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Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming.
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.
Afiliação
  • Guo X; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.
  • Gao Q; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Yuan M; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Wang G; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.
  • Zhou X; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Feng J; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Shi Z; Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, USA.
  • Hale L; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Wu L; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Zhou A; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Tian R; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Liu F; School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan, China.
  • Wu B; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Chen L; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Jung CG; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Niu S; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Li D; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Xu X; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Jiang L; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Escalas A; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Wu L; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • He Z; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Van Nostrand JD; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Ning D; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Liu X; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Yang Y; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Schuur EAG; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Konstantinidis KT; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA.
  • Cole JR; Environmental Microbiomics Research Center and School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China.
  • Penton CR; Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
  • Luo Y; Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA.
  • Tiedje JM; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
  • Zhou J; University of Chinese Academy of Sciences, Beijing, China.
Nat Commun ; 11(1): 4897, 2020 09 29.
Article em En | MEDLINE | ID: mdl-32994415
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 (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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Solo / Microbiologia do Solo / Carbono / Metagenoma / Microbiota Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Solo / Microbiologia do Solo / Carbono / Metagenoma / Microbiota Idioma: En Ano de publicação: 2020 Tipo de documento: Article