Publication
Detecting macroecological patterns in bacterial communities across independent studies of global soils
Publisher:
Springer Science and Business Media LLC
Date:
20-11-2017
DOI:
10.1038/S41564-017-0062-X
Abstract: The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial bio ersity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple in idual studies to address macroecological questions of bacterial ersity 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 s les from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial ersity measures or abundances of major taxa, we show that disparate licon 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.