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An emerging approach to data-limited fisheries stock assessment useshierarchical multi-stock assessment models to group stocks together, sharinginformation from data-rich to data-poor stocks. In this paper, we simulatedata-rich and data-poor fishery and survey data scenarios for a complex ofdover sole stocks. Simulated data for individual stocks were used to compareestimation performance for single-stock and hierarchical multi-stock versionsof a Schaefer production model. The single-stock and best performingmulti-stock models were then used in stock assessments for the real dover soledata. Multi-stock models often had lower estimation errors than single-stockmodels when assessment data had low statistical power. Relative errors forproductivity and relative biomass parameters were lower for multi-stockassessment model configurations. In addition, multi-stock models that estimatedhierarchical priors for survey catchability performed the best under data-poorscenarios. We conclude that hierarchical multi-stock assessment models areuseful for data-limited stocks and could provide a more flexible alternative todata-pooling and catch only methods; however, these models are subject tonon-linear side-effects of parameter shrinkage. Therefore, we recommend testinghierarchical multi-stock models in closed-loop simulations before applicationto real fishery management systems.

Evaluating the role of data quality when sharing information in hierarchical multi-stock assessment models, with an application to Dover Sole