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Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities

Overview of attention for article published in PLoS Computational Biology, December 2012
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Title
Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002832
Pubmed ID
Authors

James P. O'Dwyer, Steven W. Kembel, Jessica L. Green

Abstract

Microbial communities are typically large, diverse, and complex, and identifying and understanding the processes driving their structure has implications ranging from ecosystem stability to human health and well-being. Phylogenetic data gives us a new insight into these processes, providing a more informative perspective on functional and trait diversity than taxonomic richness alone. But the sheer scale of high resolution phylogenetic data also presents a new challenge to ecological theory. We bring a sampling theory perspective to microbial communities, considering a local community of co-occuring organisms as a sample from a larger regional pool, and apply our framework to make analytical predictions for local phylogenetic diversity arising from a given metacommunity and community assembly process. We characterize community assembly in terms of quantitative descriptions of clustered, random and overdispersed sampling, which have been associated with hypotheses of environmental filtering and competition. Using our approach, we analyze large microbial communities from the human microbiome, uncovering significant variation in diversity across habitats relative to the null hypothesis of random sampling.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 285 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 21 7%
Spain 3 1%
United Kingdom 3 1%
France 3 1%
Canada 3 1%
Sweden 2 <1%
Colombia 2 <1%
Germany 2 <1%
Brazil 1 <1%
Other 8 3%
Unknown 237 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 88 31%
Researcher 69 24%
Professor 24 8%
Student > Master 20 7%
Student > Doctoral Student 16 6%
Other 49 17%
Unknown 19 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 160 56%
Environmental Science 35 12%
Biochemistry, Genetics and Molecular Biology 15 5%
Immunology and Microbiology 10 4%
Computer Science 7 2%
Other 28 10%
Unknown 30 11%