title: An improved method to set significance thresholds for β diversity testing in microbial community comparisons.
abstract: Exploring the variation in microbial community diversity between locations (β diversity) is a central topic in microbial ecology. Currently, there is no consensus on how to set the significance threshold for β diversity. Here, we describe and quantify the technical components of β diversity, including those associated with the process of subsampling. These components exist for any proposed β diversity measurement procedure. Further, we introduce a strategy to set significance thresholds for β diversity of any group of microbial samples using rarefaction, invoking the notion of a meta-community. The proposed technique was applied to several in silico generated operational taxonomic unit (OTU) libraries and experimental 16S rRNA pyrosequencing libraries. The latter represented microbial communities from different biological rapid sand filters at a full-scale waterworks. We observe that β diversity, after subsampling, is inflated by intra-sample differences; this inflation is avoided in the proposed method. In addition, microbial community evenness (Gini > 0.08) strongly affects all β diversity estimations due to bias associated with rarefaction. Where published methods to test β significance often fail, the proposed meta-community-based estimator is more successful at rejecting insignificant β diversity values. Applying our approach, we reveal the heterogeneous microbial structure of biological rapid sand filters both within and across filters.
authors.affiliation: Department of Environmental Engineering, Technical University of Denmark, Building 113, Miljøvej, 2800, Kongens Lyngby, Denmark.
authors.forename: Arda
authors.initials: A
authors.lastname: Gülay
authors.affiliation: Department of Environmental Engineering, Technical University of Denmark, Building 113, Miljøvej, 2800, Kongens Lyngby, Denmark.
authors.forename: Barth F
authors.initials: BF
authors.lastname: Smets
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics*; D003627:Data Interpretation, Statistical; D059013:Microbial Consortia / Q000235:genetics* / Q000502:physiology; D012336:RNA, Ribosomal, 16S / Q000235:genetics
chemical_list: D012336:RNA, Ribosomal, 16S