Abstract Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme‐mediated soil inorganic N processes in the Microbial‐ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C‐N fluxes, microbial C:N ratios, and functional gene abundances from a 12‐year CO2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C‐N‐associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO2concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem‐level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.
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Can models adequately reflect how long-term nitrogen enrichment alters the forest soil carbon cycle?
Abstract. Changes in the nitrogen (N) status of forest ecosystems can directly and indirectly influence their carbon (C) sequestration potential by altering soil organic matter (SOM) decomposition, soil enzyme activity, and plant–soil interactions. However, model representations of linked C–N cycles and SOM decay are not well validated against experimental data. Here, we use extensive data from the Fernow Experimental Forest long-term whole-watershed N fertilization study to compare the response to N perturbations of two soil models that represent decomposition dynamics differently (first-order decay versus microbially explicit reverse Michaelis–Menten kinetics). These two soil models were coupled to a common vegetation model which provided identical input data. Key responses to N additions measured at the study site included a shift in plant allocation to favor woody biomass over belowground carbon inputs, reductions in soil respiration, accumulation of particulate organic matter (POM), and an increase in soil C:N ratios. The vegetation model did not capture the often-observed shift in plant C allocation with N additions, which resulted in poor predictions of the soil responses. We modified the parameterization of the plant C allocation scheme to favor wood production over fine-root production with N additions, which significantly improved the vegetation and soil respiration responses. Additionally, to elicit an increase in the soil C stocks and C:N ratios with N additions, as observed, we modified the decay rates of the POM in the soil models. With these modifications, both models captured negative soil respiration and positive soil C stock responses in line with observations, but only the microbially explicit model captured an increase in soil C:N. Our results highlight the need for further model development to accurately represent plant–soil interactions, such as rhizosphere priming, and their responses to environmental change.
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- PAR ID:
- 10521135
- Publisher / Repository:
- Copernicus Publications on behalf of the European Geosciences Union
- Date Published:
- Journal Name:
- Biogeosciences
- Volume:
- 21
- Issue:
- 1
- ISSN:
- 1726-4189
- Page Range / eLocation ID:
- 201 to 221
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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