Diverse communities promote the coexistence of closely-related strains through emergent equalization and stabilization

TL;DR

This study reveals how diverse microbial communities facilitate the coexistence of closely-related strains through community-mediated feedbacks, which promote equalization and stabilization, transforming competitive interactions into mutualistic-like correlations.

Contribution

It combines ecology and physics to show that indirect community interactions enable strain coexistence and alter correlation patterns without detailed interaction networks.

Findings

Community-mediated feedbacks are as strong as direct interactions.

Strain coexistence is driven by equalizing and stabilizing mechanisms.

Community context can turn competition into apparent mutualism.

Abstract

Microbial communities harbor extensive fine-scale diversity: closely-related strains of the same species coexist alongside many distantly-related taxa. Yet strain coexistence remains poorly understood, largely because most studies neglect the diverse communities in which strains are embedded. Here we combine community ecology and statistical physics to study the dynamics of closely-related strains in a community context. We demonstrate that in a diverse community, indirect interactions between strains -- mediated through the surrounding community members -- can be as strong as direct ones. These community-mediated feedbacks cause conspecific strains to behave as if they have correlated growth rates and reduced competition. Using modern coexistence theory, we show that these effects correspond to equalizing and stabilizing mechanisms which together promote strain coexistence. The same equalizing and stabilizing mechanisms also qualitatively transform strain abundance correlations: strains that compete strongly and show negative correlations in isolation instead show positive correlations in a community, appearing mutualistic despite being competitors. Our results demonstrate that strain dynamics are emergent consequences of the surrounding community, and that capturing community feedbacks does not require the full interaction network; only a small number of emergent parameters.