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 individuals is...

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Autores: Montesinos-Navarro, Alicia, Estrada, Alba, Font, Xavier, Matias, Miguel G., Meireles, Catarina, Mendoza García, Manuel, Honrado, João P., Prasad, Hari D., Vicente, Joana R., Early, Regan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/183333
Acceso en línea:http://hdl.handle.net/10261/183333
Access Level:acceso abierto
Palabra clave:Community structure
Species interactions
Relative abundance distribution
Community ecology
Plant ecology
Plants
Invasive species
Spatial and landscape ecology
http://metadata.un.org/sdg/15
Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
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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
Community structure
Species interactions
Relative abundance distribution
Community ecology
Plant ecology
Plants
Invasive species
Spatial and landscape ecology
http://metadata.un.org/sdg/15
Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
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, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza García, Manuel
Honrado, João P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author Montesinos-Navarro, Alicia
author_facet Montesinos-Navarro, Alicia
Estrada, Alba
Font, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza García, Manuel
Honrado, João P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author_role author
author2 Estrada, Alba
Font, Xavier
Matias, Miguel G.
Meireles, Catarina
Mendoza García, Manuel
Honrado, João P.
Prasad, Hari D.
Vicente, Joana R.
Early, Regan
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Fundação para a Ciência e a Tecnologia (Portugal)
Ministerio de Economía y Competitividad (España)
European Commission
Foundation for Science and Technology
Principado de Asturias
Montesinos-Navarro, Alicia [0000-0003-4656-0321]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Community structure
Species interactions
Relative abundance distribution
Community ecology
Plant ecology
Plants
Invasive species
Spatial and landscape ecology
http://metadata.un.org/sdg/15
Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
topic Community structure
Species interactions
Relative abundance distribution
Community ecology
Plant ecology
Plants
Invasive species
Spatial and landscape ecology
http://metadata.un.org/sdg/15
Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
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 be because 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
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/183333
url http://hdl.handle.net/10261/183333
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPDI-2013-16266
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI‐2015‐23498
http://dx.doi.org/10.1371/journal.pone.0197877
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling Community structure informs species geographic distributionsMontesinos-Navarro, AliciaEstrada, AlbaFont, XavierMatias, Miguel G.Meireles, CatarinaMendoza García, ManuelHonrado, João P.Prasad, Hari D.Vicente, Joana R.Early, ReganCommunity structureSpecies interactionsRelative abundance distributionCommunity ecologyPlant ecologyPlantsInvasive speciesSpatial and landscape ecologyhttp://metadata.un.org/sdg/15Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity lossUnderstanding 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 be because 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.This work was funded by FCT Project “QuerCom” (EXPL/AAG-GLO/2488/2013) and the ERA-Net BiodivERsA project “EC21C” (BIODIVERSA/0003/2011). A.M.N. was supported by a Bolsa de Investigacao de Pos-doutoramento (BI_Pos-Doc_UEvora_Catedra Rui Nabeiro_EXPL_AAG-GLO_2488_2013) and postdoctoral fellowships from the Ministry of Economy and Competitivity (FPDI-2013-16266 and IJCI‐2015‐23498). MGM acknowledges support by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme (FORECOMM). J. Vicente is supported by POPH/FSE funds and by National Funds through FCT - Foundation for Science and Technology under the Portuguese Science Foundation (FCT) through Post-doctoral grant SFRH/BPD/84044/2012. AE has a postodoctoral contract funded by the project CN-17-022 (Principado de Asturias, Spain).Peer reviewedPublic Library of ScienceFundação para a Ciência e a Tecnologia (Portugal)Ministerio de Economía y Competitividad (España)European CommissionFoundation for Science and TechnologyPrincipado de AsturiasMontesinos-Navarro, Alicia [0000-0003-4656-0321]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201920192018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/183333reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPDI-2013-16266info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI‐2015‐23498http://dx.doi.org/10.1371/journal.pone.0197877Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1833332026-05-22T06:33:51Z
score 15.812429

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