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.