Emergent properties of organic matter decomposition by soil enzymes.

Plant residues and soil organic matter are predominantly decomposed by exoenzymes. Many soil carbon models now represent enzymatic decomposition, but the mathematical formulation of this process has been debated over the last 15 years. Some models apply the traditional "forward" Michaelis-Menten equation to represent enzyme kinetics, whereas others apply the "reverse" Michaelis-Menten equation, which assumes that kinetic rates saturate at high enzyme concentrations. Recently the equilibrium chemistry approximation (ECA) has been proposed as an alternative to both Michaelis-Menten formulations. However, because of methodological limitations, in-situ enzyme kinetics—especially in the context of soil system heterogeneity—have been difficult to verify experimentally. Therefore, the overarching goal of our study was to evaluate different enzyme kinetic formulations using model-based evidence at microbial to ecosystem scales. We used a spatially explicit individual- and trait-based microbial model, DEMENT, to circumvent methodological challenges. Although DEMENT assumes forward Michaelis-Menten kinetics at local scales, at the grid scale we found saturating relationships between degradation rate and both substrate concentrations and enzyme concentrations that fit the forward and reverse Michaelis-Menten equations, respectively, at specific successional stages during decomposition. Although forward and reverse Michaelis-Menten equations emerged under some conditions, only the ECA adequately represented decay rates emerging from the spatial-temporal variation in substrate and enzyme concentrations throughout the decomposition process. Our results support a more widespread adoption of the ECA equation in soil biogeochemical modelling at ecosystem scales. 1. Representation of enzymatic decomposition in soil carbon models is under debate 2. We use a spatially explicit trait-based model to overcome methodological challenges 3. Michaelis-Menten equations fail to capture emergent spatial-temporal variation 4. Equilibrium Chemistry Approximation (ECA) captures emergent patterns very well 5. ECA can be adopted to scale up fine scale heterogeneity in ecosystem models [ABSTRACT FROM AUTHOR]