A Comparison of Three Data-Poor Stock Assessment Methods for the Pink Spiny Lobster Fishery in Mauritania - Meissa, Beyah; Dia, Mamadou; Baye, Braham C.; Bouzouma, Moustapha; Beibou, Ely; Roa-Ureta, Rubén H. (Frontiers in Marine Science, 2021)
Several data-poor stock assessment methods have recently been proposed and applied to data-poor fisheries around the world. The Mauritanian pink spiny lobster fishery has a long history of boom and bust dynamics, with large landings, stock collapse, and years-long fishery closures, all happening several times. In this work we used catch, fishing effort, and length frequency data (LFD) obtained from the fishery in its most recent period of activity, 2015 to 2019, plus historical annual catch records starting in 2006, to fit three data-poor stock assessment methods. These were the length-based Bayesian method (LBB), which uses LFD exclusively, the CMSY method, using annual catch data and assumptions about stock resilience, and generalized depletion models in the R package CatDyn combined with Pella-Tomlinson biomass dynamics in a hierarchical inference framework. All three methods presented the stock as overfished. The LBB method produced results that were very pessimistic about stock status but whose reliability was affected by non-constant recruitment. The CMSY method and the hierarchical combination of depletion and Pella-Tomlinson biomass dynamics produced more comparable results, including similar sustainable harvest rates, but both were affected by large statistical uncertainty. Pella-Tomlinson dynamics in particular demosntrated a stock experiencing wide fluctuations in abundance. In spite of uncertain estimates, a clear understanding of the status of the stock as overfished and in need of a biomass rebuilding program emerged as a management-useful guidance to steer exploitation of this economically significant resource into sustainability.