful for sustainable management. The availability of the species will prompt their need for conserving the river habitats which act as refuge to the species.

This study will also contribute in filling the knowledge gaps since no study has been done on the growth and mortality of L. altianalis along River Kuja which could be a better source of fish and improve the Lake’s diversity if well managed. Conservation of the species in the rivers will cause a reappearance of various native species that have sought refuge in the rivers for a long time and considered extinct in the lake.

There is need to conduct the research with the aim of saving the declining species diversity in Lake Victoria by studying its tributaries like River Kuja. The information on the distribution of L. altianalis along River Kuja will help various management authorities in monitoring the various anthropogenic activities in the specific parts of the river to ensure survival of the native species.

1.4 Assumptions

The major assumption in this study is that the environmental changes and the population dynamics of Labeobarbus altianalis are in a steady state from time to time. This means the changes remain the same from year to year.

6

1.5 Objectives

1.5.1 General Objective

To investigate the distribution and population characteristics of Labeobarbus altianalis in

River Kuja, Lake Victoria Basin, Kenya.

1.5.2 Specific objectives

1. To determine the distribution and abundance of Labeobarbus altianalis of River

Kuja.

2. To estimate the variation in population characteristics of Labeobarbus altianalis (growth, mortality, recruitment, length weight relationship, mean length relative condition factor, maturity, sex ratio, potential of longevity and growth performance index) along in River Kuja. 3. To determine the relationship between various physical chemical parameters (pH, temperature, conductivity, dissolved oxygen, total phosphates, total nitrates, total dissolved solids, turbidity, speed) and abundance of Labeobarbus altianalis in River Kuja.

1.6 Hypothesis

Ho: There is no significant difference in the distribution and abundance of Labeobarbus altianalis along River Kuja.

Ho: There is no significant difference in changes in population parameters of Labeobarbus altianalis at different sites along River Kuja.

Ho: There is no relationship between physical chemical parameters and distribution of

Labeobarbus altianalis in River Kuja.

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CHAPTER TWO

2.0 LITERATURE REVIEW

This chapter presents information on the genus Labeobarbus, habitat and distribution of

Labeobarbus altianalis, its ecology, food and feeding habits, growth and mortality. This information provides comparative studies done elsewhere concerning the species.

2.1 Genus Labeobarbus

Labeobarbus is a recently revised genus from the synonymy and is widely spread in (Skelton,

2001). It comprises fish normally known as the ripon barbel or large Barbus which in most cases reach large adult sizes that exceed 150 mm SL and possess scales that have longitudinal striations (Skelton, Tweddle &

Jackson, 1991). There are two species groups of the large African Barbus that were established by Banister

(1973) basing on their morphological characteristics; and . The latter species group included various species like Barbus acuticeps (Matthes,

1959), Labeobarbus altianalis (Boulengar, 1990) and Labeobarbus intermedius (Ruppel, 1836).

Labeobarbus altianalis (ripon barbel) is an East African fish that belongs to the family cyprinidae. Seegers, De Vos & Okeyo (2003) realized that most of the African Barbus do not relate to the genus at all. There are two major groups in which the African Barbus have been classified; the small size and large size (Berrebi, Kottelat, Skelton & Rab, 1996). The large size African Barbus was classified in the genus Labeobarbus (Berrebi & Valiusho, 1998;

Skelton, 2001, Berrebi &Tsigenopoulos, 2003, Banyankimbona, Vreven, Ntakimanzi &

Snoeks, 2012).

8

2.2 Habitat and Distribution

The cyprinid family constitutes the most abundant species of the riverine fisheries and are mostly found at the mid and lower reaches of the river (Lucas & Batley, 1996). Balirwa,

(1978) found out that cyprinids, particularly Barbus species are the most successful in terms of species and numbers in the Lake Victoria’s affluent rivers and have been little studied.

Cyprinids are cultured worldwide but only the common carp (Cyprinus carpio) is cultured to a large extent in Africa despite the many indigenous cyprinids in the continents’ lakes and rivers (Rutaisire, Levavi- Sivan, Aruho & Ondhoro, 2013).

Labeobarbus altianalis has its origin in the Lake Kivu, River Rusizi and River Akagera (De

Vos & Thys van den Audenaerde, 1990). It is also found in Lake George, Lake Edward and

Lake Victoria (Seegers et al., 2003, Katunzi, Mbonde, Waya & Mrosso, 2010). The species inhabits both lacustrine and riverine habitats. The juveniles are restricted to the rivers and occur in shoals while the adults inhabits both the lacustrine and riverine habitats. Mature L. altianalis migrate to the rocky upper reaches to spawn during rainy seasons (Witte & de

Winter 1995). It inhabits a depth range of 0 – 50 m (Kudhongania & Cordone, 1974).

It is uncertain whether the lake had both lacustrine and potamodromous populations of L. altianalis. There are no more large numbers of big ripe fishes crowding rivers on their migrations upstream and also the species has become common in the riverine catches according to Mugo & Tweddle, (1999). Their abundance in the rivers is inquisitive in light of their scarcity in the lake (Ojwang, Kaufman, Soule & Asila, 2007).

2.3 Ecology

Recent studies show that Labeobarbus altianalis comprises stationary populations concentrated at the mouths of the Yala, Nzoia, Nyando and Sondu-Miriu rivers in the Lake

9

Victoria watershed (Ojwang et al., 2007). There are also cases of recognized river-restricted populations of potamodromous Lake Victoria cyprinids (Chemoiwa et. al., 2013).

L. altianalis was one of the native fish species in the affluent rivers of Lake Victoria

(Chemoiwa et al., 2013) in the 1950s and 1960s (Corbet, 1961). However overfishing has caused a reduction in the riverine species from annual catches of 2500 tonnes in the 1950s to

108 tonnes in the 1980s and 1990s (Ochumba & Manyala, 1992). The introduction of more efficient gillnets and unregulated fisheries have severely impacted the stocks of riverine spawning lake-dwelling cyprinids. Fishermen could set gillnets near river mouths and block the large cyprinids off from the lake hence blocking the mature fish from reaching the upstream spawning grounds (Graaf, Marcel, Machiels & Sibbing, 2004).

Due to the declining fishing potential in rivers of the Lake Victoria basin, local communities’ livelihoods have been affected and this calls for the need of urgent management and conservation measures for the riverine fishes. Currently, the species is not listed in the catches of other landed fish species at the landing sites of the Kenyan sector of Lake Victoria.

Mugo and Tweddle (1999) found out that L. altianalis is not entirely a potamodromous fish but also has populations that dwell in the river permanently. They realized that the presence of river populations pose a challenge in the valuation of the fishery for fish migrating from the lake, since the fish from the river will differ in various population characteristics like growth and maturity from the potamodromous fishes. Further studies by Chemoiwa et al.,

(2013) displayed a strong population differentiation among four rivers which was not related to environmental changes caused by man. They realized that the riverine populations of L. altianalis are likely to have survived as distinct populations for long periods of time. They discovered strongly genetically distinguished populations in four rivers draining the Kenyan

10 side of the Lake Victoria and these populations may characterize unrecognized non- migratory populations that have survived regardless of anthropogenic effects.

2.4 Food and feeding of Labeobarbus altianalis

Admassu & Dadebo (1997), found out that the big barbus from different water bodies in Asia and Africa are omnivorous and well adapted in changing diets basing on the seasonal differences and prey availability. L. altianalis found in the riverine habitats mostly feed on algae and other herbaceous plants (Chemoiwa & Jepleting 2018). The species can adapt in any environment and be able to feed on favorable foods that are available in the specific habitat.

The species mostly feeds on gastropod mollusks, plant material, fishes, crustaceans and larvae (Witte & de Winter 1995). Other studies by Okito et al., (2017) revealed that the species mainly feeds on phytoplankton and supplements the diet with plant debris and zooplanktons. Chemoiwa and Jepleting (2018) found out that the species feeds on different diet groups namely; ephemeroptera, plant material, diptera, algae, coleopteran, gastropods, and detritus.

2.5 Growth of Labeobarbus altianalis

Proper fisheries management that ensures sustainability of the fish stock is done using various population dynamics parameters resulting from the length and weight data (Ecoutin, Albaret

& Trape, 2005). A specific population has a matchless set of dynamics; growth, recruitment, and mortality that affect its status currently and in the future (Pope, Lochmann & Young,

2010). Food and Agriculture Organization (FAO) has supported the use of length-based

11 methodologies by issuing relevant manuals, as one subsection of the various methodologies available for fisheries research (Csirke, Caddy & Garcia, 1987).

The main process that relates with growth rates is recruitment to the fishery. In most cases growth will be positively or negatively correlated with recruitment depending on the density due to a limited energy supply or the environmental conditions which may favor both recruitment and growth (Maunder, Crone, Valero & Semmens, 2015). Recruitment, mortality and selectivity are associated with growth so as to define the frequency of mature fish awaited in the catch (Wetherall, Polovina & Ralston, 1987). Information gained from various population parameters like asymptotic length (L∝), growth coefficient (K), mortality (natural and fishing) rate and exploitation level (E) are essential for regulation and management of fisheries resources (Amin, Arshad, Bujang & Siraj, 2009).

The L∞ is a growth parameter of the von Bertalanffy Growth Function (VBGF), which expresses how far the mean length of a fish from a specified population would reach if they were to grow for a markedly long time or the mean length of the old fish. Growth study basically means the definition of the body size as a function of age (Maunder et al., 2015).

The growth coefficient (K) is connected to the fish metabolic rate. The metabolic rate likewise is a function of temperature: In the tropical areas, fish tend to have high growth coefficient values compared to their counterparts in the cold-water (Ecoutin et al., 2005).

These relations are apprehensive however, by an association of K and L∞ such that at the same level of activity, small species have higher K values than large species (Sparre &

Venema, 1998).

12

The length at maturity of the female and male L. altianalis from Lake Edward and Lake

George in was estimated at 54.0 cm and 36.0 cm respectively (Breder & Rosen,

1966). Further studies by Ochumba and Manyala (1992) on the species in Sondu-Miriu River,

Lake Victoria in Kenya, estimated the length at first maturity of L. altianalis to be 7.0 cm standard length.

2.6 Mortality

The estimates of mortality are very important to fisheries managers. When determining the abundance of a fish populations, it is critical to find the mortality rates. By using the model

Z=M+F where Z is the total mortality rate M is the natural mortality rate and F is the fishing mortality rate, one can evaluate the tendency of a specific population (Arizi, Aggrey-Fynn &

Obodai, 2015). Both rates are calculated on a yearly basis. Fish mortality rates estimates are often incorporated in mathematical yield models to forecast yield levels attained under numerous exploitation scenarios. The mortality rates are able to predict the total deaths of a population which can be used in comparison with the total births or recruits of a population inorder to establish whether a population is increasing or reducing in numbers (Beverton &

Holt, 1957). Getting to understand these rates can help fisheries managers to set harvest limits to sustainable yields; maximum sustainable yield and optimum sustainable yield (Christian

& Holt, 2013).

Natural mortality coefficient (M) is directly proportional to the growth coefficient (K) of a fish and inversely proportional to the asymptotic length (L∞) and the life span (Beverton &

Holt, 1956). Fishes with higher growth coefficient have higher natural mortality and shorter life span compared to those with larger L∞ and lower growth coefficient. This relationship is frequently affected by different factors like temperature, availability of food and many more.

13

Sufficient amount of food is a source for a fish to grow up towards its maximum size, but the growth rate may not increase (Liu et al., 2016). There are no current mortality estimates of the L. altianalis species.

14

CHAPTER THREE

3.0 MATERIALS AND METHODS

This chapter presents the description of River Kuja as the study area, research methodology in terms of how the data was collected and analyzed. Analysis of the data is presented in details with all the formulas highlighted.

3.1 Study area

This study was conducted along River Kuja which is one of the major rivers flowing into

o o Lake Victoria. The river extends from a longitude of 34 7’60’’E to a latitude of 00 55’60’’S.

It has an altitude of 1,133 meters above sea level. It has a length of 149 km, an area of 6900 km2 with an average discharge of 58 m3s-1. River Kuja flows in areas with an average annual rainfall of 1300 mm. Its sub-catchment is about 3,000km² with an average annual flow of over 1000 million m³ as per the stream gauge IKBIA situated close to Gogo Falls (LBDA,

2008).

The region has an altitude ranging from 1,100 - 2,050m above sea level. The Kisii highland where the river originates has a higher population but limited land available. It drains through an agricultural area, that supports crops like coffee, tea, maize sugarcane and it’s used for the

Lower Kuja Irrigation project.

Kuja catchment experiences soil erosion as a serious problem and farming in areas bordering the river contributes to the observed sediment transport rates (Omari, 1986). The upstream of River Kuja system consist of the fertile reddish volcanic loamy soils, the midstream area is covered by fertile grayish clay soil and sandy soil while the downstream is covered by grayish clay soil. The Luo community located downstream are mainly farmers and fishermen

15 while the Kisii tribe is located in the upstream of River Kuja are mainly farmers (WRMA &

JICA, 2014).

The study was conducted on selected six sampling points along the river from Kegati to Wath

Ong’er.

Figure 2: River Kuja drainage basin with the sampled stations.

3.1.1 Sampling sites description

Six sampling sites were selected randomly along River Kuja and below is their description in terms of altitude, river bed content and surrounding activities.

16

Kegati

It’s on River Kuja’s upstream. It has an altitude of 1760 m.a.s.l with a longitude of

34.821817˚E and a latitude of 0.711408˚S. It has a water depth and width of 0.5 m and 15 m respectively. The river bed has mud/silt, sand/pebbles and boulders at a ratio of 2:1:7. Shrubs and Eucalyptus trees are the dominant vegetation and there was settlement around the river bank. Human activities noted were a water intake point with raised ramp facilitating trickling flow, a high bridge above, road transport, weir and agriculture.

Ogembo

It’s on River Kuja’s downstream from Kegati. It has an altitude of 1568 m.a.s.l with a longitude of 34.974980⁰E and a latitude of 0.609960⁰S. It has a water depth and width of

0.3 m and 20 m respectively. The river bed has mud/silt, sand/pebbles and boulders at a ratio of 1:2:2. Shrubs and grass are the dominant vegetation and there is settlement around the river bank. Human activities noted were a foot bridge above the river, car wash with discharge into the river, grazing at the river bank.

Kanga

It’s on the midstream of River Kuja’s downstream from Ogembo. It has an altitude of 1378 m.a.s.l with a longitude of 34.587100˚E and a latitude of 0.833480˚S. It has a water depth and width of 0.2 m and 15 m respectively. The river bed has mud/silt, sand/pebbles and boulders at a ratio of 5:3:2. Sugarcane and maize are the dominant crops and there is settlement around the river bank. Human activities noted were a foot bridge above the river and clearing of the river bank for agriculture.

17

Nyokal

It’s on River Kuja’s midstream and downstream of Kanga. It has an altitude of 1338 m.a.s.l with a longitude of 34.571938˚E and a latitude of 0.809173˚S. It has a water depth and width of 0.3 m and 30 m respectively. The river bed has mud/silt, sand/pebbles and boulders at a ratio of 1:6:13. Herbs and shrubs are the dominant vegetation at the river bank. Human activities noted were gold mining activities; washing of mine tailings, laundry, bathing and cattle drenching.

Gogo

It’s on River Kuja’s downstream from Nyokal. It has an altitude of 1234 m.a.s.l with a longitude of 34.349540˚E and a latitude of 0.908027˚S. It has a water depth and width of 5 m and 100 m respectively. The river bed is constituted mud. Macrophytes (Phragmites) are the dominant vegetation at the river bank. Human activities noted were a foot bridge and a hydroelectric power station.

Wath Ong’er

It’s along River Kuja’s downstream. It has an altitude of 1138 m.a.s.l with a longitude of

34.210316˚E and a latitude of 0.951535˚S. It has a water depth and width of 0.5 m and 50 m respectively. The river bed has mud/silt, sand/pebbles and boulders at a ratio of 95:3:2.

Macrophytes and grass are the dominant vegetation at the river bank. Human activities noted were a main highway bridge, drenching, bathing and laundry.

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3.2 Methodology

The six sampling sites on River Kuja were sampled once a month for physic - chemical parameters, fish abundance in numbers, length - weight frequency data for studying growth and exploitation rate and sexual maturity stages classification of L. altianalis.

3.3 Water quality

Replicate water quality recordings were made for the parameters; dissolved oxygen, conductivity, temperature, pH and turbidity once every month using handheld multiparametre meter model YSI 556 (Geo Scientific Limited) during the period November

2016 to August 2017.

3.4 Water velocity

The orange or float method was used to calculate the water velocity where the length of the river was measured and the start and finish points marked (Point A-B), then an orange was placed at the starting point and time recorded for it to roll to the finish point. The experiment was repeated three times and the velocity was expressed as an average in meters per second

(length/time = ms-1).

3.5 Collection of abundance and length frequency data

Fish samples were collected using a 400 V (10 A) electro fishing equipment (model Electra catch Wolvampton W.O 580 Winchester) that was operated by a Honda GX 240 8 horse power generator. Samples were collected once a month from six different sampling sites for a period of ten months from November 2016 to August 2017. Once all the fish were caught, they were put into plastic containers containing water and taken to the river bank, whereby

Labeobarbus altianalis was sorted out. Specimen of the species were measured to the nearest

19 centimeter total length (TL) and focal length (FL) with a fish measuring board and weighed to the nearest 0.001 g using digital weighing scale (model TP A1000) with a precision of

1.0g . Throughout the sampling period, numbers of L. altianalis caught were within manageable limits, mostly less than 300 therefore there was no sub sampling and all fish caught were measured in length and weight respectively. During the measurement process, fish were separated into males and females and their numbers recorded after which the sex ratio was calculated. Maturity stages of the fish were identified after dissection and gonadal staging done using the characteristics of various stages of sexual maturity (Table 1) quoted by Okito et al., (2017). Sexual maturity was visually categorized by examining the gonads and stage III to stage VI selected as part of the mature fish (Tiago, Vitor, Luciano & Nelson,

2016).

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Table 1: Sexual maturity stages characteristics (Adapted from Okito et al., 2017))

Stage Male Female 1  Small gonad dimension  Undeveloped ovaries shaped adjoining the spine. strips. Visible egg and  Unrecognizable gender both wrapped by a fabric spongy binocular appearance.  Unrecognizable gender both binocular 2  Gonads transparent at  transparent gonads watching observation and oocytes discernible binocular 3  Testicles pink reddish or  Ovaries clear or pinkish or whitish pinkish reddish-pink of grainy  Oocytes visible but not separable 4  Testicles whitish or whitish-  Ovaries yellowish or pink with well-developed yellow-pink or yellow- points of milt whitish

 Clearly visible and separable eggs 5  Testicles white, soft roe  Yellow ovaries - orange or expellable by finger pressure lemon yellow  Sperm duct bursting with  Presence of ova in the roe oviduct 6  Testicles pale pink and  Pink ovaries or rouges – flabby emptied of sperms brick, empty, flaccid, some small whitish granules

3.6 Data processing and analysis

Monthly length-frequency distributions of Labeobarbus altianalis were analyzed using The

Electronic Length Frequency Analysis (ELEFAN) in the FAO-ICLARM Stock Assessment

Tool (FiSAT) computer programme for assessing the von Bertalanffy growth parameters; asymptotic length (L∞) and growth co-efficient (K).

21

The VBGF is defined by the equation:

−푘(푡−푡0) 퐿푡 = 퐿∞[1 − 푒 ]………………...... equation 1

Where:

Lt – mean length at age t

L∞ - asymptotic length/ mean length of old fish

K – Growth coefficient/ curvature parameter/ how fast fish approaches their asymptotic length.

T – Age of the fish.

t0 – initial condition parameter/ hypothetical age at which the length is zero.

The Powel Wetherall method was further used to estimate the parameter L infinity and Z/K where Z is the instantaneous rate of total mortality. (Wetherall, Polovina & Ralston, 1987).

The method was used to plot Li against Li - L’ i and the above parameters estimated using the following formula:

a L∞ = ………………………………………………………….…………. equation 2 −b

푍 1+푏 = …………………………………………………………….…………. equation 3 퐾 −푏

Where a is the x- intercept and b is the y- intercept

22

After the growth parameters of VBGF were obtained, a linearized length-converted catch curve was constructed using the following formula to estimate the instantaneous total mortality (Z):

푁 ln ( 푡) = 푎 + 푏푡………………………………………………….…………. equation 4 훥푡

Where, N is the number of individuals of relative age (t) and Δt is the time needed for the fish to grow through a length class. The slope (b) of the curve with its sign changed gives Z.

Natural mortality (M) was estimated using the empirical relationship of Pauly (1980) whereby:

퐿표푔10M = − 0.0066 – 0.279퐿표푔10퐿∞ + 0.6543퐿표푔10K + 0.4634 퐿표푔10T.. equation 5

Where, M is the natural mortality, L∝ the asymptotic length, K the growth co-efficient of the

VBGF and T the mean annual habitat water temperature °C.

Once Z and M were obtained, fishing mortality (F) was estimated using the relationship:

F = Z – M ……………………………………………………………………… equation 6

The exploitation level (E) was obtained using the formula developed by Gulland (1971);

E = F/Z ……………………………………………………………………… equation 7

The recruitment pattern was acquired by projecting the length-frequency data backwards on the time axis using growth parameters and Normal distribution of the recruitment pattern was determined by NORMSEP in FiSAT (Pauly & Caddy, 1985).

23

The growth performance index ∅′ was computed according to Moreau, Bambino and Pauly

(1986):

∅′ = 퐿표푔10퐾 + 2퐿표푔10퐿∞ ……………………………………………………. equation 8

Potential longevity (tmax) of the species was calculated from Pauly (1984) formula:

tmax = 3/K …………………………………………………………………… equation 9

3.7 Length at 50% maturity

The relation between length and maturity in length classes was demonstrated on a logistic curve for estimating the total lengths at 50% maturity.

3.8 Relationship of water quality and abundance

A correlation was conducted to assess the relationship between the distributions of

Labeobarbus altianalis was investigated using Pearson correlation.

3.9 Length weight

The length (L) weight (W) relationship of the fish was expressed using the Le Cren (1951) equation;

푊 = 푎퐿푏 ……………………………………………………………………… equation 10

Where, W - weight (g), L - total length (cm), a – coefficient related to body form, b – exponential expressing relationship between length and weight.

Linear transformation was performed using natural logarithm at the observed lengths and weights and the following was applied (Ricker 1975).

Log W = Log a + b Log L ………………………………………………….. equation 11

24

The deviation of the allometric coefficient b from the theoretical value of isometric growth

(b=3) was tested by chisquare. All tests of significance were at α=0.05.

For statistical differences, data was analyzed using computer software SPSS version 23.0.

3.10 Relative condition factor

The degree of well-being or relative robustness of the fish was expressed by relative condition factor (Kn) as calculated by the formula of Fulton (1904).

Kn = Wo……………………………………………………………………. equation 12 Wp

Where Kn = relative condition factor;

Wo = weight observed

b Wp = weight predicted (aL )

25

CHAPTER FOUR

4.0 RESULTS

This chapter presents the findings of the study carried out in River Kuja from November

2016 to August 2017. The trends for the selected physicochemical parameters, Pearson correlation between physicochemical parameters and abundance, distribution and abundance of Labeobarbus altianalis along River Kuja was displayed in tables. The length frequency distribution, length weight relationship, relative condition factor and growth parameters were presented too.

4.1 Water quality

4.1.1 pH

The pH levels in all six stations ranged from 7.2 to 7.8 with a mean of 7.52 ± 0.04 (Table 2).

The highest pH was found at Ogembo and Wath Ong’er followed by Gogo, Nyokal and

Kanga respectively while Kegati station recorded the lowest.

Table 2: pH level at the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 2.2 7.21 0.68 5.8 8 Ogembo 1.7 7.8 0.51 6.9 8.6 Kanga 2 7.41 0.62 6.4 8.4 Nyokal 1.5 7.45 0.44 6.9 8.4 Gogo 1.9 7.47 0.71 6.2 8.1 Wath 2.8 7.77 0.89 6.2 9 Ong’er

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4.1.2 Temperature

The temperature readings in all six stations ranged from 17.88 oC to 24.33 oC with a mean of

21.35 ± 0.40 oC (Table 3). Wath Ong’er had the highest temperature followed by Gogo,

Kanga, Nyokal and Ogembo respectively while the lowest was found at Kegati station.

Table 3: The temperature (oC) of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 3.7 17.88 1.27 16.1 19.8 Ogembo 5.5 19.82 1.71 18.1 23.6 Kanga 5 21.91 1.9 19.5 24.5 Nyokal 15.67 21.08 4.49 9.33 25 Gogo 4.8 23.07 1.49 20.8 25.6 Wath 4.5 24.33 1.51 22.2 26.7 Ong’er

4.1.3 Conductivity

The conductivity readings in all six stations ranged from 61 - 120 µScm-1 with a mean of

90.75 ± 4.24 µScm-1 (Table 4). The highest conductivity was found at Gogo followed by

Wath Ong’er, Kanga, Nyokal and Kegati respectively while Ogembo station had the lowest.

Table 4: Conductivity (µScm-1) of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 60 69.9 20.12 48 108 Ogembo 67 61 19.65 9 76 Kanga 57 94.3 20.5 64 121 Nyokal 66 84.2 22.43 54 120 Gogo 137 120 38.32 54 191 Wath 84 115.1 27.29 90 174 Ong’er

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4.1.4 Dissolved Oxygen

The dissolved oxygen levels in all six stations ranged from 3.99 - 6.23 mgL-1 with a mean

5.03 ± 0.15 mgL-1 (Table 5). Kegati station had the highest dissolved oxygen followed by

Ogembo, Kanga, Nyokal and Wath Ong’er respectively while Gogo station had the lowest.

Table 5: Dissolved Oxygen (mgL-1) of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 2.3 6.23 0.78 5.2 7.5 Ogembo 1.6 5.67 0.63 4.8 6.4 Kanga 3 5.24 0.91 4.2 7.2 Nyokal 2.94 4.95 0.94 4 6.94 Gogo 4.9 3.99 1.39 0.3 5.2 Wath 1.6 4.13 0.54 3.6 5.2 Ong’er

4.1.5 Total Phosphates

The total phosphates concentration in all six stations ranged from 68.13 - 427.86 μgL-1 with a mean of 232.25 ± 24.79 μgL-1 (Table 6). Wath Ong’er had the highest total phosphates followed by Nyokal, Ogembo, Gogo and Kanga respectively while Kegati station had the lowest.

Table 6: The total Phosphates concentrations (μgL-1) at the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 117.14 68.13 40.36 30.57 147.71 Ogembo 511.43 242.97 164.59 22 533.43 Kanga 430.43 181.45 144.61 23 453.43 Nyokal 398.57 255.74 122.14 23.43 422 Gogo 403.29 217.36 126.81 17.71 421 Wath 960 427.86 291.28 72 1032 Ong’er

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4.1.6 Total Nitrates

The total nitrates concentration in all six stations ranged from 1100.63 - 1400.06 μgL-1 with a mean of 1242.39 ± 56.05 μgL-1 (Table 7). Ogembo station had the highest total nitrates while Kanga station had the lowest.

Table 7: The total Nitrates concentration (μgL-1) of the six sampling sites along River Kuja

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 1839 1315.33 655.19 415 2254 Ogembo 1707 1400.06 556.2 647 2354 Kanga 546 1100.63 144.96 824 1370 Nyokal 1697.8 1207.83 497.62 750 2447.8 Gogo 1137 1152.16 323.54 515 1652 Wath 854.2 1278.29 236.77 786 1640.2 Ong’er

4.1.7 Total Dissolved Solids

The total dissolved solids levels in all six stations ranged from 39.5 - 74.7 mgL-1 with a mean of 57.36 ± 2.05 mgL-1 (Table 8). Gogo station had the highest levels followed by Wath

Ong’er, Nyokal, Kanga and Ogembo respectively while Kegati station had the lowest.

Table 8: The total Dissolved Solids (mgL-1) of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 25 39.5 7.72 25 50 Ogembo 12 47.64 4.52 42 54 Kanga 22 54.1 6.77 43 65 Nyokal 21 58.9 6.14 48 69 Gogo 52 74.72 15.02 44 96 Wath 52 69.28 17.4 46 98 Ong’er

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4.1.8 Turbidity

The turbidity measures in all six stations ranged from 45.54 - 152.21 NTU with a mean of

92.43 ± 7.78 NTU (Table 9). Wath Ong’er station had the highest turbidity followed by

Kanga, Gogo, Nyokal and Ogembo respectively while Kegati station had the lowest.

Table 9: The turbidity levels (NTU) of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 15.67 45.54 5.43 32.63 48.3 Ogembo 47.36 61.06 20.84 29.64 77 Kanga 112.4 117.25 50.73 38.6 151 Nyokal 96.55 78.56 26.56 40.2 136.75 Gogo 104.25 99.94 47.45 33.75 138 Wath 246 152.21 96.67 35 281 Ong’er

4.1.9 Speed

The velocity of water in all six stations ranged from 0.64 - 0.84 ms-1 with a mean of 0.72 ±

0.03 ms-1 (Table 10). Kegati station had the highest water velocity followed by Kanga,

Nyokal, Gogo and Wath Ong’er respectively while Ogembo station had the lowest.

Table 10: Velocity (ms-1) of water of the six sampling sites along River Kuja.

Sampling Std. Range Mean Minimum Maximum station Deviation Kegati 0.58 0.84 0.19 0.52 1.1 Ogembo 0.57 0.64 0.22 0.43 1 Kanga 0.69 0.72 0.23 0.51 1.2 Nyokal 0.85 0.72 0.34 0.35 1.2 Gogo 0.85 0.71 0.35 0.35 1.2 Wath 0.59 0.66 0.21 0.41 1 Onger

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4.1.10 Correlation between physicochemical parameters and abundance (Appendix 1)

Water pH of River Kuja strongly correlated negatively with the water speed (-0.464**). The pH correlation with temperature (0.231), conductivity (0.147), total phosphates (0.099), total nitrates (0.120), total dissolved solids (0.056) and turbidity (0.042) was positively insignificant. However the pH correlation with dissolved oxygen (-0.175) and abundance (-

0.04) was negatively insignificant. Water temperature of River Kuja strongly correlated positively with conductivity (0.537**), total phosphates (0.385**), totals dissolved solid

(0.574**) and turbidity (0.384**). Water temperature of River Kuja strongly correlated negatively with dissolved oxygen (-0.731**), speed (-0.510**) and fish abundance (-0.313*).

Water conductivity of River Kuja strongly correlated positively with temperature (0.537**), total dissolved solids (0.698**) and total phosphates (0.316*). Water conductivity of River

Kuja strongly correlated negatively with dissolved oxygen (-0.553**) and slightly with fish abundance (-0.257*).

Dissolved oxygen measured from River Kuja strongly correlated positively with speed

(0.380**) and abundance (0.432**). Dissolved oxygen measured from River Kuja strongly correlated negatively with temperature (-0.731**), conductivity (-0.553**), total dissolved solids (-0.556**) and turbidity (-0.335**).

Total phosphates concentrations measured from River Kuja strongly correlated positively with temperature (0.385**), total dissolved solids (0.489**), conductivity (0.316*) and total nitrates (0.259*). Total phosphates concentrations measured from River Kuja slightly correlated negative with dissolved oxygen (-0.271*).

31

Total nitrates concentrations measured from River Kuja slightly correlated positively with total phosphates (0.259*). The total nitrates correlation with pH (0.120), dissolved oxygen

(0.079), total dissolved solids (0.052), abundance (0.049) and speed (0.109) was positively insignificant. However the total nitrates correlation with temperature (-0.168), turbidity (-

0.136) and conductivity (-0.032) was negatively insignificant.

Total dissolved solids measured from River Kuja strongly correlated positively with temperature (0.514**), conductivity (0.698**) and total phosphates (0.489**). Total dissolved solids measured from River Kuja strongly correlated negatively with dissolved oxygen (-

0.556**) and B altianalis fish abundance in River Kuja (-0.344**).

Turbidity measured from River Kuja strongly correlated positively with temperature

(0.384**) and negatively with dissolved oxygen (-0.335**). The turbidity correlation with pH

(0.042), conductivity (0.193) and total dissolved solids (0.070) was positively insignificant.

However the turbidity correlation with total phosphates (-0.064), total nitrates (-0.136), speed

(-0.186) and abundance (-0.077) was negatively insignificant.

Water speed measured from River Kuja strongly correlated positively with dissolved oxygen

(0.380**) and negatively with pH (-0.464**) and temperature (-0.510**). The water speed correlation with total phosphates (0.056), total nitrates (0.109), total dissolved solids (0.045) and Labeobarbus altianalis abundance (0.112) was positively insignificant. However the water speed correlation with conductivity (-0.081) and turbidity (-0.186) was negatively insignificant.

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4.2 Distribution and abundance of Labeobarbus altianalis

Kanga station recorded the highest number of Labeobarbus altianalis followed by Kegati station then Ogembo and Nyokal respectively. Gogo and Wath Ong’er had the lowest numbers of fish. The month of July 2017 recorded the highest number of L. altianalis followed by August 2017. The month of June 2017 recorded the least number of fish which was below 100 compared to other months that had fish above 100 (Table 11).

Table 11: Distribution of Labeobarbus altianalis (numbers) at the six sampling stations along River Kuja during the period November 2016 to August 2017

Months Kegati Ogembo Kanga Nyokal Gogo Wath Ong'er Total

Nov-16 18 18 21 15 13 17 102

Dec-16 56 14 44 34 11 5 164

Jan-17 26 26 15 13 14 12 106

Feb-17 68 21 44 19 12 8 172

Mar-17 63 26 35 5 4 3 136

Apr-17 41 28 34 6 3 2 114

May-17 11 10 40 41 0 0 102

Jun-17 33 15 23 11 3 2 87

Jul-17 52 64 92 50 0 1 259

Aug-17 12 52 169 9 2 0 244

Total 380 274 517 203 62 50 1486

The distribution of L. altianalis in the wet and dry seasons is depicted in Table 12. The dry season recorded a high abundance of fish (372) with the month of December having more

33 fish (164) compared to other months in the season. The month of April and May recorded a high fish abundance of 114 and 102 respectively during the wet season. Chi-square analysis indicated that there was a significant difference (p < 0.05; x2 = 0.008) in the L. altianalis abundance during the dry and wet season.

Table 12: Labeobarbus altianalis abundance during the dry and wet season

Dry season (372) Wet season (303)

Stations Nov-16 Dec-16 Jan-17 Apr-17 May-17 Jun-17

Kegati 18 56 26 41 11 33

Ogembo 18 14 26 28 10 15

Kanga 21 44 15 34 40 23

Nyokal 15 34 13 6 41 11

Gogo 13 11 14 3 0 3

Wath Ong'er 17 5 12 2 0 2

Total 102 164 106 114 102 87

4.2.1 Monthly temporal distribution of Labeobarbus altianalis from River Kuja

The monthly temporal distribution of L. altianalis is depicted in Table 13. The month of July

2017 had the highest abundance (259 fish) while the month of June 2017 had the least abundance (87 fish). L. altianalis sampled in the month of November had the highest mean length and mean weight (15.91± 10.87 cm TL and 111.51 ± 201.04 g) respectively. The month of August recorded the least mean length and mean weight (6.13 ± 3.01 cm TL and

5.36 ± 12.82g) respectively.

34

Table 13: Temporal distribution of Labeobarbus altianalis of River Kuja during the period November

2016 to August 2017.

Month Abundance Mean TL (cm) Mean W (g)

November 102 15.91 ± 10.87 111.15 ± 201.04

December 164 13.2 ± 5.24 34.52 ± 75.34

January 106 12.43 ± 6.50 39.08 ± 112.20

February 172 11.18 ± 7.44 37.56 ± 119.59

March 136 12.9 ± 4.64 25.56 ± 25.02

April 114 11.9 ± 7.12 44.03 ± 159.47

May 102 8.54 ± 3.34 8.14 ± 11.64

June 87 7.21 ± 3.45 7.01 ± 22.37

July 259 10.05 ± 6.38 28.28 ± 131.78

August 244 6.13 ± 3.01 5.36 ± 12.82

4.3 Mean length and mean weight of Labeobarbus altianalis

The mean lengths and weight of L. altianalis at different sampling stations on River Kuja are presented in Table 14. The sampling station at Gogo falls had fish with the highest mean length of 20.10 ± 12.58 cm TL followed by fish from Kegati with a mean length of 13.66 ±

7.10 cm TL. The sampling station at Wath Ong’er had fish with the smallest mean length of

8.21 ± 3.54 cm TL. The mean weight of L. altianalis similarly exhibited the same pattern with that of the mean length. sampling station at Gogo falls had fish with the highest mean weight of 196.27 ± 291.56 g followed by fish from Kegati station with a mean weight of

35

51.08 ± 153.30 g. Wath Ong’er station had fish with the smallest mean weight of 7.96 ± 10.65 g.

Table 14: Mean length of Labeobarbus altianalis at the six sampling sites along River Kuja

Station No. of fish (n) Mean TL (cm) Mean W (g)

Kegati 380 13.66 ± 7.10 51.08 ± 153.30

Ogembo 274 9.34 ± 4.63 13.39 ± 24.92

Kanga 517 8.69 ± 4.38 11.34 ± 20.12

Nyokal 203 9.95 ± 5.71 22.02 ± 53.63

Gogo 62 20.10 ± 12.85 196.27 ± 291.56

Wath Ong'er 50 8.21 ± 3.54 7.96 ± 10.65

4.4 Length frequency distribution.

The length frequency distribution of Labeobarbus altianalis sampled in River Kuja during the period November 2016 to August 2017 is shown in Figure 3. A total of 1,486 L. altianalis were used in the construction of length frequency distribution, which covered a size range of

0 - 54.9 cm TL. It displayed a bimodal distribution with modal frequencies at between 5.0 -

9.9 cm TL and 35 - 39.9 cm TL respectively. Two major cohorts in the length frequency distribution of L. altianalis were manifested.

36

700 652

600

500

400 371

300 200 200 167

Frequency 100 53 8 7 12 10 3 3

0

0 - 4.9 - 0 9.9 - 5

10 - 14.9 - 10 15 - 19.9 - 15 24.9 - 20 29.9 - 25 34.9 - 30 39.9 - 35 44.9 - 40 49.9 - 45 54.9 - 50 Length (cm)

Figure 3: Length frequency distribution of Labeobarbus altianalis sampled in River Kuja.

The combined percentage frequency distribution of L. altianalis per length class during the sampling period November 2016 to August 2017 is presented in Table 15. A larger percentage of L. altianalis had lengths between 0 – 10 cm TL length class followed by 10.1

– 20 cm TL length class (56.3 % and 37.3 %) respectively. The length class with the least frequency percentage was 50.1 – 60 cm TL that had 0.1 %

37

Table 15: Percentage frequency distribution of Labeobarbus altianalis per length class (cm TL) in River Kuja during the period November 2016 to August 2017.

Months Total Percentage contribution per length class (%)

number 0-10 10.1-20 20.1-30 30.1-40 40.1-50 50.1-60

of fish

November 102 41.18 34.31 4.90 18.63 0.98 0.00

2016

December 164 25.00 68.30 5.48 1.22 0.00 0.00

2016

January 2017 106 34.91 56.60 6.60 0.00 1.89 0.00

February 172 54.07 38.37 5.23 0.58 1.74 0.00

2017

March 2017 136 27.94 66.91 5.15 0.00 0.00 0.00

April 2017 114 38.6 56.14 2.63 0.00 2.63 0.00

May 2017 102 70.59 28.43 0.98 0.00 0.00 0.00

June 2017 87 90.8 8.05 1.15 0.00 0.00 0.00

July 2017 259 65.25 28.96 4.25 0.39 0.76 0.39

August 2017 244 92.62 6.15 1.23 0.00 0.00 0.00

Total 1486 56.6 37.3 3.8 1.4 0.9 0.1

4.5 Length Weight relationship

The correlation between log TL (cm) and log W (g) of combined male and female data for L. altianalis population from all the sampling sites along River Kuja is shown in Table 16.

38

The length weight relationship of the population from River Kuja was described using the equation: log W = 3.03 log TL – 2.09 (r2 = 0.97). The regression coefficient (b) was 3.03.

The correlation coefficient (r) of the equation (0.97) indicated a strong positive correlation

(p < 0.05) between weight and length of the L. altianalis in the Kuja River.

Table 16: Correlation between log TL (cm) and log W (g) of Labeobarbus altianalis population from sampling sites along River Kuja (similar superscripts letters indicate no significant difference).

Study site Weight-length relationship Sample size (n) r2

Kegati Malea: log W = 3.09 log TL - 2.16 187 0.98

Femaleb: log W = 3.01 log TL - 2.09 109 0.97

Ogembo Malea: log W = 2.91 log TL – 1.98 53 0.92

Femaleb: log W = 3.05 log TL - 2.14 48 0.95

Kanga Malea: log W = 2.78 log TL – 1.80 76 0.87

Femaleb: log W = 2.58 log TL – 1.57 74 0.77

Nyokal Malea: log W = 2.94 log TL – 1.95 36 0.94

Femaleb: log W = 3.07 log TL - 2.10 39 0.98

Gogo Malea: log W = 3.13 log TL - 2.22 34 0.97

Femaleb: log W = 3.12 log TL - 2.19 16 0.96

Wath Ong’er Malea: log W = 3.47 log TL - 2.56 9 0.82

Femaleb: log W = 3.11 log TL - 2.14 5 0.95

4.6 Relative condition factor

The monthly mean relative condition factor of L. altianalis in River Kuja is presented in figure 4. The mean ranged from 0.42 ± 0.0016 to 1.56 ± 0.0071. The fish sampled in the month of April 2017 had the highest mean condition factor (1.56 ± 0.0071) followed by 39

December 2016 (1.233 ± 0.0032). The least mean condition factor was recorded from fish sampled in November 2016 (0.42 ± 0.0016).

1.8 1.555 1.6

1.4 1.233 1.185 1.154 1.2 0.957 1 0.845 0.669 0.8 0.617 0.613 0.6 0.42 0.4

Mean condition factor conditionMean 0.2 0

Sampling months

Figure 4: Monthly changes in relative condition factor in Labeobarbus altianalis along River Kuja.

(Vertical bars are standard deviations)

Various length weight parameters of L. altianalis from six sampling stations along River

Kuja are presented in Appendix 2. The sampling station at Ogembo had the highest mean condition factor of 1.21 ± 0.0067 while Wath Ong’er had the least mean condition factor of

0.68 ± 0.0025.

40

4.7 Growth parameters of Labeobarbus altianalis

4.7.1 Powel Wetherall plot

The estimation of the asymptotic length (L∞) and ratio of the total mortality to the growth constant Z/K using the Wetherall modified version (Pauly, 1986) is depicted in Figure 5. The

-1 estimated values of L∞ and Z/K were 61.29 cm TL and 2.802 yr respectively.

a = 16.12 b = -0.263 r= 0.966

L∞ = a/b = 61.29 푍 1 + 푏 = = 2.802 퐾 푏

Figure 5: Estimation of L∞ and Z/K using the modified Wetherall method for Labeobarbus altianalis in

River Kuja.

4.7.2 ELEFAN Analysis

The resultant growth curve of the ELEFAN analysis fitted in the monthly length-frequency distributions of L. altianalis in the Kuja River is illustrated in Figure 6. The estimated

-1 asymptotic length (L∞) was 60.00 cm TL and the growth constant (K) was 0.40 yr .

41

Figure 6: Monthly length frequency distribution of Labeobarbus altianalis fitted with growth curves.

4.7 Recruitment of Labeobarbus altianalis

The recruitment pattern of L. altianalis of River Kuja during the period November 2016 to

August 2017 is shown in Figure 7. ELEFAN method plots an annual recruitment pattern of the fish. The fish recruits twice in a year during the months of Feb – April and July –

September. The months of July to Sept had a higher intensity of recruitment compared to

February to April. The strength of recruitment (% age) is depicted in appendix 4.

42

Figure 7: Recruitment pattern of Labeobarbus altianalis of River Kuja during the months of Nov 2016 to August 2017.

4.8 Estimation of Mortality and the exploitation rate

The instantaneous rate of total mortality (Z) was estimated using the length converted catch curve analysis (Figure 8). The slope (b) of the regression analysis with its changed sign suggested a total mortality (Z) rate of 1.83 yr-1 which is relatively high. By substitution the annual mean water temperature (21.4oC) of the Kuja River and growth parameters into the empirical equation of Pauly (1980), the natural mortality (M) of the population was estimated to be 0.71 yr-1. Using the Ricker’s (1975) equation, fishing mortality of L. altianalis population was estimated to be 1.12 yr-1.

The exploitation rate (E) was calculated using the fishing and total mortality rates which in the analysis gave an exploitation rate of 0.61. The exploitation rate is used in the assessment of the fishery in particular the yield per recruit which is the average yield in weight of one

43 recruited fish in the fishery that contributes to the total yield realized. However the yield per recruit was not an objective of this study hence not calculated.

Figure 8: Length converted catch curve for Labeobarbus altianalis in River Kuja.

4.10 Growth performance index (∅′) and potential longevity (tmax)

The growth performance index was estimated to be 3.16. The estimated longevity (tmax) was

7.5 years indicating that it is a fairly long lived fish.

4.13 Sex ratios

The sex ratio of L. altianalis sampled at various sites along River Kuja is shown in Table 17

The ratio of females to male was 1:1.35 (n = 684) and a chi-square test indicated a significant difference (p < 0.05; x2 = 9.62E-05) from 1:1 sex ratio. The males dominated the population of the species with a 57.46 % while females were at 42.54%. Males were more abundant than females in five out of six sampled sites. Kegati Bridge recorded the highest number of males and females, while the lowest number of males and females were recorded at Wath Ong’er

44 station. The highest number of males and females was found in the following class size: 5.0

– 13 cm TL at Ogembo, Kanga and Wath Ong’er respectively and 13.1 – 21 cm at Kegati,

Nyokal and Gogo respectively.

Table 17: Ratio of females (F) to males (M) Labeobarbus altianalis sampled at various sites along River

Kuja

Sampling stations

Wath

Kegati Ogembo Kanga Nyokal Gogo Ong'er

Class size (cm) F M F M F M F M F M F M

5.0 - 13.0 44 56 30 24 45 28 19 13 2 11 3 7

13.1 - 21.0 60 116 17 20 28 39 16 17 5 9 2 2

21.1 - 29.0 1 5 1 9 1 9 3 2 3 4 0 0

29.1 - 37.0 1 6 0 0 0 0 1 4 1 4 0 0

37.1 - 45.0 0 2 0 0 0 0 0 0 5 6 0 0

45.1 - 53.0 3 2 0 0 0 0 0 0 0 0 0 0

Total 109 187 48 53 74 76 39 36 16 34 5 9

4.14 Maturity

Out of a total specimen of 1,486 L. altianalis, 18.97 % were mature and 81.03% were immature. The observed female that was sexually mature with the smallest size (6.97 g) had a total length of 9 cm while the biggest mature fish observed (1,300g) recorded a total length

45 of 52.5 cm. On the other hand, the observed sexually mature male L. altianalis with the smallest size (4.6 g) had a total length of 8.2 cm while the biggest mature fish observed

(1,180g) recorded a total length of 50 cm.

The males to female ratio of mature fish for the different sampling sites is presented in Table

18. The smallest mature female L. altianalis was 11.4 cm TL from Wath Ong’er station, while the smallest mature male was 10.2 cm TL from Nyokal station indicating that males mature at a smaller length than females. The largest mature male and female were from

Kegati with a length of 50.0 cm TL and 52.5 cm TL, respectively. Kegati is upstream and most of the fish travel a long distance as far as Kegati and beyond to breed.

Table 18: Maturity ratio of male to female Labeobarbus altianalis sampled at various sites along River

Kuja

Sampling station

Kegati Ogembo Kanga Nyokal Gogo

Class size x2 (cm) M F x2 M F M F x2 M F x2 M F x2

10.0-15.0 49 3 1 7 1 0.99 5 1 1 3 1 0.8 2 0 0.0036*

15.1 -20.0 69 12 1 8 8 1 11 10 1 4 2 0.99 1 1 0.809

20.1-25.0 10 1 0.99 6 2 1 13 1 1 1 3 0.95 6 0 0.0016*

25.1-30.0 1 0 2.77E-07* 3 0 1.10E-05* 1 0 3.41E-07* 0 0 0 1 0 2.26E-07*

30.1-35.0 0 1 4.97E-09* 0 0 0 0 0 0 4 1 1.00 4 0 5.16E-07*

35.1 - 40.0 7 0 5.01E-06* 0 0 0 0 0 0 0 0 0 4 2 1

40.1 - 45.0 1 0 5.47E-11* 0 0 0 0 0 0 0 0 0 2 4 0.99

45.1 - 50.0 2 2 0.98 0 0 0 0 0 0 0 0 0 0 0 0

50.1 - 55.0 0 1 4.30E-13* 0 0 0 0 0 0 0 0 0 0 0 0

5.64E- 1.79E-

Total 139 20 48* 24 11 0.53 30 12 0.99 12 7 0.0007* 20 7 09*

*Indicates a significant difference between male and female ratios.

46

The analysis at which 50% of female L. altianalis in River Kuja are mature is presented in

Figure 9. It was estimated as 38.33 cm TL.

120

100

80

60

40

20 Cummulative frequency (%) frequency Cummulative 0 27.5 32.5 37.5 42.5 47.5 52.5 Length (cm)

Figure 9: Mean length at sexual maturity of female Labeobarbus altianalis in River Kuja.

The analysis at which 50% of male L. altianalis in River Kuja are mature is presented in

Figure 10. The L50 was estimated as 33.33 cm TL.

120

100

80

60

40

20 Cummulative frequency (%) frequency Cummulative

0 27.5 32.5 37.5 42.5 47.5 Length (cm)

47

Figure 10: Mean length at sexual maturity of male Labeobarbus altianalis in River Kuja.

48

CHAPTER FIVE

5.0 DISCUSSION

This chapter explains the results in reference to similar studies done previously.

5.1 Water Quality (Physicochemical parameters)

5.1.1 pH

The mean pH value of River Kuja was 7.52 ± 0.09 which represents a near neutral and slightly basic environment. The water pH in River Kuja was between the optimum ranges for survival of L. altianalis (Ophardt, 2003). The pH levels in all the six stations were basic and did not affect the fish abundance; Kanga station which had a high abundance of fish had a pH of 7.41 while Wath Ong’er which had the least abundance had a pH of 7.7. The basic pH could have been attributed to the organic effluents from agricultural factories like coffee, tea and sugarcane deposited in the river. The organic effluents change the water pH owing to the presence of basic and acidic elements used by the factories (Kumar, 2014). Similar result were found from River Kisat which is along Lake Victoria catchment that had an acidic environment due to high decomposition of the organic wastes deposited in the river (Otieno,

Kitur & Gathuru, 2017).

5.1.2 Temperature

Temperature affects the water quality by affecting the solubility of compounds in the water

(Wurts, 2012; Bhadja & Vaghela, 2013). The mean temperature of River Kuja was 21.35 ±

0.40 oC which was within the recommended NEMA standard of 20 - 29 oC (GOK, 2006).

The highest temperature was recorded at Wath Ong’er station while the lowest at Kegati station. The temperature increased downstream and this could be attributed to increase in

49 turbidity temperature through the suspended particles that absorb heat from sunlight more and increased in solar radiation downstream (Pasquero, 2008). The decrease in altitude from upstream to downstream could have contributed to the increased water temperatures too.

Water temperature varies with changes in seasons, solar radiation exposure, water depth, water speed and clouds covering the area; all these could have contributed to the temperature difference at the upstream and downstream (Otieno et al., 2017).

The two sampling stations at the downstream; Gogo and Wath Ong’er with the highest temperature recorded the lowest fish abundance of 62 and 50 respectively. Temperature affects the solubility of gases like oxygen hence an increase in temperature will decrease the solubility of oxygen in water (Perlman, 2013).

5.1.3 Conductivity

The mean conductivity of River Kuja was 90.75 ± 4.24 µScm-1. Studies reveal that rivers supporting suitable mixed fisheries have a conductivity range of between 150 and 500

µhos/cm and beyond. This range might signify that the water is unsuitable for some species

(Miller, Bradford & Peters, 1988).

The high conductivity at Gogo station could have been attributed to the increased water temperature and industrial wastes that contain dissolved solids discharged in the river (EPA,

2012). Point source pollution like sewage effluents and discharge of industrial waste can increase water conductivity due to the presence of phosphate, chloride and nitrate. A considerable change in conductivity could hence indicate pollution or a discharge from a polluted source (Miller et al., 1988). This was evident in Gogo station that had the highest conductivity, where we once found fish gasping for air at the surface due to discharge of waste in the river by a sugar factory at Oria town. Otieno et al., (2017) during his studies in

50

River Kisat, recorded higher conductivity attributed to treated and untreated industrial effluents discharged in the river.

5.1.4 Dissolved Oxygen

The mean dissolved oxygen of River Kuja was 5.036 ± 0.15 mgL-1 which is within the NEMA standard of 5 mgL-1 (GOK, 2006). The increased temperatures downstream contributed to the low oxygen levels (Scales, 2009). Anthropogenic activities like mining, car wash and waste releases from factories noted at the various stations along the river from upstream permitted the decrease of dissolved oxygen. Similar studies by Sultana, Ahmed, Biswas, Saha

& Alam (2017) reveal the effects of industrial effluents on the dissolved oxygen concentration which further affects the fish population.

5.1.5 Phosphates

The mean total phosphates of River Kuja was 232.25 ± 24.79 ugL-1 or 0.23 mgL-1 which is higher than the recommended value of 0.1 mgL-1 for rivers (Sean, 2005). This could be attributed to the fact River Kuja passes through rich agricultural lands where the riparian communities practice farming and use of phosphorus fertilizers, disposal of sewage, domestic use of detergents containing phosphorus and agricultural run-off (Masese, Muchiri & Raburu,

2009). Wath Ong’er station at the downstream had the highest total phosphates concentration and a resultant low fish abundance. The high phosphates concentration could be attributed to the accumulated nutrients downstream. When the phosphate level is too high, algal blooms dominate the aquatic ecosystem that consume large amounts of oxygen and causes anoxia that increases mortality which decreases fish abundance (Okungu et al., 2005). Kegati station at the upstream had the lowest total phosphate concentration and a resultant high fish abundance. The low phosphate concentration could be attributed to the minimum

51 anthropogenic activities in the area. WRMA and JICA (2014) during their studies in River

Kuja recorded similar total phosphates measure and attributed it to majorly agricultural runoff and use of phosphorus fertilizers. Schenone, Volpedo, Fernández and Cirelli, (2007) found out that high phosphorus is recorded during the rainy season due to increased nutrient load up and erosion.

5.1.6 Total Nitrates

The mean total nitrates of River Kuja was 1242.38 ± 56.05 ugL-1 or 1.24 mgL-1 which is within the nitrate levels for rivers (JICA, 1992). Ogembo station had the highest total nitrates concentration while Kanga station had the lowest. Nitrate is a key component of farm fertilizer and is essential for crop production. Nitrates get in to the aquatic system through erosion from the fertilized farms during rainy seasons, leaking septic tanks and car exhaust discharges (Boyle et al., 2012). The high nitrate concentration in Ogembo could be attributed to the pollution caused by the car wash that was at the river bank. Kanga station had the lowest total nitrates concentration and the highest fish abundance. It is evident that total nitrates did not affect fish abundance along River Kuja. Okungu and Opango (2005) found a higher nitrate level in River Kuja of 1.45 mgL-1 and attributed it to the high nutrient concentration due to the degraded Kisii highland environment.

5.1.7 Total dissolved solids (TDS)

The mean total dissolved solids of River Kuja was 57.36 ± 2.05 mgL-1 which is within the recommended value (Sean, 2000). Gogo station had the highest total dissolved solids levels while Kegati had the lowest levels. The total dissolved solids levels along River Kuja increased downstream. Siltation and erosion during the rainy seasons along the river could

52 have increased the total dissolved solids downstream. Over time the total dissolved solids increases the water nutrients level causing eutrophication which decreases the dissolved oxygen levels through decomposition and respiration of algal blooms that further decreases

L. altianalis abundance downstream. A high level of total dissolved solids increases water temperatures due to the increased absorption of heat by the solids compared to the absorption by the water molecules; this could have contributed to the high temperature downstream

(Wetzel, 2001). Similar studies by Ouma (2015) found high total dissolved solids along the

Kenyan Lake Victoria Basin and attributed it to the low pH that was ideal for the solubility of ions related to the total dissolved solids.

5.1.8 Turbidity

The mean turbidity of River Kuja was 92.43 ± 7.78 NTU which is above the recommended value by NEMA of 5 NTU (GOK, 2006). Wath Ong’er station that is downstream of Gogo had the highest turbidity levels due to erosion from various agricultural activities along the river. Increased erosion of the river banks increases turbidity (EPA, 2012). Turbid waters hinder sunlight penetration inhibiting the photosynthesis process that further decrease plant survival and levels of dissolved oxygen (Battelle, 1999). Turbidity caused by suspended sediments indicates erosion and this can clog the gills of fish and affect growth rates (Zeigler,

2002). Kegati station that is upstream had the lowest turbidity levels due to less erosion upstream. Otieno et al., (2017) in his studies on River Kisat found mean turbidity level of 89

NTU which is higher than the recommended NEMA value and he attributed it to erosion, bottom sediments, decaying matter, urban runoff and industrial effluents accumulated over sampling sites along the river.

53

5.1.9 Stream velocity

The mean water velocity of River Kuja was 0.72 ± 0.03 ms-1. Kegati station which is at the upstream had the highest stream velocity while Wath Ong’er station had the lowest. Water at the upstream has a higher velocity compared to downstream. The higher speed allows water aeration and increases dissolved oxygen levels hence the high D.O levels at Kegati.

Likewise, at a higher water speed, solar radiation will not penetrate fully to the bottom of the river affecting the water temperature hence the lower temperatures at Kegati. Otieno et al.,

(2017) found a low stream velocity of 0.47 ms-1 and attributed it to the low river depth and increased width from the upstream to the downstream. This could also be the contributing factor to the high stream velocity at Kegati station which had a depth and width of 0.5 m and

15 m respectively compared to Wath Ong’er station that had a water depth and width of 0.5 m and 50 m respectively.

There was also a high abundance of L. altianalis at stations with moderate stream velocity.

This could be due to favorable and stable environmental conditions like dissolved oxygen, more food and less energy used in swimming. Hockley, Wilson, Brew and Cable (2014) found a high abundance of guppies in sections of the river with moderate speed. They attributed this to the more stable and predictable environment created by the less spatial and temporal variation.

5.2 Relationship between physicochemical parameters and fish abundance.

5.2.1 pH pH correlation with abundance was negatively insignificant (-0.04) which means it did not affect the L. altianalis abundance along River Kuja. All the six sampling stations had a basic

54 environment. The pH levels were within the required range for aquatic life hence didn’t raise a concern in terms of leading to a decline of L. altianalis along River Kuja.

5.2.2 Temperature

Water temperature of River Kuja slightly correlated negatively with L. altianalis abundance

(-0.313*). This means that high temperatures in River Kuja led to a decline in the fish abundance. This is probably due to the fact that increased temperature lowers the dissolved oxygen levels which is a key parameter in fish respiration. Since oxygen solubility decreases as temperature increases, this will decrease the fish abundance (Perlman, 2013).

5.2.3 Conductivity

Water conductivity of River Kuja slightly correlated negatively with L. altianalis abundance

(-0.257*). This means that high conductivity in River Kuja led to a decline in the fish abundance. Point source pollution like sewage effluents can raise the water conductivity due to the presence of conductive ions that originate from carbonate compounds, chlorides, phosphates, dissolved salts, sulfides, inorganic materials and nitrates (Miller et al., 1988). In such a case, the fish are stressed since they can only tolerate specific conductivity ranges.

5.2.4 Dissolved Oxygen

Dissolved oxygen measured from River Kuja strongly correlated positively with L. altianalis abundance (0.432**). This means that increased dissolved oxygen in River Kuja favored a high fish abundance. Dissolved oxygen is an important water quality parameter due to its respiratory influence on the aquatic organisms (Wetzel, 2001) hence a decrease or an increase affects aquatic life.

55

5.2.5 Total Phosphates

Total phosphates concentrations correlation with L. altianalis abundance from River Kuja was negatively insignificant (-0.208) which means it did not affect the fish abundance.

Phosphates quicken the growth of phytoplankton which acts as food for most herbivorous and omnivorous fish (Dates, 1994). The increased food availability increases fish population up to a certain limit. In the case of River Kuja the phosphate level was favorable to L. altianalis population.

5.2.6 Total Nitrates

The total nitrates correlation with L. altianalis abundance (0.049) in River Kuja was positively insignificant which means it did not affect the fish abundance. Nitrates get in to the aquatic system through erosion from the fertilized farms during rainy seasons and it hasten planktonic growth and in turn provide food for fish (Boyle et al., 2012). The nitrates levels had not become harmful to the L. altianalis population in River Kuja to affect its distribution and abundance.

5.2.7 Total Dissolved Solids

Total dissolved solids measured from River Kuja strongly correlated negatively with L. altianalis fish abundance (-0.344**) in River Kuja. This means that increased total dissolved solids led to a decline in the fish abundance. Over time the total dissolved solids increases the water nutrients level causing eutrophication. This decreases dissolved oxygen levels through decomposition and respiration of algal blooms that further decreases the fish abundance (Muigai, Shiundu, Mwaura & Kamau, 2010).

56

5.2.8 Turbidity

The turbidity correlation with L. altianalis abundance (-0.077) in River Kuja was negatively insignificant which means it did not affect the fish abundance. Turbid waters hinder sunlight penetration inhibiting the photosynthesis process. The reduced photosynthesis process will decrease the plant survival and levels of dissolved oxygen (Battelle, 1999; Wetzel, 2001).

This further reduces the fish abundance. According to Chemoiwa & Jepleting (2018) the species prefers sand/gravel substrate and it is threatened by siltation of aquatic habitats which result to increased turbidity. De Graaf (2007) also found that erosion seriously affected L. altianalis population in River Kagera.

This is different from what is reveled from River Kuja.

5.2.9 Stream velocity

The water speed correlation with L. altianalis abundance (0.112) in River Kuja was positively insignificant which means it did not affect the fish abundance. High water speed causes fish to use more energy in swimming against the current (Hockley et al., 2014). In River Kuja, there was no great margin (0.28) in the stream velocity of the sampled stations hence it did not affect the abundance in the river.

5.2.10 Labeobarbus altianalis fish abundance in River Kuja

The abundance of L. altianalis fish from River Kuja strongly correlated positively with dissolved oxygen (0.432**). From the Pearson correlation, it is evident that dissolved oxygen was the key parameter that determined the abundance of L. altianalis fish in River Kuja and total dissolved solids was the key parameter that hindered the abundance of L. altianalis fish in River Kuja. Other parameters like temperature and conductivity slightly hindered the fish abundance. High temperatures, conductivity and total dissolved solids levels led a reduction

57 in the abundance of L. altianalis fish. On the other hand, dissolved oxygen favored the abundance due to its respiratory influence.

5.3 Distribution and abundance

A total of 1,486 L. altianalis fish were sampled from River Kuja. Kanga Bridge which is at the midstream had the highest population. In the mid reaches of a river, river structure such as trees and rocks play a significant role of supplying organic material like periphyton. The photosynthesis to respiration ratio is higher in this division and amounts to P: R> 1 (Pidwirny,

2006). This favors the presence of a higher abundance at the Kanga Bridge station in River

Kuja. The complexity of the habitat structure at Kanga could have also favored the high abundance (mud/silt, sand/pebbles and boulders at a ratio of 50:30:20). A study conducted by Orina, Getabu, Omondi & Sigei (2018) along River Kuja found Kanga to be the station with the highest fish catch and mean habitat quality index. Similar studies on River Awach

Seme and Kisian revealed a higher abundance of various species at the midstream (Mwangi et al., 2012).

River Kuja’s upstream and mid-stream had a higher abundance compared to the downstream that had a total of 112 fish. The high abundance in the headwaters and midreaches could also be due to migratory nature of the fish. The low abundance in the lower reaches could due to overfishing especially by the communities living in those areas who value fish for food. April to August were the months in which the highest number of fish were sampled from both upstream and midstream. This is also the period when recruitment occurs and there are fish that have migrated to the specific spawning grounds. There was a high abundance of fish during the dry season compared to the wet season. This could be attributed to the migration of the species upstream to spawn during rainy season.

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5.4 Mean length of fish

Gogo falls dam had the highest fish mean length of 20.1 cm TL while Wath Ong’er the smallest of 8.2 cm TL. Kenya Electricity Generating Company has placed a power station at

Gogo falls. The presence of a water fall and a dam at Gogo station could have contribute to the presence of larger L. altianalis compared to other stations, since the area sampled is protected from fishermen. The dam could be acting as a block to the potamodromous fish that migrate upstream to spawn hence the presence of mature large fish (Marmulla, 2001;

Jumbe, 2003). The habitat has purely mud/silt hence more food for the fish to exploit and the depth of 5 m offers a good opportunity for the fish to evade the various predators. Dams alter the downstream hydrology of the river, including flooding and this increases food availability

(Jackson & Marmulla, 2001) which could have favored the presence of large fish. The smaller length of L. altianalis at Wath Ong’er which is downstream from Gogo of the Kuja River could be attributed to fishing near the river mouth by the riparian communities. The small sizes found at the downstream could also be due to the passive drift mechanism where some cyprinid juveniles move downstream to feedings grounds by drift from the spawning grounds as discovered by (Pavlov, 1977) in the Volga and Kuban rivers. He articulated the downstream movement as an important migratory pattern phase for the potamodromous fish.

5.5 Length frequency

A larger frequency of the L. altianalis sampled from River Kuja were less than 20 cm TL while the largest fish sampled was 54 cm TL. The larger sizes were few over the months and not all sizes were fully represented over the months which could be attributed to the fact that the fish is seasonal and overexploitation of the fish over the past years in the Lake Victoria basin. The largest L. altianalis sampled along River Kuja was smaller than the maximum

59 recorded total length of 90 cm (Ntakimazi, 2006) which could be due to various anthropogenic activities in the river that affect the fish. The biggest fish was found at the upstream and this could be due to the migration for spawning purposes. The low mean total length recorded in River Kuja concur with those found in four rivers along the Lake Victoria

Basin. Most fish from the same species sampled from River Nzoia, River Yala, River Sondu and River Nyando revealed that most of the fish had less than 20cm TL (Chemoiwa, Abila,

Njenga & Barasa, 2017). The lower lengths of L. altianalis in River Kuja suggests presence of more juveniles than adults in the river water as indicated by Aruho, Walakira & Rutaisire

(2018) and overexploitation in the river.

5.6 Length Weight relationship

The b values in length- weight relationships determine the growth pattern of the fish species and the growth pattern is isometric when it is three and allometric when it is significantly different from three (Asadi, Sattari, Motalebi, Zamani & Gheytasi, 2017). The regression coefficient (b) of 3.03 showed isometric growth exhibited by L. altianalis from River Kuja that is fish becomes more robust with increasing length. A study conducted by Ondhoro et al., (2016) on the same species revealed a regression coefficient (b) of 3.27 from River Nile,

Lake Edward and Kazinga Channel in Uganda which further confirms the fish to be having an isometric growth. The silver cyprinid Rastrineobola argentea in Lake Victoria shows a positive allometric growth with a regression coefficient (b) of 3.40 (Yongo et al., 2016). The length–weight relationship that has resulted from this study could help form basis for future work on L. altianalis especially in River Kuja.

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5.7 Condition factor

From the study, mean relative condition factor of L. altianalis from River Kuja was 0.91 which reveals a fairly good condition of the fish in the habitat. Condition factor (K) designates the fitness of a specific water body for growth of fish. It’s dependent on various features of fish such as environmental factors, maturity and food accessibility in a water body

(Eagderi & Radkhah 2015). Generally all the six sampling stations along the river showed different condition factors ranging from 0.68 to 1.21 whereby the downstream had the lowest mean condition factor. A condition factor of ≥ 1 signify a good feeding level and suitable environmental conditions (Asadi et al., 2017). Ogembo and Nyokal stations at the upstream and midstream respectively revealed to be more suitable habitats with better environmental condition for L. altianalis as per their condition factors that were above 1 due to fish food resources and high environmental quality especially the dissolved oxygen levels. Since pollution in various forms was noted along the river, we can conclude that the dissolved oxygen could be the main parameter that affected the fish condition in River Kuja sampled stations.

The mean condition factor also varied from time to time in the river. Fish sampled during the month of April had a higher condition factor compared to other months and this could be due to food availability and improvement in the habitat quality since this is one of the months in the long rains. Condition factors of dissimilar population of same species provide certain information about the food source, the timing and period of breeding (Eagderi & Radkhah

2015). The month of April proved to be the perfect timing for the L. altianalis population in

River Kuja.

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The mean condition factor of L. altianalis from different habitats were: Lake Edward 1.05,

Kazinga channel 1.04 and River Nile 0.96 which do not differ significantly with that from

River Kuja (Ondhoro et al., 2016). From the condition factors stated, it is evident that a lake is a more suitable habitat for the species compared to flowing rivers due to the slightly higher value. Rapid changes in the rivers due to rainfall and different water quality parameters along its upstream and downstream could be the limiting factor. The lake environment on the other hand is more settle and has proper aeration from the wind that blows. Planktonic food resources are also poor in river compared to the lake. This needs further investigation.

5.8 Population growth.

The asymptotic length (L∞) was estimated to be 60 cm TL and the growth coefficient (K) was

0.4 yr-1 from the ELEFAN method. Powel method estimated an asymptotic length of 61.29 cm TL and a Z/K of 2.802. The L∞ is a parameter of the von Bertalanffy Growth Function

(VBGF), which expresses how far the mean length of a fish from a specified population would reach if they were to grow for an markedly long time or the mean length of the old fish. The growth coefficient K is how fast the fish approach their asymptotic length and it is related to the fish metabolic rate. River species are more active since they swim against the current and hence high metabolic rate and a high growth coefficient (Sparre & Venema

1998). Fish usually exhibit a k or r life style, on the other hand, L. altianalis seem to have an intermediate life history style due to the slightly high growth rate (0.4). They occupy both unstable and stable (riverine and lacustrine) environment, grow to larger sizes and mature at a slightly higher length (33.33cm TL for males and 38.33cm TL for females) and a slightly long life span of 7.5 years. Due to the L. altianalis ability to occupy an intermediate strategy,

62 its survival is guaranteed under a wide range of conditions hence the need for its conservation

(Winemiller & Rose, 1992; Froese, 2005).

The L∞ (60 cm TL) was lower than the recorded maximum length of 90 cm TL (De Vos and

Thys van den Audenaerde, 1990). This suggests that the L. altianalis in the Kuja River is overexploited whereby fishing has removed all the larger fish above 60 cm to 90 cm

(Chemoiwa et al., 2013). There are also various factors that stress the L. altianalis population in River Kuja further limiting its asymptotic length. Environmental stress caused by anthropogenic activities lead to reduced dissolved oxygen levels that increase mortality. This was once observed at Gogo station where fish were gasping for oxygen due to organic pollutants from the sugar factories at Oria. Since large fish have low tolerance to low dissolved oxygen compared to small fish, this affects the asymptotic length of L. altianalis in River Kuja.

Siltation caused by agriculture at the river bank, turbidity, mining, urbanization also increase mortality and the resultant low asymptotic length. Overfishing in the lake and the river mouth that began in the 1950’s that has not been contained also contributed to the low asymptotic length (Ochumba & Manyala 1992; Chemoiwa et al., 2013). The absence of large fish affects

L∞ in the population.

5.9 Mortality

Natural mortality (M) is the elimination of fish from the stock with reasons not associated with fishing while fishing mortality (F) is the removal of fish from the stock by fishing activities using any fishing gear (Christian & Holt 2013).

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The fishing mortality (F) for L. altianalis in River Kuja was estimated to be 1.12 yr-1. This has reduced over time since the larger fishable part of the stock has been overexploited. The estimated natural mortality (M) for L. altianalis in the River Kuja was 0.71 yr-1 and this could be considered normal basing on the M/K ratio of 1.78 which is within the range of 1.12 –

2.50 which has been computed for most fish populations (Beverton & Holt, 1957; Arizi et al., 2015). The natural mortality is directly proportional to the growth coefficient of a fish and inversely proportional to the asymptotic length and the life span (Beverton & Holt 1956).

Anthropogenic impacts were the major cause for mortality in the Kuja River.

5.10 Exploitation rate

The calculated exploitation rate for the L. altianalis species of River Kuja was 0.61, this was due to the high calculated fishing mortality which contributed 61% of the total mortality.

Fishing was noted at various stations especially at the river mouth. This can cause a reduction of the species in the future, decline in the protein source and dependency of the species by the riparian communities. Results suggest that the species are probably not well conserved and require appropriate management measure to ensure that it continues to provide the necessary protein resources for the subsistence communities living around the river. Poor fishing has a long fishing documentation which has been attributed to over fishing of riverine species, Labeobarbus species included (Chemoiwa et al., 2013). Therefore environmental measures which include management of agriculture, mining and fishing need to be put in place. If this measures are implemented, the survival of the species is expected to improve as well as the L∞ which means there will be availability of large fish and the catches will be sustainable which currently are not.

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5.11 Growth Performance Index (∅′)

The growth performance index (∅′) of L. altianalis estimated in River Kuja was 3.16. It was high and could be attributed to the high exploitation rate which reduced the average size of fish and resulted to a faster growth rate (Getabu, 1992). Native fish species like L. altianalis have declined in Lake Victoria Basin due to use of illegal and damaging fishing gears mainly at the river mouth and to a degree damaging breeding and nursery grounds. (Ochumba,

Gophen & Pollinger, 1991; Masai, Ojuok & Ojwang, 2002).

The calculated (∅′) of L. altianalis compares very well with the silver cyprinid in Lake

Victoria which has a growth performance index of 3.36 (Yongo, Manyala & Agembe, 2018).

5.12 Potential longevity

Potential longevity is the estimated maximum age (tmax) that a fish of a given population would reach or the life expectancy of a fish when living in an ‘ideal’ environment. The tmax of L. altianalis from River Kuja was estimated to be 7.5 years, which indicates a slightly long lived fish. The L. altianalis population in River Kuja are capable to grow to larger sizes under favorable environment. The relatively long years of 7.5 years depicted by L. altianalis in the

Kuja River might influence its growth in a way that makes the fish reach their asymptotic length at a slower rate. Studies by Chemoiwa and Jepleting (2018) on L. altianalis from River

Nyando, categorized the fish as slow growing hence taking long time to mature. This was favoured by the species environmental tolerance and omnivorous feeding habits.

5.13 Recruitment pattern

Recruitment is the entry of new fish into the population or entry of the already grown fish into the legal fishing gear. The recruitment pattern displayed a bimodal distribution meaning that L. altianalis of River Kuja has two cohorts. The annual recruitment pattern of the species

65 in the River Kuja indicated that recruitment occurred throughout the year with the major one occurring during the rainy season (Feb - April) and the minor ones occurring during the short rains (June – Aug). The major recruitment occurring during the long rains could be favored by the environmental condition when there is enough food, more breeding grounds and more cover from predators. The results are in agreement with the silver cyprinid in Lake Victoria which has a recruitment pattern with two peaks; in May and August (Yongo et al., 2018).

The species breed throughout the year just like the L. altianalis with peaks during and after the rainy seasons.

5.14 Sex ratio

From the study, the female to male ratio was 1:1.35. The male L. altianalis could have adapted fully to the environment in terms of finding food, escaping predators and therefore becoming fit for survival hence their population numbers in River Kuja during the sampling period. The fact that the males mature at a lesser length compared to the females could be a contributing factor to their abundance. They cross to the maturity stage faster and begin colonizing the environment before their fellow females who were spawned at the same time hence more susceptible to be part of the catch. For instance, the fast growth rate of male

Oreochromis niloticus from Lake Victoria cause them to be caught fast by the trawl nets compared to the smaller females. The male selection causes its dominance over females in the catch (Njiru et al., 2006). The difference in the sex ratio could also be due to differential migration of sexes whereby the male L. altianalis could have travelled a longer distance compared to their female counterparts hence their abundance (Rinne & Wanja, 1982). Further investigation is needed in explaining the sex ratio difference since there is no study of the same species that can be used for comparison.

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5.15 Maturity

The estimated length at 50% fish maturity of the females was 38.33 cm TL whereas that of the males was 33.33 cm TL. It is evident from the results that males mature at a smaller total length compared to the females in River Kuja. The males maturing faster than the females could be a reproduction adaptation to ensure that when the females are ready to spawn the males are already mature to fertilize. In a scenario where the females will lay eggs and males are not yet mature to fertilize the eggs, this may affect the breeding of the species and its population at large. The estimated length at 50% maturity of the species in River Kuja differed from those in Lake Edward and Lake George which revealed a Lm50 of 36 cm for male and 54 cm for male (Breder & Rosen, 1966). The Lm50 calculated in River Kuja is smaller compared to that in the two lakes revealing that the fish attains its maturity faster in the riverine environment compared to lacustrine. This could be due to the environmental conditions in the two habitats; the fish will mature faster in an unstable, harsh and stressful environment (Lowe- McConnell, 1969). The size at sexual maturity is of great importance in the determination of the optimal mesh size so as to ensure sustainability while exploiting the fish stock.

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CHAPTER SIX

6.0 CONCLUSION AND RECOMMENDATION

This chapter concludes the thesis findings in line with the objectives and offers recommendations basing on the study carried out.

6.1 CONCLUSION

Results from this study reveal that there are L. altianalis species in River Kuja that are able to recruit themselves into the fishery. The fish is found along the river from the upstream to the downstream though they are found in numerous numbers at the midstream Kanga station to be specific. Downstream of River Kuja had less numbers of the species.

L. altianalis from River Kuja can grow to a bigger size when conserved based on the calculated asymptotic length (L∞) and the growth coefficient (K). The curvature parameter

(K) and the growth performance index indicated that the fish grows faster and this could help promote the riverine fisheries. The potential longevity of 7.5 years depicted by L. altianalis in the River Kuja might influence its growth in a way that makes the fish reach their asymptotic length at a slower rate. The relatively long life span of the species favors its survival and can be a major tool in growth especially in captivity.

The calculated fishing mortality and a normal natural mortality favored the immature species in River Kuja. The less threat to the stock gives a better chance for their survival although the large difference between the fishing and natural mortality increased the exploitation rate slightly above the optimum. The larger species are fished especially near the river mouth and with the dam along the lower reaches, migratory routes are affected.

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River Kuja has L. altianalis populations that majorly constitute fish that are sexually immature. The juveniles need conservation in order to attain their size at 50% maturity and be recruited into the fishery. Since the species is threatened in the Lake, it might have some sedentary population in River Kuja that needs further research. The availability of these species though immature can be a future solution to the declined species diversity in Lake

Victoria Basin if well conserved. Other studies by Chemoiwa et al., (2017) revealed the presence of L. altianalis in four major rivers draining into Lake Victoria and this study has proved presence of the species to the fifth major tributary.

The mean relative condition factor of L. altianalis from River Kuja revealed a fairly good condition of the habitat. The annual recruitment pattern of L. altianalis in the River Kuja indicated that recruitment occurred throughout the year. L. altianalis was distributed along

River Kuja with more abundance in the upstream and midstream. The size at maturity for both males and females could be relevant in culturing the species since there are attempts in

Uganda in culturing the species but information is limited (Aruho, Walakira & Rutaisire,

2018). L. altianalis has a great potential of generating incomes for both local and international markets if successfully cultured. It is a high-value demanded cyprinid species with nutritional benefits.

There was a relationship between the water quality parameters and the abundance of L. altianalis in River Kuja though only two parameters strongly correlated positively and negatively with the abundance. Dissolved oxygen was the parameter that correlated positively and total dissolved oxygen correlated negatively.

The knowledge gained will act as inputs in formulating models that will be used for sustainable management of L. altianalis along River Kuja and culture. The management will

69 ensure the growth of the immature fish and conserve the L. altianalis populations of River

Kuja.

6.2 RECOMMENDATION

When well managed the River can act as a good refuge for the species and can help in resurfacing the species back to the Lake catches. L. altianalis being a potamodromous fish will require clear routes and less blockage along the river as it migrates upstream to spawn.

The presence of this species in River Kuja calls for protection of the river’s water quality to ensure survival of the species.

It is advisable to ensure that all fish in a stock reaches sexual maturity before exploiting the fishery. The study recommend more research on L. altianalis population in River Kuja like spawning, fecundity and effect of the various anthropogenic activities noticed along the river like mining on L. altianalis population. More research is also recommended on the culture of the species to increase the species diversity and longer studies on the species featuring all seasons in River Kuja.

Since dissolved oxygen is the main parameter favoring the presence of the species in River

Kuja, it is recommendable not to dispose organic waste in the river. Various methods can be used to avoid siltation like discouraging farming in the riparian lands and use of organic fertilizer in order to reduce total dissolved solid in the river. Finally more research needs to be done on rivers along the Lake Victoria basin for they seem to harbor various indigenous species and little has been studied.

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APPENDICES

Appendix 1: Pearson correlation table between physicochemical parameters and fish abundance.

pH Temp Cond DO TP TN TDS Turbidity Speed Abundance

(˚C) (µScmˉˡ) (mgLˉˡ) (µgLˉˡ) (µgLˉˡ) (mgLˉˡ) (NTU) m/s no. pH Pearson -

Correlation 1 .231 .147 -.175 .099 .120 .056 .042 .464* -.040

*

Sig. (2-tailed) .076 .262 .181 .454 .363 .670 .750 .000 .764

N 60 60 60 60 60 60 60 60 60 60

Temperature Pearson -

Correlation .231 1 .537** -.731** .385** -.168 .514** .384** .510* -.313*

*

Sig. (2-tailed) .076 .000 .000 .002 .200 .000 .002 .000 .015

N 60 60 60 60 60 60 60 60 60 60

Conductivity Pearson .147 .537** 1 -.553** .316* -.032 .698** .193 -.081 -.257* Correlation

Sig. (2-tailed) .262 .000 .000 .014 .808 .000 .139 .537 .047

N 60 60 60 60 60 60 60 60 60 60

DO Pearson - - .380* -.553** 1 -.271* .079 -.556** -.335** .432** Correlation .175 .731** *

Sig. (2-tailed) .181 .000 .000 .036 .550 .000 .009 .003 .001

N 60 60 60 60 60 60 60 60 60 60

TP Pearson .099 .385** .316* -.271* 1 .259* .489** -.064 .056 -.208 Correlation

Sig. (2-tailed) .454 .002 .014 .036 .046 .000 .626 .669 .111

N 60 60 60 60 60 60 60 60 60 60

*. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

88

Appendix 2: Pearson correlation table between physicochemical parameters and fish abundance.

pH Temp Cond DO TP TN TDS Turbidity Speed Abundance

(˚C) (µScmˉˡ) (mgLˉˡ) (µgLˉˡ) (µgLˉˡ) (mgLˉˡ) (NTU) m/s no.

TN Pearson .120 -.168 -.032 .079 .259* 1 .052 -.136 .109 .049 Correlation

Sig. (2-tailed) .363 .200 .808 .550 .046 .696 .300 .409 .710

N 60 60 60 60 60 60 60 60 60 60

TDS Pearson .056 .514** .698** -.556** .489** .052 1 .070 .045 -.344** Correlation

Sig. (2-tailed) .670 .000 .000 .000 .000 .696 .594 .732 .007

N 60 60 60 60 60 60 60 60 60 60

Turbidity Pearson .042 .384** .193 -.335** -.064 -.136 .070 1 -.186 -.077 Correlation

Sig. (2-tailed) .750 .002 .139 .009 .626 .300 .594 .156 .556

N 60 60 60 60 60 60 60 60 60 60

Speed Pearson - - Correlation .464 -.081 .380** .056 .109 .045 -.186 1 .112 .510** **

Sig. (2-tailed) .000 .000 .537 .003 .669 .409 .732 .156 .395

N 60 60 60 60 60 60 60 60 60 60

Abundance Pearson - -.313* -.257* .432** -.208 .049 -.344** -.077 .112 1 Correlation .040

Sig. (2-tailed) .764 .015 .047 .001 .111 .710 .007 .556 .395

N 60 60 60 60 60 60 60 60 60 60

*. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

89

Appendix 3. Descriptive statistics and estimated parameters of the length – weight relations for

Labeobarbus altianalis in various sampling stations along River Kuja.

Station Number Mean Mean Length Mean Weight Regression Regression Correlation

of fish condition constant coefficient coefficient

factor (a) (b) (r)

Kegati 380 0.826±0.0014 13.665±7.0982 51.080±153.3025 -2.1 3 0.9875

Ogembo 274 1.209±0.0067 9.343±4.6259 13.389±24.918 -1.9 2.9 0.9297

Kanga 517 0.711±0.0019 8.690±4.3786 11.337±20.1254 -2.2 3.1 0.9672

Nyokal 203 1.033±0.0034 9.952±5.7148 22.017±53.632 -2 3 0.965

Gogo 62 0.737±0.0015 20.103±12.8533 196.273±291.5626 -2.1 3.1 0.977

Wath 50 0.684±0.0025 8.208±3.5356 7.963±10.6455 -2.2 3.1 0.9526

Ong'er

All 1486 0.908±0.0033 10.715±6.5627 31.071±107.7694 -2.1 3 0.9729

stations

90

Appendix 4: Relative recruitment values

Relative time Percentage Recruitment

January 1.85

February 4.41

March 4.74

April 3.70

May 5.15

June 11.01

July 7.59

August 18.67

September 21.70

October 20.29

November 0.88

December 0.00

91