# Microbial cohorts: bringing ecological meaning to the modularity concept of co-occurrence networks

## TL;DR

This study shows that microbial co-occurrence networks are modular, with stable subcommunities (cohorts) that are consistent across diverse environments.

## Contribution

The study demonstrates that microbial cohorts are universal, stable, and ecologically meaningful units across ecosystems.

## Key findings

- Microbial co-occurrence networks consistently show high modularity across different biomes.
- Cohorts represent up to 90% of community composition and respond predictably to environmental gradients.
- Cohort structure is robust to sample size and inference algorithm variations.

## Abstract

Microbial communities are structured through complex interactions that are difficult to observe directly. Co-occurrence networks offer a way to infer community structure, revealing (not exclusively) potential biotic interactions. Such networks have been inferred for diverse biomes and repeatedly found to be modular, yet the ecological significance of this modularity remains underexplored. We tested whether clusters within co-occurrence networks (“cohorts”), are universal and ecologically meaningful units by assessing their ubiquity, stability, and environmental specificity across diverse ecosystems. Our meta-analysis spans 25 previously published 16S rRNA gene amplicon sequencing datasets (14 160 samples) and covers high environmental variability ranging from aquatic, terrestrial to anthropogenic environments. Microbial co-occurrence networks consistently exhibited high modularity across biomes. Inferred cohorts were ubiquitous and represented up to 90% of the community composition. Our findings demonstrate that modularity is a fundamental and generalizable feature of microbial community organization, indicating the existence of stable subcommunities. Highly similar cohorts were inferred even across different, unconnected environments and datasets, and showed consistent responses to environmental gradients, indicating that their composition is to a large degree deterministic and predictable. The overall cohort structure and environmental preferences were independent of the sample size and the inference algorithm, underlining the robustness and applicability of the results. Recognizing these microbial cohorts as a meaningful level of microbial organization will refine microbial community ecology, cultivation strategies, and predictive modelling of microbial dynamics.