基于MaxEnt模型预测苹果树腐烂病在中国的潜在地理分布

为了明确影响苹果树腐烂病发生的主要环境因子及其潜在地理区域,根据1960年至1990年30 a环境气候因子数据,利用19个生物气候因子首先建立MaxEnt模型,结果显示该模型的准确性良好,训练数据AUC值达0.884,影响MaxEnt模型最重要的影响因子是最暖季度平均温(Bio10),其对模型构建的贡献率为22.3%,其次是最冷季度降水量(Bio19)和最干月份降水量(Bio14)。随后,该模型结合以往苹果树腐烂病发生地点统计数据,对影响苹果腐烂病发生的环境因子和适生区进行预测。结果表明最冷月份最低温(Bio6)、最暖季度平均温(Bio10)和最冷季度降水量(Bio19)是影响苹果树腐烂病分布的主要环境因子。

Prediction for Potential Geographic Distribution of Valsa mali in China Based on MaxEnt Model

Apple Valsa Canker is a destructive branch disease caused by Valsa mali. To determine the main environmental variables and potential geographic influences of the disease,a model using 19 bioclimatic variables was built based on environmental climatic data from 1960 to 1990. The result revealed that the accuracy of MaxEnt model was very good when the AUC value of training data was 0.884. Mean temperature of warmest quarter(Bio10) contributed to the model by 22.3% ,followed by precipitation of coldest quarter(Bio19) and precipitation of driest month(Bio14). Subsequently,combine of the statistics of the occurrence of Valsa mali in the past,the model was able to predict the environmental factors and suitable areas affecting the occurrence of Valsa mali. The results showed that the lowest temperature in the coldest month(Bio6),the warmest quarterly average temperature(Bio10) and the coldest quarterly precipitation(Bio19) were the main environmental factors affecting the distribution of Valsa mali. This study provides theoretical references for fruit-growing farmers and policymakers.