Climate change and increasingly restrictive pesticide regulations have created a growing need for new tools to support the integrated pest management (IPM) of the olive fruit fly, Bactrocera oleae, in cultivated areas of the Mediterranean. In this study, the environmental suitability for this phytophagous insect in eastern Sicily was mapped by using geographic information system (GIS) tools and species distribution models (i.e., Random Forest and MaxEnt). The models were trained on presence data of the fly, obtained from a network of pheromone traps and locations where olive trees were present, combined with climatic, topographic and soil predictors for both current conditions and the future climate scenario (2021–2040). Correlation analysis was utilised to select ten predictors from an initial set of 33 soil and climate variables. Model performance was evaluated by using 10-fold cross-validation based on accuracy measures Area Under the Curve (AUC), True Skill Statistic (TSS), and the difference between the training and testing AUC) to minimise overfitting. Both algorithms demonstrated excellent predictive performance, producing convergent suitability maps, with high values concentrated in the foothills and hills of the Iblean–Calatino area and low values along the coastal plains and at higher altitudes, where extreme temperatures and unfavourable soil textures reduce habitat suitability. Response curves highlighted the combined influence of moderate temperature and precipitation seasonality, balanced topsoil texture, and moderate slopes in defining the species’ ecological niche. The proposed framework provides an operational basis for optimising monitoring networks and targeting IPM measures under current and near-future climate conditions.
Catalano et al. (Tue,) studied this question.