Integrating remote‐sensing and meteorological data to identify key drivers of the potential distribution of a grassland grasshopper

Abstract A detailed understanding of the mechanisms linking environmental factors to insect outbreaks is crucial for advancing fundamental ecological theory. Combining these key factors to establish reliable predictive models is essential for accurately identifying potential insect distribution and addressing broader ecological dynamics. In this study, we identified and analysed key environmental factors and the thresholds that influence the distribution of Oedaleus decorus asiaticus (O. decorus). An ensemble model (EM) incorporating these key environmental factors was then constructed to delineate the potential suitable areas for this species. The results indicate that: (i) EM offers significant advantages for monitoring suitable areas for O. decorus. The accuracy of the model was assessed using area under the curve (AUC) and true skill statistics (TSS), which yielded values of 0.973 and 0.833, respectively. (ii) Climate is the determining factor directly influencing the distribution of O. decorus, particularly because temperature often affects the entire life cycle of this taxon. The total precipitation in August is crucial in determining their distribution. Other topographical features, apart from elevation, exert minor influence on their distribution. Vegetation biomass during the oviposition and incubation periods influences the distribution of this species. The distribution of their suitable areas is strongly influenced by vegetation type but shows little correlation with soil type. (iii) The suitable areas of this species in 2022 and 2023 were identified. The most suitable areas of O. decorus are distributed in the central, southeastern and northeastern regions of Inner Mongolia, which overlapped with the zones of agro‐pastoralism. In conclusion, this study establishes EM framework as a robust and transferable method for predicting insect distributions. By synthesizing multiple key environmental factors, this framework can effectively identify areas of potential occurrence. The study offers a generalizable predictive paradigm for enhancing the monitoring and forecasting of insect populations within dynamic ecosystems.