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-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.
Highlights
Biodiversity is rapidly changing due to changes in the climate and human related activities; the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies
Detecting and monitoring species diversity and composition is critical to developing effective management strategies and conservation actions facing global c hange[3,9,10] that move us towards international biodiversity goals, including those posed by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem (IPBES) and the parallel United Nations’ (UN) Sustainable Development Goals (UN-SDGs)[8] and the upcoming post-2020 Convention on Biodiversity goals
We found that the average SES-mean phylogenetic distance (MPD) at the plot and site scales for the observed assemblages were as follows: NEONPLOT = − 0.66 ± 1.30 and NEONSITE = − 1.04 ± 1.34; bS-SDMPLOT = − 1.12 ± 1.23 and bS-SDMSITE = − 0.71 ± 1.3; cS-SDMPLOT = − 1.42 ± 1.09 and cS-SDMSITE = − 1.02 ± 1.03
Summary
Biodiversity is rapidly changing due to changes in the climate and human related activities; the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. Remote sensing products (RS-products) have been increasingly used to derive metrics that allow tracking biodiversity from s pace[18,25,26], monitoring the state of human impacts[3,27], as predictors for describing large patterns of species d iversity[28,29,30] or to derive Essential Biodiversity Variables, i.e., measures that allow the detection and quantification of biodiversity changes[12,31,32] Despite their high spatial and temporal resolution, quasi-global coverage and range of data products (e.g., precipitation, plant productivity, biophysical variables, land cover), RS-products have been rarely used as predictors for biodiversity m odels[17,30,33]. Species models derived from RS-products perform as well as those derived from interpolated climate surfaces and have the potential to provide predictions with greater spatial r esolution[30]
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