We selected and sampled different types of aquatic systems typical of the tundra polygon terrace of the valley (Fig. 2; Table 1): (1) polygonal ponds over low-centered ice wedge polygons; (2) elongated water channels over melting ice wedges (ponds formed in collapsed ice-wedge troughs, hereafter referred to as trough ponds); (3) lakes with underlying talik (unfrozen soil over permafrost), including a thermokarst (thaw) lake and a kettle (melted buried glacier ice) lake. A total of 23 ponds and lakes were sampled in June–July 2013, including 9 polygonal ponds, 12 trough ponds, and 2 lakes (1 thermokarst and 1 kettle lake). In July 2014, six water bodies (two polygonal ponds, two trough ponds, and two lakes including one thermokarst and one kettle lake) were selected and studied more intensively, including morphological measurements of ponds (depth, width and length) and lakes (bathymetry with a portable sonar as in Bouchard et al., 2015), and limnological profiles (see below).

We measured a suite of limnological characteristics during both years, including temperature, dissolved oxygen, and concentrations of dissolved organic carbon (DOC), chromophoric fraction of dissolved organic matter (CDOM), nutrients (phosphorus, nitrogen) and major ions. Temperature and dissolved oxygen profiles were recorded with a ProODO handheld meter (YSI Inc.). Water samples were filtered through 0.2 µm pre-rinsed cellulose acetate filters (2013) or pre-combusted GF/F filters (2014, nominal porosity 0.7 µm) to analyze DOC and major ions. Cations were fixed with HNO3 (0.15 % final concentration) while anions and DOC were not fixed but kept in dark and cold. DOC concentrations were measured with a Shimadzu TOC-5000A carbon analyzer calibrated with potassium biphthalate, and CDOM was quantified (in 2013 only) with the absorption coefficient of DOM at 320 nm (a320) obtained on a Cary 300 (Varian; methodological details in Laurion and Mladenov, 2013). Major anions were quantified by ionic chromatography (Dionex ICS-2000), whereas major cations by inductively coupled plasma–optical emission spectrometry (ICP-OES, Varian VISTA AX). Total phosphorus (TP) and total nitrogen (TN) were quantified from unfiltered water samples fixed with H2SO4 (0.15 % final concentration) as described by Stainton et al. (1977). Finally, the thermal structure of one trough pond (BYL27) was assessed during a full year (July 2013–July 2014) by recording water temperature at two depths (0 and 50 cm) at a 15 min interval using two submersible data loggers (Vemco Minilog-II-T, accuracy ±0.1 ∘C, resolution ±0.01 ∘C) installed on a mooring line. The line was not moored at the deepest point of the pond (∼1 m) as found upon its retrieval, but the data still provide a clear picture of the thermal stratification establishing in this type of humic ponds.

Ebullition gas samples were collected using submerged funnels (as in Wik et al., 2013) equipped with a 140 mL plastic syringe (Fig. A1 in Appendix A) and deployed for a period of 1 h to 19 days depending on the flux. The samples trapped in the syringe were transferred into 50 mL glass bottles with butyl rubber stoppers (bottles acid-washed, pre-combusted, helium flushed and vacuumed) for 14C dating (see below), and into two separate 6 mL glass vials (helium flushed and vacuumed Exetainers) for stable isotope (see below) and gas chromatography analysis (Varian 3800, COMBI PAL head space injection system, CP PoraPLOT Q 0.53 mm ID × 25 m, flame ionization detector). Ebullition flux (Fe, in mmolm-2d-1) was calculated as Fe=(pGas×V)/(A×MV×t), where pGas is the partial pressure of CO2 or CH4, V is the collected gas volume, A is the funnel area, MV is the gas molar volume at ambient air temperature, and t is the collecting time.

Surface water dissolved GHG concentrations were obtained by equilibrating 2 L of lake or pond water with 20 mL of ambient air during 3 min (Hesslein et al., 1991). The resulting gaseous headspace was transferred into 6 mL glass vials and analyzed as above by gas chromatography. Dissolved GHG concentration at the surface (Csur) was calculated using Henry's law, and departure from saturation (sink vs. source) was calculated subtracting the gas concentration in the water at equilibrium with the atmosphere (Ceq, global values of atmospheric partial pressures from IPCC, 2007, were used). To estimate diffusive flux (Fluxd), first the gas transfer coefficient (k600) standardized to a Schmidt number (Sc) of 600 (Wanninkhof, 1992) was calculated with the wind-based model of Cole and Caraco (1998): k600=2.07+0.215u101.7, where u10 is the wind speed at 10 m above the ground, and then applying the equation: Fluxd=k(Csur-Ceq), where k is the gas transfer coefficient for a given gas calculated as k=k600(Sc/600)-0.5.

Ebullition gas samples were analyzed at the Keck Carbon Cycle AMS facility at the University of California, Irvine. First, CH4 and CO2 were separated and purified by a zero air carrier gas flow-through line (Pack et al., 2015), and graphitized by the sealed tube Zn reduction method (Xu et al., 2007), then measured for radiocarbon (14C) on a compact accelerator mass spectrometer (AMS) (Southon and Santos, 2007). Data presented here are expressed as Δ14C (‰), which is normalized to radiocarbon activity of an oxalic acid standard OX1 (decay corrected to 1950) and corrected for isotopic fractionation (Reimer et al., 2004). Δ14C (‰) >0 was further used to indicate “modern” carbon (1950 to present), and Δ14C (‰) <0 for “older” carbon (pre-1950). This was particularly helpful for polygonal and trough ponds, which provided modern or very young GHG. The Δ14C analytical error was ∼2 ‰ for modern sample, based on long-term measurements of secondary standards. 14C age (yr BP) is as defined by Stuiver and Polach (1977).

Stable carbon and hydrogen isotopic compositions of GHG, δ13CO2, δ13CH4, and δDCH4, were analyzed at the Biogeochemistry Facility School of Earth and Ocean Sciences (BF-SEOS, University of Victoria). Ebullition gas samples were analyzed for δ13CH4 by introducing the gas onto a GSQ PLOT column (0.32 mm ID, 30 m) using a Valco 6-port valve and sample loop. After chromatographic separation, the CH4 passes through an oxidation oven (1030 ∘C), a Nafion water trap, and open-split interface to a continuous flow-isotope ratio mass spectrometer (CF-IRMS). The δ13CO2 was measured similarly by CF-IRMS, but bypassing the combustion oven. Precision for the δ13CH4 and δ13CO2 analyses was ±0.2 ‰, relative to Vienna PeeDee Belemnite (VPDB). Hydrogen isotope ratios of CH4 (δDCH4) were measured by a TC/EA pyrolysis unit (1450 ∘C) interfaced to a CF-IRMS. Precision for the δDCH4 analyses was ±3 ‰, relative to Vienna Standard Mean Ocean Water (VSMOW). Carbon and hydrogen isotope ratios are expressed using standard delta (δ) notation as described by deviations from a standard such that δsample‰=[(Rsample/Rstandard)-1]×1000, where R is the 13C / 12C or 2H / 1H ratio in the sample or standard. For isotope calibration, methane carbon and hydrogen standards from Isometric Instruments were used. These are traceable back to VPDB for carbon isotope ratios and VSMOW for hydrogen isotope ratios.

Ponds were generally shallow (∼0.6–1.0 and 1.0–1.5 m deep for polygonal and trough ponds, respectively) and thus froze to the bottom during winter, whereas lakes were more variable in depth depending on their origin and at least a portion of them did not freeze to the bottom in winter. The thermokarst lake was a few meters deep (<5 m), while the kettle lake was deeper (<12 m). Polygonal ponds, including different developmental stages and coalesced ponds, generally had flat bottoms covered by cyanobacterial mats (up to 5 cm thick), and stable (non-eroding) shores (Fig. 2b, c). Their surface area varied substantially (from 21 to 3350 m2) with a median of around 160 m2. Trough ponds were elongated water channels (median width ∼3 m; median length ∼10 m), and their shores were either actively eroding with collapsing decimetric peat blocks (Fig. 2f), or stable and colonized by brown mosses (Fig. 2g). The thermokarst lake had sharp edges near the shore, a shallow and gently sloping lake bottom and a deeper central basin. The kettle lake had steeper slopes along its margins, and showed a deep section that was not in the center of the lake (Bouchard et al., 2015).

Ponds and lakes showed contrasting physicochemical conditions during the 2 sampling years (Table 1). Trough ponds generally had the highest concentrations of DOC, nutrients and ions, followed by polygonal ponds, whereas lakes showed the lowest values. Trough pond BYL27, where shore erosion was active during summer time, had near- or higher-than-average concentrations, whereas trough pond BYL24, with stable shores, showed lower-than-average values. Pond DOC, nutrient and ion concentrations were substantially higher in 2014, a particularly dry year (total precipitations from January to June =27.0 mm in 2014, compared to 50.7 mm in average; Table B1 in Appendix B), with resulting low pond water levels as observed in the field. When considering specific solute species separately, all of them except NO3- and SO42- were statistically different (p<0.0001) among aquatic system types in 2013. In contrast, in 2014 only DOC (p<0.0001), total nitrogen (TN, p<0.001) and soluble reactive phosphorus (SRP, p<0.05) showed significant differences, and only between lakes and ponds (i.e., not between polygonal and trough ponds). Among all the water chemical properties and regardless of the sampling year, DOC showed the highest statistical contrasts between the different types of water bodies.

Polygonal ponds (BYL30, BYL80) had a thermally homogenous and well-oxygenated water column in July, whereas trough ponds (BYL24, BYL27) were notably stratified (Figs. 3 and 4). Thermokarst lake BYL66 was relatively well mixed over most of the water column, except near the sediment-water interface where dissolved oxygen decreased rapidly. Kettle lake BYL36, deeper than the other sampled water bodies, showed a steep gradient between the warmer, well-oxygenated epilimnion and the much colder, anoxic hypolimnion. The thermal profiles on Fig. 3 are representative of the conditions generally prevailing from July to mid August in each type of water bodies.

Radiocarbon age (Δ14C signature) and concentration of GHG (CO2 and CH4) emitted through ebullition showed strikingly different trends between the various types of aquatic systems (Fig. 5). Polygonal and trough ponds produced modern CH4 and modern to a few hundred years old (<550 yr BP) CO2, whereas lakes generally released older GHG, ranging from 510 to 1425 yr BP for CO2 and from 125 to 3405 yr BP for CH4 (Table 2). Moreover, samples from lake edges had younger and less concentrated CH4 than those coming from lake central area. No such trend was observed for CO2 in lakes. Considering all ponds and lakes as a whole, CH4 was generally 1 to 2 orders of magnitude more concentrated than CO2 in emitted bubbles in July.

Polygonal ponds were generally CO2 sinks, but they were CH4 sources with a relatively broad range of saturation levels (∼0–2.4 µM) (Fig. 6). Lakes were near the equilibrium with the atmosphere (all samples clustered near 0 for both gases), being small sinks or sources of CO2, and small sources of CH4. Trough ponds were in general supersaturated in both gases, especially when their margins were actively eroding (highest GHG saturation values) (Fig. C1). Trough ponds showed the highest diffusive flux, especially of CO2 (65.5 mmolm-2d-1; Table 3) with a median diffusive CO2 flux (21.8 mmolm-2d-1) more than 12 times higher than the median value of all sampled water bodies (1.7 mmolm-2d-1). Polygonal ponds, on the other hand, showed the highest ebullition flux for both CO2 (16.3 mmolm-2d-1) and CH4 (534.5 mmolm-2d-1), with a median ebullition CH4 flux that, although relatively low (∼1.0 mmolm-2d-1), was ∼5 times higher than the median value for all ponds and lakes (∼0.2 mmolm-2d-1). Lakes generally showed the lowest fluxes (both diffusion and ebullition). Globally, diffusion appeared as the dominant mechanism for CO2 emission, whereas CH4 was mainly emitted through ebullition. Statistical tests ran on the GHG data showed that trough ponds (BYL24, BYL27) were significantly different (p<0.001) from the other two types of water bodies (polygonal ponds and lakes), but also from each other. Furthermore, dissolved CO2 and CH4 fluxes were significantly correlated (p<0.006) with CDOM (r=0.79 and 0.78, respectively; N=22; a320 only available in 2013), but only CH4 fluxes were correlated (p<0.003) with DOC (R=0.61, N=28; data available in both years).

The stable isotope ratios of methane (δ13CH4, δDCH4) and carbon dioxide (δ13CO2) were measured on 18 ebullition samples collected in 2013 and 2014 (Table 2; Figs. 7 and 8). The δ13CH4 average values were -60.5 ‰ and ranged from -52.1 ‰ to the most 13C-depleted value of -67.6 ‰, both from polygonal ponds. The δDCH4 values, which averaged -376.80 ‰, were relatively 2H-depleted for naturally occurring methane. The δDCH4 with the most 2H-enriched value came from the thermokarst lake sample collected at its center (-319.56 ‰; BYL66; Fig. 7). In contrast, the δDCH4 values from trough ponds (BYL24 and BYL27) were consistently and extremely 2H-depleted, with values from -397.7 ‰ to a very low value of -448.1 ‰. There was no apparent correspondence between the methane concentration and δ13CH4 or δDCH4. The CO2 contents of ebullition samples were sometimes insufficient for carbon isotope measurements. For those with more CO2, the average δ13CO2 was -14.3 ‰ and varied from +0.3 (polygonal pond BYL80) to -21.8 ‰ (trough pond BYL24). There was also no apparent correspondence between the CO2 concentration and δ13CO2. However, it is worth noting that the sample with the most 13C-enriched CO2 also corresponded to the one with the most 13C-depleted CH4 (polygonal pond BYL80; Fig. 8).

We observed large variability in the age, composition and emission rate of GHG released by the studied aquatic systems. The GHG escaping through ebullition ranged from modern to a few centuries old for polygonal and trough ponds, and from a few centuries to a few millennia old for lakes (Fig. 5). Trough ponds emitted slightly but significantly older CH4 than polygonal ponds (Δ14C = 10±18 ‰ vs. 43±28 ‰, respectively; p<0.05), as observed earlier at the same site (Negandhi et al., 2013), although still classified as modern carbon, suggesting a small contribution of peat-derived carbon pool to microbial activity in trough ponds. Surprisingly, trough ponds did not emit millennium-old CH4, at least in July, despite the fact that they were exposed to eroding peat from down to the base of the active layer in the surroundings (14C dates ranging from ∼2.2 to 2.5 kyr BP; Table 2) and even older peat strata up-thrusted along ice wedges by cryoturbation and now in contact with surface waters (Fortier and Allard, 2004). Eroding peat was likely leaching old carbon into the water column, but bottom sediment interstitial water, where CH4 is mostly produced, did not predominantly emit carbon of this age. Permafrost disturbance was indeed shown to deliver millennia-old particulate organic carbon and DOC to arctic streams and rivers (Lamoureux and Lafrenière, 2014; Guo et al., 2007; Vonk et al., 2013), acting as a significant source of bioavailable carbon in Arctic freshwaters (Mann et al., 2015). We speculate that microbes were preferably using young carbon, putatively more labile and more abundant at this time of the year, and may use older carbon stocks later when primary producers are less active. If the CH4 released from trough ponds is indeed older during the autumn and spring, this could represent a positive climate feedback, but our results now indicate a limited role.

On the other hand, CH4 ebullition samples collected from lakes provided older dates, up to nearly 3500 yr BP (thermokarst lake BYL66), which is very close to the maximum known age of the permafrost peat layers in the valley (3670±110 yr BP; Fortier and Allard, 2004). It may suggest that permafrost thaw underneath this lake has proceeded through the organic layers at this site, which could result in decreased emissions in the future after the microbial exhaustion of the labile fraction of the organic matter pool (Walter et al., 2007). However the timing of this reduction is unknown. We observed a spatial gradient in the age and concentration of CH4 in bubbles emitted from the thermokarst lake, with younger and less concentrated CH4 from the lake edge (∼3 %), and older and more concentrated CH4 from the center (up to 57 %). The development of a talik (unfrozen soil under lake) explains the mobilization of deeper and older CH4 at the lake center where water remains unfrozen under the ice cover in winter (maximum lake depth >4 m, ice cover thickness ∼2 m). Methane emitted from a given location would thus be composed of a mixture of young CH4 from the edge with older CH4 from the center (Fig. D1). To our knowledge, the only other studies of thermokarst lakes presenting 14C dates on GHG are in yedoma deposits (Alaska, Siberia), which have very different ground ice, sediment and organic carbon contents, and chronostratigraphic history. For these lakes, the release of very old (>40 kyr BP) and highly concentrated (up to 90 %) CH4 from deep unfrozen lake sediments has been found (Walter et al., 2008). However, this study also reported younger ages for ebullition samples emitted from different parts of the lakes, and generally younger towards the lake center (when from background ebullition). At our study site, even though older GHG were emitted from lakes compared to ponds, ebullition fluxes remained low during the study period (July). Walter-Anthony and Anthony (2013) concluded that the classic randomized bubble-trap method for estimating mean lake ebullition is highly median-biased toward underestimation of fluxes, and this was possibly also occurring for our data set, although no systematic point source studies have been conducted so far at our study site.

We also observed strong differences in dissolved GHG flux depending on pond and lake types (Fig. 6; Table 3): polygonal ponds were CO2 sinks but CH4 sources while trough ponds were significant sources of both GHG, as previously reported in the valley (Laurion et al., 2010; Negandhi et al., 2013), and lakes were small sources of GHG. This pattern can be explained by the morpho-limnological properties of the water bodies. Polygonal ponds had stabilized shores (no apparent slumping) and more transparent waters compared to other systems, as shown by their lower CDOM content (Laurion et al., 2010). Moreover, they had flat and shallow bottoms covered by abundant cyanobacterial mats actively photosynthesizing and acting as a relatively efficient CO2 sink (flux reaching -11.8 mmolm-2d-1). This is however 1 order of magnitude lower than the net ecosystem CO2 uptake measured over the summer from a wet polygonal tundra site in Siberia (flux reaching -104.7 mmolm-2d-1; Kutzbach et al., 2007). Bottom sediments of the studied polygonal ponds were also colonized by methanotrophic bacteria (Negandhi et al., 2014), which can be a significant control mechanism on CH4 emissions as shown in polygonal ponds of the Lena region (Liebner et al., 2011).

Lakes were larger and deeper, thus they were exposed to wind-induced mixing of their epilimnetic waters promoting venting of the GHG from this layer. When the water column is seasonally stratified (like in BYL36), the hypolimnion likely stores a large fraction of the GHG produced by the lake until the autumnal overturn period (Bastviken et al., 2004), allowing more space and time for the oxidation of dissolved CH4, and for the dissolution of a fraction of ebullition CH4 (Bastviken et al., 2008). Therefore, it is possible that higher flux of old carbon would be observed later in the season. To fully account GHG emissions from lakes and compare them to other aquatic systems, summer and winter storage fluxes will need to be estimated (Boereboom et al., 2012; Langer et al., 2015; Walter-Anthony et al., 2010; Wik et al., 2011).

Trough ponds presented the highest combined (CO2+CH4; diffusion + ebullition) GHG fluxes at the time of sampling (Fig. 9). Considering a global warming potential (GWP) of 34 for CH4 on a 100-year horizon (Myhre et al., 2013), trough ponds presented the highest net carbon efflux (1.5 g CO2-equivalent m-2d-1, compared to 0.7 and 0.2 g CO2-equivalent respectively for polygon ponds and lakes). Despite their shallow depths, trough ponds were strongly stratified with oxygen-depleted and cold bottom waters. The bottom temperature in these ponds was indeed near 0 ∘C (Fig. 3) because this layer of water is lying just above the melting ice wedge (as part of the active layer), it does not mix with surface waters and it is cooled down through sensible heat transfer. Moreover, trough ponds were not colonized by photosynthesizing (CO2 sink) and methanotrophic (CH4 sink) bacteria such as in polygonal ponds (Negandhi et al., 2014). Stronger water column hypoxia generated anoxia more rapidly in the sediments, and the organic material inputs caused by active erosion likely led to higher CH4 production, although the young carbon signature of emitted CH4 is still puzzling (see below). Meanwhile, the eroding conditions and reduced light availability (higher CDOM, TP and turbidity; Table 1) in trough ponds favored net heterotrophy and net CO2 emissions, as found in subarctic thermokarst lakes (Roiha et al., 2015). Similar to polygonal ponds, the shallow depth of trough ponds reduces the chances for dissolution of CH4 bubbles into the water column and its subsequent oxidation before reaching the atmosphere. Moreover, the thermal structure of trough ponds (low transparency, microtopography) can impede mixing for several weeks (Fig. 4), thus favoring GHG summer storage in bottom waters, and likely generating stronger diffusive flux later at the autumnal overturn period. Thermal structure might become even stronger in years of low precipitations such as in 2014, when concentrations of solutes (DOC, ions) increase through evaporation, intensifying density gradients thus GHG storage. Diffusive CH4 fluxes were indeed statistically higher (p<0.01) in 2014 compared to 2013, although no such trend was observed for CO2.

The highest GHG saturation levels observed over the sampling period were measured in a trough pond the day following a major erosion event (peat block collapsing in pond BYL27; Fig. C1). This might result from the disturbance of the thermal structure and transfer of stored GHG to the surface, or from the causal effect of a new input of organic matter to microbial activity. Active shore erosion around tundra ponds, potentially increasing CH4 production by 2 to 3 orders of magnitude, has been reported from similar systems in Siberia (Langer et al., 2015), suggesting a direct impact of permafrost slumping on GHG emissions. The effect of erosion events on GHG flux must be further evaluated as other factors, such as fluctuating wind and air temperature, can also influence mixing and surface GHG concentrations (Tedford et al., 2014).

Interestingly, we also observed substantial differences in GHG concentrations among trough ponds, some presenting much lower values. Trough ponds such as BYL24 (Fig. 2g) had relatively stable (non eroding) shores, and were colonized by abundant vegetation dominated by brown mosses. Methane oxidation by bacteria associated with submerged brown mosses has been reported in Siberian ponds, contributing to smaller CH4 concentrations in these ecosystems (Liebner et al., 2011). Therefore, there might be cases where the methanotrophic community is also efficient in limiting CH4 emissions from trough ponds (Negandhi et al., 2014).

We obtained different radiocarbon ages for CO2 and CH4 within the same ebullition samples, as collected from funnels placed at the water surface (Table 2, Fig. 5), suggesting that GHG production was derived from different carbon sources. This divergence in carbon age was even more pronounced for the lakes, where it could reach almost 3000 years. The presence of unfrozen sediment layers (talik) underneath the lakes would explain the older bubbling CH4 emitted from deeper and older sediments exposed to microbial degradation, as found in thermokarst lakes of Siberia and Alaska (Walter et al., 2007). Younger CO2 could then be explained by a larger contribution of younger and shallower surface sediments to bacterial production and respiration. It could also result from lateral inputs of CO2 produced within younger organic material or from exchanges with atmospheric CO2.

On the other hand, century-old CO2 collected from ponds in parallel to modern CH4 is more difficult to explain. As stated above, emission of young CH4 suggests the preferential use of modern carbon by methanogens, and also a dominance of background ebullition mode (from surface sediments) in thaw ponds. Meanwhile, emission of older CO2 could be related to anaerobic CO2 production in water-saturated and reductive soils and its subsequent lateral transport, as observed in a flooded tundra site in Alaska (Zona et al., 2012). Characterizing organic matter properties and oxidation versus reduction (redox) potential of pond and lake sediments at our study sites are required to confirm if such a mechanism can contribute to modern CH4 emissions from surface layers and, at the same time, older CO2 emissions from deeper layers. Moreover, a quantification of lateral fluxes of carbon within the active layer (groundwater and streams), an important yet rarely mentioned process driven by the coupling between carbon and water cycles (Vonk and Gustafsson, 2013; Paytan et al., 2015), could help to better understand these results.

Notwithstanding the above-mentioned differences, the concentrations of CO2 and CH4 emitted through ebullition also need to be taken into account when evaluating the climate feedback potential of these emissions. Even though the age of CO2 could reach several centuries (>1000 yr BP for one sample; Fig. 5), it was 1 to 2 orders of magnitude less concentrated in the emitted bubbles than CH4. Hence, such emissions have a much lower potential to generate a positive feedback effect, at least during the ice-free season and under current climate conditions. Similar observations were reported from Siberian lakes, despite notably different geomorphological, geocryological and limnological conditions (Walter et al., 2007).

Methanogenesis in cold wetland systems typically proceeds via the anaerobic fermentation pathways of acetoclastic methanogenesis (AM) and/or hydrogenotrophic carbonate reduction methanogenesis (HM) (e.g., Kotsyurbenko et al., 2004; Alstad and Whiticar, 2011). AM utilizes the transfer of a CH3- group from preformed organic substrates (i.e., acetate, methanol, methylated substrates, etc.), whereas HM utilizes H2 and CO2. Numerous studies have demonstrated the ability of using methane C and H isotope signatures to discriminate AM from HM pathways, and to characterize secondarily altered methane (oxidation, mixing, etc.). Polygonal ponds and lakes had combined methane C and H stable isotope signatures that were typical for methanogenesis dominated by AM, as clearly illustrated in the plot of δ13CH4 versus δDCH4 (Fig. 7). Trough ponds shared similar δ13CH4 values with the other water bodies, but had substantially more 2H-depleted values (δDCH4 from -398 to -448 ‰; Table 2, Fig. 7). These values are among the most 2H-depleted values known for naturally occurring methane (e.g., Whiticar, 1999). Although there was some variation between sites, the isotope signatures designate that all CH4 emitted by ebullition in July is produced by AM, consistent with an earlier study at the same site (Negandhi et al., 2013). There is no indication of HM, which has a very different isotope signature, although the signature of samples collected from the center of lakes tend to lie towards the HM region, suggesting that a small proportion of the CH4 produced could be through this pathway. This finding of AM dominance is consistent with ombrotrophic bogs with higher pH (ranging from ∼6.7 to 10.0 in 2014) compared with more acidic minerotrophic wetlands, which can be HM dominated (e.g., Bowes and Hornibrook, 2006; Prater et al., 2007). The dominance of AM is likely related to the carbon precursors; our sites may have more labile organic material present (e.g., organic acids) supporting acetoclastic methanogenesis and recently made available to methanogens. As this labile carbon pool is exhausted, the methanogenic pathway shifts from acetoclastic to more recalcitrant compounds and hence hydrogenotrophic methanogenesis (e.g., Alstad and Whiticar, 2011). It is therefore possible that other periods of the year would show a stronger HM signature, which would also be consistent with the presence of a large fraction of microbes able to perform HM in thaw ponds from this site (Negandhi et al., 2013).

Previous work in this valley indicated a significant relationship between water oxygen concentration and dissolved CH4 oxidation level (Negandhi et al., 2013). This work also showed evidence that diffusive CH4 was more susceptible to oxidation in polygonal ponds where a methanotrophic community was favored (Negandhi et al., 2014). This conclusion was supported by the strong shift in δ13CH4 and δDCH4 to the heavier isotopes, as expected (Whiticar et al., 1986). In the present study, there was no evidence of methane oxidation in any of the collected ebullition samples (Fig. 8), indicating that the conditions did not favor oxidation at the production site (likely in anoxic sediment but also potentially in the water column; Grossart et al., 2011), and that the exchange with a pool of oxidized methane during the transport of bubbles to surface waters was undetectable, possibly linked to the short residence time. This was expected for shallow waters where bubbles can rapidly escape, but it was also the case in larger and deeper stratified lakes such as BYL36.

The general topography and geology of the southwest plain of Bylot, together with the distinct local conditions of the Qarlikturvik valley (e.g., glacier and outwash plain activity, valley orientation in relation to dominant winds, snow cover depth and density), have contributed to the development over thousands of years of what is arguably one of the richest ecosystems in the region. However, taken separately, most of the landscape features in the valley (e.g., tundra polygons, ice-wedges, thermokarst ponds and lakes) are widespread across the Arctic (e.g., Walter-Anthony et al., 2010; Abnizova et al., 2012; Langer et al., 2015). When compared with flux values reported in the literature, our results, representing a snapshot of mid-summer conditions, generally appear in the range of what has been observed in other ponds and lakes from northern regions (Table 4). For example, we measured total CO2 fluxes (diffusion + ebullition) of up to ∼0.8 gCm-2d-1, which is in the range of those reported from Alaska (0.7–2.3 gCm-2d-1; Kling et al., 1992; Sepulveda-Jauregui et al., 2015), Siberia (0.02–1.1 gCm-2d-1; Abnizova et al. 2012; Blodau et al., 2008), and Scandinavia (0.9–1.6 gCm-2d-1; Huttunen et al., 2003; Kankaala et al., 2013). Methane fluxes (diffusion + ebullition) at our study site varied substantially (0.0005–6.4 gCm-2d-1), but could reach values 1 order of magnitude higher than those from lakes in Alaska (0.01–0.5 gCm-2d-1; Kling et al., 1992; Sepulveda-Jauregui et al., 2015; Walter-Anthony and Anthony, 2013) and Scandinavia (0.01–0.1 gCm-2d-1; Bastviken et al., 2004; Huttunen et al., 2003; Kankaala et al., 2013). However, median values for polygonal and trough ponds (∼0.02 and 0.01 gCm-2d-1, respectively) were more similar to published ranges. Yet, these fluxes were lower than those reported from Siberian thermokarst lakes in yedoma deposits (nearly 20 gCm-2d-1; Walter-Anthony et al., 2010), which however include discrete ebullition seeps and hotspots that were not observed in our study, and most likely do not exist in the case of ponds.