Data-limited stock assessment in Lake Victoria, Kenya fisheries

The Kenyan waters of Lake Victoria, spanning approximately 4,100 km², sustain one of the country's most economically critical inland fisheries, supporting an estimated 200,000 fishers and fish traders. Despite its importance, rigorous and regular stock assessment of key commercial species, including Nile perch (Lates niloticus), Nile tilapia (Oreochromis niloticus), and dagaa (Rastrineobola argentea), remains constrained by chronic data deficiencies, underfunded monitoring systems, and an over-reliance on frame survey data that fails to capture real-time population dynamics.

This study evaluates the performance and suitability of data-limited stock assessment approaches, specifically Length-Based Spawning Potential Ratio (LBSPR) and Catch-MSY (CMSY), within Kenya's fisheries monitoring context, using secondary length-frequency and effort data from the Kenya Marine and Fisheries Research Institute (KMFRI) and Beach Management Unit (BMU) landing site records. We assess the reliability of these methods against known biological reference points and examine how variability in sampling effort across landing beaches affects output precision. Results indicate that LBSPR yields robust spawning potential ratio estimates for Nile tilapia even under conditions of moderate sampling irregularity, while CMSY performance is sensitive to the quality of historical catch reconstructions, a known limitation in this data environment. A spatially stratified, BMU-anchored monitoring design is proposed to improve input data quality without requiring costly fisheries-independent surveys.

These findings offer actionable guidance for Kenya's State Department of Fisheries and KMFRI in aligning national stock assessment practice with the Lake Victoria Fisheries Organization (LVFO) regional framework, while informing adaptive management strategies under fiscal and logistical common to inland fisheries in Sub-Saharan Africa.