基于生态位模型的薇甘菊在中国适生区的预测

    Predicting potential geographic distribution of mikania micrantha planting based on ecological niche models in China

    • 摘要: 薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘菊在中国的适生区进行预测,并运用受试者工作曲线(receiver operating characteristic, ROC)分析方法对2种模型的预测结果进行分析,选出最优模型进行预测,同时对环境变量进行刀切法分析,判断环境变量对薇甘菊分布的影响。结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.971,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于薇甘菊的适生区预测;对环境变量进行刀切法表明,海拔和季节性降水量方差对薇甘菊的分布影响最小,年温变化范围、年降水量、最湿月份降水量、最湿季度降水量、温度变化方差这5个环境变量对薇甘菊适生区预测影响最大;预测结果显示薇甘菊在中国大陆的适生区主要集中在海南、广东、广西、香港、澳门、云南、福建、西藏、贵州等省,其中西藏东南部和西南部、贵州西南部、福建中南部等地区应该加强监测及预警。

       

      Abstract: Mikania micrantha is a pernicious invasive weed in agriculture and forestry. In this paper, the potential geographic distribution of Mikania micrantha in China was predicted by genetic algorithm for rule-set production(GARP) and maximum entropy (MaxEnt) models, and determined better model by receiver operating characteristic curve. Meanwhile, environmental variables were analyzed with jackknife method to judge the influence on Mikania micrantha. The results showed that the areas under ROC curves with GARP model and MaxEnt model were 0.910 and 0.971 respectively. It meant that MaxEnt model was better than GARP model in predicting the potential geographic distribution of Mikania micrantha. The results from jackknife method indicated that attitude and the variance of seasonality precipitation had little influence on the distribution of the invasive weed, while the range of annual temperature,annual precipitation, precipitation in the wettest month, precipitation in the wettest quarter and the variance of temperature range had greater influence on the distribution of the weed. According the prediction of MaxEnt, the potential geographic distribution areas of Mikania micrantha were in Hainan, Guangdong, Guangxi, Hong Kong, Macao, Yunnan, Fujian, Tibet and Guizhou. Southeast and southwest of Tibet, southwest of Guizhou and south of Fujian were areas which should be strengthened in monitoring and early warning work.

       

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