Seasonal Dynamics of Ecosystem Carbon Exchange for a Wet Sedge Vegetation Community, Melville Island, NU

Amy Blaser1, Neal A Scott1 and Paul Treitz2, (1)Queen's University, Kingston, ON, Canada, (2)Queen's University, Geography and Planning, Kingston, ON, Canada

Contact First Author: Amy Blaser; amy.blaser@queensu.ca

Previously Published Material: Preliminary results were presented at Arctic Change 2014 conference December 8-12 (poster presentation).

Abstract ID#: 35077

 

English Abstract:
Wet sedge meadows are the most productive communities in the High Arctic. Preliminary research suggests that this plant community is a net carbon sink, yet the controls – and the scale at which those controls act – are not well understood. If climate change enhances wet sedge growth, we may observe increases in the percentage of land area occupied by these meadows, altering the carbon balance of high arctic landscapes.

We examined seasonal carbon exchange processes in three wet sedge meadows at the Cape Bounty Arctic Watershed Observatory (CBAWO). Automated and static CO2 exchange systems recorded CO2 exchange from June to August, 2014. In conjunction with these measurements, time-series NDVI images were collected to quantify the phenological stage of the community through the growing season. Simultaneous measurements of soil temperature, air temperature, PAR, soil moisture, and active layer depth were also made.

Net ecosystem exchange (NEE) measurements indicate carbon uptake through photosynthesis, and NEE rates differed in spectrally separable ‘wet’ and ‘dry’ sedge areas (-0.33 and 0.01 µmol m-2 s-1 means respectively; p<0.001). NDVI measurements captured spring greening and peak summer biomass, and will likely be a critical variable for modelling CO2 exchange. Abiotic factors such as soil and air temperature may also influence CO2 exchange, though this system is driven strongly by soil moisture.

Predictive models of ecosystem carbon fluxes will be created using NDVI and environmental measurements as predictors of carbon flux. This will allow us to evaluate the drivers of CO2 exchange in these communities –spatially and temporally – and facilitate predictions of NEE based on NDVI and/or biophysical variables. We will also evaluate the scale (largely temporal) dependency of these controls to improve predictions of annual carbon fluxes at the landscape scale.