A Comparison of Three Data-Poor Stock Assessment Methods for the Pink Spiny Lobster Fishery in Mauritania

Several data-poor stock assessment methods have recently been proposed and applied to fisheries around the world. The Mauritanian pink spiny lobster fishery has a long history of boom bust dynamics, with large landings, collapse, years-long closures, all happening several times. In this study, we used catch, fishing efforts, length-frequency data (LFD) obtained from in its most recent period activity, 2015–2019, historical annual catch records starting 2006 fit three methods. These were length-based Bayesian (LBB) method, which uses LFD exclusively, Catch-only MSY (CMSY) using assumptions about resilience, generalised depletion models R package CatDyn combined Pella-Tomlinson biomass dynamics hierarchical inference framework. All presented as overfished. LBB method produced results that very pessimistic status but whose reliability was affected by non-constant recruitment. CMSY combination more comparable results, such similar sustainable harvest rates, both statistical uncertainty. particular demonstrated experiencing wide fluctuations abundance. spite uncertain estimates, clear understanding overfished need rebuilding program emerged management-useful guidance steer exploitation economically significant resource into sustainability.