A systems approach to understanding subarctic critical zone changes in a warming climate

Climate change is dramatically altering the subarctic and Arctic Critical Zone. Permafrost, which currently holds approximately one third of global soil carbon in a relatively unavailable form, is predicted to be virtually eliminated by the end of the century. One endpoint for permafrost habitat thaw is wetlands, which are a major source of the microbially-produced greenhouse gas methane. This creates a potentially large positive feedback to climate change. Our team is using a systems approach spanning diverse geochemical (high-resolution greenhouse gas isofluxes and soil/peat geochemistry) and molecular (16S rRNA gene amplicon, metagenomic and metaproteomic sequencing) measurements to track parallel changes in carbon cycling and in situ microbiology across a natural permafrost thaw gradient. Thaw at this site results in a three-stage habitat shift from ericaceous shrubs, to peat moss, to sedges, concomitant with a substantial increase in methane emissions. Isotopically, emitted methane shifts along the thaw gradient away from hydrogenotrophic methane production, in parallel with the appearance of acetoclastic methanogens in the microbial community. Community data have also revealed the presence of a novel, highly-active methanogen from the euryarchaeal lineage Rice Cluster-II, dubbed Candidatus Methanoflorens stordalenmirensis. Its \u27;species\u27 is present in numerous other global wetland datasets, has the genomic capacity (inferred from its population genome) for hydrogenotrophic methanogenesis, and was the highest environmental correlate of emitted methane\u27s isotopic signature. In situ community global protein expression profiles (i.e. metaproteomes) revealed that it strongly expresses its hydrogentrophic methanogensis genes, and that methanogenesis is a dominant signal in the overall community proteome. As we generate a portrait of how thaw impacts this major subarctic critical zone habitat, we are working with ecosystem process modelers to integrate new understandings into prognostic models of climate change feedbacks