Predicting the Potential Distribution of <em>Galeruca daurica</em> in Inner Mongolia Under Current and Future Climate Scenarios Using the MaxEnt Model

Background: The Inner Mongolia Autonomous Region, a key ecological barrier in northern China, has recently experienced large-scale outbreaks of Galeruca daurica, a pest beetle, largely driven by climate change and grassland degradation. Assessing its potential geographic distribution under current and future climate scenarios is critical for ecological risk assessment and targeted pest management. Methods: We used the Maximum Entropy (MaxEnt) modeling approach to predict the potential distribution of G. daurica across Inner Mongolia. A total of 122 occurrence records, combined with climatic, topographic, and edaphic variables, were analyzed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and habitat suitability was categorized into four classes: unsuitable, low, moderate, and high. Future distributions were projected under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) for the 2050s and 2070s. Results: The model demonstrated high predictive accuracy (AUC &gt; 0.9). The most influential environmental predictors were precipitation of the wettest month (39.6%), annual precipitation (24.0%), and annual temperature range (8.2%). At present, suitable habitats cover approximately 44.9% of Inner Mongolia, mainly concentrated in arid and semi-arid zones. Under future climate scenarios, the extent of suitable habitat is projected to decline, with the most pronounced reduction occurring under SSP2-4.5 (a 23.56% decrease by the 2070s). A northward shift in the distribution centroid is also anticipated. Conclusions: The distribution of G. daurica is strongly regulated by climatic factors, particularly precipitation and temperature variability. Climate change is likely to contract its suitable range and drive a latitudinal shift. These findings provide a scientific basis for developing early warning systems, guiding pest management strategies, and informing ecological monitoring in climate-sensitive grassland ecosystems. Future studies should integrate biotic interactions and anthropogenic influences to improve model robustness.