CART Rule-Guided MaxEnt Model Construction and Its Application in Fishing Ground Prediction of Chub Mackerel in the Northwestern Pacific Ocean - Zuli Wu; Fenghua Tang; Yumei Wu; Shengmao Zhang; Fei Wang; Xuesen Cui (Fishes, 2026)
Chub mackerel (Scomber japonicus) is a commercially important pelagic species in the northwest Pacific Ocean. Accurate identification of its fishing grounds can provide a more robust and targeted scientific basis for fishery management and ecological research. Based on fishing effort and five environmental factors (i.e., sea surface temperature [SST], chlorophyll-a concentration [CHL], SST gradient [GSST], sea surface height [SSH], and current speed), this study developed a Classification and Regression Tree (CART) rule-guided MaxEnt model. Specifically, rules generated by the CART model were first extracted and then incorporated as constrained feature functions into MaxEnt for model training. To select the optimal model scheme, four combinations of rule compositions and feature function outputs were designed, and model performance on the validation dataset was evaluated using ROC curves. Finally, the model was further verified with in situ environmental and fisheries data from April to November 2024. Results showed that the predicted fishing grounds were highly aligned with the actual monthly fishing grounds in 2024, and the predicted migration routes matched the movement trajectory of fishing vessels. The model also exhibited satisfactory performance, achieving an average AUC of 0.722 ± 0.033, a sensitivity of 0.604, a specificity of 0.834, and a negative predictive value (NPV) of 0.978. In conclusion, the CART rule-guided MaxEnt model, integrating the interpretability of CART and the predictive power of MaxEnt, effectively predicts the spatial distribution of chub mackerel fishing grounds in the northwest Pacific Ocean, providing technical support for fishery management and ecological research.