Arctic landscapes are made up of a patchwork of various plant-community types [Kade et al., 2005; Walker et al., 2005], and the magnitude of ecosystem productivity and respiration varies by vegetation type and the associated soil conditions. Understanding the influence of vegetation composition and structure on ecosystem processes is critical when predicting changes in carbon exchange due to altered climatic conditions in the future [Soegaard et al., 2000; Street et al., 2007]. For example, moist acidic tussock tundra in Arctic Alaska shows greater carbon exchange than dry heath tundra [Welker et al., 2000]. Various studies have investigated plot-level carbon exchange with chambers that provide good estimates of CO2 fluxes for specific Arctic plant-community types [e.g., Oberbauer et al., 1991; Vourlitis et al., 1993; Welker et al., 2000; Shaver et al., 2007], but these measurements are labor intensive, usually confined to discrete sampling periods and may be unsuitable for extrapolation to larger areas with patchy vegetation. Eddy covariance measurements give continuous estimates of landscape-level net ecosystem carbon exchange in Arctic systems [e.g., Weller et al., 1995; Vourlitis and Oechel, 1999; Nordstroem et al., 2001; Harazono et al., 2003; Lafleur and Humphreys, 2008], but they do not partition out the importance of different plant communities with varying structural and functional characteristics. That is, while a given type of tundra (e.g., heath, tussock or wet sedge tundra) may be characterized by a predominant surface type, in reality each tundra type exhibits a certain amount of spatial heterogeneity. This plot-scale heterogeneity may cause varying contributions to the overall landscape-scale carbon exchange measured with eddy covariance towers. Although a few studies have scaled carbon fluxes from chamber to tower data in tundra landscapes based on physiological and micrometeorological parameters [Vourlitis et al., 2000; Zamolodchikov et al., 2003; Loranty et al., 2011], we are aware of only one study that has focused on the contribution of carbon exchange from discrete vegetation types at the plot level to those of larger areas as measured with the eddy covariance technique in Arctic ecosystems [Fox et al., 2008]. Here, we ask how the carbon exchange of various Arctic tundra plant communities at the plot level compares to eddy-covariance data during both the summer and winter season. The comparison of winter fluxes offers a unique aspect in our study, as eddy covariance data during Arctic winters are very sparse [Euskirchen et al., 2012].
 In order to document the changes in CO2, water and energy fluxes of Arctic systems due to high-latitude warming, we established three eddy covariance towers along a toposequence from ridge-top heath tundra to mid-slope moist acidic tussock tundra to valley-bottom wet sedge tundra in the Imnavait Creek Watershed in the Low Arctic in 2007 [Euskirchen et al., 2012]. The goals of the present study are to:
 (a) Examine the seasonal variations in CO2 flux among discrete tundra plant communities within the footprint of the towers. We determined CO2 flux measurements of gross ecosystem exchange (GEE), ecosystem respiration (ER) and net ecosystem exchange (NEE) at the plot level during several measurement campaigns over a full annual cycle, including collection of flux data during the winter season. We hypothesized that the summer GEE and ER would show greater absolute values in the wet sedge and tussock tundra than in the dry heath tundra, based on the lack of vegetation on the frost-disturbed heath soils, while we expected less variation in flux among vegetation types during the winter months.
 (b) Relate the plot-level CO2 flux measurements to the eddy covariance data at heterogeneous landscape levels with the help of a footprint model that determines the source areas of the fluxes. We hypothesized that plot-level and tower estimates would agree better for NEE than for ER, since ER estimates cannot be measured directly by eddy covariance towers and are difficult to derive during Arctic summer nights with almost constant light. GEE estimates should only be as accurate as ER estimates, but since GEE represents a larger value than ER, the percent error should be smaller.