Using null models to infer microbial co-occurrence networks.

ABSTRACT: Although microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incor

SUBMITTER: Connor N 

PROVIDER: S-EPMC5426617 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature