A new chapter of the Japanese beetle invasion saga: predicting suitability from long-infested areas to inform surveillance strategies in Europe

Abstract

AbstractThe Japanese beetle (Popillia japonica) is a polyphagous pest that spreads rapidly and is estimated to cost more than 460 M$/year in damage and control in the USA alone. This study provides risk maps to inform surveillance strategies in Continental Europe, following the beetle’s introduction and successive spread in the last decade. We developed a species distribution model using a machine-learning algorithm, considering factors relevant to the beetle’s biology, climate, land use and human-related variables. This analysis was performed using presence-only data from native and invaded ranges (Japan, North America, Azores archipelago - Portugal). We gathered more than 30 000 presence data from citizen science platforms and standardized surveys, and generated pseudo-absences using the target-group method. We used the environmental structure of data to randomly sample pseudo-absences, and evaluate model performanceviaa block cross-validation strategy. Our results show that climate, in particular seasonal trends, and human-related variables, are major drivers of the Japanese beetle distribution at the global scale. Risk maps show that Central Europe can be considered as suitable, whereas Southern and Northern European countries are at lower risk. The region currently occupied is among the most suitable according to our predictions, and represents less than 1% of the highest suitable area in Europe. A major cluster of high suitability areas is located near the currently infested zone, whereas others are scattered across the continent. This highlights the importance of designing surveillance strategies considering both active insect dispersal and the possibility of hitchhiking to reach distant areas.