Regional analysis of spatiotemporal trends in Colorado potato beetle abundance

Abstract Crop pests can significantly damage crops, cause economic loss, and reduce the sustainability of agroecosystems. 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 multi-year scouting data (2014–2024) linked with climate and cropping histories. We develop statistical models that account for spatial and temporal correlations, and find these approaches substantially improve fit and prediction. After accounting for spatiotemporal random effects, we find that three predictors increased 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 in order to support regional decision-making. Our results highlight the value of integrating field-level history with landscape context and explicit spatial structure when forecasting pest pressures in agroecosystems. Competing Interest Statement The authors have declared no competing interest. Footnotes Open Research Statement: This submission uses novel code, which is provided, per our requirements, in an external repository to be evaluated during the peer review process. All data and scripts are available on GitHub at https://github.com/drnursultan/Spatiotemporal_CPB_Modeling