Community structure informs species geographic distributions

Understanding what determines species' geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individua...

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Autores: Montesinos-Navarro, Alicia, Estrada, Alba, Font i Castell, Xavier, Matias, Miguel G., Meireles, Catarina, Mendoza, Manuel, Honrado, Joao P., Prasad, Hari D., Vicente, Joana R., Early, Regan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/123192
Acceso en línea:https://hdl.handle.net/2445/123192
Access Level:acceso abierto
Palabra clave:Biogeografia
Biodiversitat
Biogeography
Biodiversity
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spelling Community structure informs species geographic distributionsMontesinos-Navarro, AliciaEstrada, AlbaFont i Castell, XavierMatias, Miguel G.Meireles, CatarinaMendoza, ManuelHonrado, Joao P.Prasad, Hari D.Vicente, Joana R.Early, ReganBiogeografiaBiodiversitatBiogeographyBiodiversityUnderstanding what determines species' geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. `community structure') reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could bebecause species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species' large-scale distributions, and this information can improve the predictions of species distributions.Public Library of Science (PLoS)2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/123192Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1371/journal.pone.0197877PLoS One, 2018, vol. 13, num. 5, p. 1-16https://doi.org/10.1371/journal.pone.0197877cc-by (c) Montesinos-Navarro, A. et al., 2018http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1231922026-05-27T06:46:51Z
dc.title.none.fl_str_mv Community structure informs species geographic distributions
title Community structure informs species geographic distributions
spellingShingle Community structure informs species geographic distributions
Montesinos-Navarro, Alicia
Biogeografia
Biodiversitat
Biogeography
Biodiversity
title_short Community structure informs species geographic distributions
title_full Community structure informs species geographic distributions
title_fullStr Community structure informs species geographic distributions
title_full_unstemmed Community structure informs species geographic distributions
title_sort Community structure informs species geographic distributions
dc.creator.none.fl_str_mv Montesinos-Navarro, Alicia
Estrada, Alba
Font i Castell, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza, Manuel
Honrado, Joao P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author Montesinos-Navarro, Alicia
author_facet Montesinos-Navarro, Alicia
Estrada, Alba
Font i Castell, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza, Manuel
Honrado, Joao P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author_role author
author2 Estrada, Alba
Font i Castell, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza, Manuel
Honrado, Joao P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Biogeografia
Biodiversitat
Biogeography
Biodiversity
topic Biogeografia
Biodiversitat
Biogeography
Biodiversity
description Understanding what determines species' geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. `community structure') reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could bebecause species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species' large-scale distributions, and this information can improve the predictions of species distributions.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/123192
url https://hdl.handle.net/2445/123192
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0197877
PLoS One, 2018, vol. 13, num. 5, p. 1-16
https://doi.org/10.1371/journal.pone.0197877
dc.rights.none.fl_str_mv cc-by (c) Montesinos-Navarro, A. et al., 2018
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Montesinos-Navarro, A. et al., 2018
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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