Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae

Abstract

Ageratina adenophora, Eupatorium odoratum, and Mikania micrantha are three highly destructive invasive plants of Compositae in China. Through the screening of SDMs, random forest (RF), gradient boosting model (GBM), artificial neural network (ANN), and flexible discriminant analysis (FDA) with TSS greater than 0.8 are selected to construct a high-precision ensemble model (EM) as the prediction model. We use specimen sites and environmental variables containing climate, soil, terrain, and human activities to simulate and predict the invasion trend of three invasive weeds in China in current, the 2050s, and the 2070s. Results indicate that the highly invasive risk area of three exotic plants is mostly distributed along the river in the provinces south of 30° N. In the future scenario, the three exotic plants obviously invade northwards Yunnan, Sichuan, Guizhou, Jiangxi and Fujian. Climate is the most important variable that affects the spread of three kinds of alien plant invasions. Temperature and precipitation variables have a similar effect on A. adenophora and E. odoratum, while M. micrantha is more sensitive to temperature. It has been reported that Ipomoea batatas and Vitex negundo can prevent the invasion of three invasive plants. Hence, we also simulate the suitable planting areas for I. batatas and V. negundo. The results show that I. batatas and V. negundo are suitable to be planted in the areas where the three weeds show invasion tendency. In the paper, predicting invasion trends of exotic plants and simulating the planting suitability of crops that can block invasion, to provide a practical significance reference and suggestion for the management, prevention, and control of the invasion of exotic plants in China.

Keywords: Climate change; Ensemble model (EM); Future invasive trends; Invasive plants; Species distribution models (SDMs).