A Policy–Machine Learning Hybrid Approach to Evaluate Trap Mesh Selectivity: A Case Study on Pseudopleuronectes yokohamae

A machine learning-based policy–utility framework was developed to assess trap mesh sizes (35–80 mm) in the Marbled Flounder fishery and reframe traditional selectivity analysis into a policy-oriented decision context. A utility function integrating catch per unit effort (CPUE), the immature proportion, and the bycatch ratio was constructed from experimental data collected in 2015–2016 and assessed under multiple policy weighting scenarios. Gradient boosting models trained on the 2016 data and validated with the 2015 data demonstrated strong predictive accuracy. The empirically optimized weighting set (α* = 0.79, β* = 2.36, and γ* = 0.79) produced high agreement between predicted and observed utilities (root mean square error ≈ 0.22; r = 0.901). Variable importance analysis identified the immature proportion as the main driver of utility variation; bycatch ratio and CPUE made smaller contributions. Scenario-based simulations showed a shift in the optimal mesh size, from 65 mm in 2015 to 80 mm in 2016, that reflects interannual changes to population size structure and bycatch composition. Policy regret analysis (comparing 65 mm to 80 mm) indicated consistently low regret (ΔU ≈ 0.12–0.15) and relative regret (<80%) values. This integrated utility–regret framework provides a dynamic, policy-relevant tool for linking trap selectivity information to management objectives.