Predicting invasion success of naturalized cultivated plants in China

Abstract Plant invasions pose significant threats to native ecosystems, human health, and global economies. However, the complex and multidimensional nature of factors influencing plant invasions makes it challenging to predict and interpret their invasion success accurately. Using a robust machine learning algorithm, random forest, and an extensive suite of characteristics related to environmental niches, species traits, and propagule pressure, we developed a classification model to predict the invasion success of naturalized cultivated plants in China. Based on the final optimal model, we evaluated the relative importance of individual and grouped variables and their prediction performance. Our study identified key individual variables within each of three groupings: climatic suitability and native range size (environmental niches), phylogenetic distance to the closest native taxon and vegetative propagation mode (species traits), and the number of botanical gardens and provinces where species were cultivated (propagule pressure). Remarkably, when grouped variables were evaluated, the relative importance of grouped variables increased dramatically—by 13.5–17.7 times—compared with the cumulative importance of individual variables within a category. However, the relative importance of one category was primarily due to the number of variables within each category rather than its inherent characteristics. Synthesis and applications . Our findings emphasize the necessity of developing data‐driven predictive tools for effective invasion risk assessment using large datasets. By identifying key individual variables, we recommend prioritizing surveillance of alien plant species with large native ranges and high climatic suitability. By evaluating grouped variables, we emphasize the significance of grouped variables in enhancing model interpretability by providing deeper insights into the complex interactions among individual factors within each predefined category. This comprehensive approach can not only identify the most influential predictors of invasion success but also equip policymakers with evidence‐based strategies for surveillance, early detection, and targeted intervention of invasive plants.