Leveraging energy flows to quantify microbial traits in soils

Heat dissipation from organic matter decomposition is a well-recognized proxy for microbial activity in soils, but only a few modeling studies have used heat signals to quantify microbial traits such as maximum substrate uptake rate, specific growth rate, mortality rate, and growth efficiency. In this contribution, a hierarchy of coupled mass-energy balance models is proposed to estimate microbial traits encoded in model parameters using heat dissipation and respiration data from glucose induced microbial activity. Moreover, the models are used to explain the observed variability in calorespirometric ratios (CR)-the ratio of heat dissipation to respiration rate. We parametrized four model variants using heat dissipation and respiration rates measured in an isothermal calorimeter during the lag-phase only or during the whole growth-phase. The four variants are referred to as: (i) complex physiological model, (ii) simplified physiological model, (iii) lag-phase model, and (iv) growth-phase model. Model parameters were determined using three combinations of data: A) only the heat dissipation rate, B) only the respiration rate, and C) both heat dissipation and respiration rates. We assumed that the 'best' parameter estimates were those obtained when using all the data (i.e., option C). All model variants were able to fit the observed heat dissipation and respiration rates. The parameters estimated using only heat dissipation data were similar to the 'best' estimates compared to using only respiration rate data, suggesting that the observed heat dissipation rate can be used to constrain microbial models and estimate microbial traits. However, the observed variability in CR was not well captured by some model variants such as the simplified physiological model, in contrast to the lag- and growth-phase model that predicted CR well. This suggests that CR can be used to scrutinize how well metabolic processes are represented in decomposition models.