Thaw Transitions and Redox Conditions Drive Methane Oxidation in a Permafrost Peatland

Abstract

Permafrost peatlands are a significant source of methane (CH4) emissions to the atmosphere and could emit more CH4 with continued permafrost thaw. Aerobic methane‐oxidizing bacteria may attenuate a substantial fraction of CH4 emissions in thawing permafrost peatlands; however, the impact of permafrost thaw on CH4 oxidation is uncertain. We measured potential CH4 oxidation rates (hereafter, CH4 oxidation) and their predictors using laboratory incubations and in situ porewater redox chemistry across a permafrost thaw gradient of eight thaw stages at Stordalen Mire, a permafrost peatland complex in northernmost Sweden. Methane oxidation rates increased across a gradient of permafrost thaw and differed in transitional thaw stages relative to end‐member stages. Oxidation was consistently higher in submerged fens than in bogs or palsas across a range of CH4 concentrations. We also observed that CH4 oxidation increased with decreasing in situ redox potential and was highest in sites with lower redox potential (Eh < 10 mV) and high water table. Our results suggest that redox potential can be used as an important predictor of CH4 oxidation, especially in thawed permafrost peatlands. Our results also highlight the importance of considering transitional thaw stages when characterizing landscape‐scale CH4 dynamics, because these transitional areas have different rates and controls of CH4 oxidation relative to intact or completely thawed permafrost areas. As permafrost thaw increases the total area of semiwet and wet thaw stages in permafrost peatlands, CH4 oxidation represents an important control on CH4 emissions to the atmosphere.

Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2020

“…As a consequence of thawing of permafrost, centuries-old carbon deposits become bioavailable and undergo microbial mineralisation [ 404 ]. Rates of emission of CH 4 from Arctic and other natural environments depend on rates of methane production and consumption by methane-producing and methane-oxidising microorganisms, respectively, which are affected by temperature and other environmental factors [ 405 , 406 ]. Methane plays a critical role in determining tropospheric concentrations of OH (more CH 4 , less OH) and hence the tropospheric cleaning capacity (Sect.…”

Hysteretic temperature sensitivity of wetland CH&lt;sub&gt;4&lt;/sub&gt; fluxes explained by substrate availability and microbial activity

“…Atmospheric CH 4 concentrations have more than doubled since 1750 (Saunois et al, 2016) and have contributed about 20 % of the additional radiative forcing accumulated in the lower atmosphere (Ciais et al, 2013). Recent assessments have found that CH 4 emissions from wetland and other inland waters are the largest and most uncertain sources affecting the global CH 4 budget (Kirschke et al, 2013;Poulter et al, 2017;Saunois et al, 2016). Such CH 4 emissions account for 25 % to 32 % of current global total CH 4 emissions (Saunois et al, 2016) and contribute substantially to the renewed and sustained atmospheric CH 4 growth after 2006 (Saunois et al, 2017).…”

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

“…1B). By integrating these myriad types of datasets, the project has characterized how thaw-induced changes in hydrology and vegetation (Malmer et al, 2005;Johansson et al, 2006;Bäckstrand et al, 2010;Palace et al, 2018) drive changes in organic matter (Hodgkins et al, 2014(Hodgkins et al, , 2016Wilson et al, 2017;Wilson & Tfaily, 2018) and microbial and viral communities (Mondav et al, 2014;Trubl et al, 2016Trubl et al, , 2018Trubl et al, , 2019Singleton et al, 2018;Emerson et al, 2018;Woodcroft et al, 2018;Martinez et al, 2019;Wilson et al, 2019;Roux et al, 2019), giving rise to changes in carbon gas emissions (Wik et al, 2013(Wik et al, , 2018Hodgkins et al, 2014Hodgkins et al, , 2015McCalley et al, 2014;Burke et al, 2019;Perryman et al, 2020), and collectively these insights are allowing improvements in predictive models (Deng et al, 2014(Deng et al, , 2017Chang et al, 2019aChang et al, , 2019bWilson et al, 2019).…”