Introduction
Rewetting is a technique commonly employed to restore ecological and
biogeochemical functioning of drained fens. However, while rewetting may
reduce carbon dioxide (CO2) emissions (Wilson et al., 2016), it often
increases methane (CH4) emissions in peatlands that remain inundated
following rewetting. On a 100-year timescale, CH4 has a global warming
potential 28 times stronger than CO2 (Myhre et al., 2013), and the
factors that contribute to the magnitude and duration of increased emissions
are still uncertain (Joosten et al., 2015; Abdalla et al., 2016). Thus,
elucidating the dynamics of post-rewetting CH4 exchange is of strong
interest for both modeling studies and peatland management projects
(Abdalla et al., 2016). Although a recent increase in rewetting projects in
Germany and other European countries has prompted a number of studies of
methane cycling in rewetted peatlands (e.g., Jerman et al., 2009;
Hahn-Schöfl et al., 2011; Urbanová et al., 2013; Hahn et al., 2015;
Vanselow-Algan et al., 2015; Zak et al., 2015; Emsens et al., 2016; Putkinen et
al., 2018), the post-rewetting distribution and abundance of methane-cycling
microbes in rewetted fens has seldom been examined (but see Juottonen et al.,
2012; Urbanová et al., 2013; Putkinen et al., 2018).
Peat CH4 production and release is governed by a complex array of
interrelated factors including climate, water level, plant community,
nutrient status, site geochemistry, and the activity of microbes (i.e.,
bacteria and archaea) that use organic carbon as an energy source (Segers, 1998;
Abdalla et al., 2016). To date, the vast majority of studies in rewetted fens
have focused on quantifying CH4 emission rates in association with
environmental variables such as water level, plant community, and aspects of
site geochemistry (Abdalla et al., 2016). Site geochemistry indeed plays an
important role for methanogenic communities, as methanogenesis is suppressed
in the presence of thermodynamically more favorable terminal electron acceptors
(TEAs, Blodau, 2011). Due to a smaller pool of more favorable electron
acceptors and high availability of organic carbon substrates, organic-rich
soils such as peat rapidly establish methanogenic conditions post-rewetting
(Segers, 1998; Keller and Bridgham, 2007; Knorr and Blodau, 2009). Despite
their decisive role as producers (i.e., methanogens) and consumers (i.e.,
methanotrophs) of CH4 (Conrad, 1996), only a few studies have combined a
characterization of the CH4-cycling microbial community, site
geochemistry, and observed trends in CH4 production. Existing studies
have been conducted in oligotrophic and mesotrophic boreal fens (e.g.,
Juottonen et al., 2005, 2012; Yrjälä et al., 2011),
alpine fens (e.g., Liebner et al., 2012; Urbanová et al., 2013; Cheema et
al., 2015; Franchini et al., 2015), subarctic fens (Liebner et al., 2015), and
incubation experiments (e.g., Jerman et al., 2009; Knorr and Blodau, 2009;
Urbanová et al., 2011; Emsens et al., 2016). Several studies on
CH4-cycling microbial communities have been conducted in minerotrophic
temperate fens (e.g., Cadillo-Quiroz et al., 2008; Liu et al., 2011; Sun et
al., 2012; Zhou et al., 2017), but these sites were not subject to drainage or
rewetting. Direct comparisons of in situ abundances of methanogens and
methanotrophs in drained versus rewetted fens are scarce (Juottonen et al.,
2012; Putkinen et al., 2018), and the studied sites, so far, are
nutrient-poor fens with acidic conditions.
While studies of nutrient-poor and mesotrophic boreal fens have documented
post-rewetting CH4 emissions comparable to or lower than at pristine
sites (Komulainen et al., 1998; Tuittila et al., 2000; Juottonen et al., 2012),
studies of temperate nutrient-rich fens have reported post-flooding CH4
emissions dramatically exceeding emissions in pristine fens (e.g., Augustin
and Chojnicki, 2008; Hahn et al., 2015). These high emissions typically occur
together with a significant dieback in vegetation, a mobilization of
nutrients and electron acceptors in the upper peat layer, and increased
availability of dissolved organic matter (Zak and Gelbrecht, 2007;
Hahn-Schöfl et al., 2011; Hahn et al., 2015; Jurasinski et al., 2016). High
CH4 fluxes may continue for decades following rewetting, even in bogs
(Vanselow-Algan et al., 2015). Hence, there is an urgent need to characterize
CH4-cycling microbial communities and geochemical conditions in
rewetted minerotrophic fens. In this study, we therefore examined microbial
community composition and abundance in relation to post-flooding geochemical
conditions in two rewetted fens in northeastern Germany. In both fens,
CH4 emissions increased dramatically after rewetting, to over 200 gCm-2a-1
(Augustin and Chojnicki, 2008; Hahn-Schöfl et al., 2011;
Hahn et al., 2015; Jurasinski et al., 2016). Average annual CH4 emissions
have decreased in both fens since the initial peak (Franz et al., 2016;
Jurasinski et al., 2016). Nevertheless, fluxes remained higher than under
pre-flooding conditions (ibid.) and higher than in pristine fens (Urbanová
et al., 2013; Minke et al., 2016). In the Hütelmoor in 2012, average
CH4 emissions during the growing season were 40 gm-2 (Koebsch et
al., 2015). In Zarnekow, average CH4 emissions were 40 gm-2 for
the year 2013 (Franz et al., 2016). In comparison, a recent review paper
(Abdalla et al., 2016) estimated an average flux of 12±21 gCm-2a-1 for pristine peatlands.
Location of study sites in northeastern Germany (a) and sampling
locations within sites (b) Hütelmoor and (c) Zarnekow. Maps (b) and (c)
are drawn to the same scale. Image source: (a) QGIS; (b) and (c) Google
Earth via QGIS OpenLayers Plugin. Imagery date: 9 August 2015.
We expected patterns in microbial community composition would reflect the
geochemical conditions of the two sites and hypothesized a high abundance of
methanogens relative to methanotrophs in both fens. We also expected
acetoclastic methanogens, which typically thrive in nutrient-rich fens
(Kelly et al., 1992; Galand et al., 2005), to dominate the methanogenic community in
both fens.
Methods
Study sites
The nature reserve Heiligensee and Hütelmoor (“Hütelmoor” in the
following; approx. 540 ha; 54∘12′36.66′′ N, 12∘10′34.28′′ E)
is a coastal, mainly minerotrophic fen complex in
Mecklenburg-Vorpommern (NE Germany) that is separated from the Baltic Sea by
a narrow (∼100 m and less) dune dike (Fig. 1a and b). The
climate is temperate in the transition zone between maritime and
continental, with an average annual temperature of 9.1 ∘C and an
average annual precipitation of 645 mm (data derived from the grid product of
the German Weather Service, reference climate period 1981–2010). Episodic
flooding from storm events delivers sediment and brackish water to the site
(Weisner and Schernewski, 2013). The vegetation is a mixture of salt-tolerant
macrophytes, with dominant to semi-dominant stands of Phragmites australis,
Bolboschoenus maritimus, Carex acutiformis, and
Schoenoplectus tabernaemontani. The dominating plants are interspersed with open water bodies that are
colonized by Ceratophyllum demersum in summer (Koch et al., 2017). Intense draining and land
amelioration practices began in the 1970s, which lowered the water level to
1.6 m below ground surface and caused aerobic decomposition and concomitant
degradation of the peat (Voigtländer et al., 1996). The upper peat layer
varies in depth between 0.6 and 3 m and is highly degraded, reaching up to
H10 on the von Post humification scale (Hahn et al., 2015). Active draining
ended in 1992, but dry conditions during summertime kept the water table
well below ground surface (Schönfeld-Bockholt et al., 2008; Koebsch et
al., 2013) until concerns of prolonged aerobic peat decomposition prompted
the installation of a weir in 2009 at the outflow of the catchment (Weisner
and Schernewski, 2013). After installation of the weir, the site has been
fully flooded year-round with an average water level of 0.6 m above the peat
surface, and annual average CH4 flux increased ∼186-fold
from 0.0014±0.0006 to 0.26±0.06 kgCH4m-2a-1 (Hahn et al., 2015).
The study site polder Zarnekow (“Zarnekow” in the following; approx. 500 ha; 53∘52′31.10′′ N, 12∘53′19.60′′ E) is situated in the
valley of the river Peene in Mecklenburg-Vorpommern (NE Germany, Fig. 1a and
c). The climate is slightly more continental compared to the Hütelmoor,
with a mean annual precipitation of 544 mm and a mean annual temperature of
8.7 ∘C (German Weather Service, meteorological station Teterow,
24 km southwest of the study site; reference period 1981–2010). The fen can
be classified as a river valley mire system consisting of spring mires,
wider percolation mires, and flood mires along the river Peene. Drainage and
low-intensity agricultural use began in the 18th century when land use
changed to pastures and grassland. This was intensified by active pumping in
the mid-1970s. Due to land subsidence of several decimeters, after rewetting
(October 2004) the water table depth increased to 0.1–0.5 m above the peat surface.
The upper horizon is highly decomposed (0–0.3 m), followed by moderately
decomposed peat to a depth of 1 m and a deep layer of slightly decomposed
peat up to a maximum depth of 10 m. The open water bodies are densely
colonized by Ceratophyllum spp. and Typha latifolia is the dominant emergent macrophyte (Steffenhagen et
al., 2012). Following flooding, CH4 flux rates increased to
∼0.21 kgm-2a-1 (Augustin and Chojnicki, 2008). No
pre-rewetting CH4 flux data were available for the Zarnekow site, but
published CH4 flux rates of representative drained fens from the same
region have been shown to be negligible, and many were CH4 sinks
(Augustin et al., 1998).
Collection and analysis of peat cores and porewater samples
Peat and porewater samples were collected at four different locations
(n=4) in Hütelmoor (October 2014) and at five locations (n=5) in
Zarnekow (July 2015) and spanned a distance of 1200 and 250 m,
respectively, to cover the whole lateral extension at each site (Fig. 1b and
c). Sampling depths in the Hütelmoor were 0–5, 5–10, 10–20, 20–30,
30–40, and 40–50 cm below the peat surface, except for core numbers 1 and 4
where samples could only be obtained up to a depth of 10–20 and 30–40 cm,
respectively. Sampling depths in Zarnekow were 0–5, 25–30, and
50–55 cm
below the peat surface. Previous work at Zarnekow has revealed little
variation in peat properties with depth (e.g., Zak and Gelbrecht, 2007);
hence, a lower depth resolution in Zarnekow cores (ZCs) was chosen for this study.
Peat cores were collected with a Perspex liner (ID: 60 mm, Hütelmoor)
and a peat auger (Zarnekow). In order to minimize oxygen contamination, the
outer layer of the peat core was omitted. Subsamples for molecular analysis
were immediately packed in 15 mL sterile Falcon tubes and stored at
-80 ∘C until further processing.
Pore waters in the Hütelmoor were collected with a stainless-steel
push-point sampler attached to a plastic syringe to recover the samples from
10 cm depth intervals. Samples were immediately filtered with 0.45 µm membrane, sterile, disposable syringe filters. Pore waters in Zarnekow were
sampled with permanently installed dialysis samplers consisting of slotted
polypropylene (PP) pipes (length: 636 mm, ID: 34 mm) surrounded with 0.22 µm
polyethersulfone membrane. The PP pipes were fixed at distinct
peat depths (surface level, 20 and 40 cm depth) and connected with PP tubes
(4×6 mm ID×AD). Water samples were drawn out from the dialysis sampler pipes
with a syringe through the PP tube. Due to practical restrictions in
accessibility and sampling, permanent dialysis samplers could not be
installed at the desired locations in the Hütelmoor, resulting in the
different sampling techniques described above.
At both sites, electrical conductivity (EC), dissolved oxygen (DO), and pH
were measured immediately after sampling (Sentix 41 pH probe and a TetraCon
325 conductivity measuring cell attached to a WTW multi 340i handheld; WTW,
Weilheim). In this paper, EC is presented and was not converted to salinity
(i.e., psu), as a conversion would be imprecise for brackish waters. A
simplified equation for conversion can be found in Schemel (2001). Headspace
CH4 concentrations of porewater samples were measured with an Agilent
7890A gas chromatograph (Agilent Technologies, Germany) equipped with a
flame ionization detector and a Carboxen PLOT Capillary Column or HP-Plot Q
(Porapak-Q) column. The measured headspace CH4 concentration was then
converted into a dissolved CH4 concentration using the
temperature-corrected solubility coefficient (Wilhelm et al., 1977). Isotopic
composition of dissolved CH4 for Hütelmoor was analyzed using the
gas-chromatography–combustion technique (GC-C) and the
gas-chromatography–high-temperature-conversion technique (GC-HTC). The gas was
directly injected in a gas chromatograph (Agilent 7890A), CH4 was
quantitatively converted to CO2, and the δ13C values were
then measured with the isotope ratio mass spectrometer MAT-253 (Thermo
Finnigan, Germany). The δ13C of dissolved CH4 in Zarnekow
was analyzed using a laser-based isotope analyzer equipped with a small
sample isotope module for analyses of discrete gas samples (cavity ring-down
spectroscopy, CRDS; Picarro G2201-I, Santa Clara, CA, USA). Calibration was
carried out before, during, and after analyses using certified standards of
known isotopic composition (obtained from Isometric Instruments, Victoria,
BC, Canada; and from Westfalen AG, Münster, Germany). Reproducibility of
results was typically ±1 ‰. In the presence of
high concentrations of hydrogen sulfide interfering with laser-based isotope
analysis, samples were treated with iron(III) sulfate to oxidize and/or
precipitate sulfide. For both sites, sulfate and nitrate concentrations were
analyzed by ion chromatography (IC, Thermo Fisher Scientific Dionex) using
an IonPac AS-9-HC 4 column, partly after dilution of the sample. Dissolved
metal concentrations were analyzed by inductively coupled plasma optical emission spectrometry
(ICP-OES, iCAP 6300 DUO, Thermo Fisher Scientific). Accuracy and precision were routinely checked with a certified
CASS standard as previously described (Kowalski et al., 2012).
For the incubation experiments, peat cores were collected from Zarnekow in
March 2012 using a modified Kajak Corer with a plexiglass tube. The intact
cores were placed in a cool box and immediately transported to the
Leibniz Institute of Freshwater Ecology and Inland Fisheries in Berlin, where
they were sectioned into a total of 12 samples. Fresh, surficial organic
sediment (0–10 cm depth, 6 individual samples) was separated from the bulk
peat (10–20 cm depth, 6 individual samples) and the samples were placed in
60 mL plastic cups. The cups were filled completely and closed with
air-tight caps to minimize oxygen contamination. The samples were then
express-shipped (<24 h) to the lab at the Netherlands Institute
of Ecology for immediate processing and analysis. For CH4 production
incubations, 5 g of material and 10 mL of nitrogen (N2)-flushed
MilliQ water were weighed into three (n=3) 150 mL flasks for both surficial
organic sediment and bulk peat. The flasks were capped with rubber stoppers,
flushed with N2 for approximately 1 h, and then incubated stationarily
at 20 ∘C in the dark. For CH4 oxidation incubations, 5 g of
fresh material and 10 mL of MilliQ water were weighed into three
150 mL
flasks for both surficial organic sediment and bulk peat. The flasks were
capped with rubber stoppers and 1.4 mL of pure CH4 was added to
obtain a headspace CH4 concentration of approximately 10 000 ppm.
Incubations were performed in the dark at 20 ∘C on a gyratory
shaker (120 rpm). For all incubations, headspace CH4 concentration was
determined using a gas chromatograph equipped with a flame ionization
detector on days 1, 3, 5, and 8 of the incubation. Potential CH4
production and oxidation rates were determined by linear regression of
CH4 concentration over all sampling times.
Gene amplification and phylogenetic analysis
Genomic DNA was extracted from 0.2–0.3 g of duplicates of peat soil per
sample using an EurX GeneMATRIX soil DNA Purification Kit (Roboklon, Berlin, Germany). DNA
concentrations were quantified with a Nanophotometer P360 (Implen GmbH,
Munich, Germany) and Qubit 2.0 Fluorometer (Thermo Fisher Scientific,
Darmstadt, Germany). Polymerase chain reaction (PCR) amplification of
bacterial and archaeal 16S rRNA genes was performed using the primer
combination of S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 (Herlemann et al.,
2011) and S-D-Arch-0349-a-S-17/S-D-Arch-0786-a-A-20 (Takai and Horikoshi,
2000), respectively, with barcodes contained in the 5′ end. The PCR mix
contained 1× PCR buffer (Tris⚫ Cl, KCl, (NH4)2SO4,
15 mMMgCl2; pH 8.7) (QIAGEN, Hilden, Germany), 0.5 µM of each
primer (Biomers, Ulm, Germany), 0.2 mM of each deoxynucleoside (Thermo
Fisher Scientific, Darmstadt, Germany), and 0.025 UµL-1 hot
start polymerase (QIAGEN, Hilden, Germany). PCR samples were kept at 95 ∘C
for 5 min to denature the DNA, with amplification proceeding
for 40 cycles at 95 ∘C for 1 min, 56 ∘C for 45 s, and
72 ∘C for 90 s; a final extension of 10 min at 72 ∘C
was added to ensure complete amplification. PCR products were purified with
a Hi Yield Gel/PCR DNA fragment extraction kit (Süd-Laborbedarf,
Gauting, Germany). To reduce amplification bias, PCR products of three
individual runs per sample were combined. PCR products of different samples
were pooled in equimolar concentrations and compressed to a final volume of
10 µL with a concentration of 200 ngµL-1 in a vacuum
centrifuge Concentrator Plus (Eppendorf, Hamburg, Germany).
Illumina sequencing was performed by GATC Biotech AG using 300 bp paired-end
mode and a 20 % PhiX Control v3 library to counteract the effects of
low-diversity sequence libraries. Raw data were demultiplexed using our own
script based on CutAdapt (Martin, 2011). Ambiguous nucleotides at sequence
ends were trimmed and a 10 % mismatch was allowed for primer
identification, whereas barcode sequences needed to be present without any
mismatches and with a minimum Phred score of Q25 for each nucleotide. After
sorting, overlapping paired-end reads were merged using PEAR (Q25, p. 0.0001,
v20) (Zhang et al., 2014). The orientation of the merged sequences was
standardized according to the barcode information obtained from
demultiplexing. Low-quality reads were removed using Trimmomatic (SE,
LEADING Q25, TRAILING Q25, SLIDINGWINDOW 5:25; MINLEN 200) (Bolger et al.,
2014). Chimeric sequences were removed using USEARCH 6.1 and the
QIIME script identify_chimeric_seqs.py
(Caporaso et al., 2010). Preprocessed sequences were taxonomically assigned
to operational taxonomic units (OTUs) at a nucleotide sequence identity of
97 % using QIIME's pick_open_reference_otus.py script and the GreenGenes database 13.05
(McDonald et al., 2012) as reference. The taxonomic assignment of
representative sequences was further checked for correct taxonomical
classification by phylogenetic tree calculations in the ARB environment
referenced against the SILVA database version 119 (Quast et al., 2013). The resulting
OTU table was filtered for singletons, for OTUs assigned to chloroplasts or
mitochondria, and for low-abundance OTUs (below 0.2 % within each sample).
Archaeal and bacterial samples were processed separately while only OTUs
that were assigned to the respective domain were considered for further
analysis. For archaea, a total of 6 844 177 valid sequences were obtained,
ranging from 60 496 to 398 660 in individual samples. These sequences were
classified into 402 OTUs. For bacteria, a total of 2 586 148 valid sequences
were obtained, ranging from 22 826 to 164 916 in individual samples. These
sequences were classified into 843 OTUs. The OTU tables were then collapsed
at a higher taxonomic level to generate the bubble plots. The 16S rRNA gene
sequence data have been deposited at NCBI under the BioProject PRJNA356778.
The Hütelmoor sequence read archive accession numbers are
SRR5118134-SRR5118155 for bacterial and SRR5119428-SRR5119449 for archaeal
sequences. The Zarnekow accession numbers are
SRR6854018-SRR6854033 and SRR6854205-SRR6854220 for bacterial and archaeal
sequences, respectively.
Depth profiles of oxygen, nitrate, total iron, manganese,
and sulfate (a), and profiles of pH, EC, dissolved methane, and
the isotopic signature of methane-bound carbon (b) in both study
sites. Solid lines connect the respective means of individual wetlands
(n=4 for Hütelmoor and n=5 for Zarnekow).
qPCR analysis
Quantitative polymerase chain reaction (qPCR) for the determination of
methanotrophic and methanogenic functional gene copy numbers and overall
bacterial 16S rRNA gene copy numbers was performed via SYBR Green assays on a
Bio-Rad CFX instrument (Bio-Rad, Munich, Germany) with slight modifications
according to Liebner et al. (2015). The functional methanotrophic pmoA gene was
amplified with the primer combination A189F/Mb661 (Kolb et al., 2003)
suitable for detecting all known aerobic methanotrophic Proteobacteria.
Annealing was done at 55 ∘C after a seven-cycle-step touchdown
starting at 62 ∘C. The functional methanogenic mcrA gene was
amplified with the mlas and mcrA-rev primer pair (Steinberg and Regan, 2009), with
annealing at 57 ∘C. The bacterial 16S rRNA gene was quantified
with the primers Eub341F/Eub534R according to Degelmann et al. (2010), with
annealing at 58 ∘C. Different DNA template concentrations were
tested prior to the qPCR runs to determine optimal template concentration
without inhibitions through co-extracts. The 25 µL reactions
contained 12.5 µL of iTaq Universal SYBR Green Supermix (Bio-Rad,
Munich, Germany), 0.25 µM concentrations of the primers, and 5 µL
of DNA template. Data acquisition was always done at 80 ∘C
to avoid quantification of primer dimers. The specificity of
each run was verified through melt-curve analysis and gel electrophoresis.
Only runs with efficiencies between 82 % and 105 % were used for further
analysis. Measurements were performed in duplicates. The ratio of
methanogens to methanotrophs was determined based on gene abundances of
mcrA and pmoA. The marker gene for the soluble monooxygenase, mmoX, was neglected due to
the absence of Methylocella in the sequencing data (Fig. 4).
Data visualization and statistical analysis
All data visualization and statistical analysis were done in R (R Core
Team, 2017). The taxonomic relative abundances across samples were visualized
through bubble plots with the R package ggplot2 (Wickham, 2009). Differences
in microbial community composition were visualized with two-dimensional
non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances.
The NMDS ordinations were constructed using R package vegan (Oksanen et al.,
2017). An environmental fit was performed on the ordinations to determine
the measured geochemical parameters that may influence community
composition. The geochemical data were fitted to the ordinations as vectors
with a significance of p<0.05. Depth profiles were constructed with
the porewater geochemical data, as well as with the microbial abundances, to
elucidate depth-wise trends and assess whether differences in microbial
community and abundances among the two fens are related to differences in
their respective geochemistry.
Environmental conditions and geochemical conditions, and
microbial abundances in peat cores from the Hütelmoor, a coastal
minerotrophic fen in northeastern Germany. Environmental conditions are
described by pH and EC (electrical conductivity). Geochemical parameters
shown are dissolved methane (CH4) concentrations, the isotopic
signature of methane-bound carbon (∂13C–CH4), and
concentrations of terminal electron acceptors which are denoted with their
respective chemical abbreviations. Microbial abundances here represent the
mean value of subsamples for each depth section (n=2). nd: not detected.
Core
Depth
pH
EC
∂13C–CH4
Dissolved CH4
O2
NO3-
Fe
Mn
SO42-
16SrRNA
mcrA
pmoA
mcrA/pmoA
cm
mScm-1
mM
gene copies g dry peat-1
HC 1
0–5
7.2
1.79
-60.2
0.14
0.30
nd
0.10
0.03
0.03
2.04×1010
1.15×108
6.60×106
17.7
5–10
7.0
1.80
-60.7
0.31
0.18
nd
0.31
0.02
0.01
3.25×1010
3.36×107
6.68×107
0.51
10–15
7.0
2.35
-65.1
0.23
0.05
nd
0.60
0.03
nd
2.11×1010
8.12×107
1.76×107
6.12
15–20
7.1
2.94
-66.1
0.11
nd
0.03
1.34
0.06
nd
3.08×1010
1.21×108
2.76×107
4.41
HC 2
0–5
6.9
3.01
-57.8
0.46
0.05
0.03
0.03
0.01
nd
1.10×1011
1.13×1010
1.03×107
1170
5–10
6.7
2.60
-63.2
0.34
0.17
2.63
0.10
0.01
0.01
5.51×1010
7.27×107
1.69×107
4.73
10–20
7.2
5.73
-60.4
0.06
0.29
3.00
1.41
0.02
nd
3.13×1010
4.47×106
7.32×106
0.74
20–30
7.0
7.29
-61.8
0.08
0.08
nd
1.51
0.02
0.29
4.71×109
6.41×105
4.50×105
3.75
30–40
6.5
9.66
-64.2
0.64
nd
nd
1.68
0.02
3.66
2.09×109
6.21×105
3.90×104
18.3
40–50
6.4
9.71
-64.5
0.20
nd
nd
5.35
0.03
17.1
4.09×109
2.47×106
2.75×105
10.7
HC 3
0–5
6.6
2.93
-57.7
0.23
0.29
2.77
0.11
0.01
0.04
1.10×1011
1.34×109
3.51×108
3.86
5–10
6.6
3.00
-57.4
0.19
0.27
2.69
0.01
0.01
0.03
8.72×1010
1.40×109
3.42×107
46.6
10–20
6.4
3.77
-57.3
0.49
0.24
3.08
0.05
nd
nd
6.08×1010
5.86×108
9.35×106
63.6
20–30
6.1
6.77
-57.4
0.42
0.11
nd
0.20
nd
nd
4.26×1010
3.48×108
1.92×107
18.2
30–40
6.5
8.56
-59.4
0.08
0.03
nd
0.16
nd
nd
1.05×1010
3.20×106
1.17×106
2.74
40–50
5.6
9.36
-59.5
0.12
0.01
nd
0.02
nd
0.08
3.18×109
2.16×106
2.58×105
8.39
HC 4
0–5
6.6
2.93
-61.2
0.25
0.30
2.72
0.02
0.01
0.04
1.17×1011
3.63×109
3.09×108
11.7
5–10
6.7
2.65
-59.2
0.13
0.30
2.87
0.01
nd
0.05
4.87×1010
1.09×109
7.51×107
14.5
10–20
6.6
5.20
-60.5
0.05
0.30
3.05
0.14
nd
nd
4.85×1010
8.71×108
2.15×107
40.8
20–30
7.2
6.06
-59.1
0.05
0.01
nd
0.06
nd
0.02
9.78×109
5.82×107
7.91×106
7.36
30–40
6.6
8.11
-60.6
0.29
nd
nd
0.09
nd
0.67
1.60×109
1.58×106
1.25×106
1.27
Environmental conditions and geochemical conditions, and
microbial abundances in peat cores from Zarnekow, a freshwater minerotrophic
fen in northeastern Germany. Environmental conditions are described by pH
and EC (electrical conductivity). Geochemical parameters shown are dissolved
methane (CH4) concentrations, the isotopic signature of methane-bound
carbon (∂13C–CH4), and concentrations of terminal
electron acceptors which are denoted with their respective chemical
abbreviations. Microbial abundances here represent the mean value of
subsamples for each depth section (n=2). nd: not detected.
Core
Depth
pH
EC
∂13C–CH4
Dissolved CH4
O2
NO3-
Fe
Mn
SO42-
16SrRNA
mcrA
pmoA
mcrA/pmoA
cm
mScm-1
mM
gene copies g dry peat-1
ZC 1
0–5
6.64
1.03
-64.5
0.51
0.07
0.001
0.007
0.002
0.002
6.33×1010
1.02×109
1.49×107
69.7
25–30
6.67
1.14
-62.0
0.64
0.08
0.001
0.087
0.028
0.003
4.25×1010
8.96×108
9.14×106
98.0
50–55
6.66
1.31
-62.5
0.63
0.09
0.005
0.310
0.037
0.002
3.40×1010
3.97×108
6.85×106
58.1
ZC 2
0–5
6.91
1.00
-59.2
0.17
0.08
0.004
0.012
0.069
0.007
1.43×1011
1.14×1010
4.35×107
261
25–30
6.76
1.29
-51.3
0.15
0.10
0.001
0.215
0.033
0.013
6.44×1010
1.45×109
2.34×107
61.8
50–55
6.64
1.52
-61.1
0.62
0.04
nd
0.410
0.054
0.003
5.64×1010
5.10×108
1.50×107
34.0
ZC 3
0–5
6.88
1.17
-60.5
0.50
0.10
0.001
0.073
0.074
0.032
7.86×1010
2.78×109
3.26×107
85.7
25–30
7.04
3.39
-61.9
0.10
0.03
0.002
1.046
0.188
0.003
5.79×1010
7.81×108
1.55×107
51.8
50–55
6.92
3.82
-68.7
0.59
0.02
nd
0.779
0.123
0.003
3.41×1010
2.21×108
5.41×106
40.9
ZC 4
0–5
7.3
1.06
-61.5
0.14
0.12
0.010
0.013
0.024
0.035
7.19×1010
1.28×109
6.53×107
19.6
25–30
7.13
1.58
-65.1
0.12
0.11
0.002
0.301
0.049
0.002
7.19×1010
nd
4.60×107
–
50–55
6.89
1.51
-67.6
0.17
0.11
0.002
0.366
0.048
0.002
5.42×1010
9.47×108
4.50×107
21.0
ZC 5
0–5
6.81
0.83
-63.7
0.57
0.01
0.002
0.005
0.035
0.005
8.73×1010
8.73×108
4.97×107
17.6
25–30
6.72
0.86
-63.5
0.53
0.06
0.002
0.139
0.043
0.001
8.94×1010
5.21×108
5.57×107
93.4
50–55
6.58
1.00
-63.8
0.37
0.06
0.002
0.275
0.045
0.002
8.00×1010
2.14×108
1.44×108
14.9
Results
Environmental characteristics and site geochemistry
The two rewetted fens varied substantially in their environmental
characteristics (e.g., proximity to the sea) and porewater geochemistry
(Fig. 2, Tables 1 and 2). EC was more than 3 times higher in
Hütelmoor than in Zarnekow, averaging 5.3 and 1.5 mScm-1,
respectively. Mean values of pH were approximately neutral (6.5 to 7.0) in
the upper peat profile and comparable in both fens until a depth of about 30 cm
where pH decreased to ∼6 in the Hütelmoor.
Concentrations of the TEAs nitrate and sulfate were lower in Zarnekow and
near zero in the pore water at all depths, while nitrate and sulfate were
abundant in the upper and lower peat profile in Hütelmoor at
∼1.5 to 3.0 mM and ∼4 to 20 mM, respectively
(Fig. 2). Iron concentrations were higher in the Hütelmoor pore water,
while manganese concentrations were higher in Zarnekow pore water. Dissolved
oxygen concentrations in the upper peat profile (i.e., 0 to 25 cm depths)
were much higher in Hütelmoor than in Zarnekow (Fig. 2). Here DO
concentrations averaged ∼0.25 mM until a depth of 15 cm at
which they dropped sharply, reaching concentrations slightly below
0.05 mM
at 25 cm. In Zarnekow, DO concentrations did not exceed 0.1 mM and varied
little with depth. Regarding geochemical conditions, Hütelmoor core (HC)
1 differed from all other Hütelmoor cores and was more similar to
Zarnekow cores. In HC 1 – the core taken nearest to potential freshwater
sources (Fig. 1b) – pore water EC and DO concentrations were lower while pH
was slightly higher than in all other Hütelmoor cores. Moreover, this
was the only Hütelmoor core where nitrate concentrations were below
the detection limit (0.001 mM) (Fig. 2). In all cores we found high
concentrations of dissolved CH4 that varied within and among fens and
were slightly higher in Zarnekow pore water. Stable isotope ratios of
∂13C-CH4 (Fig. 2) in the upper peat (approx. -59 ‰) suggest a predominance of acetoclastic
methanogenesis, with a shift to hydrogenotrophic methanogenesis around
-65 ‰ in the lower peat profile. Additionally, the
observed shifts toward less negative ∂13C-CH4 values
in the upper peat layer, as in HC 1 and HC 2, could indicate partial
oxidation of CH4 occurred (Chasar et al., 2000).
Relative abundances of different bacterial lineages in
the study sites. Along the horizontal axis samples are arranged according to
site and depth. The rank order along the vertical axis is shown for the
phylum level.
Relative abundances within Proteobacteria phylum in the study
sites. Along the horizontal axis samples are arranged according to site and
depth. The rank order along the vertical axis is shown for the family level.
If an assignment to the family level was not possible the next higher
assignable taxonomic level was used.
Community composition of bacteria and archaea
Bacterial sequences could be affiliated into a total of 30 bacterial phyla
(Fig. 3). Among them, Proteobacteria, Acidobacteria, Actinobacteria,
Chloroflexi, Nitrospirae, and Bacteroidetes were present in all samples. With
mean relative abundance of 48 %, Proteobacteria was the most abundant
phylum. Some taxa (e.g., Verrucomicrobia; Atribacteria, OP9; and AD3) were
present only in Hütelmoor. Variation in community composition was larger
in Hütelmoor samples than in Zarnekow. Within Proteobacteria, the alpha
subdivision was the most dominant group, having contributed 26.7 % to all
the libraries on average (Fig. 4). The family Hyphomicrobiaceae dominated the
Alphaproteobacteria and was distributed evenly across samples but missing
in the surface and bottom peat layers in HC 2. In addition, methanotrophs
were clearly in low abundance across all samples, representing only 0.06 %
and 0.05 % of the bacterial community in Hütelmoor and Zarnekow,
respectively. Of the few methanotrophs that were detected, type II
methanotrophs (mainly Methylocystaceae) outcompeted type I methanotrophs (mainly
Methylococcaceae) in the community, while members of the genus Methylocella were absent (Fig. 4).
Relative abundances of different archaeal lineages in the
study sites. Along the horizontal axis samples are arranged according to
site and depth. The rank order along the vertical axis is shown for the
family level. If an assignment to the family level was not possible, the
next higher assignable taxonomic level was used.
Within the archaeal community, Bathyarchaeota were mostly dominating over
Euryarchaeota (Fig. 5). The miscellaneous Crenarchaeota group (MCG; mainly the order of pGrfC26) in
Bathyarchaeota prevailed across all samples but was especially abundant in
HC 2 samples. In addition to Bathyarchaeota, methanogenic archaea were
important and on average contributed 30.6 % to the whole archaeal
community. Among the methanogens, acetoclastic methanogens were more
abundant in most of the samples and Methanosaetaceae (24.8 %) were the major component.
They were present in most samples and much more dominant than
Methanosarcinaceae (2.0 %). Hydrogenotrophic methanogens, such as Methanomassiliicoccaceae (1.6 %),
Methanoregulaceae (1.2 %), and Methanocellaceae (0.6 %), albeit low in abundance, were detected in many
samples. Hütelmoor samples displayed greater variability in archaeal
community composition compared to Zarnekow samples. The putative anaerobic
methanotrophs of the ANME-2d (Raghoebarsing et al., 2006) clade occurred in patchy abundance with dominance in single spots of both sites. In HC 1 they
represented a mean relative abundance of 40.9 % of total archaeal reads
but were almost absent in all other Hütelmoor cores. In Zarnekow core 3,
ANME-2d represented up to approximately 30 % of all archaea but
were otherwise low in abundance.
NMDS plots showing (a) bacterial, (b) archaeal, and
(c) microbial (bacterial plus archaeal) community composition across the nine
peat cores. The point positions represent distinct microbial communities,
with the border colors of the symbols referring to the study sites and their
shapes representing the core number. HC 2 symbols are highlighted with red
fill to emphasize the large variation in microbial community within the
core. Environmental fit vectors with a significance of p<0.05 are
shown in green.
Environmental drivers of microbial community composition
Bacterial and archaeal population at both peatland sites showed distinct
clustering (Fig. 6) with similarly high intra- and inter-site variations but
greater overall variation in community composition in the Hütelmoor.
Community composition varied much more strongly in HC 2 than in any other
core (Fig. 6). Bacterial communities in HC 1 were more similar to
communities in all Zarnekow cores than in other Hütelmoor cores (Fig. 6a).
The archaeal community in HC 1 was more similar to Zarnekow cores as
well (Fig. 6b). Environmental fit vectors suggest pH, oxygen, and alternative
TEA availability as important factors influencing microbial community
composition. The EC vector suggests the importance of brackish conditions in
shaping microbial communities in the Hütelmoor (Fig. 6a–c).
Depth distribution of qPCR abundances for total microbial
(16S), methanogen (mcrA), methanotroph (pmoA), and ratio of mcrA to pmoA gene copy numbers
in both sites. Microbial abundances were designated as numbers of gene
copies per gram of dry peat soil. Duplicate measurements per depth section
are shown against sampling depth using log-transformed values. Solid lines
indicate mean abundances for individual wetlands (n=4 for Hütelmoor
and n=5 for Zarnekow). Note that the plot at the right was split into two
plots to capture very high mcrA/pmoA ratios in the upper peat layer.
Total microbial and functional gene abundances
Quantitative PCR results show that, in both fens, mcrA abundance is up to
2 orders of magnitude greater than pmoA abundance (Fig. 7, Tables 1 and 2). Gene
copy numbers of mcrA are overall higher and spatially more stable in Zarnekow
than in Hütelmoor. Total microbial abundance declined with depth more
strongly in Hütelmoor than in Zarnekow (Fig. 7). There was a pronounced
decrease in microbial abundances at 20 cm depth in the Hütelmoor. For
example, 16S rRNA gene and pmoA gene copy numbers in deeper samples (below
20 cm
depth) are 1 order of magnitude lower than in upper samples on average,
while the mcrA gene abundances are approximately 2 orders of magnitude lower.
Hütelmoor samples also exhibited larger heterogeneity in terms of
abundances than Zarnekow samples. Contrary to previous studies, methanotroph
abundance did not correlate with dissolved CH4 or oxygen
concentrations.
Discussion
Fen geochemistry and relations to microbial community
composition
The rewetting of drained fens promotes elevated CH4 production and
emission, which can potentially offset carbon sink benefits. Few studies
have attempted to link microbial community dynamics and site geochemistry
with observed patterns in CH4 production and/or emission in rewetted
fens, while such data are crucial for predicting long-term changes to
CH4 cycling (Galand et al., 2002; Yrjälä et al., 2011; Juottonen
et al., 2012). In this study, we show that CH4-cycling microbial
community composition is related to patterns in site geochemistry in two
rewetted fens with high CH4 emissions, high methanogen abundances, and
low methanotroph abundances. Our results suggest that high methanogen
abundances concurrent with low methanotroph abundances are characteristic of
rewetted fens with ongoing high CH4 emissions. Thus, we present
microbial evidence for sustained elevated CH4 emissions in mostly
inundated rewetted temperate fens.
The environmental conditions and associated geochemistry of the two rewetted
fens were largely different. Depth profiles of porewater geochemical
parameters show the fens differed in EC throughout the entire peat profile,
while pH and concentrations of alternative TEAs differed at certain depths.
In general, concentrations of TEAs oxygen, sulfate, nitrate, and iron were
higher in the Hütelmoor. In Zarnekow, geochemical conditions varied
little across the fen and along the peat depth profiles (Fig. 2). As
expected, the geochemical heterogeneity was reflected in microbial community
structure in both sites, suggesting the importance of environmental
characteristics and associated geochemical conditions as drivers of
microbial community composition (Figs. 2, 3, 4, 6). The NMDS ordinations
(Fig. 6) show large variation in archaeal and bacterial community
composition in the coastal brackish fen and much less variation in the
freshwater riparian fen. Environmental fit vectors (Fig. 6) suggest that
salinity (indicated by the EC vector), pH, oxygen, and alternative TEA
availability are the most important measured factors influencing microbial
communities in the two fens. Patterns in microbial community composition
have previously been linked to salinity (e.g., Chambers et al., 2016; Wen et al., 2017), pH
(e.g., Yrjälä et al., 2011; Wen et al., 2017), and TEA availability in peatlands (e.g.,
He et al., 2015).
Comparing the geochemical depth profiles (Fig. 2) with the relative
abundance of bacteria and archaea (Figs. 3 and 4) provides a more complete
picture of the relationships between microbial communities and site
geochemistry, particularly with respect to TEA utilization. While the
porewater depth profiles suggest there is little nitrate available for
microbial use in HC 1, the relative abundance plot for Archaea showed that
this core was dominated by ANME-2d. ANME-2d were recently discovered to be
anaerobic methanotrophs that oxidize CH4, performing reverse
methanogenesis using nitrate as an electron acceptor (Haroon et al., 2013).
However, ANME-2d has also been implicated in the iron-mediated anaerobic
oxidation of methane (Ettwig et al., 2016), and the HC 1 site showed slightly
higher total iron concentrations. The relevance of ANME-2d as CH4
oxidizers in terrestrial habitats is still not clear (Winkel et al., 2018). Rewetting converts the
fens into widely anaerobic conditions, thus providing conditions suitable
for the establishment of anaerobic oxidation of methane, but this has yet to
be demonstrated in fens. The patchy yet locally high abundance of ANME-2d
both in Hütelmoor and in Zarnekow suggests an ecological relevance of
this group. Shifts towards less negative δ13C-CH4
signatures in the upper peat profile, for example, from -65 ‰ to
-60 ‰ in HC 1 (where ANME-2d was abundant), may indicate
that partial oxidation of CH4 occurred, but we could only speculate
whether or not ANME-2d are actively involved in this CH4 oxidation.
Although TEA input may be higher in the Hütelmoor, here, methanogenic
conditions also predominate. This finding contrasts the measured oxygen
concentrations in the upper peat profile, as methanogenesis under
persistently oxygenated conditions is thermodynamically not possible.
However, seasonal analysis of oxygen concentrations in both sites suggests
highly fluctuating oxygen regimes both spatially and temporary (data not
shown). Such nonuniform distribution of redox processes has already been
described elsewhere, in particular for methanogenesis (Hoehler et al., 2001;
Knorr et al., 2009). It is possible that oxygen levels in both fens are
highly variable, allowing for both aerobic and anaerobic carbon turnover
processes. Recent studies from wetlands also show that methanogenesis can
occur in aerobic layers, driven mainly by Methanosaeta (Narrowe et al., 2017;
Wagner,
2017), which were detected in a high abundance in this study (Fig. 5).
Further, oxygen may not necessarily be available within aggregates entailing
anaerobic pathways and, thus, the existence of anaerobic microenvironments
may also partially explain the seemingly contradictory co-occurrence of
oxygen and the highly abundant methanogens. Anaerobic conditions are also
reflected by the extensive and stable occurrence of the strictly anaerobic
syntrophs (e.g., Syntrophobacteraceae, Syntrophaceae) in most samples, even in the top centimeters. This
suggests that syntrophic degradation of organic material is taking place in
the uppermost layer and the fermented substances are readily available for
methanogens. As geochemistry and microbial community composition differ
among the sites in this study, it is thus notable that a similarly high
abundance of methanogens, and low abundance of methanotrophs, was detected in
both fens. The dominance of methanogens implies that readily available
substrates and favorable geochemical conditions promote high anaerobic
carbon turnover despite seasonally fluctuating oxygen concentrations in the
upper peat layer.
Low methanotroph abundances in rewetted fens
Methanogens (mainly Methanosaetaceae) dominated nearly all of the various niches detected in
this study, while methanotrophs were highly under-represented in both sites
(Figs. 3 and 4). Functional and ribosomal gene copy numbers not only show a
high ratio of methanogen to methanotroph abundance (Fig. 7), irrespective of
site and time of sampling, but also a small contribution of methanotrophs to
total bacterial population in both sites. Methanotrophs constitute only
∼0.06 % of the total bacterial population in the
Hütelmoor and ∼0.05 % at Zarnekow. It should be noted
that in this study we measured only gene abundances and not transcript
abundances, and the pool both of active methanogens and methanotrophs was
likely smaller than the numbers presented here (Freitag and Prosser, 2009;
Freitag et al., 2010; Cheema et al., 2015; Franchini et al., 2015). Also, as we
were unable to obtain microbial samples from before rewetting, a direct
comparison of microbial abundances was not possible. This was, therefore, not
a study of rewetting effects. For this reason, we performed an exhaustive
literature search on relevant studies of pristine fens. Compared to pristine
fens, we detected a low abundance of methanotrophs. Liebner et al. (2015),
for example, found methanotrophs represented 0.5 % of the total bacterial
community in a pristine, subarctic transitional bog–fen palsa, while mcrA and
pmoA abundances were nearly identical. In a pristine Swiss alpine fen, Liebner
et al. (2012) found methanotrophs generally outnumbered methanogens by an
order of magnitude. Cheema et al. (2015) and Franchini et al. (2015)
reported mcrA abundances higher than pmoA abundances by only 1 order of magnitude
in a separate Swiss alpine fen. In the rewetted fens in our study, mcrA gene
abundance was up to 2 orders of magnitude higher than pmoA abundance (Fig. 7).
Due to inevitable differences in methodology and equipment, direct
comparisons of absolute gene abundances are limited. Therefore, only the
abundances of methanotrophs relative to methanogens and relative to the
total bacterial community were compared, rather than absolute abundances. We
are confident that this kind of “normalization” can mitigate the bias of
different experiments and allows a comparison of sites. Further, all primers
and equipment used in this study were identical to those used by Liebner et
al. (2012, 2015), making the comparison more reliable.
As most methanotrophs live along the oxic–anoxic boundary of the peat
surface and plant roots therein (Le Mer and Roger, 2001), the low
methanotroph abundances in both fens could be explained by disturbances to
this boundary zone and associated geochemical pathways following inundation.
In rewetted fens, a massive plant dieback has been observed along with
strong changes in surface peat geochemistry (Hahn-Schöfl et al., 2011;
Hahn et al., 2015). In addition to substrate (i.e., CH4) availability,
oxygen availability is the most important factor governing the activity of
most methanotrophs (Le Mer and Roger, 2001; Hernandez et al., 2015). The
anoxic conditions at the peat surface caused by inundation may have
disturbed existing methanotrophic niches – either directly by habitat
destruction and/or indirectly by promoting the growth of organisms that are
able to outcompete methanotrophs for oxygen. Heterotrophic organisms, for
example, have been shown to outcompete methanotrophs for oxygen when oxygen
concentrations are greater than 5 µM (van Bodegom et al., 2001). Our
microbial data support this conclusion, as Hyphomicrobiaceae, most of which are aerobic
heterotrophs, was the most abundant bacterial family in both fens. Incubation
data from Zarnekow (Fig. S1 in the Supplement) show that the CH4 oxidation potential is
high; however, incubations provide ideal conditions for methanotrophs and
thus only potential rates. It is likely that, in situ, the activity of methanotrophs
is overprinted by the activity of competitive organisms such as
heterotrophs. It is also possible that methane oxidation may occur in the
water column above the peat surface, but this was beyond the scope of this
study. Nevertheless, oxidation rates are low enough that emissions remain high, as demonstrated by the high dissolved CH4 concentrations
and ongoing high fluxes.
Comparable studies have so far been conducted in nutrient-poor or
mesotrophic fens where post-rewetting CH4 emissions, though higher than
pre-rewetting, did not exceed those of similar pristine sites (e.g.,
Yrjälä et al., 2011; Juottonen et al., 2005, 2012).
Nevertheless, there is mounting evidence linking CH4-cycling microbe
abundances to CH4 dynamics in rewetted fens. Juottonen et al. (2012),
for example, compared pmoA gene abundances in three natural and three rewetted
fens and found them to be lower in rewetted sites. The same study also
measured a lower abundance of mcrA genes in rewetted sites, which was attributed
to a lack of available labile organic carbon compounds. In peatlands, and
especially fens, litter and root exudates from vascular plants can stimulate
CH4 emissions (Megonigal et al., 2005; Bridgham et al., 2013; Agethen and
Knorr, 2018), and excess labile substrate has been proposed as one reason for
substantial increases in CH4 emissions in rewetted fens
(Hahn-Schöfl et al., 2011). Future studies should compare pre- and
post-rewetting microbial abundances along with changes in CH4
emissions, plant communities, and peat geochemistry to better assess the
effect rewetting has on the CH4-cycling microbial community.