Detecting macroecological patterns in bacterial communities across independent studies of global soils. (February 2018)

• Record Type: Journal Article
• Authors: Ramirez, Kelly; Knight, Christopher; de Hollander, Mattias; Brearley, Francis; Constantinides, Bede; Cotton, Anne; Creer, Si; Crowther, Thomas; Davison, John; Delgado-Baquerizo, Manuel; Dorrepaal, Ellen; Elliott, David; Fox, Graeme; Griffiths, Robert; Hale, Chris; Hartman, Kyle; Houlden, Ashley; Jones, David; Krab, Eveline; Maestre, Fernando; McGuire, Krista; Monteux, Sylvain; Orr, Caroline; van der Putten, Wim; Roberts, Ian; Robinson, David; Rocca, Jennifer; Rowntree, Jennifer; Schlaeppi, Klaus; Shepherd, Matthew; Singh, Brajesh; Straathof, Angela; Bhatnagar, Jennifer; Thion, Cécile; van der Heijden, Marcel; de Vries, Franciska;
• Is Part Of: Nature microbiology. Volume 3:Number 2(2018)
• Page Start: 189
• Page End: 196
• Publication Date: 2018-02
• Subjects: Microbiology -- Periodicals; 579.05
• DOI: 10.1038/s41564-017-0062-x
• Languages: English
• ISSNs: 2058-5276

Abstract

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. A machine-learning approach accounting for methodological differences in studies and complex interactions among taxa allows independent soil studies to be combined at the taxonomy-based level to assess bacterial community structure.