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Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method

文献类型：期刊论文

作者Chen, Shuai2,3; Ding, Fangyu2,3; Hao, Mengmeng2,3; Jiang, Dong1,2,3
刊名SUSTAINABILITY
出版日期2020-12-01
卷号12期号:23页码:13
关键词S invicta red imported fire ant potential distribution boosted regression tree human factors
DOI10.3390/su122310182
通讯作者Hao, Mengmeng(haomm@igsnrr.ac.cn) ; Jiang, Dong(jiangd@igsnrr.ac.cn)
英文摘要As one of the most notorious invasive species, the red imported fire ant (Solenopsis invicta Buren) has many adverse impacts on biodiversity, environment, agriculture, and human health. Mapping the potential global distribution of S. invicta becomes increasingly important for the prevention and control of its invasion. By combining the most comprehensive occurrence records with an advanced machine learning method and a variety of geographical, climatic, and human factors, we have produced the potential global distribution maps of S. invicta at a spatial resolution of 5 x 5 km(2). The results revealed that the potential distribution areas of S. invicta were primarily concentrated in southeastern North America, large parts of South America, East and Southeast Asia, and Central Africa. The deforested areas in Central Africa and the Indo-China Peninsula were particularly at risk from S. invicta invasion. In addition, this study found that human factors such as nighttime light and urban accessibility made considerable contributions to the boosted regression tree (BRT) model. The results provided valuable insights into the formulation of quarantine policies and prevention measures.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010203]
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000597620100001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/137115]  
专题中国科学院地理科学与资源研究所
通讯作者Hao, Mengmeng; Jiang, Dong
作者单位1.Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Shuai,Ding, Fangyu,Hao, Mengmeng,et al. Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method[J]. SUSTAINABILITY,2020,12(23):13.
APA Chen, Shuai,Ding, Fangyu,Hao, Mengmeng,&Jiang, Dong.(2020).Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method.SUSTAINABILITY,12(23),13.
MLA Chen, Shuai,et al."Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method".SUSTAINABILITY 12.23(2020):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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