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Complexity-stability trade-off in empirical microbial ecosystems.

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May's stability theory suggests ecosystems have a complexity limit. This study introduces a new method to estimate ecosystem complexity, finding that microbial communities exhibit trade-offs between species number and interactions, supporting stability constraints in natural microbiomes.

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Area of Science:

  • Theoretical ecology
  • Microbiome research
  • Computational biology

Background:

  • May's stability theory posits a limit to ecosystem complexity based on species number and interaction intensity.
  • Empirical validation of this theory in real-world ecosystems, especially complex natural microbiomes, remains challenging due to difficulties in reconstructing interaction networks.

Purpose of the Study:

  • To develop a computational framework for estimating ecosystem complexity without prior knowledge of interaction networks.
  • To investigate the applicability of May's stability theory to human-associated and sponge-associated microbial communities.

Main Methods:

  • Introduced a novel computational framework to estimate ecosystem complexity.
  • Applied the framework to analyze human microbiome data from various body sites.
  • Applied the framework to analyze sponge microbiome data from different geographical locations.

Main Results:

  • The developed framework allowed complexity estimation without needing a priori interaction network data.
  • Both human and sponge microbial communities demonstrated a significant trade-off between species richness and effective connectance.
  • These findings indicate that complexity in natural microbiomes is constrained by stability.

Conclusions:

  • Natural microbiomes appear to be regulated by stability constraints that limit their overall complexity.
  • The study provides empirical support for May's stability theory in complex natural microbial ecosystems.
  • The novel computational framework offers a new tool for ecological network analysis.