Sensitivity of microbial spatial self-organization to surface friction depends on metabolic interactions

TL;DR

Microbial spatial patterns are more sensitive to environmental conditions when cells interact positively, like cross-feeding, compared to when they compete.

Contribution

A new framework to quantify spatial intermixing and its sensitivity to environmental and biotic factors in microbial ecosystems.

Findings

Positive interactions like cross-feeding lead to spatial patterns more sensitive to environmental changes.

High dispersal and strong biotic interdependence promote persistent spatial intermixing.

Spatial intermixing strength can indicate ecosystem sensitivity to environmental conditions.

Abstract

Self-organizing spatial patterns are ubiquitous in microbial ecosystems, yet their sensitivity to environmental conditions remains poorly understood. Understanding spatial pattern sensitivity is particularly relevant for surface-associated microbial systems, as their functioning depends on how different cell-types self-organize across space as a consequence of their traits and environmental conditions. Here, we integrate principles from microbial systems ecology with self-organization theory to understand how environmental conditions and biotic interactions shape the sensitivity of emergent spatial intermixing, which is a critical feature of spatial patterns. Using denitrifying strains of the bacterium Stutzerimonas stutzeri that engage in negative (competitive) or positive (cross-feeding) interactions, we demonstrate that spatial intermixing emerging from positive interactions is more sensitive to environmental conditions than that emerging from negative interactions. We further develop and quantify the spatial intermixing strength as a key descriptor of spatial pattern sensitivity, revealing that high short-range dispersal and strong biotic interdependence promote persistent spatial intermixing. Our findings highlight that ecosystem sensitivity to environmental conditions can be inferred from features of emergent spatial patterns, providing a quantitative framework for understanding how environmental and biological factors jointly govern ecosystem assembly and dynamics.

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