# Phylogenetic clustering of microbial communities as a biomarker for chemical pollution

## TL;DR

This paper shows that chemical pollution affects microbial communities in predictable ways, with their evolutionary relationships being useful for tracking environmental contamination.

## Contribution

The study demonstrates that phylogenetic clustering in microbial communities can serve as a novel biomarker for chemical pollution.

## Key findings

- Bacterial isolates showed a strong phylogenetic signal in their responses to chemical stress.
- Pollutants reduced community diversity and caused shifts toward increased phylogenetic clustering.
- Mean phylogenetic distance is a potential simple metric for monitoring pollution.

## Abstract

Microbial communities play a critical role in ecosystem functioning and offer promising potential as bioindicators of chemical pollution in aquatic environments. Here we examine the responses of both bacterial isolates and microbial communities to a range of pollutants, focusing on the phylogenetic predictability of their responses. We tested the growth inhibition of environmental bacterial isolates by 168 agricultural pollutants recently shown to have off-purpose antimicrobial activity in human gut bacteria. We also tested the growth responses of whole microbial communities to the same chemical pollutants and quantified changes in the composition of select communities, to link compositional changes to functioning. We found that bacterial isolates exhibited a strong phylogenetic signal in their growth responses, with closely related taxa responding similarly to chemical stress. In microbial communities, pollutants that significantly impacted isolates also reduced community diversity and growth, causing shifts in community structure toward increased phylogenetic clustering, suggesting environmental filtering. The mean phylogenetic distance effectively captured these shifts, indicating its potential as a simple metric for monitoring pollution. Our findings highlight the predictability of microbial responses to pollution and suggest that microbial-based bioindicators, coupled with rapid sequencing technologies, could transform environmental monitoring.

We show that chemical pollution alters microbial communities in predictable ways, making their evolutionary relationships a useful tool for tracking environmental contamination.