CH4 and N2O dynamics in the boreal forest-mire ecotone

Biogeosciences, 12, 281–297, 2015 www.biogeosciences.net/12/281/2015/ doi:10.5194/bg-12-281-2015 © Author(s) 2015. CC Attribution 3.0 License. CH4and N2O dynamics in the boreal forest–mire ecotone B. ˇ Tupek1, K. Minkkinen1, J. Pumpanen1, T. Vesala2, and E. Nikinmaa1 1Department of Forest Sciences, P.O. Box 27, 00014 University of Helsinki, Finland 2Department of Physics, P.O. Box 48, 00014 University of Helsinki, Finland Correspondence to: B. ˇ Tupek ([email protected]) Received: 28 April 2014 – Published in Biogeosciences Discuss.: 4 June 2014 Revised: 13 November 2014 – Accepted: 3 December 2014 – Published: 16 January 2015 Abstract. In spite of advances in greenhouse gas research, the spatiotemporal CH4and N2O dynamics of boreal landscapes remain challenging, e.g., we need clarification of whether forest–mire transitions are occasional hotspots of landscape CH4and N2O emissions during exceptionally high and low ground water level events. In our study, we tested the differences and drivers of CH4 and N2O dynamics of forest/mire types in field conditions along the soil moisture gradient of the forest–mire ecotone. Soils changed from Podzols to Histosols and ground water rose downslope from a depth of 10m in upland sites to 0.1m in mires. Yearly meteorological conditions changed from being exceptionally wet to typical and exceptionally dry for the local climate. The median fluxes measured with a static chamber technique varied from −51 to 586µgm−2h−1for CH4and from 0 to 6µgm−2h−1for N2O between forest and mire types throughout the entire wet–dry period. In spite of the highly dynamic soil water fluctuations in carbon rich soils in forest–mire transitions, there were no large peak emissions in CH4and N2O fluxes and the flux rates changed minimally between years. Methane uptake was significantly lower in poorly drained transitions than in the well-drained uplands. Water-saturated mires showed large CH4emissions, which were reduced entirely during the exceptional summer drought period. Near-zero N2O fluxes did not differ significantly between the forest and mire types probably due to their low nitrification potential. When upscaling boreal landscapes, pristine forest–mire transitions should be regarded as CH4sinks and minor N2O sources instead of CH4and N2O emission hotspots. 1 Introduction Soil fertility, soil water content, and soil carbon storage of boreal forests varies between well-drained mineral soils mainly found in uplands and poorly drained organic soils mainly found in peatlands (Seibert et al., 2007; Weishampel et al., 2009). The CH4and N2O fluxes from mineral and organic soils are impacted by varying soil moisture conditions (Solondz et al., 2008; Pihlatie et al., 2004). Typical mineral soil forests are small sinks of CH4and small sources or sinks of N2O (Moosavi and Crill, 1997; Pihlatie et al., 2007). Sparsely forested peatlands are typically large or small sources of CH4and small sources or sinks of N2O (Martikainen et al., 1995; Nykänen et al., 1995; D’Angelo and Reddy, 1998). Field CH4and N2O studies of natural boreal forest–mire ecotones are rare (e.g., Ullah et al., 2009; Ullah and Moore, 2011) in comparison to those of typical forests or mires. However, the area of forest–mire transitions is relatively large, e.g., in Finland, forested mires with an organic horizon <30cm cover 1.5 million hectare or approximately 7% of the total forest area (Finnish statistical yearbook of forestry, 2013), and at the present time it is not clear whether the terrestrial–aquatic interfaces, such as the forest– mire transition, represents a biogeochemical hotspot of CH4 and N2O emissions (McClain et al., 2003). The lagg transitional zone in the forest–mire ecotone receives nutrients from the adjacent mineral soil runoff, and is thus more minerotrophic, biologically diverse, and productive than open mires or bogs (Howie and Meerveld, 2011). Furthermore, ecotones between forests and mires are ecological switches (Agnew et al., 1993), where the vegetation of forests and mires coincide and soils frequently undergo fluctuations in water level position and chemistry (Hartshorn et al., 2003; Howie and Meerveld, 2011), and where the CH4 Published by Copernicus Publications on behalf of the European Geosciences Union. 282 B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone and N2O dynamics of forest–mire transitions may be expected to differ generally and on a year-to-year basis from those of typical forests and mires. The CH4uptake of forest soils is a result of CH4oxidizing aerobic methanotrophs sensitive to water saturation, soil porosity, moisture, temperature, pH, and ammonium (Moosavi and Crill, 1997; Saari et al., 2004; Jaatinen et al., 2004). Unsaturated upland forest soils oxidize CH4at higher rates than more water-saturated, acidic, and ammonium rich forested peat soils (Saari et al., 2004). In contrast to the CH4 sinks of upland forest soils, and drained peatlands, natural mires emit CH4to the atmosphere (Bubier et al., 1995; Nykänen et al., 1998; Kettunen et al., 1999). CH4production in peat soil is a result of methanogenic and methanotrophic active bacteria, whose activity depends on anoxic and oxic conditions below and above the water level, temperature, and availability of carbon substrate (Kettunen et al., 1999). Increasing soil wetness increases anoxic conditions necessary for increased methanogenesis (Juottonen et al., 2005), and as a result CH4emissions increase (Saarnio et al., 1997; Ojanen et al., 2010; Yrjälä et al., 2011). Methane production potential in peat soils generally increases positively with pH (Juottonen et al., 2005; Ye et al., 2012), whereas CH4oxidation of forested peatlands has a narrow pH optimum around 5.5 (Saari et al., 2004). Increased pH levels, e.g., through the inflow of less acidic mineral soil water, typically containing greater calcium and bicarbonate concentrations than peat water (Howie and Meerveld, 2011), could increase CH4emissions from transitions. N2O emissions in well-drained boreal forest soils are controlled by soil moisture, pH, available nitrate, ammonium, oxygen, and carbon concentrations (Regina et al., 1996; Ullah et al., 2008). N2O production is limited by the amount of nitrogen and is subject to denitrification and nitrification processes (Ambus et al., 2006). In well-drained soils NO3 limitation, anoxic microsites, and larger soil porosity may also promote N2O consumption (Frasier et al., 2010). N2O consumption of soils correlates with dehydrogenase activity, which is affected by oxidation-reduction status and possibly controlled by soil moisture (Wlodarczyk et al., 2005). The N2O consumption by soils is attributed to respiratory reduction (Conrad, 1996) caused by denitrifiers and nitrifiers (Rosenkranz et al., 2006). N2O emissions increase during drier periods through increased ammonification and nitrification (Regina et al., 1996; Nykänen et al., 1995; Von Arnold et al., 2005). In water-saturated minerotrophic peatlands nitrification supplies nitrate (Wrage et al., 2001) for denitrification, which is the main but small N2O source (Wray et al., 2007; Frasier et al., 2010). In nutrient rich mires, N2O emissions increase during drier periods through increased ammonification and nitrification (Regina et al., 1996; Nykänen et al., 1995; Von Arnold et al., 2005). Nitrification and the supply of nitrate for denitrification increases with higher pH (Regina et al., 1996). However, if nitrate is available, low pH increases N2O emissions (Weslien et al., 2009). Therefore, if nitrate were present during water level drawdown, the forest– mire transitions could become sources of N2O. Our aims were (1) to test whether forest floor CH4and N2O fluxes of the forest–mire transition differ from the typical upland forests and lowland mires of natural boreal landscapes and (2) how meteorologically different years, i.e., exceptionally wet (2004), typical (2005), and exceptionally dry (2006), affect the fluxes. We addressed the question of whether increasing wetnessin forest–miretransitions promotesCH4production, and whether dry conditions reduce CH4production and increase N2O emissions. We hypothesized that forest/mire types exhibit distinct levels of CH4and N2O fluxes due to the changing soil structure from Podzols to Histosols and due to increasing soil water content from xeric to saturated. We expected that the occasionally saturated organo-mineral soils of forest–mire transitions are variable sources of CH4and N2O fluxes. In order toevaluate the underlying factors behind CH4 and N2O forest floor fluxes, we measured the fluxes and environmental variables, such as soil temperature, soil moisture, water table depth, and soil water pH, in nine sites along the forest–mire ecotone during exceptionally different meteorological conditions. In order to detect statistically significant differences between CH4and N2O fluxes of nine sites we used two-way analysis of variance, and for better understanding of flux responses to environmental factors we used linear and nonlinear regression models, and residual sensitivity analysis. 2 Material and methods 2.1 Study site characteristics The Vatiharju–Lakkasuo ecotone of nine forest and mire study sites forms a gradient in vegetation communities, soil moisture and nutrient conditions in central Finland (61◦470, 24◦190) (ˇ Tupek et al., 2008). Forest/mire types were classified using the Finnish classification systems (Cajander, 1949; Laine et al., 2004) based on soil fertility reflected by the composition and abundance of forest floor vegetation, and by the site location on the slope. The ecotone study sites are situated along a 450m transect on a hillslope with a relative relief of 15m and a 3.3% slope facing NE (Fig. 1a). The fertility of the forest/mire sites increase from the poorly fertile sites at the xeric and saturated edges of the ecotone towards the most fertile Oxalis-Myrtillus type forest (OMT) in the middle of the hillslope (Fig. 1b). Dominant vegetation composition changes with increasing soil moisture down the slope. Xeric Scots pine forest (CT – Calluna type) on the summit of glacial sandy esker gives way to subxeric Scots pine Norway spruce forest (VT – Vaccinium vitis-idaea type) on the shoulder, and mesic and herb rich Norway spruce dominated types on the back slope and footslope (MT – Vaccinium myrtillus Biogeosciences, 12, 281–297, 2015 www.biogeosciences.net/12/281/2015/ B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone 283 Table 1. Site soil water solution pH and soil properties. CT VT MT OMT OMT+KgK KR VSR1 VSR2 mean SE mean SE mean SE mean SE mean SE mean SE mean SE mean SE mean SE pH 10cm 5.57 0.36 5.14 0.42 5.24 0.08 4.68 0.39 4.58 0.30 4.46 0.14 4.37 0.22 5.06 0.39 4.80 0.44 pH 30cm 6.20 0.06 6.18 0.02 5.91 0.13 5.30 0.11 5.53 0.04 4.91 0.10 4.55 0.08 5.32 0.15 4.79 0.19 Bulk density 0–10cm 0.37 0.09 0.28 0.04 0.48 0.03 0.27 0.09 0.31 0.13 0.33 0.05 0.24 0.02 0.40 0.12 0.40 0.12 Bulk density 10–30cm 0.92 0.07 0.31 0.12 0.85 0.03 0.90 0.07 0.90 0.07 Tot C (%) 0–10cm 43.17 24.22 49.63 47.09 45.36 48.68 50.30 45.76 48.20 Tot C (%) 10–30cm 21.76 53.31 48.33 47.70 49.97 Tot N (%) 0–10cm 1.02 0.61 1.18 1.59 2.19 1.47 1.12 1.29 0.96 Tot N (%) 10–30cm 0.96 1.95 1.45 1.87 1.81 C/N 0–10cm 42.32 39.70 42.06 29.62 20.71 33.12 44.91 35.47 50.21 C/N 10–30cm 22.67 27.34 33.33 25.51 27.61 Figure 1. (a) Airborne infrared photograph shows a 450m long boreal forest–mire ecotone located on the NE slope of the glacial Vatiharju–Lakkasuo esker in Finland (61◦470, 24◦190). (b) The fisheye photographs show tree stands of xeric (1), subxeric (2), mesic (3), herb rich (4), paludified (5–7), and saturated (8–9) forest/mire types. (c) Photographs show ground vegetation and (d) soil profiles of nine forest/mire types. Upland forests: 1 CT – Calluna, 2 VT –Vaccinium vitis-idaea, 3 MT – Vaccinium myrtillus, 4 OMT – Oxalis-Myrtillus; forest–mire transition types: 5 OMT+–OxalisMyrtillus paludified, 6 KgK – Myrtillus spruce forest paludified, 7 KR – spruce pine swamp; sparsely forested wet mire types: 8 VSR1 and 9 VSR2 – tall sedge pine fen. type, OMT – Oxalis-Myrtillus type). The toe slope contains forest–mire transitions of paludified mixed spruce– pine–birch forests (OMT+–Oxalis-Myrtillus paludified, KgK – Myrtillus spruce forest paludified). There is a permanently wet mixed spruce–pine–birch swamp (KR – spruce pine swamp) at the mire edge of the forest–mire transitions. On the level of the hillslope there are birch–pine fen mires with open tree canopies (VSR1 and VSR2 – tall sedge pine fen) (Fig. 1b). The forest floor vegetation is composed of sitespecific mosses and vascular plants (Fig. 1c). Soils are formed by well-drained Haplic Podzols on the hillslope, intermediately drained Histic and Gleyic-Histic Podzols in the forest–mire transitions on the toe of the slope, and permanently wet Hemic Histosols downslope (Fig. 1d). We measured pH during summer campaign 2005 from soil water data collected on all sites by suction cup lysimeters. Three lysimeters were installed in 10cm and one in 30cm depth below the soil surface in each site. Detailed description of the lysimeters and sampling procedure can be found in Starr (1985). The pH was measured on the day of water sampling in the laboratory by pH meter equipped with a glass electrode. The mean acidity level of the sites of forest–mire ecotone was gradually increasing from pH 5.6 in uplands (CT) to 4.4 in transitions (KR), whereas mires were less acid than transitions with pH 5.1 and 4.8 (VSR1 and VSR2, respectively) (Table 1). Collected soil water from 30cm depth showed generally higher pH than soil water pH at 10cm depth. Three soil cores for each plot were taken in July 2006 from the top soil (0–10cm) in upland forests and from the two profile depths (0–10, 10–30cm) in forest–mire transitions and in peatlands. The volume of samples was measured before the oven drying at 70◦C to determine the bulk density. The bulk density of the upper organic layer ranged from 0.24gcm−3(KR) to 0.48gcm−3(MT) and was approximately half of the bulk density of the organic layer from 10 to 30cm depth (mean of transitions and mires 0.77gcm−3) (Table 1). The C/N ratio was determined once for each plot from the soil organic matter analyzed by dry combustion with Leco CNS-1000 (Leco Corp., USA). The C/N ratio was wider in the 0–10cm profile (mean 37) than in the 10–30cm profile (mean 27). The highest N content as well as the lowest C/N ratio along the ecotone was found in forest–mire www.biogeosciences.net/12/281/2015/ Biogeosciences, 12, 281–297, 2015 284 B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone transitions OMT+and KgK (Table 1). A more detailed forest/mire type characterization is given by ˇ Tupek et al. (2008). 2.2 Micrometeorological conditions The micrometeorological measurements along the Vatiharju– Lakkasuo forest–mire ecotone were taken weekly during the summers of 2004 (July–November), 2005 (May–November), 2006 (May–September), and monthly during the winters (December–April). The forest floor soil temperatures (◦C) at depths of 5, 15, and 30cm (T5,T15, and T30) were measured using a portable thermometer connected to thermocouples installed permanently in the soil. The volumetric soil moisture (%) at depths of 5, 10, and 30cm (soil water content – SWC5, SWC10, and SWC30) was measured by a portable ThetaProbe (Delta-T Devices Ltd.) in diagonally installed perforated PVC tubes, to ensure the same compactness of the soil.The depthof watertablewasmeasured insidePVC tubes (∅30mm) installed at each site. Precipitation was measured by an automated bucket system at a station for monitoring forest – atmosphere relations, SMEARII (Hari and Kulmala, 2005), located 6km north – west from the forest–mire ecotone. Missing soil temperature and moisture data of ecotone were gap filled by linear regression between continuous measurements of soil temperature and moisture at SMEARII. 2.3 CH4and N2O fluxes The field gas sampling was conducted weekly in the 2004 and 2005 seasons, bi-weekly during the 2006 season, and monthly during the winters. The gas sampling was done within 3-days interval of the micrometeorological measurements. If there was packed snow on the ground, the gas samples would be taken from the top and bottom layers; and the CH4(µgm−2h−1) and N2O (µgm−2h−1) fluxes were calculated by the snowpack diffusion method using each gas concentration difference, snow depth, porosity and temperature, and gas diffusion coefficients as in Sommerfeld et al. (1993). Otherwise, if there was no snowpack, the samples would be taken from three opaque, vented, closed, static chambers (∅315mm, h295mm) placed air tightly on preinstalled collars. On each measuring occasion a sample of ambient gas and four 15ml samples from each of the three chambers were drawn in syringes at intervals of 5, 10, 15, and 20min from chamber closure, totaling 13 samples for each site. Chamber temperature was monitored during the sampling. After the sampling event, the gas samples were stored in coolers at +4◦C and analyzed within 36h in a laboratory with a gas chromatograph. The gas chromatograph (Hewlett-Packard, USA) model number HP-5890A was fitted with a flame ionization detector (FID) for CH4and an electron capture detector (ECD) for N2O detection. The gas chromatograph was also equipped with a moisture trap. Prior to analysis of field samples and after each set of 13 samples a reference gas sample of known CH4and N2O concentration was analyzed. The CH4(µgm−2h−1) and N2O (µgm−2 h−1) fluxes were calculated from the slope of linear regression between the set of four gas concentrations and sampling time, time elapsed after the chamber closure, and by applying temperature correction. For the flux calculation we used a MATLAB (The Mathworks Inc.) script developed at the Dept. of Physics, University of Helsinki. The method quantification limit (MQL) of the gas chromatograph was based on 100 subsequently analyzed samples of reference gas of known CH4and N2O concentrations (mean±two SD: 1.837±0.055 and 0.295±0.023ppm, respectively) and reference gas samples analyzed before the set of field samples for each site. The MQL was a gas-specific standard deviation of the random fluxes derived from 1000 random sets of four CH4or N2O concentrations of reference gas samples (22µgm−2h−1for CH4and 18µgm−2h−1 for N2O). In order to minimize the random error related to gas sampling in the field, fluxes were verified using the ambient field air sample analyzed before each sequence of chamber samples adopting similar criteria as used in Alm et al. (2007). Due to gas sampling disturbances in the field and poor gas chromatograph accuracy 17% of CH4and 49% of N2O fluxes were discarded. 2.4 Statistical analysis Two-way analysis of variance (ANOVA) was used to test whether CH4and N2O fluxes of forest/mire types have common means in wet, typical, and dry years. Post hoc Tukey HSD (honest significant difference) tests were used to test the pairwise differences between the forest and mire types and years changing from wet to dry. For CH4fluxes we ran ANOVA tests twice, first on the whole data set including nine forest/mire types and then on a subset of data including upland forests and forest–mire transitions, and excluding mires. For testing significant differences between the two groups of data we performed Welch’s two sample ttest, e.g., between the N2O fluxes from the snow on the ground season (January–April in 2006) and the N2O fluxes from the snowless seasons (May–November in 2005 and May–September in 2006). In addition to ANOVA, we tested the dependence between the measured CH4(µgm−2h−1) and the gap filled half-hourly environmental variables in separate models for: (a) the upland forests on mineral soils (CT, VT, MT, OMT), and (b) forest–mire transitions on organo-mineral soils and (OMT+, KgK, and KR) (c) mires (VSR1, VSR2). CH4fluxes (µgm−2h−1) of uplands and transitions were fitted by two linear mixed-effects regression models with a random effect for forest types (Pinheiro et al., 2013). For both groups of forest types, we evaluated the effect of all our environmental variables on CH4together and their combinations iteratively by selecting the model combination of variables that were significant. Biogeosciences, 12, 281–297, 2015 www.biogeosciences.net/12/281/2015/ B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone 285 The CH4fluxes for upland forests and transitions included soil moisture at 10cm (%) (SWC10) and soil temperature at 5cm (◦C) (T5) as predictors in separate models (Eqs. 1 and 2): yuij =βCTSWC10 +βVTSWC10 +βMTSWC10 (1) +βOMTSWC10 +βCTT5+βVTT5+βMTT5+βOMTT5 +bCT +bVT +bMT +bOMT +εij , ytij =βOMT+SWC10 +βKgKSWC10 +βKRSWC10 (2) +βOMT+T5+βKgKT5+βKRT5+bOMT+ +bKgK +bKR +εij , where yuij and ytij are the CH4flux (µgm−2h−1) for upland forests or transitions and for a particular ith forest type and the jth observation, βCT through βKR are the fixed effect coefficients for a particular ith forest type (CT, VT, MT, OMT Eq. 1, or OMT+, KgK, and KR Eq. 2), SWC10, and T5are the fixed effect variables (predictors) for observation jin forest type iwhere each forest type’s predictor is assumed to be multivariate normally distributed, bCT through bKR are intercepts for the random effect for a particular ith forest type, and εij is the error for case jin forest type i where each forest type’s error is assumed to be multivariate normally distributed (Table 2). The CH4fluxes (µgm−2h−1) of mires were fitted by using a multiplicative nonlinear regression model with a combined response to water table depth and soil temperature at 5cm Eq. (1): yij =a0e−0.5WT-WTopt WTtol 2e−0.5T5-Topt Ttol 2+εij ,(3) where yij is the CH4flux (µgm−2h−1) for the ith mire (VSR1,VSR2) and for the jth case, WT (cm) is water table depth, T5 (◦C) is soil temperature at 5cm, and a0, WTopt, WTtol, Topt, and Ttol are parameters (Table 3). The N2O fluxes (µgm−2h−1) of all forest/mire types were fitted by using one multiplicative nonlinear regression model with a combined response to soil moisture and soil temperature at 5cm Eq. (4): zij =a0SWC5e−0.5T5-Topt Ttol 2+εij ,(4) where zij is the N2O flux (µgm−2h−1) for the ith mire (VSR1,VSR2) and for the jth case, SWC5(%) is soil moisture at 5cm, and T5 (◦C) is soil temperature at 5cm,and a0, Topt, and Ttol are parameters (Table 4). To illustrate the sensitivity of CH4and N2O flux response to environmental factors we performed a residual analysis by simulating a value for each data point with only one factor allowed to vary and the other set to its mean level. To examine correlations between CH4and N2O fluxes and pH, and soil properties we preformed the Pearson’s correlation tests. The statistical analyses were performed in MATLAB R2012a (The Mathworks Inc.) and in R (R Core Team 2013) software environments. 3 Results 3.1 Micrometeorological conditions The largest differences between years 2004, 2005, and 2006 were seen in changing summer precipitation patterns (measured nearby the SMEARII station). The average June– August monthly precipitation was reduced from 94 to 44mm from a wet 2004 to a dry 2006, while ambient temperature increased from 14 to 17◦C. In the coldest summer (2004) the average precipitation in June and July was over 117mm, and dropped to 47mm in August. In the typically warm summer of 2005 the monthly precipitation gradually increased up to 123mm in August, and dropped to 58mm in September. However, in the warmest summer (2006) the monthly precipitation never reached more than 48mm. In July 2006, two rainless weeks induced a drought. By drought we mean that the soil water content in the upper soil layer (in mineral soils) was so low that mosses wilted and dried (all along the ecotone). The drought conditions lessened in mid-August and ended in September with increasing rains towards autumn. Late autumn was exceptionally warm and snowless. Monthly median soil temperatures at 5cm (T5) ranged from around 5◦C in May, culminated to around 15–16◦C in July and August, and subsided again to around 5◦C in October. The non-vegetative season T5minimum was close to 0◦C. The warmest T5was in upland forest CT and the coldest was in upper forest–mire transition OMT+. Soil temperature slightly increased from forest–mire transitions towards mires. In spite of the ambient air temperature difference throughout all the months in the 3 years, we detected differences mainly during early and late season in 2004, 2005, and 2006 T5(Fig. 2a). The median water table (WT) showed the obvious rise from 10m at the summit of the hill, to around 1m in the mid-slope, between 0.5 and 0.1m at the toe slope, and close to 0.01m on the level (Fig. 2b). The seasonal WT rise in 2005 was observed between the July and August medians. During the drought of 2006, the WT values dropped less than 0.1m for the uppermost forest sites, but dropped heavily by ∼1m in the forest–mire transitions, and more than 0.5m in the lowermost peatland sites. Volumetric SWC in 10cm depth ranged from a dry value of around 10% in the mineral soils to a water-saturated value of around 80% in swamp and mires (Fig. 2c). The largest drought reduction of SWC was in August 2006 on the welldrained sandy Podzols at the summit of the hill, and also on the poorly drained Histic Podzols on the toe slope. 3.2 CH4fluxes The median fluxes from the forest floor varied from −51 to 586µgm−2h−1for CH4among individual sites during the entire period (Fig. 3a). The small negative CH4fluxes associated with prevailing oxidation were mostly observed www.biogeosciences.net/12/281/2015/ Biogeosciences, 12, 281–297, 2015 286 B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone Table 2. Parameter estimates and their standard errors for trend coefficients of CH4fluxes (µgm−2h−1) of the upland forest types (CT, VT, MT, and OMT, Eq. 1), and for the forest–mire transitions (OMT+, KgK, and KR, Eq. 2). Both equations are functions of volumetric soil moisture at 10cm (%) and soil temperature at a depth of 5cm (◦C). Eq. (1) bi Group bi Group bi SE βi1βi1 SE βi2βi2 SE NRMSE CT −39.345 −43.632 9.102 0.762a0.299 −1.249 0.223 137 35.2 VT −26.213 143 25.1 MT −50.984 139 25.2 OMT −57.985 144 32.1 Eq. (2) OMT+ −49.898 −50.248 7.507 0.638 0.105 −0.109b0.226 139 22.3 KgK −48.216 146 17.9 KR −52.630 149 31.5 Eq. (2) soil temperature excluded from fitting OMT+ −51.799 −52.466 6.341 0.660 0.099 139 22.3 KgK −50.404 146 17.9 KR −55.196 149 31.5 p < 0.001 for all parameters, except ap=0.011,bp=0.629.βi1– soil moisture at 10cm,βi2– soil temperature at 5cm. Table 3. Parameter estimates and their standard errors for trend coefficients of CH4fluxes (µgm−2h−1) of the mires (VSR1, VSR2, Eq. 3). Equation (3) is a function of water table depth (cm) and soil temperature at a depth of 5cm (◦C). Eq. (3) a0a0 SE Topt Topt SE Ttol Ttol SE WTopt WTopt SE WTtol WTtol SE NRMSE mires 1207.1 126.7 13.9 1.4 6.4 1.3 −18.0 2.2 16.6 2.8 324 656 VSR1 1570.3 155.1 13.0 0.8 5.8 0.8 −18.6 1.6 15.5 1.7 162 424 VSR2 801.3 190.8 16.6a6.8 8.7b4.5 −17.3c5.3 20.7d9.7 162 558 pvalues<0.001, except ap=0.016,bp=0.053,cp=0.002,dp=0.035. in uplands and in transitions, while mires typically showed large positive CH4fluxes associated with prevailing production. The CH4flux dynamics changed exponentially with increasing levels of the ground water table from small uptake to large emissions (Figs. 2, 3). The median CH4fluxes of uplands (CT, VT, MT, OMT), transitions (OMT+, KgK, KR), and mires (VSR1, VSR2) varied from −38, −48, and 392µgm−2h−1, respectively (Fig. 3b). Momentary CH4 fluxes of uplands and transitions ranged from −342 to 143µgm−2h−1, whereas in mires the fluxes ranged from −12 to 6808µgm−2h−1(Fig. 3b). The median CH4fluxes for one upland (VT) and all the transitions (OMT+, KgK, KR) were found inside the range of the gas chromatograph detection limits (MQLCH4=22µgm−2h−1). In forest–mire transitions the ground water level in August 2005 increased towards the surface and approached the levels typically found in mires (Fig. 2b), but the soil water saturation in transitions was not followed by CH4emissions such as those found in mires. ANOVA showed that forest floor CH4fluxes differed significantly for the nine forest/mire types of the ecotone F(8, 1252) =108, p < 0.001 and for the wet, typical, and dry years F(2, 1252)=10, p < 0.001. There was a significant interaction between CH4fluxes of forest/mire types and wet, typical, and dry years F(16, 1252)=5, p < 0.001. The post hoc Tukey comparison of the nine forest/mire types indicated that the mires had significantly higher CH4fluxes than the forests. Differences in means (M) and 95% confidence limits (CI) ranged from minimum VSR2–KgK (M=481, 95% CI [352, 610]) to maximum VSR1–OMT (M=793, 95% CI [668, 918]) at p < 0.001. Also the CH4fluxes of the mires were significantly different from each other VSR2– VSR1 (M= −260, 95% CI [−384, -137]), p < 0.001. Differences between the years were significant at p <0.001 for dry–typical (M= −96, 95% CI [−149, −43]) when CH4 fluxes of mires were highly reduced. The comparison of mean CH4fluxes of typical–wet (M=51, 95% CI [−6, 108]), p=0.089, and dry–wet years did not show a significant difference (M= −45, 95% CI [−111, 20]), p=0.237. Differences between the forest types (transitions, uplands) were not significant when analyzed together with the CH4fluxes of mires, but became significantly different F(6, 976)=71, p < 0.001, when ANOVA was run without mires. Though unlike the nine forest/mire type data set, for the Biogeosciences, 12, 281–297, 2015 www.biogeosciences.net/12/281/2015/ B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone 287 Figure 2. The panels (a–c) show the monthly medians of environmental variables: (a) soil temperature at a depth of 5cm, (b) ground water level, and (c) volumetric soil moisture at 10cm depth observed along the forest–mire ecotone during wet (2004), intermediate (2005), and dry years (2006). The top–down arrangement of sites mimics the locations on the slope (see Fig. 1). The error bars represent the 25th and 75th percentiles. Table 4. Parameter estimates and their standard errors for forest floor N2O fluxes (µgm−2h−1) of all forest/mire types (CT–VSR2) in one group Eq. (4). Eq. (4) is function of volumetric soil moisture at 5cm (%) and soil temperature at a depth of 5cm (◦C). Eq. (4) a0a0 SE Topt Topt SE Ttol Ttol SE NRMSE forests/mires 4.034 0.635 11.268 0.183 1.414 0.181 400 36.2 p < 0.001 for all parameters. group of uplands with transitions there was no difference between wet, typical, and dry years F(2, 976)=1, p=0.292, or their interactions F(12, 976)=1, p=0.135. The mean CH4 uptake of the upland forests (−42.9µgm−2h−1) was for the whole period significantly larger than the mean CH4uptake of the forest–mire transitions (−12.8µgm−2h−1) according to Welch’s two sample ttest t(994)=15.56, p < 0.001. The post hoc Tukey comparison of the differences in the mean CH4fluxes for 21 pairs of seven upland and transitional forest types was significant for 17 pairs at p < 0.001 and ranged from OMT–VT (M= −35, 95% CI [−45, −25]) to KR– OMT (M=51, 95% CI [41, 61]). The post hoc Tukey comparisons showed non-significant pvalues for 4 of the 21 pairs of CH4fluxes of transitional and upland forest types(MT–CT 0.056, OMT+–VT 0.965, OMT–MT 0.431, and KR–KgK 0.999). www.biogeosciences.net/12/281/2015/ Biogeosciences, 12, 281–297, 2015 288 B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● CT VT MT OMT OMT+ KgK KR VSR1 VSR2 0 1000 2000 3000 4000 5000 6000 7000 forest/mire types forest floor CH4 (ug m−2 h−1) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● CT VT MT OMT OMT+ KgK KR −200 −100 0 100 (a) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● uplands transitions mires 0 1000 2000 3000 4000 5000 6000 7000 groups of forest/mire types forest floor CH4 (ug m−2 h−1) (b) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● uplands transitions −200 −100 0 100 Figure 3. The box plots of forest floor CH4fluxes (µgm−2h−1) for each forest/mire type (a), and (b) for uplands (CT, VT, MT, OMT), transitions (OMT+, KgK, KR), and mires (VSR1, VSR2) during the whole period. The left–right arrangement of sites mimics the locations on the slope (see Fig. 1). 3.3 Factors controlling CH4fluxes The mean level of CH4fluxes of upland and transitional forests differed (Table 2, parameter group bi), though the sensitivity response to environmental factors was similar (Fig. 4). The largest part of the CH4fluxes remained unexplained with our models, as the proportion of explained variance was relatively low for uplands (10%) and transitions (15%) and slightly higher for mires (22%). The modeled CH4flux response for the upland and transitional forest sites to soil moisture at 10cm was nearly flat, although the soil moisture parameter was significant (p=0.011, Table 2). In the transitional Oxalis-Myrtillus paludified forest type OMT+, where the soil moisture at 10cm ranged from 20% (in the uplands) to over 70% (in the mires), the modeled CH4flux response between dry and water-saturated soil differed by 50µgm−2h−1. A stronger gradient than that in the soil moisture was detected by modeling stronger temperature responses of CH4fluxes for the uplands and the nearly flat response for the transitions (Fig. 4). The model parameter to soil temperature at 5cm in the uplands was highly significant at p < 0.001, in contrast to transitions where the temperature parameter was insignificant p=0.629 (Table 2). In the mires the observed range of water level during wet, typical, and dry years spanned from the surface to a depth of 54cm and showed a sigmoidal response with lower CH4fluxes towards the extreme ends. The optimum water level for CH4 emissions was 18cm below the surface with 16.6cm tolerance which is deviation of water level up to 60% of CH4 flux maximum (Fig. 4; p < 0.001, WTopt and WTtol in Table 3). Optimum near-surface peat temperature for the CH4 emissions was found at 13.9◦C with 6.4◦C tolerance (Fig. 4; p < 0.001, Topt and Ttol in Table 3). 3.4 N2O fluxes During the typical and dry years the momentary forest floor N2O fluxes of forest/mire types ranged from −107 to 248µgm−2h−1. The median N2O fluxes were similar for the forest/mire types and ranged only from 0 to 6µgm2h−1 (Fig. 5). The median N2O fluxes of all forest/mire types were found inside the range of the method quantification limits (MQLN2O=18µgm−2h−1). The N2O fluxes of the snow on the ground period were significantly lower than the N2O fluxes of the snowless period according to Welch’s two sample ttest t(297)=5.094, p < 0.001. Forest floor N2O fluxes did not differ significantly for the nine forest/mire types of the ecotone for the snowless periods F(8, 284)=0.708, p=0.684. Though, the momentary N2O fluxes were significantly different in typical and dry snowless seasons F(1, 284)=6.157, p < 0.014. N2O fluxes were lower during dry snowless seasons and a small increase was observed only in one forest–mire transition (KR – spruce pine swamp) and in one mire (VSR2 – tall sedge pine fen) (Fig. 6). In general N2O fluxes were low and did not show clear spatial differences in relation to increasing soil moisture from Biogeosciences, 12, 281–297, 2015 www.biogeosciences.net/12/281/2015/ B. ˇ Tupek et al.: CH4and N2O dynamics in the boreal forest–mire ecotone 289 ●● ● ● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ●● ● ●●●● ● ● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ●● ● ●●●● ● ● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●●● ● ●● ● ●● ● ●● 8 9 11 13 −300 −200 −100 0 100 ●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●●●● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●● CT r2 = 0.3 % ●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ● 10 20 30 ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●● ● VT r2 = 7.3 % ●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● 10 12 14 16 18 20 ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ●● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● MT r2 = 8.5 % ●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● 10 20 30 ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● OMT r2 = 9.3 % ●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● 20 40 60 80 ●● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● OMT+ r2 = 16.6 % ●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● 55 65 75 85 ●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ● ● ● KgK r2 = 3.1 % ●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●● 60 65 70 75 ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●●●● ● ● ●●● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ●● KR r2 = 3.1 % ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● 10 30 50 0 2000 4000 6000 water table depth (cm) adjusted CH4 (ug m−2 h−1) ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●●● ●●● ● ● ●●● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●● ●●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● VSR1 r2 = 9.8 % ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● 5 15 25 35 water table depth (cm) adjusted CH4 (ug m−2 h−1) ● ●●● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●●●●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●●● ●● ● ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● ● ●●● ● ● ● ● ●●● ●● ● ●●● ● ●● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●●● ●●● ● VSR2 r2 = 1 % ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● 0 5 10 15 20 −300 −200 −100 0 100 ●● ● ●●● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● ●●● ● ● ● ●● ● ●●●●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ●● ●● ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ● ● ● ●●● ●●● ●● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● CT r2 = 6.6 % ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● 0 5 10 15 ●● ● ●●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●●●●●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ●●●● ●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● VT r2 = 2.4 % ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● 0 5 10 15 ●● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ● ●● ●● ● ●●● ●●● ●● ●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● MT r2 = 11.3 % ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● 0 5 10 15 ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● ●● ● ●● ● ●● ● ●●● ●●● ●● ●● ●●● ● ● ●●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●● ●●●● ●● ● ●● OMT r2 = 18.4 % ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● 0 5 10 15 ●● ● ●●● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ●●● ● ● ● ●● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●●● ●● ● ●●● ● ● ●●●●● ●●●● ● ● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● OMT+ r2 = 4.8 % ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● 0 5 10 15 ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●● ●● ●●●● ●● ● ● ● ●● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● KgK r2 = 2.5 % ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● 0 5 10 15 ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●●● ●●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● ●● ● ●● ● ● ● ● ●●● ●●● ●●●● ●●●● ● ●●● ●● ● ● ●● ● ● ●●● ●● ● ●●●● ●●●● ●● ● ●● KR r2 = 0.5 % ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● 0 5 10 15 0 2000 4000 6000 water table depth (cm) adjusted CH4 (ug m−2 h−1) ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ●●● ●● ● ●● VSR1 r2 = 22.4 % ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● 0 5 10 15 water table depth (cm) adjusted CH4 (ug m−2 h−1) ●● ● ●● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ●● ●●● ●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●●● ● ● ● ●●●● ●●● ● ●● ● ●● VSR2 r2 = 12.3 % soil moisture at 10 cm (%) water table depth (cm) soil temperature at 5 cm (°C) adjusted CH4 (ug m−2 h−1) Figure 4. Comparison of sensitivity of forest floor CH4fluxes (µgm−2h−1) to environmental factors for nine forest/mire types. Modeled in the upper panels is CH4flux response to soil moisture at 10cm (uplands and transitions) or to water table depth (cm) (mires) for uplands (CT, VT, MT, OMT) Eq. (1), for transitions (OMT+, KgK, KR) Eq. (2), and for mires (VSR1, VSR2) Eq. (3). Water table depth is indicated as negative when it is above the soil surface. In the lower panels, CH4flux response (Eqs. 1–3) is modeled to soil temperature at 5cm of the same forest/mires types and during the same period as in the upper panel. The CH4flux response for each individual environmental factor is illustrated so that the simulated value for each data point was recalculated by allowing only one factor at a time to vary while the other was set to its mean level. To the adjusted CH4flux responses (black points) the corresponding residual of each data point was added in order to describe the unexplained model variation (gray points). The r2(%) is the proportion of explained variance. The left–right arrangement of sites mimics the locations on the slope (see Fig. 1). ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● CT VT MT OMT OMT+ KgK KR VSR1 VSR2 −100 −50 0 50 100 150 200 250 forest/mire types forest floor N2 O (ug m−2 h−1) Figure 5. The box plot of forest floor N2O fluxes (µgm−2h−1) for each forest/mire type (uplands – CT, VT, MT, OMT; transitions – OMT+, KgK, KR; and mires – VSR1, VSR2) during the period including typical and dry years. The left–right arrangement of sites mimics the locations on the slope (see Fig. 1). xeric uplands to water-saturated mires, but the N2O fluxes were lower in the dry than in the typical year. 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