Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale
Pseudomonas syringae is a pathogenic bacterium that poses a significant threat to global agriculture, necessitating a deeper understanding of its ecological dynamics in the context of global warming. This study investigates the current and projected future distribution of P. syringae, focusing on th...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402417048X |
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| author | Sameh M.H. Khalaf Monerah S.M. Alqahtani Mohamed R.M. Ali Ibrahim T.I. Abdelalim Mohamed S. Hodhod |
| author_facet | Sameh M.H. Khalaf Monerah S.M. Alqahtani Mohamed R.M. Ali Ibrahim T.I. Abdelalim Mohamed S. Hodhod |
| author_sort | Sameh M.H. Khalaf |
| collection | DOAJ |
| description | Pseudomonas syringae is a pathogenic bacterium that poses a significant threat to global agriculture, necessitating a deeper understanding of its ecological dynamics in the context of global warming. This study investigates the current and projected future distribution of P. syringae, focusing on the climatic factors that influence its spread. To achieve this, we employed Maximum Entropy (MaxEnt) modeling based on Geographic Information Systems (GIS) to analyze species occurrence records alongside relevant climate data. The MaxEnt model was calibrated using 75 % of the occurrence data, with the remaining 25 % reserved for validation. The model's performance was meticulously assessed utilizing the area under the curve (AUC) and true skill statistics (TSS), resulting in an AUC score of 0.92, indicating excellent predictive capability. Our analysis identified key climatic parameters—temperature, precipitation, and humidity—that significantly affect the presence of P. syringae. Notably, our findings project an expansion of the bacterium's geographic range in the coming decades, with optimal conditions shifting toward the poles. This research underscores the significant influence of climate change on the distribution of P. syringae and provides valuable insights for developing targeted disease management strategies. The anticipated increase in bacterial infections in crops highlights the urgent need for proactive measures to mitigate these effects. |
| format | Article |
| id | doaj-art-232b2938c3be4aabb8491cee25d7ae24 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-232b2938c3be4aabb8491cee25d7ae242025-08-20T01:58:31ZengElsevierHeliyon2405-84402024-12-011024e4101710.1016/j.heliyon.2024.e41017Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scaleSameh M.H. Khalaf0Monerah S.M. Alqahtani1Mohamed R.M. Ali2Ibrahim T.I. Abdelalim3Mohamed S. Hodhod4Faculty of Biotechnology, October University for Modern Sciences & Arts (MSA University), 6th October City, 12566, Egypt; Corresponding author.Biology Department, Faculty of Science, King Khalid University, Abha, 61413, Saudi ArabiaFaculty of Biotechnology, October University for Modern Sciences & Arts (MSA University), 6th October City, 12566, EgyptFaculty of Biotechnology, October University for Modern Sciences & Arts (MSA University), 6th October City, 12566, EgyptFaculty of Biotechnology, October University for Modern Sciences & Arts (MSA University), 6th October City, 12566, EgyptPseudomonas syringae is a pathogenic bacterium that poses a significant threat to global agriculture, necessitating a deeper understanding of its ecological dynamics in the context of global warming. This study investigates the current and projected future distribution of P. syringae, focusing on the climatic factors that influence its spread. To achieve this, we employed Maximum Entropy (MaxEnt) modeling based on Geographic Information Systems (GIS) to analyze species occurrence records alongside relevant climate data. The MaxEnt model was calibrated using 75 % of the occurrence data, with the remaining 25 % reserved for validation. The model's performance was meticulously assessed utilizing the area under the curve (AUC) and true skill statistics (TSS), resulting in an AUC score of 0.92, indicating excellent predictive capability. Our analysis identified key climatic parameters—temperature, precipitation, and humidity—that significantly affect the presence of P. syringae. Notably, our findings project an expansion of the bacterium's geographic range in the coming decades, with optimal conditions shifting toward the poles. This research underscores the significant influence of climate change on the distribution of P. syringae and provides valuable insights for developing targeted disease management strategies. The anticipated increase in bacterial infections in crops highlights the urgent need for proactive measures to mitigate these effects.http://www.sciencedirect.com/science/article/pii/S240584402417048XClimate changeDisease managementGISMaxent modelingPseudomonas syringae |
| spellingShingle | Sameh M.H. Khalaf Monerah S.M. Alqahtani Mohamed R.M. Ali Ibrahim T.I. Abdelalim Mohamed S. Hodhod Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale Heliyon Climate change Disease management GIS Maxent modeling Pseudomonas syringae |
| title | Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale |
| title_full | Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale |
| title_fullStr | Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale |
| title_full_unstemmed | Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale |
| title_short | Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale |
| title_sort | using maxent modeling to analyze climate change impacts on pseudomonas syringae van hall 1904 distribution on the global scale |
| topic | Climate change Disease management GIS Maxent modeling Pseudomonas syringae |
| url | http://www.sciencedirect.com/science/article/pii/S240584402417048X |
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