Gangsheng Wang

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2 degrees C (baseline temperature 12 degrees C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from -8% to 8% under +2 degrees C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.

Microbial dormancy improves development and experimental validation of ecosystem model

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C–CO2 and 14C–CO2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20–71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.

A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling

We attempted to reconcile three microbial maintenance models (Herbert, Pirt, and Compromise) through a theoretical reassessment. We provided a rigorous proof that the true growth yield coefficient (Y(G)) is the ratio of the specific maintenance rate (a in Herbert) to the maintenance coefficient (m in Pirt). Other findings from this study include: (1) the Compromise model is identical to the Herbert for computing microbial growth and substrate consumption, but it expresses the dependence of maintenance on both microbial biomass and substrate; (2) the maximum specific growth rate in the Herbert (μ(max,H)) is higher than those in the other two models (μ(max,P) and μ(max,C)), and the difference is the physiological maintenance factor (m(q) = a); and (3) the overall maintenance coefficient (m(T)) is more sensitive to m(q) than to the specific growth rate (μ(G)) and Y(G). Our critical reassessment of microbial maintenance provides a new approach for quantifying some important components in soil microbial ecology models.

Representation of Dormant and Active Microbial Dynamics for Ecosystem Modeling

Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

Differential effects of warming and nitrogen fertilization on soil respiration and microbial dynamics in switchgrass croplands

The mechanistic understanding of warming and nitrogen (N) fertilization, alone or in combination, on microbially mediated decomposition is limited. In this study, soil samples were collected from previously harvested switchgrass (Panicum virgatum L.) plots that had been treated with high N fertilizer (HN: 67 kg N ha−1) and those that had received no N fertilizer (NN) over a 3‐year period. The samples were incubated for 180 days at 15 °C and 20 °C, during which heterotrophic respiration, δ13C of CO2, microbial biomass (MB), specific soil respiration rate (Rs: respiration per unit of microbial biomass), and exoenzyme activities were quantified at 10 different collections time. Employing switchgrass tissues (referred to as litter) with naturally abundant 13C allowed us to partition CO2 respiration derived from soil and amended litter. Cumulative soil respiration increased significantly by 16.4% and 4.2% under warming and N fertilization, respectively. Respiration derived from soil was elevated significantly with warming, while oxidase, the agent for recalcitrant soil substrate decomposition, was not significantly affected by warming. Warming, however, significantly enhanced MB and Rs indicating a decrease in microbial growth efficiency (MGE). On the contrary, respiration derived from amended litter was elevated with N fertilization, which was consistent with the significantly elevated hydrolase. N fertilization, however, had little effect on MB and Rs, suggesting little change in microbial physiology. Temperature and N fertilization showed minimal interactive effects likely due to little differences in soil N availability between NN and HN samples, which is partly attributable to switchgrass biomass N accumulation (equivalent to ~53% of fertilizer N). Overall, the differential individual effects of warming and N fertilization may be driven by physiological adaptation and stimulated exoenzyme kinetics, respectively. The study shed insights on distinct microbial acquisition of different substrates under global temperature increase and N enrichment.

Multiple models and experiments underscore large uncertainty in soil carbon dynamics

Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.