期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 25, 页码 9692-9698出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1206721109
关键词
metagenomics; microbial ecology
资金
- Food Safety Research Response Network
- National Research Initiative of the US Department of Agriculture Cooperative State Research, Education, and Extension Service [2005-35212-15287]
- US Department of Agriculture/National Research Initiative [2006-35206-16652, 2007-35212-18046]
- L.S. Edelheit Family Biological Physics Fellowship
- Institute of Genomic Biology Fellows Program
The theoretical description of the forces that shape ecological communities focuses around two classes of models. In niche theory, deterministic interactions between species, individuals, and the environment are considered the dominant factor, whereas in neutral theory, stochastic forces, such as demographic noise, speciation, and immigration, are dominant. Species abundance distributions predicted by the two classes of theory are difficult to distinguish empirically, making it problematic to deduce ecological dynamics from typical measures of diversity and community structure. Here, we show that the fusion of species abundance data with genome-derived measures of evolutionary distance can provide a clear indication of ecological dynamics, capable of quantifying the relative roles played by niche and neutral forces. We apply this technique to six gastrointestinal microbiomes drawn from three different domesticated vertebrates, using high-resolution surveys of microbial species abundance obtained from carefully curated deep 16S rRNA hypervariable tag sequencing data. Although the species abundance patterns are seemingly well fit by the neutral theory of metacommunity assembly, we show that this theory cannot account for the evolutionary patterns in the genomic data; moreover, our analyses strongly suggest that these microbiomes have, in fact, been assembled through processes that involve a significant nonneutral (niche) contribution. Our results demonstrate that high-resolution genomics can remove the ambiguities of process inference inherent in classic ecologicalmeasures and permits quantification of the forces shaping complex microbial communities.
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