以江苏省松材线虫2007年75个定位发生数据和68个环境变量为主要信息源,采用分类与回归树模型(CART)、基于规则的遗传算法(GARP)、最大熵法(Maxent)和逻辑斯蒂回归(LR)4种生态位模型,建立松材线虫在江苏的潜在生境预测模型,在此基础上预测各县(区)松材线虫发生概率和发生面积。结果表明: CART模型的总体预测精度较高; 坡度、降水季节性变化(bio15)、复合地形指数(CTI)、最干燥季节平均温度(bio9)、南北坡向(northness)和最温暖月份最高温度(bio5)是影响松材线虫空间分布的6个主要环境因子; 预测江苏省松材线虫发生面积占江苏省松林面积的39.04%,是已发生面积的2.73倍;预测宜兴、溧阳和句容感染松材线虫的风险最大,宜兴、溧阳和南京市区松材线虫病发生面积最大。
Prediction of spatial distribution and occurrence area of alien forest pests and diseases is a prerequisite for making managing measures of biological invasions. In this paper data from 75 pine wilt disease occurrence points with geographic coordinates and 68 environmental variables were gathered in 2007. Four habitat modeling methods of Classification and Regression Trees(CART),Genetic Algorithm for Rule-set prediction(GARP),maximum entropy method (Maxent),and Logistic Regression(LR) were introduced to generate potential geographic distribution maps for invasions of pine wood nematode in Jiangsu province. Then the occurrence area was predicted in each county of Jiangsu province. Results showed that CART outperformed other three models. Slope, precipitation seasonality (bio15), compound topographic index (CTI), mean temperature of driest quarter (bio9), slope aspect, maximum temperature of warmest month (bio5) were the six forcing environmental factors. CART model predicted that future occurrence area of pine wilt disease would account for 39.04% of total pine forest, 2.73 times of present infected area of the pest. Yixing, Liyang and Jurong would be the most susceptible counties to the pest, while Yixing,Liyang and Nanjing urban would have the largest infected forest area.
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