Microbial decomposers face large stoichiometric imbalances when feeding on nutrient-poor plant residues. To meet the challenges of nutrient limitation, microorganisms might: (i) allocate less carbon (C) to growth vs. respiration or excretion (i.e., flexible C-use efficiency, CUE), (ii) produce extracellular enzymes to target compounds that supply the most limiting element, (iii) modify their cellular composition according to the external nutrient availability, and (iv) preferentially retain nutrients at senescence. These four resource use modes can have different consequences on the litter C and nitrogen (N) dynamics–modes that selectively remove C from the system can reduce C storage in soil, whereas modes that delay C mineralization and increase internal N recycling could promote storage of C and N. Since we do not know which modes are dominant in litter decomposers, we cannot predict the fate of C and N released from plant residues, in particular under conditions of microbial nutrient limitation. To address this question, we developed a process-based model of litter decomposition in which these four resource use modes were implemented. We then parameterized the model using ∼80 litter decomposition datasets spanning a broad range of litter qualities. The calibrated model variants were able to capture most of the variability in litter C, N, and lignin fractions during decomposition regardless of which modes were included. This suggests that different modes can lead to similar litter decomposition trajectories (thanks to the multiple alternative resource acquisition pathways), and that identification of dominant modes is not possible using “standard” litter decomposition data (an equifinality problem). Our results thus point to the need of exploring microbial adaptations to nutrient limitation with empirical estimates of microbial traits and to develop models flexible enough to consider a range of hypothesized microbial responses.

Abstract. The distribution of organic substrates and microorganisms in soils is spatially heterogeneous at the microscale. Most soil carbon cycling models do not account for this microscale heterogeneity, which may affect predictions of carbon (C) fluxes and stocks. In this study, we hypothesize that the mean respiration rate R‾ at the soil core scale (i) is affected by the microscale spatial heterogeneity of substrate and microorganisms and (ii) depends upon the degree of this heterogeneity. To theoretically assess the effect of spatial heterogeneities on R‾, we contrast heterogeneous conditions with isolated patches of substrate and microorganisms versus spatially homogeneous conditions equivalent to those assumed in most soil C models. Moreover, we distinguish between biophysical heterogeneity, defined as the nonuniform spatial distribution of substrate and microorganisms, and full heterogeneity, defined as the nonuniform spatial distribution of substrate quality (or accessibility) in addition to biophysical heterogeneity. Four common formulations for decomposition kinetics (linear, multiplicative, Michaelis–Menten, and inverse Michaelis–Menten) are considered in a coupled substrate–microbial biomass model valid at the microscale. We start with a 2-D domain characterized by a heterogeneous substrate distribution and numerically simulate organic matter dynamics in each cell in the domain. To interpret the mean behavior of this spatially explicit system, we propose an analytical scale transition approach in which microscale heterogeneities affect R‾ through the second-order spatial moments (spatial variances and covariances). The model assuming homogeneous conditions was not able to capture the mean behavior of the heterogeneous system because the second-order moments cause R‾ to be higher or lower than in the homogeneous system, depending on the sign of these moments. This effect of spatial heterogeneities appears in the upscaled nonlinear decomposition formulations, whereas the upscaled linear decomposition model deviates from homogeneous conditions only when substrate quality is heterogeneous. Thus, this study highlights the inadequacy of applying at the macroscale the same decomposition formulations valid at the microscale and proposes a scale transition approach as a way forward to capture microscale dynamics in core-scale models.

Microbial growth is a clear example of organization and structure arising in nonequilibrium conditions. Due to the complexity of the microbial metabolic network, elucidating the fundamental principles governing microbial growth remains a challenge. Here, we present a systematic analysis of microbial growth thermodynamics, leveraging an extensive dataset on energy-limited monoculture growth. A consistent thermodynamic framework based on reaction stoichiometry allows us to quantify how much of the available energy microbes can efficiently convert into new biomass while dissipating the remaining energy into the environment and producing entropy. We show that dissipation mechanisms can be linked to the electron donor uptake rate, a fact leading to the central result that the thermodynamic efficiency is related to the electron donor uptake rate by the scaling law η∝μED−1/2 and to the growth yield by η∝Y4/5. These findings allow us to rederive the Pirt equation from a thermodynamic perspective, providing a means to compute its coefficients, as well as a deeper understanding of the relationship between growth rate and yield. Our results provide rather general insights into the relation between mass and energy conversion in microbial growth with potentially wide application, especially in ecology and biotechnology.