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Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method
Jiang, Dong1,2,3; Chen, Shuai1,2; Hao, Mengmeng1,2; Fu, Jingying1,2; Ding, Fangyu1,2
2018-08-30
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume8Pages:8
Corresponding AuthorHao, Mengmeng(haomm.16b@igsnrr.ac.cn)
AbstractThe spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losses caused by codling moths are immeasurable. It is essential to understand the potential distribution of codling moths to reduce the risks of codling moth establishment. In this study, we adopted the Maxent (Maximum Entropy Model), a machine learning method to predict the potential global distribution of codling moths with global accessibility data, apple yield data, elevation data and 19 bioclimatic variables, considering the ecological characteristics and the spread channels that cover the processes from growth and survival to the dispersion of the codling moth. The results show that the areas that are suitable for codling moth are mainly distributed in Europe, Asia and North America, and these results strongly conformed with the currently known occurrence regions. In addition, global accessibility, mean temperature of the coldest quarter, precipitation of the driest month, annual mean temperature and apple yield were the most important environmental predictors associated with the global distribution of codling moths.
DOI10.1038/s41598-018-31478-3
WOS KeywordSPECIES DISTRIBUTIONS ; MAXENT ; FUTURE ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFC1201300]
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000443160800033
PublisherNATURE PUBLISHING GROUP
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54371
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHao, Mengmeng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China
3.Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Dong,Chen, Shuai,Hao, Mengmeng,et al. Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method[J]. SCIENTIFIC REPORTS,2018,8:8.
APA Jiang, Dong,Chen, Shuai,Hao, Mengmeng,Fu, Jingying,&Ding, Fangyu.(2018).Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method.SCIENTIFIC REPORTS,8,8.
MLA Jiang, Dong,et al."Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method".SCIENTIFIC REPORTS 8(2018):8.
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