Machine learning reveals microclimate-specific drivers of a cosmopolitan supervector's population dynamics

Forecasting pest population dynamics under variable microclimates is essential for understanding and managing ecological interactions in agroecosystems. In this study, we employed machine learning to model the population dynamics of a cosmopolitan supervector across two contrasting production environments—open fields and high tunnels. Using data from 1686 weekly trap observations (standardized to 2254 modeling units) and 16 environmental predictors, we developed and compared Random Forest, Gradient Boosting Machine (GBM), and XGBoost models to identify key abiotic and biotic drivers of population fluctuations. Random Forest achieved the highest predictive accuracy in open fields (87.7%), while XGBoost performed best under high-tunnel conditions (84.9%). Parent (seed) population and temperature consistently emerged as dominant predictors, with humidity and wind showing secondary effects. Models trained in one microclimate failed to predict populations in the other (≤44% accuracy), revealing distinct ecological processes governing pest dynamics in adjacent systems. These results demonstrate that machine learning can disentangle nonlinear interactions among environmental variables and improve predictive understanding of vector population ecology. Our framework illustrates how ecological informatics can integrate environmental sensing, population monitoring, and data-driven modeling to forecast biologically meaningful patterns across heterogeneous agroecosystems. • Machine learning models accurately forecast Frankliniella occidentalis populations. • Random Forest outperformed GBM and XGBoost in open-field conditions. • XGBoost achieved the highest accuracy for high-tunnel population prediction. • Parent population and temperature were key predictors of thrips severity. • Field and high-tunnel systems function as distinct pest micro-ecosystems.