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

Wang, Gangsheng; Gao, Qun; Yang, Yunfeng; Hobbie, Sarah E.; Reich, Peter B.; Zhou, Jizhong

doi:10.1111/gcb.16036

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

Journal Article · 24 December 2021 · Global Change Biology

DOI: https://doi.org/10.1111/gcb.16036 · OSTI ID:1961811

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Wuhan Univ. (China); Univ. of Oklahoma, Norman, OK (United States)
Tsinghua Univ., Beijing (China)
Univ. of Minnesota, St. Paul, MN (United States)
Univ. of Minnesota, St. Paul, MN (United States); Univ. of Western Sydney, NSW (Australia). Hawkesbury Inst. for the Environment
Univ. of Oklahoma, Norman, OK (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

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 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. In conclusion, 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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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDA; National Science Foundation (NSF); National Science Foundation of China (NSFC); Second Tibetan Plateau Scientific Expedition and Research (STEP) Program

Grant/Contract Number:: AC02-05CH11231; FG02-96ER62291; SC0004601; SC0010715; SC0014079; SC0016247; SC0020163

OSTI ID:: 1961811

Journal Information:: Global Change Biology, Journal Name: Global Change Biology Journal Issue: 5 Vol. 28; ISSN 1354-1013

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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soil enzymes

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

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