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Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt
Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt
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Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt
Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt

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Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt
Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt
Journal Article

Investigating sand dunes flora conservation based on remote sensing and predictive modeling in the Mediterranean coastal region, Egypt

2025
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Overview
Climate change and anthropogenic activities present significant long-term threats to global biodiversity. To effectively reduce species loss, conservation strategies must rely on detailed insights into suitable habitats for different species and the factors influencing their distribution. This research aimed to provide a checklist of the plant species on the Mediterranean coastal sand dunes in Egypt and investigated the potential impact of anthropogenic and climate change on its distribution in Egypt over the next several decades using remote sensing and species distribution modeling (SDM). The current study revealed that Egypt’s Mediterranean coastal sand dunes host 236 plant taxa, comprising 212 species, 5 varieties, and 19 subspecies, distributed across 75 genera and 39 families. An ensemble modeling approach was employed, incorporating five key. Among these, Random Forest (RF), Boosted Regression Trees (BRT) demonstrated the highest performance. For remote sensing, Landsat imagery (including Thematic Mapper (TM), and Operational Land Imager (OLI) sensors) was utilized. Support Vector Machine (SVM) classification, implemented via Google Earth Engine (GEE), was employed to analyze land use/cover changes. Additionally, future land use projections for 2080 were generated using the CA-Markov simulation model. The study detected major changes in land use along the Mediterranean coast from 1988 to 2024, with projections extending to 2050 and 2070. Based on the ensemble model, the primary environmental drivers of sand dune vegetation were distance from the shoreline, soil organic carbon, precipitation patterns (both total in the wettest month and seasonality), and sand content. The landscape was significantly transformed by the expansion of urban areas, agriculture, and aquaculture. Vegetation diversity is abundant in the Eastern and Western Mediterranean dunes, but scarce in the Deltaic Mediterranean dunes. Future projections using the IPSL-CM6A-LR GCM model for 2050 and 2070, under two climate scenarios (sustainable and low-emissions future: SSP126 and very high greenhouse gas emission: SSP585), suggested a decline in suitable habitat compared to current conditions. These findings emphasize the pressing need for conservation actions, including species reintroduction and strategic planning for both in situ and ex situ conservation in viable habitats.