Peatlands are a large source of methane (CH4) to the
atmosphere, yet the uncertainty around the estimates of CH4 flux from
peatlands is large. To better understand the spatial heterogeneity in
temperate peatland CH4 emissions and their response to physical and
biological drivers, we studied CH4 dynamics throughout the growing
seasons of 2017 and 2018 in Flatiron Lake Bog, a kettle-hole peat bog in
Ohio. The site is composed of six different hydro-biological zones: an open
water zone, four concentric vegetation zones surrounding the open water, and
a restored zone connected to the main bog by a narrow channel. At each of
these locations, we monitored water level (WL), CH4 pore-water
concentration at different peat depths, CH4 fluxes from the ground and
from representative plant species using chambers, and microbial community
composition with a focus here on known methanogens and methanotrophs.
Integrated CH4 emissions for the growing season were estimated as 315.4±166mgCH4m-2d-1 in 2017 and 362.3±687mgCH4m-2d-1 in 2018. Median CH4 emission was highest in
the open water, then it decreased and became more variable through the
concentric vegetation zones as the WL dropped, with extreme emission
hotspots observed in the tamarack mixed woodlands (Tamarack) and low
emissions in the restored zone (18.8–30.3 mgCH4m-2d-1).
Generally, CH4 flux from above-ground vegetation was negligible
compared to ground flux (<0.4 %), although blueberry plants were
a small CH4 sink. Pore-water CH4 concentrations varied
significantly among zones, with the highest values in the Tamarack zone,
close to saturation, and the lowest values in the restored zone. While the
CH4 fluxes and pore-water concentrations were not correlated with
methanogen relative abundance, the ratio of methanogens to methanotrophs in
the upper portion of the peat was significantly correlated to CH4
transfer velocity (the CH4 flux divided by the difference in CH4 pore-water concentration between the top of the peat profile and the
concentration in equilibrium with the atmosphere). Since ebullition and
plant-mediated transport were not important sources of CH4 and the peat
structure and porosity were similar across the different zones in the bog,
we conclude that the differences in CH4 transfer velocities, and thus
the flux, are driven by the ratio of methanogen to methanotroph relative
abundance close to the surface. This study illustrates the importance of the
interactions between water-level and microbial composition to better
understand CH4 fluxes from bogs and wetlands in general.
Introduction
Methane (CH4) fluxes from natural and anthropogenic sources play a
significant role in determining atmospheric climate forcing (Ciais et al.,
2013). Changes to CH4 fluxes from natural systems are of significant
concern due to their potential to drive positive feedback cycles in the
global climate system (Bridgham et al., 2013; Dean et al., 2018). Natural
wetlands emit approximately 30 % of all the methane (CH4) released into
the atmosphere (Kirschke et al., 2013), yet the uncertainty around wetland
CH4 flux is the highest of all the components of the global CH4
budget (Kirschke et al., 2013). This uncertainty partly arises from the
complexity of physical and biological interactions that result in the
production and oxidation of CH4 and its eventual release to the
atmosphere (Lai, 2009). Generally, water level (WL) is the most important
driver of CH4 emissions from wetlands, and especially peatlands, as its
position in the soil or peat profile defines the boundary between anaerobic
CH4 production (methanogenesis) in the catotelm (i.e. the lower anoxic
portion of the peat) and aerobic CH4 oxidation (methanotrophy) in the
acrotelm (the upper oxic peat; Kettunen, 2003; White et al., 2008).
However, a plethora of environmental variables can also influence CH4
fluxes in peatlands, including temperature (Bohn et al., 2007; Kim et al.,
1999; Segers, 1998); peat origin (e.g. Sphagnum, woody peat, and fen and reed peat; Bridgham and Richardson, 1992); degree of humification (Glatzel et al.,
2004); availability of labile carbon in the peat (Updegraff et al., 1995);
concentrations of lignin, long-chain fatty acids, and polysaccharides along
the peat profile (Hoyos-Santillan et al., 2016); phosphorous content, which
regulates anaerobic decomposition of organic matter (Basiliko et al., 2007);
the abundance of other electron acceptors, especially Fe (Chamberlain et al.,
2018); and pH, as methanogens occur at greater abundances in neutral to
slightly alkaline conditions (Wang et al., 1993). It is also important to be
cognisant of reports of CH4 production in aerobic soil (Angle et al.,
2017) and an increased awareness of the importance of anaerobic oxidation of
CH4 (Smemo and Yavitt, 2011).
The microbiota of a site can have complex interactions with WL and other
physical conditions, which result in variable CH4 fluxes. Despite the
increasingly complex picture emerging of peatland CH4 cycling, it has
been estimated that methanotrophy can oxidize 60 %–90 % of the CH4
produced in wetlands before it can escape to the atmosphere (Le Mer and
Roger, 2001). Research has also shown that water table drawdowns reduce the
abundance of methanogens (Kim et al., 2008) and that changes in ecosystem
vegetation and structure can affect microbial community composition and, in
turn, the CH4 biochemistry of wetlands (McCalley et al., 2014).
Generally, peat bogs are nutrient-poor sites dominated by hydrogenotrophic
methanogenesis, but when disturbance occurs, a change from hydrogenotrophic
to acetoclastic methanogenesis can occur due to an increase in pH and
nutrients (Kelly et al., 1992; Kim et al., 2008; Kotsyurbenko et al., 2004).
Kettle-hole peat bogs are peatlands created by the accumulation of peat in
areas previously occupied by kettle lakes. Kettle-hole peat bogs, which are
frequently found in eastern North America (Cai and Yu, 2011; Moore, 2002),
often consist of water bodies surrounded by different vegetation zones.
Closest to the open water there is often a mat of floating vegetation
followed by concentrically organized vegetation zones that ultimately
support shrubs and trees (Vitt and Slack, 1975). This vegetation
heterogeneity can be an important driver of CH4 fluxes (Lai et al.,
2014), particularly in ombrotrophic peat bogs where vegetation communities
and water levels are strongly associated (Malhotra et al., 2016).
Measurements of CH4 flux in different vegetation zones are important in
understanding site-level flux estimates at the bog scale that are affected by
the relative cover and arrangement of different vegetation zones (Nadeau et
al., 2013). Most importantly, a better understanding of the biological,
chemical, and physical processes controlling fluxes at these low resolutions
is necessary to scale up CH4 fluxes at the ecosystem level (Bridgham
et al., 2013). The objectives of this study were to (1) calculate the
growing-season CH4 budget of a kettle-hole peat bog in Ohio by
upscaling flux measurements from different vegetation zones, (2) quantify the
effects of biotic and abiotic controls on below-ground vertical profiles of
CH4 pore-water concentration and related fluxes, and (3) determine the links
between microbial community structure and associated CH4 dynamics.
Brief comparisons of CH4 dynamics between restored and undisturbed
section are discussed but not in detail, as the evaluation of the effect of
restoration on CH4 fluxes is not the objective of this paper.
Map of the study site showing the different hydro-biological
zones and the sampling locations. The map was created with QGIS, and the image was downloaded from the Ohio Geographically Referenced Information Program (OGRIP) (http://gis5.oit.ohio.gov/geodatadownload/, last access: 2 February 2018, image reference number: N2280505 and N2280500).
MethodsStudy site
We studied Flatiron Lake Bog, a ca. 14.4 ha kettle-hole peat bog located in
northeastern Ohio (41∘02′40.67′′ N, 81∘21′59.81′′ W; Fig. 1). The site is a state nature preserve and has been owned by The
Nature Conservancy (TNC) since 1984. The greater part of the site typifies
the characteristic abiotic and biotic zonation found in similar sites
throughout eastern North America. A small area (ca. 1120 m2) of open
water (Water) is located at the center of the site and is surrounded by a
series of concentrically organized vegetation zones. The vegetation
community of the site, including the bog vegetation and upland zones, was
described in detail by Colwell (2009). The closest zone to the open water,
hereafter called the Sphagnum-leatherleaf mat (Mat), consists of a floating
mat of Sphagnum fallax (H. Klinggr.), with abundant cover of swamp loosestrife (Decodon verticillatus (L.)
Elliot) and leatherleaf (Chamaedaphne calyculata (L.) Moench). Further away from the open water,
and surrounding the Mat, is a narrow band of tamarack mixed woodland
(Tamarack). The Tamarack zone is characterized by tamarack (Larix laricina (Du Roi) K. Koch) and yellow birch (Betula alleghaniensis Britton), with a ground layer dominated by S. fallax.
Further towards the bog's periphery, one can find a large area of mixed
ericaceous shrubs (Shrubs) dominated by highbush blueberry (Vaccinium corymbosum L.) and
huckleberry (Gaylussacia baccata (Wangenh.) K. Koch), with a ground layer of Sphagnum and scattered
sedges, ferns, and forbs. The Shrubs zone also includes occasional patches
dominated by winterberry (Ilex glabra (L.) A. Gray) or mature hardwoods, such as red
maple (Acer rubrum L.) and yellow birch. Finally, the outermost area consists of a lagg
or moat, hereafter called the winterberry lagg (Lagg). The Lagg is typically
inundated during the first half of the growing season but dry during
extended periods of the year. The dominant vegetation on the Lagg includes
winterberry (I. glabra) and buttonbush (Cephalanthus occidentalis L.). The Water, Mat, and Tamarack zones
generally present water levels that are always at (Water and Mat), or near
(Tamarack) the surface, and together they are hereafter referred to as the
permanently wetted area. In contrast, the Shrubs and Lagg zones have deeper
water tables with more pronounced fluctuations in water level and are
hereafter referred to as the intermittently wetted area. Peat coring and manual
depth probing revealed a gradient in peat depths from the margin of the site
to the interior. Measured peat depths varied from >0.3m in the
Lagg areas to >10m close the center of the site. The immediate
upland area surrounding the bog is mostly forested, with dominant tree
species including the American beech (Fagus grandifolia L.), black oak (Quercus velutina Lam.), and red maple
(Acer rubrum L.). The width of this forested buffer varies, and some parts of the site
are in proximity to areas under arable production, roads, or buildings
(Fig. 1).
In addition to this relatively unaltered core area of the site, there is a
restored section (Res) in the southern part of the bog, which is connected
to the main area by a narrow channel (Fig. 1) and comprises 19 % of the
total peatland (ca. 23 430 m2). During the 1950s, this area was
disturbed and drained to provide water for gravel- and sand-mining activities
in adjacent areas. Peat coring in this area has revealed evidence of fire
disturbance with significant deposits of charcoal and char layers. Between
2001 and 2003 TNC implemented a few restoration interventions in this area.
This included opening of the channel to reconnect the two sections of the
bog and the installation of a water-control structure to raise the water
table at the restored section. Elevated water tables suppressed red maple
trees that had colonized the site since the disturbance and enabled the
establishment of bog vegetation. The latter process was aided by the
transfer of Sphagnum diaspores and the planting of Vaccinium spp. The current vegetation
community for the restored section is dominated by winterberry (I. glabra),
buttonbush (C. occidentalis), invasive glossy buckthorn (Frangula alnus Mill.), and a remnant population
of red maple trees. Thin discontinuous mats of Sphagnum spp. and Carex spp. dominated
the ground layer. Due to its limited connection to the core of the site, and
its history of modification, degradation, and restoration, we consider the Res
zone to be a distinct hydro-biological zone, and due to its large variation in
water level, we consider this zone to be part of the intermittently wetted area
as well.
Experimental design
Across the site, we established multiple sampling locations to assess
ecosystem carbon fluxes, CH4 pore-water concentrations, peat
properties, water table dynamics, and microbial community composition.
Monitoring included locations within both the undisturbed and restored
sections of the bog. In the permanently wetted area we initiated two
transects with their start points located to the north and south of the open
water in the center of the bog (Fig. 1). Each transect included three
sampling locations, each associated with a vegetation zone: Water, Mat, and
Tamarack. In the intermittently wetted area, sampling locations for Shrubs
and Lagg were selected as shown in Fig. 1. Most locations were established
in summer 2017, but the Tamarack location on the north transect and the Lagg
location were added in the spring of 2018. For the restored section two
randomly selected locations were sampled, a northern location towards the
center of the restored section (Res-N) and a southern location near the
edge (Res-S). Fewer sampling locations in the restored section were
justified by the more homogenous vegetation composition at the section
scale and the section's smaller area.
Surface CH4 flux chamber measurements
In 2017, CH4 gas transfer at the peat surface was measured monthly
between June and October using non-steady-state chambers. We sampled two to four chambers monthly in each sampling location at each zone. Chambers were
deployed on top of semi-permanent collars that were installed 3 months prior
to the first round of sampling. The collars in the Water, Tamarack, Shrubs,
and Res zones were made of rectangular high-density polyethylene (HDPE)
boxes, with dimensions of 38cm×56cm and a height of 26 cm.
During sampling, the collars and the chambers had a foam seal and were held
together with clamps. For the open-water chambers, closed-cell polyethylene
pipe insulation (1.3 cm internal diameter) was attached to the bottom edge
of the chamber to facilitate flotation and create a seal with the water
surface (Rey-Sanchez et al., 2018). For the Mat zone, we used tall chambers
with a volume of 121 L (height 82 cm and radius 28 cm), with circular collars
with a 28 cm radius and a height of 59 cm that were inserted ca. 30 cm into the
mat for a total chamber height of ca. 121 cm. The height of the chambers
was necessary to fit the tall and abundant loosestrife and leatherleaf
plants. Due to their larger volume, these chambers included fans at 30
and 85 cm above the surface to improve air mixing within the chamber during
sampling. The volume of the plants within the chamber was considered
negligible.
All chambers included a thermometer to measure air temperature, a 3 m long
Tygon tube (1.6 mm internal diameter) used as a vent for stabilizing
pressure, and a 20 mm gray butyl stopper that served as a sampling port. In
2017 gas samples were extracted from the chambers using a syringe (30 mL).
Here 20 mL of the gas sample was introduced into evacuated 10 mL vials to
keep it over-pressurized. We used a closure time of 30 min for each
chamber and extracted a sample every 5 min for a total of seven samples per
chamber. The gas extracted from the chamber was transported to the
laboratory to be analyzed on a gas chromatograph (Shimadzu GC-2014, Shimadzu
Scientific Instruments, Kyoto, Japan). Fluxes were calculated from the slope
of the linear regression of the molar density of the greenhouse gas vs. time.
We incorporated selection criteria for rejecting outliers from individual
chamber measurements as described in Morin et al. (2017). Specifically, if
the r2 value of the linear regression of molar density vs. time was not
sufficiently high (r2≥0.85), and the p value was higher than
0.05, we removed one outlier point (identified as the point with the highest
residual value) from the regression. This was done up to twice per chamber,
and if the accumulation rate regression still did not meet the selection
requirements, the entire chamber observation was rejected. This approach
leads to the exclusion of cases where ebullition events occur during the
sampling, creating a non-linear change in concentration. The procedure for
calibration of the gas chromatograph is based on previous studies at the
same facility (Nahlik and Mitsch, 2010; Sha et al., 2011) and was fully
described in Morin et al. (2017).
In 2018, surface fluxes were measured monthly and at the same locations as
in 2017 and the additional Lagg and Tamarack locations. We used a portable
infrared gas analyzer (Picarro GasScouter G4301, Picarro Inc., Santa Clara,
CA) adapted to sample the same chambers as used in 2017. Given the higher
sampling rate of the Picarro (one point per second) the fluxes were calculated
based on a linear regression of the molar density of CH4 over 2–4 min depending on the volume of the chamber and the strength of the
response of gas concentration vs. time. Due to the higher number of points
(146–293 per regression), a stricter p value was implemented (p<0.001) to determine the significance of the regression. A lack of a
significant correlation within a chamber measurement set was assumed to
equal a zero flux.
Diurnal patterns of CH4 emissions for the four main zones in the bog
(Open, Mat, Tamarack, and Shrubs) were measured in September in O-S, Mat-S,
Tamarack-S, and Shrubs locations (Fig. 1). Four individual chamber
measurements per location were completed throughout a full 24 h cycle
with a frequency of approximately 3 h. Chamber measurements were
accompanied by measurements of surface or water temperature when appropriate.
CH4 flux from plants
To estimate potential emission of CH4 through the plant tissues of
larger sub-canopy and canopy trees and shrubs, which would be missed by
chambers, we measured plant fluxes in dominant vascular species near the
location of the surface measurements. Fluxes from plants were sampled
monthly in June, July, and September 2018 using the Picarro gas scouter
with chambers adapted to fit individual leaves or branch sections.
Measurements were taken at multiple times during the day in June, July, and
September, while a full diurnal pattern was performed in September.
To measure fluxes coming directly through the plant tissue in the Mat zone,
we used small chambers on loosestrife stems, the most abundant plant species
in this zone. These chambers had a small opening in the corner of one of the
sections to allow the stem to sit uncompressed. The spaces around the stem
hole were sealed with putty. This loosestrife-stem chamber enclosure had
dimensions of 34cm×21cm×12.4cm and a volume of 11.4 L.
We used fully mature and healthy-looking loosestrife stems with more than
200 cm2 of area for plant-flux calculations. Stems were measured five
times throughout the day in June and twice in July and September, adding up
to nine observations throughout the season. After 2–3 min of measurements,
the stem was cut, wrapped in a moist paper towel, and put in a cooler for
calculation of leaf area. The leaves were detached from the stem and
petioles, arranged on a sheet of paper, and put on a scanner with a
reference scale. The images were analyzed with the software ImageJ
(Schneider et al., 2012) for calculation of total leaf area.
Plant-flux measurements at the Tamarack zone were conducted on stems and
trunk sections of Tamarack, while fluxes at the Shrubs were measured from
blueberry stems. To measure fluxes coming from trunk sections we used an
adaptation of the chambers used by Pangala et al. (2013) for tropical
wetlands. These chambers had two sections that were sealed with insulation
foam that closed around the trunk and that were held together tightly with
clips. When holes around the trunk were present, additional layers of
insulation foam were added to guarantee a good seal. The volume of the trunk
inside the chamber was measured to subtract from the total volume of the
chamber, which was 106 L. The dimensions of all the enclosures were 76cm×112cm×52cm. The understory fluxes from the low stems of the
Tamarack as well as blueberry, the most abundant plant in the understory in
the Tamarack and Shrubs zones, were measured using stem chambers. Trunk
fluxes were measured six times in the months of May and July. For stem flux
calculation, we used fully mature and healthy tamarack stems growing at a
reachable height. Stems were measured twice in May, seven times in June,
five times in July, and twice in September. Blueberry twigs were sampled at
multiple locations within the Shrubs zones, four times in June, four times
in July, and twice in September.
Upscaling of CH4 fluxes
To scale up the fluxes from each of the zones we extrapolated monthly mean
chamber measurements to the entire area of each zone. We then integrated the
monthly observed flux to calculate the total seasonal CH4 budget for
each zone and added the contribution of all the zones for the total seasonal
site total. When fluxes from plants were significant, we calculated the
total contributions by first multiplying the per-leaf-area rate observed by
the plant chamber measurement by the leaf-area index, then multiplying by
the area of the zone, and finally integrating in time for the whole season.
The leaf-area index (LAI) was calculated based on the MODIS LAI product (image collection
ID: MODIS/006/MCD15A3H, available through the Google Earth Engine) for the
period of study. Due to the low resolution of the imagery with respect to
the site (500 m), we calculated the average LAI of the two images
intersecting the site, which comprised similar areas.
Due to the lack of strength in the signal of the diurnal pattern, we did not
correct the monthly measurements by time of day. The measurements in 2017
encompassed a total of 122 d for which the integration of fluxes was
performed. The length of this period was higher in 2018 and was equal to 149 d.
Vertical profiles of CH4 pore-water concentration and methane
transfer velocity
We used in situ, dialysis, pore-water samplers (“peepers”; Angle et al., 2017;
MacDonald et al., 2013) to measure vertical pore-water concentration
profiles of dissolved CH4. In total, seven peepers were installed
throughout the site: five in the undisturbed section and two in the restored
section. Peepers were placed adjacent to the gas flux chambers. Each peeper
had 10 sampling windows located at depths from 1.4 to 51.8 cm and spaced
every 5.6 cm. Each window (8.89 cm×2.28cm area and 3.02 cm
depth), which was filled with deionized water that equilibrates with the
surrounding pore water through a semi-permeable membrane (pore size 0.2 µm; Sterlitech Corporation, Kent, WA), was connected to two
UV-resistant Tygon tubes that extended to the surface. When one tube water
was suctioned using a syringe, the other was connected to a nitrogen bag to
replace the volume of water extracted. Extracted samples were stored in 10 mL glass vials, each containing 100 µL of hydrochloric acid (2 M) to
prevent any biological reactions. Samples were kept in a cooler at low
temperatures (ca. 4 ∘C) for no longer than 2 d before
processing.
Samples were processed with the goal of measuring the concentration of
dissolved gases in the water; 5 mL of the water sample was extracted from each
vial and placed in a syringe pre-filled with 20 mL of N2 gas. The
syringes were shaken vigorously for 15 min, and 20 mL of the headspace was
extracted into a new 10 mL glass vial. The pore-water concentrations of the
samples were calculated based on the headspace concentration of the gas in
equilibrium with the liquid sample according to Henry's law of equilibrium
of gases in a liquid–air interface. The coefficient of equilibrium for
CH4 was 67.13 LMPamol-1. The gas samples were analyzed in a gas
chromatograph with a flame ionization detector (FID; Shimadzu GC-2014, Shimadzu Scientific
Instruments, Kyoto, Japan).
By combining pore-water concentration at the surface with the associated
fluxes, estimations of methane transfer velocity were obtained as in
previous studies in forested ponds and lakes (Holgerson et al., 2017;
Schilder et al., 2016; Wanninkhof, 2014). Through this approach, the flux at
the water–air interface can be calculated using the bulk formulation:
FCH4=k(Cw-Ceq),
where FCH4 is the diffusive CH4 flux (molm-2s-1), k is the
CH4 transfer velocity (ms-1), Cw is the concentration of
methane in the pore water at the surface (molm-3), and Ceq is the
concentration of CH4 in equilibrium with the atmosphere (molm-3).
Ceq is calculated by multiplying the mixing ratio of CH4 in the
atmosphere (r; in molmol-1) by the atmospheric pressure (P; in MPa) and
dividing by Henry's law coefficient of equilibrium for CH4 (KH) of
0.067 m3MPamol-1 as in Eq. (2):
Ceq=rPKH.Ceq was calculated first with a constant r (2 µmolmol-1)
and second with the value of the average of the initial r of the chamber
measurements associated with each flux calculation. These two methods
produced nearly identical results in Ceq when compared to the much
higher values of Cw. The constant mixing ratio was chosen for the rest
of the analyses given the uncertainty associated with the initial
concentration from the chambers. In the case of our peat bog, Cw can be
calculated by multiplying pore-water concentration ([CH4]) by peat
porosity (Φ; see ancillary measurements below):
Cw=[CH4]Φ,
where [CH4] was calculated in the top stratigraphic layer of the peat
(ca. 10 cm). Finally, methane transfer velocity can be calculated as
k=FCH4Cw-Ceq.
We focus on the top 10 cm because, first, this is the section where the
atmospheric exchange occurs. Secondly, this section should be the most
active one for both methanogens and methanotrophs (Angle et al., 2017), since it
includes the more aerobic acrotelm as well as less well-humified peat
(greater labile C availability).
Core sampling, DNA extraction, and 16S rRNA amplicon sequencing and
analysis
We analyzed the microbial composition of peat cores adjacent to the peepers.
Three cores were extracted in August 2017 from within 5 m of the
peepers located in the Mat-S, Tamarack-S, Shrubs, Res-N, and Res-S zones. The
cores were extracted using a rectangular Wardenaar peat corer with an
aperture area of 12 cm×12cm and >50cm length. Core
horizons were sampled in the field according to obvious stratigraphy (by
color, texture, and von Post humification). Representative ca. 10 cm long
samples of each horizon were stored at 4 ∘C and processed the
next day for microbial analyses. Processing involved dividing each section
vertically into three sub-samples, which were homogenized before a 0.25 g sub-sample was extracted from each. A fourth 0.25 g sub-sample was taken
following homogenization of all the remaining material from a given section.
All sub-samples were stored at -20∘C for no more than 3
months until DNA extraction. DNA was extracted using DNeasy PowerSoil Kit
(Qiagen, Hilden, Germany) following the manufacturer's protocol. Extracted
DNA was quantified with NanoDrop 8000 (Thermo Fisher Scientific, Waltham,
WA). The 16S rRNA V4 region was then amplified and sequenced on the Illumina
MiSeq platform (Illumina, San Diego, CA), at Argonne National Labs, via the
Earth Microbiome Project (http://www.earthmicrobiome.org/, last access: 3 January 2019) post-2015
barcoded primer set. These primers (515F; Parada et al., 2016; CGTGYCAGCMGCCGCGGTAA –
806R, April, GGACTACNVGGGTWTCTAAT, forward-barcoded; Parada et al., 2016,
and Apprill et al., 2015) are adapted for Illumina HiSeq2000 and MiSeq by
the addition to the forward primer of a 5 ft Illumina adapter to support
paired-end sequencing, a 12-base barcode sequence to support sample
pooling in each lane and forward pad and linker sequences, and the addition
to the reverse primer of a 3 ft Illumina adapter and reverse pad and linker
sequences (Caporaso et al., 2010, redesigned by Walters et al., 2016).
Each 25 µL polymerase chain reaction (PCR) contained 12 µL of MO BIO PCR water
(certified DNA-free), 10 µL of 5PRIME HotMasterMix (1×), 1 µL of
forward primer (5 µM concentration, 200 pM final), 1 µL Golay
barcode-tagged reverse primer (5 µM concentration, 200 pM final), and
1 µL of template DNA. The conditions for PCR were as follows: 94 ∘C for
3 min to denature the DNA, with 35 cycles at 94 ∘C for 45 s,
50 ∘C for 60 s, and 72 ∘C for 90 s, with a final
extension at 72 ∘C for 10 min to ensure complete amplification.
The PCR amplicons were quantified using PicoGreen (Invitrogen, Carlsbad, CA)
and a plate reader. Once quantified, various volumes of each of the
amplicons were pooled into a single tube for equal representation of each
sample. This pool was then cleaned using UltraClean PCR Clean-Up Kit (MO BIO
Laboratories, Inc.) and quantified using the Qubit (Invitrogen, Carlsbad,
CA). After quantification, the molarity of the pool was determined and
diluted to 2 nM, denatured, and then diluted to a final concentration of
4.0 pM with a 10 % PhiX spike for sequencing on the Illumina MiSeq via the
protocol with 2×150 base pairs.
Sequence data were processed with the bioinformatic software QIIME 1.9.1
(Caporaso et al., 2010) using a 16S RDS pipeline (Nelson et al., 2014) with
slight modifications. The subset of amplicon-based lineages identified as
genera of known methanogens and methanotrophs (Appendix A, Table A1) were
then further profiled for this study. Sub-samples were averaged to obtain
one mean value for each section within each core.
Ancillary measurements
Data from nearby NOAA meteorological stations WBAN:14813 and WBAN:14985
(https://www.ncdc.noaa.gov/cdr, last access: 8 November 2018) were used to obtain hourly and daily
averages of air temperature, precipitation, and atmospheric pressure. Eight
dip wells adjacent to the peepers (Mat-N, Mat-S, Tamarack-N, Tamarack-S,
Shrubs, Lagg, Res-N, and Res-S) were used for monthly measurements of water
level. Water level was measured continuously between June 2017 and October 2018 in four of the eight dip wells (Mat-S, Tamarack-S, Shrubs, Res-S).
Water levels at other locations were estimated based on an offset between
manual readings of water level. To calculate water levels we used HOBO
pressure sensors (Onset Computer Corporation, Bourne, MA) that were
corrected using atmospheric pressure data from the NOAA stations. Adjacent
to each peeper, we measured vertical profiles of dissolved oxygen two to four times
a year using a probe equipped with a fiber-optic sensor and a temperature
sensor (PreSens Precision Sensing GmbH, Regensburg, Germany). The probe was
inserted to a depth of 80 cm and allowed to stabilize for ca. 30 min. The
probe was then moved upwards in 10–20 cm increments to complete a profile up
to the level of the water table.
Water from the eight dip wells was sampled for chemical analysis roughly
monthly between 27 June and 3 October 2017 in four sampling events. All
dip wells were perforated at 60–90 cm below ground level to ensure that
water samples were collected from a consistent depth. Before collecting
water samples, dip wells were emptied completely using a vacuum syringe and
allowed to refill. When it was impossible to completely empty a dip well due
to rapid recharge, a volume of water equivalent to the volume of the
dip well was removed before collecting samples. Electrical conductivity (EC) and pH were measured in
the field using a YSI Pro1030 pH, conductivity, and salinity instrument. When
dip-well recharge was insufficient for EC and pH measurements in the field,
these measurements were made within 48 h in the lab using a YSI EcoSense
EC30A conductivity and TDS pen tester and a YSI EcoSense EH10A
pH and temperature pen tester. Water samples were then filtered using Whatman
binder-free glass microfiber 0.7 µm filters that had been combusted at
500 ∘C to remove organic contamination. Water samples were
stored in HDPE-coated bottles and frozen at -22∘C for 10 months prior to analysis. Inductively coupled plasma–optical emission
spectrometry (ICP-OES; US EPA, 2015b) was carried out using a Varian
Vista-MPX to measure concentration of Al, Ca, Fe, K, Mg, Mn, Na, P, S, and
Zn. Concentrations of NO3+NO2 nitrogen (measured as a combined
value) and NH4 nitrogen were determined by colorimetry using Lachat's
QuikChem® 8500 Series 2 Flow Injection Analysis System (US
EPA, 2015a). Quality assurance and quality control protocols were followed
for both the ICP-OES and flow injection analyses. Recoveries of matrix
spikes and serial dilutions were at least 75 % and 90 %,
respectively. The reporting limit (RL) for each batch of samples was the
lowest concentration in the calibration curve. The RL for NH4-N was 0.1 mgL-1, and the RL for all other analytes was 0.01–0.05 mgL-1. Where
concentrations were below the reporting limit, the measured concentration
was substituted with one-half the reporting limit. Check standards and
blanks were analyzed every 10 samples. Check standard recoveries did not
exceed ±10 % error, and blanks did not exceed reporting limits. No
blanks were allowed to exceed the reporting limits. Accuracy of pH and EC
measurements was ensured through regular calibration of equipment.
In 27 June 2017, one core from the Shrubs zone and one from the Res zone
were extracted for analysis of peat bulk density and porosity. The core was
sliced every 2.5 cm to a depth of 50 cm. Samples were packed and sealed in
plastic bags and taken to the laboratory to measure wet weight. Samples were
then dried in an oven at 60 ∘C for 2–3 d until the weight was
stable. Peat bulk density was calculated based on the weight of dry soil
occupied by slices of 2.5 cm×12 cm×12cm. Porosity was calculated as 1
minus the ratio of peat bulk density to soil particle density, which was
estimated as 1.45 Mgm-3 for Sphagnum peat soils (Oleszczuk and Truba, 2013).
Data analysis
Data preparation was completed in MATLAB (R2017b, Mathworks), and
statistical analyses were completed in R version 3.5.1 (R Development Core Team, 2018).
Differences in CH4 fluxes between hydro-biological zones were evaluated
using a linear mixed-effect model (lmm) through the function “lmer”
implemented in R in the package “lmerTest” version 3.0-1 (Kuznetsova et
al., 2017). Transformation of CH4 flux data to their logarithm base 10 was applied to improve the normality of the data and the normality of the
residuals of the model. The fixed effects in the model were Zone (Water, Mat,
Tamarack, Shrubs, and Res), a categorical value for year (Year), a categorical
value for the month of measurements (Month), temperature 10 cm below the surface
(Tsurf), mean water level for a month before the flux measurements (WLm), and a
continuous variable representing the time to noon in hours (t2noon). The transect (north or
south) was specified as a random effect. We also tested for the interactions
between Zone and Month, Zone and WLm, and Zone and Tsurf, but they were not significant. The final
statistical model for both CH4 flux is described in Eq. (5):
Flux∼Zone+Tsurf+WLm+Year+Month+t2noon+(1|Transect).
Pair-wise differences in emissions among the zones were evaluated through
testing differences in the marginal means of the reference grid of the mixed
model using the package “emmeans” in R (Lenth et al., 2018). The overall
effect of the factors within the model was evaluated with an ANOVA of the
model. Significance in the model was defined with a p value of 0.05. To
evaluate if plant fluxes were significantly different from zero, we used a
one-sample Wilcoxon test.
Pore-water concentrations of CH4 were evaluated using a linear mixed
model. We used a similar model to evaluate pore-water CH4 concentrations
except that we added depth to the surface (Depth) as a fixed effect (see Eq. 6). We deleted the interaction between depth and zone because it was not
significant. The final model for pore-water concentrations of CH4 is
described in Eq. (6):
CH4 Pore-water conc.∼Zone+Depth+WLm+Tsurf+Year+Month+(1|Transect).
Pair-wise differences in pore-water concentrations between zones were tested
by evaluating differences in the marginal means in the same way as for the
model of CH4 flux. The overall effect of the factors in the model was
evaluated with an ANOVA.
Chemical analyses were not included in the model, as chemistry data were only
available for 2017. Instead, a principal component analyses was run on the
chemical variables (12 chemical species plus EC and pH; Appendix A, Table A2) at the eight sampling locations, and the scores of the first principal
component were correlated to mean CH4 fluxes, mean CH4 pore-water
concentration, and mean CH4 transfer velocity. Differences in element
concentrations between different vegetation zones and between different
locations were evaluated using ANOVA. Pair-wise comparisons were evaluated
using a Tukey HSD (honestly significant difference) post hoc test. Differences in peat bulk density were
evaluated using an ANCOVA of zone and depth. The relationship between
microbiota and methane fluxes was evaluated through a correlation of the
ratio of the relative abundance of methanogens to the relative abundance of
methanotrophs versus CH4 flux mean CH4 pore-water concentration
and CH4 transfer velocity.
ResultsInter- and intra-annual variation in abiotic conditions
The mean air temperature during the growing season (1 May to
31 October) was 20.4 ∘C in 2017 and 22.5 ∘C in 2018, as
measured by standard meteorological stations. In 2017 and 2018, total
precipitation for the growing season was 196 and 356 mm, respectively
(Fig. 2). The water level ranged from -45.4 to 19.7 cm in 2017 and -55.1 to 27.3 cm
in 2018, where negative levels indicate a water table below the ground
surface (Fig. 2). As expected, the intermittently wetted area (Shrubs, Lagg, and Res zones) experienced substantial fluctuations in water level,
while in the permanently wetted area (Tamarack) the water level remained at
or close to the surface (Fig. 2). Fluctuations were smaller in the Tamarack
zone, with the water table drawing down to a maximum depth of 12 cm compared
to a maximum of 53 cm in the Shrubs (Fig. 2).
Water-level (WL) fluctuations in the tamarack mixed woodland (Tam)
zone, the mixed ericaceous shrub (Shrubs) zone, and the restored (Res) zone
of the bog. Vertical dashed lines indicated the 10 times of pore-water
sampling, and the solid lines indicate the two times of core sampling: gray
for Tam, Shrubs, and Res-S and teal for Mat and Res-N. The secondary axis
shows daily values of precipitation.
pH was similar throughout the bog, with higher values occurring in the
restored (Res) zone than in the undisturbed zone (Appendix A, Table A2), but
with no significant differences among the hydro-biological zones (F=0.98, p=0.43). The Lagg zone had significantly higher concentrations of
Fe, Ca, Mg, and Mn when compared to other hydro-biological zones (p<0.05 for all paired relationships). The restored section had
significantly higher concentration of Mn (F=3.80, p=0.01) and Na (F=3.78, p=0.01). Concentrations of Ca and P tended to be higher in the
restored section as well; however, the differences were not significant when
comparing all hydro-biological zones (F=2.88, 2.47; p=0.05, 0.07;
respectively). Interestingly, concentrations of ammonia (NH4+) were
significantly higher in the Tamarack zone (F=10.6, p<0.001)
than in all the other zones, while concentrations of nitrate (NO3-) were
generally low and did not significantly differ among zones (F=0.05, p=0.91).
The northern section of the bog collected runoff from adjacent agricultural
fields and, consequentially, had higher pH, electrical conductivity, and
concentration of elements including S, Na, Mn, Mg, Fe, and Ca than the rest
of the bog. Notably, when considering location-wise comparisons, the
concentrations of S, Ca, and Mn were significantly higher in Tamarack-N
than in all other locations of the undisturbed bog (Appendix A, Table A2).
Location differences also occurred in the restored section. pH was
significantly higher in the Res-N location (p<0.05), and P was
significantly higher in the Res-S location (p<0.05).
Vertical profiles of dissolved oxygen confirmed the existence of anoxic
conditions below the water level. Dissolved oxygen concentrations below the
water level were always less than 0.1 mgL-1, whereas above the water
level the concentration increased sharply. The only exception was the
profiles taken at the Mat, which had an average dissolved oxygen
concentration of 0.27 mgL-1.
Peat bulk density was significantly lower in the Shrubs than in the Res zone
(F=34.5, p<0.001), with averages ±SD of 0.08±0.02 and 0.12±0.03gm-3, respectively. Calculated porosities
assuming a peat particle density of 1.45 gm-3 (Oleszczuk and Truba,
2013) were equal to 94.5 % and 91.8 %. Because the peat was
saturated at the time of extraction this porosity is equivalent to the
volumetric water content. There was not a significant effect of depth on
peat bulk density (F=0.05, p=0.82).
CH4 fluxes for the different hydro-biological zones in
Flatiron Lake Bog. Integrated fluxes are based on a 122 d period for 2017
and 149 d period for 2018. Values in parenthesis for mean fluxes are the
standard error and for the subsequent rows the propagated standard error.
a BB: blueberry leaves occupy the area of the Tamarack, Shrubs, and Res
zones. Fluxes from other plant species and from the Lagg zone were not
significantly different from zero.b Total emissions per zone (mgCH4d-1) were added and divided by
the area of the bog (excluding the Lagg zone) to produce the final result of
315.4±166mgCH4m-2d-1 in 2017 and 362.3±687mgCH4m-2d-1 in 2018.n/a: not applicable.
The effect of different hydro-biological zones and water level on
CH4 emissions
There were higher CH4 emissions towards the central, permanently wetted
part of the bog (Table 1). The fluxes from the Lagg zone were not
significantly different than zero (t test, p=0.185) and were therefore
excluded for future comparisons among zones. Mean CH4 fluxes were
significantly different between hydro-biological zones (F=1.14, p<0.001). The fluxes from the Water zone were not significantly
higher than the fluxes from the other units within the permanently wetted
area (t ratio =-1.45, p=0.59, and t ratio =1.27, p=0.70, for Mat
and Tamarack, respectively), but they were significantly higher than fluxes
in the intermittently wetted area (Shrubs zone: t ratio =5.83, p<0.001; Res zone: t ratio =6.53, p<0.001). CH4
emissions from the restored section were significantly lower than the
emissions from units in the permanently wetted area and the Mat (t ratio =-4.6, p<0.001) and Tamarack zones (t ratio =-6.1, p<0.001). However, CH4 emissions from the restored section were
not significantly different from the emissions from the Shrubs zone (t ratio =-0.17, p=0.99).
Mean water level (WLm) had a significant effect on CH4 flux (F=8.49,
p=0.003), with higher emissions occurring when WLm was more positive (higher
WLm). The effect of water level on CH4 fluxes was not significant when
considering instantaneous water levels at the time of the measurements but
was significant when considering the average water-level data throughout the 30 d prior to the flux measurement. The effect of temperature (Tsurf) was not
significant (F=0.71, p=0.40).
Monthly fluxes of methane for each of the five hydro-biological
zones of the study. Fluxes from the Lagg were not significantly different
than zero and are therefore not shown. Standard errors are for all sample
locations within the same month and zone (variable number; see Sect. 2.3).
Temporal variations in CH4 fluxes
There was a substantial temporal variability in CH4 fluxes. The open
water zone was the only zone that had a distinct and consistent seasonal
cycle, where the fluxes increased from May to the middle of the growing
season, peaking in early September and declining in October
(Fig. 3). In the Tamarack zone, fluxes declined
over the growing season in 2017, but in 2018 the flux peaked in early
September, where there were two extremely high flux measurements at the
northern transect of 27 180 and 8605 nmolm-2s-1 that skewed the
average to a total of 6748 nmolm-2s-1. There was no significant
relationship between month of measurement and CH4 flux (F=2.21, p=0.05). Across all hydro-biological zones, CH4 fluxes were not
significantly different in 2017 and 2018 (F=2.59, p=0.11).
Diurnal patterns of CH4 emission measured over a 24 h
period in September 2018. Note a smaller y axis maximum in the Shrubs zone.
Error bars represent the standard error of four individual chamber measurements
within the same 30 min period at each location. Secondary axis (and black
lines) shows the temperature at 10 cm below the surface either in the open
water (Water) or in the peat (Mat, Tamarack, and Shrubs). The Res zone was
not sampled.
Although the relationship of CH4 emissions with time to noon was
significant (F=13.1, p<0.001), the diurnal measurements from
September 2018 (Fig. 4) did not indicate strong diurnal patterns of CH4
emissions. In the open water zone, CH4 emissions decreased during the
late afternoon to early evening, which approximately coincided with a peak in
water surface temperature (Fig. 4). In the Tamarack zone emissions increased
with warmer temperatures in the afternoon. In the Mat zone there was a peak
in the middle of the morning, but there was no apparent relationship with
surface temperature. There was no clear diurnal pattern of CH4
emissions in the Shrubs zone, likely a consequence of very low CH4
emissions during the time of measurements.
Plant-mediated CH4 fluxes from loosestrife leaves (Mat
zone), tamarack leaves, tamarack Stems, and blueberry leaves (Tamarack and
Shrubs zone). Only fluxes from the blueberry were significantly different
from zero (p=0.01).
Plants fluxes and upscaling of CH4 emissions
Fluxes from plant tissues were negligible compared to the fluxes from the
peat or open water surfaces (Fig. 5). Measurements from loosestrife, the
most abundant vascular plant in the Mat, and from tamarack stems and stems
were not significantly different from zero (p=0.83, p=0.48, and p=0.06, respectively). Fluxes from the blueberry leaves were significantly
different than zero (p=0.01) and averaged at -1.11nmolm-2s-1,
indicating net uptake of CH4 by or through blueberry plants (Fig. 5).
The peat bog emitted a total of 4.8±1.9 and 5.5±8.4t of
CH4 during the growing seasons of 2017 and 2018, respectively. The high
uncertainty in 2018 was due to the larger variation in fluxes produced by
high fluxes in the Tamarack zone, which emitted a total of 0.12±0.14t of CH4 in 2017 but a much higher 5.4±8.2t of CH4
in 2018.
Blueberry leaves acted as a slight sink of atmospheric CH4, with a mean
flux of -1.11nmolm-2s-1. The total sink of CH4 from
blueberry bushes was equal to -46.9±20 and -57.4±24kg of
CH4 for 2017 and 2018, respectively. These values were equal to a small
offset of the total daily emissions by 0.37 % for 2017 and 0.14 % for
2018.
Because the length of the measurement periods in the growing seasons was
not equal among years, total emissions (Table 1) were divided by the length
of the measurement period to produce estimates of mean total flux per day.
These values were then divided by the area of the bog (excluding the Lagg
zone) to produce the final result of 315.4±166mgCH4m-2d-1 in 2017 and 362.3±687mgCH4m-2d-1 in
2018.
Vertical profiles of CH4 pore-water concentrations by zone.
The error bars represent the standard deviation of the monthly measurements
for 2017 and 2018 combined. A minor y-axis jitter has been added to more
clearly distinguish zone patterns. Note that the concentrations in the
Tamarack zone at depth approach saturation (1.44 mM at 20 ∘CCH4).
Dissolved CH4 pore-water concentrations and methane transfer
velocity
Excluding the Mat zone, the mean CH4 pore-water concentration per zone
followed a pattern similar to the fluxes, with higher concentrations in the
Tamarack zone, followed by Shrubs, Res, and Lagg zones (Fig. 6). Pore-water
CH4 concentrations were significantly higher in the Tamarack zone than
in the Mat zone (t ratio =3.3, p=0.003) and in the Shrubs zone
(t ratio =6.4, p<0.001). Pore-water CH4 concentrations were
significantly lower in the Res zone than in the Mat zone (t ratio =-7.2,
p<0.001), the Tamarack zone (t ratio =-17.1, p<0.001),
and the Shrubs zone (t ratio =-6.8, p<0.001) but not
significantly different from concentrations in the Lagg (t ratio =0.28, p=0.77; Fig. 6). Differences in CH4 pore-water concentration between
Mat and Shrubs zones were not significant (t ratio =1.98, p=0.19). It
is important to note that times for which the water table was below the
level of a certain peeper sampling window were considered to be missing values because there was no pore water
at that given height.
There was a significant relationship between CH4 concentration and
depth (F=85.3, p<0.001), with pore-water concentrations of CH4
increasing with depth. CH4 pore-water increased significantly with
increasing temperature 10 cm below the surface at the time of measurement
(Tsurf) (F=20.9, p<0.001) and with the average water level during
the month preceding the measurement (WLm; F=16.2, p<0.001).
Higher water tables were associated with increased CH4 pore-water
concentration throughout the whole profile.
Per location, average (mean±SD) CH4 pore-water concentration in
the top 50 cm of the peat was the highest in Tamarack-S (0.86±0.62mM), followed by Tamarack-N (0.76±0.36mM), Shrubs (0.30±0.26mM), Mat-N (0.21±0.12mM), Mat-S (0.19±0.12mM), Res-N
(0.14±0.13mM), Lagg (0.10±0.08mM), and Res-S (0.09±0.08mM). Ammonium concentration was positively correlated with CH4
pore-water concentration averaged for the whole profile (r2=0.70,
p=0.005) and for the top peat layer (r2=0.83, p<0.01).
CH4 pore-water concentrations were significantly different among
months, with concentrations always lower in May (p<0.001 for all
paired relationships) and June (p<0.001 for all paired
relationships) and higher in August, around the peak of the growing season,
and October, at the end of the growing season. CH4 pore-water
concentrations were significantly higher in 2018 than in 2017 (F=24.9, p<0.001).
Overall, there was no significant relationship between average concentration
and surface fluxes (r2<0.01, p=0.95), even when
considering only the top layers of the peat column, where a better
relationship was expected (r2=0.08, p=0.11; Fig. 7a). The lack
of a relationship was surprising, as the values included only those times at
which the top stratigraphic layer of the peat was saturated. Methane
transfer velocity was calculated from these data (Fig. 7b) and from the
times when the microbiology data were available for comparison (Fig. 7c).
(a) Relationship between CH4 pore-water concentration and
CH4 flux for times where the WL was high and within the top
stratigraphic layer of the peat. (b)CH4 transfer velocity calculated
from the upper plot; (c) same as previous, but with the data relevant for
microbial analysis only. Note that microbial samples for Tam-N, Mat-S, and
Lagg are not available and therefore not used in the following comparisons
of CH4 transfer velocity against microbial activity. The error bars are
the standard error.
The first principal component (PC1) of the chemical analytes explained
37.6 % of the variation in the dataset, while the second explained 28.5 %
(Appendix B, Fig. B1). The 10 variables that contributed the most to PC1
were, in order, Mn, Ca, Mg, S, P, Al, EC, NA, NO3, and K. There was no
significant relationship between PC1 and CH4 flux (r2=0.17, p=0.17), CH4 pore-water concentration (r2=0.15, p=0.80),
or CH4 transfer velocity (r2=0.12, p=0.65).
Both methanogens and methanotrophs were more abundant in
permanently wetted zones
Overall, both methanogens and methanotrophs were at higher relative
abundances (as a portion of the overall microbial communities) in the
permanently wetted zones Mat-S and Tamarack-S (where they accounted for 1.8 %
and 2.0 % of the microbial communities, respectively, by amplicon
percentages) than in the intermittently wetted zones Shrubs, Res-N, and
Res-S (0.2 %, 0.1 %, and 0.1 %, respectively; Fig. 8). In addition,
hydrogenotrophic methanogens (Methanobacterium and Methanoregula) were much more abundant than
acetoclastic methanogens (Methanosaeta and Methanosarcina) at all sites (Fig. 8). Among the
hydrogenotrophs, Methanobacterium was broadly present, while Methanoregula was generally a larger
component of the methanogen community in saturated, undisturbed peat (Mat-S,
Tamarack-S, and deep Shrubs). Among the acetoclastic methanogens Methanosaeta was observed only in
the permanently wetted zone Mat-S and Tamarack-S and accounted for a small
proportion of total methanogens except at 50 cm in Mat-S. In the restored
zones, where acetoclastic methanogens had higher relative abundances, the genus Methanosarcina was
predominant.
Relative abundances of methanogens and methanotrophs in the Mat-S,
Tam-S, Shrubs, Res-N, and Res-S zones of the bog at different depths in the
peat column, with the mean water level from June 2017 through August 2017
(mean WL) and the water level at time of sampling (core WL; in Mat-S these
were both at 0 cm; in Tam-S, the mean WL was at 0 cm). Panel (a) shows
overall methanogen (Mgens) and methanotroph (Mtrophs) abundances along
with the average dissolved oxygen profile over the preceding month (from
coring; see Methods). The observed genera of methanogens and methanotrophs
are shown in panels (b) and (c), respectively, with variable
x axes. Methanobacterium (Mbac) and Methanoregula (Mreg) are hydrogenotrophic methanogens, and
Methanosaeta (Msaet) and Methanosarcina (Msarc) are acetoclastic methanogens. Methylomonas (Mmonas) and
Methylosinus (Msinus) are methanotrophs.
Methanotrophs were mostly present in the permanently flooded zones Mat-S and
Tamarack-S and were particularly abundant in peat strata closer to the
surface (0–20 cm). Methylomonas accounted for most of the methanotroph sequences found
in this study and dominated the methanotrophs of the Mat-S and Tamarack-S
zones, while Methylosinus was a much larger portion of the methanotrophs in the Shrubs
and Res zones (Fig. 8) even as overall methanotroph relative abundance
dropped to less than 0.05 % of the microbial community.
Methane fluxes were not correlated to the relative abundance of methanogens
(r2=0.01, p=0.74) or methanotrophs (r2=0.01, p=0.78). In addition, mean CH4 concentrations were also not correlated to
the relative abundance of methanogens (r2=0.01, p=0.83) or
methanotrophs (r2=0.01, p=0.70). However, for the principal
coordinates analysis of sites based on geochemistry, PC1 was significantly
negatively correlated to methanogens' relative abundance (r2=0.90,
p<0.01). As indicated above, most of the variation in PC1 was
driven by Mn, Ca, Mg, and S, and there was a significant relationship between
mean methanogen relative abundance and manganese (r2=0.90, p=0.007) and sulfur concentrations (r2=0.74, p=0.03). When
considering only the bottom 25 cm of the peat profile, the layer from which
pore water was taken for chemical analyses, methanogen relative abundance
was negatively correlated to electrical conductivity (r2=0.85, p=0.01). In the top layer of the peat, where methanotrophs are more
active, there was a negative correlation between methanotroph relative
abundance and magnesium concentration (r2=0.79, p=0.03).
DiscussionThe CH4 budget and its heterogeneity among hydro-biological zones
There were relatively high CH4 emissions in Flatiron Lake Bog compared
to previously reported fluxes in other northern peatlands. Average daily
CH4 emissions were equal to 315.4±166mgCH4m-2d-1 in 2017 and 362.3±687mgCH4m-2d-1 in
2018. These values were higher than emissions in ombrotrophic peat bogs in
Minnesota (monthly average range: 27–240 mgCH4m-2d-1; Chasar et al., 2000; 117 mgCH4m-2d-1; Dise, 1993) and
Michigan (0.6–209 mgCH4m-2d-1; Shannon and White, 1994)
and in a boreal bog in northern Quebec (57 mgCH4m-2d-1; Nadeau et al., 2013). Higher CH4 fluxes compared to other bogs are
likely the result of the higher temperatures experienced in Ohio, which are
at the southern limit of northern peatland distribution.
Methane fluxes were highly heterogeneous, with a variation of over 4 orders
of magnitude and with a skewed distribution due to extreme events of
CH4 flux (median: 33.7 nmolm-2s-1; range: -12.2–27 186 nmolm-2s-1). The skewed distribution of CH4 fluxes and
heterogeneity has also been found by Christen et al. (2016) in a Canadian
undisturbed scrub-pine Sphagnum bog (median: 42 nmolm-2s-1; range: 5–3500 nmolm-2s-1) and by Treat et al. (2007) in temperate fen in
New Hampshire (range: 6.3–2772 nmolm-2s-1). We found higher
emissions in the open water (mean: 122; median: 61.9; range: 0.14–1823 nmolm-2s-1) than in the other hydro-biological zones. This pattern
was also found by Christen et al. (2016), who found that fluxes from open
waters or ponds had an average of 3336 nmolm-2s-1 and a median
value of 2670 nmolm-2s-1 compared to collars on the ground
containing vegetation that had mean and median values of 986 and 47 nmolm-2s-1, respectively. In an analysis of a variety in peatlands in
Minnesota, Crill et al. (1988) also found that mean CH4 emissions were
294 mgm-2d-1 in open bogs, while in forested bogs the mean was
equal to 77 mgm-2d-1. This result agrees with our calculations,
where we find daily normalized fluxes averaged for both years of 279 mgCH4m-2d-1 in open water and 224.72 mgCH4m-2d-1 in the mixed ericaceous shrub units.
There were extremely high CH4 flux measurements from the northern
transect of the Tamarack zone in September 2018 (27 180 and 8605 nmolm-2s-1) and in October 2018 (2808 and 6609 nmolm-2s-1). These measurements were not ebullition events, since the increase
in concentration with time was steady (Appendix B, Fig. B2) and the
coefficient of correlation for both flux events was higher than 0.97. They
were not localized events either, since the two collars were about 1.5 m
apart from each other. Unfortunately, a core was not taken at the northern
transect where this event occurred, so the abundance of methanogens and
methanotrophs could not be tested. Interestingly, the concentration of
sulfur was significantly higher in this zone, indicating that the Tamarack-N
possesses an environment that is highly reduced where both methanogenesis
and sulfate reduction take place at extremely high rates. This was
corroborated by the detection of a potent smell of hydrogen sulfide while
measuring these extremely high CH4 fluxes. It is also possible that specific
plant–soil relationships, such as higher polysaccharides in the form of
tree-root exudates (Lai, 2009), have enhanced CH4 production in the Tamarack
zone. However, more research on the characteristics of the peat at this site
is needed to reach conclusions about these extreme events.
Although higher heterogeneity in CH4 fluxes within peat bogs can be
encountered, it is likely that the same patterns of CH4 flux along
hydro-biological zones occur in other kettle-hole peat bogs due to the tight
relationships between water-level fluctuations and vegetation composition in
these ecosystems (Malhotra et al., 2016). It is also possible that the
higher rates of CH4 emission in this Ohio peat bog are replicated in
similar peat bogs located at lower latitudes, where warmer temperatures have
the potential to not only drive much higher productivity (Cai and Yu, 2011)
but also increase methane emissions due to the effect of higher temperatures
on CH4 emissions in peatlands (Moore and Dalva, 1993; Pugh et al.,
2018).
The role of plants in the CH4 cycle in peat bogs
The presence of different plant species was strongly associated with
variations in CH4 emissions in peatlands. For example, the presence of
sedges, such as Eriophorum vaginatum L., in ombrotrophic peat bogs was observed to be an important
transport of CH4 to the atmosphere (Greenup et al.,
2000). In our study site, however, there was no active plant transport of
CH4. This lack of plant transport in ombrotrophic peat bogs has also
been reported by Chasar et al. (2000) and can be likely attributed to a low
abundance of sedges.
Lai et al. (2014) found that fluxes varied significantly among plant
communities at the ombrotrophic Mer Bleue bog in Canada. In this bog, low
fluxes were found in Chamaedaphne (32–22 mgCH4m-2d-1) and
Maianthemum and Ledum (83–53 mgCH4m-2d-1) communities, whereas the highest were
found in the Eriophorum-dominated community (122–124 mgCH4m-2d-1).
The magnitude of these fluxes was much lower than the average daily
emissions from the mixed ericaceous shrubs of 224.72 mgCH4m-2d-1.
Interestingly, we found that blueberry plants were slight but statistically
significant sinks of CH4. This result was also reported by Sundqvist et
al. (2012), who found that boreal plants of spruce (Picea abies), birch (Betula pubescens), rowan
(Sorbus aucuparia), and pine (Pinus sylvestris) showed a net uptake of CH4. The values found by
Sundqvist et al. (2012) fluctuated between 1 and 2 nmolm-2s-1, which
is similar to the values found in this study. The mechanism behind this
process is still uncertain, but it has been reported that this process could
be mediated by epiphytic bacteria capable of consuming CH4
(Raghoebarsing et al., 2005). Sundqvist et al. (2012) believe that the
response is mediated by gross primary productivity and stomatal conductance through mechanisms not
yet understood.
We did not find a clear diurnal pattern of CH4 emissions in the bog.
Similarly, summer season measurements of eddy covariance in an ombrotrophic
bog did not find clear diurnal patterns either (Nadeau et al., 2013). In
contrast, studies in other wetlands have found a mid-morning peak in
CH4 emissions in fen (Whiting and Chanton, 1992) and marshes (Kim et
al., 1999; Rey-Sanchez et al., 2018; Van der Nat et al., 1998). This
discrepancy is likely due to the fact that CH4 emissions in marshes
(Chu et al., 2014; Hatala et al., 2012; Morin et al., 2014, 2017), and in
fen (Chasar et al., 2000; Treat et al., 2007; Waddington and Day, 2007),
are largely dominated by plants that transport CH4 through their
aerenchyma.
Fluctuations in water level explain variability in CH4 emissions
Methane fluxes were different among hydro-biological zones, but given that
plants were not a pathway of CH4 flux, the reported differences were
most likely driven by the water-level differences among hydro-biological
zones. The length of dry conditions preceding permanently wetted conditions
has important consequences for the magnitude of CH4 fluxes (Turetsky et
al., 2014). While the highest CH4 flux occurs after a period of 30 d
of antecedent wet conditions (Turetsky et al., 2014), longer dry periods
reduce the capacity of methanogens to acclimate to stable environmental
conditions, therefore reducing methanogenesis. Indeed, we found that the
average water-level data throughout the 30 d prior to the flux measurement,
not the instantaneous water level, had a significant effect in CH4 fluxes.
We hypothesize that this is a general ecological response by which community
composition lags behind environmental change. In our case, it may take
several weeks for methanogens to acclimate to new water levels after the
water level has been raised, therefore not responding to instantaneous
changes in water level. Both Res and Shrubs zones were characterized by high
fluctuations in water level, which was likely the cause of lower CH4
emissions in these zones when compared to the more permanently wetted
Tamarack, Mat, and Water zones. Higher WL fluctuations in the Shrubs zones in
2018 (range: -40.4–6.1 cm) than in 2017 (range: -31.6–8.0 cm) could also
explain the higher CH4 emissions in 2017 than in 2018 in the Shrubs
zone.
Our conclusion is that methanogen inhibition associated with longer dry
periods in the Shrubs and Res zones is likely the cause of lower CH4
emissions. However, reduced CH4 emissions are also the result of an
increase in the amount of methanotrophy in the upper, oxic layers. We can
confirm this, as we observed pore-water concentrations of CH4 that were
much higher in the Shrubs zone than in the Res zone despite similar WL
fluctuation. Yet the fluxes were not significantly different between these
two zones, indicating higher levels of methanotrophy in the Shrubs zones.
Indeed, methanotroph relative abundance in the top section was twice as much
in the Shrubs zone than in the Res zone.
We did not find a significant correlation between CH4 flux and surface
temperature. This is partially explained by the fact that the effect of
temperature on peatland CH4 emissions is significant when the water
table is near the surface (Strack and Zuback, 2013), and our site had
significant water-level fluctuations. For example, Lai et al. (2014) found
that the relationship between temperature and CH4 flux was only
significant when the water table was less than 30 cm depth in average. It is
possible that due to monthly variations in the water level in the Shrubs and
Tamarack sites, the response of CH4 emissions to temperature was
confounded. The temporal resolution of the measurements was also a reason
for the lack of correlation. At a higher temporal resolution, such as the
measurements of the diurnal pattern, the effect of temperature on CH4
emissions may be more easily discerned.
Pore-water CH4 concentrations were higher in the undisturbed
section
Pore-water CH4 concentration was high throughout the undisturbed
section of the bog and significantly lower in the restored section. Although
concentrations of key electron acceptors, such as nitrates or sulfates, were
low and not significantly different among zones, we found that the restored
section had significantly higher concentration of Mn (F=3.80, p=0.01) and Na (F=3.78, p=0.01), suggesting that bacterial manganese
reduction could compete against methanogens in the restored zone.
Excluding the Mat zone, pore-water CH4 concentration followed a similar
pattern of variation to the fluxes, with higher concentrations in the
Tamarack zone followed by Shrubs, Res, and Lagg zones. Low concentrations but
higher fluxes in the Mat zones indicate a higher CH4 transfer velocity.
This could be the consequence of different porosities in the peat that
affect the rate of transfer. However, because the porosity throughout the
peat bog was uniform, it is likely that CH4 transfer velocity is being
driven by microbial activity rather than physical properties (see Sect. 4.6).
Pore-water CH4 concentration was the highest in the Tamarack zone, with
concentration at deeper levels close to the saturation point (1.2 mM).
Similarly, in a study in an ombrotrophic peat bog in Minnesota, Chasar et
al. (2000) reported high CH4 pore-water concentrations in bogs of 1.2 and 1.5 mM for pore water at about 1 m of depth for June and July,
respectively. Chasar et al. (2000) also reported much higher pore-water
CH4 concentrations in bogs than in fen and suggested that this is
related to negligible plant transport in peat bogs that causes CH4 to
accumulate in the pore water, diffuse upwards, and be oxidized in the top
layers of the peat. Methanotrophy in the shallow layers of the peat was also
reported by Chasar et al. (2000), where analysis of isotopes in shallow pore
water versus associated fluxes indicated oxidation of CH4 in the
pore water before diffusive transport to the atmosphere.
Concurrent measurements of pore-water CO2 concentrations indicated that
the CH4:CO2 ratio was similar at the top of the profiles, while at
the bottom of the profiles there was a clear difference between restored and
undisturbed sites (Appendix B, Fig. B3). This difference could indicate that
there is a higher competition for respiratory processes in the disturbed
section, while methanogenesis is more favored in the undisturbed section.
The analysis of CO2 fluxes is not, however, within the scope of this
paper and is presented here only as a preamble for future studies.
Methane-cycler abundance depends on vegetation zone and water level
Consistent with expectations based on their anaerobic lifestyle, we found
higher relative abundances of methanogens in the permanently wetted areas
Mat and Tamarack than in the intermittently wetted areas (Shrubs, Res-N, and
Res-S). Hydrogenotrophic methanogens, which are typically dominant in
nutrient-poor sites (Kelly et al., 1992; Kim et al., 2008; Kotsyurbenko et
al., 2004) and are typical of Sphagnum-dominated bogs (Chasar et al., 2000;
Kelly et al., 1992; Lansdown et al., 1992), dominated both the undisturbed
and restored sections, while acetoclastic methanogens were rare and only
slightly more common in the restored section. We hypothesized that the
restored section had gained more nutrients due to higher degree of
mineralization; however, the dominance of hydrogenotrophic methanogens
suggests that the restored section may still be nutrient-poor, despite the
disturbance and apparent mineralization of the soil. This is also evident in
the low concentration of key constituents, such as nitrates, iron, ammonium,
phosphorous, and magnesium (although note relatively higher concentrations of
manganese and calcium in the restored section; Appendix A, Table A2). It is
possible that 15 years of restoration efforts have effectively restored this
section's trophic status and that acetoclastic methanogenesis was higher there in the past.
Alternately, the original disturbance may have had minimal impact on the
microbial composition such that the restored section retains a community
similar to its pre-disturbance state, when it was part of the Shrubs zone of
the then-undisturbed section. Basiliko et al. (2013) similarly found that
mining-based disturbance and subsequent restoration of Canadian peatlands
did not affect archaeal microbial community composition.
At the genus level, however, there were differences in methanogen
composition between the undisturbed and restored sections. While
hydrogenotrophic genera strongly dominated both, there was a shift from
Methanoregula-dominated communities in the undisturbed sections to strongly
Methanobacterium-dominated communities in the restored sections. Based on our prediction of
a higher nutrient status in the restored site, we would have expected the
opposite trend in Methanoregula dominance, since Methanomicrobiales (the order containing Methanoregula) have been
observed as preferring nutrient-rich sites (Godin et al., 2012); their dominance
is further indication that the restored section is not as high in nutrients as
we expected. In contrast to the hydrogenotrophs, the acetoclastic methanogens did not
show genus-level differences from undisturbed to restored zones but rather
from inundated (Mat-S and Tamarack-S) to intermittently flooded (Shrubs,
Res-N, and Res-S) ones. When acetoclastic methanogens were present, Methanosaeta dominated their
community, consistent with observations of Methanosaeta in nutrient-poor acidic sites
(Godin et al., 2012). However, in the inundated zones,
Methanosarcina was also present. This is actually the opposite pattern we would have
expected based purely on likely oxygen concentrations, as Methanosaeta
typically dominates anaerobic environments while Methanosarcina can produce
methane under partially oxic conditions (Angel et al., 2011). We therefore
interpret Methanosaeta's presence in FSL-S and TMW-S to arise from its
greater metabolic versatility – in addition to acetate, it can also use CO2
or methylated compounds (Liu and Whitman, 2008) – and thus conclude that these sites
may have distinct substrate profiles.
Methanotrophic lineages, like methanogens, were at the highest relative
abundances in the undisturbed, inundated sites, where they primarily
occurred near the peat surface. The higher abundance of methanotrophs in
inundated zones may be related to the presence of Sphagnum mosses in these zones, as
methanotrophs are a common, abundant member of the Sphagnum microbiome (Dedysh,
2011; Kostka et al., 2016); the DNA extraction method may have accessed
microbiota on and within the moss as well as from the bulk peat.
Alternatively the higher methanotrophs in the inundated sections may have
been in the bulk peat and may simply be due to the higher supply of methane in
those areas.
Microbiota drive CH4 transfer velocity
While methanogens control the production of CH4 through the peat
column, methanotrophs interact with plants and physical processes to mediate
the portion of produced CH4 that is oxidized before being emitted to
atmosphere. We therefore examined the relationship between resident CH4
cyclers and the CH4 transfer velocity in the top stratigraphic layer of
the peat. Generally, when the water level is near the surface, CH4
diffuses directly from the surface pore water to the overlying air and can
also be transported via plant tissue. However, at our site, we measured no
significant CH4 transport through vascular plants (see Sect. 4.2).
Therefore, the transport pathway at the upper layers of the soil in all
zones (except Water) should occur through the ubiquitous Sphagnum mat and thus have
similar resistance throughout the site. We also found no significant
correlation between CH4 pore-water concentration at the top soil layers
and surface CH4 flux (Fig. 7a). Thus, with all zones expected to be
similar in their physical transport processes, observed differences in
CH4 transfer velocity among zones should represent differences in
microbial processes. Indeed, we found a significant correlation between
CH4 transfer velocity and the ratio of total methanogens to total
methanotrophs (r2=0.33, p=0.03; based on the relative abundances
of lineages in each functional guild, see Methods; Fig. 9). A related
result was reported in a rice paddy system, where the ratio of the gene
expression of the two diagnostic marker genes for methanogenesis and
methanotrophy, mcrA and pmoA, in the upper 10 cm of the soil was highly correlated to
CH4 flux (Lee et al., 2014). However, in our site, this correlation was
not significant when using CH4 flux data alone (r2=0.01, p=0.75) or pore-water data alone (r2=0.03, p=0.57). It is
intriguing that, despite the fact that presence does not necessarily imply
activity, and relative abundances do not represent absolute abundances, in
our study we see this relationship between the 16S rRNA gene amplicons of
known methanogenic and methanotrophic lineages and the CH4 fluxes in
both undisturbed and restored peatlands. This result illustrates the utility
of examining microbiota to explain differences between CH4 production
and emissions to the atmosphere.
Relationship between the ratio of the relative abundance of
methanogenic–methanotrophic lineages (Mgen/Mtroph) and the CH4
transfer velocity in the top stratigraphic layer of the peat profile: 0–6.7 cm for Mat and 0–12.5 cm for the other zones. CH4 transfer velocity
was calculated as the average for the 3 months prior to coring, during
which the water level was within or above the top stratigraphic layer.
Conclusions
Flatiron Lake Bog had high rates of CH4 emission that included several
extreme fluxes from the Tamarack mixed woodland zone that were not driven by ebullition.
CH4 emissions decreased with distance from the center of the bog, from
regularly wetted sections to those that had higher water-level fluctuations.
Pore-water concentrations followed a similar pattern of increase with depth,
except for the Mat zone, which is adjacent to the open water and thus has
better vertical mixing. Longer dry periods in the Shrubs and Res zone likely
inhibited methanogens, lowering their abundance and thus decreasing CH4
accumulation in the pore water and associated emissions. Although pore-water
chemistry explained some of the variation in pore-water CH4
concentration, water level explained the largest component of variation in
CH4 fluxes due to its effects on methanogenesis and methanotrophy at
the top soil levels. Given that plants were not an appreciable pathway of
CH4 flux, the reported differences in CH4 transfer velocity when
the water level was high were explained by the ratio of the relative
abundance of methanogens to methanotrophs in the top layer.
Why would two locations with similar near-surface CH4 concentrations
have different fluxes if they also have similar diffusivities and negligible
ebullition and plant transport? Our results show that the answer is that they
have different transfer velocities for CH4. Transfer velocities are
normally a function of wind speed, but beneath the shrub and tree canopy of
peat bogs wind speeds are very low, so something else is affecting this
transfer velocity. The upper layer of the bog's peat mass is a dynamic
region with both methanotrophs and methanogens living within the oxic layer
(Angle et al., 2017). Within this layer, higher abundance of methanogens
drives higher transfer velocities if the concentration of CH4 is assumed
to be at a quasi-steady state. At the same time, however, methanotrophs
consume much of the methane produced. Therefore, methanogen abundance, when
normalized by methanotroph abundance, can explain CH4 transfer velocity
differences in a peat bog where diffusive transport from pore water in
saturated layers is dominant. We conclude that microbial communities, and
their control by variation in water table depth, are the key drivers of
variability in CH4 fluxes across multiple hydro-biological zones in
kettle-hole peat bogs. Future research should examine whether such patterns
can be confirmed in other ecosystems where plant-mediated transport of
CH4 is low.
Code and data availability
The data and the code used in this paper are available upon request.
Subset of amplicon-based lineages identified as genera of known
methanogens and methanotrophs The genera found in the study are shown in
bold letters.
Water chemistry of the pore water in the eight locations of the
study. The means (SD) are averages of four measurements taken throughout the
growing season of 2017. Asterisks indicate means that are significantly
higher than at least one other mean.
Principal component analysis of the 14 variables listed in Table A2.
Chamber measurement during the September hotspot in the Tam-N
location. Note the steady increase in concentration that indicates that
ebullition was not the reason for the high magnitude of the flux at this
location.
Vertical profiles of CH4 and CO2 pore-water
concentrations (a, b) and the resulting CH4:CO2 ratios (c).
Author contributions
CRS, GB, and GMD designed the experiments. CRS, JS, RGA, and YH conducted
field and laboratory observations. VIR and YFL generated the microbial
analyses. CRS prepared the paper, with contributions from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Bryan Cassidy, Dominique Hadad, Anna Thompson, Julio Quevedo,
Austin Rechner, and Alexa Baratucci for their assistance in the field in
2017 and Tasmina Uddin, Tim Becker, Charles Davis, Jorge Villa, Theresia Yasbeck, Taylor Stephen, Yang Ju, and Cassandra Rey for their assistance in
the field in 2018. We thank Julian Deventer for insights on the analysis of
the data. We also thank Karen Seidel and The Nature Conservancy for granting
access to the site.
Financial support
This research has been supported by the Ohio State University Presidential Fellowship to Camilo Rey-Sanchez, the Ohio Water Resources Center (grant no. G16AP00076) and the Ohio Agricultural Research and Development Center, Ohio State University (grant no. SEEDS 2016-055).
Review statement
This paper was edited by Tina Treude and reviewed by two anonymous referees.
ReferencesAngel, R., Matthies, D., and Conrad, R.: Activation of Methanogenesis in Arid
Biological Soil Crusts Despite the Presence of Oxygen, PLOS ONE, 6,
e20453, 10.1371/journal.pone.0020453, 2011.Angle, J. C., Morin, T. H., Solden, L. M., Narrowe, A. B., Smith, G. J.,
Borton, M. A., Rey-Sanchez, C., Daly, R. A., Mirfenderesgi, G., Hoyt, D. W.,
Riley, W. J., Miller, C. S., Bohrer, G., and Wrighton, K. C.: Methanogenesis
in oxygenated soils is a substantial fraction of wetland methane emissions,
Nat. Commun., 8, 1567, 10.1038/s41467-017-01753-4, 2017.Apprill, A., McNally, S., Parsons, R., and Weber, L.: Minor revision to V4
region SSU rRNA 806R gene primer greatly increases detection of SAR11
bacterioplankton, Aquat. Microb. Ecol., 75, 129–137,
10.3354/ame01753, 2015.Basiliko, N., Blodau, C., Roehm, C., Bengtson, P., and Moore, T. R.:
Regulation of decomposition and methane dynamics across natural,
commercially mined, and restored northern peatlands, Ecosystems, 10,
1148–1165, 10.1007/s10021-007-9083-2, 2007.Basiliko, N., Henry, K., Gupta, V., Moore, T. R., Driscoll, B. T., and
Dunfield, P. F.: Controls on bacterial and archaeal community structure and
greenhouse gas production in natural, mined, and restored Canadian
peatlands, Front. Microbiol., 4, 215, 10.3389/fmicb.2013.00215, 2013.Bohn, T. J., Lettenmaier, D. P., Sathulur, K., Bowling, L. C., Podest, E.,
McDonald, K. C., and Friborg, T.: Methane emissions from western Siberian
wetlands: heterogeneity and sensitivity to climate change, Environ. Res.
Lett., 2, 4, 10.1088/1748-9326/2/4/045015, 2007.Bridgham, S. D. and Richardson, C. J.: Mechanisms controlling soil
respiration (CO2 and CH4) in southern peatlands, Soil Biol. Biochem.,
24, 1089–1099, 10.1016/0038-0717(92)90058-6, 1992.Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q. L.:
Methane emissions from wetlands: biogeochemical, microbial, and modeling
perspectives from local to global scales, Glob. Change Biol., 19,
1325–1346, 10.1111/gcb.12131, 2013.Cai, S. and Yu, Z.: Response of a warm temperate peatland to Holocene
climate change in northeastern Pennsylvania, Quaternary Res., 75, 531–540,
10.1016/j.yqres.2011.01.003, 2011.Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.
D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon,
J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R.
E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J.,
Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko,
T., Zaneveld, J., and Knight, R.: QIIME allows analysis of high-throughput
community sequencing data, Nat. Methods, 7, 335–336,
10.1038/nmeth.f.303, 2010.Chamberlain, S. D., Anthony, T. L., Silver, W. L., Eichelmann, E., Hemes, K.
S., Oikawa, P. Y., Sturtevant, C., Szutu, D. J., Verfaillie, J. G., and
Baldocchi, D. D.: Soil properties and sediment accretion modulate methane
fluxes from restored wetlands, Glob. Change Biol., 24, 4107–4121,
10.1111/gcb.14124, 2018.Chasar, L. S., Chanton, J. P., Glaser, P. H., and Siegel, D. I.: Methane
concentration and stable isotope distribution as evidence of rhizospheric
processes: Comparison of a fen and bog in the Glacial Lake Agassiz Peatland
complex, Ann. Bot., 86, 655–663, 10.1006/anbo.2000.1172, 2000.Christen, A., Jassal, R. S., Black, T. A., Grant, N. J., Hawthorne, I.,
Johnson, M. S., Lee, S. C., and Merkens, M.: Summertime greenhouse gas fluxes
from an urban bog undergoing restoration through rewetting, Mires Peat, 17, 1–24,
10.19189/MaP.2015.OMB.207, 2016.Chu, H. S., Chen, J. Q., Gottgens, J. F., Ouyang, Z. T., John, R.,
Czajkowski, K., and Becker, R.: Net ecosystem methane and carbon dioxide
exchanges in a Lake Erie coastal marsh and a nearby cropland, J. Geophys.
Res.-Biogeo., 119, 722–740, 10.1002/2013jg002520, 2014.Ciais, P., Gasser, T., Paris, J. D., Caldeira, K., Raupach, M. R., Canadell, J. G., Patwardhan, A., Friedlingstein, P., Piao, S. L., and Gitz, V.: Attributing the increase in atmospheric CO2 to emitters and absorbers, Nat. Clim. Change, 3, 926, 2013.Colwell, S. R.: Characterization of Upland/Wetland Community Types: Changes to Flatiron Lake Bog over a 24-Year Period, The Ohio State University, available at: https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ETD_SUBID:68741 (last access: 23 August 2019), 2009.Crill, P. M., Bartlett, K. B., Harriss, R. C., Gorham, E., Verry, E. S.,
Sebacherl, D. I., Madsar, L., and Sanner, W.: Methane flux from minnesota
peatlands, Glob. Biogeochem. Cy., 2, 371–384,
10.1029/GB002i004p00371, 1988.Dean, J. F., Middelburg, J. J., Röckmann, T., Aerts, R., Blauw, L. G.,
Egger, M., Jetten, M. S. M., Jong, A. E. E. de, Meisel, O. H., Rasigraf, O.,
Slomp, C. P., in't Zandt, M. H., and Dolman, A. J.: Methane Feedbacks to the
Global Climate System in a Warmer World, Rev. Geophys., 56, 207–250,
10.1002/2017RG000559, 2018.Dedysh, S. N.: Cultivating Uncultured Bacteria from Northern Wetlands:
Knowledge Gained and Remaining Gaps, Front. Microbiol., 2, 184,
10.3389/fmicb.2011.00184, 2011.Dise, N. B.: Methane emission from minnesota peatlands – spatial and
seasonal variability, Glob. Biogeochem. Cy., 7, 123–142,
10.1029/92gb02299, 1993.Glatzel, S., Basiliko, N., and Moore, T.: Carbon dioxide and methane
production potentials of peats from natural, harvested, and restored sites,
eastern Quebec, Canada, Wetlands, 24, 261–267,
10.1672/0277-5212(2004)024[0261:cdampp]2.0.co;2, 2004.Godin, A., McLaughlin, J. W., Webster, K. L., Packalen, M., and Basiliko, N.:
Methane and methanogen community dynamics across a boreal peatland nutrient
gradient, Soil Biol. Biochem., 48, 96–105,
10.1016/j.soilbio.2012.01.018, 2012.Greenup, A. L., Bradford, M. A., McNamara, N. P., Ineson, P., and Lee, J. A.: The role of Eriophorum vaginatum in CH4 flux from an ombrotrophic peatland, Plant Soil, 227, 265–272, 10.1023/a:1026573727311, 2000.Hatala, J. A., Detto, M., and Baldocchi, D. D.: Gross ecosystem
photosynthesis causes a diurnal pattern in methane emission from rice,
Geophys. Res. Lett., 39, L06409, 10.1029/2012gl051303, 2012.Holgerson, M. A., Farr, E. R., and Raymond, P. A.: Gas transfer velocities in
small forested ponds, J. Geophys. Res.-Biogeo., 122, 1011–1021,
10.1002/2016JG003734, 2017.Hoyos-Santillan, J., Lomax, B. H., Large, D., Turner, B. L., Boom, A.,
Lopez, O. R., and Sjogersten, S.: Quality not quantity: Organic matter
composition controls of CO2 and CH4 fluxes in neotropical peat profiles,
Soil Biol. Biochem., 103, 86–96, 10.1016/j.soilbio.2016.08.017, 2016.Kelly, C. A., Dise, N. B., and Martens, C. S.: Temporal variations in the
stable carbon isotopic composmon of methane emitted from minnesota
peatlands, Glob. Biogeochem. Cy., 6, 263–269, 10.1029/92gb01478,
1992.Kettunen, A.: Connecting methane fluxes to vegetation cover and water table
fluctuations at microsite level: A modeling study, Glob. Biogeochem. Cy.,
17, 1051, 10.1029/2002gb001958, 2003.Kim, J., Verma, S. B., and Billesbach, D. P.: Seasonal variation in methane
emission from a temperate Phragmites-dominated marsh: effect of growth stage
and plant-mediated transport, Glob. Change Biol., 5, 433–440,
10.1046/j.1365-2486.1999.00237.x, 1999.Kim, S. Y., Lee, S. H., Freeman, C., Fenner, N., and Kang, H.: Comparative
analysis of soil microbial communities and their responses to the short-term
drought in bog, fen, and riparian wetlands, Soil Biol. Biochem., 40,
2874–2880, 10.1016/j.soilbio.2008.08.004, 2008.Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J.,
Krummel, P. B., Lamarque, J. F., Langenfelds, R. L., Le Quere, C., Naik, V.,
O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R.
G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, 10.1038/ngeo1955, 2013.Kostka, J. E., Weston, D. J., Glass, J. B., Lilleskov, E. A., Shaw, A. J.,
and Turetsky, M. R.: The Sphagnum microbiome: new insights from an ancient
plant lineage, New Phytol., 211, 57–64, 10.1111/nph.13993, 2016.Kotsyurbenko, O. R., Chin, K. J., Glagolev, M. V., Stubner, S., Simankova,
M. V., Nozhevnikova, A. N., and Conrad, R.: Acetoclastic and hydrogenotrophic
methane production and methanogenic populations in an acidic West-Siberian
peat bog, Environ. Microbiol., 6, 1159–1173,
10.1111/j.1462-2920.2004.00634.x, 2004.Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B.: lmerTest
Package: Tests in Linear Mixed Effects Models, J. Stat. Softw., 82,
1–26, 10.18637/jss.v082.i13, 2017.
Lai, D. Y. F.: Methane Dynamics in Northern Peatlands: A Review, Pedosphere,
19, 409–421, 2009.Lai, D. Y. F., Moore, T. R., and Roulet, N. T.: Spatial and temporal
variations of methane flux measured by autochambers in a temperate
ombrotrophic peatland, J. Geophys. Res.-Biogeo., 119, 864–880,
10.1002/2013jg002410, 2014.Lansdown, J. M., Quay, P. D., and King, S. L.: CH4 production via CO2
reduction in a temperate bog: A source of 13C-depIeted CH4, Geochim.
Cosmochim. Ac., 56, 3493–3503, 10.1016/0016-7037(92)90393-W, 1992.Lee, H. J., Kim, S. Y., Kim, P. J., Madsen, E. L., and Jeon, C. O.: Methane
emission and dynamics of methanotrophic and methanogenic communities in a
flooded rice field ecosystem, FEMS Microbiol. Ecol., 88, 195–212,
10.1111/1574-6941.12282, 2014.Le Mer, J. and Roger, P.: Production, oxidation, emission and consumption of
methane by soils: A review, Eur. J. Soil Biol., 37, 25–50,
10.1016/s1164-5563(01)01067-6, 2001.Lenth, R., Singmann, H., Love, J., Buerkner, P., and Herve, M.: emmeans:
Estimated Marginal Means, aka Least-Squares Means, available at:
https://CRAN.R-project.org/package=emmeans, last access: 24 December 2018.
Liu, Y. C. and Whitman, W. B.: Metabolic, phylogenetic, and ecological
diversity of the methanogenic archaea, in: Incredible Anaerobes: From
Physiology to Genomics to Fuels, vol. 1125, edited by: Wiegel, J.,
Maier, R. J., and Adams, M. W. W., pp. 171–189, 2008.MacDonald, L. H., Paull, J. S., and Jaffe, P. R.: Enhanced semipermanent
dialysis samplers for long-term environmental monitoring in saturated
sediments, Environ. Monit. Assess., 185, 3613–3624,
10.1007/s10661-012-2813-8, 2013.Malhotra, A., Roulet, N. T., Wilson, P., Giroux-Bougard, X., and Harris, L.
I.: Ecohydrological feedbacks in peatlands: an empirical test of the
relationship among vegetation, microtopography and water table,
Ecohydrology, 9, 1346–1357, 10.1002/eco.1731, 2016.McCalley, C. K., Woodcroft, B. J., Hodgkins, S. B., Wehr, R. A., Kim, E.-H.,
Mondav, R., Crill, P. M., Chanton, J. P., Rich, V. I., Tyson, G. W., and
Saleska, S. R.: Methane dynamics regulated by microbial community response
to permafrost thaw, Nature, 514, 478–481, 10.1038/nature13798,
2014.Moore, P. D.: The future of cool temperate bogs, Environ. Conserv., 29,
3–20, 10.1017/s0376892902000024, 2002.
Moore, T. R. and Dalva, M.: The influence of temperature and water-table
position on carbon-dioxide and methane emissions from laboratory columns of
peatland soils, J. Soil Sci., 44, 651–664, 1993.Morin, T. H., Bohrer, G., Naor-Azrieli, L., Mesi, S., Kenny, W. T., Mitsch,
W. J., and Schaefer, K. V. R.: The seasonal and diurnal dynamics of methane
flux at a created urban wetland, Ecol. Eng., 72, 74–83,
10.1016/j.ecoleng.2014.02.002, 2014.Morin, T. H., Bohrer, G., Stefanik, K. C., Rey-Sanchez, A. C., Matheny, A.
M., and Mitsch, W. J.: Combining eddy-covariance and chamber measurements to
determine the methane budget from a small, heterogeneous urban floodplain
wetland park, Agric. Forest Meteorol., 237, 160–170,
10.1016/j.agrformet.2017.01.022, 2017.Nadeau, D. F., Rousseau, A. N., Coursolle, C., Margolis, H. A., and Parlange,
M. B.: Summer methane fluxes from a boreal bog in northern Quebec, Canada,
using eddy covariance measurements, Atmos. Environ., 81, 464–474,
10.1016/j.atmosenv.2013.09.044, 2013.Nahlik, A. M. and Mitsch, W. J.: Methane Emissions From Created Riverine
Wetlands, Wetlands, 30, 783–793, 10.1007/s13157-010-0038-6, 2010.Nelson, M. C., Morrison, H. G., Benjamino, J., Grim, S. L., and Graf, J.:
Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene
Amplicon Surveys, PLOS ONE, 9, e94249, 10.1371/journal.pone.0094249,
2014.Oleszczuk, R. and Truba, M.: The analysis of some physical properties of
drained peat-moorsh soil layers, Ann. Wars. Univ. Life Sci. 00 SGGW Land
Reclam., 45, 41–48, 10.2478/sggw-2013-0004, 2013.Pangala, S. R., Moore, S., Hornibrook, E. R. C., and Gauci, V.: Trees are
major conduits for methane egress from tropical forested wetlands, New
Phytol., 197, 524–531, 10.1111/nph.12031, 2013.Parada, A. E., Needham, D. M., and Fuhrman, J. A.: Every base matters:
assessing small subunit rRNA primers for marine microbiomes with mock
communities, time series and global field samples, Environ. Microbiol.,
18, 1403–1414, 10.1111/1462-2920.13023, 2016.Pugh, C. A., Reed, D. E., Desai, A. R., and Sulman, B. N.: Wetland flux
controls: how does interacting water table levels and temperature influence
carbon dioxide and methane fluxes in northern Wisconsin?, Biogeochemistry,
137, 15–25, 10.1007/s10533-017-0414-x, 2018.Raghoebarsing, A. A., Smolders, A. J. P., Schmid, M. C., Rijpstra, W. I. C.,
Wolters-Arts, M., Derksen, J., Jetten, M. S. M., Schouten, S., Damste, J. S.
S., Lamers, L. P. M., Roelofs, J. G. M., den Camp, H., and Strous, M.:
Methanotrophic symbionts provide carbon for photosynthesis in peat bogs,
Nature, 436, 1153–1156, 10.1038/nature03802, 2005.R Development Core Team: R: A language and environment for statistical
computing., available at: http://www.R-project.org (last access: 7 January 2019), 2018.Rey-Sanchez, A. C., Morin, T. H., Stefanik, K. C., Wrighton, K., and Bohrer,
G.: Determining total emissions and environmental drivers of methane flux in
a Lake Erie estuarine marsh, Ecol. Eng., 114, 7–15,
10.1016/j.ecoleng.2017.06.042, 2018.Schilder, J., Bastviken, D., Hardenbroek, M., and van and Heiri, O.:
Spatiotemporal patterns in methane flux and gas transfer velocity at low
wind speeds: Implications for upscaling studies on small lakes, J. Geophys.
Res.-Biogeo., 121, 1456–1467, 10.1002/2016JG003346, 2016.Schneider, C. A., Rasband, W. S., and Eliceiri, K. W.: NIH Image to ImageJ:
25 years of image analysis, Nat. Methods, 9, 671–675,
10.1038/nmeth.2089, 2012.Segers, R.: Methane production and methane consumption: a review of
processes underlying wetland methane fluxes, Biogeochemistry, 41, 23–51,
10.1023/a:1005929032764, 1998.Sha, C., Mitsch, W. J., Mander, U., Lu, J. J., Batson, J., Zhang, L., and He,
W. S.: Methane emissions from freshwater riverine wetlands, Ecol. Eng.,
37, 16–24, 10.1016/j.ecoleng.2010.07.022, 2011.
Shannon, R. D. and White, J. R.: 3-year study of controls on methane
emissions from 2 Michigan peatlands, Biogeochemistry, 27, 35–60, 1994.Smemo, K. A. and Yavitt, J. B.: Anaerobic oxidation of methane: an underappreciated aspect of methane cycling in peatland ecosystems?, Biogeosciences, 8, 779–793, 10.5194/bg-8-779-2011, 2011.Strack, M. and Zuback, Y. C. A.: Annual carbon balance of a peatland 10 yr following restoration, Biogeosciences, 10, 2885–2896, 10.5194/bg-10-2885-2013, 2013.Sundqvist, E., Crill, P., Molder, M., Vestin, P., and Lindroth, A.:
Atmospheric methane removal by boreal plants, Geophys. Res. Lett., 39, L21806,
10.1029/2012gl053592, 2012.Treat, C. C., Bubier, J. L., Varner, R. K., and Crill, P. M.: Timescale
dependence of environmental and plant-mediated controls on CH4 flux in a
temperate fen, J. Geophys. Res.-Biogeo., 112, G01014,
10.1029/2006jg000210, 2007.Turetsky, M. R., Kotowska, A., Bubier, J., Dise, N. B., Crill, P.,
Hornibrook, E. R. C., Minkkinen, K., Moore, T. R., Myers-Smith, I. H.,
Nykanen, H., Olefeldt, D., Rinne, J., Saarnio, S., Shurpali, N., Tuittila,
E. S., Waddington, J. M., White, J. R., Wickland, K. P., and Wilmking, M.: A
synthesis of methane emissions from 71 northern, temperate, and subtropical
wetlands, Glob. Change Biol., 20, 2183–2197, 10.1111/gcb.12580,
2014.Updegraff, K., Pastor, J., Bridgham, S. D., and Johnston, C. A.:
Environmental and substrate controls over carbon and nitrogen mineralization
in northern wetlands, Ecol. Appl., 5, 151–163, 10.2307/1942060,
1995.US EPA, O.: EPA Method 350.1: Determination of Ammonia Nitrogen by
Semi-Automated Colorimetry, US EPA, available at:
https://www.epa.gov/homeland-security-research/epa-method-3501-determination-ammonia-nitrogen-semi-automated-colorimetry
(last access: 5 March 2019), 2015a.US EPA, O.: SW-846 Test Method 6010D: Inductively Coupled Plasma-Optical
Emission Spectrometry (ICP-OES), US EPA, available at:
https://www.epa.gov/hw-sw846/sw-846-test-method-6010d-inductively-coupled-plasma-optical-emission-spectrometry-icp-oes
(last access: 5 March 2019), 2015b.Van der Nat, F., Middelburg, J. J., Van Meteren, D., and Wielemakers, A.:
Diel methane emission patterns from Scirpus lacustris and Phragmites
australis, Biogeochemistry, 41, 1–22, 10.1023/a:1005933100905, 1998.Vitt, D. H. and Slack, N. G.: An analysis of the vegetation of Sphagnum-dominated
kettle-hole bogs in relation to environmental gradients, Can. J. Bot.,
53, 332–359, 10.1139/b75-042, 1975.Waddington, J. M. and Day, S. M.: Methane emissions from a peatland
following restoration, J. Geophys. Res.-Biogeo., 112, 112,
10.1029/2007jg000400, 2007.Walters, W., Hyde, E. R., Berg-Lyons, D., Ackermann, G., Humphrey, G.,
Parada, A., Gilbert, J. A., Jansson, J. K., Caporaso, J. G., Fuhrman, J. A.,
Apprill, A., and Knight, R.: Improved Bacterial 16S rRNA Gene (V4 and V4-5)
and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial
Community Surveys, mSystems, 1, e00009-15, 10.1128/mSystems.00009-15,
2016.
Wang, Z. P., Delaune, R. D., Masscheleyn, P. H., and Patrick, W. H.: Soil
redox and ph effects on methane production in a flooded rice soil, Soil Sci.
Soc. Am. J., 57, 382–385, 1993.Wanninkhof, R.: Relationship between wind speed and gas exchange over the
ocean revisited, Limnol. Oceanogr.-Methods, 12, 351–362,
10.4319/lom.2014.12.351, 2014.White, J. R., Shannon, R. D., Weltzin, J. F., Pastor, J., and Bridgham, S.
D.: Effects of soil warming and drying on methane cycling in a northern
peatland mesocosm study, J. Geophys. Res.-Biogeo., 113, G00A06,
10.1029/2007jg000609, 2008.Whiting, G. J. and Chanton, J. P.: Plant-dependent CH4 emission in a
subarctic canadian fen, Glob. Biogeochem. Cy., 6, 225–231,
10.1029/926b00710, 1992.