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Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables
The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not y...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581929/ https://www.ncbi.nlm.nih.gov/pubmed/36261485 http://dx.doi.org/10.1038/s41598-022-21367-1 |
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author | Aidoo, Owusu Fordjour Ding, Fangyu Ma, Tian Jiang, Dong Wang, Di Hao, Mengmeng Tettey, Elizabeth Andoh-Mensah, Sebastian Ninsin, Kodwo Dadzie Borgemeister, Christian |
author_facet | Aidoo, Owusu Fordjour Ding, Fangyu Ma, Tian Jiang, Dong Wang, Di Hao, Mengmeng Tettey, Elizabeth Andoh-Mensah, Sebastian Ninsin, Kodwo Dadzie Borgemeister, Christian |
author_sort | Aidoo, Owusu Fordjour |
collection | PubMed |
description | The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not yet been well established. Using MaxEnt model, the potential distribution of the pests has been defined on a global scale. The results show that large areas of the globe, important for production of palms, are suitable for and potentially susceptible to these pests. The main determinants for O. monoceros distribution were; temperature annual range, followed by land cover, and precipitation seasonality. The major determinants for O. rhinoceros were; temperature annual range, followed by precipitation of wettest month, and elevation. The area under the curve values of 0.976 and 0.975, and True skill statistic values of 0.90 and 0.88, were obtained for O. monoceros and O. rhinoceros, respectively. The global simulated areas for O. rhinoceros (1279.00 × 10(4) km(2)) were more than that of O. monoceros (610.72 × 10(4) km(2)). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms. |
format | Online Article Text |
id | pubmed-9581929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95819292022-10-21 Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables Aidoo, Owusu Fordjour Ding, Fangyu Ma, Tian Jiang, Dong Wang, Di Hao, Mengmeng Tettey, Elizabeth Andoh-Mensah, Sebastian Ninsin, Kodwo Dadzie Borgemeister, Christian Sci Rep Article The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not yet been well established. Using MaxEnt model, the potential distribution of the pests has been defined on a global scale. The results show that large areas of the globe, important for production of palms, are suitable for and potentially susceptible to these pests. The main determinants for O. monoceros distribution were; temperature annual range, followed by land cover, and precipitation seasonality. The major determinants for O. rhinoceros were; temperature annual range, followed by precipitation of wettest month, and elevation. The area under the curve values of 0.976 and 0.975, and True skill statistic values of 0.90 and 0.88, were obtained for O. monoceros and O. rhinoceros, respectively. The global simulated areas for O. rhinoceros (1279.00 × 10(4) km(2)) were more than that of O. monoceros (610.72 × 10(4) km(2)). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms. Nature Publishing Group UK 2022-10-19 /pmc/articles/PMC9581929/ /pubmed/36261485 http://dx.doi.org/10.1038/s41598-022-21367-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Aidoo, Owusu Fordjour Ding, Fangyu Ma, Tian Jiang, Dong Wang, Di Hao, Mengmeng Tettey, Elizabeth Andoh-Mensah, Sebastian Ninsin, Kodwo Dadzie Borgemeister, Christian Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title | Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title_full | Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title_fullStr | Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title_full_unstemmed | Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title_short | Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
title_sort | determining the potential distribution of oryctes monoceros and oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581929/ https://www.ncbi.nlm.nih.gov/pubmed/36261485 http://dx.doi.org/10.1038/s41598-022-21367-1 |
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