Using Machine Learning to Inform Harvest Control Rule Design in Complex Fishery Settings

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Observational analysis improved harvest control rules in Walleye fisheries, indicating optimal strategies for policy design.

Optimised harvest control rules significantly enhance performance metrics compared to standard feedback policies in fisheries management.
Key findings include the importance of monitoring mean fish weight alongside stock biomass for better policy decisions.
Assessment using reinforcement learning and Bayesian optimisation provided more effective strategies for complex fishery settings.
This research highlights the need for adaptive management in fisheries to address the complexities of ecological dynamics.

Montealegre‐Mora et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark