Multi-model assessments to characterize occurrences of emerald ash borer (Coleoptera: Buprestidae).

Introduction and spread of nonindigenous species present a formidable threat to forest health. The emerald ash borer (EAB), Agrilus planipennis, is an East Asian-origin insect that has devastated ash (Fraxinus spp.) trees across the United States and parts of Canada since 2002. Proactive surveillance using high-performing predictive models could aid in mitigating pest risk. Predictor variables and modeling methods are important considerations in such analysis. Therefore, we assessed whether relevant single predictors, a combination of predictors grouped under a certain driver category, or multiple key predictors comprising several drivers, alter the goodness-of-fit of logistic regression models to EAB occurrence data (2002 to 2018) from Canada. The predictors used in models included spatial, topographic/positional, transport pathways/human hotspots, host-related factors, and climate-related variables. Using predictors from the best candidate logistic regression model, we tested the performance of 7 different model types including an ensemble model. Our findings showed that predictors from a wide range of drivers better characterized EAB occurrences than single predictors or a combination of predictors from any given driver category. In multi-model comparisons, random forest outperformed all other models, including the ensemble model. Elevation, infestation pressure, accumulated degree-days (>10 °C), and human population density were important predictors of EAB presence. Random forest and ensemble model forecasts for the city of Edmonton, Alberta, Canada, indicated an area of potential concern for EAB. Our research strongly underscores the utility of comparative multi-model approaches in invasive risk assessments that could have important implications for pest surveillance and management.