Regional analysis of spatiotemporal trends in Colorado potato beetle abundance

BACKGROUND Crop pests can significantly damage crops, cause economic loss, and reduce the sustainability of agroecosystems. Control of pests is challenging because of the development of pesticide resistance, increasing environmental variation, the continual emergence of invasive pests, and growing demand for sustainable production. New ecoinformatic approaches are needed to understand the drivers of pest population dynamics and improve pest management practices. Here we analyze spatiotemporal drivers of Colorado potato beetle (CPB, Leptinotarsa decemlineata) abundance across Wisconsin potato fields using multiyear scouting data (2014-2024) linked with climate and cropping histories. We develop statistical models that account for spatial and temporal correlations while testing the effect of predictor variables, and find spatiotemporal statistical methods substantially improve fit and prediction. RESULTS After accounting for spatiotemporal random effects, we find that three predictors affected CPB abundance: cumulative growing degree days (GDD), recent potato intensity in the surrounding landscape, and winter coldest-day temperature. Cumulative GDD and potato intensity are positively associated with abundance, and warmer winter minima (higher coldest-day temperatures) are likewise associated with higher abundance, consistent with improved overwintering survival. Using the best-performing model, we generate a preliminary, statewide risk surface for Wisconsin to support regional decision-making. CONCLUSION Our results highlight the value of integrating field-level history with landscape context and explicit spatial structure when forecasting pest pressures in agroecosystems.