Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models
<i>Salurnis marginella</i> causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests b...
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| author | Jae-Woo Song Jaho Seo Wang-Hee Lee |
| author_facet | Jae-Woo Song Jaho Seo Wang-Hee Lee |
| author_sort | Jae-Woo Song |
| collection | DOAJ |
| description | <i>Salurnis marginella</i> causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests becomes difficult to control after invasion, it is essential to establish measures to minimize losses through pre-emptive monitoring and identification of high-risk areas, which can be achieved through model-based predictions. The aim of this study was to evaluate the potential distribution of <i>S</i>. <i>marginella</i> by developing multiple species distribution modeling (SDM) algorithms. Specifically, we developed the CLIMEX model and three machine learning-based models (MaxEnt, random forest, and multi-layer perceptron), integrated them to conservatively assess pest occurrence under current and future climates, and overlaid the host distribution with climatically suitable areas of <i>S. marginella</i> to identify high-risk areas vulnerable to the spread and invasion of the pest. The developed model, demonstrating a true skill statistic >0.8, predicted the potential continuous distribution of the species across the southeastern United States, South America, and Central Africa. This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. Possible areas of spread were identified in Asia and the southwestern United States, considering the host distribution. This study provides data for the proactive monitoring of pests by identifying areas where <i>S. marginella</i> can spread. |
| format | Article |
| id | doaj-art-9d83be42d47c4eb1b42fd972d3e92b95 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-9d83be42d47c4eb1b42fd972d3e92b952025-08-20T02:48:06ZengMDPI AGAgriculture2077-04722025-01-0115329710.3390/agriculture15030297Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution ModelsJae-Woo Song0Jaho Seo1Wang-Hee Lee2Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea<i>Salurnis marginella</i> causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests becomes difficult to control after invasion, it is essential to establish measures to minimize losses through pre-emptive monitoring and identification of high-risk areas, which can be achieved through model-based predictions. The aim of this study was to evaluate the potential distribution of <i>S</i>. <i>marginella</i> by developing multiple species distribution modeling (SDM) algorithms. Specifically, we developed the CLIMEX model and three machine learning-based models (MaxEnt, random forest, and multi-layer perceptron), integrated them to conservatively assess pest occurrence under current and future climates, and overlaid the host distribution with climatically suitable areas of <i>S. marginella</i> to identify high-risk areas vulnerable to the spread and invasion of the pest. The developed model, demonstrating a true skill statistic >0.8, predicted the potential continuous distribution of the species across the southeastern United States, South America, and Central Africa. This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. Possible areas of spread were identified in Asia and the southwestern United States, considering the host distribution. This study provides data for the proactive monitoring of pests by identifying areas where <i>S. marginella</i> can spread.https://www.mdpi.com/2077-0472/15/3/297climate changeclimatic suitabilityensemble methodmultiple species distribution evaluation<i>Salurnis marginella</i> |
| spellingShingle | Jae-Woo Song Jaho Seo Wang-Hee Lee Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models Agriculture climate change climatic suitability ensemble method multiple species distribution evaluation <i>Salurnis marginella</i> |
| title | Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models |
| title_full | Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models |
| title_fullStr | Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models |
| title_full_unstemmed | Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models |
| title_short | Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models |
| title_sort | spatial evaluation of i salurnis marginella i occurrence according to climate change using multiple species distribution models |
| topic | climate change climatic suitability ensemble method multiple species distribution evaluation <i>Salurnis marginella</i> |
| url | https://www.mdpi.com/2077-0472/15/3/297 |
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