Posted by Design Studio
23 January 2026
Challenge 2: Soil Carbon
Introduction
Microbial traits can vary with environmental conditions and are difficult to constrain in models. Eco-evolutionary optimality approaches resolve this issue by assuming that traits vary so as to maximize a measure of fitness – that means, they assume that microbes are always adapted optimally to given conditions and express “optimal traits”.
A primer for eco-evolutionary approaches
In this perspective article, we offer a primer for understanding how eco-evolutionary optimality models work. We first classify the existing approaches according to the way fitness is defined and the time scale at which the optimization is performed. We then compare the mathematical formulations and predictions across a range of contrasting optimization approaches applied within the same model structure.
Are different eco-evolutionary approaches comparable?
Even if models share the same structure, applying contrasting eco-evolutionary approaches can yield different predictions of optimal traits that maximize microbial fitness (here we focused on microbial investment in extra-cellular enzymes that drive decomposition). We thus conclude that it is difficult to generalize insights from a specific combination of model structure and eco-evolutionary approach, but systematically comparing results from different model structures and approaches and validating them with observational data can help find consistent patterns in the response of microbial traits to environmental conditions.
Eco-evolutionary optimality approaches determine functional traits that are selected by evolutionary or ecological processes because they maximize microbial fitness. The schematic illustrates this approach for the microbial resource allocation between biomass and extracellular enzyme production, but this principle can be applied to many other traits as well.
Read the publication in full https://doi.org/10.1111/ele.70278
