Revealing Seasonal Environmental Associations and Spatial Heterogeneity of Pacific Yellowfin Tuna CPUE Using an Interpretable Neural Network Framework

Understanding the spatial distribution patterns of pelagic species such as yellowfin tuna (Thunnus albacares) is essential for ecosystem-based fisheries management. However, characterizing CPUE–environment relationships remain challenging because these relationships may be nonlinear and spatially heterogeneous across large oceanic regions. To address these challenges, we developed an interpretable spatial modeling framework, geographically neural network weighted regression integrated with GeoShapley analysis (GNNWR-GeoShapley), which combines the nonlinear learning capability of neural networks with spatially explicit characterization and interpretation of model relationships. Using Pacific longline fishery data and multi-source environmental variables from 2004 to 2023, we constructed quarterly models of CPUE–environment relationships and compared the performance of GNNWR with Generalized additive model (GAM), geographically weighted regression (GWR), graph neural network (GNN) models, and Geographical Random Forest (GRF). The results demonstrated that GNNWR showed the best overall performance across seasons, effectively capturing nonlinear relationships and spatial heterogeneity in yellowfin tuna nominal CPUE. GeoShapley analysis further revealed that sea surface and subsurface (150 m) temperature and salinity were among the most important environmental variables associated with nominal CPUE variations. Nonlinear response patterns indicated that SST values above approximately 25 °C and T150 values above approximately 19 °C were associated with positive model contributions, whereas higher salinity values (>35) exhibited negative contributions. Moreover, spatial effects represented by the geographical location variable (GEO) and their interactions with environmental variables revealed pronounced spatial heterogeneity, with the contribution patterns of environmental factors varying across seasons and regions. This study provides an interpretable spatial modeling framework for characterizing complex species–environment relationships and offers new insights into the spatial variability of Pacific yellowfin tuna nominal CPUE for fisheries oceanography and sustainable resource management.