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 oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON), we assessed the performance of stacked species distribution models (S-SDMs), constructed using satellite remote sensing as covariates and under a Bayesian framework, in order to build the next-generation of biodiversity models. 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 three main results. First, environmental predictors derived entirely from satellite remote sensing represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, not necessarily results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), this kind of biodiversity models do not accurately recover the observed species composition at plot level or ecological scales (NEON plots), however, they do return reasonable predictions at macroecological scales, i.e., mid to high correct assignment of species identities at the scale of NEON sites. 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.

Modeling present and future distribution of plankton populations in a coastal upwelling zone: the copepod Calanus chilensis as a study case

“…To estimate the potential distribution of C. chilensis, the predictions (i.e. probability of presence) were converted into binary predictions using a threshold at which TSS is maximized (max TSS) 97,98 . The Bayesian spatial method allows the incorporation of spatial correlation of the variables and the uncertainty of the parameters in the modeling process, resulting in a better quantification of the uncertainty (credible intervals) 99,100 .…”

Predicting ecosystem pattern across landscapes

“…vegetation physiognomy - Yang et al 2017), compositional (e.g. community -Pinto-Ledezma andCavender-Bares 2021), and functional (e.g. productivity -O'Neill 2001) ecosystem features.…”