Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

2024 Abstracts

Response of Methanotroph Communities to Warming Temperatures in a Recently Thawed Fen


Cheristy P. Jones1* ([email protected]), Rachel M. Wilson2, Jeff P. Chanton2, Fen Li3, Lyreshka Castro Morales1, McKenzie A. Kuhn1, Jared Ellenbogen4, Kelly C. Wrighton4, Virginia I. Rich3, Ruth K. Varner1


1University of New Hampshire–Durham; 2Florida State University–Tallahassee; 3The Ohio State University–Columbus; 4Colorado State University–Fort Collins


To resolve key unknowns in the grand challenge of understanding the fate of carbon (C) in thawing permafrost, researchers focus on C cycle climate feedbacks to warming in high-methane (CH4) emitting landscapes in an Arctic mire ecosystem. The team’s aims are to (1) identify and resolve key gaps in the understanding of microbial C processes consequential to C storage and CH4 emission; (2) identify and resolve a mystery of microbial oxidation rates and controls consequential to emission mitigation; and (3) integrate resolved consequential unknowns into next-generation ecosystem models.


As the climate warms, permafrost thaw is fueling high CH4 emissions, particularly in permafrost peatlands. Aerobic methane- oxidizing bacteria (i.e., methanotrophs) could dampen these emissions, but how these microbes will respond to rising temperatures remains unknown. Researchers conducted laboratory incubations to investigate how methanotroph communities respond to warming temperatures at different peat depths in a recently thawed fen in a thawing permafrost peatland located in Stordalen Mire, Sweden. The team used 16S rRNA to characterize microbial community composition at 20°C and 25°C. Oxidation rates did not differ across peat depths (10 cm increments from 0 to 40 cm). Isotopic analysis of 13C-CH4 and 13C-CO2 will reveal sources of CH4. Future work will investigate the metabolomic and genomic controls on CH4 oxidation. These results will inform a trait-based ecosystem model to improve emission predictions under climate change.

Funding Information

DOE (DE-SC0023456)