Hierarchical surplus production stock assessment models improve management performance in multi-species, spatially-replicated fisheries

Managers of multi-species fisheries aim to balance harvests of target and non-target species that vary in abundance, productivity, and degree of technical interactions. In this paper, we evaluated management performance of five surplus production stock assessment methods used in such a multi-species context. Production models included single-species and hierarchical multi-species models, as well as methods that pooled data across species and spatial strata. Operating models included technical interactions between species intended to produce choke effects often observed in output controlled multi-species fisheries. Average annual yield of each method under three data scenarios were compared to annual yield obtained by a simulated omniscient manager. Yield and conservation performance of hierarchical multi-species models was superior to all other methods under low, moderate, and high data quantity scenarios. Results were robust to a wide range of prior precision in assessment model biomass parameters, hierarchical prior precision for catchability and productivity, and future survey precision; however, results were sensitive to prior precision in assessment model productivity parameters under the low data scenario, where the hierarchical multi-species method had similar performance to the data pooling models and was no longer clearly the best option.

Automated summary

The study evaluated the management performance of five surplus production stock assessment methods used in multi-species fisheries. Results showed that hierarchical multi-species models performed better in terms of yield and conservation under low, moderate, and high data quantity scenarios, but were sensitive to prior precision in assessment model productivity parameters under low data scenario.