Predictive mapping of plant diversity in an arid mountain environment (Gebel Elba, Egypt)

Prepared by Maged M. Abutaha, Ahmed A. El-Khouly, Norbert Jürgens & Jens Oldeland

Evergreen vegetation (Olea woodland) at higher elevations of Gebel Elba, south-eastern Egypt. Left: Mahmoud Ali, right: Maged Abutaha. Photo credit: Maged Abutaha.

Gebel Elba, the core of the largest national park in Egypt (Gebel Elba National Park), is situated in the southeast corner of Egypt, nearly 15 km west of the Red Sea coast. This mountain is surrounded by a hyper-arid desert, but orographic precipitation on its northern slopes provides favourable climatic conditions for a richer growth of Afrotropical species than any similar region in Egypt. The main obstacles to assess its diversity are the rugged topography and the inaccessibility of the region. Therefore, using remote sensing is helpful to predict the diversity of this unique mountain. We used data derived from very high-resolution PlanetScope satellite images and a digital elevation model (DEM) to model alpha and beta diversity.

The predictive model-based plant diversity maps show the importance of using combined environmental variables in addition to elevation. In more details, the modified soil-adjusted vegetation index (MSAVI2) and topographic variables play important roles in explaining spatial diversity patterns. Moreover, the predictive maps highlight the importance of wadis (drainage systems) for plant growth and diversity in arid environments and show clear differences between the desert and mountain ecosystems, because the water availability and accumulation in wadi systems are much higher than in the open desert. Thus, it is important for conservation efforts to focus on wadi areas of Gebel Elba. Given the global availability of remote sensing products, we recommend that emphasis should be put on sampling high-quality vegetation data to serve as ground truth.

This is a plain language summary for the paper of Maged Abutaha et al. published in Applied Vegetation Science (https://doi.org/10.1111/avsc.12582).