Food and Foraging Ecology of Limnothrissa Miodon in The

FOOD AND FORAGING ECOLOGY OF LIMNOTHRISSA MIODON IN THE

SANYATI BASIN, LAKE KARIBA

Takudzwa Comfort Madzivanzira

A thesis submitted in partial fulfilment of the requirements for the degree of Master of

Science in Tropical Resource Ecology.

University of Zimbabwe

Faculty of Science

Department of Biological Sciences

Tropical Resource Ecology Programme

May 2016 Abstract The aim of the research was to study the feeding behaviour of Limnothrissa miodon (kapenta) with reference to food items in the Sanyati Basin of Lake Kariba. Sampling was carried out from June to December 2015. Fish samples for dissection were collected per haul in three nights per month at three different sites. Plankton and physicochemical variables were also taken on these sites. Food items in kapenta stomachs were identified and quantified for frequency of occurrence and electivity index. Sardine stomachs were also classified according to fullness. Zooplankton dominated by rotifers and ostracods had the highest frequency of occurrence in the stomachs of sardines. Macroinvertebrates also highly occurred in the stomachs of sardines and had the highest electivity index in all sampled months. Frequency of occurrence of unpalatable prey was significantly different between sampled months (ANOVA, p<0.05) whilst that of most macroinvertebrates, palatable phytoplankton and all zooplankton was not significant different between months (ANOVA, p>0.05). Of the 2970 sardines that were analysed, 69% had empty stomachs, with the remaining 31% stomachs that had food being constituted by 19% ¼-full, 5% half full, 3% ¾-full and 4% full stomachs. There were no significant differences between sampled months with respect to all the stomach classes (ANOVA, p>0.05). The number of kapenta with empty stomachs significantly outnumbered those that had stomachs which were at least ¼-full in all months (χ2 = 376.7, df = 6, P < 0.003). Fullness index ranged from 0.32 in June to 0.14 in November and was significantly different among months (ANOVA, p<0.05). Fullness index also showed a significant difference with respect to the interaction between month and site (ANOVA, p<0.05) and no significant difference between the sampled sites (ANOVA, p>0.05). Total length of fish and weight ranged between 32-70 mm and 0.12-2.63 g respectively. Body condition index for Limnothrissa miodon ranged between 0.67 and 0.73 recorded in July and November respectively. Body condition was not significantly different (ANOVA, p>0.05) with both site and month and however significantly different with respect to the interaction between site and month. Body condition had close relationships with the highly preferred prey groups found in the sardine stomachs. Temperature had a negative correlation with green algae (r = -0.9). All the measured lake water physical and chemical properties except dissolved oxygen displayed significant differences (p<0.05) among months. A total of 44 phytoplankton species were recorded in the Sanyati Basin, which comprised of 6 Bacillariophyta, 21 Chlorophyta, 8 Cyanophyta, 3 Dinophyta, 3 Euglenophyta, 2 Chrysophyta and 1 Xanthophyta. Overall, Cylindrospermopsis raciboskii dominated Cyanophyta had the highest cellular concentrations making up 85% of the total phytoplankton concentrations followed by Chlorophyceae which had 7% contribution. Dinophyceae contributed 5% and the rest of the phytoplankton classes contributed less than 5% to the total population. The high densities of Cyanophyta affected the species diversity of phytoplankton in the Sanyati Basin which ranged between 0.84 and 1.42 recorded in November and June respectively. A total of 26 zooplankton species were recorded which comprised of 3 Cladocera, 7 Copepoda and 16 Rotifera. Keratella cochlearis dominated the in the rotifer taxonomic group and had the highest cellular concentrations making up 83% of the total zooplankton concentrations. Copepoda contributed 12% whilst Cladocera contributed the least

i of 5%. The density of palatable prey was very low due to the strained environment which is a possible cause of the starvation of Limnothrissa miodon.

Acknowledgements This research project is credited to the invaluable assistance and guidance from my supervisors,

Professor Christopher H.D. Magadza and Portia C. Chifamba. Thank you for the scholastic guidance, constant confidence and support in me. I also want to thank Godwin Mupandawana from the ULKRS who added the practical technical aspect to my theoretical ecology knowledge together with his daughters and sons from various institutions attached at the station. All the lab work was carried out at the ULKRS and I thank the Director, Dr. Tamuka Nhiwatiwa, the chief technician Elmon Dhlomo and his team for the support they gave me. The project would not have been possible also if McMaster and Irene fishing company had not allowed me to go into the lake on their rigs to collect free kapenta samples. I also thank their friendly staff, who gave me information about their experiences in kapenta fishing. My sincere gratitude goes to my family and friends for the continued encouragement throughout the TREP programme. The

TREP programme would not have been enjoyable without you Nyasha Mabhumbo-Rugwete,

Kudzanai Dhliwayo, Chipo Mungenge, Ernest Manunure, Innocent Shoshore, Marshall Gonye and Definate Mudzamiri. Last but definitely not least, I give thanks to God for his everyday blessings and guidance.

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Dedication The entire work is dedicated to the most cherished memories of my beloved late grandmother

Rabecca “Shava” Madzivanzira whose strong words of encouragement and moral support

were always my inspiration.

Table of Contents Abstract ...... i Acknowledgements ...... iii Dedication ...... iv List of Figures ...... viii List of Tables ...... ix List of Appendices ...... x List of Plates ...... xi CHAPTER 1 ...... 1 Introduction ...... 1 1.1 General Introduction ...... 1 1.2 Problem Statement ...... 2 1.3 Justification ...... 3 1.4 Objectives ...... 4 1.4.1 Other Objectives ...... 4 1.5 Null hypothesis ...... 4 CHAPTER 2 ...... 5 Literature Review...... 5 2.1 Introduction ...... 5 2.2 Phytoplankton studies ...... 5 2.3 Zooplankton studies ...... 7 2.4 Kapenta studies ...... 10 2.5 Physicochemical studies in Lake Kariba ...... 12 2.6 The present study ...... 14 CHAPTER 3 ...... 15 Materials and Methods ...... 15 3.1 Study Area ...... 15 3.2 The Sampling Sites...... 16 3.3 Field Sampling ...... 17 3.4 Dissolved substance analysis ...... 17 3.4.1 Nitrates ...... 18 3.4.2 Orthophosphates ...... 18 3.4.3 Total Nitrogen (TN) ...... 18 3.4.4 Total Phosphorous (TP) ...... 19

3.4.5 Ammonia...... 19 3.4.6 Chlorophyll-a ...... 19 3.5 Phytoplankton sampling ...... 20 3.6 Zooplankton sampling ...... 21 3.7 Kapenta analysis ...... 22 3.8 Data Analysis ...... 24 3.8.1 Diversity index of plankton ...... 24 3.8.2 Monthly frequency of occurrence of prey in stomachs ...... 25 3.8.3 Monthly stomach fullness index ...... 26 3.8.4 Monthly comparison of stomach fullness classes ...... 26 3.8.5 Body condition L. miodon...... 27 3.8.6 Prey preference ...... 27 3.8.7 The relationship between plankton densities, physicochemical variables body 28 condition, frequency of occurrence of prey...... 28 CHAPTER 4 ...... 29 Results ...... 29 4.1 Lake basin physicochemical variables ...... 29 4.2 Phytoplankton...... 34 4.2.1 Species composition and abundance ...... 34 4.3 Zooplankton ...... 40 4.3.1 Species composition and abundance ...... 40 4.4 Feeding habits of L. miodon ...... 43 4.4.1 Stomach fullness classifications ...... 43 4.4.2 The diet of L. miodon ...... 45 4.4.3 Preferred food items ...... 51 4.4.4 Body condition ...... 53 4.4.5 Breeding classes of sardines ...... 55 4.5 Relationships between variables ...... 56 4.5.1 Correlations between variables ...... 56 4.5.2 Principal component analysis ...... 57 4.5.3 Cluster analysis ...... 61 CHAPTER 5 ...... 62 Discussion ...... 62

5.1 Feeding habits of L. miodon ...... 62 5.2 Body condition of kapenta ...... 64 5.3 Plankton dynamics ...... 65 5.3.1 Phytoplankton ...... 65 5.3.2 Zooplankton ...... 66 5.4 Lake water physicochemical variables ...... 68 5.5 Relationships between variables as explored by PCA and cluster analysis and canonical correlation...... 71 5.6 Conclusion ...... 71 5.7 Recommendations ...... 72 References ...... 74 Appendices ...... 83 Plates ...... 94

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List of Figures Figure 3.1. Map showing (a) location of Lake Kariba, (b) study sites in the Sanyati Basin. .. 15 Figure 4.1. Changes in (a) temperature (°C), (b) pH, (c) DO (mg l-1), (d) % oxygen saturation, (e) conductivity (μScm-1), (f) turbidity (mg l-1) in the Sanyati Basin, Lake Kariba from June to December 2015 ...... 31 Figure 4.2. Changes in (a) nitrate (mg l-1), (b) phosphates (mg l-1), (c) TN (mg l-1), (d) TP (mg l-1), (e) ammonia (mg l-1), (f) chlorophyll-a (mg l-1) in the Sanyati Basin, Lake Kariba from June to December 2015...... 32 Figure 4.3. Temperature and dissolved oxygen profiles in the Sanyati Basin from June to December, 2015...... 33 Figure 4.4. Nitrate and phosphate profiles in the Sanyati Basin from June to December, 2015...... 34 Figure 4.5. Percentage occurrence of each phytoplankton classes the Sanyati basin, from June to December, 2015...... 35 Figure 4.6. Monthly mean abundance of (a) Cyanophyceae and Chlorophyceae, (b) Bacillariophyceae, Euglenophyceae and Dinophyceae, (c) Chrysophyceae and Xanthophyceae in the Sanyati Basin, Lake Kariba from June to December, 2015...... 39 Figure 4.7. Percentage contribution of each zooplankton taxa to the total zooplankton abundance in the Sanyati basin, from June to December, 2015...... 40 Figure 4.8. Monthly mean abundance of zooplankton taxa in the Sanyati Basin, Lake Kariba from June to December, 2015...... 41 Figure 4.9. (a) Percentages of stomach classes. (b) Monthly percentage for stomach classes and fullness index...... 44 Figure 4.10. (a) Total frequency of occurence of prey groups in kapenta stomachs (b) Percentage frequency of occurence of different prey taxa in the Sanyati Basin, Lake Kariba from June to December, 2015...... 48 Figure 4.11. Monthly frequency of occurrence for (a) phytoplankton prey (b) zooplankton prey (c) macroinvertebrates and scales in the Sanyati Basin, Lake Kariba from June to December, 2015...... 49 Figure 4.12. Variations in electivity index for (a-c) phytoplankton taxa, (d) zooplankton taxa and macroinvertebrates taxa in Limnothrissa miodon in the Sanyati Basin, Lake Kariba from June to December, 2015...... 52 Figure 4.13. Mean (a) total length and weight, (b) body condition of Limnothrissa miodon in the Sanyati Basin, Lake Kariba from June to December, 2015...... 54 Figure 4.14. Percentages of various breeding categories of L. miodon in the Sanyati Basin from June to December, 2015...... 55 Figure 4.15. Monthly percentages of various breeding categories of L. miodon in the Sanyati Basin from June to December, 2015...... 56 Figure 4.16. Scree plot of principal components...... 59 Figure 4.17. Biplot showing loadings of variables...... 60 Figure 4.18. Cluster analysis dendrogram. (Gut contents denoted by a black square, so as not to confuse gut contents and prey in the column) ...... 61

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List of Tables Table 3.1. The kapenta sampling sites in the Sanyati Basin ...... 17 Table 4.1. Means (±SE) of physical and chemical variables recorded at all the sites from July (2015) to February (2016) ...... 30 Table 4.2. Mean abundances (±SD) (individuals per ml) of phytoplankton in the Sanyati Basin, Lake Kariba from June to December, 2015...... 36 Table 4.3. Mean abundances (±SD) (individuals per 25ml) of zooplankton in the Sanyati Basin, Lake Kariba from June to December, 2015...... 42 Table 4.4. Two way ANOVA output for fullness index. (Significant differences at α 0.05 between months denoted by *) ...... 45 Table 4.5. Diet composition of Limnothrissa miodon in the Sanyati Basin, Lake Kariba from June to December, 2015...... 47 Table 4.6. One way ANOVA test for monthly significant differences in frequency of occurrence of prey items in the Sanyati Basin, Lake Kariba from June to December, 2015. (Significant differences at α 0.05 between months denoted by *) ...... 50 Table 4.7. Two way ANOVA output for body condition. (Significant differences at α 0.05 between months denoted by *) ...... 53 Table 4.8. Weight of the variables along the main principal components axis ...... 57 Table 5.1. Comparison of mean temperatures (calculated from all depth readings combined) in the Sanyati Basin from 1986 to the present study through 2011 ...... 69

List of Appendices Appendix 1. Table of canonical correlations between variables ...... 83 Appendix 2. Summary of PCA ...... 86 Appendix 3. Correlation matrices of principal components ...... 86 Appendix 4. Chi-square test for significant difference between stomach classes ...... 90 Appendix 5. One way ANOVA test for significant difference between stomach classes ...... 90 Appendix 6. Kapenta analysis recording sheet ...... 91 Appendix 7. Plankton recording sheet ...... 92 Appendix 8. Physicochemical variables recording sheet ...... 93

List of Plates Plate 1. Ceratium sp. (centre right) with a Lecane sp. (bottom) from a sardine gut ...... 94 Plate 2. Themocyclops sp. from a sardine gut ...... 94 Plate 3. Nauplius from a sardine gut ...... 95 Plate 4. Pediastrum sp. from a sardine gut ...... 95 Plate 5. (a) Keratella cochlearis with spine and (b) Keratella cochlearis tecta with no spine from a sardine gut ...... 96 Plate 6. Diaphanosoma excisum from the water column ...... 96 Plate 7. Garmin fish finder mounted on a kapenta rig ...... 97 Plate 8. Godwin Mupandawana (ULKRS) helping fishermen to empty L. miodon from the net into kapenta crates (December, 2015)...... 97 Plate 9. Proposed ULKRS rig (Photo edited from the one taken at Chawara harbour. Rig belongs to McMaster Fishing Company) ...... 98

CHAPTER 1

Introduction 1.1 General Introduction

The construction of Lake Kariba offered opportunities for an inland fishery industry that previously did not exist in southern Africa south of the Kafue Flats River fishery (Magadza,

2006). Gillnet fishing became the common fish industry after the construction of the lake in the early 1960s which was dominated by indigenous riverine fish in the littoral areas (less than

20 m deep) (Balon and Coche, 1974; Magadza, 2006).

Limnothrissa miodon (locally known as kapenta) was introduced in 1968/69 from Lake

Tanganyika in order to increase the lake’s productivity (Bell-Cross and Bell-Cross, 1971;

Magadza, 2006). This was as a result of the failure by riverine fish species that were present in the Zambezi River before damming, to occupy the pelagic niche which constitutes the bulk of the lake (Chifamba, 2000; Magadza, 2006). The kapenta managed to completely colonise Lake

Kariba within five years after the introduction resulting in its dominance in the fishing industry

(Magadza, 2006). By May 1970, they were present throughout the lake with some invading

Lake Cahora Bassa through the hydroelectric turbines (Chifamba, 2000). Late 1980’s, the annual catch of the kapenta fishery peaked, with an annual Zambian and Zimbabwean yield of about 37000 metric tonnes (Magadza, 2010). Post-1990, total annual landings declined steadily

(Magadza, 2006, 2011).

Algae are the primary producers in almost every water ecosystem, and are the food source for planktonic consumers (Davis et al., 2009). The primary producers are very sensitive to an alteration of their environment as temperature variations have been shown to affect temporal natural plankton communities’ dynamics on scales varying from days to years (de Senerpont

Domis et al., 2013). Pelagic fishery is dependent on the availability of adequate plankton in

Lake Kariba (Cochrane, 1978). The distribution of zooplankton is patchy (Magadza, 1980), which possibly explains the patchy distribution of kapenta (Marshall, 1988, Lindem, 1988).

Studies on feeding habits of kapenta by gut examination have been carried out by Masundire

(1991), Mandima (1999) and Muvengwi et al. (2012). They provide useful information about diet composition. Since the last detailed feeding ecology study by Mandima (1999), there is a gap in our current knowledge of the kapenta diet. To study the diet of kapenta through prey choice, stomach analysis provides direct information about predator-prey relations.

Gut content analysis gives information about the type of food material that is available to the kapenta in the food chain (Babare et al., 2013). As the area is warming at a faster rate than predicted for the region by the IPCC (2007), it is therefore necessary for regular monitoring of the lake (Mahere et al., 2014). Since starvation is considered as a major cause of fish mortality

(Shepherd and Cushing, 1980), stomach analysis and classification according to fullness answers the question whether the kapenta are having enough to eat in Lake Kariba. The feeding habits of fish contributes an active area of research, not only to those seeking to understand the interrelationships among or within species but also for fisheries managers concerned with factors that affect the fishery.

1.2 Problem Statement

Limnothrissa miodon catches have been declining in Lake Kariba since the early 1990s

(Magadza, 2006; Magadza, 2010; LKFRI, 2010; Ndebele-Murisa, 2011). This has had an impact not only on the livelihoods of the riparian community of the lake, but also on most of

2 the underprivileged Zimbabweans who rely on this cheap protein source. Fish is an important food resource in the dry region unsuitable for agriculture (Scudder, 1972; Magadza, 2006).

Gwembe valley, where Lake Kariba is located, has warmed up by an average of 2.6°C

(Magadza, 2010; Magadza, 2011; Ndebele-Murisa, 2011). Temperatures across Africa have increased by 0.5-2.0°C over the past century (IPCC, 2014). The IPCC (2014) showed projections of 3.4-4.2°C towards the end of the 21st century in areas over the Sahara and semi- arid parts of southern Africa such as the Zambezi Valley, (Lake Kariba’s location). These temperatures far exceed increases in temperature from natural climatic variability (IPCC,

2014). Owing to these impacts of climate change, different components of the climate system have been shown to affect temporal natural plankton dynamics on scales varying from days to years (De Senerpont Domis et al., 2013). African countries are among the most vulnerable to climatic changes (IPCC, 2001). The trends in climate for the past 50 years shows that the earth is warming up.

1.3 Justification

Considering that Limnothrissa miodon used to contribute to the gross domestic product (Khali, pers. communication), research on its foraging ecology becomes necessary since there is a continuous decline in catches (Muvengwi et al., 2012). Studies on the dynamics of plankton are therefore important as different components of the climate system have been shown to affect temporal plankton communities’ dynamics (De Senerpont Domis et al., 2013). Studying dynamics of plankton gives a clear insight of ecosystem relationships, thereby enabling a sound and correct resource management.

1.4 Objectives

The main objective of the study was to examine the feeding behaviour of Limnothrissa miodon in the Sanyati Basin of Lake Kariba.

1.4.1 Other Objectives

• to investigate the degree of satiation of L. miodon.

• to determine the selection of food items by L. miodon.

• to determine the frequency of occurrence of prey in L. miodon stomachs.

• to determine the monthly fullness index and body condition of L. miodon.

• to determine the monthly community compositions and diversities of plankton in the

Sanyati Basin of L. Kariba.

• to determine the relationship between plankton densities in the water column, prey in

lake and prey in the gut , fullness index, body condition and physicochemical variables.

1.5 Null hypothesis

• H0: There is no significant difference in proportion of empty stomachs and those

with a stomach classification of at least ¼-full by month.

• H0: L. miodon in L. Kariba are not selective feeders.

• H0: Frequency of occurrences of prey in guts of L. miodon does not vary among

months.

• H0: There is no difference between the relative frequencies of kapenta food items in

the water column and those on the stomach contents of the fish.

CHAPTER 2

Literature Review 2.1 Introduction

In the determination of the natural productivity of the lake and water quality, the relationship between plankton and fish is vital (Mageed and Konsowa, 2002). The plankton communities are subjected to different levels of stress that includes wide environmental variations and fish predation. This chapter presents plankton and Limnothrissa miodon literature as well as the physicochemical aspects in freshwater ecosystems with particular reference to Lake Kariba.

2.2 Phytoplankton studies

The phytoplankton community of Lake Kariba originally comprised of riverine species which totalled to 171 species dominated by Chlorophyceae (Thomasson, 1965). Ramberg (1984) did the first quantitative study of L.Kariba phytoplankton. From his study, 0.29 mg/l chlorophyll- a was observed, with 80% of the phytoplankton biomass occurring in the rainy season. Sixty percent comprised of Cyanophyceae genera dominated by Cylindrospermopsis and Anabaena sp. A total of 82 algal species were recorded by Ramberg (1987) from the Sanyati Basin with

Cylindrospermopsis, Anabaena and Pseudoanabaena sp. dominating. However, in studies by

Cronberg (1997), 155 phytoplankton species were recorded from samples that were collected between 1986 and 1990. Cronberg (1997) concluded that the algae population in the lake had stabilised from an original riverine type rich in desmids and large algal species to a lacustrine type dominated by Chlorophyceae with a typical seasonality, where the Cyanophyceae dominate in summer and diatoms dominate at winter turn-over.

Sibanda (2005) compared the responses to temperature of selected phytoplankton classes mainly looking at Chlorophyceae and Cyanophyceae in laboratory cultures. From her findings, growth rate of Chlorophyceae declined at temperatures above 24°C, becoming negative after

28°C. In contrast, Cyanophyceae increased almost exponentially up to 34°C. The dominance of cyanobacteria in the laboratory experiment by Sibanda (2005) describes the phytoplankton status in Lake Kariba with cyanobacteria (particularly Cylindrospermum raciborskii) dominating (Magadza, 2011). Magadza (2011) determined how changes in climatic variables affect kapenta fish stocks in Lake Kariba through altering the phytoplankton composition as determined by Sibanda (2005) in her laboratory culture.

Blue-green algae comprising 18 species exhibited the highest relative cellular concentration

(78.05%) in a study by Ndebele-Murisa (2011). Chlorophyceae had 40 species (9.7%),

Dinophyceae, four species (7.2%), Bacillariophyceae, 10 species (1.7%), Euglenophyceae 3 species (1.6%), Chrysophyceae (1.3%) and Xanthophyceae (0.45) with one species each.

Cyanophyceae dsominated by Cylindrospermopsis raciborskii increased from a previously reported 50-65% to 78%. Cyanophyceae maximum had a notable shift as previously reported in summer stratification peak to the winter turnover peak. Bacillariophyceae and Dinophyceae declined from previously reported 18% and 13% to 1.7% and 7.2%, respectively.

Chlorophyceae species richness increased in total from the previously reported 30 to 40 whilst

Cyanophyceae and Euglenophyceae declined from previously reported 41 and 8 to 18 and 3, respectively (Ndebele-Murisa, 2011). These shifts in phytoplankton concentrations and species composition to less palatable Cyanophyceae that are competitively more abundant at higher temperatures were attributed to nutrient availability and elevated water temperatures with climate warming (Ndebele-Murisa, 2011).

2.3 Zooplankton studies

Fryer (1957) initially investigated free living Crustacea from the flooded backwaters of the

Zambezi River. He recorded seven Cladocera species dominated by Diaphanosoma excisum and Moina micrura, five species of cyclopoids dominated by Mesocyclops major, the calanoid

Tropodiaptomus cf. kraepelini, and the Decapod shrimp Caridina nilotica (JFRO, 1959).

Thomasson (1965) between May 1959 and September 1963, recorded four Protozoa and 34

Rotifera, the most common were Branchionus falcatus, Keratella tropica, Lecane bulla, Filinia opoliensis and Hexarthra mira, and 13 Cladocera, comprising Diaphanosoma excisum,

Chydorus sphaericus, Daphnia lumholtzi and Bosmina longirostris, as well as one calanoid

(Tropodiaptomus cf. kraepelini), one cyclopoid (Mesocyclops major), and one Chaoborus sp.

Harding and Rayner (2001) concluded that the zooplankton composition of the lake was typical of warm water bodies similar to those found in sub-Saharan artificial tropical lakes.

The zooplankton concentration in Lake Kariba varies in response to phytoplankton biomass, and it is has been suggested that zooplankton production is determined to a larger extent by phytoplankton availability than fishing pressure and predation (Marshall, 1984; Masundire,

1989), with a distinct correlation also evident between zooplankton biomass and river inflows

(Magadza, 1980; Masundire, 1992). Noteworthy is that the Cladocera species has declined since the introduction of the Tanganyika sardine, whilst that of Copepoda had remained relatively stable (Cochrane, 1978; Masundire, 1989).

According to Magadza (1980), zooplankton abundance has a clear reliance on annual inputs of nutrients (Magadza, 1980). The survey by Magadza (1980) revealed differences in size distribution, with low numbers and small bodied zooplankters being registered in pelagic

7 stations while riverine and littoral stations showed more abundant and large bodied zooplankters. Masundire (1989) showed that zooplankton was abundant in waters that had low transparency to combat predation by L. miodon which is a visual feeder (Masundire, 1989;

Mandima, 1999). The flooding period of the Sanyati River reduced from 6-7 m to 2-3 m which resulted in the prevalent of calanoids and large cladocerans (such as Diaphanosoma excissum and Daphnia lumholtzi). Water transparency therefore may play a vital role in determining the absence or presence of these large zooplankters (Masundire, 1997). The zooplankton community was dominated by Bosmina longirostris and cyclopoid species (Masundire, 1989).

Masundire (1994) showed that the zooplankton biomass in the lake was very low for considerable periods during the warm periods.

Ndebele-Murisa (2011) examined the spatial, temporal and depth variations in zooplankton species richness and concentrations in Lake Kariba and, compared these with those in other tropical African lakes, and ascertained whether measured changes in zooplankton composition were be linked to climate warming. The whole lake samples comprised 77 species, including

48 species of Rotifera of which Keratella cochlearis was the most abundant, 15 species of

Copepoda of which Thermocyclops albidus was the most abundant, 14 species of Cladocera of which Bosmina longirostris was the most abundant. In the Sanyati Basin, 52 species were enumerated, comprising 38 Rotifera, 8 Copepoda, 6 Cladocera, of which B. longirostris was the most abundant Cladocera, T. albidus and Tropodiaptomus congruens were the most abundant Copepoda, and K. cochlearis was the most abundant rotifer. All three zooplankton classes displayed concentration peaks in August, following stratification breakdown which were consistent with earlier reports. However, only the Copepoda exhibited the summer peak in, implying a change in the seasonality and a decline in concentration of the zooplankton during summer.

A comparison of zooplankton assemblages of Ndebele-Murisa (2011) with those of the previous studies (Masundire, 1991) shows an overall decline of zooplankton densities, with substantial decreases in the Cladocera of up to 93.3% and increases in the Rotifera species richness and concentrations which constituted 64.4% of total zooplankton counts. In addition, a number of zooplankton species seem to have disappeared in Lake Kariba’s waters. These include the midge larvae Chaoborus anomalis, C. ceratopogones and C. edulis, and the

Cladocera Ceriodaphnia dubia, whilst only Bosmina longirostris has remained dominant among the Cladocera. Ndebele-Murisa (2011) concluded that the zooplankton composition of

Lake Kariba is similar to that of other African lakes. Differences in species richness are attributed to predation, predominantly by the clupeid sardine, L. miodon, and recent changes in lake’s physicochemical properties, especially elevated water temperatures, the latter affecting phytoplankton composition and biomass. Ndebele-Murisa (2011) concluded that the decline in zooplankton biomass and altered zooplankton composition, towards increased abundance of small sized Rotifera over the large sized Copepoda and Cladocera in Lake Kariba, point to a control by phytoplankton caused by the proliferation of unpalatable species, such as

Microcystis, Ceratium and Peridioniopsis, on zooplankton abundance, rather than to predation by L. miodon in compliance with the cascade hypothesis (Paulsen, 1994).

2.4 Kapenta studies

The economic role as well as the ecological role of kapenta stimulated a lot of research on the sardine’s general biology, ecology and population dynamics (Mandima, 1999). Qualitative aspects of the feeding biology of L. miodon in Lake Kariba were conducted by Begg (1974),

Cochrane (1978, 1984), Langerman (1979), Marshall (1985) and Mandima (2000), who mainly listed the diet composition. They showed the diverse diet of L. miodon which constituted of

Cladocera, Copepoda, Rotifera, aquatic insect larvae and phytoplankton. Begg (1974) found that B. longirostris was their main prey, constituting about 80% of the food items in their guts.

On the contrary, they comprised on average only 26% of the food items in samples collected by Cochrane (1978) with Mesocyclops sp. making up 56% of the items. Cochrane's work showed that the stomach contents of the sardine correlated with plankton availability, for example, the utilisisation of Ceriodaphnia dubia extensively when it was most abundant in the lake. L. miodon may have caused a decline in some larger zooplankton species which were more abundant before 1971.

Begg (1974) found that insects (for example ephemeropterans, hemipterans, chironomids and trichopterans) also constituted the diet of L. miodon and Mitchell (1976), found a sample which had 50% food items of terrestrial origin. Mitchell (1976) also discovered scales, vertebrate and fin rays in L. miodon stomachs concluding that they prey on other small fish. In a 64 mm sardine’s stomach dissected by Mitchell (1976), a Pseudocrenilabrus philander was found. In

Lake Kariba, it has been suggested that L. miodon are cannibalistic, as they are in Lake Kivu since fish have been found in the gut of individuals over 80 mm in length (Chifamba, 1992). It appears that L. miodon can ingest specimens half their own length. An example is a 57 mm fish

10 which was recorded in the stomach of a 125 mm long fish and a 46 mm in a 93 mm fish

(Chifamba, 1992).

Despite the similarity in pelagic primary productivity in the Lake Kariba and Tanganyika as noted by Machena (1983), the sardines have a marked difference in size (Marshall, 1993).

Several explanations can be attributed to the size differences which include genetic change, slower growth, environmental unpredictability, food deficiency and high predation. If the smaller size of kapenta in Lake Kariba had arisen from high mortalities due to predation, early maturation enables the fish to maintain a constant proportion of mature adults in spite of intense predation on adults (Fryer and Iles, 1972). Their biology is typical of an r-selected species characterised by small bodies which rapidly grow in unstable environments (Chifamba, 1992).

This is relevant to the sardine as it invest more energy into reproduction than large sardines

(Marshall, 1993). The phenomenon was scientifically proven by Reznick et al. (1990)’s example. The predation of adult guppies, Poecilia reticulata by the cichlid, Crenicichla alta, caused them to mature earlier, increase their reproductive effort and reduce the size of their offspring (Reznick et al., 1990). L. miodon therefore has a very high potential productivity in

Lake Kariba attributable to their short life cycle and high production/biomass (P/B) ratio

(Marshall, 1984).

Quantitative studies on L. miodon were carried out by Paulsen (1994), Mandima (1999),

Muvengwi et al. (2012). Mandima (1999) concluded that L. miodon was a visual feeder which depended on daylight to locate its prey. Kapenta prey included cladocerans mainly B. longirostris, Bosminopsis deitersi and Ceriodaphnia cornuta and the copepods, mainly

Mesocyclops sp., Thermodiaptomus sp. and Tropodiaptomus sp. Aquatic macroinvertebrate larvae, rotifers and ostracods also occurred infrequently in the diet. Mandima (1999) however noted that in earlier studies, large cladocerans of Daphnia sp. dominated in the diet of kapenta.

In his study, smaller cladocerans dominated which was a reﬂection of a shift in prey size as a result of increased predation which is a common phenomenon in classical feeding ecology

(Brooks and Dodson, 1965). Mandima (1999) noted the presence of Bosminopsis deitersi, which was never recorded in earlier zooplankton studies in Lake Kariba. He observed this species as a commoner in zooplankton samples and kapenta stomach contents. But however, planktonic peaks in Lake Kariba coincide with nutrient fluxes caused by both river inflow and turnover, and these in turn are followed by peaks in fish production (Marshall, 1982; Zengeya and Marshall, 2008; Ndebele-Murisa, 2011). Chifamba (2000), also found that the sardine biomass correlated with planktonic gradients.

Muvengwi et al. (2012) explored the diet composition of kapenta employing frequency of occurrence methods. He categorised kapenta lengths into juveniles, sub-adults and adults looking at feeding in each length category. He observed the presence of chironomids and rare large cladocerans for example Daphnia lumholtzi, in the diet of different size and age classes of kapenta in the Sanyati Basin.

2.5 Physicochemical studies in Lake Kariba

Physicochemical variables with subsequent monitoring of the thermal regime of Lake Kariba have been investigated several times (Harding 1961; Begg 1970, 1974; Coche, 1974;

Bowmaker, 1976; Marshall et al., 1982; Mtada, 1987; Lindmark, 1997; Magadza et al., 1987,

2010, 2011; Ndebele-Murisa, 2009, 2011; Mahere et al., 2014). The first study to indicate the

12 impacts of climate change was done by Magadza (2010) who suggested that Lake Kariba had warmed up by about 2.0°C in the last 50 years since its creation thereby altering the thermal regime characterised by a much reduced epilimnion. Magadza (2010) concluded that the warming favoured the dominance of Cyanophyceae over the palatable Chlorophyceae affecting the food chain and productivity of the lake and, therefore, a decline in the pelagic fishery yield.

This was supported by Ndebele-Murisa et al. (2011) who elaborated the impacts of the changing climate on fisheries productivity, a view challenged by Marshall (2012) on the grounds that fishing effort had not been taken into account.

The recent study by Mahere et al. (2014) examined the limnology of Lake Kariba. In his findings, the lake temperature rose by 0.7°C, a rate equivalent to 0.03°C y−1, which was however not uniform. March and May had pronounced increases of 1.5°C and 1.4°C respectively with least increases in August and October (0.1°C in both). Mahere et al. (2014) concluded that the changes thermal regime which now seems to be less predictable than before could have been disrupted by the changes resulting in weakened thermal gradients have weakened. Oxyclines therefore are presently less pronounced and persistent than in the past

(Mahere et al., 2014). Mahere et al. (2014)’s work contradicted with previous limnological studies that observed the strengthened stratification of Lake Kariba due to warming causing the thermocline to rise thus reducing the productivity of the pelagic fishery. His work found out that there was a downward transfer of heat that caused the thermocline to fall and weaken thereby reducing thermal stability.

2.6 The present study

Due to the changing climate as documented by IPCC (2001, 2007, 2014), Magadza (2010,

2011), Ndebele-Murisa (2011), Muvengwi et al. (2012), Mahere et al. (2014), need for continuous research on plankton dynamics and kapenta feeding ecology is necessary. Since the last detailed kapenta diet analysis study by Mandima (1999), there is a gap in our current knowledge of the kapenta diet since Africa is warming at an accelerated rate than predicted by the IPCC (2001, 2014). Magadza (1994) demonstrated the inability of Daphnia lumholtzi and

Bosmina longirostris to ﬁlter feed on Microcystis aeruginosa with an effective particle diameter of 4 μm. The increase in phytoplankton by either cell size or colony formation makes zooplankton unable to graze on the phytoplankton. Since the blue-greens are dominating, what is happening to the Bosmina species, the main prey for Limnothrissa miodon (Masundire, 1991;

Mandima, 1999)? The present study aims to investigate whether the kapenta are having enough to eat, owing to the changes that were documented to be occurring particularly the climate aspect. The study also looks at the current plankton status in relation to the physicochemical variables in Lake Kariba in the Sanyati Basin where the majority of previous limnological studies have been conducted (Begg, 1970, 1974, 1976; Cochrane, 1978, 1984; Magadza, 1980,

2006, 2010, 2011; Marshall, 1984, 1985, 1993; Randberg, 1987; Masundire, 1991, 1994, 1997;

Paulsen, 1994; Cronberg 1997; Lindmark, 1997; Mandima, 1999; Chifamba, 2000; Muzavazi,

2007; Ndebele-Murisa, 2010; Ndebele-Murisa, 2011; Muvengwi et al., 2012; Mahere et al.,

2014).

CHAPTER 3

Materials and Methods 3.1 Study Area

The study was conducted in Lake Kariba (16.5°S, 28.8°E; 518 m above sea level). Kariba lies wholly in Natural Region V of Zimbabwe, which is semi-arid, characterised by low and erratic rainfall.

(a) (b )

Figure 3.1. Map showing (a) location of Lake Kariba, (b) study sites in the Sanyati Basin.

Lake Kariba is about 280 km long, volume and area of 185 km3 and 687 049 km2 respectively

(Magadza, 2006). The mean depth is 29 m and its maximum depth is 97 m. The lake has five hydrological basins defined by Begg (1970) and Mitchell (1970) which are the Mlibizi, Binga,

Sengwa, Ume and Sanyati.

The Sanyati Basin where most limnological studies have been carried out, is the one most accessible to the University Lake Kariba Research Station (ULKRS). The Basin is fed by

Sanyati, Gache Gache, Nyaodza and Charara rivers. The most influential river in the basin is the Sanyati River which exerts a significant influence on the Sanyati Basin in terms of productivity (Sanyanga and Mhlanga, 2004). Sanyati River brings highly mineralised water from farms, sewage and mining drainage effluent from Kwekwe through Sebakwe River

(Magadza, 1980).

3.2 The Sampling Sites

Samples were collected at three sampling sites chosen from the fishing sites that the kapenta companies fish. Physicochemical variables, were also measured on the fishing sites with consequent plankton sampling. The sampled sites and general designation are described in

Table 3.1. Fishing was however not done at a specific location in the Sanyati Basin, but at radii of approximately 2 km (derived from calculating speed = distance/time) from each sampling point which then defines a fishing perimeter allocated for a particular rig.

Table 3.1. The kapenta sampling sites in the Sanyati Basin.

SITE NAME DESIGNATION

1 Sanyati The area is fed by the Sanyati River.

2 Nyaodza The area is fed by the Nyaodza River.

3 Redcliff The area is in between Long Island, Rhino Island and Redcliff.

3.3 Field Sampling

Sampling was done monthly from June to December (2015). Water temperature, pH, dissolved oxygen, oxygen saturation percentage and conductivity, were measured in situ using a multi- meter measuring the physical variables at different water depths (0, 5, 10, 15, 20, 25 and 30 m). A turbidity meter was used to measure water turbidity. A 5 litre Ruttner sampler was used to collect water at different depths. Water transparency was measured with a standard Secchi disk according to standard methods by Wetzel (1983). Water samples from the above- mentioned depths were collected in polythene bottles for dissolved nutrients analysis. To reduce chemical reactions, these were stored in cooler boxes with ice in the field and kept frozen in the lab until analysis.

3.4 Dissolved substance analysis

Chemical analysis of water was conducted in the Wet Chemistry Laboratory at the ULKRS.

Samples analysed for nitrates and phosphates were first filtered using Whatman GF/C filter papers of 47 mm diameter. The filter papers were wrapped with an aluminium foil and placed in the fridge-freezer anterior to chlorophyll-a analysis.

3.4.1 Nitrates

Dissolved nitrates in the water samples were determined by the EPA method 353.3 whereby filtered samples were passed through a copperised cadmium column to reduce nitrates to nitrites (USEPA, 1979). Under acidic conditions, nitrites react with sulphanilamide forming a diazo compound that reacts with N-(1-Naphthyl)ethylenediamine to form a reddish purple azo dye. A spectrophotometer (HACH DR/2010) was then used to convert the observed colours into concentrations at 545 nm in 1 cm3 cuvettes.

3.4.2 Orthophosphates

Phosphates were also determined by colorimetric methods using the PhosVer-3 procedure

(Hach method 8048, USEPA Method 365.2 and Standard Method 4500-P E). In an acidic solution, reactive ions reacted with ions of molybdate and antimony to form an antimonyl- phosphomolybdate complex. This is then reduced by ascorbic acid to phosphomolybdenum blue. Absorbance was then read at 882 nm.

3.4.3 Total Nitrogen (TN)

TN was determined colorimetrically whereby nitrogenous compounds in the water samples from the basin were oxidised to nitrate by heating with an alkaline persulphate solution.

Nitrites (which were originally present together with reduced nitrate) were determined by diazotisation with 4-Aminobenzenesulfonamide and reacting with N-(1-

Naphthyl)ethylenediamine dihydrochloride forming a coloured azo dye which was read at 545 nm.

3.4.4 Total Phosphorous (TP)

TP was determined by the use of the acid persulphate digestion procedure (Hach 8190 and

Standard Methods 4500 P-E). Samples for total phosphorous were first digested in an autoclave converting the combined phosphate to the ortho form. Orthophosphate reacts with molybdate in an acid medium with ascorbic acid acting as the reducing agent. The resulting intense molybdenum blue colour is read at 882 nm.

3.4.5 Ammonia

Ammonia was determined using EPA method 350.1. Ammonia reacts with hypochlorite in slightly alkaline forming monochloroamine, which in the presence of phenol and surplus of hypochlorite gives indophenols blue. Sodium nittroprusside catalyses the reaction and absorbance is read at 635 nm. All the above-mentioned chemical variables were calculated using the equation:

y = bx + a

where y = concentration

x = absorbance

b = a constant, the slope with units y/x

a = a constant, concentration at zero absorbance, units of y.

3.4.6 Chlorophyll-a

Filter papers from 3.3 were crushed and placed in vials in 7 ml 90% acetone was added to extract chlorophyll-a. The sample was allowed to extract in the refrigerator for 1-3 hours and centrifuged for 10 minutes at 3000 revolutions/second (Aminot and Rey, 2000). Supernatant solutions are then transferred into 1 cm3 cuvettes and read at 630; 647; 664 and 750 nm. The

19 concentration of chlorophyll-a was then calculated according to Aminot and Rey (2000) equation:

Chlorophyll-a =11.85 (E664 – E750) − 1.54 (E647 – E750) −

Ve 0.08 (E630 – E750) × ( ) L x Vf

Where L = cuvette light path in cm

Ve = extraction volume in ml

Vf = filtered volume in l

3.5 Phytoplankton sampling

Plankton samples were collected using a standardised method presented in Edmondson and

Winberg (1971). A composite sample was collected per site using vertical hauls with a phytoplankton net of 40 cm diameter and 22 μm mesh size from as close to the bottom as possible to the surface through the water column at an approximate speed of 7 m min-1

(Masundire, 1991). The depth of the sampling site was determined by a Ruttner sampler which was used to take water samples at various depths. Samples were stored in labelled dark 250 ml plastic bottle after the addition of Lugol’s solution and stored in a cooler box. For analysis, the sample was thoroughly shaken and 1 ml was taken for sedimentation using an adjustable 1 ml micropipette. Phytoplankton was then enumerated under an inverted microscope after the removal of supernatant liquid according to the method of Utermöhl (1958). Phytoplankton guides by van Vuuren et al. (2006), Bellinger and Sigee (2010) were used in the identification of phytoplankton species. The number of phytoplankton in the 1 ml was expressed over the volume of water that passed the net.

The volume of sampled water that passed through the net estimated by the formula by

Masundire (1991), Nhiwatiwa (2004):

2 VL = π r .d

Where VL = volume of water filtered by the plankton net,

r = radius of the mouth of the net

d = distance the net pulled through

The density of plankton was computed after Masundire (1991) as follows: count of species i in sub-sample = n volume of sub-sample = v

volume of sample bottle = vb

-1 number of organisms of species i in sample (Ni) = n . v . vb

-3 -1 density of species i in water column (ind. m ) (NW) = Ni . VL

-1 -1 density of species i in water column (ind. L ) (nL ) = NW/1000

3.6 Zooplankton sampling

Zooplankton samples were collected using vertical hauls with a zooplankton net of 40 cm diameter and 62 μm mesh size from as close to the bottom as possible to the surface through the water column at an approximate speed of 7 m min-1 (Masundire, 1991). The depth of the

21 sampling site was determined by depth finders mounted on kapenta rigs and also by lowering the Ruttner sampler to the bottom of the site. Zooplankton samples in 250 ml peanut butter bottles were immediately fixed with 70% alcohol and stored in a cooler box. In the laboratory,

25 ml were sedimented for at least an hour in a sedimentation chamber and enumerated after the removal of supernatant liquid. Zooplankton were identified using guides by Thorp and

Covich (2001); Fernando (2002) and expressed as the number of organisms per litre.

3.7 Kapenta analysis

Three nights per month were spent collecting kapenta samples from the three of ten fishing sites fished by McMaster and Irene fishing company. Hauling of the net was determined by concentration of the sardines by light which was observed on the fish finder (Plate 7). About

100 g of kapenta samples were collected per haul which were immediately put into a cooler box with ice. These were examined in fresh state for length (to the nearest millimetre) and mass

(to the nearest 0.001 g) in the laboratory before being dissected over a dissecting microscope.

Stomach samples were attained by removing of the whole stomach between the oesophagus and the intestines (Källgren, 2012). Fish awaiting analysis were kept in ice granules to slow down the decomposition of the stomach contents.

Each stomach was weighed initially as “intact” just after retrieval and once more after the removal of all gut contents as “empty tissue” to determine weight of stomach contents

(Källgren, 2012). The intensity of feeding was determined based on the degree of stomach wall distension and volume occupied by the contents. Classification of food in the stomach as, ‘full’,

‘¾ full’, ‘½ full’, ‘¼ full’ and ‘empty’ was according to Suseelan and Somasekharan (1969).

Basing on individual assessment of the stomach and the above classifications, a subjective

22 stomach fullness coefficient was determined (full = 4, ¾ full = 3, ½ full = 2, ¼ full = 1 and empty = 0) (Headley et al., 2009).

Stomach contents for each sardine were analysed over a dissecting microscope at X40 and large identifiable contents were recorded and isolated. A known volume of distilled water (0.5-1.0 ml) was added to the stomach contents using a micropipette and the Petri dish was shaken slightly. The contents were viewed over an inverted microscope at X100. These were identified using plankton guides (Thorp and Covich, 2001; Gerber and Gabriel, 2002; Fernando, 2002; van Vuuren et al., 2006; Bellinger and Sigee, 2010).

In each category of stomach fullness, a ratio of identifiable to unidentifiable stomach contents was applied as some stomachs would have fed but the stage of digestion made it difficult for identification. The following codes were used to justify some of the stomach weighs:

1 - about 10% of the contents identifiable

2 - about 50% of the contents identifiable

3 - about 75% of the contents identifiable

4 - almost every prey constituent identifiable

Intestines were visually assessed to determine the degree of fullness to give a reflection on feeding prior to being caught. Basing on individual assessment of the intestine, a subjective intestinal fullness coefficient was determined (75%-100%, full = 2; 10-75%, ½ full = 1 and

0%-10% empty = 0). These classifications determine the monthly differences in feeding intensity by calculating the empty coefficient and the fullness index. The sex and gonadal status

23 were also recorded for each individual using reference of the Zimparks classification. The following classification codes were used:

Female active (FA) - actively breeding female = 1

Female spent (FS) - female released eggs for breeding = 2

Female inactive (FIA) - female has not matured sexually = 3

Male active (MA) - actively breeding male = 4

Male spent (MS) - male released sperms for breeding = 5

Male inactive (MIA) - male has not matured sexually = 6

3.8 Data Analysis

The data recorded on field sheets (Appendix 6, 7 and 8) was entered into Microsoft Excel and analysed using STATISTICA version 7 (STATSOFT Inc., Tulsa, OK, USA) , PAST Version

2.17 and 3.11. Data was first tested for normality before analysis using the Shapiro-Wilk test to give certainty to use parametric or non-parametric tests.

3.8.1 Diversity index of plankton

After identification, diversity indices were used to provide more information about community composition than simply species richness of the different sites. Diversity indices are mathematical measures of species diversity in a given community (Magurran, 2004). These indices are based on the species richness and species abundance. Species richness is the number of species present in a community and the abundance is the number of individuals per species.

The Shannon-wiener index is an information statistic index and assumes that all the species are represented in a sample and are randomly sampled. It ranges between 1.5 and 3.5 and rarely exceeds 4 (Magurran, 2004). The Shannon index increases as the species richness and evenness of the community increase. The Shannon-Wiener diversity index of Shannon and Wiener

(1949) used was calculated using the formula:

Hʹ = – ∑ 푝푖 ln 푝푖

푠 Where; 푝 = 푖 푖 푁

푠푖 = number of individuals of one species

N = total number of all the individuals in

analysed sample

ln = the logarithm to base e

3.8.2 Monthly frequency of occurrence of prey in stomachs

The prey in the kapenta gut was analysed by the frequency of occurrence index (Hyslop, 1980) to express the proportion by number of the prey category in the gut. Occurrence of different food items in the gut were detected when at least one individual of a taxonomic group was present, thus indicating the number of sardines that would have eaten a particular prey group.

Frequency of occurrence was calculated using the formula:

%F.O = (Nfish, i/Nfish) x 100

where FO = the estimated percentage of prey i in the diet

Nfish, i = number of individuals containing prey i in

their diet

Nfish = total number of fish examined

One way ANOVA test was also performed on frequency of occurrence to test whether there were/were no significant any differences between months.

3.8.3 Monthly stomach fullness index

To determine the relative fullness of stomachs in different months, a standard fullness index was calculated which standardises the weight of ingested food as a percentage of the total fish weight. Values were calculated for all sardines regardless of the presence or absence of gut contents, so as to provide unbiased estimates of feeding intensity (Sagarese et al., 2011). This was calculated according to Hureau (1969):

FI = (stomach contents weight/weight of fish) x 102

Two way ANOVA was performed on the FI values to test whether there were/were no significant differences between months and site.

3.8.4 Monthly comparison of stomach fullness classes

A one way ANOVA was performed on all the stomach classes to test whether there were/were no significant differences between months. The proportion of sardines with stomach classes of at least ¼-full were added together and compared with sardines with empty stomachs. The monthly proportions were then tested using a chi-square test if there were/were no significant differences between the sardines with empty stomachs and those which had food.

3.8.5 Body condition L. miodon

Length-weight data was used to calculate the body condition of kapenta using Fulton’s mathematical formula (1902):

K = 100 ∗ W/L3

Where K = condition coefficient

W = weight of fish in grams

L = length of fish in cm

Two way ANOVA was performed on the K values to test whether there were/were no significant differences between months and site.

3.8.6 Prey preference

Prey abundance in both the water column and stomach contents was expressed as a percentage of the total prey so that proportions were used to compute Ivlev’s electivity index (EI) (1961).

EI was calculated using the formula:

푟 −푝 EI = 푖 푖 푟푖+푝푖

Where 푟푖 = proportion of the i-th food type in gut

푝푖 = proportion of the i-th food type in water

column

The purpose of the index according to Ivlev is to characterise the degree of selection of particular prey species. The index has values that range between -1 and +1, with negative values indicating avoidance and positive values indicating active selection.

3.8.7 The relationship between plankton densities, physicochemical variables body condition, frequency of occurrence of prey.

Temporal trends in plankton abundance were explored by examination of a time series graph.

Canonical correlation analysis was used in assessing relationship strength between variables.

Principal Component Analysis (PCA) which also is a multivariate technique was used to combine all the variables to derive new components which will produce a simpler description of the dataset. Cluster analysis was used to sort the different variables into groups based on their similarity coefficients. The clusters join observations that are most similar merging them to construct a dendrogram.

CHAPTER 4

Results Sampling was carried out monthly from June to December (2015). Data for kapenta analysis was normally distributed (p>0.05) and parametric tests were used.

4.1 Lake basin physicochemical variables

Physicochemical data was not normally distributed (p>0.05) and hence the Kruskal-Wallis (H) test was used. Table 4.1 summarises the mean values of environmental variables in the Sanyati

Basin of Lake Kariba for the study period. All the measured lake water physical and chemical properties displayed significant differences (H, p<0.05) between months except DO as shown in Table 4.1. The highest mean water temperatures were recorded in December (27.72°C) and the lowest in July (22.44°C). The pH displayed a fluctuating trend during the sampling period.

At the start of the sampling period, the values were just above the neutral point and increased in the month of July. The pH values had a sharp decrease in September before increasing in

October up to December recording pH ranges of between 7.64 and 7.74.

June and September recorded the highest mean conductivities of 93.87 and 93.20 μS cm-1 respectively. Conductivity in July dropped sharply to just above 80 μS cm-1 in site 3 (Redcliff)

(Figure 4.1 (e)) despite recording the highest conductivity of 99.78 μS cm-1. Conductivity at

Redcliff peaked again above other sites in September to 98.7 μS cm-1 before decreasing and stabilising below other sites (below 90 μS cm-1). Conductivity values in other months were fairly above 88 μS cm-1. Highest DO values of 7.17 mg/l were recorded in August which decreased to below 6 mg/l in the summer season. Oxygen saturation percentage values also followed a similar trend as that of DO (Figure 4.1 (c) and (d)). Fothergill site had the highest

DO values in summer.

Table 4.1. Means (±SE) of physical and chemical variables recorded at all the sites from July (2015) to February (2016).

Variable/Site 1 2 3 4 5 6 7

June July August September October November December p

Temperature (oC) 23.79±0.13 22.44±0.06 23.73±0.11 24.84±0.19 25.73±0.28 28.27±0.34 28.72±0.52 0.0000

pH 7.29±0.06 7.88±0.02 7.71±0.07 7.45±0.08 7.64±0.10 7.71±0.14 7.74±0.18 0.0000

DO (mg l-1) 6.42±0.11 6.74±0.12 7.17±0.25 5.65±0.42 5.08±0.47 5.79±0.40 5.47±0.68 0.0680

% Saturation 89.16±1.61 77.41±1.23 87.97±3.13 71.97±5.40 66.06±6.32 79.73±5.82 77.74±10.2 0.0104

Conductivity (μS cm-1) 93.87±2.18 88.62±1.00 90.21±0.82 93.20±1.61 88.37±0.41 89.54±0.44 91.39±0.35 0.0019

Turbidity (NTU) 1.77±0.20 1.89±0.16 1.54±0.34 3.19±1.24 2.30±1.01 4.13±2.55 1.60±0.41 0.0005

Nitrate (mg l-1) 0.007±0.01 0.015±0.00 0.015±0.00 0.021±0.00 0.039±0.01 0.028±0.00 0.023±0.00 0.0001

Phosphate (mg l-1) 0.007±0.00 0.015±0.00 0.015±0.00 0.004±0.00 0.010±0.00 0.011±0.00 0.011±0.00 0.0000

TN (mg l-1) 0.359±0.02 0.502±0.03 0.502±0.03 0.409±0.03 0.561±003 0.800±.0.08 0.870±0.09 0.0000

TP (mg l-1) 0.022±0.00 0.022±0.00 0.049±0.03 0.022±0.00 0.032±0.00 0.042±0.00 0.037±0.00 0.0000

Ammonia (mg l-1) 0.024±0.00 0.030±0.00 0.030±0.01 0.074±0.01 0.055±0.02 0.038±0.01 0.040±0.01 0.0188

Chlorophyll-a (mg l-1) 0.004±0.00 0.005±0.00 0.005±0.00 0.002±0.00 0.005±0.00 0.007±0.00 0.005±0.00 0.0003

(a) (b)

(e) (f)

Figure 4.1. Changes in (a) temperature (°C), (b) pH, (c) DO (mg l-1), (d) % oxygen saturation,

(e) conductivity (μScm-1), (f) turbidity (mg l-1) in the Sanyati Basin, Lake Kariba from June to

December 2015.

(a) (b)

(e) (f)

Figure 4.2. Changes in (a) nitrate (mg l-1), (b) phosphates (mg l-1), (c) TN (mg l-1), (d) TP (mg l-1), (e) ammonia (mg l-1), (f) chlorophyll-a (mg l-1) in the Sanyati Basin, Lake Kariba from

June to December 2015.

Lake water was more turbid between August and November with a peak value of about 8 NTU

at Fothergill site. Lowest mean nitrate concentrations were measured in June (0.007 mg/l) and

the highest in October (0.039 mg/l). Mean orthophosphate concentrations showed a peak in

July and decreased sharply recording the lowest concentrations in September (0.004 mg/l)

before steadily increasing up to December. June and September recorded low mean TN

concentrations with December recording the highest (0.870 mg/l). TP concentrations were

fairly stable except in August where the mean was affected by high TP of 0.686 mg/l recorded

for Redcliff site. Ammonia showed a mean peak of 0.074 mg/l in September with June

recording the least concentrations (Figure 4.2 (e)). Chlorophyll-a concentrations increased

steadily from June to August dropping in September where low concentrations of 0.002 mg/l

were recorded. The increasing trend resumed from October to December, recording high mean

chlorophyll-a concentrations of 0.007 mg/l in November. Chlorophyll-a had a pronounced

peak of 0.011 mg/l in October at Nyaodza site (Figure 4.2 (f)).

Temperature (0C) DO (mg/l) 22.0 24.0 26.0 28.0 30.0 32.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 0 0 June 10 July 10

August 20 September 20 October

Depth(m) 30 Depth(m) 30 November December 40 40

50 50

Figure 4.3. Temperature and dissolved oxygen profiles in the Sanyati Basin from June to

December, 2015.

Nitrates (mg/l) Phosphates (mg/l) 0.00 0.05 0.10 0.15 0.20 0.000 0.010 0.020 0 June 0 July 10 10 August

20 September 20 October

30 30 Depth(m) November Depth(m) December 40 40

50 50

Figure 4.4. Nitrate and phosphate profiles in the Sanyati Basin from June to December, 2015.

Surface temperatures ranged between 24-32°C as shown in Figure 4.3. June, July and August were characterised by low temperature and the temperature of the surface water was almost equal to the bottom water which facilitated turnover. Lake turnover was also reflected in the nutrients as there was little difference in nutrient concentrations from the epi- to the hypolimnion Figure 4.4). The lake was then stratified from September to December.

4.2 Phytoplankton

4.2.1 Species composition and abundance

A total of 44 phytoplankton genera were recorded in the Sanyati Basin, which comprised of 6

Bacillariophyta, 21 Chlorophyta, 8 Cyanophyta, 3 Dinophyta, 3 Euglenophyta, 2 Chrysophyta and 1 Xanthophyta. Overally, Cyanophyta had the highest cellular concentrations making up

85% of the total phytoplankton concentrations followed by Chlorophyceae which had 7% contribution. Dinophytes contributed 5% and the rest of the phytoplankton classes contributed less than 5% to the total population (Figure 4.5).

Bacillariophytes Chlorophytes Cyanophytes Dinophytes Euglenophytes Chrysophytes Xanthophytes

Figure 4.5. Percentage occurrence of each phytoplankton classes the Sanyati basin, from June to December, 2015.

Cyanophyta dominated completely in all the months with Cylindrospermopsis sp. contributing the greater percentage in terms of abundance. The highest densities of Cyanophyceae (1544 individuals per litre) were recorded in the cool dry season (Figure 4.6 (a)) and the lowest in

September. The density of Chlorophyta was highest (138 ind./l) during the cold dry period

(June-July) and recorded the highest number of genera in June. In June, 17 Chlorophyceae genera were recorded with Chlorella and Staurastum sp. dominating. Chrysophyceae,

Dinophyceae and Euglenophyceae recorded their highest densities in June and had low densities in other months. Diatoms had a peak in August whilst Xanthophyceae were only encountered in July. In comparison with other months, species diversity and evenness was high in June and low in November (Table 4.2). The highest number of genera was recorded in June

(36) and the lowest in October (23).

Table 4.2. Mean abundances (±SD) (individuals per ml) of phytoplankton in the Sanyati Basin, Lake Kariba from June to December, 2015.

Species June July August September October November December Bacillariophyceae Aulacoseira sp. 0.9±1.36 - 10.9±7.44 10.5±6.96 20.8±6.91 1.8±2.49 1.75±2.38 Nitzschia sp. 126.5±25.87 - 394.3±81.21 378.6±61.13 - 0.1±0.33 0.75±1.39 Cylotella sp. 329.5±36.05 - 864.3±63.84 0.9±1.36 - - 15.50±4.57 Synedra sp. 73.5±17.39 - - - - - 1.13±1.55 Navicula sp. - 4.4±5.87 - 2.8±3.34 3.1±5.09 - - Surirella sp. - 0.6±0.99 31.3±15.65 39.4±11.15 66.0±13.36 67.5±14.05 46.50±16.17 Chlorophyceae Staurastrum sp. 760.6±42.05 755.0±74.31 684.5±142.79 220.1±59.29 270.1±54.27 176.4±60.58 157.00±50.48 Mougeotiasp. 15.6±14.02 5.9±6.99 0.8±1.39 - - - - Chlorella sp. 616.6±98.29 681.0±43.71 546.1±122.46 237.0±35.04 204.5±33.61 182.6±15.55 187.63±77.96 Cosmarium sp. 49.0±13.86 2.8±3.56 - - - - - Golenkima sp. 87.0±15.91 42.6±13.02 - - - - - Pediastrum sp. 188.0±53.80 212.0±36.32 153.8±73.47 50.4±11.32 22.4±7.61 20.8±5.49 30.00±35.93 Tetrahedron sp. 2.1±2.42 0.6±0.99 - - - - - Coleastrum sp. 48.0±15.70 33.5±20.32 79.0±17.62 12.1±14.76 1.1±1.05 1.3±1.30 5.88±8.64 Scenedesmus sp. 38.5±22.47 - 2.9±3.37 2.9±2.80 3.8±4.49 1.0±1.22 2.75±3.88 Oocystis sp. 1.6±1.65 ------Staurodesmussp. 1.1±1.96 ------Euastrum sp. 0.5±1.32 0.8±1.30 - 0.3±0.43 - - - Gloeocystis sp. 0.1±0.33 - - 1.3±1.39 - - - Ankistrodesmus sp. - - - 2.9±4.68 - 1.3±2.28 - Cerasterias sp. 0.6±0.99 - - 0.1±0.33 0.1±0.33 0.6±0.99 0.50±1.07 Nephrochlamys sp. - - - 0.5±1.00 - - - Chlamydomonas sp. 0.5±0.71 ------

Eudorina sp. 2.4±3.08 ------Pandorina sp. 2.8±3.60 0.5±1.00 - - 4.9±2.89 1.5±2.60 - Oodegonium sp. ------Micramasterias sp. - - - 0.8±1.30 - 1.3±2.28 - Cyanophyceae 18210.8±1369.5 16661.0±1474.7 15839.0±1474.5 5968.1±1011.1 Cylindrospermopsis sp. 7 0 4 6290.6±899.14 1 7076.0±896.70 6843.63±715.26 Anabaena sp. 808.9±147.01 568.9±73.87 413.6±57.23 423.0±68.74 339.5±55.02 356.4±42.58 452.88±43.28 Microcystis sp. 623.1±231.60 539.9±141.65 402.1±93.04 160.9±45.06 166.3±84.00 277.3±27.07 177.00±24.05 Chlorococcus sp. 0.3±0.43 1.0±1.32 9.8±9.59 3.8±6.57 0.5±1.00 0.3±0.66 - Oscillatoria sp. 8.4±15.39 5.4±6.58 5.4±9.68 6.0±5.50 2.0±2.06 6.3±6.68 1.38±1.51 Nostoc sp. - - - 2.3±3.93 - - 19.75±14.89 Lyngbya sp. 610.9±227.00 791.3±104.41 687.9±74.55 232.9±55.36 162.4±29.27 157.047.04 155.63±43.48 Meris sp. - - 0.1±0.33 1.0±1.66 - - - Dinophyceae Ceratium sp. 921.5±202.42 486.9±126.80 151.8±45.56 205.3±34.05 216.0±23.99 216.0±26.25 230.63±37.33 Peridiniopsis sp. 803.4±88.67 363.8±44.78 44.4±21.89 80.4±15.16 78.5±26.01 72.6±40.82 84.25±30.65 Peridinium sp. 447.8±76.61 40.4±47.91 0.8±1.30 2.1±4.54 4.4±6.54 1.6±1.93 2.13±1.81 Euglenophyceae Euglena sp. 102.5±17.02 131.1±23.55 61.4±19.86 0.4±0.70 0.9±1.36 0.1±0.33 4.25±6.32 Trachelomonas sp. 284.0±29.48 60.9±13.21 2.3±2.38 - - 0.1±0.33 1.88±2.30 Phacus sp. 20.9±20.10 - - - - - 4.50±4.96 Chrysophyceae Dinobryon sp. 1044.0±232.08 84.1±27.82 - - - - - Goniochloris sp. 3.9±6.88 ------Xanthophyceae Tribonema sp. - 0.1±0.33 - - - - -

Total individuals 26236 21474.3 20386.0 8368.9 7535.3 8619.6 8427.3 Species richness 36 26 23 29 21 24 24 Shannon-Wiener 1.42 1.05 1.04 1.11 0.95 0.84 0.88 Evenness 0.40 0.32 0.33 0.33 0.31 0.26 0.28

Cyanophytes (a) Chlorophytes 2000 150

1500 (ind./l) 100 1000

500 Chlorophyceae Chlorophyceae (ind./l) Cyanophyceae 0 0 June BacillariophytesJuly August September October November December (b) 200 Dinophytes Month 40 Euglenophytes

150 30 (ind./l)

100 20

(ind./l)

50 10 Euglenohyceae

0 0 June July August September October November December Bacillariophyceae/Dinophyceae Bacillariophyceae/Dinophyceae Chrysophytes Month 90 Xanthophytes (c) 0.012 80 0.010 70 0.008

60 (ind./l) 50 0.006 40 30 0.004 20 0.002

10 Xanthophyceae Chrysophyceae (ind./l) Chrysophyceae 0.000 0 -10 -0.002 June July August September October November December Month

Figure 4.6. Monthly mean abundance of (a) Cyanophyceae and Chlorophyceae, (b)

Bacillariophyceae, Euglenophyceae and Dinophyceae, (c) Chrysophyceae and Xanthophyceae in the Sanyati Basin, Lake Kariba from June to December, 2015.

4.3 Zooplankton

4.3.1 Species composition and abundance

A total of 26 zooplankton species were recorded in the Sanyati Basin from June to December

(2015), which comprised of 3 Cladocera, 7 Copepoda and 16 Rotifera. Rotifers had the highest cellular concentrations making up 83% of the total zooplankton concentrations (Figure 4.7).

Copepods contributed 12% whilst Cladocerans contributed the least of 5%.

Cladocera Copepoda Rotifera

Figure 4.7. Percentage contribution of each zooplankton taxa to the total zooplankton abundance in the Sanyati basin, from June to December, 2015.

August was characterised by peaks in cladocerans and rotifers densities of 0.05 ind./l and 0.48 ind./l respectively (Figure 4.8). Cladocerans densities in all the months (excluding August) ranged between 0.002 and 0.004 ind./l. Bosmina longirostris was the dominant Cladocera whilst Keratella cochlearis was the dominant Rotifera. Highest Copepod densities were recorded in October and November (0.05 ind./l), recording a mini-peak in August. Nauplii contributed almost 50% of the total Copepoda taxa although they were not identified to their

respective species class (Table 4.3). The diversity indices and mean densities of zooplankton

are shown in Table 4.3.

Rotifera Cladocera Copepoda 0.50 0.06

0.40 0.04 0.30

0.20

0.02 (ind./l)density Cladocera/Copepoda Cladocera/Copepoda

Rotifera density (ind./l) density Rotifera 0.10

0.00 0.00 June July August September October November December Month

Figure 4.8. Monthly mean abundance of zooplankton taxa in the Sanyati Basin, Lake Kariba

from June to December, 2015.

Table 4.3. Mean abundances (±SD) (individuals per 25 ml) of zooplankton in the Sanyati Basin,

Lake Kariba from June to December, 2015.

June July August September October November December Division Cladocera B. longirostris 9±0.6 13±2.2 99±3.7 - 4±0.8 2±0.5 - D. excisum - - 34±3.2 - 4±0.8 9±1.5 4±0.8 B. meridionalis - - 3±0.7 - - - - Copepoda T. schemeili 6±0.7 12±1.6 - - - - T. albidus 14±1.3 2±0.5 19±1.3 6±1.2 - 31±3.2 - M. albidus 5 9 - - - 7±1.1 M. major - 10±1.2 18±0.8 - - - - T. hylinus - 1±0.4 1±0.0 - 53±4.3 - 10±1.7 M. albidus ------T. congruens - - - 4±0.8 - 20±2.3 11±1.5 Nauplii - 9±1.0 19±1.4 - 73±6.0 81±4.4 26±2.6 Rotifera K. cochlearis 36±3.4 15±1.0 78±2.6 59±4.5 171±9.0 48±2.6 67±4.8 K. cochlearis tecta 67±4.3 61±1.7 541±9.4 171±5.7 429±9.3 92±4.3 154±5.0 K. quadratta - - 17±2.4 - - - - F. longiseta - 12±1.2 40±3.8 - - - - F. opoliensis 21±1.8 - 20±3.9 - - - - F. terminalis - - - 18±1.6 - - - T. chattoni - 10±1.5 27±2.3 - - - - H. brehmi 34±1.5 23±2.9 37±2.4 5±0.9 81±5.6 - - L. bulla - - 166±6.3 10±1.3 246±6.1 - - L. patella - - 14±2.3 - - - - Lecane sp. - - - - - 17±1.5 16±1.7 T. longisetta - - 57±6.6 - - - - B. caudatus - - 46±4.4 - 10±1.6 - - B. angularis - - 5±1.6 - - - - B. falcatus - - 166±6.3 - - - - A. volvociola - - - - - 4±1.1 3±0.5 Total individuals 192 156 1428 273 1071 304 298 Species richness 8 10 22 7 9 9 9 Shannon-Wiener 1.77 1.88 2.25 1.14 1.61 1.77 1.47 Evenness 0.85 0.82 0.73 0.59 0.73 0.81 0.67

Species richness in all months generally ranged between 7 and 10 excluding August which recorded the highest (22). Species diversity was also high in August and low in September.

Species were more evenly distributed in June with a value of 0.85 and less evenly distributed in September (0.59).

4.4 Feeding habits of L. miodon

4.4.1 Stomach fullness classifications

Of the 2970 sardines that were analysed for stomach contents, 69% had empty stomachs, with the remaining 31% stomachs that had food being constituted by 19% ¼-full, 5% half full, 3%

¾-full and 4% full stomachs (Figure 4.9 (a)).

The percentage of L. miodon with empty stomachs was very high in all the months sampled with October and June recording the highest (78%) and lowest (59%) respectively (Figure

4.9(b)). Percentages of stomach classes which were at least ¼-full were high in June as compared to other months as it recorded the highest number of ones, twos, threes and fours than the rest of the months. In comparison with other months, percentages of stomach classes which were at least ¼-full were low in October recording the lowest number of ones, twos, threes and fours than the rest of the months (Figure 4.9 (b)). There were no significant differences between months with respect to all the stomach classes (ANOVA, p>0.05)

(Appendix 5). The number of kapenta with empty stomachs significantly outnumbered those that had stomachs which were at least ¼-full in all months (χ2 = 376.7, df = 6, P < 0.003)

(Appendix 4).

80 70 (a) 60

50 40 30 Percentage 20 10 0

EMPTY 1/4 FULL 1/2 FULL 3/4 FULL FULL Stomach fullness class

90 0.40 (b) 80 70 0.30 60 50 empty

0.20 FI 40 1/4 full

30 1/2 full % of of % stomachs 20 0.10 3/4 full

10 full 0 0.00 FI

Month

Figure 4.9. (a) Percentages of stomach classes. (b) Monthly percentage for stomach classes and fullness index.

Fullness index was generally low in all the months ranging from 0.32 in June to 0.14 in

November (Figure 4.9 (b)). Fullness index was significantly different between months

(ANOVA, p<0.05) as shown in Table 4.4. Fullness index a showed significant difference with

44 respect to the interaction between month and site. There was however no significant difference between the sampled sites.

Table 4.4. Two way ANOVA output for fullness index. (Significant differences at α 0.05 between months denoted by *)

SS Degrees of Freedom MS F p Intercept 0.383601 1 0.383601 156.3676 0.000001* Month 0.062488 6 0.010415 4.2453 0.026309* Site 0.005588 3 0.001863 0.7593 0.544613 Error 0.022079 9 0.002453

4.4.2 The diet of L. miodon

Thirty seven prey items were observed in the 31% sardines that had stomach classification of at least a ¼-full. The prey items found in the dissected sardines are summarised in Table 4.5.

Zooplankton were present in 49% of the sardines that had food in their guts with rotifers and ostracods dominating in the prey group (Figure 4.10 (a) and (b)). Macroinvertebrates were also present in the sardine stomachs contributing 31% to the total prey. Phytoplankton contributed

17% to the total food items with Chlorophyceae and Bacillariophyceae contributing 45% and

24% respectively to that percentage. Dinophyceae and Cyanophyceae contributed 30% whilst

Euglenophyceae contributed the least (1%) to the 17% total phytoplankton. Scales made up the least contribution (2%) to the total prey under the “other” prey category (Figure 4.10 (b)).

The frequency of occurrence of rotifers was also higher than any other prey in all the months recording a high percentage (23%) in December and a low (5%) in October (Figure 4.11 (b)).

Higher frequencies of ostracods occurred in June, November and December with the later recording the highest (20%) frequency of occurrence. Frequency of occurrence of Cladocera

45 was high in June and August whilst copepod occurrence was high in December (Figure 4.11

(b)). Of the phytoplankton taxa, Chlorophyceae were frequently encountered more than other phytoplankton taxon (Figure 4.11 (a)). June recorded a high frequency of occurrence of green algae (8%) and low occurrence (0.7%) was recorded in October. Blue-green algae infrequently occurred (≤1%) in L. miodon stomachs in the first 5 months sampled and had sudden shoot in

December recording a high percentage of 8%. Dipterans highly occurred in sardine stomachs over other macroinvertebrates with the highest percentage (8%) recorded in June (Figure 4.11

(c)). Odonata and Trichoptera recorded high occurrence percentages in sardine stomachs in

December of 6% and 9% respectively.

Table 4.5. Diet composition of Limnothrissa miodon in the Sanyati Basin, Lake Kariba from

June to December.

Crustaceans Rotifers Phytoplankton Macroinvertebrates

Bosmina longirostris Keratella sp. Peridiniopsis sp. Chaoborus sp.

Diaphanosoma Lecane sp. Ceratium sp. Diptera

excisum Brachionus sp. Aulacoseira sp. Ephemeroptera

Mesocyclops sp. Filinia sp. Nitzschia sp. Trichoptera

Macrocyclops sp. Horaella sp. Synedra sp. Coleoptera

Thermocyclops sp. Surirella sp. Hemiptera

Nauplii Microcystis sp. Odonata

Pediastrum sp.

Chlorella sp.

Volvox sp.

Coelastrum sp.

Tetraedron sp.

Pandorina sp.

Mougeotia sp.

Straurastrum sp.

Scenedesmus sp.

Euglena sp.

Trachelomonas sp.

60 (a) 50

% total occurence total % 10

0 zooplankton phytoplankton macro other invertebrates

Prey group

12 (b)

4 2 frequency occurrence of % 0

Scales

Diptera

Rotifera

Odonata

Cladocera Ostracoda

Copepoda

Hemiptera

Coleoptera Trichoptera

Dinophyceae

Cyanophyceae

Chlorophyceae

Ephemeroptera

Euglenophyceae Bacillariophyceae Prey item

Figure 4.10. (a) Total frequency of occurence of prey groups in kapenta stomachs (b)

Percentage frequency of occurence of different prey taxa in the Sanyati Basin, Lake Kariba from June to December, 2015.

10 (a)

Euglenophyceae 6 Dinophyceae Chlorophyceae 4 Cyanophyceae Bacillariophyceae

% frequency frequency occurrence of % 2

0 June July August September October November December 25 (b)

10 Cladocera Copepoda 5 Rotifera

% frequency frequency occurrence of % 0 Ostracoda 10 (c)

8 Month Odonata 6 Diptera 4 Scales Ephemeroptera 2 Trichoptera % frequency frequency occurrence of % 0 Coleoptera Hemiptera

Month

Figure 4.11. Monthly frequency of occurrence for (a) phytoplankton prey (b) zooplankton prey

2015.

Table 4.6. One way ANOVA test for monthly significant differences in frequency of occurrence of prey items in the Sanyati Basin, Lake Kariba from June to December, 2015.

(Significant differences at α 0.05 between months denoted by *)

Prey June July August September October November December P

Euglenophyceae 0.25 0.44 0.15 0.00 0.00 0.00 0.00 0.1818

Dinophyceae 4.69 1.93 0.44 0.25 0.37 0.74 1.10 0.0176*

Chlorophyceae 8.15 5.19 4.15 3.70 0.74 1.48 6.04 0.0557

Cyanophyceae 0.25 0.00 0.00 0.00 1.11 1.48 7.68 0.0001*

Bacillariophyceae 8.40 2.22 0.00 1.48 0.74 1.11 1.65 0.0228*

Cladocera 7.65 3.70 6.67 3.21 1.11 0.74 1.10 0.0501

Copepoda 5.19 2.37 4.89 2.72 1.11 5.19 18.11 0.2628

Rotifera 14.81 9.78 10.37 12.84 4.81 11.48 23.05 0.4990

Ostracoda 12.84 3.85 2.07 2.47 0.74 10.74 20.30 0.0034*

Odonata 5.68 3.41 1.04 2.72 1.48 0.74 6.04 0.0307*

Diptera 7.65 6.37 4.74 5.19 4.07 2.96 3.29 0.1614

Scales 2.47 3.56 1.04 2.22 1.85 0.00 2.19 0.0011*

Ephemeroptera 0.00 1.63 1.93 3.21 2.96 0.37 1.10 0.1102

Trichoptera 2.47 4.15 2.52 5.19 3.70 1.85 8.78 0.7837

Coleoptera 0.00 1.04 0.30 1.98 0.37 0.00 1.65 0.0412*

Hemiptera 0.99 6.52 0.44 2.47 4.44 0.00 0.55 0.0758

The frequency of occurrence of prey categories, Cyanophyceae, Dinophyceae,

Bacillariophyceae, Odonata, scales, Coleoptera and Ostracoda in sardine stomachs was significantly different (ANOVA, p<0.05) between the sampled months (Table 4.6).

Euglenophyceae, Chlorophyceae, Cladocera, Rotifera, Copepoda, Diptera, Ephemeroptera,

Trichoptera and Hemiptera occurrence was not significantly different between the sampled months (ANOVA, p>0.05).

4.4.3 Preferred food items

Limnothrissa miodon’s preference for Euglenophyceae was high in August (0.71) and decreased from September to December (-1.00) (Figure 4.12 (a)). Dinophytes were prefered in

June to August (>0.5) decreasing in September. Chrysophytes were not prefered during the study recording an EI of zero and below(Figure 4.12 (b)). Diatoms were highly prefered (1.0) in cold dry period and were highly not prefered in August (-0.1). Preference of diatoms then showed a gradual increase from September to December. Cyanophytes were less prefered in all months recording an EI of less than -0.5 in all the sampled months (Figure 4.12 (c)). The highest EI for Cyanophytes was -0.48 recorded in December and lowest was recorded in July to August (-1.0). Chlorophytes were highly prefered in all months except in September and

October where they recorded negative EI values. Cladocera preference was fairly high during the first four months sampled recording the highest EI of 1.0 in September before falling into the negative zone in October to December. Copepods were highly prefered in August (0.67), than in other months shown by negative EIs. Rotifers were less prefered in all the months sampled recording a highest EI value of 0.07 in November. Ostracods as well as macroinvertebrates were highly prefered during all the months that were sampled showing a constant EI value of 1.0.

(a) (b)

(e) 1.2 ostracoda 1.0 0.8 odonata Chaoborus 0.6 scales 0.4 ephemeroptera

Electivity index Electivity 0.2 trichoptera 0.0 coleoptera June July August September October November December Hemiptera Month

Figure 4.12. Variations in electivity index for (a-c) phytoplankton taxa, (d) zooplankton taxa,

and macroinvertebrates taxa and ostracods in Limnothrissa miodon in the Sanyati Basin, Lake

Kariba from June to December, 2015.

4.4.4 Body condition

Body condition was not significantly different (ANOVA, p>0.05) with respect to both site and month (Table 4.7). Mean total length was relatively stable between June and September ranging between 39 mm and 40 mm (Figure 4.13 (a)). It increased uniformly from September to

December. The maximum length of L. miodon was 70 mm recorded in December at Sanyati and Redcliff sites. The minimum length was 32 mm recorded in June and July. Mean weight showed a similar trend as that of length. The highest weight of L. miodon was 2.63 g recorded in December at Redcliff site. The lowest weight (0.21 g) was recorded in June at Nyaodza site.

Body condition index for L. miodon ranged between 0.67 and 0.73 from June to December

(Figure 4.13 (b)). The highest K value was recorded in November whilst low values were recorded in July and August.

Table 4.7. Two way ANOVA output for body condition. (Significant differences at α 0.05 between months denoted by *)

SS Degrees of Freedom MS F P Intercept 4.046261 1 4.046261 8175.099 0.000000* Month 0.007245 6 0.001208 2.440 0.110625 Site 0.000595 3 0.000198 0.401 0.755764 Error 0.004455 9 0.000495

Total length 55 Weight 1.1 (a) 1.0 50 0.9

0.8 45 0.7

0.6 40 0.5 (g) Weight

(mm) length Total 35 0.4 0.3 30 0.2 June July August September October November December Month

0.80 (b)

0.75

0.70

Body Body condition 0.65

0.60 June July August September October November December

Month

Figure 4.13. Mean (a) total length and weight, (b) body condition of Limnothrissa miodon in the Sanyati Basin, Lake Kariba from June to December, 2015.

4.4.5 Breeding classes of sardines

A total of 2970 sardines that were analysed comprised of 52% females and 48% males. Females were constituted by 30% FA, 18% FS and 4% FIA whilst 28% MA, 19% MS and 1% MIA constituted the male percentage. Figure 4.14 illustrates the percentage contributions made by each breeding category.

(%) 20

Contribution 10

0 FA FS FIA MA MS MIA Sex and gonadal status

Figure 4.14. Percentages of various breeding categories of L. miodon in the Sanyati Basin from

June to December, 2015.

The percentage of females classified as active was low in October and high in June, August and September (Figure 4.15). The percentage of male active was high in September and

October and low in October and June. The highest percentage of sardines that had a gonadal classification of ‘spent’ was high in November in both sexes. The proportion of inactively breeding sardines was low (<10%) in all the moths (Figure 4.15).

(%) 25 FA FS 20 FIA

15 MA Contribution 10 MS

MIA 5

0 June July August September October November December Month

Figure 4.15. Monthly percentages of various breeding categories of L. miodon in the Sanyati

Basin from June to December, 2015.

4.5 Relationships between variables

4.5.1 Correlations between variables

Appendix 1 shows a summary of canonical correlations between physicochemical variables

plankton variables, prey in gut, body index and fullness index. Fullness index strongly

correlated with green algae, odonata and diptera (r > 0.8). Body condition strongly correlated

with most macroinvertebrates, copepods, rotifers and green algae (r > 0.5). Both

Chlorophyceae and Cyanophyceae negatively correlated with month, temperature, nitrates (r >

-0.6) and positively correlated with transparency (r > 0.8). Cyanophyceae had a strong negative

correlation with month, temperature and nitrate, strongly positively correlating with

Chlorophyceae and transparency. Cladocera correlated with DO, rotifers and

Bacillariophyceae (r > 0.6).

4.5.2 Principal component analysis

Five components contributed a significant 71% to the total variance of all the variables that were analysed. PC1 accounted for 29% of the total variance which was attributed to gut contents (mainly green algae, Dinophyceae, dipterans, odonata, diatoms, cladocerans, scales), body condition and phytoplankton in the water column (mainly blue-green algae, green algae and Euglenophyceae) (Table 4.8). PC2 accounted for 15% of the total variance which was attributed to body condition, month, and gut contents (mainly trichopterans, coleopterans, rotifers, copepods and blue green algae) (see appendix). PC3 accounted for 11% of the total variance which was attributed to pH, phosphates cladocerans and rotifers. Figure 4.16 shows the scree plot output of the principal components.

Table 4.8. Weight of the variables along the main principal components axis (Numbers in bold under the same column represent weights of the important variables contributing to the different factors. Variables not in caps refer to stomach contents).

Variable PC 1 PC 2 PC 3 PC 4 PC 5 SECCHI 0.47 -0.71 0.19 0.24 -0.15 TEMP -0.74 0.42 -0.19 0.24 0.30 pH -0.37 0.07 0.55 0.37 0.13 DO 0.37 -0.37 0.48 -0.16 0.26 % O2 SATURATION 0.13 -0.28 0.04 -0.03 0.65 CONDUCTIVITY 0.28 0.05 -0.50 -0.51 0.15 TURBIDITY -0.06 -0.02 -0.24 -0.36 -0.30 NITRATE -0.59 0.41 0.10 0.11 -0.06 PO4 0.02 -0.33 0.60 0.64 -0.04 TN -0.52 0.36 0.02 0.37 0.03 TP 0.26 0.19 0.44 0.51 -0.08 NH3 -0.30 0.25 -0.07 -0.49 -0.39 CHLOROPHYLL-a -0.11 -0.09 -0.03 0.63 0.12 CLADOCERA 0.20 -0.29 0.68 -0.33 0.43 COPEPODA -0.59 0.10 0.16 0.43 0.34 ROTIFERA -0.15 -0.25 0.61 -0.34 0.35 BACILLARIOPHYTES 0.34 -0.40 0.38 -0.54 0.39

CHLOROPHYTES 0.73 -0.59 0.16 0.05 -0.01 CYANOPHYTES 0.79 -0.51 0.13 0.21 0.04 DINOPHYTES 0.65 -0.35 -0.56 0.18 -0.06 EUGLENOPHYTES 0.77 -0.40 -0.40 0.18 -0.04 CHRYSOPHYTES 0.55 -0.38 -0.59 0.07 0.07 XANTHOPHYTES 0.38 0.23 0.32 0.50 -0.29 Euglenophyceae 0.61 -0.01 0.21 0.22 -0.16 Dinophyceae 0.81 0.16 -0.25 0.27 0.08 Chlorophyceae 0.87 0.31 0.10 0.01 0.05 Cyanophyceae -0.22 0.54 -0.19 0.15 0.37 Bacillariophyceae 0.76 0.21 -0.40 0.15 0.10 Cladocera 0.78 0.14 0.26 -0.16 0.24 Copepoda 0.37 0.51 0.03 -0.05 0.53 Rotifera 0.65 0.55 0.04 -0.18 0.30 Ostracoda 0.54 0.46 -0.40 0.22 0.43 Odonata 0.79 0.41 -0.15 0.14 0.00 Diptera 0.72 0.28 0.28 -0.03 -0.06 Scales 0.72 0.37 0.24 0.04 -0.37 Ephemeroptera 0.08 0.29 0.52 -0.29 -0.26 Trichoptera 0.42 0.70 0.21 -0.09 -0.05 Coleoptera 0.20 0.55 0.19 -0.19 -0.33 Hemiptera 0.18 0.16 0.23 -0.02 -0.65 Fullness Index 0.78 0.50 0.06 0.02 0.08 Body condition 0.50 0.68 0.20 -0.07 0.10 Eigenvalue 12.39 6.54 4.60 3.65 3.24 % variance 28.81 15.21 10.71 8.49 7.53 Cumulative % 28.81 44.02 54.73 63.22 70.75 Extracted from PAST 3.11-Principal Components Analysis

Figure 4.16. Scree plot of principal components.

The inter-relationships between varifactors tabulated in Appendix 3 are illustrated in Figure

4.17 as scores of samples drawn and loading of variables. PC1, L. miodon gut contents (mainly green algae, Dinophyceae, dipterans, odonata, diatoms, cladocerans, scales), body condition and phytoplankton in the water column (mainly green algae, blue-green algae and

Euglenophyceae) are strongly correlated. These variables have a strong negative correlation with month, temperature, TN, nitrate and copepods in the water column. In PC2, body condition, month, and gut contents (mainly trichopterans, coleopterans, rotifers, copepods and blue green algae) had a strong positive correlation. These variables had a significant negative correlation with conductivity, Dinophyceae and Chrysophyceae in the water column. The canonical correlation matrix (Appendix 1) also confirmed these correlations between the variables.

Figure 4.17. Biplot showing loadings of variables.

4.5.3 Cluster analysis

In the dendrogram illustrated in Figure 4.18, relationships that were shown in the biplot in

Figure 4.17 were also illustrated. The strong association between leaves of gut contents (green algae, Dinophyceae, dipterans, odonata, diatoms, cladocerans, scales, Euglenophyceae, rotifers), body condition and phytoplankton in the water column (blue-green algae, green algae and Euglenophyceae) illustrated in the dendrogram made up PC1 in appendix 2. The result from the cluster analysis also confirmed the relationship between water column phytoplankton

(Chlorophyceae, Dinophyceae, Euglenophyceae, Chrysophyceae) as illustrated by the biplot.

Close relationships between variables was displayed due to smaller linkage distance.

Figure 4.18. Dendrogram showing similarities between variables. (Gut contents denoted by a black square, so as not to confuse gut contents and prey in the column)

CHAPTER 5

Discussion 5.1 Feeding habits of L. miodon

The results of the stomach analysis indicate a high incidence of empty stomachs (69%). A chi- square test for differences between sardines with empty stomachs and those with at least quarter-full showed significant differences between the two implying that empty stomachs outnumbered stomachs with food (Appendix 4). Masundire (1991) however found that 6% of the sardines he analysed had empty stomachs showing a marked difference. The high percentage of empty stomachs and low FI values suggests that the sardines are not having enough prey to feed on. This is as a result of low densities of prey in the water column recorded in the sampled months. The highest FI value was recorded in June in which low number of empty stomachs were recorded than any other months. June was also characterised by low temperatures and that is when green algae recorded the highest densities. Green algae in June had highest frequency of occurrence in sardine stomachs and their EI was high. The highest number of empty stomachs were recorded in October which also had a low FI value. In this month, water temperatures were high and green algae densities were low.

The diet of L. miodon is diverse as recorded in Table 4.5 in the previous chapter. Of the 31% sardines that had food in their stomachs, zooplankton had the highest frequency of occurrence

(49%). The zooplankton prey group was dominated by rotifers which also were abundant in the water column. Rotifers, despite being abundant in the Sanyati Basin, they had low EI values meaning they were less preferred. A prey item is selected on the basis of its profitability and since rotifers have a low biomass according to Gophen (2012) they are less selected. Ostracods also highly occurred in the sardine stomachs and had high EI values in all the months meaning that they were highly preferred than rotifers. In a study by Mandima (1999), ostracods

62 infrequently occurred in the stomachs of sardines. This was because then B. longirostris, a cladoceran was abundant in the water column and highly occurred in stomach contents.

Cladocerans highly occurred in stomachs in June and August recording a high EI in September, a month after their peak confirming feeding strategy characterised by selective and opportunistic feeding. Macroinvertebrates, which had the second highest frequency of occurrence (31%), were highly preferred by sardines as shown by high EI values in all the months. This may be attributed to the energy value of the macroinvertebrates in comparison with rotifers and phytoplankton. Macroinvertebrates were however absent in the water samples that were taken monthly from the four sampled sites.

According to Masundire (1991) and Mandima (1999), cladocerans particularly B. longirostris used to dominate in the diet of kapenta with Masundire recording 98% of sardines with

Bosmina. In the present study, the few number of sardines that had zooplankton were dominated by rotifers and ostracods which had the highest frequency of occurrence. Copepods had a high frequency of occurrence in December, a month after their peak and were highly preferred in August. Frequency of occurrence was not significantly different between months with respect to Euglenophyceae, Chlorophyceae, all zooplankton taxa and most macroinvertebrates. Blue-green algae infrequently occurred in sardine stomachs in all the sampled months. Blue-green algae also had negative EI values in all the months implying that they are highly unfavoured. Despite the low densities of prey in the water column, L. miodon still thrive to locate the desired prey.

The increased catches in August support Cochrane (1978)’s findings that catches are related to food availability. August was characterised by peaks in Cladocera, Rotifera, and

Bacillariophyceae but macroinvertebrates were highly preferred over the abundant plankton.

EI for Chlorophyceae was higher than that of cladocerans in August which were abundant in that month. Blue-green algae infrequently occurred in sardine stomachs recording the highest frequency of occurrence in December. EI for blue-green algae was always negative in all the months meaning that the phytoplankton class is highly not preferred. Blue green algae is abundant in the Sanyati Basin of Lake Kariba and is dominated by C. raciborskii which was never encountered in any sardine stomach. A few stomachs that were analysed contained

Microcystis sp.

5.2 Body condition of kapenta

Body condition is determined by the relationship between length and weight. High body condition values were recorded in October and November and this was after peaks in zooplankton. The improved body condition recorded could be attributed to nourishment from the zooplankton that was abundant in August. The body condition values between months were however not significantly different.

The average total length and mass of L. miodon was 42 mm and 0.52 g respectively. Total length of fish caught in this study ranged from 32 mm to 70 mm whilst those analysed by

Masundire (1991) ranged between 30 mm and 90 mm. The weight of L. miodon ranged from

0.21 g to 2.63 g. According to Marshall (1993), the lakes in which kapenta occur are very different from each other with particular focus on size. Sardines from Lake Tanganyika are larger than Lake Kariba’s. Marshall (1993) attributed the smaller size of sardines in Lake

Kariba to high mortalities due to fishing and predation. As an adaptive strategy, kapenta in

Lake Kariba now mature earlier as a 35 mm sardine could be seen with eggs. This phenomenon is important as the small sized fish invest more energy into reproduction (Marshall, 1993).

General surveying revealed that most consumers of kapenta prefer small sized from Kariba to large sized cheap imports from Mozambique that are flooding the Mbare market in Harare

(Mbare vendor, pers. communication). Small sized sardines have small stomachs and hence fewer stomach contents than large sized. Small sized kapenta dry well as the surface area is higher than that of large sized sardines. Large sardines therefore have an unpleasant taste brought about by stomach contents since they do not dry well as compared to small sardines.

5.3 Plankton dynamics

5.3.1 Phytoplankton

The results of the phytoplankton analysis of water samples showed the dominance of blue- green algae in all the months. The highest densities of the C. raciborskii dominated taxa were recorded in the winter period associated with low temperatures. The dominance of blue-green algae in the Sanyati Basin is consistent with earlier studies (Ramberg, 1984, 1987; Cronberg,

1997; Ndebele-Murisa, 2010). Cyanobacteria is successfully dominating in the Sanyati Basin because of its ability to tolerate elevated water temperatures (Robarts and Zohary, 1987), ability to fix nitrogen in nitrogen scarce conditions (Blomqvist et al., 1994), resistance against zooplankton grazing (Haney, 1987; Magadza, 1994) and ability to regulate their buoyancy, thus their water column position (Reynolds et al., 1987). If they are able to regulate their buoyancy, it therefore means that they can be able to utilise nutrients that may be locked in certain depths. The characteristics of cyanobacteria described above provide them with a competitive advantage over other phytoplankton taxa.

The high density of Chlorophyceae in June and their high species richness characteristic conforms to previous studies in Lake Kariba by Ndebele-Murisa (2011). Chlorella sp. and

Staurastrum sp. dominated in the months sampled. According to a study by Sibanda (2005),

65 blue-green algae concentrations increased with subsequent decrease in green algae concentration when temperature was raised above 24°C. The same scenario that was demonstrated by Sibanda (2005) could be happening in the Sanyati Basin of Lake Kariba whereby epilimnion temperatures in most months are above 24°C. Green algae was common in the cold months (June and July) and their densities decreased in August up to December.

The high epilimnion temperatures could be inhibiting the increase in green algae densities.

Since the epilimnion temperatures are not suitable for high green algae dominance, the species cannot do well in lower depths as the light intensity decreases with depth. Green algae require sunlight to photosynthesise which is profuse in the epilimnion, but has unfavourable temperatures.

An unstressed community will have high numbers of species characterised by fairly low densities or high evenness (Harrison, 2001). This will translate to a high diversity index which however was low in the Sanyati Basin ranging from 0.88 to 1.40 according to the Shannon

Wiener index of diversity. Species evenness was also low in all the months meaning that species were not evenly distributed in the Sanyati Basin. This uneven distribution is as a result of blue-green algae abundances which outnumber other phytoplankton taxa thereby affecting diversity and evenness.

5.3.2 Zooplankton

Visually observations of lake samples of zooplankton before analysis showed that densities were low in the Sanyati basin. August was marked by a peak in zooplankton which was attributed to increased edible phytoplankton abundance due to the nutrients released from the winter turnover (Figure 4.4). These findings support those of Marshall (1988), Masundire

(1991), Ndebele-Murisa (2010). High species diversity calculated for August resulted from the availability of most zooplankton species in the lake samples. The results of the zooplankton analysis of water samples showed the dominance of rotifers in all the months. The highest densities of the K. cochlearis dominated taxonomic group were recorded in August after the winter period. The dominance of Rotifera in the Sanyati Basin is consistent with studies by

(Ndebele-Murisa, 2010). Cladocerans had a peak in August and B. longirostris was the dominant cladoceran. As observed by Ndebele-Murisa (2010), the densities of cladocerans have fallen from 15 individuals per litre (Cochrane, 1978) in 1975, to far less than 1 individual per litre. The largest Cladoceran, Daphnia lumholtzi, calanoid copepods previously recorded by Magadza (1980) and Masundire (1991), seems to have disappeared completely in the

Sanyati Basin. Copepod density was high in October and November with Themocyclops sp. dominating.

The low densities of Cladocerans observed in the Sanyati Basin have been attributed to predation by fish, interspecific competition, warming up of the lake and reduced densities of edible phytoplankton (Ndebele-Murisa, 2010). The elevated temperatures are unfavourable for the survival of edible phytoplankton and therefore, their densities diminish resulting in

Cladocerans with less food. According to Gilbert and MacIsaac (1989), Keratella cochlearis exists in two forms namely Keratella cochlearis and Keratella cochlearis tecta with the former characterised by a posterior spine. These two forms also exist in the Sanyati Basin and the tecta form is more abundant than the one with a spine (See plate 5). The spine helps the rotifer to prevent predation from Copepods and Cladocerans. The dominance of Keratella cochlearis tecta in the Sanyati Basin therefore means that predation is low thereby justifying the low densities of Cladocerans and Copepods in the basin. According to Thorp and Covich (2001), the Keratella sp. is able to survive with low quantities of prey, and therefore oftenly found in

67 food-stricken habitats which are not able to sustain larger zooplankton. The Sanyati Basin is characterised by low edible phytoplankton densities and hence the dominance of the Keratella sp. Despite the abundance of Keratella sp. in the Sanyati Basin, they were less preferred by L. miodon. This is because Keratella sp. have low biomass (Mandima, 1999; Gophen, 2012).

Rotifers tolerate temperature variations (Barnes, 1968), as they undergo cyclomorphogenic changes under harsh conditions (Hickman et al. 2001).

Some species that seem to have disappeared in the basin, could have been missed out in zooplankton sampling, but could have been detected in the stomach of at least one sardine. In the 1970s, Cochrane noted that larger cladocerans of the Daphnia sp. had a very high frequency of occurrence in the diet of L. miodon. The absence of these species in both the water column and sardine guts confirms their possible total disappearance in the basin. Kapenta introduction into Lake Kariba resulted in Daphnia sp. decline (Green, 1985). The same scenario also occurred in Lake Kivu where the introduction of L. miodon resulted in the disappearance of

Daphnia curvirostris accompanied by a decline in total zooplankton biomass (Isumbisho et al.,

2006).

5.4 Lake water physicochemical variables

All the physicochemical variables were significantly different between the months that were sampled except DO. Comparing the current lake temperatures and that of 1986 and 2011 by

Magadza (1987) and Mahere et al (2014). respectively, the lake is generally showing a warming trend. This is illustrated in the Table 5.1.

Table 5.1. Comparison of mean temperatures (calculated from all depth readings combined) in the Sanyati Basin from 1986 to the present study through 2011.

Month Magadza, 1986 (°C) Mahere, 2011 (°C) Current study (°C)

June 23.43 24.19 23.79

July 22.10 23.10 23.44

August 21.94 22.09 23.73

September 22.86 23.27 24.84

October 24.61 24.70 25.73

November 28.27

December 28.72

The canonical correlation analysis showed a negative correlation between green algae and temperature. This implies that an increase in temperature results in declines in green algae densities as demonstrated in the laboratory by Sibanda (2005). Green algae was abundant to some extent, in June and July as the average temperatures were below 24°C. Since the temperatures in Table 5.1 were averages, green algae densities were low in August as the epilimnion temperatures were high. Green algae depend on the epilimnion to fix much light, but if the epilimnion temperatures are high, green algae production and survival is negatively affected.

In tropical African lakes nitrogen (N) and phosphorous (P) are the major nutrients that govern primary production and phytoplankton biomass (Talling and Talling, 1965). Lake Kariba water

69 has been shown to be P-limited for most of the year with a possibility of N co-limiting at other times (Moyo, 1991; Magadza, 1992). This study supports the oligotrophic status that was shown by Ndebele-Murisa (2010) and predicted by Balon and Coche (1974). Species that have the ability to fix nitrogen then have an advantage to dominate in the oligotrophic lake which is an explanation why blue-green algae is successfully dominating (Ndebele-Murisa, 2010).

From the measured physicochemical variables, it was interesting to note that there was no relationship between turbidity and secchi readings. Turbidity readings ranged from 1.54 to 3.19

NTU although these were averages and were greatly affected by water close to the bottom of the lake which sometimes got disturbed by the Rutner sampler thereby recording high turbidity values. The epilimnion recorded low turbidity values with as low as 0.00 NTU being recorded at different sites. Looking at these turbidity readings, one would expect to get a secchi reading of above 5 m. Secchi readings in this study ranged from 2 m to 4 m. This is because of the reflection of light caused by the presence of mica flakes in the lake which has a net effect of affecting secchi readings (Magadza, pers. communication).

5.5 Relationships between variables as explored by PCA and cluster analysis and canonical correlation.

A correlation matrix was used to calculate principal components since the variables had different scales and units. PCA result shows that in PC1, body condition of L. miodon is positively associated with fullness index, phytoplankton in the water column and stomach contents. This close association can be attributed to the importance of the phytoplankton in the water column as well as the macroinvertebrates in the diet of L. miodon which therefore improves body condition. The strong association was also illustrated in the dendrogram. The positively correlated variables are however negatively correlated with temperature, month and nitrates as shown by the PCA biplot, dendrogram and canonical correlation matrix. The strong negative correlations between temperature and green algae implies that an increase in temperature results in decline in green algae densities thereby supporting Sibanda (2005)’s laboratory findings and Ndebele-Murisa (2010)’s Lake Kariba’s findings. Green algae densities were high in the months that recorded low lake temperatures (June and July) decreasing in August to December which had high temperatures. Edible phytoplankton is important for the sardines as they improve their body condition and thereby increasing fullness index of stomachs. Cyanophyceae was the only outlier substantially different and arbitrarily fused with other variables at a high distance. Cyanophytes dominated in the water column and were less preferred by L. miodon. This taxonomic group however associated with other phytoplankton taxa in the PCA biplot as these were found together in the water column.

5.6 Conclusion

Evidence of the high incidences of empty stomachs of kapenta is a result of the low densities of edible prey in the water column. The water column is dominated by unpalatable phytoplankton, C. raciboskii and low biomass zooplankton, K. cochlearis. The densities of C.

71 raciboskii affect the species evenness and diversity of phytoplankton. As proposed by Magadza

(2011), food chain and productivity has been greatly affected and this has attributed to warming of the lake. The diet of L. miodon has changed significantly from a Daphnia sp. and B. longirostris dominated diet in the 1970s and 1990s respectively to a macroinvertebrate and rotifer dominated. The study shows that L. miodon highly selects macroinvertebrates than any other prey items as they recorded high EI values in all the months sampled. Frequency of occurrence of most prey that had high EI values was not significantly different between months implying that kapenta still thrive to look for the preferred prey despite the low abundances and unavailability in the water column. Unpalatable prey occurrence in guts was low and significantly different between months meaning that these were eminently unfavoured and rarely occurred despite being abundant in the water column. The warming trend in Lake Kariba is continuing and unpalatable prey is dominating well in the warming lake. Body condition of sardines between months was not significantly different. Body condition showed a relationship between highly preferred prey groups according to the PCA which contribute to high K values.

5.7 Recommendations

Kariba is located in the Zambezi valley in which Hulme et al. (2001) predicted to warm up by as much as 2.5°C by 2050. As confirmed by this research and Magadza (2010), the warming trend is likely to surpass the one predicted by Hulme et al. (2001) which will further strain the artificial lake. It is therefore necessary to implement ways to curb the continued release of greenhouse gases that are causing the warming of the earth. The magnitude of the warming problem is large as it is not only being experienced in Zimbabwe, but globally. It is now the time to start implementing the documented activities that restore ecosystems with net effects of regulating climates. Activities that include intensive planting of trees may seem not to have a link with the Lake Kariba situation of “low prey density”. Tree plantations can help in

72 sequestering the greenhouse gas, carbon dioxide (CO2) thereby reducing its continued loading of into the atmosphere. This will have a net effect of stabilising the temperature in the long run which will provide conditions for green algae to at least survive. The green algae are then grazed by zooplankton which therefore provide biomass for L. miodon.

It is unfortunate that nothing can be done for the sardines in terms of solving the food situation in the water column. It will remain a worry to the poor fishermen who spend 281 nights in the lake catching very few kilograms of fish per night. The National Parks and Wildlife

Management Authority (Zimparks) prohibit fishing usually in the last week of every month which is commonly referred to as the full-moon period in Kariba. According to the Zimparks, in this period, the kapenta are given time to breed. One week seems not to be enough for kapenta to breed. The little that can be done by Zimparks is to regulate the number of rigs, arrest unlicenced rigs and implement measures to curb poaching. The government of

Zimbabwe and Zambia should collaborate in fighting poachers who do not even spare the

“maternity” areas (where kapenta breed) which are not open for fishing.

More research should be done on the shallow areas to investigate the composition and distribution of prey as most food items encountered in L. miodon stomachs was never encountered in the pelagic water samples. The ULKRS carries a routine water quality monitoring program every month which should also be done for the sardines. The ULKRS under the department of Biosciences, University of Zimbabwe could purchase a kapenta rig

(Plate 9) so that it easies sampling. I had to choose the sampling sites from company’s routine fishing areas and sometimes one would want to define his/her sampling sites and time of sampling.

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Personal Communication

Zenzo Khali Senior Technician-ULKRS

Kenny Chawara Foreman-McMaster Fishing Company

Professor Christopher H.D. Magadza Lecturer-Biosciences (UZ)

Several fishermen McMaster Fishing company

Mr. Orange (not real name) Mbare vendor

Appendices Appendix 1. Table of canonical correlations between variables. Significant correlations between variables are bolded.

Green Blue- Cladoce Copepo Rotifer Bacillariophyc Chlorophyc Cyanophyc Dinophyce Euglenophyc Chrysophyc Xanthophyc Eugle Dinoo algae green algae ra da a eae eae eae ae eae eae eae Month -0.209 0.578 0.026 -0.388 -0.885 -0.848 -0.725 -0.825 -0.635 -0.190 -0.464 -0.499 -0.481 0.627 Site -0.178 0.084 0.010 0.003 0.005 0.079 0.001 -0.022 0.082 0.083 -0.240 -0.178 -0.376 -0.050 Secchi 0.293 -0.247 0.091 0.322 0.819 0.807 0.469 0.610 0.361 0.182 0.490 0.275 0.154 -0.565 Temperature -0.378 0.676 -0.039 -0.537 -0.845 -0.782 -0.453 -0.602 -0.376 -0.221 -0.469 -0.365 -0.503 0.593 pH 0.087 0.350 0.134 -0.262 -0.222 -0.194 -0.488 -0.452 -0.579 0.230 0.045 -0.339 -0.235 0.208 DO 0.580 -0.197 0.310 0.495 0.531 0.479 0.100 0.238 0.026 -0.044 0.390 0.069 0.228 -0.310 % Saturation 0.301 0.102 0.210 0.313 0.189 0.211 0.232 0.215 0.265 -0.207 0.033 0.084 0.079 0.025 Conductivity -0.121 -0.360 -0.155 0.152 0.048 0.044 0.309 0.302 0.290 -0.540 0.124 0.258 0.144 -0.052 Turbidity -0.159 -0.357 -0.218 -0.090 -0.106 -0.190 -0.029 -0.048 -0.043 -0.060 0.031 -0.080 -0.066 -0.050 Nitrate -0.226 0.704 0.124 -0.449 -0.640 -0.682 -0.484 -0.584 -0.481 -0.181 -0.284 -0.345 -0.422 0.167 Phosphate 0.287 0.257 0.188 -0.067 0.401 0.409 -0.090 0.055 -0.220 0.354 0.306 -0.055 -0.068 -0.144 TN -0.229 0.595 -0.204 -0.552 -0.560 -0.567 -0.355 -0.431 -0.398 -0.092 -0.097 -0.268 -0.300 0.220 TP 0.045 0.089 0.026 -0.043 0.033 0.275 -0.130 -0.053 -0.151 0.774 0.450 0.352 0.399 -0.056 Ammonia -0.273 -0.118 -0.012 -0.196 -0.487 -0.565 -0.291 -0.373 -0.278 -0.080 -0.189 -0.215 -0.163 -0.119 Chlorophyll -0.132 0.440 -0.172 -0.234 0.022 0.159 0.105 0.080 0.084 0.076 -0.078 -0.014 -0.216 0.025 Cladocera 1.000 -0.029 0.728 0.833 0.426 0.359 -0.238 -0.069 -0.150 -0.070 0.086 -0.100 0.181 -0.214 Copepoda -0.029 1.000 0.268 -0.246 -0.482 -0.451 -0.403 -0.468 -0.341 -0.130 -0.271 -0.270 -0.431 0.121 Rotifera 0.728 0.268 1.000 0.651 0.128 0.007 -0.414 -0.313 -0.296 -0.158 -0.093 -0.269 -0.114 -0.144 Bacillariophycea 0.833 -0.246 0.651 1.000 0.493 0.430 -0.004 0.123 0.125 -0.161 0.052 0.026 0.254 -0.324 e Chlorophyceae 0.426 -0.482 0.128 0.493 1.000 0.926 0.607 0.768 0.503 0.176 0.438 0.428 0.447 -0.423 Cyanophyceae 0.359 -0.451 0.007 0.430 0.926 1.000 0.649 0.795 0.573 0.296 0.481 0.541 0.517 -0.363 Dinophyceae -0.238 -0.403 -0.414 -0.004 0.607 0.649 1.000 0.961 0.939 0.015 0.301 0.608 0.398 -0.205 Euglenophyceae -0.069 -0.468 -0.313 0.123 0.768 0.795 0.961 1.000 0.878 0.097 0.407 0.681 0.498 -0.268 Chrysophyceae -0.150 -0.341 -0.296 0.125 0.503 0.573 0.939 0.878 1.000 -0.059 0.139 0.513 0.301 -0.175 Xanthophyceae -0.070 -0.130 -0.158 -0.161 0.176 0.296 0.015 0.097 -0.059 1.000 0.321 0.456 0.484 -0.101 Eugle 0.086 -0.271 -0.093 0.052 0.438 0.481 0.301 0.407 0.139 0.321 1.000 0.525 0.560 -0.237 Dinoo -0.100 -0.270 -0.269 0.026 0.428 0.541 0.608 0.681 0.513 0.456 0.525 1.000 0.814 -0.057 Green algae 0.181 -0.431 -0.114 0.254 0.447 0.517 0.398 0.498 0.301 0.484 0.560 0.814 1.000 -0.057 Blue-green algae -0.214 0.121 -0.144 -0.324 -0.423 -0.363 -0.205 -0.268 -0.175 -0.101 -0.237 -0.057 -0.057 1.000 Diatoms -0.179 -0.280 -0.313 0.004 0.336 0.425 0.608 0.650 0.532 0.331 0.444 0.935 0.713 -0.077 Clado 0.466 -0.292 0.172 0.487 0.486 0.489 0.266 0.391 0.235 0.348 0.496 0.651 0.833 -0.266

Cope 0.181 -0.132 -0.026 0.142 -0.005 0.077 -0.018 0.052 0.021 0.118 0.198 0.360 0.542 0.472 Roti 0.177 -0.310 -0.113 0.197 0.160 0.235 0.132 0.216 0.105 0.186 0.314 0.522 0.673 0.169 Ostracoda -0.158 -0.109 -0.377 -0.091 0.081 0.200 0.366 0.389 0.327 0.180 0.283 0.729 0.596 0.400 Odonata -0.165 -0.386 -0.333 -0.016 0.294 0.383 0.486 0.512 0.340 0.414 0.536 0.789 0.804 0.092 Diptera 0.213 -0.303 -0.027 0.233 0.410 0.467 0.288 0.362 0.172 0.374 0.275 0.498 0.724 -0.112 Scales -0.014 -0.417 -0.189 -0.017 0.365 0.370 0.274 0.375 0.113 0.478 0.561 0.571 0.715 -0.092 Ephemeroptera 0.210 -0.046 0.235 0.209 -0.087 -0.088 -0.292 -0.263 -0.289 0.028 0.161 -0.143 0.204 -0.120 Trichoptera 0.046 -0.264 -0.076 -0.020 -0.038 -0.002 -0.121 -0.071 -0.152 0.505 0.139 0.310 0.512 0.226 Coleoptera -0.100 -0.364 -0.181 -0.056 -0.158 -0.112 -0.222 -0.216 -0.254 0.442 0.021 0.002 0.288 -0.032 Hemiptera -0.084 -0.215 -0.100 -0.234 0.155 0.060 0.048 0.111 -0.101 0.082 0.351 0.043 0.162 -0.040 Fullness Index 0.070 -0.338 -0.190 0.093 0.319 0.389 0.358 0.422 0.231 0.305 0.373 0.648 0.826 0.168 Body Bondition 0.141 -0.149 -0.080 0.051 0.045 0.107 0.011 0.074 -0.056 0.131 0.288 0.374 0.594 0.349

Appendix 1 (continued).

Fullness Body Diatoms Clado Cope Roti Ostracoda Odonata Diptera Scales Ephemeroptera Trichoptera Coleoptera Hemiptera Index Bondition Secchi 0.217 0.219 -0.235 -0.096 -0.105 0.079 0.126 0.185 -0.106 -0.260 -0.249 0.140 -0.016 -0.225 Temperature -0.307 -0.539 0.033 -0.266 0.012 -0.303 -0.463 -0.476 -0.181 -0.115 -0.145 -0.253 -0.327 -0.133 pH -0.447 -0.178 -0.002 -0.213 -0.273 -0.205 -0.115 -0.059 0.047 -0.026 -0.056 -0.001 -0.192 -0.138 DO 0.008 0.403 0.059 0.191 -0.115 0.109 0.323 0.251 0.072 -0.035 -0.136 0.069 0.186 0.047 % Saturation 0.085 0.239 0.128 0.069 0.084 0.098 0.094 -0.129 -0.234 -0.150 -0.319 -0.387 0.067 -0.129 Conductivity 0.451 0.178 0.221 0.404 0.329 0.276 0.016 0.079 -0.046 0.022 -0.007 -0.049 0.199 0.157 Turbidity -0.043 -0.052 -0.014 -0.076 -0.092 -0.069 -0.112 -0.043 -0.072 -0.074 -0.051 0.044 -0.104 -0.172 Nitrate -0.302 -0.367 -0.148 -0.152 -0.181 -0.345 -0.215 -0.178 0.237 -0.009 0.010 0.106 -0.211 0.092 Phosphate -0.225 -0.011 -0.133 -0.227 -0.216 -0.169 0.037 0.110 0.009 -0.156 -0.232 0.225 -0.097 -0.077 TN -0.253 -0.285 -0.032 -0.203 -0.033 -0.236 -0.357 -0.217 -0.034 -0.074 -0.084 0.099 -0.230 -0.033 TP 0.181 0.263 0.199 0.157 0.132 0.281 0.314 0.294 0.196 0.261 0.210 0.020 0.234 0.167 Ammonia -0.126 -0.154 -0.231 -0.128 -0.304 -0.137 -0.094 0.013 0.186 0.080 0.229 0.192 -0.176 -0.123 Chlorophyll -0.035 -0.221 -0.103 -0.170 0.052 -0.046 -0.024 -0.119 -0.071 -0.168 -0.132 -0.201 -0.026 -0.003 Cladocera -0.179 0.466 0.181 0.177 -0.158 -0.165 0.213 -0.014 0.210 0.046 -0.100 -0.084 0.070 0.141 Copepoda -0.280 -0.292 -0.132 -0.310 -0.109 -0.386 -0.303 -0.417 -0.046 -0.264 -0.364 -0.215 -0.338 -0.149 Rotifera -0.313 0.172 -0.026 -0.113 -0.377 -0.333 -0.027 -0.189 0.235 -0.076 -0.181 -0.100 -0.190 -0.080 Bacillariophyceae 0.004 0.487 0.142 0.197 -0.091 -0.016 0.233 -0.017 0.209 -0.020 -0.056 -0.234 0.093 0.051 Chlorophyceae 0.336 0.486 -0.005 0.160 0.081 0.294 0.410 0.365 -0.087 -0.038 -0.158 0.155 0.319 0.045 Cyanophyceae 0.425 0.489 0.077 0.235 0.200 0.383 0.467 0.370 -0.088 -0.002 -0.112 0.060 0.389 0.107 Dinophyceae 0.608 0.266 -0.018 0.132 0.366 0.486 0.288 0.274 -0.292 -0.121 -0.222 0.048 0.358 0.011 Euglenophyceae 0.650 0.391 0.052 0.216 0.389 0.512 0.362 0.375 -0.263 -0.071 -0.216 0.111 0.422 0.074

Chrysophyceae 0.532 0.235 0.021 0.105 0.327 0.340 0.172 0.113 -0.289 -0.152 -0.254 -0.101 0.231 -0.056 Xanthophyceae 0.331 0.348 0.118 0.186 0.180 0.414 0.374 0.478 0.028 0.505 0.442 0.082 0.305 0.131 Eugle 0.444 0.496 0.198 0.314 0.283 0.536 0.275 0.561 0.161 0.139 0.021 0.351 0.373 0.288 Dinoo 0.935 0.651 0.360 0.522 0.729 0.789 0.498 0.571 -0.143 0.310 0.002 0.043 0.648 0.374 Green algae 0.713 0.833 0.542 0.673 0.596 0.804 0.724 0.715 0.204 0.512 0.288 0.162 0.826 0.594 Blue-green algae -0.077 -0.266 0.472 0.169 0.400 0.092 -0.112 -0.092 -0.120 0.226 -0.032 -0.040 0.168 0.349 Diatoms 1.000 0.604 0.357 0.596 0.766 0.787 0.421 0.503 -0.231 0.392 0.110 -0.037 0.597 0.341 Clado 0.604 1.000 0.491 0.663 0.442 0.621 0.588 0.567 0.218 0.498 0.192 -0.043 0.619 0.460 Cope 0.357 0.491 1.000 0.743 0.692 0.369 0.243 0.248 0.170 0.442 0.168 -0.187 0.526 0.577 Roti 0.596 0.663 0.743 1.000 0.716 0.642 0.624 0.535 0.153 0.738 0.457 0.004 0.786 0.783 Ostracoda 0.766 0.442 0.692 0.716 1.000 0.681 0.311 0.281 -0.261 0.396 0.057 -0.199 0.635 0.565 Odonata 0.787 0.621 0.369 0.642 0.681 1.000 0.671 0.736 0.055 0.578 0.351 0.114 0.835 0.598 Diptera 0.421 0.588 0.243 0.624 0.311 0.671 1.000 0.714 0.302 0.547 0.371 0.286 0.886 0.682 Scales 0.503 0.567 0.248 0.535 0.281 0.736 0.714 1.000 0.469 0.593 0.422 0.588 0.744 0.647 Ephemeroptera -0.231 0.218 0.170 0.153 -0.261 0.055 0.302 0.469 1.000 0.182 0.305 0.360 0.227 0.438 Trichoptera 0.392 0.498 0.442 0.738 0.396 0.578 0.547 0.593 0.182 1.000 0.775 0.170 0.624 0.658 Coleoptera 0.110 0.192 0.168 0.457 0.057 0.351 0.371 0.422 0.305 0.775 1.000 0.115 0.369 0.390 Hemiptera -0.037 -0.043 -0.187 0.004 -0.199 0.114 0.286 0.588 0.360 0.170 0.115 1.000 0.264 0.367 Fullness Index 0.597 0.619 0.526 0.786 0.635 0.835 0.886 0.744 0.227 0.624 0.369 0.264 1.000 0.845 Body Bondition 0.341 0.460 0.577 0.783 0.565 0.598 0.682 0.647 0.438 0.658 0.390 0.367 0.845 1.000

Appendix 2. Summary of PCA

PC 1 2 3 4 5 6 7 8 9 10 11 12 13 Eigenvalue 12.39 6.54 4.60 3.65 3.24 2.26 1.86 1.42 1.27 0.95 0.94 0.74 0.69 % variance 28.81 15.21 10.71 8.49 7.53 5.25 4.34 3.30 2.95 2.22 2.19 1.71 1.61

Appendix 2 (continued).

PC 14 15 16 17 18 19 20 21 22 23 24 25 26 Eigenvalue 0.48 0.38 0.33 0.31 0.20 0.18 0.14 0.13 0.12 0.07 0.05 0.04 0.02 % variance 1.13 0.88 0.76 0.71 0.47 0.42 0.32 0.30 0.28 0.16 0.12 0.09 0.05

Appendix 3. Correlation matrices of principal components

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8 PC 9 PC 10 PC 11 PC 12 PC 13 SECCHI 0.47 -0.71 0.19 0.24 -0.15 0.06 0.01 0.01 -0.13 -0.04 0.21 0.15 0.07 TEMP -0.74 0.42 -0.19 0.24 0.30 0.03 0.16 0.05 0.09 0.00 -0.11 0.06 -0.04 pH -0.37 0.07 0.55 0.37 0.13 -0.22 0.53 -0.19 0.08 -0.02 0.03 0.01 -0.03 DO 0.37 -0.37 0.48 -0.16 0.26 0.01 0.56 -0.10 0.18 0.00 0.15 -0.03 0.04 % O2 SATURATION 0.13 -0.28 0.04 -0.03 0.65 -0.06 0.54 0.03 0.40 0.00 -0.05 -0.06 -0.10 CONDUCTIVITY 0.28 0.05 -0.50 -0.51 0.15 0.19 0.21 0.07 -0.08 0.10 0.42 0.17 0.01 TURBIDITY -0.06 -0.02 -0.24 -0.36 -0.30 -0.34 0.27 -0.09 -0.30 0.29 -0.07 -0.43 0.35 NITRATE -0.59 0.41 0.10 0.11 -0.06 0.37 -0.03 0.31 0.14 0.13 0.06 0.09 0.24 PO4 0.02 -0.33 0.60 0.64 -0.04 0.13 0.08 -0.16 -0.10 -0.07 0.07 0.03 0.11 TN -0.52 0.36 0.02 0.37 0.03 0.36 0.25 0.15 -0.12 -0.30 0.12 -0.24 -0.16

TP 0.26 0.19 0.44 0.51 -0.08 -0.42 -0.06 0.21 -0.13 0.26 -0.07 0.04 -0.13 NH3 -0.30 0.25 -0.07 -0.49 -0.39 -0.22 0.43 0.27 0.17 0.05 -0.10 0.04 0.06 CHLOROPHYLL a -0.11 -0.09 -0.03 0.63 0.12 0.22 -0.31 -0.04 0.24 0.30 0.28 -0.30 0.09 CLADOCERA 0.20 -0.29 0.68 -0.33 0.43 0.11 -0.13 0.01 -0.12 -0.13 -0.07 -0.14 0.07 COPEPODA -0.59 0.10 0.16 0.43 0.34 0.27 -0.04 0.39 0.10 0.11 -0.02 0.03 0.14 ROTIFERA -0.15 -0.25 0.61 -0.34 0.35 0.08 -0.15 0.24 -0.02 0.02 -0.21 0.26 0.13 BACILLARIOPHYTES 0.34 -0.40 0.38 -0.54 0.39 -0.03 -0.25 0.09 -0.03 0.02 -0.05 -0.02 -0.07 CHLOROPHYTES 0.73 -0.59 0.16 0.05 -0.01 0.10 -0.06 -0.16 -0.02 -0.10 -0.01 0.00 0.10 CYANOPHYTES 0.79 -0.51 0.13 0.21 0.04 0.00 -0.11 -0.14 0.01 0.02 0.03 0.01 -0.01 DINOPHYTES 0.65 -0.35 -0.56 0.18 -0.06 0.17 0.07 -0.02 0.17 0.02 -0.11 -0.03 -0.10 EUGLENOPHYTES 0.77 -0.40 -0.40 0.18 -0.04 0.16 0.06 -0.05 0.07 -0.01 -0.07 0.00 -0.02 CHRYSOPHYTES 0.55 -0.38 -0.59 0.07 0.07 0.10 -0.08 0.03 0.17 0.05 -0.18 -0.03 -0.18 XANTHOPHYTES 0.38 0.23 0.32 0.50 -0.29 -0.53 -0.11 0.14 0.02 -0.12 -0.14 -0.04 0.01 Euglenophyceae 0.61 -0.01 0.21 0.22 -0.16 0.07 0.36 0.14 -0.40 0.11 0.19 0.07 -0.11 Dinophyceae 0.81 0.16 -0.25 0.27 0.08 -0.05 0.07 0.30 -0.11 0.01 -0.13 0.08 0.07 Chlorophyceae 0.87 0.31 0.10 0.01 0.05 -0.06 0.04 0.14 -0.09 -0.01 -0.18 -0.08 -0.12 Cyanophyceae -0.22 0.54 -0.19 0.15 0.37 0.01 0.02 -0.53 -0.13 0.03 -0.29 0.17 0.00 Bacillariophyceae 0.76 0.21 -0.40 0.15 0.10 -0.07 0.05 0.34 -0.05 -0.07 0.06 0.13 0.13 Cladocera 0.78 0.14 0.26 -0.16 0.24 -0.04 0.04 0.32 -0.08 -0.10 -0.02 -0.19 -0.03 Copepoda 0.37 0.51 0.03 -0.05 0.53 -0.11 -0.02 -0.19 -0.32 0.16 0.05 -0.04 -0.12 Rotifera 0.65 0.55 0.04 -0.18 0.30 -0.03 -0.04 -0.07 -0.01 -0.02 0.26 0.03 0.12 Ostracoda 0.54 0.46 -0.40 0.22 0.43 -0.03 -0.02 -0.01 -0.20 -0.02 0.05 0.03 0.11 Odonata 0.79 0.41 -0.15 0.14 0.00 -0.06 0.13 0.05 0.12 0.01 0.00 0.04 -0.05 Diptera 0.72 0.28 0.28 -0.03 -0.06 0.08 -0.03 -0.06 0.39 0.11 -0.14 -0.10 0.17 Scales 0.72 0.37 0.24 0.04 -0.37 0.12 0.16 -0.04 0.10 0.09 -0.01 0.10 -0.02 Ephemeroptera 0.08 0.29 0.52 -0.29 -0.26 0.26 -0.10 0.06 0.03 0.47 0.03 0.01 -0.36 Trichoptera 0.42 0.70 0.21 -0.09 -0.05 -0.22 -0.13 -0.07 0.17 -0.27 0.10 0.05 0.13 Coleoptera 0.20 0.55 0.19 -0.19 -0.33 -0.32 -0.25 -0.08 0.28 -0.23 0.32 -0.01 -0.14

Hemiptera 0.18 0.16 0.23 -0.02 -0.65 0.47 0.23 -0.21 -0.10 -0.03 -0.17 0.19 0.10 Fullness Index 0.78 0.50 0.06 0.02 0.08 0.15 0.01 -0.14 0.18 0.09 -0.09 -0.06 0.09 Body Bondition 0.50 0.68 0.20 -0.07 0.10 0.33 -0.09 -0.20 0.02 0.14 0.01 0.02 0.09

Appendix 3 (continued).

PC 14 PC 15 PC 16 PC 17 PC 18 PC 19 PC 20 PC 21 PC 22 PC 23 PC 24 PC 25 PC 26 MONTH -0.10 0.01 -0.02 0.01 0.05 -0.04 0.04 0.02 0.00 -0.03 0.01 -0.01 0.00 SITE 0.14 -0.05 -0.05 -0.07 0.13 0.01 0.05 -0.03 0.03 0.05 -0.02 0.04 -0.04 SECCHI 0.04 -0.02 -0.03 -0.02 0.01 0.03 -0.06 0.20 0.10 0.01 0.06 -0.02 0.02 TEMP -0.05 0.06 0.04 0.06 -0.01 0.07 0.01 0.03 0.05 -0.08 0.05 0.01 0.01 pH 0.05 -0.01 -0.10 0.01 -0.06 0.07 -0.07 0.03 0.00 0.00 0.00 0.05 0.01 DO -0.01 -0.03 0.03 -0.01 0.05 -0.10 0.06 -0.01 -0.05 -0.04 -0.03 0.01 0.02 % O2 SATURATION -0.01 0.09 0.07 -0.02 -0.01 -0.05 0.02 0.03 -0.03 0.04 0.02 -0.03 0.01 CONDUCTIVITY -0.13 -0.02 0.01 0.19 -0.02 0.13 -0.03 0.02 -0.05 0.01 0.00 0.02 0.01 TURBIDITY 0.01 0.12 0.02 -0.05 0.03 0.07 0.00 0.07 0.02 -0.01 0.01 0.01 0.01 NITRATE 0.20 0.05 0.03 -0.13 -0.16 -0.09 0.00 0.11 -0.08 0.01 -0.03 0.01 0.00 PO4 0.08 0.01 -0.03 0.12 -0.07 0.01 -0.04 -0.10 0.05 -0.04 0.05 -0.05 0.00 TN 0.06 -0.07 0.02 0.03 0.09 0.08 0.01 0.07 0.07 0.05 -0.10 0.00 -0.02 TP -0.15 -0.13 0.24 0.06 0.00 0.06 -0.05 0.01 -0.05 0.01 -0.02 0.00 0.01 NH3 0.05 -0.03 0.10 0.19 -0.08 -0.10 -0.04 -0.05 0.14 0.05 0.01 -0.02 -0.01 CHLOROPHYLL a -0.15 0.20 0.05 0.17 0.00 -0.05 0.05 -0.03 0.02 0.03 0.00 0.00 -0.01 CLADOCERA -0.02 0.01 0.08 0.04 0.04 -0.06 -0.04 0.08 0.01 -0.05 0.02 0.07 -0.07 COPEPODA 0.05 -0.04 0.00 -0.09 0.15 0.06 0.04 -0.02 0.00 0.05 0.06 0.00 0.03 ROTIFERA -0.05 0.19 0.05 0.02 -0.09 0.14 0.06 -0.04 0.08 -0.02 -0.06 0.03 0.00 BACILLARIOPHYTES -0.13 0.00 0.00 0.00 0.10 -0.07 0.02 0.06 0.06 0.02 -0.01 -0.05 0.04 CHLOROPHYTES 0.06 0.05 -0.08 0.01 -0.06 0.00 0.06 -0.03 0.01 0.07 -0.06 -0.03 0.00 CYANOPHYTES -0.02 -0.04 0.06 0.08 -0.05 -0.01 -0.04 0.02 -0.05 0.03 0.00 0.05 0.00

DINOPHYTES 0.09 0.05 0.07 -0.06 -0.04 0.04 -0.01 0.02 0.03 0.01 0.01 0.00 -0.01 EUGLENOPHYTES 0.09 0.03 0.03 0.03 -0.02 0.03 0.02 0.00 0.03 -0.02 -0.01 0.02 0.03 CHRYSOPHYTES 0.15 0.08 0.21 -0.04 0.06 0.01 -0.05 0.00 0.03 -0.05 0.00 0.02 0.00 XANTHOPHYTES 0.07 0.05 0.01 0.06 0.01 -0.06 0.02 0.05 0.01 -0.02 -0.05 0.00 0.03 Euglenophyceae -0.16 0.12 0.14 -0.26 -0.06 -0.04 0.04 -0.05 0.01 -0.01 0.00 -0.03 -0.02 Dinophyceae -0.04 -0.01 -0.08 0.12 -0.01 -0.08 0.00 0.04 -0.08 -0.01 -0.03 0.02 0.03 Chlorophyceae -0.01 -0.08 -0.08 0.02 -0.06 0.00 0.13 0.01 -0.05 0.06 0.06 0.02 -0.03 Cyanophyceae -0.08 0.15 0.00 0.02 -0.03 -0.03 -0.06 0.08 -0.05 0.06 0.00 -0.04 0.00 Bacillariophyceae -0.02 0.05 -0.03 0.10 0.04 -0.03 0.02 0.06 0.01 -0.02 0.02 -0.03 -0.05 Cladocera 0.13 0.10 -0.09 -0.01 0.02 0.06 -0.09 -0.11 -0.05 0.05 0.05 0.01 0.02 Copepoda 0.30 0.01 0.04 0.09 -0.04 0.07 0.12 0.01 0.04 -0.01 0.01 -0.02 0.00 Rotifera 0.15 -0.10 0.12 -0.04 0.02 0.02 -0.05 -0.02 -0.03 -0.05 -0.04 -0.04 0.01 Ostracoda 0.02 -0.09 -0.03 -0.03 0.00 -0.13 -0.02 -0.03 0.12 0.00 -0.01 0.01 0.01 Odonata -0.23 0.11 -0.17 -0.12 0.01 0.05 -0.04 0.00 0.08 0.01 -0.02 0.02 -0.01 Diptera -0.08 -0.23 -0.01 -0.03 -0.02 0.11 -0.01 0.03 0.00 -0.03 0.01 -0.07 -0.03 Scales 0.06 0.15 -0.15 0.05 0.14 -0.03 0.03 -0.01 -0.03 -0.11 -0.02 0.00 -0.01 Ephemeroptera 0.14 0.06 -0.12 0.03 0.01 -0.02 -0.06 0.05 0.02 0.02 -0.01 -0.01 -0.01 Trichoptera 0.07 0.21 0.11 -0.01 0.09 0.07 -0.08 -0.01 -0.03 0.06 0.00 -0.02 -0.02 Coleoptera -0.02 0.06 0.08 -0.04 -0.08 -0.01 0.11 0.05 0.03 0.00 0.05 0.03 0.02 Hemiptera 0.00 -0.01 0.15 0.07 0.15 0.01 0.10 0.00 -0.01 0.05 0.03 0.02 0.01 Fullness Index -0.07 -0.11 -0.03 -0.03 -0.06 0.07 0.06 0.02 0.05 0.00 -0.02 0.04 0.02 Body Bondition -0.02 -0.06 0.07 -0.04 0.01 -0.10 -0.08 -0.05 0.07 0.03 0.01 0.06 0.02

Appendix 4. Chi-square test for significant difference between stomach classes

Observed vs. Expected Frequencies (stomach classes.sta) Chi- Square = 376.7323 df = 6 p < 0.003 NOTE: Unequal sums of obs. & exp. frequencies

Appendix 5. One way ANOVA test for significant difference between stomach classes

Analysis of Variance (stomach classes.sta) Bolded effects are significant at p < .05000 SS df MS df MS Effect Effect Effect SS Error Error Error F p ZEROS 408.9087 6 68.15145 421.947 12 35.16225 1.9382 0.155073 ONES 184.8977 6 30.81629 131.3983 12 10.94986 2.81431 0.060056 TWOS 23.84194 6 3.973656 57.26658 12 4.772215 0.832665 0.567164 THREES 16.0464 6 2.6744 31.94422 12 2.662018 1.004651 0.465686 FOURS 19.10813 6 3.184688 25.12391 12 2.09366 1.521111 0.252233

Appendix 6. Kapenta analysis recording sheet

UNIVERSITY OF ZIMBABWE, FACULTY OF SCIENCE, BIOLOGICAL SCIENCES DEPARTMENT, TROPICAL RESOURCES ECOLOGY PROGRAMME TAKUDZWA C MADZIVANZIRA, R077061X KAPENTA ASSESSMENT SITE...... GPS CO-ODIRNATES...... DATE...... TIME...... Fish Tot. Stan. M. M. Sto. Contents Sto. Inte. Rati S Eugl Din Chloro Cyan Bacilla Clado Cope Rotife Ostra Dipte Odon Trico Hemi Coleo Other No. Lengt Lengt before after Weight weight class Class o e eno op phyta ophy riophy cera poda ra r ata ptera ptera tera h h (g) (g) (g) (g) if x phyt hyt ta ta (mm) (mm) a a 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 ...... Notes……………………………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..………………………………………………….… ……………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..… …………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..………………………..… …………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..…………… ………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………....…………………..…………………………………………………......

Appendix 7. Plankton recording sheet

UNIVERSITY OF ZIMBABWE, FACULTY OF SCIENCE, BIOLOGICAL SCIENCES DEPARTMENT, TROPICAL RESOURCES ECOLOGY PROGRAMME TAKUDZWA C MADZIVANZIRA, R077061X ZOOPLANKTON ASSESSMENT Month…………………………………….. Date……………………………… ROTIFERA SITE Species A B C D

Notes……………………………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..………………………………………………….… ……………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..… …………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..………………………..… …………..……………………………………..…………………………………………………………………………..……………………………………..……………………………………..…………………………………………………………………………..……………………………………..……………

Appendix 8. Physicochemical variables recording sheet UNIVERSITY OF ZIMBABWE, FACULTY OF SCIENCE, BIOLOGICAL SCIENCES DEPARTMENT, TROPICAL RESOURCES ECOLOGY PROGRAMME TAKUDZWA C MADZIVANZIRA, R077061X WATER QUALITY RECORDING SHEET DEPTH (m) JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Temp0 0 5 10 15 20 25 30 40 50 pH 0 5 10 15 20 25 30 40 50 Conductivity 0 5 10 15 20 25 30 40 50 Turbidity 0 5 10 15 20 25 30 40 50

Plates

Plate 1. Ceratium sp. (centre right) with a Lecane sp. (bottom) from a sardine gut.

Plate 2. Themocyclops sp. from a sardine gut.

Plate 3. Nauplius from a sardine gut.

Plate 4. Pediastrum sp. from a sardine gut.

Plate 5. (a) Keratella cochlearis with spine and (b) Keratella cochlearis tecta with no spine from a sardine gut.

Plate 6. Diaphanosoma excisum from the water column.

Plate 7. Garmin fish finder mounted on a kapenta rig.

Plate 8. Godwin Mupandawana (ULKRS) helping fishermen to empty L. miodon from the net into kapenta crates (December, 2015).

Plate 9. Proposed ULKRS rig (Photo edited from the one taken at Chawara harbour. Rig belongs to McMaster Fishing Company).