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

基因信息分解模型预测由于微生物持续适应变暖,土壤碳损失会减少

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作者:Xue Guo #, Qun Gao #, Mengting Yuan #, Gangsheng Wang #, Xishu Zhou, Jiajie Feng, Zhou Shi, Lauren Hale, Linwei Wu, Aifen Zhou, Renmao Tian, Feifei Liu, Bo Wu, Lijun Chen, Chang Gyo Jung, Shuli Niu, Dejun Li, Xia Xu, Lifen Jiang, Arthur Escalas, Liyou Wu, Zhili He, Joy D Van Nostrand, Daliang Ning, 

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.

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