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

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  • Shuai Chen

    (State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Fangyu Ding

    (State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Mengmeng Hao

    (State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Dong Jiang

    (State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land &Resources, Beijing 100101, China)

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 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.

Suggested Citation

  • Shuai Chen & Fangyu Ding & Mengmeng Hao & Dong Jiang, 2020. "Mapping the Potential Global Distribution of Red Imported Fire Ant ( Solenopsis invicta Buren) Based on a Machine Learning Method," Sustainability, MDPI, vol. 12(23), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10182-:d:457640
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    References listed on IDEAS

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    1. Rongfeng Yang & Yi Luo & Kun Yang & Liang Hong & Xiaolu Zhou, 2019. "Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017," Sustainability, MDPI, vol. 11(11), pages 1-15, May.
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