Data from: Predicting invasion success of cultivated naturalized plants in China

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 to 17.7 times—compared to 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. We also highlight the importance of
grouped variables in enhancing model interpretability. For practical
application in China, we recommend prioritizing surveillance of alien
plant species with large native ranges and high climatic suitability.
Implementing a tiered risk assessment system based on our random forest
model can allow for a more effective allocation of resources for
monitoring and managing invasive species. Ultimately, interdisciplinary
collaboration is crucial for implementing and applying these predictive
tools, thereby protecting biodiversity, ecosystem services, and economic
interests.