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

TL;DR

This study evaluates how data quality impacts the effectiveness of hierarchical multi-stock assessment models in fisheries, demonstrating their advantages over single-stock models especially with limited data, through simulations and real data application.

Contribution

It introduces a comprehensive evaluation of hierarchical multi-stock models' performance with varying data quality, highlighting their potential benefits and limitations in data-limited fisheries assessment.

Findings

Multi-stock models often outperform single-stock models with low statistical power data.

Hierarchical priors for survey catchability improve model performance in data-poor scenarios.

Testing in simulations is recommended before applying to real fisheries management.

Abstract

An emerging approach to data-limited fisheries stock assessment uses hierarchical multi-stock assessment models to group stocks together, sharing information from data-rich to data-poor stocks. In this paper, we simulate data-rich and data-poor fishery and survey data scenarios for a complex of dover sole stocks. Simulated data for individual stocks were used to compare estimation performance for single-stock and hierarchical multi-stock versions of a Schaefer production model. The single-stock and best performing multi-stock models were then used in stock assessments for the real dover sole data. Multi-stock models often had lower estimation errors than single-stock models when assessment data had low statistical power. Relative errors for productivity and relative biomass parameters were lower for multi-stock assessment model configurations. In addition, multi-stock models that estimated hierarchical priors for survey catchability performed the best under data-poor scenarios. We conclude that hierarchical multi-stock assessment models are useful for data-limited stocks and could provide a more flexible alternative to data-pooling and catch only methods; however, these models are subject to non-linear side-effects of parameter shrinkage. Therefore, we recommend testing hierarchical multi-stock models in closed-loop simulations before application to real fishery management systems.