Solanum rostratum is a globally regulated invasive species, known for its detrimental impacts on local biodiversity, human and livestock health, and agricultural productivity. This study employed the Biomod2 ensemble modeling framework to analyze the geographic distribution of S. rostratum in China, identify key environmental factors limiting its spread, and provide a scientific basis for its management and control. By integrating species distribution data with multiple environmental variables, we predicted the potential geographic distribution of this species. Pearson correlation analysis and variance inflation factor (VIF) testing were applied to identify significant environmental variables constraining its spread, including precipitation seasonality (bio15), mean temperature of the wettest quarter (bio8), precipitation of the warmest quarter (bio18), isothermality (bio3), precipitation of the driest month (bio14), and human footprint. Three Biomod2-based ensemble models (EMmean, EMca and EMwmean) were based on the receiver operating characteristic curve (ROC), true skill statistic (TSS), and Kappa coefficient. Of these, EMca demonstrated the highest predictive accuracy. The model identified highly suitable habitats for S. rostratum primarily in semi-arid and semi-humid regions with high human activity, including the Northeast Plain, bounded by the Greater Khingan, Lesser Khingan, and Changbai Mountains; the northern North China Plain extending to the Shandong Hills and Yellow River basin; and the Junggar Basin extending to the Altai Mountains. These regions should be prioritized for future monitoring and control efforts. This study provides both empirical data and theoretical insights to accurately delineate potential invasion zones of S. rostratum, enhancing surveillance and guiding effective prevention and control strategies.
Zhang et al. (2026) studied this question.