Potential distribution of <i>Bactericera cockerelli</i> , vector of potato purple top and zebra chip diseases in Colombia under current and future climates: A multi-scale spatial epidemiology approach

Abstract Background : Potato is a vital food security crop in Colombia that faces increasing threats from emerging pests, particularly potato purple top and zebra chip, both transmitted by Bactericera cockerelli . Despite the growing risks in this regard, epidemiological data in the Andean region remain limited, constraining proactive pest management. Methods : To assess potential risks in Colombia, we developed multi-scale species distribution models using MaxEnt and random forest at meso (&lt;2000 km) and global (&gt;10,000 km) scales, projecting the potential establishment of B. cockerelli under current conditions and for 2040 using the SSP2-4.5 and SSP5-8.5 climate scenarios. Presence records were compiled from ICA (Instituto Colombiano Agropecuario) official surveillance and free worldwide dataset GBIF (Global Biodiversity Information Facility), while background points were selected using stratified random sampling in environmental space. Results : Model performance was highest at the mesoscale (AUC = 0.997–0.999), surpassing global-scale predictions (AUC = 0.785–0.969), which highlights the importance of fine-scale environmental and spatial factors for accurate forecasting. Our results indicate that B. cockerelli establishment will largely remain restricted to the Eastern Cordillera, primarily due to the constraints of temperature limits. Pixel-level analyses revealed contrasting algorithmic outputs: MaxEnt suggested relative distributional stability, whereas random forest predicted potential expansions into new areas. Areas where both models agree identify high-confidence zones that warrant focused surveillance and management interventions, while regions of model disagreement flag emerging risk areas requiring proactive monitoring. Conclusions : These findings provide a spatially explicit, evidence-based framework for prioritizing surveillance, early detection, and integrated pest management in Colombia’s potato systems. By integrating climate-adapted predictive modeling with risk mapping, this study supports informed decision making to mitigate pest impacts, safeguard yields, and enhance the resilience of potato production under changing climatic conditions.