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Functional attractors in microbial community assembly.

Sylvie Estrela1, Jean C C Vila1, Nanxi Lu1

  • 1Department of Ecology and Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA.

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|October 15, 2021
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Summary
This summary is machine-generated.

Microbial communities show predictable metabolic organization but unpredictable taxonomic divergence. This study explains how resource partitioning and population dynamics create these patterns in microbial assembly.

Keywords:
alternative statesdynamical systems theorymicrobial community assemblymicrobial metabolismpopulation dynamics

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Area of Science:

  • Microbial ecology
  • Community assembly
  • Predictive modeling

Background:

  • Microbiome biology requires identifying reproducible and predictable community features.
  • Understanding variability in microbial community assembly is crucial for predictive power.

Purpose of the Study:

  • To experimentally investigate parallelism and convergence in microbial community assembly.
  • To identify the drivers of reproducibility and variability in microbial communities.
  • To explain family-level convergence and taxonomic divergence in replicate habitats.

Main Methods:

  • Experimental study of microbial community assembly in replicate glucose-limited habitats.
  • Analysis of community structure and metabolic organization.
  • Application of resource-partitioning models and population dynamics theory.

Main Results:

  • Observed family-level convergence reflects reproducible metabolic organization.
  • Ratio of dominant metabolic groups explained by a resource-partitioning model.
  • Taxonomic divergence arises from multistability in population dynamics.
  • Multistability can lead to alternative functional states in closed ecosystems.

Conclusions:

  • Evolutionary conservation of metabolic traits, multistability, and stochasticity drive microbial community patterns.
  • Findings illustrate how different levels of organization exhibit reproducibility and variability.
  • This work advances microbiome biology towards a more predictive science.