Spatial Risk Assessment of Alien Plants in China Using Biodiversity Resistance Theory

Abstract

In the present study, the potential occurrence risk of invasive plants across different provinces of China is studied using disease risk mapping techniques (empirical Bayesian smoothing and Poisson-Gamma model). The biodiversity resistance theory which predicts that high-biodiversity areas will have reduced risk of species invasion serves as the base for performing spatial risk assessment of plant invasion across provinces. The results show that, both risk mapping methods identified that north-eastern part of China have the highest relative risk of plant invasion. In contrast, south-western and south-eastern parts of China, which have high woody plant richness, are predicted to possess low relative risks of plant invasion. Through spatial regression analysis (simultaneous autoregression model), nine environmental variables representing energy availability, water availability, seasonality, and habitat heterogeneity are used to explain the relative risk of plant invasion across provinces of China. The fitting results suggest that, PRECrange and TEMrange are the most two important covariates correlated with the occurrence risks of alien plants at provincial level in China. As indicated by Moran’s I index, spatial regression analysis can effectively eliminate the potential biases caused by spatial autocorrelation.