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journal.pone.0176751.pdf
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FInal Published Version
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Univ Arizona, Dept Soil Water & Environm SciIssue Date
2017-05-11
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PUBLIC LIBRARY SCIENCECitation
Using null models to infer microbial co-occurrence networks 2017, 12 (5):e0176751 PLOS ONEJournal
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© 2017 Connor et al. This is an open access article distributed under the terms of the Creative Commons Attribution LicenseCollection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Although microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.Note
Open access journal.ISSN
1932-6203Version
Final published versionAdditional Links
http://dx.plos.org/10.1371/journal.pone.0176751ae974a485f413a2113503eed53cd6c53
10.1371/journal.pone.0176751