Local knowledge corroborates threats of local extinctions in Kenya's exploited reef fishes
2025
Coral reef fisheries play a critical role in poverty alleviation, economic development, and as a primary protein source for over 1.5 million people in Kenya. However, widespread overfishing and use of destructive fishing methods are driving certain fish species to local extinctions. Assessing these extinctions in data-poor regions like Kenya is challenging and current species-at-risk assessments often overlook valuable local knowledge. This study addresses this gap by integrating anecdotal insights from fishers to validate local extinctions and enhance data reliability. Additionally, it examines fishers' perceptions of shifting baselines for 23 species previously identified to be at risk of local extinction. Our findings revealed that most fishermen perceived population decline and rarity in more than half of the species identified as threatened with local extinction by a framework developed in 2019, suggesting that the findings likely reflect genuine ecological patterns, rather than local attitudes about the general state of fisheries. Gear types use appeared to influence perceptions of species rarity and population declines, indicating that fishers using diverse gear types tend to have broader ecological knowledge of reef fish populations compared to those specializing in a single type of gear. However, fishing experience did not affect perceptions of species rarity and population decline, supporting the concept of homophily and highlighting the effective knowledge transmission from older to younger fishers along the Kenyan coastline. Overall, this study demonstrates that local ecological knowledge is a valuable tool for assessing fish species' threat status and offers complementary insights that can enhance fisheries management efforts.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par University of Pretoria
Découvrez la collection de ce fournisseur de données dans AGRIS