Fuente: Sustainability - Revista científica (MDPI) Sustainability, Vol. 18, Pages 4730: Floristic Diversity and Phytogeography of Qatar
Sustainability doi: 10.3390/su18104730
Authors:
Ahmed Elgharib
María del Mar Trigo
Elsayed Elazazi
Mohamed M. Moursy
Alaaeldin Soultan

Despite the ecological importance of desert ecosystems in Qatar, quantitative analyses integrating species diversity, phytogeographical regionalisation, and environmental drivers remain limited. This study applied species distribution models (SDMs) to delineate phytogeographical regions of Qatar, followed by the identification of indicator species and associated environmental drivers of each region using indicator species analysis and Relative Environmental Turnover (RET). Species distributions were developed for 112 perennial species to address sampling incompleteness, and converted into a presence–absence matrix, which was subjected to UPGMA clustering to identify phytogeographical regions. The analysis delineated three distinct phytogeographical regions: Shrubland–Gravel, Coastal Halophytic, and Inland Sandy Desert. Species richness exhibited a clear spatial gradient, with high richness (>60 species per site) concentrated in northeastern Qatar and declining towards the south. Indicator species analysis identified nine species as strong regional indicators, reflecting pronounced habitat specialisation. RET analysis revealed that soil nitrogen and organic carbon were strongly associated with the Coastal Halophytic region, indicating enhanced nutrient availability in these saline environments. Although the other regions did not exhibit statistically significant environmental clustering, descriptive patterns suggested tendencies toward higher precipitation in the Shrubland–Gravel region and elevated sand content and temperature in the Inland Sandy Desert region. Collectively, these findings demonstrate that vegetation patterns in Qatar are structured by the interaction of soil properties, precipitation variability, and landform heterogeneity. The integration of SDMs with clustering and environmental analyses provides a robust framework for phytogeographical analysis and supports biodiversity conservation and sustainable land management in hyper-arid environments.