Relationship between lake level fluctuations and fisheries
The reported yield of Lake Baringo’s fisheries has fluctuated greatly since the early 1980’s. Annual yields from 1982-2020 (years of available data on annual fisheries) ranged from approximately 8 metric tons in 1994 to 496 metric tons in 2017 averaging close to 227 metric tons per year (Figure 7). Linear regression analysis showed a significant relationship (p < 0.05) between annual fisheries yields and the WLs (amplitude) indicating the direct effect of WLFs on the lake’s fisheries production (Fishery yield = 78.15 + (29.94 * WLS); R² = 0.66; Figure 7). These results suggest that water level changes have an influence on fish catchability in the lake.
The fishery variables (fish yields and fish condition factor) were correlated with the WLFs indicators as per the Pearson’s correlation coefficients (r) shown in Table III. The results indicated a strong significant positive relationship (p < 0.001) between the condition factor of the endemic tilapia species, Oreochromis niloticus baringoensis , with the yearly lake water level amplitude (r = 0.69). However, there were potential significant correlations (r = ⁓ 0.5) between the condition factor of the lungfish, Protopterus aethiopicus with DLTM (r = 0.48), and between the annual yield of the barb, Barbus intermedius, and DLTM (r = 0.50) (Table III). These results demonstrated positive and negative relationships for P. aethiopicus and B. intermedius with mean WLFs,respectively. There were no significant relationships between either DLTM or amplitude (WLFs) and total biomass as well as with fish species yields in the lake except for B. intermedius (Table III).
DISCUSSION
The results demonstrated inter-annual water level changes in Lake Baringo and the existence of patterns across years for the long-term database (1956-2020). They also indicated a near two decades periodicity or oscillation in extreme peak water levels across a 64-year time frame. This oscillation is likely associated with periodicity in abnormal rainfall events due to climate cycle variability in the lake watershed over time (Ngaira 2006; Aur et al., 2020). The mean annual WLs and amplitude intensities have been fluctuating in the Lake Baringo watershed over the last six decades as also evidenced in other African lakes (Kolding and Van Zwieten, 2012) and elsewhere (Blenckner, 2005; Neckles, et al ., 1990; White et al., 2008). The annual fluctuations are characterized by variable water quality changes ranging from poor to good water quality years with resultant impacts on lake ecology (Hickley et al ., 2004) and livelihoods (Aura et al ., 2020). In the long-term, the lake level has fluctuated from a low of 1.47 m in 1956 to peak levels (13.95, m) in 2017.
The results showed relations between WLF indices and lake water quality variables and with lake fisheries yields over time. These relations suggest a significant influence of water level fluctuations in the ecological functions of the lake. The results also indicated the impact of hydrological variable changes on the lake’s fisheries productivity through some mechanisms related to water-level mediated changes in fish species catchability and condition factor as also reported for Lake Turkana in Kenya (Kolding et al ., 1993a) and elsewhere (Koldinget al ., 2012). Studies on concordance between lake WLFs and biota conducted elsewhere (Neckles, et al., 1990; White et al.,2008; Gownaris et al ., 2015) showed similar patterns between WLFs and biota variables demonstrating the important role of water levels in a lakes’ habitat availability, macroinvertebrates’ richness and their temporal distribution.
The concordance of water quality variables with WLFs is expected as water levels are directly controlled by hydrological inputs driven by severe droughts and floods in the region. A rise or draw-down estimated at 1 m in water level causes a shift in the lake’s hydrological budget that might affect the ecological process in the lake. The relationship between water level (WL) and the lake surface area indicated that a 1 m increase in water level leads to about 90 km2 change in Lake Baringo’s surface area. This finding is a demonstration that high water levels create flood pulse regimes that provide enhanced habitat, food, and breeding areas for fish species that contribute to increased fisheries yields (Gownaris et al ., 2015). The littoral habitat area is important for enhanced fisheries production in semi-arid lakes such as Baringo. For instance, in Lake Baringo, the lake level decline leads to large losses of the open water habitat thereby likely reducing the carrying capacity of species that dominate its pelagic zone. Moreover, the lake level decline shrinks the floodplain area and leads to losses of littoral habitats, feeding and breeding habitats of some species (Karenge and Kolding, 1995; Grown et al ., 2015; Mageria and Kibwage, 2009). This might probably be the reason for the low catchability of some commercially valuable fishery species (e.g.O. niloticus barinoensis and B. intermediens ) of Lake Baringo during the years of lower levels in the lake (Mlewa et al ., 2005; Nyakeya et al ., 2020). In Lake Turkana, Kenya, the majority of the lake’s endemic species are found below the 10-m contour indicating that declines in inshore and offshore habitats would have severe ecological consequences for these species (Hopson, 1982).
The effect of WLFs on habitat variations and their effects on the fisheries of Lake Baringo is also a function of the depth of the lake and human population density settlements around the lake. The three zones of study (north, central, and south) in the lake will be differently affected depending on their depths and the human activities along the shores (Welcomme, 2008). Therefore as a result of changes in lake level, there is increased interaction between the aquatic and terrestrial ecotones during the rising water level years requiring an integrated approach to the management of the lake-terrestrial ecosystems. Regression and Gaussian analyses outputs demonstrated the highly responsive nature of turbidity and WQI to increasing DLTM indicating that hydrological indices are the major drivers of the water quality changes in the lake. We found positive correlations between nutrient components and WLFs except nitrogen forms (NO2, NO3, NH4+) and Chl-a which either showed negative relation with WLFS or did not demonstrate any relationship (TN and Chl-a) with any WLFs indices due probably to the decrease of these nutrient concentrations and increase of Chl-a levels with the increased DLTM in the lake. These results are in agreement with the findings from most tropical reservoirs and lakes. For example, in Lake Tana, Ethiopia, the plant nutrients are quickly exhausted during draw-downs and their effects on biological production become less important (Karengea and Kolding, 1995). The results of this study also indicated that the years with decreased WLs were characterized by lower lake primary production measured as chlorophyll-a concentrations indicating the effect of WLFs on the lake production in terms of phytoplankton biomass and likely the reported zooplankton limitation in Lake Baringo (Schagerl and Oduor, 2003; Tarras-Wahlberg et al ., 2003).
CONCLUSION
The results of the study highlight the link between water quality properties and WLFs in Lake Baringo. The lake’s fishery yields and species condition factors appear to be also influenced by WL changes. These linkages result from the influence of WLFs indices on the critical water quality parameters like turbidity, DO, conductivity, temperature, and the water quality index WQI). There is a direct link between years of high lake level and increased fisheries landings perhaps mediated through increased habitat availability as nursery and feeding grounds for species.
During periods of droughts with less inflow, water level decrease leads to a decline in littoral habitat due to the water volume reduction and this might force the species including juveniles to find a refugee in open water habitat enhancing the predatory degree and reducing the refugia areas in the lake for juveniles and some species (eg.Oreochromis niloticus baringoensis ). If the water level declines severely, the lake’s physio-chemical characteristics will be altered with an eventual reduction in DO due to the decrease of the euphotic zone in the lake, increase in water temperature, and high conductivity making the conditions in the lake harmful for the aquatic communities. This emphasizes the need for long-term monitoring of the lake’s condition and catchment for purposes of integrated lake management. Such management will require monitoring of upstream developments in order to maintain natural inflows during draw-down seasons.
ACKNOWLEDGMENTS
This research was funded by European Union (EU) through the Collaborative Training in Fisheries and Aquaculture in Eastern, Central, and Southern Africa Project (COTRA project). We thank the Kenya Fisheries Department and Water Resource Authority (WARA) for providing long-term data on fishery and water levels of the lake. Our thanks go to the technicians from KMFRI at Kisumu station, Mrs. Mwanchi, for nutrient analysis and Benjamin Bett Arwait, Julius Kiplagat, Barongo, Caroline Kibet, and Winnie Chelagat from KMFRI at Baringo station for assistance in the field and sample analysis.
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DATA AVAIL ABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.