Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity via abiotic effects on soil or mediated by changes in plant inputs. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the source of these problems in archetypal microbial models and propose a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. We compare these models to a synthesis of long-term (>5 years) C-input field manipulations, including the Detritus Input and Removal Treatment (DIRT) experiments, and identify key benchmarks to validate the inherent dynamics of each model structure.
Results/Conclusions
We find that widely used first-order models and microbial models without community-level regulatory mechanisms (e.g., density-dependent growth and mortality) cannot readily capture the range of long-term responses observed across the DIRT experiments as a direct consequence of their model structures. Incorporating such regulatory mechanisms provides an additional negative feedback that limits the activity and size of the microbial biomass pool, and thus, strongly affects biogeochemical projections. The proposed formulation improves predictions of long-term C-input changes, and implies greater SOC storage associated with CO2-fertilization-driven increases in C inputs over the coming century compared to recent microbial models. Finally, we discuss our findings in the context of improving microbial model behavior for inclusion in Earth System Models.