Representation of Dormant and Active Microbial Dynamics for Ecosystem Modeling

TL;DR

This paper introduces a new microbial physiology model that incorporates dormancy, enabling better representation of microbial activity and states in ecosystem models, especially under varying substrate conditions.

Contribution

The paper presents a novel microbial physiology model that accounts for dormancy and active states, improving predictions of microbial dynamics in ecosystem modeling.

Findings

Model explains low active microbial fractions in undisturbed soils.

Respiration data can robustly determine key microbial parameters.

Model can be integrated into existing ecosystem models for improved accuracy.

Abstract

Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in major model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and is majorly parameterized with the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-mediated processes and to provide improved estimates of microbial activities.