Significance: Interactions between nutrient supply and plant demand dictate key terrestrial ecosystem feedbacks to global climate change. We investigated these responses in a marsh ecosystem and found that plants and soils respond to warming at different temperatures. Modest warming caused plant demand for nitrogen (N) to outpace the soil N supply, while more extreme warming caused the N supply to outpace plant N demand. These responses changed when elevated CO2was added to the warming treatments. Globally, terrestrial plant N demand has exceeded soil N supply in unfertilized ecosystems over the past century; our results reveal that this imbalance is driven primarily by elevated CO2and forecast that N supply will likely increase over plant demand as warming exceeds 2 °C.
11 December 2013 in Journal of Geophysical Research: Biogeosciences
[1] Peatlands are a large natural source of atmospheric methane (CH4), and the sedge Carex rostrata plays a critical role in the production, oxidation, and transport of CH4 in these systems. This 4 year clipping experiment examined the changes in CH4 emissions from a temperate peatland after removing all aboveground C. rostrata biomass. Methane fluxes, dissolved CH4, and environmental variables were measured during spring, summer, and fall from 2008 to 2011. Clipping and removing the C. rostrata leaves and stems caused an immediate decrease in CH4 emissions that persisted over 4 years of this study. There was a strong seasonal trend in CH4 flux, with the largest treatment effects occurring during the fall months when the sedges were senescing. As expected, there was a strong positive correlation between C. rostrata green-leaf area and CH4 flux, implying that the presence of C. rostrata increases CH4 emissions from this peatland. Large interannual variability in vegetation distribution and biomass, water table depth, and temperature was observed in this study, indicating the importance of multiyear studies for understanding the interactions among these factors to determine how they could be incorporated into biogeochemical models to predict CH4 emissions under changing environmental conditions.