Modeling interacting microbial and litter quality constraints on decomposition: The intersection of metabolic, stoichiometric and ecoenzymatic theories

Background/Question/Methods: The activities of decomposer microorganisms typically drive litter decay but mathematical models of this process often omit microorganisms or drive their dynamics as a result of changes in litter mass and chemistry. A challenge to devising microbially-based models is capturing the interacting controls of litter chemistry and microbial activities, both of which constrain the decomposition process. We extended the enzyme-based decomposition model of Schimel and Weintraub (2003), which simulates the dynamics of one enzyme that degrades one substrate, to two enzymes that degrade two qualitatively different substrate pools: a carbon-only pool (e.g. cellulose) and a carbon+nitrogen pool (e.g. chitin or protein). Substrate hydrolysis was estimated as a “reverse” Michaelis-Menten function of enzyme saturation, rather than the more common approach based on substrate saturation. Patterns of decay resulted from both microbial and substrate limitations.

Results/Conclusions: The allocation of extracellular enzyme activities (EEA) to simultaneously meet the energetic and stoichiometric demands of decomposer microorganisms varied with both C:N content of C+N substrate pool (CN₁) and availability of a second, C-only pool. Access to a C-only pool reduced N-mineralization and increased microbial biomass and respiration when CN₁ was less than the quotient of the microbial C:N ratio (CN_M) divided by the C-utilization efficiency of substrate (SUE); this quotient is commonly known as the threshold element ration (i.e., TER≈CN_M/SUE). In every set of simulations, maximum microbial biomass and respiration corresponded with maximum total enzyme pool size, which in turn corresponded to a balanced allocation of enzymes between pools (i.e., EEA≈50:50). This optimal EEA occurred when CN₁=CN_M. However, this threshold varied with key model parameters (SUE, enzyme half-saturation coefficients, and maximum rates of substrate hydrolysis). Moreover, the combination of selective substrate utilization and progressive recycling of microbial products led to convergences in substrate chemistry and patterns of microbial activity consistent with recently published syntheses of changing litter chemistry and extracellular enzyme activities during advanced decay. Sensitivity analysis showed that variations in these parameters explained over 90% of variation in subsequent model behaviors, despite the non-linear relationships between enzyme pool sizes, biomass, respiration and enzyme activity. Model results also showed that variations in TER with respect to gross litter chemistry could be explained by finer scale mechanisms of specific enzymes hydrolyzing specific substrates in response to microbial requirements and substrate qualities.