Community structure informs species geographic distributions

Alicia Montesinos-Navarro, Alba Estrada, Xavier Font, Miguel G. Matias, Catarina Meireles, Manuel Mendoza, Joao P. Honrado, Hari D. Prasad, Joana R. Vicente, Regan Early

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)e0197877
JournalPLOS ONE
Volume13
Issue number5
DOIs
StatePublished - May 23 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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 ofEconomy 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). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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