中国森林病虫2026，Vol.45Issue(2)：15-24,10.DOI:10.19688/j.cnki.issn1671-0886.20260008
基于MaxEnt模型的森林天幕毛虫适生区预测
Suitable region prediction of Malacosoma disstria based on MaxEnt model
摘要
Abstract
To predict the distribution pattern of Malacosoma disstria suitable region and evaluate the impact of future climate change on its suitable region range in China,global distribution data combined with current and future environmental factors under different climate scenarios were utilized.The parameters of the MaxEnt model were optimized using the Kuenm package in R software,followed by analysis of the optimized model outputs.The results indicated that the optimal model parameters were determined as regularization multiplier(Rm)of 0.5,with feature combinations(Fc)including linear(L),quadratic(Q),threshold(T),and hinge(H)features.Model evaluation metrics revealed that the area under the curve(AUC)of receiver operating characteristic curve(ROC)was 0.947 and the true skill statistic(TSS)was 0.834,indicating that the model predictions were highly reliable.The primary environmental factors influencing the suitable habitat were identified as precipitation in the driest month(bio14),precipitation in the wettest quarter(bio16),and maximum temperature in the warmest month(bio5).Under current climatic conditions,the pest's suitable regions in China were concentrated in the northeastern,central,eastern,southern,and southwestern regions.Moderately suitable region and high suitability region in provinces including Jilin,Liaoning,Henan,Shandong,Anhui,Jiangxi,Fujian,and Yunnan accounted for 35.06%of Chinese total forest coverage.Under future climate scenarios,the distribution range of M.disstria suitable region in China is expected to remain relatively stable.关键词
森林天幕毛虫/最大熵模型/适生区/有害生物风险分析/定殖风险Key words
Malacosoma disstria/MaxEnt model/suitable region/pest risk analysis/colonization risk分类
农业科技引用本文复制引用
何旭诺,颜素娟,林莉,李盼畔,武目涛,赵菊鹏..基于MaxEnt模型的森林天幕毛虫适生区预测[J].中国森林病虫,2026,45(2):15-24,10.基金项目
广州市重点研发计划项目"口岸危险性有害物快速鉴定及入侵机制研究"(2023B04J0154) （2023B04J0154）
广州海关科研计划项目"甲虫类'异虫'风险评估及重要的多虫态精准识别综合应用研究"(2023GZCK02) （2023GZCK02）
