| Title: |
A global map of groundwater-dependent vegetation in the Mediterranean biome |
| Authors: |
El-Hokayem, Léonard; Damasceno, Gabriella; Sabatini, Francesco M.; Bruelheide, Helge; Bonari, Gianmaria; Dwyer, Ciara; Gonçalves, Fernando; Jiménez-Alfaro, Borja; Millett, Jonathan; Peñuelas, Josep; Svenning, Jens-Christian; Altman, Jan; Chen, Han Y. H.; Dziuba, Tetiana; El-Sheikh, Mohamed A.; Güler, Behlül; Hending, Daniel; Hérault, Bruno; Hatim, Mohamed Z.; Jansen, Florian; Meyer, Carsten; Mukul, Sharif A.; Pielech, Remigiusz; Rodrigues, Flávio; Wang, Hua-Feng; Conrad, Christopher |
| Contributors: |
El-Hokayem, Léonard; Damasceno, Gabriella; Sabatini, Francesco M.; Bruelheide, Helge; Bonari, Gianmaria; Dwyer, Ciara; Gonçalves, Fernando; Jiménez-Alfaro, Borja; Millett, Jonathan; Peñuelas, Josep; Svenning, Jens-Christian; Altman, Jan; Chen, Han Y. H.; Dziuba, Tetiana; El-Sheikh, Mohamed A.; Güler, Behlül; Hending, Daniel; Hérault, Bruno; Hatim, Mohamed Z.; Jansen, Florian; Meyer, Carsten; Mukul, Sharif A.; Pielech, Remigiusz; Rodrigues, Flávio; Wang, Hua-Feng; Conrad, Christopher |
| Publication Year: |
2026 |
| Collection: |
Università degli Studi di Siena: USiena air |
| Subject Terms: |
Biodiversity; Groundwater-dependent vegetation; Machine learning; Mediterranean; Phreatophytes; Remote sensing; Terrestrial ecosystem |
| Description: |
Groundwater-dependent vegetation (GDV) plays a vital role in maintaining biodiversity and ecosystem services in the Mediterranean biome but is increasingly threatened by climate and land use change. Large-scale GDV mapping in arid regions is critical for effective conservation and water resource management but remains challenging due to limited ground-truth data and the lack of high-resolution remote sensing-based spatial models. In this study, we mapped GDV across the world's five Mediterranean climate regions using a multi-level approach combining species-occurrence, vegetation-plot, and remote-sensing data. At the species level, we compiled a global list of phreatophyte and groundwater-associated species, which we used to extract occurrence records from GBIF. At the community level, we classified vegetation-plot data from the sPlot database based on phreatophyte presence and coverage, resulting in a unique ground-truth species-community dataset. At the biome level, we trained Random Forest models with eleven predictor variables in order to map GDV distribution at 30 m resolution for the period 2018–2023. We identified 482,000 km2 of GDV, covering 28% of the study area and predicted GDV hotspots for the Western Iberian Peninsula, Southern France, California, Chile, and the West Coast of Australia. The largest absolute area of GDV was found in the Mediterranean Basin (306,000 km2), while the highest relative coverage was found in California (42%) and Chile (40%). Notably, only about a quarter of Mediterranean GDV lies within protected areas. Key environmental predictors include soil sand content, dry season vegetation vitality, and elevation. By integrating species, community, and remote-sensing data, our high-resolution GDV map provides a crucial basis for monitoring ecosystem response to global change, conservation planning, sustainable groundwater management, and risk assessment in the drought-stressed Mediterranean biome. |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001685947800001; volume:183; journal:ECOLOGICAL INDICATORS; https://hdl.handle.net/11365/1311939; https://www.sciencedirect.com/science/article/pii/S1470160X2600066X |
| DOI: |
10.1016/j.ecolind.2026.114669 |
| Availability: |
https://hdl.handle.net/11365/1311939; https://doi.org/10.1016/j.ecolind.2026.114669; https://www.sciencedirect.com/science/article/pii/S1470160X2600066X |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.C8701D8A |
| Database: |
BASE |