Predicting species distributions and community composition using satellite remote sensing predictors

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9 Scopus citations


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-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. 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 that accurately predict and monitor ecological assemblages through time and space.

Original languageEnglish (US)
Article number16448
JournalScientific Reports
Issue number1
StatePublished - Aug 12 2021

Bibliographical note

Funding Information:
J.N.P.-L. was supported in part by the University of Minnesota College of Biological Sciences’ Grand Challenges in Biology Postdoctoral Program and by the National Science Foundation through the Macrosystems Biology & NEON-Enabled Science program (DEB 2017843 grant to J.N.P.-L. and J.C.-B.) with additional support provided by the National Aeronautics and Space Administration (20-BIODIV20-0048) and the National Science Foundation (DBI 2021898).

Publisher Copyright:
© 2021, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't


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