Prediction of Climate Change Impacts on the Suitable Habitat of Hyphantria cunea in China Based on Biomod2 Ensemble Models

Global climate warming has intensified in recent years, with extreme weather events occurring more frequently and severely impacting ecosystems and social production. According to the “China Climate Change Blue Book (2023),” China’s temperature rise rate exceeds the global average, with increasingly significant impacts on ecosystems. Hyphantria cunea, an invasive forest pest first discovered in China in 1979, has spread widely, causing serious damage to forestry and agriculture and posing a significant threat to China’s ecological security. To address this threat, this study employed seven modeling algorithms (GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt) from the R Biomod2 package to develop an ensemble model. The core research objective of this work is to quantify climate-driven range shifts of H. cunea under ongoing global climate change. Previous nationwide SDM studies on invasive forest pests have consistently demonstrated that climatic variables dominate broad-scale nationwide suitable habitat patterns at the macro-regional level. Supplementary topographic, vegetation cover, and human land-use disturbance layers were incorporated to capture fine-scale habitat filtering effects and long-distance pest dispersal facilitated by human activities, which together fully characterize the suitable regional environments of this pest. By integrating climate, topography, vegetation, and human disturbance data, we predicted the potential geographical distribution of H. cunea in China under four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The ensemble model achieved excellent performance with TSS and ROC values of 0.901 and 0.984, respectively. Currently, highly suitable areas for H. cunea are concentrated in 12 provinces, including Shandong, Jiangsu, Hebei, Henan, and Anhui, covering 56.33 × 104 km2, with Shandong showing the highest proportion (25.48%). The suitable habitat range is projected to expand northeastward, with significant increases under high emission scenarios (SSP5-8.5). Analysis of environmental variables reveals that nighttime light brightness, precipitation in the warmest season, the seasonal temperature variation coefficient, and average temperature in the driest season are key factors influencing H. cunea distribution. Nighttime light brightness shows the highest contribution (27.7%), indicating significant human impact on species spread. Response curves suggest that H. cunea favors warm, humid areas with pronounced seasonal changes. This study demonstrates that climate change will increase H. cunea expansion risk, necessitating strengthened cross-regional monitoring and biological control techniques. These findings provide a scientific foundation for understanding H. cunea spatiotemporal distribution patterns under future climate scenarios and for developing effective prevention and control strategies.