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Sustainability
Volume 12
Issue 23
10.3390/su122310182
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Article

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

by
Shuai Chen
1,2,†,
Fangyu Ding
1,2,†,
Mengmeng Hao
1,2,* and
Dong Jiang
1,2,3,*
1
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land &Resources, Beijing 100101, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2020, 12(23), 10182; https://doi.org/10.3390/su122310182
Received: 19 October 2020 / Revised: 30 November 2020 / Accepted: 3 December 2020 / Published: 6 December 2020
(This article belongs to the Special Issue Biodiversity Conservation and Sustainability—Current Challenges and Opportunities)
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Abstract

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 × 5 km2. 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. View Full-Text
Keywords: S. invicta; red imported fire ant; potential distribution; boosted regression tree; human factors S. invicta; red imported fire ant; potential distribution; boosted regression tree; human factors
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MDPI and ACS Style

Chen, S.; Ding, F.; Hao, M.; Jiang, D. Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method. Sustainability 2020, 12, 10182. https://doi.org/10.3390/su122310182

AMA Style

Chen S, Ding F, Hao M, Jiang D. Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method. Sustainability. 2020; 12(23):10182. https://doi.org/10.3390/su122310182

Chicago/Turabian Style

Chen, Shuai, Fangyu Ding, Mengmeng Hao, and Dong Jiang. 2020. "Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method" Sustainability 12, no. 23: 10182. https://doi.org/10.3390/su122310182

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