Model performance
Ensemble models were widely adopted in many researches related to species distribution(Hao, Elith, Lahoz-Monfort, & Guillera-Arroita, 2020). Previous studies showed that uncertainty of model result would be brought out by many factors such as species distribution models, dispersal strategies, environmental factors and so on(W. Thuiller, Guéguen, Renaud, Karger, & Zimmermann, 2019). Although there were lack of strong quantitative conclusions about the predictive performance of ensemble models(Hao, Elith, Guillera-Arroita, & Lahoz-Monfort, 2019). There was a popular belief that ensemble model would reduce the uncertainty due to sole algorithm, and it would perform better than sole one(Araújo et al., 2019; Araújo & New, 2007). In our study, except SRE, the AUC values of all the other nine models were greater than 0.8, and the TSS values were greater than 0.6. The values of ensemble model were 0.971 and 0.835 respectively. Based on this, an obvious result could easily draw out that ensemble model outperformed any sole algorithm, which was consistent with the conclusion ahead. This result also showed that the simulation was with high accuracy and could be used to predict the current and future potential distributions of A. annua .