Detecting macroecological patterns in bacterial communities across independent studies of global soils.
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Authors
Ramirez, Kelly S.Knight, Christopher G.
de Hollander, Mattias
Brearley, Francis Q.
Constantinides, Bede
Cotton, Anne
Creer, Si
Crowther, Thomas W.
Davison, John
Delgado-Baquerizo, Manuel
Dorrepaal, Ellen
Elliott, David R.

Fox, Graeme
Griffiths, Robert I.
Hale, Chris
Hartman, Kyle
Houlden, Ashley
Jones, David L.
Krab, Eveline J.
Maestre, Fernando T.
McGuire, Krista L.
Monteux, Sylvain
Orr, Caroline H.
van der Putten, Wim H.
Roberts, Ian S.
Robinson, David A.
Rocca, Jennifer D.
Rowntree, Jennifer
Schlaeppi, Klaus
Shepherd, Matthew
Singh, Brajesh K.
Straathof, Angela L.
Bhatnagar, Jennifer M.
Thion, Cécile
van der Heijden, Marcel G. A.
de Vries, Franciska T.
Affiliation
Netherlands Institute of EcologyUniversity of Manchester
Manchester Metropolitan University
University of Sheffield
Bangor University
University of Tartu
University of Colorado
Umeå University
University of Derby
Centre of Ecology and Hydrology
University of Warwick
Agroscope
Universidad Rey Juan Carlos
University of Oregon
Teeside University
Wageningen University
Duke University
Natural England
Western Sydney University
Boston University
University of Aberdeen
Issue Date
2017-11-20
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Show full item recordAbstract
The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential ‘indicator’ taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past.Citation
Ramirez, K. S. et al (2018) 'Detecting macroecological patterns in bacterial communities across independent studies of global soils,' Nature Microbiology, vol 3. pp. 189-196, DOI: 10.1038/s41564-017-0062-xPublisher
NatureJournal
Nature MicrobiologyDOI
10.1038/s41564-017-0062-xAdditional Links
http://www.nature.com/articles/s41564-017-0062-xType
ArticleLanguage
enISSN
20585276ae974a485f413a2113503eed53cd6c53
10.1038/s41564-017-0062-x
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Except where otherwise noted, this item's license is described as Archived with thanks to Nature Microbiology