Ecology theory disentangles microbial dichotomies

TL;DR

This paper develops a unified ecological framework using Monod curves to interpret microbial growth strategies, reconciling traditional dichotomies and explaining microbial diversity through a quantitative, theory-based approach.

Contribution

It introduces a model that combines ecology theory with Monod curves to unify microbial growth dichotomies within a comprehensive life history strategy framework.

Findings

Microbial dichotomies can be interpreted as different points on a life history strategy triangle.

The model links Monod curve parameters to ecological strategies like r/K selection.

The framework helps explain microbial diversity in natural communities.

Abstract

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We are able to interpret microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a classical laboratory tool of bacterial physiology. Monod curves relate the specific growth rate of a microbe with the concentration of a limiting nutrient, and provide quantities that directly correspond to key ecological parameters in McArthur and Wilsons r/K selection theory, Tilmans resource competition and community structure theory and Grimes triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We analyzed some ecological context by considering the known viable carbon sources for heterotrophic microbes in the framework of community structure theory. This partly explains the microbial diversity observed using metagenomics. In sum, ecology theory in combination with Monod curves can be a unifying quantitative framework for the study of natural microbial communities, calling for the integration of modern laboratory and field experiments.