Bayesian Hierarchical Modelling of Artisanal Fisheries Data for Stock Assessment in Data-Limited West African Settings

Data-limited stock assessment is a persistent challenge for fisheries management in West Africa, where most species are harvested by dispersed artisanal fleets with inconsistent monitoring coverage. This research develops and validates a Bayesian hierarchical modelling framework for artisanal fisheries stock assessment that explicitly incorporates data uncertainty, spatial heterogeneity, and informative prior distributions derived from life history meta-analyses. The framework is demonstrated through application to artisanal catch and effort data for six data-limited species in the Bight of Benin coastal fisheries of Edo, Delta, and Ondo States. The hierarchical model operates in three levels: individual fisher catch per unit effort, community-level fishing pressure, and regional stock biomass trajectory. Prior distributions for growth and natural mortality parameters were constructed from FishBase and GloNAF life history databases using phylogenetic hierarchical shrinkage. Markov Chain Monte Carlo sampling was implemented in Stan. Model validation was conducted through retrospective analysis and simulation testing. The Bayesian hierarchical framework reduced relative bias in biomass estimates by 38 percent compared to classical surplus production models when applied to simulated sparse data scenarios. Applied to real artisanal catch data, the model produced well-constrained posterior distributions for exploitation status of all six target species, with four species classified as over-exploited with greater than 0.9 posterior probability. The framework explicitly propagated monitoring uncertainty into management advice, demonstrating that naive point estimates systematically underestimate overfishing probability. This research makes a significant theoretical contribution by establishing the hierarchical prior structure required for principled data-limited stock assessment in tropical artisanal fisheries. Keywords: Bayesian hierarchical models, data-limited stock assessment, artisanal fisheries, Bight of Benin, MCMC.