Effects of Fishing Selectivity and Dynamics on the Performance of Catch-Based Data-Limited Assessment Models for Species with Different Life History Traits

Abstract

The assessment of fish stocks is often limited by a lack of comprehensive data. Therefore, catch-based methods are increasingly being used because of the availability of more catch data. However, catch-based models may perform differently for species with different traits and fishing histories. In this study, we investigated the performance of catch-based models for species with different life history traits, fishing histories, and under different length selections. We compared simulated biomass with estimated stock status from three widely used catch-based models (Catch-MSY model [CMSY]; catch-only model-sampling importance resampling model [COM-SIR]; state-space catch-only model [SSCOM]) under three fishing history scenarios (constant, increasing then decreasing, and continuously increasing fishing mortality) and three length selectivity scenarios (no selectivity, preferring smaller individuals, preferring larger individuals). Our results showed that CMSY performed the best, particularly when fishing mortality remained constant. Catch-based models performed better for opportunistic species that had larger individuals selected for fishing and equilibrium species that had smaller individuals selected. However, the models tended to overestimate stock status when fishing mortality continued to increase. Therefore, caution should be exercised when applying catch-based methods to data-poor stocks with diverse life history traits, fishing history, and those sensitive to selective fishing.

Keywords

• catch-based model
• selective fishing
• stock synthesis
• data-poor fisheries
• diverse life history traits