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

Gatekeepers of Arctic Carbon Loss: Landscape-Scale Metabolites-to-Ecosystems Profiling to Mechanistically Map Climate Feedbacks


McKenzie Kuhn1*, Virginia Rich2,  Gene Tyson3, Simon McIlroy3, Kelly Wrighton4, Malak Tfaily5, Scott Saleska5, Jeff Chanton6, Rachel Wilson6, Jinyun Tang7, and Ruth Varner1



1University of New Hampshire; 2The Ohio State University; 3Queensland University of Technology; 4Colorado State University; 5University of Arizona; 6Florida State University; and 7Lawrence Berkeley National Laboratory


The team proposes to identify and characterize microbes and metabolites critical for C transformation in high-latitude interconnected terrestrial and aquatic sediment systems, which are undergoing rapid climate change. Discontinuous permafrost in these regions s rapidly thawing and the potential climate feedbacks are substantial. These landscapes encompass various permafrost thaw stages and lesser-studied lakes, which can be the exit point for a significant fraction of CH4 lost post-thaw via ebullition (bubbling) and are projected to increase with warming. At the same time, thaw-initiated succession of plant communities can increase net soil C storage and potentially also plant-derived inhibitory compounds that dampen C processing. Accurate predictive understanding of the net effect of these simultaneous coupled loss, gain, and stabilization processes, under increasing temperatures is an area of urgent study.

The team will leverage a model terrestrial ecosystem with site-specific genomes, metabolite spectra, C gas emissions and isotopes profiles—including lake sediments with anaerobic CH4 oxidizers present at some of the highest abundances ever observed in a natural system. The team will compare integrated substrate-microbiome-emissions in the two habitats, recently thawed terrestrial fens and lake sediments, which together dominate climate feedbacks (via >90% of landscape CH4 emissions), quantify rates and controls, and distill insights into models, to more accurately predict C cycling and climate feedbacks.