Supporting a population of 40 million World Bank , the Lake Basin provides a variety of economic and development opportunities, including fisheries, tourism and transboundary conservation. However, these opportunities are hindered by a number of threats that include eutrophication, over-fishing, introduced exotic species and the impacts of climate change.
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The Mara River catchment differs from the other three catchments in terms of land use and water quality stressors. The river is also the most hydrologically varied, with tributaries being predominantly seasonal and with high EC. The basin has the smallest proportion of cropland, but the largest population of livestock and wildlife in the protected areas of the MMNR in Kenya and the SNP in Tanzania.
Studies have shown that the middle reach rangelands of the catchment contain large herds of livestock, with more than , cattle estimated to live in the Talek subcatchment Ogutu et al. Such high livestock and wildlife numbers collectively contribute to a high deposit of organic matter and nutrients into the river Subalusky et al. Analysis of the water quality parameters identified two key stressors as nutrient loading from diffused agricultural sources and organic loading, mainly from large herds of domestic and wild grazers in the Mara River Kanga et al.
These stressors were shown to have significant and distinct impacts on fish communities in the rivers. Despite these disturbances and natural system modifications Makalle et al.
The previously high biodiversity of species richness and endemism in the LVB Darwall et al. We found greater species diversity and abundance within the protected areas in the Mara catchment and low-order streams in the Nyando catchment than in other sites within the same catchments. This does not exclude sensitive or intolerant species in these rivers as the methodology used to rank species sensitivity required the species to be sampled in more than one site; hence, some nine fish species were not ranked, and five of these were either in the Mara or Nyando catchment.
This included Bagrus docmak , which had previously been ranked as sensitive Raburu and Masese, , and Enteromius magdalenae and Labeobarbus bynni. The advantage of ranking species sensitivity calculated from the NB concept is that it does not require expert judgment on species response to stressors.
It determines species response to the specific environmental gradients measured rather than a generalized response to stressors and therefore can be applied to the different stressor gradients within a catchment to compare how a species responds to different pollution gradients.
However, it is not applicable to a species that has a narrow geographical range or is endangered and therefore difficult to sample, with the exception of samples that can be found at varying environmental gradients in the same location. Species composition and abundance was generally lower than previous studies, an indication that catchment management is a critical concern and an immediate consideration, whereas conservation of headwaters and low-order streams that are still species rich will be critical to prevent further loss.
Of the 19 fish species ranked in this study, the tolerance of nine species, A. Four species, C. In this study, the ranking of four species C. When comparing the two methodologies, some intolerant species were ranked as moderately tolerant or moderately tolerant species ranked as tolerant by the expert judgment method. This could be as a result of a species-specific response to a particular stressor; however, expert judgment generalized this response to that of multiple stressors.
Differences in fish composition in the catchments could be attributed to variations in the relative abundance of six species, predominantly L. Moreover, CCA related the tolerant species C. Moderately tolerant species did not show any strong relationship with either organic load or agriculture. A clear depiction of the relationship between stressors, species sensitivity tolerant, moderately tolerant and intolerant , and land use forest and agriculture was shown with PCNA, confirming that the significant difference in species distribution, abundance and composition at the catchments was a response to stressors, as shown by the CCA.
Apart from the stressors measured in this study, it is most likely that the fish assemblages also respond to other stressors, including those that are basin-specific.
This suggests that management of riverine fish assemblages will be more effective at basin or subcatchment scales rather than at the larger LVB level Achieng et al. Fish species richness, tropic structures, taxonomic composition, species sensitivity and diversity indices were used as metrics to compute IBI, based on the concept that the values of these metrics change as a response to stressors.
Studies have shown them to decline with increasing nutrients, organic matter and ionic material pollution Kim and An, , Mamun and An, and therefore an indication of disturbance.
Although species richness and composition were high in the Nyando and Mara rivers, the proportions in the categories of trophic structure and number of benthic and pelagic species were quite low. This could be due to the high dominance of two species L.
With all IBI scores in the four catchments ranging between 26 and 34, they were all evaluated to be in fair health. Riverine fish species richness and composition in the LVB have declined in the past decades in response to increasing complexity and multiple stressors in the catchments of many rivers.
This has resulted in the loss of sensitive species, species migration to headwaters, low-order streams and less polluted subcatchments or to protected areas with restricted access and increased levels of monitoring, conservation and management, as observed in the Mara catchment.
It is difficult to quantify the number of species lost in the past decades due to a scarcity of data and the lack of regular monitoring. Our results demonstrate that the cumulative effect of stressors can adequately rank fish species tolerance to disturbance gradients and help to further develop regional metrics to assess and monitor river health.
Multivariate methods have proven to be reliable in ranking species tolerance and can be used without prior knowledge of species biology and ecology. They can combine the effects of multiple variables and factors into species-specific responses along gradients of degradation, including some intrinsic characteristics, which are not easily observable.
Although the measured variables were limited to nutrient and organic loading, which are significant contributors to catchment degradation, it is most likely that the fish assemblages also respond to hydrological variable, such as flow rates and discharge, and other stressors that are basin-specific, indicating that the management of riverine fish populations will be more effective at individual river basin or subcatchment levels rather than at an LVB scale.
The fish-based IBI showed that all the catchments were in a fair health, although the evaluation of additional stressors may record different levels of species response and is therefore most likely to provide a more detailed assessment of ecological conditions in the rivers. Ecological conditions could also be evaluated at the site level, so as to eliminate confounding effects caused by upstream—downstream effects of pollutants and other disturbances. We also recommend conservation and management of the catchments with the protection of headwaters and lowland streams, which are still species rich, to prevent further loss of the exceptional biodiversity, which are native and endemic to the LVB.
The animal study was reviewed and approved by the National Commission for Science, Technology and Innovation. TC and CF participated in conceptualizing and drafting the manuscript. All authors contributed to the article and approved the submitted version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank technicians at Kenya Marine and Fisheries Research Institute, Kisumu, for help with field sampling. We were grateful to Augustine Sitati, Henry Lubanga and George Alal for assistance offered during field sampling and laboratory processing of samples.
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The watershed is subdivided into sub-basins that are spatially related to one another. Routing in stream channel is divided in to Water, Sediment, nutrients and organic chemical routing Neitsch et al. The stations had data ranging from to , though they had missing gaps. Rainfall data were available for twelve rainfall recording stations in and around the basin.
The collected data ranges between and though there were quite a number of missing data. The other weather data used were temperature data maximum and minimum for Kericho and Kisumu Meteorological stations. An increase in the initial curve number CN2 increases the stream flow, but the effect is more pronounced on the effects on surface run off. The slightly increase in total stream flow could be as a result of ration of surface run off to base flow.
The goodness of fit between observed and simulated stream flow was assessed for the aforementioned 1GD03 station, the R 2 was found to be 0. The low value of R 2 and NSE could be attributed to lots of data gaps in the station and also the effects of combined tributaries.
The station is located about 10 km upstream of Ahero Bridge just before the flood plain. The model over estimated the low flows at this station while the high flows were well estimated.
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