# 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.

## Key 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.