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...

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Autores principales:	Jesús N. Pinto-Ledezma, Jeannine Cavender-Bares
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0030fb815054b09b89bad869a406d0e
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spelling	oai:doaj.org-article:c0030fb815054b09b89bad869a406d0e2021-12-02T15:08:39ZPredicting species distributions and community composition using satellite remote sensing predictors10.1038/s41598-021-96047-72045-2322https://doaj.org/article/c0030fb815054b09b89bad869a406d0e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96047-7https://doaj.org/toc/2045-2322Abstract 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.Jesús N. Pinto-LedezmaJeannine Cavender-BaresNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jesús N. Pinto-Ledezma Jeannine Cavender-Bares Predicting species distributions and community composition using satellite remote sensing predictors
description	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.
format	article
author	Jesús N. Pinto-Ledezma Jeannine Cavender-Bares
author_facet	Jesús N. Pinto-Ledezma Jeannine Cavender-Bares
author_sort	Jesús N. Pinto-Ledezma
title	Predicting species distributions and community composition using satellite remote sensing predictors
title_short	Predicting species distributions and community composition using satellite remote sensing predictors
title_full	Predicting species distributions and community composition using satellite remote sensing predictors
title_fullStr	Predicting species distributions and community composition using satellite remote sensing predictors
title_full_unstemmed	Predicting species distributions and community composition using satellite remote sensing predictors
title_sort	predicting species distributions and community composition using satellite remote sensing predictors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c0030fb815054b09b89bad869a406d0e
work_keys_str_mv	AT jesusnpintoledezma predictingspeciesdistributionsandcommunitycompositionusingsatelliteremotesensingpredictors AT jeanninecavenderbares predictingspeciesdistributionsandcommunitycompositionusingsatelliteremotesensingpredictors
_version_	1718388085042970624

Predicting species distributions and community composition using satellite remote sensing predictors

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