Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin
ABSTRACT This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting cli...
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2025-01-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.70753 |
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| author | Marco Bianchini Mohamed Tarhouni Matteo Francioni Marco Fiorentini Chiara Rivosecchi Jamila Msadek Abderrazak Tlili Farah Chouikhi Marina Allegrezza Giulio Tesei Paola Antonia Deligios Roberto Orsini Luigi Ledda Maria Karatassiou Athanasios Ragkos Paride D'Ottavio |
| author_facet | Marco Bianchini Mohamed Tarhouni Matteo Francioni Marco Fiorentini Chiara Rivosecchi Jamila Msadek Abderrazak Tlili Farah Chouikhi Marina Allegrezza Giulio Tesei Paola Antonia Deligios Roberto Orsini Luigi Ledda Maria Karatassiou Athanasios Ragkos Paride D'Ottavio |
| author_sort | Marco Bianchini |
| collection | DOAJ |
| description | ABSTRACT This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC‐ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios. Two spectral indices, NDVI (temperate area) and SAVI (arid area), served as vegetation proxies, classified into three classes using K‐means (NDVI: high = mainly associated with woodlands, medium = shrublands, continuous grasslands and crops, low = discontinuous grasslands, bare soil, and rocks; SAVI: high = mainly associated with woods, olive trees, and crops, medium = shrublands and sparse olive trees, low = bare soil and saline areas). Classes validated with ESA WorldCover 2020 data and sampled via 1390 presence‐only points. A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. Vegetation changes varied by area: in the temperate area, woodlands and shrublands were stable, but discontinuous grasslands expanded. In the arid area, woodlands gained suitable habitat, while bare soil declined under the pessimistic SSP 585 scenario. Both areas showed an upward shift for shrublands and grasslands. The models indicated significant shifts in areal distribution and environmental conditions, affecting habitat suitability and ecosystem dynamics. MaxEnt emerged as the most reliable algorithm for small presence‐only datasets, effectively predicting habitat suitability. The findings highlight significant vegetation redistribution and altered ecosystem dynamics due to climate change, underscoring the importance of these models in planning for future ecological challenges. |
| format | Article |
| id | doaj-art-8e61d7b94547473a90db5425e4388820 |
| institution | Kabale University |
| issn | 2045-7758 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
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| series | Ecology and Evolution |
| spelling | doaj-art-8e61d7b94547473a90db5425e43888202025-01-29T05:08:41ZengWileyEcology and Evolution2045-77582025-01-01151n/an/a10.1002/ece3.70753Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean BasinMarco Bianchini0Mohamed Tarhouni1Matteo Francioni2Marco Fiorentini3Chiara Rivosecchi4Jamila Msadek5Abderrazak Tlili6Farah Chouikhi7Marina Allegrezza8Giulio Tesei9Paola Antonia Deligios10Roberto Orsini11Luigi Ledda12Maria Karatassiou13Athanasios Ragkos14Paride D'Ottavio15Department of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyPastoral Ecosystems, Spontaneous Plants and Associated Microorganisms Laboratory Arid Regions Institute‐University of Gabes Medenine TunisiaDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyPastoral Ecosystems, Spontaneous Plants and Associated Microorganisms Laboratory Arid Regions Institute‐University of Gabes Medenine TunisiaPastoral Ecosystems, Spontaneous Plants and Associated Microorganisms Laboratory Arid Regions Institute‐University of Gabes Medenine TunisiaPastoral Ecosystems, Spontaneous Plants and Associated Microorganisms Laboratory Arid Regions Institute‐University of Gabes Medenine TunisiaDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyLaboratory of Rangeland Ecology, School of Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki GreeceAgricultural Economics Research Institute Hellenic Agricultural Organization – DIMITRA Athens GreeceDepartment of Agricultural, Food and Environmental Sciences Università Politecnica delle Marche Ancona ItalyABSTRACT This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC‐ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios. Two spectral indices, NDVI (temperate area) and SAVI (arid area), served as vegetation proxies, classified into three classes using K‐means (NDVI: high = mainly associated with woodlands, medium = shrublands, continuous grasslands and crops, low = discontinuous grasslands, bare soil, and rocks; SAVI: high = mainly associated with woods, olive trees, and crops, medium = shrublands and sparse olive trees, low = bare soil and saline areas). Classes validated with ESA WorldCover 2020 data and sampled via 1390 presence‐only points. A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. Vegetation changes varied by area: in the temperate area, woodlands and shrublands were stable, but discontinuous grasslands expanded. In the arid area, woodlands gained suitable habitat, while bare soil declined under the pessimistic SSP 585 scenario. Both areas showed an upward shift for shrublands and grasslands. The models indicated significant shifts in areal distribution and environmental conditions, affecting habitat suitability and ecosystem dynamics. MaxEnt emerged as the most reliable algorithm for small presence‐only datasets, effectively predicting habitat suitability. The findings highlight significant vegetation redistribution and altered ecosystem dynamics due to climate change, underscoring the importance of these models in planning for future ecological challenges.https://doi.org/10.1002/ece3.70753climate changemachine learningpastoral systemspredictive vegetation modelsrangelands |
| spellingShingle | Marco Bianchini Mohamed Tarhouni Matteo Francioni Marco Fiorentini Chiara Rivosecchi Jamila Msadek Abderrazak Tlili Farah Chouikhi Marina Allegrezza Giulio Tesei Paola Antonia Deligios Roberto Orsini Luigi Ledda Maria Karatassiou Athanasios Ragkos Paride D'Ottavio Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin Ecology and Evolution climate change machine learning pastoral systems predictive vegetation models rangelands |
| title | Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin |
| title_full | Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin |
| title_fullStr | Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin |
| title_full_unstemmed | Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin |
| title_short | Modeling Climate‐Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin |
| title_sort | modeling climate driven vegetation changes under contrasting temperate and arid conditions in the mediterranean basin |
| topic | climate change machine learning pastoral systems predictive vegetation models rangelands |
| url | https://doi.org/10.1002/ece3.70753 |
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