Incorporatingmicrobial ecology concepts into global soil mineralization models to improve predictions of carbon and nitrogen fluxes

Global models of soil carbon (C) and nitrogen (N) fluxes become increasingly needed to describe climate change impacts, yet they typically have limited ability to reflect microbial activities that may affect global-scale soil dynamics. Benefiting from recent advances in microbial knowledge, we evaluated critical assumptions on microbial processes to be applied in global models. We conducted a sensitivity analysis of soil respiration rates (Cmin) and N mineralization rates (Nmin) for different model structures and parameters regarding microbial processes and validated them with laboratory incubation data of diverse soils. Predicted Cmin was sensitive to microbial biomass, and the model fit to observed Cmin improved when using site-specific microbial biomass. Cmin was less affected by the approach of microbial substrate consumption (i.e., linear, multiplicative, or Michaelis-Menten kinetics). The sensitivity of Cmin to increasing soil N fertility was idiosyncratic and depended on the assumed mechanism of microbial C:N stoichiometry effects: a C overflow mechanism upon N limitation (with decreased microbial growth efficiency) led to the best model fit. Altogether, inclusion of microbial processes reduced prediction errors by 26% (for Cmin) and 7% (for Nmin) in our validation data set. Our study identified two important aspects to incorporate into global models: site-specific microbial biomass and microbial C:N stoichiometry effects. The former requires better understandings of spatial patterns of microbial biomass and its drivers, while the latter urges for further conceptual progress on C-N interactions. With such advancements, we envision improved predictions of global C and N fluxes for a current and projected climate. Key Points We modeled soil C and N flux with various microbial parameters and structures Mineralization rates were validated with lab incubation data from diverse soils Inclusion of microbial biomass and C:N stoichiometry improves global models