Predictive mapping of plant diversity in an arid mountain environment (Gebel Elba, Egypt)
2021
Abutaha, Maged M. | El‐Khouly, Ahmed A. | Jürgens, Norbert | Oldeland, Jens
AIM: This study aimed to predict the alpha and beta plant diversity of an arid mountain based on environmental variables derived from remotely sensed and ground truth data. LOCATION: Gebel Elba, Egypt. METHODS: Based on 133 vegetation plots of 100 m², we calculated alpha (Shannon index) and beta [the first ordination axis of nonmetric multidimensional scaling (NMDS1)] plant diversity. Generalized additive models (GAMs) were used to map alpha and beta diversity based on various environmental variables derived from a digital elevation model, the SoilGrids dataset, and very high resolution PlanetScope satellite imagery. The predictive models for alpha and beta diversity were mapped within the northern slopes of Gebel Elba. An ANOVA post hoc test was used to compare Shannon index and NMDS1 values among plant communities. RESULTS: The selected models revealed the importance of altitude, landforms, solar insolation, catchment area, and modified soil‐adjusted vegetation index for Shannon diversity and NMDS1. The GAMs explained 54.9% of Shannon diversity and 80.6% of NMDS1. The predicted diversity maps showed that the mountainous area was more diverse and substantially different from the open desert. The post‐hoc test revealed a clear separation of mountain and desert vegetation. CONCLUSIONS: Employing remotely sensed variables combined with ground truth data offers great opportunities for exploring spatial patterns of biodiversity. By mapping alpha and beta diversity, it was possible to determine the spatial distribution of plant diversity in Gebel Elba; the results highlighted the importance of the wadi systems and higher slopes of this mountain area. We expect our findings can be generalized to similar arid mountains in the region.
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