Improving data-limited stock assessment with sporadic stock index information in stock reduction analysis

Abstract

As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA priors (a deterministic prior set at 40% of the unfished biomass and a catch-based prior) with estimates from the likelihood-based assessments of 91 fish stocks from the RAM Legacy database. We extended our analysis by evaluating performance based on life history attributes and two depletion levels with BT/BMSY equaling 1 as the breakpoint. Results suggest index-based priors enhance accuracy for fish stocks at both depletion levels. We demonstrate that performance of SRA can be affected by three factors: the reliability of priors for BT/B1, recent depletion level, and life history.