2018/04/10 by Samuel D. N. Johnson, Johnson, Samuel D. N., Sean Cox +1
Environmental Science · #FOS: Biological sciences #Fish Ecology and Management Studies #Marine Bivalve and Aquaculture Studies #Marine and fisheries research #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.1804.03353
openalex publication_date 2018/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An emerging approach to data-limited fisheries stock assessment uses\nhierarchical multi-stock assessment models to group stocks together, sharing\ninformation from data-rich to data-poor stocks. In this paper, we simulate\ndata-rich and data-poor fishery and survey data scenarios for a complex of\ndover sole stocks. Simulated data for individual stocks were used to compare\nestimation performance for single-stock and hierarchical multi-stock versions\nof a Schaefer production model. The single-stock and best performing\nmulti-stock models were then used in stock assessments for the real dover sole\ndata. Multi-stock models often had lower estimation errors than single-stock\nmodels when assessment data had low statistical power. Relative errors for\nproductivity and relative biomass parameters were lower for multi-stock\nassessment model configurations. In addition, multi-stock models that estimated\nhierarchical priors for survey catchability performed the best under data-poor\nscenarios. We conclude that hierarchical multi-stock assessment models are\nuseful for data-limited stocks and could provide a more flexible alternative to\ndata-pooling and catch only methods; however, these models are subject to\nnon-linear side-effects of parameter shrinkage. Therefore, we recommend testing\nhierarchical multi-stock models in closed-loop simulations before application\nto real fishery management systems.\n