Spatial Heterogeneity-Based Explainable Machine Learning Methods—Modeling the Relationship Between Yellowfin Tuna Fishery Resources and the Environment in the Pacific Ocean

Yellowfin tuna (Thunnus albacares) constitutes a critical global fishery resource, and its distribution pattern is correlated to varying degrees with the marine environment. This study utilized longline fishing data from the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) spanning 2004 to 2020, categorized by quarter and combined with surface and 0–200 m depth environmental variables. Geographical random forests (GRF) were employed to examine spatially non-stationary relationships between yellowfin tuna resources and environmental factors. Additionally, by integrating GRF with GeoShapley explainable methods, we quantitatively evaluated the mechanistic impacts of environmental drivers on tuna distribution across spatial scales. The findings indicated that (1) the GRF model demonstrated superior performance throughout all four quarters, with the goodness of fit on the 20% test set (R2 = 0.72–0.85) consistently surpassing that of conventional random forest (RF) (R2 = 0.68–0.79) and extreme gradient boosting random forest (XGBRF) (R2 = 0.68–0.80). Moreover, in most cases, it had a lower RMSE and MAE, while effectively addressing spatial heterogeneity issues in yellowfin tuna fishery resources across most regions. (2) GeoShapley spatial explainable analysis revealed distinct environmental drivers, showing that the sea surface temperature and temperature at 105 m depth significantly influenced yellowfin tuna resources across all quarters, following a “high-value promotion, low-value inhibition” pattern, with salinity and dissolved oxygen at 105 m depth in Q2–Q3 and mixed-layer depth in Q3 also demonstrating notable effects. (3) Significant spatiotemporal heterogeneity was observed. The main spatial effects and temperature–depth–locality interactions remained significant throughout the year; mixed-layer depth–locality interactions were prominent in Q1, Q3, and Q4, dissolved oxygen–locality interactions in Q2 and Q4, and 105 m salinity–locality interactions exclusively in Q2. This study used geographical random forests (GRF) to integrate spatial statistics and machine learning to model the relationship between Pacific yellowfin tuna fishery resources and environmental factors. This approach demonstrates potential in improving spatial predictions of heterogeneous tuna resources and may help to identify key environmental drivers influencing their distribution. These findings provide essential insights for the formulation of science-based management strategies for Pacific yellowfin tuna fisheries.