Abstract
Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. 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. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. 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. 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 demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.
Highlights
The study of co-occurrence and co-abundance patterns has a long history in ecological research
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
Species subsampled from a metacommunity were used to produce local communities and community population dynamics were simulated until steady state abundances were reached
Summary
The study of co-occurrence and co-abundance patterns has a long history in ecological research. In macro-ecological surveys non-random species co-occurrence patterns are often observed, indicating that community structure is imprinted by interactions between species. Research on macro-ecological interaction networks and their topologies has revealed that communitywide interaction patterns maximize robustness and functionality (Montoya et al, 2006; Thébault and Fontaine, 2010; Saavedra et al, 2011). They are fundamental units for understanding community dynamics and productivity. Because interactions can affect population dynamics it is expected that signatures of microbial interactions are imprinted in microbial survey datasets
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