Microbial networks inferred from metabarcoding data lack replicability: consequences for next-generation biomonitoring

Microbial interaction networks support many ecosystem services, including the regulation of crop diseases. One of the current challenges is automatically reconstructing these networks from metabarcoding data and monitoring their responses to environmental change. Here, we evaluated the ability of network inference methods to detect changes in crop-associated microbial networks. We used grapevine as a model plant system and assessed the impact of vineyard management (conventional versus organic) on the alpha- and beta-diversity of the fungal communities of grapevine leaves. We also inferred replicated networks of fungal associations, to compare network alpha- and beta-properties between management systems and to generate hypotheses concerning fungus-fungus interactions. We found that the richness, diversity and evenness of fungal communities were significantly higher in organic plots, and that community composition differed between management systems. Erysiphe necator, the causal agent of grapevine powdery mildew, was significantly more abundant in conventional plots, consistent with visual records of disease symptoms, whereas several yeast species were significantly more abundant in organic plots. Vineyard management also had a significant impact on the beta-properties of fungal association networks, but the high turnover of associations between plots precluded the generation of robust hypotheses concerning interactions between fungal taxa, casting doubts on the relationship between microbial association networks and plant health. Network inference methods therefore require improvement and validation before use in the next-generation biomonitoring of disease control services provided by the crop microbiota. As things stand, community-level data appear to be a more reliable and statistically powerful option than network-level data for monitoring the ecosystem services provided by the plant microbiota.