Detecting macroecological patterns in bacterial communities across independent studies of global soils

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. ORCID: https://orcid.org/0000-0002-3341-4547; 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. ORCID: https://orcid.org/0000-0001-7290-4867; 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.. 2018 Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nature Microbiology, 3 (2). 189-196. 10.1038/s41564-017-0062-x

Before downloading, please read NORA policies.

Preview	Text N519281PP.pdf - Accepted Version Download (2MB) | Preview
Preview	Text N519281SUPPMAT.pdf Download (991kB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1038/s41564-017-0062-x
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2058-5276
Additional Keywords:	biodiversity, microbial ecology
NORA Subject Terms:	Ecology and Environment Agriculture and Soil Science Biology and Microbiology
Date made live:	13 Feb 2018 12:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/519281

Actions (login required)

View Item

Document Downloads

More statistics for this item...