基于最大熵模型(MaxEnt)预测北沙柳潜在地理分布

Potential Geographic Distribution of Salix psammophila based on MaxEnt

为确定北沙柳在我国的适生情况,本研究将北沙柳地理分布信息与6个气候因子基于最大熵模型(MaxEnt)对其潜在分布进行模拟预测。AUC值大于0.9,表明预测结果良好;影响北沙柳分布主导环境因子有年平均温度、温度季节变化、海拔、最湿月降水量、昼夜温差与年温差比值、最干月降水量、昼夜温差月均值;计算出北沙柳在我国的存在概率,DIVA-GIS自动划分为4个等级,分别为不适应生境(p<0.07)、低度适应生境(0.07≤p<0.24)、中度适应生境(0.24≤p<0.49)、高度适应生境(0.49≤p<0.95);当前时期到未来(2050年)时期4个情景(RCP 2.6、RCP 4.5、RCP 6.0、RCP 8.5)下的适生区面积均有所增加。模型预测结果显示,北沙柳适生区主要集中在鄂尔多斯市和榆林市交汇处、中卫市与阿拉善左旗交汇处。结合北沙柳的实际地理分布信息与基于MaxEnt模型预测的当前时期的潜在分布基本吻合,表明MaxEnt模型对北沙柳的潜在生境概率预测可靠,得出的结果对北沙柳的推广与引种具有一定参考价值。

In order to determine the suitability of Salix psammophila in China,this study has simulated and predicted its potential distribution based on the maximum entropy model(MaxEnt)with the geographic distribution information of S.psammophila and six climate factors.The value of AUC is bigger than 0.9,which shows a fine predicting result.The dominant environment factors affecting the distribution of S.psammophila are mean annual temperature,seasonal temperature variation,elevation,the wettest month precipitation,ratio of diurnal temperature difference and annual temperature difference,the driest month precipitation and monthly mean value of diurnal temperature difference.The existence probability of S.psammophila in China has been calculated,DIVA-GIS has divided into four grades automatically,they are inadaptive habitat(p<0.07),low adaptation habitat(0.07≤p<0.24),moderate adaptation habitat(0.24≤p<0.49)and high adaptation habitat(0.49≤p<0.95).The area of suitable region of four scenarios(RCP 2.6,RCP 4.5,RCP 6.0,RCP 8.5)has increased from the current period to the future period(2050).The prediction results of the model show that the suitable areas of S.psammophila are mainly concentrated at the intersection of Ordos and Yulin,and the intersection of Zhongwei and Alagxa Left Banner.Because the actual geographic distribution information of S.psammophila and the potential distribution of the current period predicted with the MaxEnt model are basically consistent,it is indicated that the MaxEnt model is reliable in predicting the potential habitat probability of S.psammophila.And the obtained results are useful for the promotion and introduction of S.psammophila.