Comparing performance of catch-based and length-based stock assessment methods in data-limited fisheries

摘要

The quantity of data from many small-scale fisheries is insufficient to allow for the application of conventional assessment methods. Even though in many countries they are moving to closed-loop simulations to assess the performance of different management procedures in data-limited situations, managers in most developing countries are still demanding information on stock status. In this study we use the common metric of harvest rate to evaluate and compare the performance of the following catch-only and length-only assessment models: catch - maximum sustainable yield (Catch-MSY), depletion-based stock reduction analysis (DBSRA), simple Stock Synthesis (SSS), an extension of Catch-MSY (CMSY), length-based spawning potential ratio (LBSPR), length-based integrated mixed effects (LIME), and length-based Bayesian (LBB). In general, results were more biased for slightly depleted than for highly depleted stocks and for long-lived than for short-lived species. Length-based models, such as LIME, performed as well as catch-based methods in many scenarios, and among the catch-based models, the one with the best performance was SSS followed by CMSY.