Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables
Abstract 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 h...
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| Format: | Article |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21367-1 |
| _version_ | 1828140530646646784 |
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| author | Owusu Fordjour Aidoo Fangyu Ding Tian Ma Dong Jiang Di Wang Mengmeng Hao Elizabeth Tettey Sebastian Andoh-Mensah Kodwo Dadzie Ninsin Christian Borgemeister |
| author_facet | Owusu Fordjour Aidoo Fangyu Ding Tian Ma Dong Jiang Di Wang Mengmeng Hao Elizabeth Tettey Sebastian Andoh-Mensah Kodwo Dadzie Ninsin Christian Borgemeister |
| author_sort | Owusu Fordjour Aidoo |
| collection | DOAJ |
| description | Abstract 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 × 104 km2) were more than that of O. monoceros (610.72 × 104 km2). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms. |
| first_indexed | 2024-04-11T19:11:04Z |
| format | Article |
| id | doaj.art-b170ed9b511a47f993585d622887bfc2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T19:11:04Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b170ed9b511a47f993585d622887bfc22022-12-22T04:07:37ZengNature PortfolioScientific Reports2045-23222022-10-0112111310.1038/s41598-022-21367-1Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variablesOwusu Fordjour Aidoo0Fangyu Ding1Tian Ma2Dong Jiang3Di Wang4Mengmeng Hao5Elizabeth Tettey6Sebastian Andoh-Mensah7Kodwo Dadzie Ninsin8Christian Borgemeister9Department of Biological, Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable DevelopmentState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesCouncil for Scientific and Industrial Research (CSIR), Oil Palm Research Institute, Coconut Research ProgrammeCouncil for Scientific and Industrial Research (CSIR), Oil Palm Research Institute, Coconut Research ProgrammeDepartment of Biological, Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable DevelopmentCentre for Development Research (ZEF), University of BonnAbstract 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 × 104 km2) were more than that of O. monoceros (610.72 × 104 km2). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms.https://doi.org/10.1038/s41598-022-21367-1 |
| spellingShingle | Owusu Fordjour Aidoo Fangyu Ding Tian Ma Dong Jiang Di Wang Mengmeng Hao Elizabeth Tettey Sebastian Andoh-Mensah Kodwo Dadzie Ninsin Christian Borgemeister Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-21367-1 |
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