Introduction
Almost a third of the world's soil carbon is estimated to be stored in
boreal and subarctic wetlands (Gorham, 1991) yet greenhouse gas (GHG)
emissions are still poorly constrained (e.g. Bridgham et al., 2013).
Furthermore, the potential feedbacks between high-latitude carbon and the
global atmospheric radiative balance is not fully understood or accurately
accounted for in coupled climate–carbon cycle models (Koven et al., 2011).
Boreal nitrogen (N) stocks are significantly understudied compared to C.
However, boreal forests are known to be significant stocks of organic N,
peatlands are estimated to contain approximately 10–15 % of the global N
pool, and permafrost regions are thought to contain between 40 and 60 Pg of N
(Abbott and Jones, 2015; Loisel et al., 2014; Valentine et al., 2006).
It is now accepted that global surface air temperatures are rising and the
rate of increase is greatest in these high-latitude areas (Pachauri and
Reisinger, 2007). Hence understanding both the current magnitude of GHG
emissions and the drivers is essential to monitor and predict climate-driven
changes and climate feedback mechanisms.
Whilst it is important to understand the direct implications of increased
temperature on net GHG emissions (CO2, CH4 and N2O), it is
also critical to consider the indirect impact through secondary drivers such
as permafrost thaw, changes in vegetation community structure, substrate
availability, soil hydrological regimes and flow path dynamics. These
factors, both individually and via interactions, are likely to alter both
net GHG emissions and GHG speciation; for example, a recent meta-analysis showed that the
temperature sensitivity of CH4 was greater than that of CO2,
suggesting that increased temperature may lead to changes in the
CH4 : CO2 emission ratio (Yvon-Durocher et al., 2014). The
sensitivity of CH4 fluxes to these environmental controls is not
currently well understood; for example, previous studies show differing
responses to water table dynamics (e.g. Aerts and Ludwig, 1997; Olefeldt et
al., 2013; Turetsky et al., 2014; Waddington et al., 1996). This limits the
ability of mechanistic models to accurately simulate actual net fluxes. A
significant research focus is required to fully explain the drivers of GHG
emissions to provide a solid basis for future prediction.
Much of the previous research effort in this field has been focussed on
CO2, the most abundant atmospheric GHG, often followed by CH4 and
to a much lesser extent N2O. Knowledge of the distribution of N2O
fluxes across high-latitude ecosystems is in fact almost entirely lacking.
Examples of mean growing season net ecosystem exchange values across
subarctic/boreal regions include an uptake of
1.7 g CO2 m-2 d-1 (Lafleur, 1999) and
5.47 g CO2 m-2 d-1 (Fan et al., 1995), both from Canadian
forest sites, and an uptake of 3.86 g CO2 m-2 d-1 (Aurela
et al., 2002) from a Finnish mesotrophic flark fen. Whilst CH4 and
N2O emissions are generally lower than net CO2 emissions, the
greater radiative forcing they produce, as described by the global warming
potential (GWP), justifies their inclusion in GHG studies. The 100-year GWPs
with and without climate-carbon feedbacks, respectively, are currently
estimated as 28 and 34 for CH4 and 265 and 298 for N2O (Myhre et
al., 2013). Overall, boreal forests appear to be a small sink for CH4
and a small source of N2O (Moosavi and Crill, 1997; Pihlatie et al.,
2007) whilst wetlands typically represent sources of CH4, and a small
sink for N2O (e.g. Bubier et al., 1993; Drewer et al., 2010b; Huttunen
et al., 2003). Growing season emissions of CH4 from subarctic and boreal
ecosystems are estimated as 112.2 ± 6.2 and
72.7 ± 1.3 mg m-2 d-1, respectively (Turesky et al.,
2014), compared to modelled estimates of N2O emissions from tundra,
forest tundra and boreal forest of 0.02, 0.09 and
0.15 mg m-2 d-1, respectively (Potter et al., 1996). Most
studies focus primarily on growing season fluxes. Whilst the logistics of
making winter measurements in these ecosystems certainly plays an important
role, the growing season has also been shown to represent the period of
greatest emissions and therefore the most suitable time to study drivers.
Jackowicz-Korczyński et al. (2010) found that summer season CH4
emissions represented 65 % of the annual flux (with the shoulder seasons
representing 25 % and the winter season only 10 % of annual flux) in
a subarctic peatland. Similarly, Panikov and Dedysh (2000) found that winter
CH4 emissions contributed only 3.5 to 11 % of total annual flux in
western Siberian boreal peat bogs and Dise (1992) reported winter CH4
fluxes representing between 4 and 21 % of total annual flux in peatlands
across northern Minnesota.
Net CH4 emissions are controlled by the balance of activity between
anaerobic methanogenic and oxidizing aerobic methanotrophic bacteria. Hence
the degree of soil saturation, which controls the position of the oxic–anoxic
boundary and the associated soil redox potential, has been identified as an
important driver of net CH4 emission (Bubier et al., 1995; Kettunen et
al., 1999; Nykanen et al., 1998). Other factors such as temperature,
substrate availability, soil porosity and pH are also commonly reported
drivers of CH4 emissions (Baird et al., 2009; Dinsmore et al., 2009b;
Levy et al., 2012; Strack et al., 2004; Yvon-Durocher et al., 2014). Whilst
the rate of methanogenesis and methanotrophy are both influenced by
temperature, methanogenesis is generally considered to be more
temperature sensitive, resulting in a positive relationship between
temperature and net CH4 emission (Dunfield et al., 1993; van Hulzen et al.,
1999).
CH4 produced within the soil environment is then transported to the
atmosphere via diffusion, ebullition or plant-mediated transport.
Vegetation can exert either a direct control on CH4 emission via
plant-mediated transport or an indirect control via its contribution to soil
structure, moisture, anaerobic microsites and substrate availability. The
development of aerenchyma is an adaptation to waterlogged conditions found
in many vascular wetland species. Where such species are present they can
act as gas conduits, allowing GHGs produced in the anoxic layer to be
transported to the atmosphere with minimal oxidation, subsequently
increasing emissions by up to an order of magnitude (Dinsmore et al., 2009a;
MacDonald et al., 1998; Minkkinen and Laine, 2006). Vegetation community
structure also provides a useful proxy for environmental variables that are
themselves difficult to measure, such as long-term water table dynamics
(Gray et al., 2013; Levy et al., 2012).
The primary processes controlling N2O emissions from soils, including
boreal soils, are nitrification processes, where ammonium is oxidized to
nitrate under aerobic conditions, and denitrification processes where
oxidized nitrogen species are reduced to N2O or N2 under anaerobic
conditions (Firestone and Davidson, 1989). As CH4, also N2O production
is a microbial process. The main drivers regulating N2O production are
nitrogen, such as ammonium and nitrate, temperature and factors which
regulate the ratio of aerobic to anaerobic soil microsites, such as soil
moisture (Butterbach-Bahl et al., 2013). In peatlands, transport through
aerenchyma is also a potential transport mechanism for N2O.
A number of different in situ methods are available for the measurement of
GHG emissions. Eddy covariance methods can produce high temporal resolution
measurements integrated at the field and ecosystem (Baldocchi et al., 2001;
Hargreaves and Fowler, 1998); whilst useful for field scale quantification,
the method does not allow separation of individual landscape components.
Traditional chamber-based studies allow a more targeted experimental design
where individual microtopographical features or vegetation communities can
be selected and compared (Dinsmore et al., 2009b; Drewer et al., 2010a). By
explaining small-scale spatial variability we can gain a greater
understanding of GHG drivers and begin to predict how climate or land-use
management changes will alter the GHG balance over the full landscape.
Furthermore, both CH4 and N2O can be measured simultaneously
within the same chamber allowing greater confidence in comparisons between
the flux estimates.
There exists a fundamental mismatch between the scale of measurements
required to increase process level understanding of CH4 and N2O
emissions, and the scale required to make useful assertions about the
magnitude of emission sources that are relevant to the global GHG budget.
Whilst land-surface models provide one way to bridge this mismatch of scale,
they are often limited by the availability of specific input variables, e.g.
water table depth, which cannot be measured at the spatial resolution
required to provide an accurate output. As a result, modelled estimates of
northern high-latitude wetland CH4 sources are highly variable between
studies ranging from approximately 20 to 157 Tg CH4 yr-1 (Zhu et
al., 2013 and references therein). An alternative method of
upscaling is empirically mapping emission factors onto spectral data
provided by high-resolution satellite imagery. This method utilizes the
spectral signatures of different vegetation types and vegetation-specific
differences in GHG emissions to create a landscape-scale emission map.
In this study we use static chambers and satellite imagery to assess the
primary spatio-temporal drivers of CH4 and N2O emissions in
subarctic/boreal Finland and upscale this to a 4 km2 landscape
containing both forest and wetland ecosystems.
Methods
Site description
The Arctic Research Centre of Sodankylä (67∘22′ N
26∘39′ E, 179 m a.s.l.) is located in central Lapland, northern
Finland, approximately 100 km north of the Arctic Circle. The
centre is run by the Finnish Meteorological Institute, is part of the
Pallas-Sodankylä GAW station and includes a level 1 ICOS ecosystem
station. Whilst referenced as an Arctic site in respect to stratospheric
meteorology and geographical location, it is considered to be within the
subarctic/boreal vegetation zone and is not underlain by permafrost. Mean
annual temperature and precipitation on site for the period 1981–2010 was
-0.4 ∘C and 527 mm, respectively. Records of mean annual air
temperature on site have shown an increase of 0.02 ∘C yr-1
over the period 1961–2000; the rate of increase specifically during March to
May was 0.04 ∘C yr-1 (Aurela et al., 2004; Tuomenvirta et
al., 2001). The mean snow depth (mid-March) is 75 cm with median snowfall
start and end dates of 26 September and 14 May (Finnish
Meteorological Institute). Permanent snow cover starts approximately at the end of
October/beginning of November. Scots pine forests and wetlands are the two
dominant ecosystems in this region. Both ecosystems were covered by the
greenhouse gas flux measurements in order to enable the landscape scale
upscaling of the results.
The forest (67∘21.708′ N 26∘38.290′ E, 179 m a.s.l.) is
classified as an Uliginosum–Vaccinium–Empetrum (UVET) type Scots pine
(Pinus sylvestris) forest on a sandy podzol. The mean vegetation
height within the forest is 12 m in the area where our measurements were
made with an average stand age of 60–100 years and tree density of 2100 ha-1. The forest floor contains a varying degree of lichen
(Cladonia spp.), which is heavily dependent on the presence/absence
of reindeer. We located static chambers evenly between three forest sites
(unfenced, 12-year enclosure, 50-year enclosure) to ensure that variability in
GHG emissions due to lichen cover was included in our results. The nearby
Halssiaapa wetland (67∘22.111′ N 26∘39.269′ E, 180 m a.s.l.) is described as a eutrophic fen dominated by large, treeless
flarks with abundant sedge vegetation and intermittent brown moss and
Sphagnum cover. Intermediate, low ridges consist of birch fen
vegetation interspersed with pubescent birch trees (Betula pubescens), with a dominant height of approximately 5–7 m. The most common
shrubs are Betula nana, Andromeda polifolia and
Vaccinium oxycoccos; herbaceous plants are primarily
Potentilla palustris and Menyanthes trifoliata; and
grasses are predominantly Carex species (several different species
observed) or Scheuchzeria palustris. Across the duration of the
study water table levels varied substantially and no consistently submerged
areas which could be easily distinguished as flarks existed; we have
therefore avoided subjective classification of ridges and flarks within our
plots and refer only to measurable environmental variables such as water
table depth and temperature.
When set within a wider 2 km × 2 km landscape unit (to which we will upscale
measurements), the proportion of wetland to forest was almost 2 : 1, with
wetlands making up 61 % of the area, and forests 32 %. The remaining
7 % included open water and grass, bare soil and buildings primarily
associated with the Sodankylä Arctic Research Centre. Within the larger
regional area described in an associated study by O'Shea et al. (2014)
(http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster)
forests made up a much greater proportion of the landscape with coniferous
and mixed forests representing 33 and 16 % of the land area,
respectively, and wetlands 23 %.
Field methodology
Measurements were carried out during growing season 2012 in two measurement
campaigns (summer: 12 July–2 August; autumn: 22 September–14 October) with the intention of capturing peak summer CH4 emissions and
the subsequent shoulder season.
A total of 60 static chambers were measured, 21 within the forest and 39
within the wetland. Within the forest, seven chambers were located in each of
three subplots representing no enclosure, 12-year enclosure (built in summer
2000) and an approximately 50-year enclosure. Within the wetland, chambers
were strategically located to cover the perceived range of both vegetation
communities and water table depths covering both hummock and hollow
microtopographic types (chamber numbers per microtopographic type: hummocks, 16; hollows, 11; neither hummock nor hollow, 12). Fluxes were
measured on approximately 2-day intervals resulting in a total of 10 measurements for all chambers during the summer campaign, seven for the
forest and eight for the wetland chambers during the autumn campaign.
Static chambers were constructed from 40 cm diameter opaque polypropylene
pipe following the guidelines discussed in Clough et al. (2015). Wetland
chambers were located so that sampling could be carried out from an existing
boardwalk – this served the dual purpose of avoiding disturbance during
chamber enclosure and minimized the environmental impact of footfall on the
site. The ground surface within the forest plots was considered to be solid
and therefore no such precautions were required. Shallow bases (10 cm depth)
were inserted into the ground the day before the first sampling; bases were
left in situ for the remainder of the study period. Fluxes calculated from
the first sampling day were not significantly different from subsequent
sampling occasions. The short settling period after base installation is
therefore considered to have had no significant effect on subsequent fluxes,
which as a result were included in the data analysis. Chamber lids, consisting
of a 25 cm section of polypropylene pipe with a closed metal top, pressure
compensation plug and draft excluder tape for sealing, were attached and
sealed to the in situ bases during the 45 min flux measurement period.
Chamber air (100 mL) was sampled four times throughout the approximately 45 min sampling period and flushed through 20 mL glass vials sealed with
butyl rubber plugs using a double needle system; vials were kept at
atmospheric pressure reducing problems associated with pressure changes
during transportation. Vials were returned to the laboratory at the Centre
for Ecology and Hydrology, Edinburgh, for analysis within approximately 1 month. Samples were analysed on an HP5890 Series II gas chromatograph
(Hewlett Packard (Agilent Technologies) UK Ltd, Stockport, UK) with electron
capture detector (ECD) and flame ionization detector (FID) for N2O
(detection limit < 7 µg L-1) and CH4 analysis (detection
limit < 70 µg L-1), respectively. Soil temperature was
recorded at a depth of 10 cm from four replicate points immediately outside
the chamber bases on each sampling occasion using the Omega HH370
temperature probe (Omega Engineering UK Ltd, Manchester, UK). Within the
forest plots, four replicate volumetric soil moisture content (VMC)
measurements were made, adjacent to each chamber base, using a Theta probe
HH2 moisture meter (Delta T-Devices, Cambridge, UK). Within the wetland, a
total of 21 dip wells constructed from 5 cm internal diameter pipe were
installed either adjacent to, or where chambers were located close together,
between chamber bases. All wetland chambers had at least one dip well located
within a 50 cm radius; where more than one dip well was located equidistant
from the chamber, the mean water table depth from the adjacent dip wells was
calculated. Soil respiration (note whilst we refer to this as soil
respiration throughout, it also includes respiration from the ground surface
vegetation defined as anything with a height of less than 2 cm above ground
surface) was measured using a PP Systems SCR-1 respiration chamber (10 cm
diameter) attached to an EGM-4 infrared gas analyser (IRGA, PP Systems;
Hitchin, Herts, UK) on each sampling occasion. Soil respiration
was measured adjacent to each forest chamber and adjacent to 14 chambers
within the wetland, chosen to cover the perceived range of spatial
variability. Vegetation within each chamber was recorded upon visual
inspection.
A pair of cation and anion Plant Root Simulator (PRS)™ probes were
deployed adjacent to each of the 60 chamber bases during both sampling
campaigns. The PRS probes utilize ion-exchange resin membranes to provide an
index of relative plant nutrient availability (Hangs et al., 2002), measured
ions included total N, NO3-N, NH4–N, Ca, Mg, K, P, Fe, Mn, Zn, B,
S, Pb, Al and Cd. During the summer campaign probes were deployed on
11 and 12 July, and recovered on 1 August. During the autumn
campaign forest probes were deployed on 22 and 23 September and
recovered between 13 and 15 October. As part of the standard
analytical processing, concentrations from each probe are corrected for
length of deployment. After recovery, probes were processed and cleaned with
deionized water following the standard procedure supplied by the
manufacturers and returned to Western Ag Innovations Inc., Canada, for
analysis.
Data analysis
Fluxes and confidence intervals from static chambers were calculated using
GCFlux, version 2, which calculates fluxes based on five methods before
choosing the most appropriate fit for individual chamber sets (Levy et al.,
2011). Reported CH4 fluxes correlate to the best-fit model for
individual chambers (either linear or asymptotic). Due to the larger
uncertainty in calculated N2O concentrations which are often close to
the GC detection limits, reported N2O fluxes were calculated from the
linear model approach only. Instantaneous fluxes are presented in units of
nmol m-2 s-1. Confidence intervals include errors
introduced by a combination of natural variability in the flux over the
measurement period, methodological and analytical limitations and
uncertainty in model fitting. When these range from negative to positive, no
sign can be accurately attributed to the flux and therefore it is treated as
indistinguishable from zero.
The data distribution of fluxes, from all chambers, and over the full study
period, had a strong positive skew (Fig. 1). To summarize the data and
account for the skewed distributions, geometric means were calculated across
time points for all chambers. Where periods of uptake and emission were both
present within a time series, geometric means were calculated for each flux
direction independently. The presented geometric means are the
frequency-weighted sum of emissions and uptake. The resulting spatial
dataset had a distribution much closer to normal and is therefore summarized
throughout using arithmetic means. Upscaled emission estimates are presented
in units of either g C m-2 h-1 or g N m-2 h-1 for
CH4 and N2O, respectively.
In all further analysis, log transformations were applied where datasets
displayed non-normal distributions; given the time between measurements,
autocorrelation within datasets was never significant. To summarize the
complex vegetation and soil data, principal component analysis (PCA) was
performed using the princomp function within the R stats package (R version 3.1.1); this uses a spectral decomposition approach which examines the
covariances and correlations between variables. Correlation analyses were
carried out with principal components PC1, PC2 and PC3
against spatial CH4 fluxes and the most appropriate component taken
forward into subsequent explanatory models. No attempt to correlate
vegetation or soil components was made with N2O fluxes given the large
proportion of near-zero fluxes.
Frequency plot showing distribution of all fluxes across both
campaign periods.
Spatial variability between chambers on all sampling occasions was large. To
allow temporal variability to be considered it was necessary to group
chambers. Rather than subjectively assign chambers to groups based on
observed landscape features we carried out a cluster analysis (R, version 3.1.1) based on emission rates. This method produced independent groups
which could also be used in further analyses to consider the environmental
controls of emissions. The total number of clusters was chosen to be five –
after multiple cluster analysis runs this was considered the most
appropriate number taking into consideration the complexity for further
analyses and clear distinctions between groups. ANOVA and Tukey pairwise
comparisons were used to explore the differences in environmental variables
between clusters; tested variables included means of soil temperature, water
table depth and soil respiration alongside vegetation principal component
and soil principal component.
Optical remote sensing imagery was acquired by the Pleiades satellite on
28 August 2012. This provided data in the blue, green, red and
near-infrared (NIR) part of the spectrum for the 2 km × 2 km region around the
chamber sites, with 2 m resolution on the ground. From these data both the
simple ratio (SR = NIR / Red) and normalized difference vegetation index
(NDVI = [NIR - Red] / [NIR + Red]) were calculated. The optical data for
each chamber location were extracted and related to the geometric mean of
the CH4 flux at that location. Multiple regression modelling was then
carried out using R (version 3.1.1) to describe the CH4 fluxes of
individual chambers initially utilizing all four wavebands and the two
calculated ratios. The best-fit model was used to upscale CH4 fluxes to
the full image domain (4 km2). Due again to large uncertainties in the
flux estimates, the large proportion of fluxes indistinguishable from zero, and
subsequent inability to accurately model the data, upscaling of N2O
emissions was not carried out using satellite imagery.
Mean ± SE CH4 and N2O fluxes split by both campaign
period (summer, autumn) and site (forest, wetland).
Summer
Autumn
Full period
CH4 (mg C m-2 h-1)
Forest
-0.06 ± < 0.01
-0.03 ± < 0.01
-0.04 ± < 0.01
Wetland
3.35 ± 0.44
0.62 ± 0.09
1.56 ± 0.20
N2O (µg N m-2 h-1)
Forest
0.75 ± 0.33
1.29 ± 1.39
1.06 ± 0.44
Wetland
1.63 ± 0.64
-1.60 ± 1.18
0.73 ± 0.40
Results
Confidence intervals calculated from each chamber measurement, which include
errors introduced by a combination of natural variability in the flux over
the measurement period, methodological and analytical limitations and
uncertainty in model fitting, show a high proportion of calculated fluxes
which are indistinguishable from zero. Given the high relative variability
in individual chambers and low fluxes in N2O, only 8 and 9 % of
fluxes were significant in the wetland and forest, respectively. The
proportion of chambers displaying significant N2O fluxes could not be
linked to any measured environmental factors and were distributed randomly
across the dataset. For CH4, whilst only 56 % of fluxes were
significantly different from zero in the forest, the wetland was much
clearer with zero excluded from the confidence range in 94 % of cases.
When separated by site (forest, wetland) and by campaign period (summer,
autumn) the highest instantaneous CH4 fluxes, greatest skew and largest
range were all observed in the wetland chambers during the summer period
(Fig. 1). These equated to a mean flux of 3.35 ± 0.44 mg C m-2 h-1, compared to only 0.62 ± 0.09 mg C m-2 h-1 in
the wetland during the autumn period. The mean CH4 flux across the
whole measurement period represented an emission of 1.56 ± 0.20 mg C m-2 h-1 from the wetland chambers, compared to a mean uptake of
0.04 ± < 0.1 mg C m-2 h-1 from the forest
chambers (Table 1).
N2O fluxes had a mean emission across the full sampling period of 1.06 ± 0.44 and
0.73 ± 0.40 µg N m-2 h-1 from forest and wetland chambers, respectively (Table 1).
Spatial variability
Surface cover data (vegetation and presence of standing water) was
summarized using a PCA analysis; combined, the top three principal components
explained 51 % of the total variation between chamber vegetation
communities, with principal components PC1, PC2 and PC3 explaining
24, 15 and 11 %, respectively. PC1, PC2 and PC3 were subsequently
tested for correlations with CH4 fluxes. Spatial variability in
CH4 emissions among wetland chambers was best captured using PC2 (r=0.40, P < 0.01). PC2 also correlated strongest with CH4
emissions when all chambers (both wetland and forest) were included (r=0.31, P < 0.01); however, PC1 showed the best correlation with forest
chambers alone (r=0.25, P < 0.01). PC2 was therefore used
throughout future analysis to describe the spatial variability in CH4
emissions.
Loading values for principal components 1 and 2 of the chamber
vegetation analysis.
Loading values for principal components 2 and 3 of the chamber soil
concentration analysis.
Relationships between geometric mean CH4 flux
(nmol m-2 s-1) against measured environmental variables across
full sampling period. Text refers to the results from statistical
correlations, where “ns” refers to a non-significant results ∗ and
∗∗ represent P < 0.05 and P < 0.01,
respectively.
PC2 (which best described CH4 fluxes) showed a strong dependence on the
proportion of green Sphagnum species within the chamber with
positive PC2 values indicating a high prevalence (Fig. 2). Due to the
strongly non-normal distribution of the data, Sphagnum sp. alone
could not be correlated with emissions, thus the principal component method
provides an indirect measure of the relationship. Low PC2 scores indicate a
higher abundance of non-Sphagnum moss species and high proportion
of open water within the chambers. Of the measured environmental variables
relating to spatial variability (soil temperature, soil moisture, water
table depth and soil respiration), PC2 only correlated significantly with
water table depth (r=0.17, P < 0.01) with PC2 scores increasing
with water table depth.
A similar PCA analysis was carried out to summarize the available soil
nutrient availability data from the PRS probes. The first three principal
components combined explained 56 % of total variation with PC1, PC2 and
PC3 individually accounting for 31, 15 and 10 % of variability,
respectively. PC1 gave the best correlation with CH4 emissions when all
data were combined and for forest chambers alone. PC2 gave a better
correlation with wetland chambers alone (PC2: r=0.40, P < 0.01).
PC2 was therefore utilized throughout the remainder of the analysis due to
the greater magnitude of wetland versus forest CH4 emissions, and their
subsequent importance to landscape scale emissions.
PC2 was influenced strongly by total N and NH4+ concentrations
with high concentrations resulting in a low PC2 score (Fig. 3). The only
environmental variable significantly correlated with PC2 was water table
depth (r=0.19, P < 0.01) with high PC2 scores indicating a deep
water table. However, when wetland chambers were considered alone soil
respiration also showed a significant positive correlation with PC2 (r=0.31, P < 0.01).
Spatial variability in GHG emissions were tested against the measured
environmental variables as well as the most appropriate PCA score for both
vegetation and soil, as described above. CH4 flux was not
statistically correlated to water table depth in the wetland chambers
(Fig. 4). However, a relatively strong positive correlation was seen
between CH4 flux and the PCA score from the vegetation analysis; a high
score from the vegetation principal component represented a deep mean water
table depth. Positive correlations were also found between CH4 flux,
mean soil temperature and the principal component from the soil analysis
when the wetland chambers were considered alone. Within the forest chambers,
only the soil principal component was statistically correlated to CH4
flux.
To further summarize the CH4 data and provide a method for both
upscaling and consideration of temporal variability, chambers were grouped
independently based on net emissions. Data distributions within each cluster
group are shown in Fig. 5. The cluster identified with the lowest
emissions contained all the forest chambers and an additional two low
emitting wetland chambers; for explanatory purposes this cluster is
subsequently referred to as the “forest” cluster. The remaining clusters,
with sequentially increasing emissions, are labelled wetland_a, wetland_b, wetland_c and
wetland_d, respectively.
Chambers clustered based on emissions with n indicating the number
of chambers within each group.
Box plots showing range of measured environmental variables within
each of the CH4 clusters. Letters represent results from the Tukey family
test statistic where clusters with similar letters are not significantly
different from one another at 95 % confidence level. Clusters Wetland_a
to Wetland_d represent groups with sequentially increasing CH4
emissions. Note that water table was not measured within the forest plots;
therefore the water table values given in the “forest” cluster actually
represent only the two wetland chambers which have quantitatively been assigned
to this cluster and have therefore been excluded.
ANOVA showed significant between cluster variability in all tested
environmental variables (soil temperature, water table depth, soil
respiration, vegetation principal component and soil principal component)
with the exception of water table depth (Fig. 6). The patterns in soil
temperature, PCA_veg and PCA_soil are in line
with the previously discussed correlation analysis. When the components of
PCA_veg are considered independently the results highlight
the importance of Sphagnum cover and open water in controlling the
CH4 emissions within the wetland clusters; however, this relationship
is complicated by the high variability shown by large standard deviations
from the mean cluster values (Table 2). Wetland clusters “a” and “b”, which
represent the two lowest emitting wetland groups, had the lowest proportions
of Sphagnum moss species and the greatest proportion of chambers
containing open water.
Between-group differences in soil nutrient concentrations were also
considered using ANOVA; only nutrients which displayed significant
between-group differences are displayed in Fig. 7. The strongest
between-group difference was evident in the soil Fe concentrations, with
high Fe linked to high CH4 emitting chambers (F=62.0, P < 0.01); positive correlations with mean group CH4 emissions were also
seen for B (F=49.2, P < 0.01), Zn (F=39.0, P < 0.01)
and Mg (F=49.2, P < 0.01). Negative correlations were seen
between mean group CH4 emission and K (F=10.6, P < 0.01),
NO3-N (F=6.38, P < 0.01), and NH4-N (F=6.36, P < 0.01). Within the wetland, total-N was lowest in groups with the
highest CH4 emission; however the pattern is less clear when forest
chambers are included as these displayed a wide range of total-N but a low
CH4. Only the forest had distinct soil Ca concentrations.
Mean ± SD ground cover data for wetland clusters. Only variables which showed
significant between cluster variability are included. Test statistic refers
to the F value with ∗ and ∗∗ indicating P vales of
< 0.05 and < 0.01, respectively.
Sphagnum sp.
Open water
Wetland_a
39.3 ± 46.0
59.5 ± 62.0
Wetland_b
68.6 ± 47.4
11.4 ± 18.6
Wetland_c
95.7 ± 11.3
5.71 ± 9.32
Wetland_d
50.0 ± 70.7
20.0 ± 28.2
ANOVA test statistic
4.62∗∗
3.59∗
Box plots showing range of soil variables within each of the CH4
clusters. Units represent probe supply rate (µg per 10 cm2
across burial period). Letters represent results from the Tukey family test
statistic where clusters with similar letters are not significantly different
from one another at 95 % confidence level. Clusters Wetland_a to
Wetland_d represent groups with sequentially increasing CH4 emissions.
Temporal variability across the two field campaigns in CH4
emissions, separated by clusters, with shaded area representing loess
smoothing. Clusters Wetland_a to Wetland_d represent groups with
sequentially increasing CH4 emissions.
Drivers of temporal variability in CH4 fluxes, separated by
clusters, with shaded area representing loess smoothing. Clusters Wetland_a
to Wetland_d represent groups with sequentially increasing CH4
emissions.
Temporal variability
Temporal variability, summarized by cluster, is displayed in Fig. 8 for
both the summer and autumn campaign periods. CH4 emissions remain
relatively constant throughout both campaign periods despite a significant
drop in emissions between them. Despite the low temporal variability,
emissions appear to peak around mid-July in the higher emitting chamber
clusters (e.g. wetland_c and wetland_d).
CH4 emissions did not follow linear relationships with the measured
environmental variables (soil temperature, air temperature, water table
depth and soil respiration) (Fig. 9). CH4 emissions peaked at a soil
temperature of approximately 12 ∘C and an air temperature of
approximately 15 ∘C, after which they began to fall. The time
series suggests a general decrease in CH4 emissions with rising water
table; however, the relationship appears to be chamber specific and
non-linear suggesting a greater complexity than is usually accounted for. In
the high-emitting chambers, there is a peak in CH4 emissions as the
water level reaches the surface, the emissions drop until water tables of
approximately 5 cm depth and then rise again as the water level deepens
further. Chamber clusters associated with lower total CH4 emissions did
not show this peak associated with surface water tables but instead followed
a smoother, but still non-linear, increase in emissions with increasing
water table depth. No relationship was observed between soil temperature and
water table depth, ruling out a potential interaction as the cause of the
peaks associated with particular water table depths or soil
temperatures.
Spectral analysis and upscaling
A multiple regression model including blue, green, red, NIR, SR and NDVI
explained 45 % of the variance in the spatial CH4 flux.
Transformations of the data and more complex models were explored, but did
not substantially improve the model fit. A simpler model containing only SR,
NDVI and the blue and NIR wavebands performed equally as well as the full
model also explaining 45 % of the spatial variation (Table 3); this
simpler model was therefore used in subsequent analysis. To predict mean
CH4 flux over our sampling period at landscape scale, we applied the
regression model to the optical data over the whole 2 km × 2 km domain. This
predicted high CH4 fluxes in the wetland areas in the northeast and at
forest edges (Fig. 10). Using the optical data to scale up the chamber
measurements, the mean CH4 flux over the whole domain between 12 July and 14 October is estimated to be
47.4 ± 14.1 nmol CH4 m-2 s-1 or 2.05 ± 0.61 mg C m-2 h-1.
By comparison, if the flux over the whole spatial domain were estimated
simply as the arithmetic mean of the individual chamber measurements
(geometric mean to summarize temporal variability) the value would be
significantly lower (23.0 ± 3.78 nmol CH4 m-2 s-1). If
we account for the differences between wetland and forest alone using an
appropriate area weighting factor (61 % wetland; 32 % forest), ignoring
variability within these landscape units, estimated emissions are 21.6 ± 2.85 nmol CH4 m-2 s-1, also substantially lower than
our modelled approach.
Model summary utilizing spectral data to estimate CH4
emissions.
Estimate
t value
p value
Intercept
-233
0.00002
< 0.01
SR
354
0.00002
< 0.01
NDVI
-283
0.03883
< 0.05
Blue
0.99
0.00365
< 0.01
NIR
-0.91
0.00022
< 0.01
Model-adjusted r2
0.45
Model p value
< 0.01
Mean (a) and SE (b) of CH4 fluxes
extrapolated over a 2 km × 2 km area predicted from chamber flux
measurements (black circles), and satellite spectral data. Coordinates are in
WGS84.
Discussion
Fluxes of CH4 from the forest and wetland areas within the landscape
were significantly different at -0.06 ± < 0.01 and 3.35 ± 0.44 mg C m-2 h-1, respectively. Whilst the error
displayed here suggests confidence in the forest as a net sink for CH4,
when individual chamber measurements are considered, only 56.3 % of the
measured fluxes had an error bar that did not cross the zero line. Hence we
can only be confident that the sign of the flux is correct in just over half
of our forest data. On removal of all fluxes with an uncertain sign, the
mean remains negative in the forest chambers. This gives confidence that
whilst the calculated flux is very small, it is a small sink rather than a
source. In the wetland however, 94.4 % of the measured fluxes differed
significantly from zero, so we can be confident that the wetland represented
a strong source of CH4.
A similar analysis was carried out on the N2O flux data and here due to
very high uncertainties in the sign of individual flux measurements (only
8.68 and 7.79 % of measurements in the forest and wetland,
respectively, did not have error bars crossing the zero line) we cannot
differentiate either the forest or wetland as being a net sink or source
over the campaign period. We can simply state that N2O fluxes in both
landscape units were near-zero. N2O fluxes were therefore not an
important component of this study area. Whilst minimal, the near-zero result
is still an important finding given the lack of N2O emissions reported
in the current literature. Assuming these near-zero fluxes are similar
across the region we have an important baseline from which to monitor change
related to future climate or land-use practices. However, due to
consistently near-zero fluxes little could be concluded about the drivers of
N2O emissions within our landscape area.
Drivers of CH4 emissions
The relationship between CH4 emissions and water table position was not
straightforward. Considering the mean CH4 flux for each chamber and
testing this against the mean water level position of that chamber showed no
significant relationship (Fig. 4), suggesting that water table was not an
important factor in controlling spatial variability in emissions across the
site. Furthermore, when chambers were clustered based on their CH4
emissions, there was high within-group variability in water table and
subsequently no significant differences in water table between groups
(Fig. 6). Whilst much of the previous literature suggests water level as
the primary driver of CH4 (Aerts and Ludwig, 1997; Hargreaves and
Fowler, 1998; Waddington et al., 1996) due to its role in controlling the
oxic/anoxic boundary, there is a growing body of evidence which suggests
this is true only in drier ecosystems (Hartley et al., 2015; Olefeldt et
al., 2013; Turetsky et al., 2014). The water levels used in this analysis
only represented the water level during the campaign periods, with no
consideration of longer term means. Due to the presence of alternative
electron acceptors and the delay in returning to favourable redox
conditions, fluctuations in the water level can result in a reduced
population and a subsequent reduction in CH4 production, even after
water levels and anoxic conditions recover (Freeman et al., 1994; Kettunen
et al., 1999). Hence whilst soil conditions may appear suitable for CH4
production at the time of measurement, an unfavourable water table in the
days to weeks prior to the measurement can limit methanogenesis and mask the
expected relationship.
CH4 in the wetland correlated positively and significantly with a
component from the vegetation PCA analysis. The vegetation component that
best described CH4 emissions (PC2) related primarily to
Sphagnum cover within the chambers and also linked low scores to a
high proportion of open water. Sphagnum is an indicator of long-term near-surface water table position, hence whilst the directly measured
water table did not correlate significantly with CH4 emissions, the
vegetation analysis suggests that longer-term water level conditions do
correlate with spatial variability in CH4. Several other studies have
also highlighted the importance of vegetation as an indirect indicator of
CH4 flux as it integrates across multiple ecological variables (e.g.
Bubier et al., 1995; Davidson et al., 2016; Gray et al., 2013; Oquist and
Svensson, 2002). It is also this link to vegetation that makes upscaling
such as that described below possible as the spectral data are primarily
picking up spatial variability in above-ground plant community cover.
Vegetation can also play an important direct role in GHG emissions via
plant-mediated transport and the supply of labile substrate, thought to be
particularly important for methanogenesis (e.g. McEwing et al., 2015;
Ström et al., 2005). Sphagnum mosses can be additionally
important in controlling CH4 emissions through their association with
methanotrophic bacteria, an association that has been shown to exist across
the globe and across a range of microtopographic features (Kip et al.,
2010). Here we find no correlation between CH4 emissions and soil
respiration and a positive influence of Sphagnum cover. This
suggests that the role of vegetation as an indirect indicator of other
environmental factors is more important to CH4 emissions in this
landscape than methanotrophic associations or substrate availability.
The water table relationship is further complicated by the presence of
standing water which related to low emitting chambers. This may be a
consequence of reduced diffusion from the soil to the atmosphere rather than
a result of reduced production. If standing water remains for long periods
of time, the sustained anoxic conditions can alter the vegetation and soil
chemistry. For example reduced nitrification, an oxic process, can lead to a
build-up of NH4+ in water-logged conditions. Soil PCA component 2,
which correlated positively with CH4 emissions, showed a strong link to
the concentration of NH4+; high concentrations were linked to low
PCA scores and low CH4 emissions. NH4+ in this case may be
acting as an indicator of the chambers which were inundated with surface
water for sustained time periods.
Our chambers were not specifically designed to measure emissions from water
surfaces and as a result cut out all wind-driven turbulence which is likely
to be an important driver of the evasion flux (MacIntyre et al., 1995). It
is therefore difficult to identify whether standing water produced a
decrease in CH4 production, a real decrease in flux due to low
diffusivity through the water column, or if our results were a consequence
of our methodology artificially reducing gas transfer across the water–air
boundary. A previous study showed an increase in CH4 emissions along a
water table gradient from 35 cm depth to 5 cm above the soil surface. Above
5 cm the relationship with increasing water level was negative (Pelletier et
al., 2007). Whilst our results are not as clear as those presented by
Pelletier et al. (2007) a similar mechanism of reduced CH4
diffusion through standing water may be responsible in both cases.
Figure 7 shows a clear positive relationship between Fe, Zn and CH4
emissions, with high-emitting clusters also displaying the highest
concentrations. These cations reflect the redox potential of the soil with
increasing concentrations indicating a lowering of the redox potential. The
CH4 water table relationship is indirect, with water table used as a
proxy for soil oxygen content and redox potential. Here we find that cation
concentrations have a greater explanatory power than water table hence they
may represent a more appropriate indicator of soil redox status and
methanogenic potential.
When we consider the temporal patterns in CH4 emissions across the two
campaign periods we see a similar response as in the spatial analysis, with
emissions falling as the water level rises between approximately 15 and 5 cm
depth. No relationship was found between water table and soil temperature,
ruling out an interaction as the primary cause of the water table
relationship. Tupek et al. (2015) measured increasing CH4 emissions in
response to a rising water table until a peak at approximately 20 cm depth
in a central Finnish mire, after which the relationship changed with
emissions decreasing as the water table approached the surface. Water table
depths measured in this study covered a smaller range and therefore we can
assume that similar dynamics may be apparent if the water level was to drop below
20 cm. Similarly a recent synthesis (Turetsky et al., 2014) involving 71
wetlands found the optimum water table depth for CH4 emissions to be
23.6 ± 2.4 cm for bog ecosystems, again suggesting that the negative water
table relationship observed here is due to water table depth being
consistently above the optimum. Potential explanations for the inhibition of
CH4 emissions at high water levels given by Turetsky et al. (2014)
include limited diffusion of CH4 through standing water as discussed
above, reduced CH4 production due to lower plant biomass and associated
labile C inputs, or unfavourable redox conditions resulting from inputs of
oxygen-rich water potentially containing alternative electron acceptors.
Whilst we saw no clear correlations between the percentage of bare soil and
that of open water in our chambers, a reduction in plant activity may have
occurred during submersion so reduction in C inputs for methanogenesis cannot
be ruled out to explain the temporal changes in CH4 emissions across the
growing season. Neither do we have the data to rule out a change in redox
potential due to water flow. A more detailed analysis under controlled
conditions would be required to accurately explain the mechanism for high-water CH4 limitation at this site.
As the water table rose between 5 cm depth and the soil surface, emissions
appear to increase again peaking at approximately the soil surface and then
decreasing with increasing water depth above the soil surface. This could be
due to physical forcing of CH4 out of the soil pore space as it reaches
the soil surface. Importantly, what our results clearly show is that there
are a number of driver mechanisms interacting to produce the observed
CH4–water table relationship.
A significant positive spatial relationship was seen between soil
temperature and CH4 (Figs. 4 and 6). The relationship between
CH4 emission and temperature is a well-established one often observed
in the literature (Segers, 1998) as a result of the greater sensitivity of
methanogenesis than methanotrophy; however, most studies focus on the
implications of temporal variation rather than the spatial pattern. The
spatial variability in soil temperature is likely to be linked to a
combination of soil water content and the surface reflectance of the
vegetation cover. Changing soil temperature therefore represents an
important by-product of other environmental changes that needs to be
accounted for in predictive mechanistic models.
The temporal relationship between CH4 emissions and temperature showed a
Gaussian response curve typical of microbial control. Peak CH4 emission
occurred at a soil temperature of ∼ 12 ∘C. A similar pattern
was observed in a central Finland mire by Tupek et al. (2015), who recorded a
peak in emissions corresponding to 14 ∘C.
Upscaling
The wetland CH4 fluxes calculated here (3.35 mg C m-2 h-1
during the summer season and 1.56 mg C m-2 h-1 when the autumn
period is included) are similar in magnitude to those described in a
multisite analysis by Turetsky et al. (2014) for subarctic
(3.51 ± 0.19 mg C m-2 h-1) and boreal wetlands (2.27 ± 0.04 mg C m-2 h-1). However, given the large differences
between fluxes calculated within the forest and wetland, and the
heterogeneous mix of these two primary ecosystem types across the
subarctic/boreal system, landscape-scale emissions are of greater importance
in understanding global CH4 source estimates than wetland emissions
alone. By extending our sampling site to a 2 km × 2 km landscape we can
calculate emissions which are more relevant to the region as a whole. Based
on a weighted average of fluxes from the forest and wetland within the
landscape, and assuming CH4 emissions from the other landscape units
are zero, we can calculate average landscape scale emissions of 0.93 ± 0.12 mg CH4-C m-2 h-1.
Whilst calculations at this level of detail have previously been shown to
give good agreement with more top-down methodologies (O'Shea et al., 2014),
significant information is lost regarding spatial variability which we have
already shown to be large, especially within the wetland. Utilizing spectral
data across the 2 km × 2 km landscape and a multiple regression model, we
calculated average CH4 flux over the growing season as 47.4 ± 14.1 nmol CH4 m-2 s-1 or
2.05 ± 0.61 mg C m-2 h-1. This is significantly higher than the 100 km2 landscape
scale CH4 flux of 1.1 to 1.4 g CH4 m-2 during the May to
October growing season (0.19 to 0.23 mg C m-2 h-1)
calculated by Hartley et al. (2015). The Hartley et al. (2015) study was
based on field measurements collected approximately 240 km north of our
study site, up-scaled using aerial imagery and satellite data. Even when
utilizing data presented from only July–September, Hartley et al. (2015)
still recorded much lower landscape scale fluxes (approximately 0.24 mg C m-2 h-1) than this study due to the different landscape units
and proportions of vegetation communities. Whereas we carried out our
upscaling over an area characterized by 61 % wetlands and 32 % forest,
the landscape unit measured by Hartley et al. (2015) contained only
∼ 22 % wetland (classified as both mire and mire edge) and
60 % forest. Heikkinen et al. (2004) also upscaled chamber-based CH4
emissions to the landscape, in this instance a 114 km2 catchment in the
eastern European Russian tundra, concluding a mean summer CH4 emission
rate of 0.43 mg C m-2 h-1. CH4 emissions from areas
classified as peaty tundra (including intermediate flarks, Carex + Sphagnum and hummocks), which ranged from 0.15 to
4.25 mg C m-2 h-1, were similar to those presented here. However, again
it is the proportion of wetland within the landscape (16.1 %) and to a
lesser extent the distribution of emissions within the wetland that appears
to be most important in defining the landscape scale flux.
Open water has not been included in this study as it was not an important
feature in the 2 km × 2 km study area. However, given the large proportion of
lakes and ponds across subarctic and boreal ecosystems, and the potential
increase in surface water as the changing climate alters subsurface
hydrology, this is something that will become more important both in the
future and as we scale to larger or regional landscapes. Methane emissions
from Arctic lakes are estimated to total 11.86 Tg yr-1, varying
spatially over high latitudes from 3.46 mg C m-2 h-1 in Alaska to
0.40 mg C m-2 h-1 in northern Europe (Tan and Zhuang, 2015); this
puts lake fluxes in the same order of CH4 emissions as northern
high-latitude wetlands and comparable to the values measured in this study.
There is still considerable uncertainty in extrapolating to our 2 km × 2 km
landscape despite optical remote sensing data having complete coverage and a
reasonably well-defined relationship with CH4 flux. Greatest emissions
and subsequently the greatest uncertainty are observed in an area to the
northeast of our landscape which represents an area of yellow/green
Sphagnum. Further flux measurements are required to reduce the
uncertainty in this area. Therefore, in addition to providing upscaled
emission estimates, this spectral approach could also potentially be applied
to define specific areas for future research focus, maximizing the potential
explanatory power of future campaigns.
We were unable to carry out a similar upscaling exercise for N2O;
however, given the near-zero fluxes across the majority of study chambers, a
detailed spatial method is not required to say with a large degree of
certainty that N2O emissions are not currently a major component of the
growing season GHG balance of our landscape. Whilst potentially subject to
changes in temperature and hydrology as a result of climate, our site was
not underlain by permafrost and therefore is not going to be affected by
thaw-related processes. Recent studies have shown potentially large
increases in N2O emissions related to permafrost thaw (Elberling et
al., 2010; Abbott and Jones, 2015); thus,f whilst negligible here, N2O
emissions across the wider northern boreal and subarctic zone may become
increasingly important to the total GHG balance of the landscape and should
therefore continue to be monitored in future research.
Conclusions
Our results showed a significant proportion of measured N2O fluxes,
across both wetland and forest, and CH4 fluxes within the forest, were
not distinguishable from zero. Considering only those fluxes that did differ
significantly from zero we can be confident that the wetland represented a
strong source of CH4, especially during the summer peak growing season
(3.35 ± 0.44 mg C m-2 h-1), and the forest a small CH4
sink (summer: -0.06 ± < 0.01 mg C m-2 h-1). We
conclude that N2O fluxes were near-zero across the landscape in both
forest and wetland. Despite the small magnitude of N2O fluxes this is
still an important result given the current lack of data available for
N2O across northern boreal, subarctic regions, and the potential for
future increases in relation to climate and land use.
We did not observe a direct water table control on spatial variability in
CH4 emissions but instead found a relationship with vegetation
communities, in particular the presence of Sphagnum mosses, and
with soil chemistry which we attribute to redox potential. Both these
parameters suggest that water table level and water table variability over a
longer timescale prior to flux measurements is required to accurately
predict CH4 emissions. When temporal variability across the campaigns
was considered we found a decrease in CH4 emissions as water table
approached the soil surface and the soil became fully saturated. We
attribute this apparent reversal of the literature described relationship
between CH4 and water table to the water table depth being consistently
above the optimum. As water levels continue to rise beyond this point
diffusion becomes restricted and the flux diminished. We also found a
temporal relationship between CH4 emissions and soil temperature with
peak emissions at approximately 12 ∘C.
To upscale the chamber measurements of CH4 to a 2 km × 2 km landscape area
we utilized PLEIADES PA1 satellite imagery and could account for 45 % of
spatial variability in CH4 flux using SR, NDVI, Blue and NIR spectral
data. Applying this model to the full area gave us an estimated CH4
emission of 2.05 ± 0.61 mg C m-2 h-1. This was higher than
landscape estimates based on either a simple mean or weighted by
forest/wetland proportion alone (0.99 ± 0.16,
0.93 ± 0.12 mg C m-2 h-1, respectively). Hence whilst there
are clearly uncertainties associated with the modelled approach, excluding
spatial variability as with the latter two methods is likely to lead to
underestimations in total emissions. This approach therefore has
considerable potential for increasing the accuracy of future landscape scale
emission estimates and making better use of the wide variety of chamber
measurements currently presented in the literature. When compared to similar
upscaling studies our landscape estimate showed significantly higher
CH4 emissions, even when individual chamber scale fluxes were similar.
Whilst spatial variability within the wetland area was important, the
primary difference was the proportion of ecosystem units within the
measurement landscape, e.g. the proportion of wetland vs forest or tundra. It
is therefore not applicable to take the results presented here and simply
apply the landscape mean to a larger area given that the proportion of wetland will
change substantially with scale; however, with the addition of further
ground-truthing and a larger spectral image, larger areas could be similarly
modelled.