There is growing interest in approaches to improve ecosystem models by incorporating microbial mechanisms. Still, microbial communities are complex, and it remains unclear how to model the relationship between microbial diversity and ecosystem processes, such as decomposition. Trait-based approaches, both empirical and theoretical, offer promise for addressing this challenge. By measuring and modeling the relationships among microbial traits, it becomes more tractable to predict how environmental responses translate into functional consequences. I developed the trait-based DEMENT model to predict rates of decomposition and nutrient cycling driven by diverse microbial communities under varying environmental conditions. The microbial taxa in the model are defined solely by their traits, which are assigned based on ecological, evolutionary, and physiological constraints. Empirical studies inform these constraints by quantifying trait distributions and correlations.
Using the DEMENT model, I have found that feedbacks involving microbial traits can potentially stabilize decomposition responses to environmental change. For example, tradeoffs among resource use rate and growth efficiency mitigate soil carbon losses when microbial communities respond to warming temperatures. For microbial communities responding to drought, interactions among different enzyme production traits stabilize rates of decomposition. These simulation results are informed by molecular datasets on the response of specific microbial taxa and enzyme genes to drought and changes in resource availability. Empirical measurements of decomposition rates under different environmental conditions validate the simulation outputs. Together, these results demonstrate how traits can be used to translate from genes to ecosystem processes in the context of environmental change.