Emergent properties of organic matter decomposition by soil enzymes

Abstract

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.

Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

“…Hence, our study contributes to resolving the challenge of upscaling microbial regulation mechanisms from the microhabitat scale to larger scales relevant for soil management and global environmental change . SpatC predictions of microbial and C dynamics are, however, dependent on the assumed biokinetic rate laws at the mm-scale, which have been shown to differ from rate laws at µm-scale (Chakrawal et al, 2019;Wang and Allison, 2019). Similarly, an exploratory analysis of SpatC revealed a very high sensitivity of model dynamics to some key biokinetic parameters that partly increased the observed mild effect of spatial heterogeneity (Supplementary Figures 28-30).…”

“…In addition, the representation of biological community interactions remains limited. Crucial extensions could include the explicit representation of enzyme dynamics (Burns et al, 2013;Moyano et al, 2018;Wang and Allison, 2019) and the implementation specific fungal traits (Yang and van Elsas, 2018). Similarly, microbial dispersal and chemotactic behavior (Valdés-Parada et al, 2009; see e.g., Gharasoo et al, 2014;Locey et al, 2017;König et al, 2018) should be included in future.…”

Dynamic upscaling of decomposition kinetics for carbon cycling models

“…The ratio of the two timescales defines the Damköhler number, Da = τ diff /τ react , which represents the relative importance of mass transport of the substrate via diffusion vs. reaction (Dentz et al, 2011). For a relevant substrate such as glucose, D diff is of the order of 10 −11 m 2 s −1 (Watt et al, 2006), the turnover time is of the order of ∼ 1 d, and the length scale is of the order of ∼ 50 µm. With these values, Da 1, which characterizes a reactionlimited system in well-mixed conditions.…”

“…This heterogeneity occurring at scales from ∼ 10 to 200 µm is generally neglected in C cycling models. Below the ∼ 50 µm scale, diffusion timescales can be assumed to be faster than advection and reaction timescales (Watt et al, 2006). Thus, it can be argued that below ∼ 50 µm the assumption of homogeneity is likely to hold, while it is no longer valid above this threshold (see Sect.…”

“…Three types of upscaling approaches are often used for dynamical systems such as those used to describe soil biogeochemical processes: (i) the spatial averaging of numerically simulated C flux fields, (ii) the definition of effective parameters to capture fine-scale heterogeneity, and (iii) scale transition theory or the volume averaging of the equations at the microscale. Spatial averaging of simulated dynamics at the microscale is relatively common (Allison, 2012;Kaiser et al, 2014;Yan et al, 2016;Wang and Allison, 2019), but this approach does not lend itself to analytical solutions that would offer insights into the effects of heterogeneity on macroscopic properties. The effective parameter approach, more common in subsurface hydrology (e.g., Dagan, 1987), has been used to relate the macroscopic decomposition rate to the characteristic parameters of microscale heterogeneity, but only in a minimal "lumped" model (Manzoni et al, 2008).…”