Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

Background: Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results: This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. Conclusion: This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

In their recent Opinion article, Banerjee, Schlaeppi and van der Heijden claim that microbial networks can identify keystones; that is, taxa with a high impact on the structure and functioning of ecosystems (Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567-576 (2018)) 1. Although we agree with the authors on the importance of keystones, we doubt that a highly connected taxon in a microbial network (a hub) is necessarily a keystone, and we therefore want to moderate their claim that over 200 microbial keystones have been identified. Keystones are of particular interest because they have a greater impact on the ecosystem than other taxa. The classic experimental validation of keystones involves comparing the effects of keystone removal and/or addition with the removal and/or addition of other community members. As such experiments are difficult to carry out, only few microbial taxa have been experimentally confirmed as keystones 2-5. If keystones could be accurately predicted from microbial networks, experiments would no longer be required. However, edges in microbial networks usually do not represent known ecological interactions, but rather statistically significant co-occurrences or mutual exclusions of taxa in sequencing data. The question is how accurately inferred microbial networks can identify keystones. Weiss and colleagues did not validate keystones, but showed that the prediction accuracy for ecological interactions in inferred microbial networks is low 6 .