Identifying spatiotemporal patterns of relative abundance and potential source environments in an invasive non‐native species using an INLA – SPDE model

Invasive non‐native species are major drivers of biodiversity loss and pose significant economic and public health concerns. To effectively implement strategies that prevent or mitigate their impacts, it is crucial to understand their spatial distribution patterns and source environments. In this study, we evaluated the effectiveness of the Stochastic Partial Differential Equation approach with the Integrated Nested Laplace Approximation (INLA–SPDE model) for assessing spatiotemporal distribution patterns of an invasive non‐native species. This approach enables efficient estimation of fine‐scale spatiotemporal variation over large spatial extents, addressing limitations of static species distribution models while avoiding the high computational cost of fully dynamic models. Using the invasive non‐native raccoon Procyon lotor on Hokkaido Island, Japan, as a case study, we (1) estimated relative abundance and (2) identified potential source environments. The model captured spatiotemporal variation in raccoon relative abundance and outperformed static models by accounting for spatial dependence. The predictive performance of the model improved with longer training data (up to 15 years) but decreased with increasing forecast horizon, indicating strong potential for informing spatial management while highlighting limitations of long‐term forecasting. The relative abundance was higher in resource‐rich, low‐disturbance habitats (e.g. forest edges, crop fields and areas with milder winter conditions), with heterogeneous responses to anthropogenic landscapes indicating context‐dependent effects of human‐modified habitats. Although capture‐based data may include spatial bias in trapping effort, consistent reporting and the model's spatiotemporal structure suggest that the overall patterns are robust, thereby supporting the identification of priority source areas for management. Synthesis and applications: High‐resolution spatiotemporal modelling revealed strong temporal persistence in raccoon density, suggesting that high‐density areas should be prioritized for future management. Targeted trapping and preventive measures on forest edges, open habitats, riverine habitats and adjacent crop fields may help reduce raccoon populations, biodiversity impacts and human–wildlife conflicts.