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ARS Home » Pacific West Area » Parlier, California » San Joaquin Valley Agricultural Sciences Center » Water Management Research » Research » Publications at this Location » Publication #377671

Research Project: Develop Water Management Strategies to Sustain Water Productivity and Protect Water Quality in Irrigated Agriculture

Location: Water Management Research

Title: Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming

Author
item GUO, XUE - Tsinghua University
item GAO, QUN - Tsinghua University
item YUAN, MENGTING - University Of California
item WANG, GANGSHENG - University Of Oklahoma
item ZHOU, XISHU - Central South University
item FENG, JIAJIE - University Of Oklahoma
item SHI, ZHOU - University Of Oklahoma
item Hale, Lauren
item WU, LINWEI - University Of Oklahoma
item ZHOU, AIFEN - University Of Oklahoma
item TIAN, RENMAO - University Of Oklahoma
item LIU, FEIFEI - Northern Arizona University
item WU, BO - University Of Oklahoma
item CHEN, LIJUN - University Of Oklahoma
item JUNG, CHANG GYO - Northern Arizona University
item NIU, SHULI - Chinese Academy Of Agricultural Sciences
item LI, DEJUN - Chinese Academy Of Agricultural Sciences
item XU, XIA - University Of Oklahoma
item JIANG, LIFEN - Northern Arizona University
item ESCALAS, ARTHUR - University Of Oklahoma
item WU, LIYOU - University Of Oklahoma
item HE, ZHILI - University Of Oklahoma
item VAN NOSTRAND, JOY - University Of Oklahoma
item NING, DALIANG - University Of Oklahoma
item LIU, XUEDUAN - Central South University
item YANG, YUNFENG - Tsinghua University
item SCHUUR, EDWARD - Northern Arizona University
item KONSTANTINIDIS, KONSTANTINOST - Georgia Institute Of Technology
item COLE, JAMES - Michigan State University
item PENTON, C. RYAN - Arizona State University
item LUO, YIQI - Northern State University
item TIEDJE, JAMES - Arizona State University
item ZHOU, JIZHONG - University Of Oklahoma

Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/21/2020
Publication Date: 8/29/2020
Citation: Guo, X., Gao, Q., Yuan, M., Wang, G., Zhou, X., Feng, J., Shi, Z., Hale, L.E., Wu, L., Zhou, A., Tian, R., Liu, F., Wu, B., Chen, L., Jung, C., Niu, S., Li, D., Xu, X., Jiang, L., Escalas, A., Wu, L., He, Z., Van Nostrand, J.D., Ning, D., Liu, X., Yang, Y., Schuur, E.A., Konstantinidis, K., Cole, J.R., Penton, C., Luo, Y., Tiedje, J.M., Zhou, J. 2020. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. Nature Communications. 11:4897. https://doi.org/10.1038/s41467-020-18706-z.
DOI: https://doi.org/10.1038/s41467-020-18706-z

Interpretive Summary: Microbial decomposition rates of soil organic carbon (SOC) and litter (i.e. heterotrophic respiration) have shown short-term increases in response to warmer temperatures, which if persistent on a grand scale, could greatly enhance greenhouse gas emissions and provide a net positive feedback to climate warming. This 7-year study revealed that thermal adaptation of heterotrophic respiration dampened this response and was attributed to shifts in microbial community structure and soil moisture losses. Incorporating relative abundances of microbial genes involved in SOC decomposition into Earth Systems Models improved accuracy and reduced uncertainty. This enhanced model should be assessed across wider biomes to evaluate its suitability to improve prediction accuracy of ecosystem feedbacks to climate warming.

Technical Abstract: Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (e.g., Q10) in a temperate grassland ecosystem persistently decreases by 12.0±3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6±7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.