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

Authors:

McKenzie Kuhn^1*, Virginia Rich², Gene Tyson³, Simon McIlroy³, Kelly Wrighton⁴, Malak Tfaily⁵, Scott Saleska⁵, Jeff Chanton⁶, Rachel Wilson⁶, Jinyun Tang⁷, and Ruth Varner¹

Institutions:

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

Abstract

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.