4.8 Article

Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling

Journal

GLOBAL CHANGE BIOLOGY
Volume 28, Issue 5, Pages 1935-1950

Publisher

WILEY
DOI: 10.1111/gcb.16036

Keywords

elevated CO2; functional traits; metagenomics; microbial ecological modeling; microbial functional genes; nitrogen enrichment; predictive ecology; soil enzymes

Funding

  1. U.S. Department of Energy [DE-SC0004601, DE-SC0010715, DE-SC0014079, DE-SC0016247, DE-SC0020163]
  2. U.S. National Science Foundation [DEB-0620652, DEB-1234162, DEB-1831944]
  3. United States Department of Agriculture [2007-35319-18305]
  4. Long-Term Research in Environmental Biology [DEB-1242531, DEB-1753859]
  5. Biological Integration Institutes [NSF-DBI-2021898]
  6. Ecosystem Sciences [DEB-1120064]
  7. Biocomplexity [DEB-0322057]
  8. U.S. Department of Energy Program for Ecosystem Research [DE-FG02-96ER62291]
  9. National Science Foundation of China [NSFC 41825016]
  10. Second Tibetan Plateau Scientific Expedition and Research [2019QZKK0503]
  11. U.S. Department of Energy (DOE) [DE-SC0014079, DE-SC0020163, DE-SC0016247, DE-SC0010715] Funding Source: U.S. Department of Energy (DOE)

Ask authors/readers for more resources

The study developed a competitive dynamic enzyme allocation scheme and detailed enzyme-mediated soil inorganic N processes in the Microbial-ENzyme Decomposition (MEND) model, using soil enzymes as indicators of soil function. The model successfully simulated soil CO2 fluxes and multiple N variables, as well as predicted microbial C:N ratios and their response to enriched N supply accurately. Validation showed that changes in enzyme activities in response to elevated atmospheric CO2 concentration were better explained by measured gene abundances.
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 x N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2 fluxes 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 CO2 concentration. 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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