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

Ramirez, KS and Knight, CG and de Hollander, M and Brearley, Francis and Constantinides, B and Cotton, A and Creer, S and Crowther, TW and Davison, J and Delgado-Baquerizo, M and Dorrepaal, E and Elliott, DR and Fox, G and Griffiths, RI and Hale, C and Hartman, K and Houlden, A and Jones, DL and Krab, EJ and Maestre, FT and McGuire, KL and Monteux, S and Orr, CH and van der Putten, WH and Roberts, IS and Robinson, DA and Rocca, JD and Rowntree, J and Schlaeppi, K and Shepherd, M and Singh, BK and Straathof, AL and Bhatnagar, JM and Thion, C and van der Heijden, MGA and de Vries, FT (2017) Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nature Microbiology, 3. pp. 189-196. ISSN 2058-5276

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Abstract

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.

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