Deciphering microbial interactions and detecting keystone species with co-occurrence networks

By D. Berry, Stefanie Widder. Frontiers in Microbiology, 2014

1,538 citations recorded by the source corpus.

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.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c8be97da104a9c0ce681353152d44673aca87b19

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Citations in the corpus

Co-occurrence is not evidence of ecological interactions. (2020, background): “…associations in co-occurrence data as a potential sign of biotic interactions towards the reconstruction of entire ecological networks derived from large presence absence data sets (Faust & Raes, 2012; Wisz et al., 2013; Berry & Widder, 2014; Zelezniak et al., 2015; Mandakovic et al., 2018).”
Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation (2016, background): “Keystones are rather defined by the quantity of overall interactions in the network that are dependent on them [44].”
Correlation detection strategies in microbial data sets vary widely in sensitivity and precision (2016, background): “One previous effort in this area tested mainly basic correlation measures for one type of model system (Berry and Widder, 2014).”
Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming (2018, background): “Keystone taxa are thought to frequently interact with many other taxa, thereby playing an important role in the overall community [67, 68].”
Microbial diversity and ecological networks as indicators of environmental quality (2017, methodology): “Among these different methods, Berry and Widder (2014) recommend using the Spearman or Pearson correlation coefficient to identify the co-occurrence links within a community.”
Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots (2018, background): “degree and highest closeness centrality, and the lowest betweenness centrality scores were 174 considered as the keystone taxa [43].”
Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China (2016, background): “Specifically, nodes with high degree (4100) and low betweenness centrality values (o5000) are recognized as keystone species in co-occurrence networks (Berry and Widder, 2014).”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, background): “The nonrandom co-occurrence patterns of species observed in ecosystems may be interpreted that the community structure is shaped primarily by microbial interactions [57].”
Microbiome definition re-visited: old concepts and new challenges (2020, background): “The interpretation of the pathogenicity data, however, should be taken with caution [83].”
Ecology and exploration of the rare biosphere (2015, background): “Infer ring ecological roles from microbial community surveys poses a substantial challenge; however, cooccurrence networks show promise in identifying keystone species in HTS data 89 , 90 .”
Biogeographic patterns of microbial association networks in paddy soil within Eastern China (2020, background): “Second, association networks can be used to identify keystone species (that is, species highly associated with other species) (Berry and Widder, 2014), which may cause a large shift of microbial community composition as well as functions upon their removal (Berry and Widder, 2014).”