Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming
Abstract
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 ( Q 10 ) 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.
- Authors:
- more »
- Publication Date:
- Research Org.:
- Univ. of Oklahoma, Norman, OK (United States)
- Sponsoring Org.:
- USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
- OSTI Identifier:
- 1668434
- Alternate Identifier(s):
- OSTI ID: 1801595
- Grant/Contract Number:
- SC0004601; SC0010715
- Resource Type:
- Published Article
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Name: Nature Communications Journal Volume: 11 Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Science & Technology - Other Topics
Citation Formats
Guo, Xue, Gao, Qun, Yuan, Mengting, Wang, Gangsheng, Zhou, Xishu, Feng, Jiajie, Shi, Zhou, Hale, Lauren, Wu, Linwei, Zhou, Aifen, Tian, Renmao, Liu, Feifei, Wu, Bo, Chen, Lijun, Jung, Chang Gyo, Niu, Shuli, Li, Dejun, Xu, Xia, Jiang, Lifen, Escalas, Arthur, Wu, Liyou, He, Zhili, Van Nostrand, Joy D., Ning, Daliang, Liu, Xueduan, Yang, Yunfeng, Schuur, Edward. A. G., Konstantinidis, Konstantinos T., Cole, James R., Penton, C. Ryan, Luo, Yiqi, Tiedje, James M., and Zhou, Jizhong. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. United Kingdom: N. p., 2020.
Web. doi:10.1038/s41467-020-18706-z.
Guo, Xue, Gao, Qun, Yuan, Mengting, Wang, Gangsheng, Zhou, Xishu, Feng, Jiajie, Shi, Zhou, Hale, Lauren, Wu, Linwei, Zhou, Aifen, Tian, Renmao, Liu, Feifei, Wu, Bo, Chen, Lijun, Jung, Chang Gyo, Niu, Shuli, Li, Dejun, Xu, Xia, Jiang, Lifen, Escalas, Arthur, Wu, Liyou, He, Zhili, Van Nostrand, Joy D., Ning, Daliang, Liu, Xueduan, Yang, Yunfeng, Schuur, Edward. A. G., Konstantinidis, Konstantinos T., Cole, James R., Penton, C. Ryan, Luo, Yiqi, Tiedje, James M., & Zhou, Jizhong. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. United Kingdom. https://doi.org/10.1038/s41467-020-18706-z
Guo, Xue, Gao, Qun, Yuan, Mengting, Wang, Gangsheng, Zhou, Xishu, Feng, Jiajie, Shi, Zhou, Hale, Lauren, Wu, Linwei, Zhou, Aifen, Tian, Renmao, Liu, Feifei, Wu, Bo, Chen, Lijun, Jung, Chang Gyo, Niu, Shuli, Li, Dejun, Xu, Xia, Jiang, Lifen, Escalas, Arthur, Wu, Liyou, He, Zhili, Van Nostrand, Joy D., Ning, Daliang, Liu, Xueduan, Yang, Yunfeng, Schuur, Edward. A. G., Konstantinidis, Konstantinos T., Cole, James R., Penton, C. Ryan, Luo, Yiqi, Tiedje, James M., and Zhou, Jizhong. Tue .
"Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming". United Kingdom. https://doi.org/10.1038/s41467-020-18706-z.
@article{osti_1668434,
title = {Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming},
author = {Guo, Xue and Gao, Qun and Yuan, Mengting and Wang, Gangsheng and Zhou, Xishu and Feng, Jiajie and Shi, Zhou and Hale, Lauren and Wu, Linwei and Zhou, Aifen and Tian, Renmao and Liu, Feifei and Wu, Bo and Chen, Lijun and Jung, Chang Gyo and Niu, Shuli and Li, Dejun and Xu, Xia and Jiang, Lifen and Escalas, Arthur and Wu, Liyou and He, Zhili and Van Nostrand, Joy D. and Ning, Daliang and Liu, Xueduan and Yang, Yunfeng and Schuur, Edward. A. G. and Konstantinidis, Konstantinos T. and Cole, James R. and Penton, C. Ryan and Luo, Yiqi and Tiedje, James M. and Zhou, Jizhong},
abstractNote = {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 ( Q 10 ) 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.},
doi = {10.1038/s41467-020-18706-z},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United Kingdom},
year = {Tue Sep 29 00:00:00 EDT 2020},
month = {Tue Sep 29 00:00:00 EDT 2020}
}
https://doi.org/10.1038/s41467-020-18706-z
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