Vegetation and hydrology are important controlling factors in
peatland methane dynamics. This study aimed at investigating the role of
vegetation components, sedges, dwarf shrubs, and Sphagnum mosses, in methane fluxes
of a boreal fen under natural and experimental water level drawdown
conditions. We measured the fluxes during growing seasons 2001–2004 using the
static chamber technique in a field experiment where the role of the
ecosystem components was assessed via plant removal treatments. The first
year was a calibration year after which the water level drawdown and
vegetation removal treatments were applied. Under natural water level
conditions, plant-mediated fluxes comprised 68 %–78 % of the mean growing
season flux (1.73±0.17 g CH4 m-2 month-1 from June to
September), of which Sphagnum mosses and sedges accounted for one-fourth and three-fourths,
respectively. The presence of dwarf shrubs, on the other hand, had a
slightly attenuating effect on the fluxes. In water level drawdown
conditions, the mean flux was close to zero (0.03±0.03 g CH4 m-2 month-1) and the presence and absence of the plant groups had a
negligible effect. In conclusion, water level acted as a switch; only in
natural water level conditions did vegetation regulate the net fluxes. The
results are relevant for assessing the response of fen peatland fluxes to
changing climatic conditions, as water level drawdown and the consequent
vegetation succession are the major projected impacts of climate change on
northern peatlands.
Introduction
Approximately one-third of all terrestrial carbon is stored in boreal and
subarctic peatlands (e.g., Yu, 2012) that generally act as
CO2 sinks in current climatic conditions. However, pristine wetlands,
including peatlands, marshes, and floodplains, are also the largest natural
source of methane (CH4) in the atmosphere
(Ciais
et al., 2014; Kirschke et al., 2013; Saunois et al., 2016). The carbon sink
function of peatlands is mostly due to the slow decomposition rate resulting
from waterlogged, anaerobic conditions sustained by a high water level,
which simultaneously favor CH4 production. CH4 is the end product
of anaerobic decomposition by strictly anaerobic methanogenic archaea. It is
released from the peat into the atmosphere via diffusion through the peat
column, ebullition or plant-mediated transport
(Lai, 2009). A considerable part, from 20 % to up
to 90 % (Le
Mer and Roger, 2001; Pearce and Clymo, 2001; Whalen, 2005) of the CH4
diffusing through the upper, aerobic part of the peat layer is oxidized to
CO2 by methanotrophic bacteria (MOB) before reaching the atmosphere.
Vegetation is a major factor controlling peatland CH4 fluxes
(Koelbener et al., 2010; Ström et al., 2005, 2012). Fresh root litter and exudates are
important substrates for the methanogenic microbes, and a significant
proportion of the CH4 is formed from this easily available organic
matter instead of from old, recalcitrant peat
(Koelbener et al., 2010; Ström et al., 2012). Therefore, CH4 fluxes have a strong,
positive correlation with the CO2 uptake
(Bellisario et al., 1999; Christensen et al., 2000; Rinne et al., 2018), since higher
primary productivity leads to a higher input of substrate. Of the vegetation
components, deep-rooting aerenchymatous species such as sedges (Cyperaceae)
and aerenchymatous herbs are especially important
(Leppälä et al., 2011; Ward et al., 2013). In sedge-dominated wetlands, most of the CH4 is released
through vascular plants
(Kelker and Chanton, 1997; Ding et al., 2004; Ström et al., 2005), thus bypassing the aerobic
peat layer where CH4 oxidation takes place. On the other hand, oxygen
transport through the aerenchyma to the rhizosphere may inhibit CH4
production
(Whalen and Reeburgh, 2000; Fritz et al., 2011) and stimulate CH4 oxidation (King, 1994;
Popp et al., 2000). The net effect of the presence of aerenchymatous species
on CH4 fluxes is positive in most cases (Bellisario
et al., 1999; Greenup et al., 2000; Rinnan et al., 2003; Couwenberg and
Fritz, 2012; Ward et al., 2013), although opposite results have also been
reported (Roura-Carol and Freeman, 1999; Strack et
al., 2006). Although the influence of the non-aerenchymatous species on the
fluxes has been studied relatively little, Gray et al. (2013) showed that plant functional groups based on
more complex traits than those related to aerenchyma were good proxies of
CH4 flux. In open boreal peatlands, the most abundant
non-aerenchymatous vascular plant functional group is dwarf shrubs, which are
generally shallow rooted (Korrensalo et al., 2018a)
and have a negligible CH4 transport capacity (Shannon et al.,
1996; Garnet et al., 2005) compared to deep-rooting aerenchymatous species.
In plant removal experiments, the presence of shrubs has been shown to
decrease CH4 fluxes (Ward et al., 2013; Robroek et
al., 2015). Recently, trees have been shown to transport significant amounts
of CH4 from soil in certain ecosystems, but so far not in forested
boreal peatlands (Covey and Megonigal, 2019). Sphagnum
mosses, in turn, have an impact on CH4 oxidation as they host partly
endophytic methanotrophs in the water-filled, hyaline cells of their leaves
and stem (Raghoebarsing
et al., 2005; Larmola et al., 2010; Putkinen et al., 2012).
Water level regulates the volume ratio of the aerobic and anaerobic peat
and, consequently, the extent of the CH4 production and oxidation
zones. Therefore, a positive correlation between the water level and
CH4 fluxes has been reported in numerous studies (Moore
and Roulet, 1993; Laine et al., 2007a; Pearson et al., 2015; Turetsky et
al., 2014; Chimner et al., 2017). However, the relationship between the
water level and CH4 fluxes is complex due to the vegetation–water
level interaction. Because the plant communities in the wettest habitats are
often associated with the sparsest vascular plant cover and lowest
productivity (Waddington
and Roulet, 2000; Laine et al., 2007b; Riutta et al., 2007b), less substrate
for CH4 production is available in those communities. At the dry end of
the water level gradient, fewer roots reach the anaerobic layer of the peat
(Waddington et
al., 1996; Kutzbach et al., 2004). Hence, CH4 fluxes may also show a
unimodal relationship to water level (Strack et
al., 2004; Brown et al., 2014) or no relationship at all
(Rask et al., 2002; Korrensalo et al.,
2018b).
In this study, we aim to disentangle the intertwined relationships among
water level, vegetation, and fen CH4 fluxes. We test the role assumed
for different plant functional groups based on earlier literature and
quantify how these roles are modulated by changing water level. Our
objective is to quantify the contribution of the different components of fen
plant community, namely sedges, dwarf shrubs, Sphagnum mosses, and the underlying
peat, to the CH4 fluxes under wet and dry conditions. To
achieve this, we applied removal treatments of plant functional groups under both
natural and experimentally lowered water level in a factorial study
design. We hypothesized that aerenchymatous plant species enhance CH4
fluxes and that this effect would be less pronounced under lowered water
level as a smaller proportion of the roots would extend to the anaerobic
peat layer. Further, we hypothesized Sphagnum mosses and dwarf shrubs to reduce
CH4 fluxes.
Materials and methodsStudy site
The study was carried out at Lakkasuo peatland complex, an eccentric raised
bog with minerotrophic laggs situated in the southern boreal vegetation zone (Ahti
et al., 1968) in southern Finland (61∘47′ N, 24∘18′ E). Annual precipitation in the region totals 710 mm, of which about a third
falls as snow. The average temperatures for January and July are -8.9 and
15.3 ∘C, respectively (Juupajoki Hyytiälä weather
station).
The study site was situated on a nutrient-poor, oligotrophic, treeless fen
part of the peatland complex. Surface topography at the site is uniform,
mostly lawn. The pH of the surface peat at the site was 4.9 (Juottonen et al., 2005). The field layer is
dominated by sedges and dwarf shrubs. The most abundant sedge species is
Carex lasiocarpa Ehrh. (percentage of cover in 2001 3.4±3.9, mean ± standard deviation
of 40 inventory plots), and other common sedge species are Eriophorum vaginatum L. (0.9±1.8) and Trichophorum cespitosum (L.) Hartm. (0.5±2.4). The most abundant shrubs are the
deciduous Betula nana L. (4.0±4.2) and ericaceous Andromeda polifolia L. (6.6±5.7) and
Vaccinium oxycoccos L. (4.9±4.2). Note that due to the erect growth form of sedges, their percentage of cover is lower than that of shrubs, although their leaf area is higher;
see Table 1 and Fig. 2. The moss layer forms a continuous carpet dominated
by Sphagnum papillosum Lindb. (40.1±31.3) and the species of the S. recurvum complex (S. fallax (Klinggr.)
Klinggr., S. flexuosum Dozy & Molk, and S. angustifolium (C.E.O.Jensen ex Russow) C.E.O.Jensen)
(together 32.7±24.0). The vegetation inventory and variation
conducted at the site are described in detail in
Kokkonen et al. (2019).
Growing season average (standard deviation) water level (WL)
relative to moss surface (in centimeters), with negative values indicating water
level below the surface, growing season peak LAI of sedges (LAIC) and
dwarf shrubs (LAID), and projection cover of Sphagnum mosses (Spha) (unit is
square meters per square meter) in different plant removal treatments in wet and WL
drawdown subsites. The year 2001 was a calibration year without the WL drawdown
and plant removal treatments, which were implemented in 2002. Vegetation
treatments: PSCD – plots with intact vegetation, consisting of peat,
Sphagnum mosses, sedges, and shrubs; PSC – plots consisting of peat, Sphagnum mosses, and sedges
(shrubs removed); PS – plots consisting of peat and Sphagnum mosses (shrubs and
sedges removed); P – plots consisting of bare peat (all vegetation removed).
The study was carried out during four growing seasons from 2001 to 2004. The
first season of the study, 2001, served as a calibration year without the
water level drawdown (WLD) treatment, which was implemented in April 2002. The study site was
divided into two subsites approximately 20 m apart, namely the wet and the
drier WLD subsite, by surrounding the WLD subsite
with a shallow ditch that lowered the water level by an average 17±1.6 cm (22±3.0 cm in 2002, 12±3.4 cm in 2003, and 16±1.9 cm in 2004). The shallow ditch was located approximately 10 m from the
wet subsite and drained to a larger, old ditch.
We studied the contribution of the ecosystem components to the net CH4
fluxes in wet and dry conditions by means of plant removal treatments. At
the site, we established permanent sample plots of 56 cm × 56 cm
consisting of
peat, Sphagnum mosses, sedges, and dwarf shrubs (PSCD, intact vegetation, n=8 at
the wet subsite and n=8 at the WLD subsite)
peat, Sphagnum mosses, and sedges (PSC, dwarf shrubs removed, n=5+4)
peat and Sphagnum mosses (PS, sedges, and shrubs removed, n=3+3)
peat (P, all vegetation removed, n=4+4).
The plant removal treatment plots (PSC, PS, and P) were established April 2002. In the plant removal treatment plots vascular plants were cut with
scissors to the level of the moss (PS plots) or peat (P plots) surface and
their aboveground litter was removed. In the P plots the top 1.5 cm of the
Sphagnum moss carpet was cut off with scissors. All emerging regrowth was clipped
off once a week as necessary. Over the course of the study, progressively
less clipping was needed, with hardly any in 2004. Prior to CH4 flux
measurements, sedge stubble in P and PS plots was treated with paraffin wax
to seal the aerenchymatous pathway of CH4.
Measurements
CH4 fluxes were measured using the closed-chamber method. A stainless
steel collar (56 cm × 56 cm × 30 cm, length × width × height)
was permanently inserted into each sample plot prior to the start of the
study. The collars had a water groove to allow chamber placement and
airtight sealing during the measurement. For the flux measurements, an
aluminum chamber of 60 cm × 60 cm× 30 cm was placed on the
water groove of the collar. After the chamber placement, a vent on the
chamber roof that ensured pressure equilibration was sealed with a septum
plug. A battery-operated fan circulated the air inside the chamber. A 40 mL
air sample was drawn into a polypropylene syringe at 5, 15, 25, and 35 min after closure. The samples were stored at +4∘C before
analysis, which was carried out within 36 h. Samples were analyzed with
a HP-5710A gas chromatograph (GC) from 2001 to 2003 and with a HP-5890A GC
in 2004. Both GCs were equipped with a 1 mL loop, 6×1/8′′ packed
column (HayeSep Q in HP-5710A; Porapak Q in HP-5890A) and flame ionization
detector. The carrier gas was helium with a flow rate of 30 mL min-1.
Column and detector temperatures were 40 and 300 ∘C,
respectively. The precision of the analysis was ±0.16 %, determined
as the coefficient of variation of the replicate samples.
To relate the fluxes to prevailing environmental conditions, peat
temperatures at 5, 10, 20, and 30 cm below the moss surface and water level
in a perforated tube adjacent to each plot were measured during the flux
measurements. Air and peat temperatures and precipitation were also
continuously recorded at the weather station at the site. Green leaf area
index (LAI) of each vascular plant species in each plot was determined with
the method of Wilson et al. (2007) from April until November,
as a product of the total number of leaves (counted monthly) and the average
leaf size of marked individuals (measured every 2 weeks). Species-specific
Gaussian curves (Wilson et al., 2007) were fitted to the observations to
describe the continuous development of LAI throughout the season. LAI of
different species was summed up to sedge, dwarf-shrub, and total LAI
(LAIC, LAID, and LAIT, respectively). Moss cover at each plot
was visually estimated annually.
In addition to CH4 exchange, CO2 exchange was measured at the
study site. The methods and results are reported elsewhere
(Riutta et al., 2007a) in more detail, but some CO2 exchange
estimates are used here to study the relationship between the CO2 and
CH4 fluxes. In summary, net ecosystem CO2 exchange (NEE) was
measured weekly or once every 2 weeks by employing the closed-chamber technique in the
same plots and during the same period as the CH4 fluxes. Measurements
were carried out in both light and dark, which enabled the partitioning of
the fluxes into gross photosynthesis and ecosystem respiration. We
constructed nonlinear regression models for photosynthesis and respiration,
with water level, temperature, and LAI as explanatory factors, separately for
each vegetation treatment, to reconstruct the fluxes for the whole growing
season.
Data analyses
CH4 flux was calculated as the linear change in CH4 concentration as a function of time by fitting a least-squares regression
line. Of the 1300 measurements, <0.5 % were rejected due to clear
errors, such as leakage or problems in the GC analysis, and 2 % were
classified as episodic fluxes.
To reconstruct seasonal (June–September) estimates for each sample plot, the
fluxes measured once every 2 weeks were linearly interpolated between measurement days,
and the obtained daily values were integrated. In the interpolation,
rejected values and episodic fluxes were replaced with the median flux of
the corresponding vegetation and water level treatment on the same
measurement day. The impact of the episodic fluxes on the seasonal flux was
taken into account by using the episodic values as the CH4 flux
estimates of the day they were measured. The reconstructed seasonal fluxes
at the wet and WLD subsites were converted to CO2 equivalent according
to Myhre et al. (2013).
We used linear mixed-effect models to test the impact of the plant removal
treatments and the WLD treatment on WL, LAI, and daily measured CH4
flux. First, we tested the differences in WL, LAIC, LAID,
LAIT, and CH4 flux between the wet and WLD subsites before the WLD
treatment was applied (year 2001) and over the years after the WLD treatment
(2002–2004), with WLD treatment, year, and their interaction as potential
fixed predictors. This model included only the plots with intact vegetation
(PSCD). The wet subsite in 2001 was the constant against which WLD and other
years were compared. Therefore, the difference between the wet and WLD
treatment in the model describes the pre-treatment difference among the two
subsites in the calibration year 2001, and the interaction between WLD treatment
and the years 2002–2004 describes the impact of WLD after the treatment.
Second, we tested the impact of plant removal on CH4 flux over the
years and the interaction of the plant removal treatments with the WLD
treatment with data from the years 2002 to 2004 (no plant removal treatments in
2001). For each year separately, we fitted a model with plant removal
treatments, WLD treatment, and the interaction between them as potential
fixed predictors.
Third, we tested the response of CH4 flux to leaf area and
environmental variables by extending the model fitted to the data of the year
2004, which had the maximum amount of time for stabilization after the
treatments. In addition to plant removal and WLD treatments, potential fixed
predictors were LAIC, LAID, cover of Sphagnum mosses, measured WL,
temperature in the chamber, and peat temperature at the depths of 5, 10, 20,
and 30 cm (T5, T10, T20, and T30) as well as the potential interactions among
these parameters. Potential new predictors were sequentially added and after
each addition the significance of all predictors was tested. We reported
both models separately for the year 2004: one including plant removal and WLD
treatments as fixed predictors for CH4 flux and another including the
response of CH4 flux to leaf area and cover of plant groups and
environmental variables.
In each case, a conditional F test was used to test if the full model with
all fixed predictors and their interactions was significantly better
(p<0.05) than a simpler model. Plot and date were included as
crossed random effects. Resulting models are reported in Table 2. The models
were fitted using the function lmer of the package lme4 (Bates et
al., 2015) of RStudio version 1.1.383.
Parameter estimates of the linear mixed-effect model describing
(a) the differences, water level (WL, cm), total, sedge, and dwarf-shrub leaf
area index (LAIT, LAIC, and LAID), and CH4 flux between
wet (WLD0) and water level drawdown (WLD1) subsites and years before (2001)
and after (2002–2004) the WLD treatment in plots without vegetation removal,
(b) the differences in CH4 flux between the vegetation removal
treatments in the years 2002–2004 and (c) the response of CH4 flux in the year
2004 to leaf area and environmental variables. Vegetation treatments: PSCD
– intact vegetation; PSC – plots consisting of peat, Sphagnum mosses, and sedges
(shrubs removed); PS – plots consisting of peat and Sphagnum (sedges and shrubs
removed); P – plots consisting of bare peat (all vegetation removed).
The pre-treatment water level did not differ between the wet and WLD
subsites (p=0.174, comparison between wet and WLD treatment during the
calibration year 2001) (Fig. 1a, Table 1). Following the drainage in April
2002, the water level was significantly lower in the WLD subsite (p<0.001, interaction between WLD and the year 2002). The WLD treatment
lowered the water level by approximately 17 cm, except in July and August 2003 when a severe drought lowered the water level below the ditch,
resulting in similar water levels at wet and WLD subsites. At the wet
subsite, the water level during the years 2001 and 2004 was similar to the
long-term average of the site, approximately 5 to 10 cm below
the moss surface (Table 1) (Laine et al., 2004). During July and August 2002
and 2003, however, the water level was lower than the long-term average.
More information on the weather conditions during the study is given in
Riutta et al. (2007b).
Mean (a) water level (WL), (b) leaf area index (LAI), and (c)CH4 flux in plots with intact vegetation at wet and water level
drawdown (WLD) subsites. Error bars are standard errors of the mean. Units
on the x axis give the day of year. The start of the water level drawdown
treatment is indicated with the vertical dashed line in 2002. Water level is
negative when it is below the moss surface. Positive CH4 fluxes
indicate emission to the atmosphere.
Prior to the drainage, vegetation composition in the plots with intact
vegetation (PSCD) was similar at both subsites (Table 1, Fig. 1b). In the
mixed-effect model, LAIC, LAID, and LAIT did not differ
between wet and WLD subsites in the year 2001 (p values 0.996, 0.656, and 0.878,
respectively). In 2001 the peak season average LAIT was approximately
1.0 m2 m-2, of which sedges composed 70 %. The mean Sphagnum cover was
80 %. By the third year since WLD, 2004 LAIC had decreased (p<0.001) and LAID increased (p<0.001) at the WLD subsite,
resulting in an overall decrease in LAIT (p=0.007) (Table 1, Fig. 1b).
In the PSCD plots, the pre-treatment CH4 fluxes did not differ between
the wet and WLD subsites (p=0.654) (Fig. 1c). After the treatment, in
2002–2004, fluxes were significantly lower in the WLD than at the wet
subsite (p<0.001 for all years). During the 3-year WLD
treatment, the mean flux was approximately 51 and 7.0 mg CH4 m-2 d-1 at the wet and WLD subsites, respectively. Converted to CO2
equivalents, the seasonal reconstructed fluxes at the wet and WLD subsites
in 2002–2004 were 236 and 32 g CO2 eq. m-2 per growing season,
respectively.
Impact of the plant removal treatments
Plant removal treatments did not lead to major changes in vegetation
composition beyond the clipped target groups. Vascular plant removal did not
affect the Sphagnum moss cover, and the removal of dwarf shrubs did not change the
LAI of sedges. LAIC was similar in PSC and PSCD plots (data for 2004
shown in Table 1) during all years at the wet subsite and during 2003 and
2004 at the WLD subsite (all p values > 0.05). LAIC was
higher in the PSC plots than in the PSCD plots at the WLD subsite in 2002
(p=0.016).
During the first season of the removal treatments (2002) at the wet subsite,
CH4 fluxes were higher in the plant removal plots (P, PS, and PSC) than
in the intact plots (PSCD), in some cases almost triple (p<0.05
for all treatments, Fig. 2a–c). The fluxes in the plant removal
treatment plots also showed a stronger seasonal pattern and larger spatial
variation. After the first year of removal treatments, the fluxes of the P,
PS, and PSC plots decreased, and in 2003 P plots had a similar CH4 flux
to the intact plots (p=0.908), while PS and PSC plots still had a higher
flux than PSCD plots (p=0.033 and p=0.005, respectively).
Difference of the measured CH4 fluxes in plots with plant
removal treatments and the mean flux in the plots with intact vegetation on
each measurement day at the control subsite (a, b, c) and water level
drawdown subsite (d, e, f). Positive values indicate that fluxes in the
plant removal treatment plots are higher than in the intact plots. Units on
the x axis give the day of year. Note the difference scales of the y axes in
the upper and lower panels. Error bars are standard errors of the mean.
Vegetation treatments: PSC – plots consisting of peat, Sphagnum mosses, and sedges
(shrubs removed); PS – plots consisting of peat and Sphagnum (sedges and shrubs
removed); P – plots consisting of bare peat (all vegetation removed). Intact
plots consisted of peat, Sphagnum mosses, sedges, and shrubs. Removal treatments were
established in 2002.
By the third year of the plant removal treatments (2004), the fluxes in all
treatments showed a seasonal pattern similar to that of the intact plots.
Bare peat plots had lower fluxes than the intact PSCD plots (p<0.001). Fluxes of the PSC plots (shrubs removed) were marginally
significantly higher (p=0.060) than those of the PSCD plots (shrubs
present). In WLD conditions, the fluxes in the plant removal plots (P, PS,
and PSC) were mostly lower than the fluxes in the intact PSCD plots during
all three vegetation treatment years (Fig. 2d–f), but the
differences were not significant (Table 2b). WLD and plant removal
treatments had a significant interaction: in 2004 WLD lowered the fluxes
more in PSCD plots than in the P plots and more in PSC plots than in the P
and PS plots (p<0.05 for the interaction terms). Seasonal fluxes
visualize the patterns tested with the nonlinear mixed-effect models: at the
WLD subsite fluxes were lower than at the wet subsite in all plant removal
treatments (Fig. 3b). In wet conditions, the seasonal flux of the P and PS
plots was lower than that of the PSCD and PSC plots in which vascular plants
were present (Fig. 3a). Taking the fluxes from bare peat plots as a
baseline, the presence of vegetation enhanced the fluxes. Compared with the
situation of sedges and Sphagna present (PSC), the presence of shrubs (PSCD) seemed
to slightly attenuate the fluxes (Fig. 3b, c). In WLD conditions, the
differences between plant removal treatments were negligible. The
differences between the plant removal treatments can be used as an estimate
of the contribution of each plant group to the total flux, although due to
the propagation of the errors, uncertainty in these estimates is large. In
normal hydrological conditions, plant-mediated flux accounted for 68 % ± 23 % (comparison of P and PSCD plots) or 78 % ± 17 %
(comparison of P and PSC plots) of the total growing season flux, of which
Sphagnum mosses and sedges accounted for approximately one-fourth and
three-fourths, respectively (Fig. 3c).
Seasonal (June–September) CH4 flux (mean ± 1 standard
error) at wet and water level (WLD) drawdown subsites (a) in plots with
intact vegetation (PSCD) during the 4 study years (2001 was a calibration
year before the implementation of the WLD treatment), (b) in different plant
removal treatments plots in 2004, and (c) by each plant group, the
contribution of which to the total flux in 2004 was estimated from
differences between the different plant removal treatments. Letters above
bars denote differences among treatments, where bars with no letter in
common are significantly different based on a mixed-effect models presented
in Table 2 (a, b) and based on two-way ANOVA test with Tukey
pairwise comparisons (c). Plant removal treatments in (b): PSCD –
plots with intact vegetation, consisting of peat, Sphagnum mosses, sedges, and shrubs;
PSC – plots consisting of peat, Sphagnum mosses, and sedges (shrubs removed); PS –
plots consisting of peat and Sphagnum mosses (shrubs and sedges removed); P – plots
consisting of bare peat (all vegetation removed). Plant groups in (c): P –
bare peat; S – Sphagnum mosses; C – sedges; D – dwarf shrubs.
The seasonal CH4 fluxes displayed a clear positive, exponential
relationship with the seasonal net CO2 flux (Fig. 4). The relationship
was similar among the plant removal treatments in wet and dry conditions.
However, the plots with intact vegetation (PSCD) were an exception; they had
lower CH4 fluxes than could have been expected based on their net
CO2 flux, pointing towards the potential suppressing effect of shrubs
on CH4 emissions.
(a) The relationship between the net ecosystem CO2 uptake
(NEE) and CH4 flux during the growing season of 2004 described with an
exponential model and (b) the residuals of the model, in the different plant
removal treatments at wet (solid symbols) and water level drawdown (open
symbols) subsites. Vegetation treatments: PSCD – plots with intact
vegetation, consisting of peat, Sphagnum mosses, sedges, and shrubs; PSC – plots
consisting of peat, Sphagnum mosses, and sedges (shrubs removed); PS – plots
consisting of peat and Sphagnum mosses (shrubs and sedges removed); P – plots
consisting of bare peat (all vegetation removed). NEE is positive when the
fen is a net sink of atmospheric CO2. Methane flux is positive when the
fen is a source of CH4 to the atmosphere.
Response of CH4 flux to environmental variables and interaction with leaf area
The best predictors of the CH4 flux in the extended model for the year
2004 were the categorical WLD treatment (which was a better predictor than
the measured WL), T20 (best out of the measured temperatures), and LAIC
(which was a better predictor than the categorical vegetation removal
treatment). The abundance of the other plant functional groups, LAID,
or Sphagnum cover did not have a significant effect on the fluxes. CH4 flux was
increased by LAIC and T20 in wet conditions (Table 2c). In the WLD
conditions, however, neither LAIC nor T20 had any impact on the fluxes
(coefficient estimates for LAIC*WLD1 and T20*WLD1 cancel out the
coefficient estimates for LAIC and T20 in wet conditions; Table 2c).
The positive coefficient of the WLD treatment seemingly indicated a larger
flux at the WLD treatment site compared with the wet site, when LAIC
and T20 both equal zero; however, the measured minimum T20 during the
growing season in 2004 was 6.1 ∘C, and the model was not intended for
any extrapolation. The predicted CH4 flux in the WLD treatment was
similar to or lower than the flux in the wet treatment in the observed T20
and LAIC range.
DiscussionThe effect of the plant types and substrate on the CH4 fluxes in natural water level conditions
In line with previous studies, the plant removal treatments of this
study indicated that sedges were the most important plant group in
regulating CH4 fluxes. In other sedge-dominated sites, plant-mediated
flux has accounted for 75 % to 97 % of the total flux (Schimel,
1995; Kelker and Chanton, 1997; Ström et al., 2005; Sun et al., 2012;
Noyce et al., 2014) and plant removal experiments have shown that of
different plant functional types, removal of graminoids causes the largest
decrease in CH4 production and flux (Ward et al., 2013; Robroek et
al., 2015). Compared with the bare peat surfaces, the presence of Sphagnum mosses
seemed to have a slight, although not statistically significant, enhancing
effect on the CH4 fluxes, similar to the results of Roura-Carol and
Freeman (1998), who
found the presence of mosses to have a slightly attenuating effect on the
fluxes, while Greenup et al. (2000) did not find
significant differences in fluxes after Sphagnum removal. Based on this, the
CH4 oxidation by the loosely symbiotic methanotrophs within Sphagnum mosses (Raghoebarsing
et al., 2005; Larmola et al., 2010; Putkinen et al., 2012) seems to play a
minor role in CH4 dynamics at our site.
Similarly to Ward et al. (2013), we found that the
presence of shrubs seemed to have a slightly attenuating effect on the
fluxes under natural water level conditions. Robroek et al. (2015) made a similar finding with potential
CH4 production. In contrast, an aerenchymatous shrub, Myrica gale, supported
similar potential CH4 production to a sedge, Carex aquatilis, and did not suppress
CH4 flux (Strack et al., 2017). Furthermore, in line
with the attenuating effect of shrubs, the CH4 flux : NEE ratio was
lower in the plots with intact vegetation (PSCD, shrubs present) than in the
other vegetation treatments. Mechanisms behind that might relate to the impact
of shrubs on soil chemistry, microbial community, or the biomass allocation
of sedges. Shrub litter has higher lignin and leaf dry matter content than
sedges, which are both related to lower methanogenesis (Yavitt et al., 2019). Shrub
removal has been observed to result in higher dissolved organic C and N and
lower C : N ratio (Ward et al., 2013) as well as higher fungal biomass (Robroek
et al., 2015). A study on the competitive ability and biomass allocation of a
wetland grass, Molinia caerulea, revealed that M. caerulea allocated more biomass to the roots when it
did not face competition by shrubs (Aerts et al.,
1991). Similarly, in our study, sedges in the plots where shrubs were
removed may have allocated more biomass to the roots than the sedges growing
in the sedge and shrub mixture. As a result, methanogenic microbes may have
benefited from the higher substrate availability in the shrub removal plots
(PSC). CH4 production has a negative relationship and CH4
oxidation has a positive relationship with the concentration of certain
woody lignin compounds in peat pore water (Yavitt et al., 2000). In our study, this
may be the reason behind the lower fluxes in the presence of the arboreals.
The results concerning the attenuating effect of shrubs on CH4 fluxes
are, however, only indicative and further process-orientated research is
needed.
Delay in the plant removal treatment effect
We observed a considerable disturbance in the fluxes following the plant
removal treatments. In other clipping studies in which the shoots were cut
above the water level, clipping increased the CH4 flux during the
first growing season after clipping (Schimel, 1995),
had no effect (Kelker and Chanton, 1997;
Greenup et al., 2000), or decreased the flux (Waddington et al., 1996; Rinnan et
al., 2003). Thus, we assumed that the higher fluxes at the clipped plots
during the first 2 years after the vegetation removal treatments were
mainly caused by treatment artifacts. The removal of the aboveground parts
of vascular plants led to the gradual death of the belowground parts,
creation of an unnatural amount of new root necromass, and, thereby, a peak in
the amount of available substrate. Methanogenesis at the study site may have
been substrate limited (Bergman et al., 1998;
Rinne et al., 2007), which could explain the initially high fluxes in the
plant removal plots. The mass loss of Carex roots and rhizomes is only 10 % to
45 % during the first 12 months of decomposition, although the litter
quality deteriorates (Scheffer and Aerts, 2000). However,
after 2 years the mass loss can be as much as 75 % of the original mass
(Thormann et al., 2001),
which gives more confidence in the results of the third year of the plant
removal treatments. Thus, we used the third year of the plant removal
treatments to quantify the contribution of the vegetation components to the
fluxes and the response of fluxes to environmental conditions. King et al. (1998) likewise reported the effects of
the plant removal 2 years after the treatment began. Shrub litter,
especially belowground litter, decomposes slower than sedge litter
(Moore et al., 2007), due to the high lignin content
(Yavitt et al., 2019). On the
other hand, the majority of dwarf shrub roots grow in the uppermost 20 cm
peat layer, while sedge roots extend deeper (Korrensalo et al., 2018a;
Mäkiranta et al., 2018), causing a larger proportion of dwarf shrub roots
to decompose in oxic conditions, thus counteracting the differences in
litter quality. Even 2 years after the start of the vegetation removal
treatments, some shrub roots still probably remained. However, they were
mostly located above the CH4 production zone.
Water level regulates the role of the vegetation
Experimental water level drawdown has been used to mimic climate change
impact on northern peatland CH4 fluxes in the mesocosm (Freeman
et al., 1992; Blodau et al., 2004; Dinsmore et al., 2009) and in the field
studies ranging from bogs to rich fens (Laine
et al., 2007a; Strack and Waddington, 2007; Turetsky et al., 2008;
Ballantyne et al., 2014; Munir and Strack, 2014; Pearson et al., 2015;
Peltoniemi et al., 2016; Chimner et al., 2017; Olefeldt et al., 2017). In
line with our results, all these studies report some level of decrease in
CH4 flux due to WLD ranging from 3 to ∼20 cm. Together
with temperature and vegetation, water level is a major regulator of
CH4 flux (Lai,
2009; Turetsky et al., 2014). However, the mechanistic understanding of this
process is still limited. While Strack et al. (2004) found
only small differences in the CH4 production and consumption potentials
between control and WLD sites, and thus attributed the decrease in
fluxes mainly to the change in the volume ratio of the anaerobic and aerobic
zones, Yrjälä et al. (2011) and Peltoniemi et al. (2016) found that WLD had a stronger impact on emissions
through decreasing CH4 production than through increasing oxidation,
In this study, the presence or absence of the plant types or LAIC had
no effect on the CH4 flux in the WLD conditions. This supports the
findings by Waddington et al. (1996) as well
as Strack et al. (2006) that the impact of the vegetation
on the fluxes is strongly dependent on the water level conditions. CH4
flux also responded to peat temperature only in wet conditions. A similar
result with water level and temperature response has been previously
reported by Moosavi et al. (1996). Our
results showed that water level acts as a switch; it turns CH4 flux on
and off, after which temperature and vegetation regulate the flux magnitude.
This result is further emphasized by the response model, where WLD treatment
including change in the ecosystem following a new WT regime rather than
seasonally varying WL was a better predictor for CH4 fluxes. In
conclusion, vegetation is a major controlling factor of the peatland
CH4 dynamics, but only in wet conditions.
Conclusions
Vegetation, sedges in particular, regulates the level of fen CH4 fluxes
in normal hydrological conditions, but this vegetation control is strongly
dependent on the water level regime. In water level drawdown conditions,
CH4 fluxes are significantly lowered, practically to zero, and
vegetation composition has no influence on the fluxes. The results are
relevant for assessing the response of fen peatlands to changing climatic
conditions, as water level drawdown and the consequent vegetation changes
are the major projected impacts of climate change on northern peatlands.
Data availability
The data associated with the paper are published in the PANGAEA
repository (https://doi.pangaea.de/10.1594/PANGAEA.911742, Riutta et al., 2020a, and https://doi.pangaea.de/10.1594/PANGAEA.911740, Riutta et al., 2020b).
Author contributions
The study was designed and the field experiment established by EST, TR, and
JL. Fieldwork, flux calculation, and flux reconstruction was conducted by
TR. Statistical analysis was done by AK. TR wrote the first version of the
manuscript, which was further processed by all other authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Jouni Meronen for technical support and the field team for
assistance in the study site. Meeri Pearson kindly revised the language.
Financial support
This research has been supported by the Academy of Finland (grant nos. 287039, 50707, 201623, and 202424), the Koneen Säätiö, the Jenny ja Antti Wihurin Rahasto, the Faculty of Science and Forestry, University of Eastern Finland, and the Graduate School in Forest Sciences, University of Helsinki.
Review statement
This paper was edited by Alexey V. Eliseev and reviewed by Tariq Munir and one anonymous referee.
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