Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change
Biological invasions pose significant threats to ecosystem structure and function, disrupt ecosystem services, cause high economic losses, and negatively impact human well-being. However, accurate prediction of species distribution is a critical challenge in ecological and biodiversity conservation....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Global Ecology and Conservation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2351989425002586 |
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| author | Hua Cheng Kasper Johansen Baocheng Jin Shiqin Xu Xuechun Zhao Liqin Han Matthew F. McCabe |
| author_facet | Hua Cheng Kasper Johansen Baocheng Jin Shiqin Xu Xuechun Zhao Liqin Han Matthew F. McCabe |
| author_sort | Hua Cheng |
| collection | DOAJ |
| description | Biological invasions pose significant threats to ecosystem structure and function, disrupt ecosystem services, cause high economic losses, and negatively impact human well-being. However, accurate prediction of species distribution is a critical challenge in ecological and biodiversity conservation. This study compares the predictive performance of 10 machine learning algorithms, including random forests, maximum entropy, support vector machines, and others, by integrating global occurrence records with climatic, edaphic, and human activity variables to identify the most robust model for predicting the global distribution of the invasive weed, Conyza sumatrensis (Retz.) E.Walker. Different algorithms yielded large variations in the predicted area of C. sumatrensis. Among these, random forests had the highest performance accuracy metrics and high agreement of predictions, aligning well with global occurrence records, and are used to explain and predict the potential distribution of C. sumatrensis. Distributions of C. sumatrensis are mainly influenced by temperature variables, adapted to a wide range of precipitation and various soil conditions, and facilitated by human activities. Currently, C. sumatrensis is distributed widely across all continents (6.20 Mkm2). The suitable habitat for C. sumatrensis is projected to have an increase of 8.03–8.78 % by 2041–2060 and 0.84–3.29 % by 2081–2100. In addition, the global extent of suitable environmental conditions for the establishment and spread of C. sumatrensis was anticipated to expand in urban and farmland by 2081–2100. The results provide an early warning of specific land-use types at higher risk of C. sumatrensis extent, offering valuable insights for managers to develop targeted prevention and control strategies. Additionally, to enhance predictive accuracy, our study underscores the critical role of selecting suitable algorithms and integrating human activity factors into invasive species distribution models. |
| format | Article |
| id | doaj-art-10123bcd039e4bc8aec6e4449c76a9a1 |
| institution | Matheson Library |
| issn | 2351-9894 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Ecology and Conservation |
| spelling | doaj-art-10123bcd039e4bc8aec6e4449c76a9a12025-06-27T05:51:51ZengElsevierGlobal Ecology and Conservation2351-98942025-09-0161e03657Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate changeHua Cheng0Kasper Johansen1Baocheng Jin2Shiqin Xu3Xuechun Zhao4Liqin Han5Matthew F. McCabe6Climate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; School of Tourism, Henan Normal University, Xinxiang 453007, China; Corresponding author at: Climate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.Climate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaCollege of Animal Science, Guizhou University, Guiyang 550025, ChinaClimate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaCollege of Animal Science, Guizhou University, Guiyang 550025, ChinaSchool of Tourism, Henan Normal University, Xinxiang 453007, ChinaClimate and Livability Initiative, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaBiological invasions pose significant threats to ecosystem structure and function, disrupt ecosystem services, cause high economic losses, and negatively impact human well-being. However, accurate prediction of species distribution is a critical challenge in ecological and biodiversity conservation. This study compares the predictive performance of 10 machine learning algorithms, including random forests, maximum entropy, support vector machines, and others, by integrating global occurrence records with climatic, edaphic, and human activity variables to identify the most robust model for predicting the global distribution of the invasive weed, Conyza sumatrensis (Retz.) E.Walker. Different algorithms yielded large variations in the predicted area of C. sumatrensis. Among these, random forests had the highest performance accuracy metrics and high agreement of predictions, aligning well with global occurrence records, and are used to explain and predict the potential distribution of C. sumatrensis. Distributions of C. sumatrensis are mainly influenced by temperature variables, adapted to a wide range of precipitation and various soil conditions, and facilitated by human activities. Currently, C. sumatrensis is distributed widely across all continents (6.20 Mkm2). The suitable habitat for C. sumatrensis is projected to have an increase of 8.03–8.78 % by 2041–2060 and 0.84–3.29 % by 2081–2100. In addition, the global extent of suitable environmental conditions for the establishment and spread of C. sumatrensis was anticipated to expand in urban and farmland by 2081–2100. The results provide an early warning of specific land-use types at higher risk of C. sumatrensis extent, offering valuable insights for managers to develop targeted prevention and control strategies. Additionally, to enhance predictive accuracy, our study underscores the critical role of selecting suitable algorithms and integrating human activity factors into invasive species distribution models.http://www.sciencedirect.com/science/article/pii/S2351989425002586Human footprintInvasive speciesLand-useRandom forestsSpecies distribution modelsUncertainty |
| spellingShingle | Hua Cheng Kasper Johansen Baocheng Jin Shiqin Xu Xuechun Zhao Liqin Han Matthew F. McCabe Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change Global Ecology and Conservation Human footprint Invasive species Land-use Random forests Species distribution models Uncertainty |
| title | Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change |
| title_full | Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change |
| title_fullStr | Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change |
| title_full_unstemmed | Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change |
| title_short | Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change |
| title_sort | human footprint with machine learning identifies risks of the invasive weed conyza sumatrensis across land use types under climate change |
| topic | Human footprint Invasive species Land-use Random forests Species distribution models Uncertainty |
| url | http://www.sciencedirect.com/science/article/pii/S2351989425002586 |
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