Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling

Accurate assessment of pest potential distributions is needed to identify their establishment risks that play a key role in pest management in agricultural ecosystems. We used a correlative niche modelling method (Maxent) to predict and map the spatial distributions of two important rice stem borers, Chilo suppressalis and Sesamia cretica, in paddy fields of Iran. In total, 195 presence occurrence records (101 records for C. suppressalis and 94 records for S. cretica) were compiled. A set of environmental and topographic variables, with the highest effects on the species distributions and the lowest correlations among themselves, were used. The results showed that mainly the northern parts of Iran were the most suitable areas for C. suppressalis, and north, north‐east and south‐west of Iran as the most suitable areas for S. cretica. Both models performed well, with an area under the receiver operating characteristic curve (AUC) of 0.983 and 0.786 for C. suppressalis and S. cretica, respectively. The Maxent models showed higher accuracy for predicting the distribution of the specialist pest with the small range sizes compared to the generalist species. Assessing the importance of environmental variables, which were derived from the jackknife test, showed the precipitation as the variable with the highest contribution (66%) in explaining the spatial distribution of C. suppressalis compared to the other variables. The distribution of S. cretica was influenced by a set of variables derived from both the precipitation and temperature. The Maxent predictions were useful to map the geographical distributions of the risk for both rice stem borers that is needed to develop effective management strategies.