Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling
Abstract
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 CO 2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO 2 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 CO 2 concentration. Our results demonstrated that using enzymes as indicators of soil function andmore »
- Authors:
-
[1];
[2];
[2];
[3];
[4];
[5]
- 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)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- 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
- OSTI Identifier:
- 1961811
- Alternate Identifier(s):
- OSTI ID: 1837392
- Grant/Contract Number:
- AC02-05CH11231; FG02-96ER62291; SC0004601; SC0010715; SC0014079; SC0016247; SC0020163; DEB-0620652; DEB-1234162; DEB-1831944; DEB-1242531; DEB-1753859; NSF-DBI-2021898; DEB-1120064; DEB-0322057; NSFC 41825016
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Global Change Biology
- Additional Journal Information:
- Journal Volume: 28; Journal Issue: 5; Journal ID: ISSN 1354-1013
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; elevated CO2; functional traits; metagenomics; microbial ecological modeling; microbial functional genes; nitrogen enrichment; predictive ecology; soil enzymes
Citation Formats
Wang, Gangsheng, Gao, Qun, Yang, Yunfeng, Hobbie, Sarah E., Reich, Peter B., and Zhou, Jizhong. Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling. United States: N. p., 2021.
Web. doi:10.1111/gcb.16036.
Wang, Gangsheng, Gao, Qun, Yang, Yunfeng, Hobbie, Sarah E., Reich, Peter B., & Zhou, Jizhong. Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling. United States. https://doi.org/10.1111/gcb.16036
Wang, Gangsheng, Gao, Qun, Yang, Yunfeng, Hobbie, Sarah E., Reich, Peter B., and Zhou, Jizhong. Fri .
"Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling". United States. https://doi.org/10.1111/gcb.16036. https://www.osti.gov/servlets/purl/1961811.
@article{osti_1961811,
title = {Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling},
author = {Wang, Gangsheng and Gao, Qun and Yang, Yunfeng and Hobbie, Sarah E. and Reich, Peter B. and Zhou, Jizhong},
abstractNote = {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 CO 2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO 2 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 CO 2 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.},
doi = {10.1111/gcb.16036},
journal = {Global Change Biology},
number = 5,
volume = 28,
place = {United States},
year = {Fri Dec 24 00:00:00 EST 2021},
month = {Fri Dec 24 00:00:00 EST 2021}
}
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