Functional attractors in microbial community assembly

For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly. [Display omitted] •We study convergence and divergence in microbiome assembly in replicate habitats•Functional convergence reflects an emergent metabolic self-organization•Taxonomic divergence arises from multistability in population dynamics•Simple models can explain observed quantitative patterns in microbiome assembly Microbiomes may be described at different levels of organization: from strains to metabolic functions. The predictability of microbiome assembly often increases as we zoom out and look at their emergent functional behavior. Due to the significant challenges of studying microbiomes in their natural habitats, these recurrent patterns remain poorly understood. Here, we investigate laboratory ecosystems exhibiting a similar pattern of functional convergence despite fine-scale taxonomic divergence. By combining experiments and modeling, we provide a mechanistic explanation for these patterns.