Cardinale, Massimiliano
- Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences
Abstract
Ensuring the sustainability of fisheries worldwide requires that scientific advice remain effective even when data and capacity are limited. To address these challenges, we propose a hierarchical assessment framework (HAF) capable of integrating auxiliary information, such as empirical indicators for fishing pressure, within a Bayesian state-space biomass dynamic modelling framework. The aim is to provide risk-equivalent advice to ensure that management does not penalise data-limited fisheries with undue precaution (and loss of potential yield), nor expose them to a higher risk of overexploitation. To achieve this, we evaluated performance using classification skill metrics, such as true skill, for stock status relative to maximum sustainable yield (MSY)-based reference points. Results demonstrate that incorporating auxiliary data, particularly fishing mortality indices from periods of high exploitation, substantially improves the accuracy of stock status classification. Adoption of hierarchical assessment frameworks will support targeted data collection and evidence-based, adaptive fisheries management.
Keywords
Bayesian stock assessment; biomass-based; calibration; classification; length-based indicators; prediction skill; validation
Published in
Sustainability
2025, volume: 17, number: 21, article number: 9383
SLU Authors
Winker, Henning
- Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences
- Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences
UKÄ Subject classification
Fish and Wildlife Management
Publication identifier
- DOI: https://doi.org/10.3390/su17219383
Permanent link to this page (URI)
https://res.slu.se/id/publ/144400