Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (α- and β-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining α- and β-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map α- and β-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape.

► We present an approach for mapping α- and β-diversity based on remotely sensed data (as surrogates of productivity and habitat structure factors) and the spatial structure of the sampling sites. ► The relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity were evaluated. ► The associations of field diversity data with productivity-habitat structure and spatial variables were used to map α- and β-diversity. ► The accuracy of predictions was enhanced when considering both remotely sensed data and spatial structure.