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Community structure informs species geographic distributions

AuthorsMontesinos-Navarro, Alicia; Estrada, Alba ; Font, Xavier; Matias, Miguel G.; Meireles, Catarina; Mendoza, Manuel; Honrado, Joao P.; Prasad, Hari D.; Vicente, Joana R.; Early, Regan
KeywordsCommunity structure
Species interactions
Relative abundance distribution
Community ecology
Plant ecology
Invasive species
Spatial and landscape ecology
Issue Date23-May-2018
PublisherPublic Library of Science
CitationPLoS ONE 13(5): e0197877 (2018)
AbstractUnderstanding 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.
Publisher version (URL)http://dx.doi.org/10.1371/journal.pone.0197877
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