Predicting species distributions and community composition using satellite remote sensing predictors.

ABSTRACT: Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models-including assemblage diversity and composition-obtained by stacking next-generatio

SUBMITTER: Pinto-Ledezma JN 

PROVIDER: S-EPMC8361206 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature