Abstract

Abstract. The reasons for spatial and temporal variation in methane emission from mire ecosystems are not fully understood. Stable isotope signatures of the emitted methane can offer clues to the causes of these variations. We measured the methane emission (FCH4) and 13C signature (δ13C) of emitted methane by automated chambers at a hemiboreal mire for two growing seasons. In addition, we used ambient methane mixing ratios and δ13C to calculate a mire-scale 13C signature using a nocturnal boundary-layer accumulation approach. Microbial methanogenic and methanotrophic communities were determined by a captured metagenomics analysis. The chamber measurements showed large and systematic spatial variations in δ13C-CH4 of up to 15 ‰ but smaller and less systematic temporal variation. According to the spatial δ13C–FCH4 relations, methanotrophy was unlikely to be the dominating cause for the spatial variation. Instead, these were an indication of the substrate availability of methanogenesis being a major factor in explaining the spatial variation. Genetic analysis indicated that methanogenic communities at all sample locations were able to utilize both hydrogenotrophic and acetoclastic pathways and could thus adapt to changes in the available substrate. The temporal variation in FCH4 and δ13C over the growing seasons showed hysteresis-like behavior at high-emission locations, indicative of time-lagged responses to temperature and substrate availability. The upscaled chamber measurements and nocturnal boundary-layer accumulation measurements showed similar average δ13C values of −81.3 ‰ and −79.3 ‰, respectively, indicative of hydrogenotrophic methanogenesis at the mire. The close correspondence of the δ13C values obtained by the two methods lends confidence to the obtained mire-scale isotopic signature. This and other recently published data on δ13C values of CH4 emitted from northern mires are considerably lower than the values used in atmospheric inversion studies on methane sources, suggesting a need for revision of the model input.

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