![]() Fundamentally, interactions can be either positive or negative ( Faust and Raes, 2012), and consequently lead to either aggregation or alternatively avoidance or exclusion ( Cody and Diamond, 1975 Stone and Roberts, 1992 Gotelli and McCabe, 2002). In macro-ecological surveys non-random species co-occurrence patterns are often observed, indicating that community structure is imprinted by interactions between species. The study of co-occurrence and co-abundance patterns has a long history in ecological research. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets. We also identify topological features associated with keystone species in co-occurrence networks. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. ![]() We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. 2CUBE-Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, AustriaĬo-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members.1Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
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