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African Journal of Aquatic Science 2007, 32(3): 291–298 Printed in South Africa — All rights reserved
AFRICAN JOURNAL OF AQUATIC SCIENCE EISSN 1727–9364 doi: 10.2989/AJAS.2007.32.3.9.308
Influence of environmental factors on seasonal changes in clupeid catches in the Kigoma area of Lake Tanganyika IA Kimirei1 and YD Mgaya2* 1
Tanzania Fisheries Research Institute, Box 90, Kigoma, Tanzania Faculty of Aquatic Sciences and Technology, University of Dar es Salaam, PO Box 60091, Dar es Salaam, Tanzania * Corresponding author, e-mail:
[email protected]
2
Received 9 March 2006, accepted 5 April 2007 An investigation into the relationship between the fluctuating physico-chemical environment and variability in fish catches in the Kigoma, Tanzania, area of Lake Tanganyika was conducted from January to December 2003. Catch per unit effort (kg fishing unit–1 night–1) showed two strong peaks in February and August–September, which followed peak catches of Stolothrissa tanganicae. Stolothrissa was the most abundant species in the pelagic catches, followed by the centropomid Lates stappersi and Limnothrissa miodon. Chlorophyll a concentrations correlated positively with high clupeid catches. Wind speed accounted for over 56.7%, of the variability in clupeid catches, while chlorophyll and phosphate, accounted for 44.0% and 55.4%, respectively. The results confirm seasonal fluctuations in pelagic fish catches and the dependence of the lake’s hydrodynamics on the weather system prevailing in the lake region. However, fluctuations in nutrient concentrations in the photic zone were less apparent than in previous studies. Regression analysis indicated that environmental factors have a significant impact on the fluctuations of the pelagic fish catches. More multidisciplinary data are required to confirm the dependence of clupeid catches on environmental factors. Keywords: chlorophyll, Clupeidae, fisheries, limnology, nutrients, Stolothrissa
Introduction One of the distinctive features of Lake Tanganyika, setting it apart from the other African Great Lakes, is the presence in it of two endemic freshwater clupeids, Limnothrissa miodon (Boulenger 1906) and Stolothrissa tanganicae (Regan 1917) (Coulter 1991). Limnothrissa miodon was introduced into Lakes Kivu and Kariba and from the latter invaded Lake Cahora Bassa. In each lake, it altered the pelagic food web by eliminating larger zooplankton, including cladocerans and Chaoborus (Gliwicz 1984, Dumont 1986, Marshall 1991). Selective predation by these clupeids probably explains the absence of cladocerans and other large zooplankton in the offshore waters of Lake Tanganyika (Coulter 1991), unlike the other East African Great Lakes, where they are abundant. In Lake Tanganyika, the two pelagic clupeids and one of their predators, the endemic centropomid Lates stappersi, (Boulenger 1914), make up one of the most productive and important pelagic fisheries in the African Great Lakes region. It yields about 184 000t of fish per annum (65% clupeids, 30% L. stappersi and 5% other Lates spp.) and provides between 25% and 40% of the animal protein consumed in Tanzania, Zambia, Burundi and the Democratic Republic of Congo (Mölsä et al. 1999, 2002). The fishery is particularly important in the Kigoma area, Tanzania, as a source of income, where the earnings of fishermen are up to twice the national average (Bosma et al. 1997). There are about 12 516 fishermen on the
Tanzanian side of the lake (Reynolds and Mölsä 2000), about 6 000 of these operating from the Kigoma area, where fishing probably supports more people than any other part of the rural economy. The pelagic catches fluctuate considerably between years, seasons and areas, often with dramatic shifts in the relative abundance of clupeids and Lates (Plisnier 1997, Mölsä et al. 1999, Chitamwebwa and Kimirei 2005). Although the total catch and catch per unit effort (CPUE) of the pelagic fishery have declined in some areas (Shirakihara et al. 1992, Bayona 1993, Phiri 1993), only a few workers have attempted to determine the causes of these fluctuations (Plisnier 1996, 2004, Phiri and Shirakihara 1999, Mulimbwa 2006, Sarvala et al. 2006) or of the decline of the fishing industry (Bayona et al. 1992). Lake Tanganyika has two main seasons: a four-month ‘dry season’ from May to August, characterised by cooler dry conditions and fairly constant southerly winds, and a ‘wet season’ during the rest of the year when the winds are generally lighter and mainly northerly (Coulter and Spigel 1991). The hydrodynamics and limnological characteristics of Lake Tanganyika and of its productivity are to a large extent linked to these weather patterns (Plisnier et al. 1999, Plisnier and Coenen 2001). The nutrients required in the euphotic zone for primary production depend primarily on regeneration from the hypolimnion through upwelling, internal mixing and entrainment of the nutrient-rich hypolimnetic
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waters into the euphotic zone. The strength of these upwelling events depends on the strength of the south-east trade winds that set them in motion. Increased nutrient regeneration into the productive layer should result in increased primary and secondary productivity, leading a few months later to high pelagic fish catches. The present study was designed to investigate seasonal variability in the pelagic fish catches in the Kigoma area of Lake Tanganyika, which are assumed to be determined by fluctuations in the physical and chemical environment of the lake, which in turn is driven by climatic conditions around the lake. Methods Samples were collected once every two weeks from January–December 2003. Physical and chemical variables were measured at a pelagic site (referred to as ‘LimSite’ 4°51’S, 29°35’E) (see Figure 1) in water more than 200m deep about 5km off Kigoma, while fish samples and catch statistics were collected at Katonga, Kibirizi and Kigodeco landing beaches in the town itself, within two days after the limnological samples. The samples were pooled to give monthly means for the catch of individual species and the total catch. Air temperature and wind speed data were obtained from a meteorological station, MONITOR II (Davis Instruments), located about 50m from the lake shore. Light penetration was measured with a 20cm-diameter Secchi disc, while water temperature, conductivity and dissolved oxygen were measured automatically with a CTD meter (Sea-Bird Electronic, Model 19). Water samples for nutrient analysis and chlorophyll a determinations were taken, at intervals of 20m to a depth of 100m, using a 5-litre Hydro-Bios water
0
4°50S
1
2
3km
AFRICA Tanzania
LimSite
Kibirizi Kigoma Bay
Kigodeco LAKE TANGANYIKA
KIGOMA
Kenya
TANZANIA Zambia
Bangwe Point
TANZANIA
INDIAN OCEAN
Nondwa Point
Sampling site
Katonga 4°55S 29°35E
Kitwe Point
29°40E
Figure 1: Map of Lake Tanganyika showing the locations of the limnological sampling site, LimSite, and the three landing beaches, Kibirizi, Katonga and Kigodeco
sampling bottle. Samples for nutrients and chlorophyll a analysis were transferred into 5-litre pre-rinsed plastic bottles, which were then stoppered tightly and stored on ice in coolboxes. In the laboratory, the water samples were filtered through a GF/C glass-fibre filter before being analysed for nutrients. Wherever possible, these analyses were done on the day the samples were collected but, when that was not possible, the samples were kept frozen and analysed on the following day. Soluble reactive phosphorus (SRP) was determined spectrophotometrically in the form of phosphomolybdenum blue using the ascorbic acid method, and nitrate was determined using a sulphanilamide method (APHA 1992). Calibrations between readings were performed with negligible variability between readings (±0.0001mg l–1) and standard curves were prepared by reading a series of standard dilutions and blanks in a spectrophotometer. Euphotic depth, i.e. the depth at which 1% of incident light is detected, was derived from Secchi depth (SD) by calculating the light attenuation coefficient (k = 1.57 SD–1); a LICOR™ light meter was used to measure photosynthetic active radiation (PAR), which was used to calculate the conversion factor. Chlorophyll a was extracted with 90% (v/v) acetone after first disrupting the cells using high frequency sound waves for 15min in a Branson Sonicator, after which the samples were kept overnight in a freezer at 4°C. The next day, the samples were again sonicated for 15min and 2ml of the extract was sent for chlorophyll a analysis using High Performance Liquid Chromatography (HPLC) at the Freshwater Ecology Laboratory, University of Namur, Belgium. At the fish landing beaches, the number of 60kg boxes filled separately with clupeids and L. stappersi was recorded and the total catch calculated. In the case of mixed species catches (normally S. tanganicae and juvenile L. stappersi and/or L. miodon), a sub-sample of the catch was obtained, the different fish species sorted, and the composition of catches by species computed on a monthly basis. Biweekly data from the 0–40m photic zone on nutrients (PO 4-P), chlorophyll a and physical parameters (transparency, water temperature and wind speed) were averaged, to get monthly means. Monthly means from individual species catches and total catches (sum of catches per species) were calculated and related to the physico-chemical and chlorophyll a data, using correlation/regression analysis (JMP, SAS Institute Inc.). Regression analysis was used to determine how much of the variability in nutrients (PO 4 -P), chlorophyll a and clupeid catches could be explained by wind speed, phosphate, and chlorophyll a concentrations, respectively. Environmental variables — including wind speed, PO 4 -P, water temperature, air temperature and chlorophyll a concentration — were used to generate principal components on which clupeid catches were regressed. In analysing the effect of wind speed and nutrients (PO4-P) on clupeid catch, no time lag was applied, but three- and two-month lag periods were applied in analysing the effect of wind speed and chlorophyll a, respectively, on clupeid catches. The periods September–April and May–August were defined as wet and dry seasons, respectively.
African Journal of Aquatic Science 2007, 32(3): 291–298
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There was a marked seasonal variation in both air temperature and wind speed. The air was cooler in July (mean ± SE = 23.4 ± 0.2°C) and warmer in September, at the end of the dry season (mean ± SE = 25.9 ± 0.1°C). Generally, the first half of the year was cooler than the second half. The dry season was windier than the wet season (Figure 2). Transparency was lowest (9.4m) in September and highest (17.9m) in April. The mean transparency over the study period, based on three observers’ average values, was (mean ± SD) 13.5 ± 1.96m. The euphotic zone (Zeu) fluctuated between 25m and 56m (average 40.5m) over the study period. The average surface (0m) water temperatures ranged between 25.9 and 27.7°C, with the lowest being recorded in August (25.9°C) and the highest in February (27.7°C). Temperature of the water column (0–100m) ranged between 27.7 and 24.2°C. There was a significant (U’ = 147, p = 0.0005) drop in surface water temperatures during the dry season (mean ± SD: 27.1 ± 0.43 and 26.3 ± 0.43°C during the wet and dry seasons, respectively). The thermocline was shallowest (40m) during the wet season and deepest (70m) in July, with the temperatures being almost uniform to this depth (Figure 3a). Generally, there was an increase in conductivity of the water with depth. During June/July, conductivity values were almost uniform from surface to about 70m, signifying downward tilting of the thermocline (Figure 3b). The values recorded over the study period ranged from 653.8 to 680 μS cm–1 at the surface and at 100m, respectively. Soluble reactive phosphorus was present in low concentrations in the photic zone (0–40m) (range: 0.0–20.80μg PO 4-P/l); however, it increased with depth to higher values at 100m (range: 5.00–118.00μg PO4 P l–1). The highest average chlorophyll a concentration (mean ± SD = 0.99 ± 0.47μg chl a l –1 ) in the photic zone was recorded in September and the lowest (0.41 ± 0.015μg chl a l–1) in March. Chlorophyll a concentration was significantly higher during the second half of the year (median for July–December and January–June were 0.665 and 0.458,
respectively (U’ = 31, p < 0.05)). There was a significant negative correlation (r 2 = 0.562, p = 0.005) between chlorophyll a in the photic zone and water temperature. Dissolved oxygen was more or less evenly distributed in the photic zone during the study period (mean ± SD = 6.3 ± 0.63mg l–1; January not included) with the highest values recorded in September (7.3mg l–1) and October (7.2mg l–1) at the end of the dry season (Figure 3c), probably as a result of high productivity during that period. Both total CPUE (Kg fishing unit–1 night–1, abbreviated as Kg FU –1 night –1) and clupeid CPUE showed two strong peaks, occurring in February and August–September (Figure 4). Stolothrissa tanganicae was the most abundant species, constituting about 84.5% of the catches of pelagic
°C
(a) 20
27.2
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26.4
60
25.6 24.8
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µS cm1
(b) 20 DEPTH (m)
Results
676
40 668 60 660
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652
mg l1
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Wind Speed Air Temperature
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M
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J J 2003
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S
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AIR TEMPERATURE ( ºC )
WIND SPEED (m s1)
(c)
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Figure 2: Seasonal variations in air temperature and wind speed at Kigoma, January–December 2003. Mean ± standard error
20
7.5
40 5.0 60 2.5
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0.0 J
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Figure 3: Isopleths of (a) temperature (°C), (b) conductivity (μS cm –1) and (c) dissolved oxygen (mg l –1) (February–December 2003) at the limnological sampling station off Kigoma in January–December 2003
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Discussion Lake Tanganyika is oligotrophic and meromictic in nature, with a permanent thermocline. The thermocline deepens, tilting downwards, substantially during the dry season as a
4
300
3
200
2
100
CPUEClupeids = 301.5 23.6 WS, r2 = 0.081 (1)
J
F
M
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M
J J 2003
A
S
O
PO4 P(µg l1)
8 6 4 2 PO4 = 3.42 + 2.72 WS, r2 = 0.574**
0
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 WIND SPEED (m s1) 0.70
(b)
0.65 0.60 0.55 0.50 0.45
Chl [a] = 0.45 + 0.02 PO4, r2 = 0.612**
0.40 0
N
1 D
Figure 4: Seasonal variations in total and clupeid CPUE of the pelagic fishes from the Kigoma area of Lake Tanganyika during January–December 2003. The equations represent regressions of (1) clupeid and (2) total CPUE against wind speed at zero time lag
CATCH (Clupeids in kg x 103)
5
WIND SPEED (m s1)
CPUE (Kg FU1 night1)
CPUE Total CPUE Clupeids Wind Speed
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(a)
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6 8 PO4 (µg l1)
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(c)
CPUETotal = 345.9 25.3 WS, r2 = 0.069 (2)
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result of wind stress caused by the relatively higher wind speeds during that period. The seasonal south-east winds control the hydrodynamic events (Plisnier and Coenen 2001) and, as evidenced by Figure 5b, seem to drive the production systems of the lake. The major climatic patterns, particularly winds, have been reported to regulate the seasonal thermal regime of the lake — evaporation, water flow and vertical mixing and transport of water masses (Coulter and Spiegel 1991, Huttula 1997) — and are responsible for intra-seasonal
CHLOROPHYLL [a] (µg l1)
species studied, followed by Lates stappersi (14.2%) and Limnothrissa miodon (1.3%). The peak catches of Stolothrissa tanganicae corresponded well with peaks in their total relative abundance (CPUE), indicating that it was the main contributor to the latter and that its seasonality would significantly affect the fishery. Figure 5 depicts linear regressions of: (a) phosphate (b) chlorophyll a and (c) clupeid catch against wind speed, phosphate and chlorophyll a, respectively. Wind speed explained 57.4% (F = 10.78, p = 0.011) of phosphate variability, while phosphate and chlorophyll a concentrations explained 61.2% (F = 12.61, p = 0.008) and 73.2% (F = 24.63, p = 0.0008) of the variability in chlorophyll a and clupeid catches, respectively. In order to explain the variability in the clupeid catches caused by different environmental variables, principal component analysis was used to reduce the environmental variables (wind speed, PO4-P, water temperature, air temperature and chlorophyll a concentrations) into principal components. Clupeid catches were then regressed on the first principal component, without any time lags. The results indicate that the environmental variables explained 60.1% of the variability in the catches (r2 = 0.601, F = 15.017, p = 0.003) (Figure 6). Different environmental factors contributed significantly to the first principal component used in this analysis. Eigenvectors indicate that water temperature contributed the most to the magnitude of –0.61, while phosphates and chlorophyll a had almost equal importance, contributing 0.497 and 0.496, respectively; however, wind speed and air temperature contributed the least (Table 1). Notably, it is difficult to separate the effects of correlated environmental factors. Wind speed and air temperature are the ultimate factors causing changes in water temperature and stratification, and consequently in phosphate levels and chlorophyll a.
12 10
Catch = 5.17 + 16.83 Chl [a], r2 = 0.732***
8 6 4 2 0.4
0.5 0.6 0.7 0.8 0.9 CHLOROPHYLL [a] (µg l1)
1.0
Figure 5: Linear regressions of (a) phosphate, (b) chlorophyll a concentrations and (c) clupeid catches at zero time lag on wind speed, phosphate and chlorophyll a concentrations, respectively; ** = highly significant, *** = very highly significant
African Journal of Aquatic Science 2007, 32(3): 291–298
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CLUPEIDS CATCHES (kg x 103)
variability of the lake’s hydrodynamics (Naithani et al. 2002). The replenishment of nutrients into the photic zone and primary production depends mainly on an internal regeneration process (Plisnier and Coenen 2001) when conditions are right, the process being controlled by the hydrophysical phenomena (Mölsä et al. 1999). The seasonal variations, between wet and dry seasons, in water temperature, chlorophyll a, water transparency and conductivity are, most probably, the result of wind forcing on the lake. The mechanism whereby this is accomplished has been suggested by Plisnier and Coenen (2001), who maintained that the hydrodynamics of the lake depend on the dry season’s south-east monsoon wind strength, which causes upwelling in the south, and the resultant long-period internal waves, which Huttula (1997) showed to be 23.4 and 34.8 days long during the dry and wet seasons, respectively. The internal waves and mixing events, such as the main upwelling events (May–September), result in pulsed primary production (Plisnier and Coenen 2001) which main-
12 10
r2 = 0.601, F = 15.07, P = 0.003
8 6 4 2 1.0
0.5 0.0 0.5 1.0 PRINCIPAL COMPONENT 1
1.5
Figure 6: Regression analysis of clupeid catches and principal component 1 generated from principal component analysis of environmental variables including wind speed, PO4-P, water temperature, air temperature and chlorophyll a concentration
Table 1: Eigenvectors indicating relative impacts of different environmental variables on different principal components. Note the contribution of the environmental factors on the first principal component. Chl a = chlorophyll a concentration, PO4-P = phosphates, Temp = water temperature, WS = wind speed, Air temp = air temperature
1 Eigenvalue per cent Cumulative per cent Eigenvectors Chl a Temp PO4-P WS Air temperature
Principal Components 2 3 4
5
2.504 50.079 50.079
1.102 0.706 0.614 0.075 22.031 14.118 12.276 1.496 72.110 86.228 98.504 100.000
0.497 –0.610 0.496 0.204 0.305
–0.204 –0.076 –0.720 –0.052 0.162 0.041 0.194 –0.386 0.602 0.753 0.613 –0.085 –0.592 0.666 0.334
0.434 0.773 0.453 0.088 0.044
tains good clupeid catches during the wet season and enough food to feed the spawning stock and fry thereafter. The CPUE and Stolothrissa catch peaks in February and August–September could have been the result of a successful October/November 2002 and May/June 2003 spawning, since each recruitment to the fishery results from a spawning that happens four months previously (Roest 1977, Mukirania et al. 1988, Mölsä et al. 2002). During the wet season, the air was comparatively warmer and there were relatively lower wind speeds, as compared to the dry season. This, together with increased cloud cover, could have caused high water temperatures, strong stratification and a shallower thermocline, resulting in reduced chlorophyll a levels. However, during the windy dry season, water temperatures decreased appreciably as a result of evaporative cooling by the winds; the thermocline deepened, entraining into the hypolimnion. As the water temperatures decreased, allowing some mixing and/or diffusion of nutrients from the nutrient-rich hypolimnion across the thermocline into the epilimnion, chlorophyll a, which is a proxy for primary production, increased in the photic zone (upper 40m); this was indicated by the significant negative correlation between water temperature and chlorophyll a. Physical forcing has recently been shown to have an impact on the magnitude of fluctuation of primary production, zooplankton community and fish stocks, and their migrations and horizontal distribution (Mölsä et al. 2002, Langenberg et al. 2003). Regression analyses of data with zero lag periods indicate that wind speed explained about 57.4% of phosphate variability in the lake. Phosphate concentrations explained about 61.2% of chlorophyll a concentration, while the latter explained about 73.2% of the variability in clupeid catches. This finding stresses the importance of environmental variability as a significant contributor to the observed fluctuations in pelagic fish catches both in the study area and in the lake as a whole. Notably, the ultimate effect of wind on the lake is to cause mixing events that result in more nutrients being brought into the photic zone, thereby increasing the chlorophyll a concentration. As a consequence, clupeids are affected by the reduced water transparency, which gives them an advantage over their predators as well as concentrating their prey (zooplankton) which follows the phytoplankton blooms at dusk and dawn. Clupeids were usually caught at dusk or dawn (IAK, pers. obs.). The greatest correlation between chlorophyll a and clupeid catches was at the zero lag period, indicating that the distribution of clupeids in the water was related to chlorophyll a abundance. However, this could indicate the effect of chlorophyll a on the catchability of the clupeids, rather than on their productivity. Phytoplankton attracts zooplankton, which indirectly attracts the clupeids. Stolothrissa tanganicae has been found to be patchily distributed, most probably following the patchy distribution of their preferred mesozooplankton prey (copepods) and predator avoidance (Mannini 1998). Chlorophyll a probably affects S. tanganicae production three to four months after its bloom by ensuring food availability to fish larvae. Water transparency also has a significant effect on the catchability of the Tanganyikan clupeids. Clupeids respond negatively
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to increased water transparency, a phenomenon related to predator avoidance, since they are preyed upon by Lates stappersi, a visual predator that favours clear transparent water (Coulter 1991, Plisnier 1997). Depending on wind strength, wind-driven mixing and internal waves cause vertical and horizontal distribution of heat, dissolved substances and nutrients from the bottom layer. This in turn alters the distribution and productivity of phytoplankton and fishes (Naithani et al. 2003). As a result of reduced wind stress and the resultant internal waves, the mixing of bottom and surface water masses over the length of the lake is also reduced. This translates into reduced phytoplankton and fish production (Plisnier 2000, 2004, Naithani et al. 2003, O’Reilly et al. 2003, Verburg et al. 2003), leading to long-term catch fluctuations in the area. An increase in primary production (chlorophyll a) during the dry season resulted in increased fisheries catches, mainly of the clupeid S. tanganicae. This is supported by the synchronous peaking of CPUE, chlorophyll a and dissolved oxygen around September, which may also indicate the efficiency of S. tanganicae in locating its zooplankton prey, (that congregate at dusk and dawn to graze on blooms of phytoplankton). Stolothrissa tanganicae is a zooplanktivore (Coulter 1991); Chlorophyll a is, therefore, an important direct and indirect indicator of food abundance for this species and, through the food chain, to its main predator, Lates stappersi. The regression analysis between clupeid catches and principal component 1, generated from a principal component analysis of different environmental variables, explained about 60% of the catch variability in the study area. This shows that environmental factors have a significant impact on the fluctuations of pelagic fish catches — particularly of clupeids — that the lake experiences. Chifamba (2000), using model-fitting, found similar effects of environmental variables on catches of Limnothrissa miodon in Lake Kariba. The mechanism by which these factors impact on individual species and the catch in Lake Tanganyika could be through seasonal fluctuations in the hydrodynamics of the lake caused by seasonal winds and/or weather patterns, as explained by Plisnier and Coenen (2001), who pointed out the importance of air temperature in particular. Seasonal changes/fluctuations in catches — as observed in this and other studies (Coulter 1991, Plisnier 1997, Coenen et al. 1998) — may, however, reflect changes in the sizes of individuals and their abundances. Important increases or decreases in abundance may also be linked to the size and success of individual cohorts. The observed high catches during February and September 2003 could have been the result of successful spawnings in November 2002 and May 2003. These patterns have been observed by Shirakihara et al. (1992) on the Burundi side of Lake Tanganyika. Density-dependent (Shirakihara et al. 1992) and densityindependent reproduction and recruitment could both be taking place in Lake Tanganyika. Successful recruitment seems to result from early wet season spawning. However, year-to-year shifts in spawning and recruitment peaks exist, probably as a result of variable environmental conditions
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(Langenberg et al. 2002) and exploitation pressure, respectively. The wet season is characterised by relatively calm and stable water conditions, with presumably optimal temperatures that allow the hatching of larvae, their migration to inshore nursery grounds and feeding on the abundant phytoplankton food available (Coulter 1970). Successful spawning during the early wet season could, therefore, be related to these conditions. The rough weather and lowered water temperature, together with episodic hypoxia of the dry season are, most probably, not suitable for the survival of eggs and fry. These conditions would lead to increased mortality and hence to reduced subsequent catches (i.e. density-independency) in the fishery. In conclusion, this study confirms the existence of seasonal variability in fish catches in relation to meteorological, physical and chemical factors. However, fluctuations in nutrient concentrations in the photic zone were less apparent here than in previous studies (Plisnier et al. 1999, Langenberg et al. 2003). This could be due to fewer upwelling and mixing events, resulting from reduced winds and a stronger thermocline, respectively, probably caused by warming of the lake (O’Reilly et al. 2003, Verburg et al. 2003) and by high nutrient turnover rates (Järvinen et al. 1999) and their rapid utilisation by phytoplankton. The impact of wind speed on nutrient availability in the productive layer and the consequential increase in chlorophyll a and the impact of the latter on clupeid catches, as evidenced in Figure 5a–c, is highly noteworthy. It is not easy to identify the effect of nutrients on catches because of the oligotrophic nature of the lake and the complex processes involved. However, when two or more parameters are considered together, their effect on pelagic species catches appears to be more significant. Therefore, it is imperative that the fluctuations in catches in the lake be studied from a multidisciplinary viewpoint, and also that a long-term data set be developed, in order to make sound conclusions. Acknowledgements — This research was supported by the Belgium Science Policy Office, through the CLIMLAKE Project, and by the Tanzania Fisheries Research Institute (TAFIRI). We thank the TAFIRI staff in Kigoma for their hospitality and logistic a l s u p p o r t d u r i n g t h e f i e l d w o r k . We t h a n k D r G a m b a Nkwengulila, Department of Zoology and Wildlife Conservation, University of Dar es Salaam, and Dr Catherine O’Reilly, Bard University, USA, for providing valuable comments on early versions of the manuscript, and also two anonymous reviewers for their constructive criticism.
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