Lawrence Berkeley National Laboratory
Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths.
- Author(s): Johnston, Eric R
- Hatt, Janet K
- He, Zhili
- Wu, Liyou
- Guo, Xue
- Luo, Yiqi
- Schuur, Edward AG
- Tiedje, James M
- Zhou, Jizhong
- Konstantinidis, Konstantinos T
- et al.
Published Web Locationhttp://10.0.4.49/pnas.1901307116
Northern-latitude tundra soils harbor substantial carbon (C) stocks that are highly susceptible to microbial degradation with rising global temperatures. Understanding the magnitude and direction (e.g., C release or sequestration) of the microbial responses to warming is necessary to accurately model climate change. In this study, Alaskan tundra soils were subjected to experimental in situ warming by ∼1.1 °C above ambient temperature, and the microbial communities were evaluated using metagenomics after 4.5 years, at 2 depths: 15 to 25 cm (active layer at outset of the experiment) and 45 to 55 cm (transition zone at the permafrost/active layer boundary at the outset of the experiment). In contrast to small or insignificant shifts after 1.5 years of warming, 4.5 years of warming resulted in significant changes to the abundances of functional traits and the corresponding taxa relative to control plots (no warming), and microbial shifts differed qualitatively between the two soil depths. At 15 to 25 cm, increased abundances of carbohydrate utilization genes were observed that correlated with (increased) measured ecosystem carbon respiration. At the 45- to 55-cm layer, increased methanogenesis potential was observed, which corresponded with a 3-fold increase in abundance of a single archaeal clade of the Methanosarcinales order, increased annual thaw duration (45.3 vs. 79.3 days), and increased CH4 emissions. Collectively, these data demonstrate that the microbial responses to warming in tundra soil are rapid and markedly different between the 2 critical soil layers evaluated, and identify potential biomarkers for the corresponding microbial processes that could be important in modeling.