Prediction of potential geographic distributions is important for species protection and habitat restoration. Ulmus lamellosa is an endangered and endemic species in China for which conservation efforts are required. ...Prediction of potential geographic distributions is important for species protection and habitat restoration. Ulmus lamellosa is an endangered and endemic species in China for which conservation efforts are required. The maximum entropy （MaxEnt） model was used to predict the current and future geographic distribution （from 2030 to 2070） of U. lamellosa in China and discuss the reasons for changes in climatic suitability. The MaxEnt model provided a good fit to our data as confirmed by an AUC value of 0.948. The suitable areas for U. lamellosa were primarily projected in the northern part of China from 2030 to 2070, especially in Liaoning province. The variables ＂temperature seasonality＂, ＂precipitation of wettest month＂ and ＂precipitation of warmest quarter＂ were the most influential climatic variables in limiting the distribution of U. lamellosa. Our results clearly predict the future impacts of climate change on the geographic distribution of U. lamellosa and this can help prioritize design of localized conservation strategies in China.展开更多
基金supported by the Shanxi Natural Science Foundation(2015011069)the University Innovation program of Science and Technology of Shanxi Province(20161109)
文摘Prediction of potential geographic distributions is important for species protection and habitat restoration. Ulmus lamellosa is an endangered and endemic species in China for which conservation efforts are required. The maximum entropy （MaxEnt） model was used to predict the current and future geographic distribution （from 2030 to 2070） of U. lamellosa in China and discuss the reasons for changes in climatic suitability. The MaxEnt model provided a good fit to our data as confirmed by an AUC value of 0.948. The suitable areas for U. lamellosa were primarily projected in the northern part of China from 2030 to 2070, especially in Liaoning province. The variables ＂temperature seasonality＂, ＂precipitation of wettest month＂ and ＂precipitation of warmest quarter＂ were the most influential climatic variables in limiting the distribution of U. lamellosa. Our results clearly predict the future impacts of climate change on the geographic distribution of U. lamellosa and this can help prioritize design of localized conservation strategies in China.
