Predicting species distributions and community composition using remote sensing products

Accurate predictions of species composition and diversity are critical to the development of conservation actions and management strategies. In this paper using oak assemblages distributed across the conterminous United States as study model, we assessed the performance of stacked species distribution models (S-SDMs) and remote sensing products in building the next-generation of biodiversity models. This study represents the first attempt to evaluate the integrated predictions of biodiversity models, including assemblage diversity and composition, obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from remote sensing products represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus cS-SDM), biodiversity models do not recover the observed species composition with high spatial resolution, i.e., correct species identities at the scale of individual plots. However, they do return reasonable predictions at macroecological scales (1 km). Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.