Introduction
Methane (CH4) is the third most abundant greenhouse gas contributing to
climate change (IPCC, 2014) – exceeded only by water vapor and carbon dioxide. Despite much lower concentrations than
carbon dioxide, it has a 32 times higher accumulative radiative forcing
potential (Etminan et al., 2016) over a time span of 100 years. In the ocean, the two major sources of methane are ongoing biogenic
production by microbes in anoxic sediment (Formolo,
2010; Reeburgh, 2007; Whiticar, 1999) and release of fossil methane from
geological storage (summarized by
Kvenvolden and Rogers, 2005; Saunois et al., 2016). Other sources include
release from permafrost, river runoff, submarine groundwater discharge (Lecher et al., 2016; Overduin et al., 2012), and
production from methylated substrates under aerobic conditions (Damm et al., 2010; Karl et al., 2008;
Repeta et al., 2016). More than 90 % of the methane sourced in the seabed
is oxidized within the sediment by anaerobic and aerobic oxidation (Barnes and Goldberg, 1976; Boetius and
Wenzhöfer, 2013; Knittel and Boetius, 2009; Reeburgh, 1976). The
remaining methane either diffuses into the water at the sediment surface or
is released as bubbles, which completely or partially dissolve while rising
through the water column (Leifer and Patro, 2002).
Dissolved methane is diluted by the surrounding water column (e.g., Damm and Budéus, 2003; Gentz et al., 2014),
in which it is used as a substrate and oxidized by aerobic methanotrophic
bacteria (methane-oxidizing bacteria, MOB; Hanson and Hanson, 1996; Murrell, 2010).
As a result, oceanic methane concentrations are frequently at low nanomolar
levels, leaving only a small fraction of sediment-sourced methane to
eventually exchange with the atmosphere (Karl et al., 2008;
Reeburgh, 2007).
By contrast, in the subarctic and Arctic shelf areas, shallow water depths
and seasonal sea-ice cover complicate the picture. High concentrations of
methane have been reported from the entire water column up to the surface
around Svalbard (Damm et al., 2005; Mau et al., 2013; Myhre et al., 2016),
the Siberian Shelf (Shakhova et al., 2010), and the Beaufort Sea (Lorenson et
al., 2016). In addition, during periods of near 100 % sea-ice cover, gas
exchange from the water column to the atmosphere is restricted (Loose et al.,
2011). Under ice-free conditions, methane concentrations are frequently found
in the range of 15 to 30 nmol L-1 or up to 7 times supersaturated with
regard to atmospheric equilibrium, while winter concentrations are often 10
to 100 times higher. Maximal concentrations of 5000 nmol L-1, or
oversaturation of 1600 times, have been reported from the Siberian Shelf
(Lorenson et al., 2016; Shakhova et al., 2010; Zhou et al., 2014).
Along with factors like oxygen and trace-metal availability (Crespo-Medina
et al., 2014; Sansone et al., 2001; Semrau et al., 2010), as well as local
oceanographic and geologic conditions (Schmale et al., 2015; Steinle et al., 2015),
dissolved methane concentration can be a
control on the community of MOB and thus methane oxidation rates (Crespo-Medina
et al., 2014; Kessler et al., 2011; Mau et al., 2013). Methane hotspots,
promoted by limited gas exchange under sea ice, might thus be candidate
locations for accumulation of methane oxidizers. In addition, sea ice,
particularly the ice–water interface, is a hotspot for microbial activity. The
ice surface, penetration of light, and constant exchange with the underlying
water column favor the development of communities composed of small
eukaryotic organisms, microalgae, prokaryotes, and viruses; the biomass often
being several orders of magnitude denser than in the underlying water
column (Thomas and Dieckmann, 2002).
MOB use methane as their sole carbon and energy source
(Hanson and Hanson, 1996). In the first step, methane is oxidized to methanol
catalyzed by the enzyme methane monooxygenase. Since methane monooxygenase is
characteristic of nearly all aerobic MOB (Knief, 2015), pmoA, the
gene encoding for a subunit of the membrane-bound particulate methane
monooxygenase, has been used as a specific molecular marker for detection and
characterization of aerobic MOB (Knief, 2015; Lüke and Frenzel, 2011;
reviewed by McDonald et al., 2008; Tavormina et al., 2008). Methanol is
further metabolized to formaldehyde, from which it is either mineralized to
carbon dioxide (CO2) or assimilated into organic compounds and
finally biomass (reviewed by Hanson and Hanson, 1996; reviewed by Strong et
al., 2015). Different types of MOB are distinguished by their phylogeny and
assimilation pathways for formaldehyde. While γ-Proteobacteria or
Type I MOB assimilate formaldehyde via the ribulose monophosphate pathway,
α-Proteobacteria or Type II MOB use the serine pathway (Hanson
and Hanson, 1996). Besides these two proteobacterial groups, MOB also occur
in the phylum Verrucomicrobia (e.g., Dunfield et al., 2007; Pol et al., 2007).
Methane-derived carbon is also assimilated in non-methane-utilizing
methylotrophs (non-MOB methylotrophs) or other bacteria in freshwater and
temperate marine environments. These non-methane oxidizers are suggested to
cross-feed on metabolites produced by the MOB (Hutchens et al., 2003; Jensen et al.,
2008; Saidi-Mehrabad et al., 2013).
Knowledge of the microbial communities responsible for methane oxidation in
the Arctic and subarctic is still sparse. During the last few years, the first
studies have determined methane oxidation rates from seawater in these
regions to cover a range from 10-4 up to 3.2 nmol L-1 d-1
(Gentz et al., 2014; Lorenson et al., 2016; Mau et al., 2013, 2017; Steinle
et al., 2015). In only two of these studies, both performed off Svalbard,
oxidation rate measurements were combined with analysis of the microbial
community. Steinle et al. (2015) quantified MOB by fluorescence in situ
hybridization and microscopy. Low but relatively constant cell-specific
oxidation rates were determined from the oxidation rates and MOB abundance,
indicating that MOB community size is an important control on the total
methane oxidation rate in the system. Mau et al. (2013) analyzed the
bacterial community with denaturing gradient gel electrophoresis (DGGE) of
the 16S gene and compared patterns of PCR products for pmoA.
Different MOB communities were observed in the meltwater layer and deep water
in this stratified system, also reflecting the observed differences in
methane oxidation rates. Only 1 of the 11 analyzed DGGE bands was
identified as methanotroph (from the genus Methylosphaera) from the
deep water in this study, while 0 were detected in the meltwater, possibly
due to the limitations of the method. To our knowledge, no high-throughput
sequencing studies of MOB in the Arctic have been
published in peer-reviewed literature to date.
We studied methane-oxidizing communities from seawater sampled on the
Beaufort Sea shelf close to Utqiagvik, Alaska. Incubation experiments were
performed under different methane concentrations to directly compare the
bacterial community structure with methane oxidation rates. Seawater
incubations, freshly sampled seawater, and sea ice were analyzed for their
entire community diversity (16S rDNA) and the presence of MOB (16S rDNA and
pmoA) using high-throughput Illumina MiSeq sequencing. The aim of this study was
to (1) investigate the response of the entire microbial community to an
increase in methane abundance, (2) identify types of MOB involved in the
oxidation of methane, (3) test for the presence of MOB in natural seawater
and sea ice communities, and (4) relate these community features to methane
oxidation rates.
Methods
Study site
Samples were collected at two sites between 7 and 15 April 2016 in
the Beaufort Sea (Table 1). Site Elson Lagoon (EL) is located north of
Utqiagvik, Alaska, (7 April 2016; 71.334∘ N,
-156.363∘ W). At the time of sampling, EL was covered with 1.5 m
thick sea ice; at approx. 1.5 m water depth, this left only a narrow layer of
water between the sea ice and the sediment. Site “ice mass balance buoy”
(IMB) is located 1 km offshore of Utqiagvik, close to the ice mass balance
buoy of the sea ice physics group of the University of Alaska, Fairbanks
(7 April 2016; 71.373∘ N, -156.548∘ W and 9 April 2016;
71.372∘ N, -156.540∘ W). This site was characterized
by 1 m thick fast ice cover and a water depth of approximately 7 m.
Station and sample list.
Name1
Date
Position
Samples
Parameters2
EL
7 April 2016
71.334∘ N, -156.363∘ W
water
in situ CH4, ox rate, T/S, DNA,
cell counts, nutrients
IMB 1
7 April 2016
71.373∘ N, -156.548∘ W
water
ox rate, DNA3, cell counts, nutrients3
IMB 2
9 April 2017
71.372∘ N, -156.540∘ W
water
ox rate, T, DNA3, cell counts,
nutrients3
ice core 1
in situ CH4, T/S, DNA
IMB 3
11 April 2015
71.372∘ N, -156.540∘ W
water
T/S3, DNA, nutrients, cell counts
IMB 4
15 April 2017
71.372∘ N, -156.540∘ W
water
in situ CH4, T/S, DNA3
ice core 2
in situ CH4, T/S, DNA, cell counts
1Station abbreviations are Elson Lagoon (EL) and ice mass balance buoy
(IMB). 2Parameters: in situ concentration and δ13CH4(in
situ CH4), net oxidation/production rate (ox rate), temperature and
salinity (T/S), collection of biomass for DNA extraction (DNA), cell counts,
nutrients. 3No complete depth profile available.
Sampling and instrument deployment
Seawater temperature and salinity were recorded with a YSI Professional
Plus probe (YSI, Ohio, USA) and a YSI 600 OMS V2 sonde (YSI, Ohio, USA).
Water was collected using either a peristaltic pump (Masterflex
Environmental Sampler, Cole-Parmer, Illinois, USA) or submersible pump
(Cyclone, Proactive Environmental Products, Florida, USA) from different
water depths. For determination of methane concentration and isotope ratios,
water samples were collected as described in Uhlig and Loose (2017a). Briefly,
in the field, 0.7 to 0.9 L of seawater was
transferred bubble-free directly into foil sample bags (no. 22950,
Restek, Pennsylvania, USA). On return to the laboratory, a 0.1 L headspace
of ultra-high purity nitrogen (Air Liquide, Anchorage, AK) was introduced
into the bags through the septa, and the samples were equilibrated at
30 ∘C for at least 6 h to measure in situ methane concentration and
carbon isotope ratios.
For DNA extractions, between 1 and 2 L of seawater were filtered onto
Sterivex® filter cartridges (Millipore) with 0.2 µM PES
filter membranes directly in the field, or were filled into foldable
polypropylene containers and filtered upon return to the laboratory. For
nutrient analysis, an aliquot of the flow-through of the
Sterivex® filters was collected in 15 mL polypropylene tubes
(Falcon Brand, Corning, New York, USA) and frozen at -80 ∘C.
Seawater was fixed with 2 % final concentration formaldehyde (Mallinckrodt
Chemicals, Surrey, UK) and stored at 5 ∘C for later
determination of the cell abundance.
Additionally, at site IMB, seawater temperature, salinity, and velocities
were recorded with an Aquadopp Profiler (Nortek AS, Norway) and a salinity
temperature recorder (SBE37SMP, Sea-Bird Scientific, Washington, USA). These
were deployed at about 7 m depth on the seafloor between 9 and 15 April.
Sea ice was collected at site IMB only, using a Kovacs Mark II ice corer
(Kovacs, Roseburg, Oregon, USA). The ice cores were sectioned into 15 cm and
split lengthwise. The outside was cleaned with a sterilized knife to remove
microbes possibly transferred from the sampling equipment. The core sections
were sealed into custom-made gas-tight tubes (Loose et al., 2011) for
determination of methane concentration and isotope ratios. In the laboratory,
the gas-tight tubes were flushed with ultrapure nitrogen for several gas
volumes (Lorenson and Kvenvolden, 1995).
Ice core 1 (IC1) was melted at 5 ∘C within a week due to technical limitations,
while ice core 2 (IC2) was melted with frequent mixing at room temperature within a day.
Samples for molecular biology and cell counts were collected from the melted sea ice similar to the
procedure described for seawater. In addition, the bottom 2 cm of one ice
core was sampled into a sterile sample bag (Whirlpak, Nasco, Fort Atkinson,
WI, USA) for molecular biology processing only. Sea-ice brine volume
fractions were calculated according to Cox and Weeks (1983).
Net methane oxidation/production and determination of isotope
fractionation factors
Rates for net methane oxidation/production were determined from the methane
mass balance according to Uhlig and Loose (2017a). In short,
seawater was sampled into multi-layer foil bags. In addition to a headspace
of hydrocarbon-free air (Air Liquide, Anchorage, AK), some sampling bags
were supplied with a spike of methane. Final dissolved methane
concentrations ranged between 3.0 and 4000 nmol L-1, representing
approximately 0.2 times (no methane addition, resulting in degassing of in
situ methane to the headspace, 0.2×), 2 times (2×), 10 times (10×), and 200
times (200×) of the in situ methane concentration. Samples were incubated at
0 to 1 ∘C for 5 to 46 days. Some variation in the incubation
period was introduced by logistical constraints. To account for potential
diffusive loss of methane, a killed control was prepared for the 200×
treatment by adding 0.1 M NaOH.
Assuming first order kinetics for oxidation of methane (Reeburgh et
al., 1991; Valentine et al., 2001), net oxidation/production rate constants
(kox) were determined from the methane mass balance in the incubations (Uhlig and Loose, 2017a) as
lnnCH4total,tinCH4total,ti-1=-kox,ppm×ti-i-1
with nCH4total,ti being the total molar mass
of methane in the bag at time ti.
The net oxidation/production rate (rox) was calculated from the first
order constant and the in situ concentration of methane in the water:
rox=kox×c(CH4)w,insitu.
Isotopic fractionation factors of methane oxidation (αox=k12k13)
were determined as described in Preuss et
al. (2013), using the isotope fractionation approach (Coleman et al.,
1981).
lnc(CH4ti)c(CH4t0)1αox-1=ln1000+δ13CH4ti1000+δ13CH4t0,
where the isotope ratios are described in δ-notation δ13CH4=RsampleRstandard-1, and R is the isotope ratio of
13CH4 / 12CH4 in the sample or standard (VPDB, Vienna
Peedee Belemnite; McKinney et al., 1950).
Alpha can be determined as αox=1m+1 from the slope (m)
of the linear regression between lnc(CH4ti)c(CH4t0) and ln1000+δ13CH4ti1000+δ13CH4t0.
Analytical procedures
Methane concentration and stable isotope ratios
Methane concentrations and stable isotope ratios were determined with a
Picarro G2201-i cavity ring-down spectrometer (Picarro, Santa Clara,
California, USA) coupled to the Picarro A0314 small sample isotope module (SSIM) as described by Uhlig and Loose (2017a). After
equilibration, the headspace above the seawater or melted ice was subsampled
with a gas tight syringe and 1 to 15 mL was injected into the SSIM.
Measurements were performed in fast measurement mode. Dissolved methane
concentrations were calculated as described in Magen et al. (2014), with the
equilibrium constant according to Yamamoto et al. (1976).
Nutrient analysis and flow cytometry
Phosphate, nitrate, and nitrite concentrations were determined using a
QuickChem QC8500 automated ion analyzer (Lachat, Loveland, Colorado, USA).
The total number of prokaryotic cells was counted on a BD
InfluxTM flow cytometer with BD FACSTM software.
Formol-fixed samples were stained with a final concentration of 1 ×
SYBR Green I (Invitrogen, Molecular Probes, Eugene, Oregon, USA) for 20 to
45 min at room temperature in the dark before analysis.
Nucleic acid extraction and sequencing
DNA was extracted with the PowerWater® DNA extraction kit
(MoBio, Carlsbad, California, USA). To remove the filter membrane, the
Sterivex® cartridge was opened with a pair of sterilized
pliers. The filter membrane was cut out along the edge with a scalpel,
transferred into the bead tube, and DNA subsequently extracted according to
the manufacturer's protocol. A minor modification was made: the tube was
vortexed once for 3 min, rotated 180∘, and then vortexed for
another 3 min. The DNA was eluted in 80 µL buffer PW6, after
incubating the buffer for 1 min on the membrane. Quantification was
conducted with a Qubit®2.0 fluorometer
(Invitrogen, Carlsbad, California, USA).
The V4-V5 region of the 16S rRNA gene was amplified with forward primer 518F
(5′-xx-CCAGCAGCYGCGGTAAN-3′), and an 8:1:1 mix of the reverse primers 926R1
(5′-yy-CCGTCAATTCNTTTRAGT-3′), R2 (5′-yy-CCGTCAATTTCTTTGAGT-3′), and R3
(5′-yy-CCGTCTATTCCTTTGANT-3′) (Nelson et al., 2014). Primers included 33 base
pair (bp) adapters (xx, yy) at the 5′ end. The final volume of
20 µL PCR reaction contained 0.2 µL PfuUltra II
fusion HS DNA polymerase (Agilent Technologies, Santa Clara, California,
USA), 50 µM each forward and reverse primer, 25 µM each
dNTPs (Thermo Scientific, Waltham, Massachusetts, USA),
10 µg mL-1 BSA (Thermo Scientific, Waltham, Massachusetts,
USA) and 1 ng template DNA. After initial denaturation for 2 min at
95 ∘C, DNA was amplified in 30 cycles of 30 s 95 ∘C
denaturation, 30 s 55 ∘C annealing, and 30 s at 72 ∘C for
extension, with a final extension of 2 min at 72 ∘C. The
pmoA subunit of the particulate monooxygenase (pMMO) was amplified
with primer pair 189f (5′-xx-GGNGACTGGGACTTCTGG-3′) and mb661r
(5′-yy-CCGGMGCAACGTCYTTACC-3′) (Holmes et al., 1999; Lyew and Guiot, 2003).
The PCR conditions were the same as described for the V4-V5 amplicon. All
amplicons were purified with Agencourt®
AMPure® XP magnetic beads (Beckman Coulter,
Indianapolis, Indiana, USA) at a ratio of 0.7 × bead solution per
PCR reaction volume and washed with 80 % ethanol.
The primer sequences specified above included adapter sequences (xx, yy) to
attach Nextera indices and adapters in a second PCR reaction of 6 cycles
with 50 ng template DNA
(http://web.uri.edu/gsc/next-generation-sequencing/, last access: 27 May 2018). Amplicons were
sequenced with Illumina MiSeq at 2 × 250 bp read length.
Sequence analysis
V4-V5 region of 16S rRNA gene
Demultiplexing and adapter removal was performed with Illumina software.
V4-V5 sequence quality control and clustering was performed in mothur (Schloss et al., 2009) as follows.
Contigs were prepared from forward and reverse reads and culled if they
contained ambiguous bases or homopolymers longer than 6 bases. Contaminating
sequences observed in kit and filter blanks accounted for 1.4 % of all
sequences and were removed from all samples. After alignment to the Silva
small subunit reference database (v123; Quast et al.,
2013), the 408 bp long sequences were preclustered (1 % variability
allowed) and filtered for chimeras (de novo algorithm) with the UCHIME (Edgar et al., 2011) wrapper in mothur. Sequences
identified as chloroplast, mitochondria, Archaea, Eukaryota, or unknown were
removed and operational taxonomical units (OTUs were built at a 3 %
distance level with the opticlust algorithm. OTUs with fewer than 2 reads
were removed from further analysis.
Visualization and further analysis of sequencing data were performed in R
version 3.2.3 (R CoreTeam, 2015) in RStudio Version 0.99.903. Species
diversity was analyzed using the phyloseq package (McMurdie and Holmes, 2013)
to determine richness (Shannon and Simpson indices) and differences in
community structures (Bray–Curtis dissimilarities). Differences in community
structure associated with different methane spike concentrations were
determined via analysis of similarity (ANOSIM) in the package vegan (Oksanen
et al., 2017) on three predefined groups: in situ (n = 9), 0.2×
(n = 2), and 10× (n = 3). Groups 1×, 200× short,
and 200× long with n = 1 (Table 2) were excluded from the analysis.
Samples sequenced for V4-V5 and pmoA.
Treatment1
Station
V4–V5
pmoA
no. of samples
no. of samples
in situ
IMB
9
4
EL
1
1
sea ice
7
0
0.2×, 10 days
IMB 1
2
3
EL
1
1
2×, 5 days
IMB 2
1
1
10×, 46 days
IMB 1
3
2
EL
1
1
200×, 6 days
IMB 2
1
1
200×, 41 days
IMB 2
1
1
1The different incubation times resulted from logistical constraints.
Identifying potential methane-oxidizing bacteria
To select groups representing methylotrophs and methanotrophs, 16S OTUs were
filtered according to their phylogenetic annotation assigned by mothur for
containing the string “meth” on family, order, and genus level. This filter
is expected to find 97 % of taxonomically annotated methanotrophs,
according to a current review on the diversity of methanotrophs (Knief, 2015).
Further, phylogenetic groups potentially involved in methane dynamics were
identified as differentially more abundant 16S OTUs between incubations
(0.2×, 10×, 200×) and in situ samples using DESeq2 (Love et
al., 2014). Only OTUs with an adjusted p value in DESeq2 < 0.05 were
kept for further analysis. OTUs identified from spike concentration 0.2× were considered
to represent groups favored due to the incubations (the “bottle effect”)
rather than addition of methane, and removed from further analysis.
Treatments EL 0.2×, EL 10×, and IMB 2× (Table 2) were not included in this
analysis, since no replicate samples were available. The abundance of all
candidate 16S OTUs, identified as described above, was determined within
every in situ or incubated sample.
Absolute numbers of methanotrophs and methylotrophs were calculated by
multiplying the relative 16S sequence abundance with flow cytometric cell
counts. The absolute numbers were further corrected for the mean of the 16S
gene copy number for the lowest taxonomic rank (class to genus) available in
the rrnDB database (Stoddard et al., 2015).
Particulate methane monooxygenase: pmoA
In addition to 16S genes, the alpha subunit of the particulate methane
monooxygenase (pmoA) was used as a molecular marker for MOB. Only
pmoA forward reads were analyzed. High-quality pmoA reads
were retrieved according to the following protocol. Using mothur (Schloss et
al., 2009), all reads were trimmed to a length of 225 bp to remove sequence
fractions with a mean quality score below 30 (fastqc; Andrews, 2010). In
addition, reads were clipped whenever the average quality score over a 50 bp
window dropped below 30. Sequences with ambiguous bases and homopolymers
larger than 6 bp were culled. Only sequences that translated into
uninterrupted protein reading frames (Emboss 6.60 / transseq; Rice et al.,
2000) were kept for further analysis. Nucleic acid sequences were aligned to
a reference dataset of pmoA sequences (fungene; Fish et al., 2013)
and sequences of a length of at least 220 bp were preclustered (1 %
variability allowed). De novo chimera filtration was run with the UCHIME
(Edgar et al., 2011) wrapper in mothur. A similarity of 93 % between
pmoA sequences was defined to match the 97 % cutoff as species
definition for the 16S gene (Lüke and Frenzel, 2011). PmoA OTUs
were built at a maximal distance of 7 % between the furthest neighbors to
maximize resolution between OTUs due to the short read length and limited
number of unique sequences (Table S1 in the Supplement). To determine the
phylogenetic relationship of pmoA sequences, nucleotide sequences
were aligned against selected reference sequences in Mafft 7.017 (Katoh and
Standley, 2013) and a neighbor joining tree calculated in Clustal 2.1 (Larkin
et al., 2007) with 1000 replications.
Results
Water column properties
On 7 April 2016, the narrow layer of water between the sediment and ice in
EL (n = 1) had a salinity of 21 and a temperature of
-1.5 ∘C. Phosphate and nitrate concentrations were
0.74 µM and 4.87 µM, respectively. Methane concentration
for EL (n = 1) was 53.2 nmol L-1 with a stable isotope
ratio of -73.8 ‰ (Fig. 1) and cell density 7.7 × 104
cells mL-1. For most days during the sampling period, the water column
at station IMB was characterized by temperatures around -1.8 ∘C
and salinities of 33.9 to 36.4 (Fig. 1, Figs. S1 and S2 in the Supplement).
Between 11 and 13 April warmer water (max. temperature observed
-0.9 ∘C) was advected, coinciding with a change in current
direction. A lower salinity of 27.5 at the ice–water interface indicates
melting of the sea ice. Phosphate concentrations at station IMB were
0.99 ± 0.33 µM (n = 9) and nitrate
6.59 ± 4.04 µM (n = 9), with neither showing any trends in
the depth profiles (data not shown). Nitrite concentrations were below
detection (0.3 µM based on technical replicates). Water column
methane concentrations at station IMB ranged between 9.2 and
25.3 nmol L-1 (16.3 ± 7.2 nmol L-1, n = 5; Fig. 1),
with stable isotope signatures between -55.4 ‰ and -70.5 ‰
(-60.6 ‰ ± 6.3 ‰, n = 5). Total prokaryotic cell
densities, determined as SYBR Green stained cells with flow cytometry, were
6.9 × 104 ± 5.7 × 103 cells mL-1 (n = 16).
Ice cores
Temperature and salinity profiles of the two sea-ice cores sampled on 9 and 15 April are shown in Fig. 2. Brine volume fractions above 5 % indicate
that the ice was permeable to water and gases (Golden et al., 1998) in the bottom 50–100 cm,
while the upper part of the ice was impermeable. Methane concentrations in
the ice were higher than in the water (83.9 ± 35.0 nmol kg-1, n = 9),
while the isotope signatures were close to seawater
(-60.4 ‰ ± 3.5 ‰, n = 9). Ice
core 1 (IC1), sampled on 7 April, had generally higher methane
concentrations and more positive isotope signatures (72.3–144.3 nmol kg-1, -54.4 ‰ to 62.0 ‰ )
than ice core 2 (IC2), sampled on 15 April (53.3–77.6 nmol kg-1, -59.0 ‰ to -61.6 ‰ ). Microbial
activity during storage of IC1 at 5 ∘C for 1 week before
analysis might have led to the differences in methane concentrations and
isotope ratios. For ice samples, cell counts were performed on IC2 only;
they show an increase from 1.0 × 104 cells mL-1 in the
top layers to 8.2 × 105 cells mL-1 in the bottom two centimeters
of the ice core.
Net methane oxidation/production and isotope fractionation
The methane oxidation potential of microbial seawater communities at stations
EL and IMB was determined from the methane mass balance in incubation
experiments (Table 3; Uhlig and Loose, 2017a). Final dissolved methane
concentrations ranged between 3.0 and 4000 nmol L-1,
representing approximately 0.2 times (0.2×) to 200 times (200×)
the in situ concentration. Oxygen concentrations at the end of the long
incubations ranged between 116 and 126 % saturation, while oxygen
concentrations at the end of the short incubations were not determined.
Methane oxidation parameters during long-term incubation
experiments. n: number of replicates,
c(CH4)initial: approximate initial methane
concentration, kox: net oxidation/production rate constant,
rox: net oxidation/production rate at in situ concentration,
αox: isotopic fractionation factor during oxidation.
Oxidation rates and rate constants are replicated from Uhlig and
Loose (2017a).
Treatment
na
Incubationb
c(CH4)initial
c(CH4)final
kox
rox
αox
(days)
(nmol L-1)
(nmol L-1)
(d-1)
(nmol L-1d-1)
0.2× EL
1
10
12.7
12.9
0c
0c
0.9591
10× EL
1
46
132.3
67.7
1.01 × 10-2
0.54
1.0230
0.2× IMB 1
5
10
4.4 ± 0.5
5.0 ± 0.4
-1.05 × 10-2
negatived
0.994 ± 0.0113
2× IMB 2
4
5
37.9 ± 1.8
36.5 ± 1.4
0c
0c
0.9898 ± 0.0104
10× IMB 1
5
46
123.0 ± 5.5
69.4 ± 36.5
9.18 × 10-3
0.15 ± 0.02
1.0225 ± 0.0005
200× IMB 2 short
7
6
3937.9 ± 148.7
3427.6 ± 160.4
0c
0c
1.0005 ± 0.0005
200× IMB 2 long
2
41
4089.5 ± 26.1
129.6 ± 95.5
6.62 × 10-2
1.08 ± 0.17
1.0103 ± 0.0002
200× IMB 2 NaOH
1
41
3953.7
3620.7
0c
0c
0.9998
aReplicates are from different water depth. bThe
different incubation times resulted from logistical constraints.
cOxidation rate constants were not significantly different from 0 at a
95 % confidence level. dNegative net oxidation rate constant
indicating methane production.
Water column properties during the time series near Utqiagvik.
Salinity (a), temperature (b), density (c), cell abundance (d), methane
concentration (e) and stable isotope ratios (f). Error bars on cell numbers
(d) represent the standard deviation on two technical replicates.
Salinity and temperature (a and b) were determined with a YSI hand held (circle) and
YSI sonde (triangle). Salinity for the YSI hand held on 15 April was
determined in the laboratory, thus in situ temperature is missing.
Salinity for the YSI sonde on 9 and 15 April is missing due to freezing of
the sensor. Methane data is only available for EL on 7 April and for IMB 4 on 15 April.
Sea ice temperature (a), bulk salinity (b), brine volume fraction (c),
prokaryotic cells mL-1 sea ice (for IC2 only) (d), methane
concentration (e), and stable isotope ratios (f). The vertical red dotted
line in (c) shows a brine volume fraction of 5 %, the threshold for
permeability (Golden et al., 1998). IC1 had sediment included into the ice matrix at depths 30–46 cm, indicated by the
gray box.
Shannon indices of alpha diversity for V4-V5 amplicons.
Net oxidation rates discussed here were published in Uhlig and Loose (2017a)
and are summarized for comparison with the microbial community structure.
Short incubations (≤ 10 days) did not show significant oxidation, while
long-term incubations (41–46 days) did. Surprisingly, four out of five
replicates of treatment 0.2× IMB showed a statistically significant
increase in methane of about 0.62 ± 0.21 nmol L-1 (n = 5)
within 10 days (Fig. S3). In long-incubation samples with significant methane
oxidation (10× and 200× spikes), the isotopic signature of the
residual methane increased toward heavier (more positive) signatures with
fractionation factors α of 1.0230 (10× EL), 1.0225 (10×
IMB), and 1.0103 (200× IMB).
Bacterial community structure
The V4-V5 region of the 16S rRNA gene was sequenced from a total of 10
seawater samples and 7 ice samples (Table 2). Non-metric multidimensional
scaling analysis of the Bray–Curtis diversity revealed high similarity
across the in situ water samples analyzed for 16S diversity (Fig. 4).
Samples from site IMB clustered together repeatedly, and we did not observe
any differences in community structure coinciding with water depth or
temperature (Fig. 4). For the IMB samples, IMB 2 was the only sample
slightly different from the other in situ samples, though IMB 1, IMB 2, and IMB 4
are all characterized by a colder water mass. Only IMB 3 showed some
influence of an incoming warm water mass in the YSI profile (Fig. 1),
though not yet reaching the bottom (Fig. S1), but this shift
is not seen in the community structure. In contrast to the in situ water
samples, the community structure of incubated samples is driven by
incubation time. While communities in the short-incubation treatments (5–10 days; 0.2×, 2×, 200× short) were similar to the in situ samples, the long
incubations (41–46 days; 10×, 200× long) clearly deviated from the in situ
samples. In both the long and short incubated clusters samples originating
from IMB 1 (0.2×, 10×) and IMB 2 (2×, 200×) are present (Fig. 4, Table 2).
Microbial communities in ice cores were clearly distinct from those in the
water samples and were more distant to each other than were the communities
in water samples.
In the in situ seawater communities, Proteobacteria were dominant with
relative sequence abundances of 59.5 and 65.5 % ± 2.5 % for EL
(n = 1) and IMB (n = 9), respectively (Fig. S4). Within the
phylum of Proteobacteria,
α- and γ-Proteobacteria made up
the majority. The second most abundant phylum was Bacteroidetes with 23 and 19.6 % ± 1.4 % for EL and IMB, respectively.
Similar to the seawater, sea ice (n = 7) showed a dominance in
Proteobacteria (58.9 % ± 9.8 %), but Bacteroidetes sequences
(29.1 % ± 11.7 %) were slightly more abundant in the ice than in
the water. γ-Proteobacteria dominated in all but one sample (IC2
30–46 cm). This one sample, which had clearly visible sediment included
into the sea ice structure, was dominated by α-Proteobacteria.
In all incubated samples that were sequenced (n = 10), species richness
decreased (Fig. 3) and the communities shifted toward higher fractions of
γ-Proteobacteria over time. In short incubations (5–10 days; n = 5)
γ-Proteobacteria dominated with 61.8 % ± 2.9 % of
sequences, while reaching 81.0 % ± 11.1 % in long-incubation
samples (41–46 days; n = 4). In particular, one operational taxonomical
unit (OTU), from the genus Oleispira, was very abundant in the long-incubation
samples, with 50.1 to 76.3 %, compared to abundances < 0.04 % in
the in situ samples. The same OTU was only slightly more abundant in the
short-incubation treatments (0.5 to 1.6 %) compared to in situ
abundances. In addition to the shift in community structure, total cell
densities increased to 1.9 × 105 and 3.3 × 106
cells mL-1 for short and long incubations, respectively, based on flow
cytometric cell counts.
Methanotrophs, methylotrophs, and differentially abundant OTUs
Using their 16S taxonomic annotation, we identified six groups of aerobic
methanotrophs (MOB; Fig. 5). With a maximum of 1.76 % ± 0.73 %,
the relative abundance of MOB was low in all samples (Table 4). Four MOB
grouped in the Methylococcales (γ-Proteobacteria), specifically
Marine Methylotrophic Group 1 and 2 (MMG1, MMG2), unclassified
Methylococcales and the Milano-WF1B-03 family. The three remaining MOB OTUs
belonged to the genera Methylobacterium and Methyloceanibacter (α-Proteobacteria) and Candidatus “Methylacidiphilum”
(Verrucomicrobia). MOB OTUs were more abundant in natural seawater samples
than in sea ice (maximal 0.11 % in IC1 0–16 cm), but in contrast to the
seawater, α-Proteobacteria MOB dominated in the sea ice.
Non-metric multidimensional scaling analysis (unitless) of
Bray–Curtis dissimilarities of the 16S read data. The low 2-D stress of 0.06
indicates a good two-dimensional representation of the multidimensional
dataset with very low prospect of misinterpretation.
Relative abundances (a) and inferred cell numbers (b) of
methylotroph OTUs by family. Sampling sites for water samples are Elson
Lagoon (EL) and ice mass balance buoy (IMB). Ice cores (IC1 and IC2) were
collected at site IMB on 9 and 15 April, respectively. The sample name
indicates the methane spike concentration compared to in situ methane
concentration for IMB and EL, and the ice core section in centimeters from top (0 cm,
ice–snow interface) to bottom (ice–water interface). IMB in situ, 0.2× and
10× are averages of the respective number (n) of samples, all other samples
were n = 1. Red and yellow shades indicate MOB, while blue shades indicate
non-MOB methylotrophs. (a) α-Proteobacteria (A),
β-Proteobacteria (Beta), and γ-Proteobacteria are shown;
Verrucomicrobia Incertae Sedis were <0.003 % in (a). Scale for
α-Proteobacteria is the same as for β- and γ-Proteobacteria. (b) Cell numbers were calculated from the relative
abundances shown in (a) with the cell counts from flow cytometry and
corrected for the 16S copy number per cell. Verrucomicrobia Incertae Sedis
and α-Proteobacteria were <8 cells mL-1.
Relative abundance of methylotroph OTUs in situ, split into
methanotrophs (MOB) and non-MOB methylotrophs (Methy)
in situ
in situ
0.2×, 2×
10×
200×
sea ice
seawater
(short)
(long)
(short + long)
n
7
10
4
4
2
Mean ± sd
MOB
0.04 % ± 0.04 %
0.24 % ± 0.09 %
0.09 % ± 0.01 %
0.17 % ± 0.15 %
1.76 % ± 0.73 %
Methy
0.74 % ± 0.50 %
0.65 % ± 0.12 %
0.34 % ± 0.13 %
0.70 % ± 0.62 %
0.61 % ± 0.29 %
min.
MOB
0.00 %
0.06 %
0.08 %
0.06 %
1.03 %
Methy
0.11 %
0.51 %
0.23 %
0.20 %
0.32 %
max.
MOB
0.11 %
0.45 %
0.11 %
0.43 %
2.49 %
Methy
1.53 %
0.83 %
0.56 %
1.72 %
0.90 %
Furthermore, four clades of non-methane-utilizing methylotrophs (non-MOB
methylotrophs) were identified, grouping into γ-Proteobacteria
Marine Methylotrophic Group 3 (MMG3) and Methylophaga, and to the β-Proteobacteria Methylophilaceae (Methylotenera, OM43 clade). Non-MOB methylotroph OTUs
were more abundant than MOB OTUs with the exception of the 200× incubation
treatments (Fig. 5, Table 4). Ice samples showed the largest difference in
abundance between non-MOB methylotrophs and MOB, with a ratio of 21:1
between the two groups. Ice samples also had the highest overall relative
abundance of methylotrophs (MOB and non-MOB) of all in situ samples (max:
1.63 %, IC1 0–16 cm). Only the 200× long incubations had a higher total
number of methylotrophs (3.3 %), while this sample was in addition
dominated by MOB (2.49 %). The second highest relative abundance of MOB
was found for in situ EL and IMB with 0.24 % ± 0.09 % (n = 10).
Taking into account the total cell number, a strong increase in MOB groups
MMG1 (2 to 700 times) and Milano-WF1B-03 (25 to 75 times) was observed for
the 10× and 200× long-incubation samples compared to in situ conditions
(Fig. 5b).
Taxonomic groups that became differentially more abundant in the incubated
samples than in natural communities were the y-Proteobacteria
Oleispira, Colwellia, and Glaciecola, as well as
Rhodobacteracea (α-Proteobacteria). Except for Oleispira,
which became dominant, the other taxa had relative sequence read abundances
from 1.1 to 12.6 % after the oxidation experiments, compared to
abundances < 0.25 % for in situ samples (Fig. S5).
Particulate methane monooxygenase (pmoA) sequences
A 225 bp section of the particulate methane monooxygenase gene
(pmoA) was sequenced in a total of 15 samples (Table 2). The
absolute abundance of pmoA fragments obtained in sequences ranged
from 9331 (IMB in situ, 6.5 m depth) to 72781 (IMB 200× long) reads.
In general, incubations with higher methane concentration had more
pmoA reads than incubations with lower methane concentration or in situ samples.
About three times more reads were filtered from the EL in situ sample (33 844 reads, n = 1)
than the IMB in situ samples
(11700 ± 1833, n = 4).
Two of the 59 pmoA OTUs made up 96.8 % of all sequences, while all other OTUs
individually represented ≤ 1 % of the pmoA sequences. The most abundant
OTU (71.0 % of all sequences) clustered with two uncultured isolates from
methane seeps (NCBI accession: HQ738559, EU444875) in the deep sea-3/OPU3
subgroup of γ-Proteobacteria Type I MOB (Hansman et al., 2017; Knief, 2015;
Lüke and Frenzel, 2011). The second most abundant (25.8 %) OTU was
related to Methyloprofundus sedimentii, another Type I MOB. Most of the low-abundance OTUs also
clustered within the Type I MOB, while only three OTUs (0.07 % of all
pmoA sequences) clustered with Type II α-Proteobacteria MOB pmoA sequences
(Methylocystis, Methylosinus).
Discussion
Methane concentration and stable isotope ratios in seawater and
ice
Seawater methane concentrations in April 2016 close to Utqiagvik, Alaska were
supersaturated 2.5 times to 7 times compared to atmospheric equilibrium
(3.6 nmol L-1). The concentration at site EL (52.90 nmol L-1,
n = 1, 7 April 2016) was in the range of a study by Lecher et al. (2016)
in EL under ice-free conditions (3.3–124.0 nmol L-1). At
site IMB, concentrations were slightly lower
(9.5–25.2 nmol L-1 : n = 5, 15 April 2018) than previously
reported from the same area for ice-free (Lecher et al., 2016; mean:
40.6 nmol L-1), and ice-covered conditions (Zhou et al., 2014;
March/April: 37.5 ± 6 nmol L-1). Shallower depths at IMB exhibit
lower methane concentrations (Fig. 2), and the isotopic signature mirrors
this pattern with more positive values toward the surface. This indicates
that methane might be biologically oxidized on the way through the water
column, after being released from the sediment.
The sea-ice bulk methane concentrations observed in this study
(53–144 nmol kg-1) are significantly higher than in a study from the
same area (Zhou et al., 2014), but fall within values reported for the
Beaufort Sea (5–1260 nM, Lorenson and Kvenvolden, 1995).
Methane carbon isotopic signatures (-54.4 to -63.8 ‰)
are comparable to the higher end of previous studies for bulk sea ice (-52.1 to -83.4 ‰ , Lorenson and
Kvenvolden, 1995) and sea-ice brine (-75 ‰ , Damm et
al., 2015).
Although both ice cores were sampled within 300 m distance from each other
at site IMB, they differ in concentration and isotope signature. These
differences could either be caused by spatial variability between the two
ice cores or differences in the processing procedure described in Sect. 2.2. Spatial variability as driving difference between the two ice cores is
corroborated by the sediment present at 30–46 cm depth in IC1, which was
not observed in IC2, indicating that both ice cores have different freezing
histories. The same event that led to inclusion of the sediment into IC1
possibly resulted in inclusion of higher methane concentrations into IC1
compared to IC2 during freeze-up. In addition, microbial processes like
oxidation of methane or methanogenesis could have taken place in situ or
during sample processing and storage. Microbial oxidation of methane,
particularly in the two middle sections (30–46 and 52–86 cm depth),
might have led to the observed shift toward more positive carbon isotope
ratios (Fig. 2). The different bacterial community introduced through the
sediment (Fig. S4) might have favored oxidation in those two
sections compared to the top and bottom sections. MOB identified by our
approach were, however, neither more abundant nor phylogenetically distinct
in the sediment-loaded section compared to the other sections (Fig. 4a).
Another microbial process that may have led to the discrepancies between IC1
and IC2 could be methane production from ice algae-derived organic carbon in
IC1. With typical carbon isotopic signatures of -20 to
-30 ‰ for ice-derived carbon (e.g., Wang et al., 2014),
methane produced from this substrate would be enriched
in 13C (more positive) compared to the initial pool of methane (about
-60 ‰, Figs. 2 and 6). Yet sequences of bacterial
taxa that might indicate anoxic conditions (Eronen-Rasimus et al., 2017), which would favor anaerobic methane production, were not
significantly more abundant in IC1 than in IC2 (Table S2).
δ13CH4 vs. reciprocal of CH4 concentration
(Keeling-type plot) of ice cores. Within each ice core, a shift to more
positive δ13CH4 values in combination with a decrease in
CH4 concentration indicates microbial oxidation. Comparing IC2 to IC1,
the shift toward higher concentrations and more positive δ13CH4 (see also Fig. 2) in IC1 might indicate CH4 production
from a substrate with heavier isotope signature, compared to the values in
IC2.
Compared to the underlying water column, methane concentrations in the sea
ice were 2 to 5 times higher. Further, the isotope signatures indicate
less oxidized methane (-60.4 to -63.8 ‰) in most of the ice sections compared to the
upper water column (-55 ‰). Lorenson and Kvenvolden (1995) report higher methane concentrations in sea ice than in
the water column for the Beaufort Sea. They attributed the high methane
concentrations in the fast ice to inclusion of sediment-sourced methane
during the initial freeze-up over the shallow shelf at < 10 m water
depth (Lorenson et al., 2016). Methane concentrations in IC2,
which are close to water column concentrations reported in previous studies
for our study region (Lecher et al., 2016; Zhou et al., 2014), suggest the same process for our ice cores. Further, in our
study, the lower methane concentrations together with more positive
(heavier) isotopic signature in seawater compared to ice, might indicate
that the microbial community in the water column is oxidizing more methane
during the ice-covered period than in the freeze-up period. Higher oxidation
rates during ice-covered periods compared to ice-free conditions were
previously reported for the Beaufort Sea. Due to reduced sea–air gas
exchange, higher methane concentrations can build up under sea-ice cover,
which might lead to higher oxidation rates (Lorenson and
Kvenvolden, 1995).
Methane dynamics at different methane concentrations
Net methane oxidation/production rates were determined from water sampled at
stations IMB 1 and IMB 2 on 7 and 9 April 2016. Both days were characterized
by the cold water temperatures (≤-1.8 ∘C; Fig. 1). Different
water masses have previously been reported to influence the methane
oxidation potential of water column microbial communities off Svalbard (Steinle et al., 2015). In this study, we observed a change
in current direction and water temperature consistent with advection of a
different water mass into the study area (Fig. S1). However,
this change occurred on 12 April subsequent to sampling IMB 3, and thus this
event would not have influenced the net oxidation potential determined in
this study.
Net oxidation rates of the long-incubation treatments at 10× (46 days) and
200× (41 days) methane concentration fall into the mid-range of rates
published for Arctic and subarctic environments (Damm
et al., 2015; Gentz et al., 2014; Lorenson et al., 2016; Mau et al., 2013,
2017; Steinle et al., 2015) or marine sites with high oxidation rates at oil
spills or gas flares (Leonte et al.,
2017; Redmond et al., 2010; Valentine et al., 2010), as discussed in Uhlig
and Loose (2017a). The fractionation factors (αox) that we observed are higher than previously reported from
cold marine environments with a range of αox
from 1.002 to 1.017 (Cowen et al., 2002; Damm et al., 2008; Grant and Whiticar, 2002; Heeschen et al., 2004; Keir et
al., 2009; Tsunogai et al., 2000). However, some of these fractionation factors,
which were calculated from in situ data, might be underestimates due
to mixing effects in the water column (Grant and Whiticar,
2002). The fractionation factors in our study seem to be inversely dependent
on the methane spike concentration, with higher fractionation in the 50× (1.023, n = 6)
treatments than in the 200× (1.010, n= 2) treatments. The
relative and absolute abundances of MOB, as well as the dominant MOB types,
differed between both treatments, possibly providing explanations for the
differences in fractionation rates. Logistical constraints forced us to stop
several incubations already after 5 to 6 days. These short-incubation 2× and
200× treatments did not resolve oxidation of methane. While the 2×
treatments did not meet the sensitivity threshold for the method (Uhlig and Loose, 2017a), the 200× short treatments were likely
just about to leave the lag phase when the experiments were stopped. A lag
phase of 6 days was observed for the long-incubation 200× samples, in which
the microbial community possibly shifted toward an abundance of MOB that
was large enough to cause detectable methane oxidation. To facilitate
comparisons between treatments, incubation duration should be kept constant
in future studies.
The increase in methane concentration in treatment IMB 0.2× (10 days incubation) is surprising since experiments were performed under aerobic
conditions. Since the seawater was not pre-filtered through a larger
pore-size filter, which would exclude larger particles but allow bacterial
cells to pass, production of methane in microanoxic zones (de Angelis and Lee, 1994; Oremland, 1979) should be
considered. Furthermore, several studies suggested pathways for methane
production in oxygenated marine systems from methylated compounds or
dissolved organic matter (Damm et al., 2010;
Florez-Leiva et al., 2010; Karl et al., 2008; Repeta et al., 2016). The
methane production rate of 0.06 nmol L-1 d-1 observed in our
study is 2 to 6 orders of magnitude lower than previously published
methane production rates under aerobic conditions (Damm et al., 2010; Karl et al., 2008). In
addition to biological processes, we cannot rule out an abiotic effect
leading to the increased methane concentrations, since our experimental
setup did not include a killed control at the same methane concentration.
Abundances of MOB and non-MOB methylotrophs control the methane
oxidation potential
We found a strong linear correlation between the net oxidation rate constant
(kox) and the relative abundance of 16S MOB sequences (Spearman rank
order coefficient ρs= 0.79, p = 0.006; Fig. 7a, Table 5).
This strong correlation is confirmed when correlating against the total
abundance or DESeq2 normalized abundance of 16S MOB sequences (Table 5). The
correlation to kox is even stronger for the absolute abundance of
pmoA sequences retrieved from the respective datasets (ρs= 0.86,
p = 0.006; Fig. 7b). This presentation of a direct and statistically
significant linear relationship is the first to our knowledge. It agrees
with other qualitative reports of positive correlations between methane
oxidation rates and abundance of pmoA or MOB 16S rRNA genes determined using a
variety of methods – quantitative PCR, FISH, or sequencing – for marine
water column and lake sediments (Crespo-Medina
et al., 2014; Deutzmann et al., 2011; Rahalkar et al., 2009; Steinle et
al., 2015). Future application of marine-specific pmoA primers may further
improve this correlation (Tavormina et al., 2008).
Correlation between net oxidation rate constant (kox) and the
relative abundance of sequences in 16S-MOB-OTUs, R(MOB-OTUs∼kox)2= 0.84
(a) and number of pmoA
sequences with R(pmoA∼kox)2= 0.85 (b). For correlation to
the number of total methylotroph OTUs (which includes MOB and
non-MOB methylotrophs in total 16S) R(Meth-OTUs∼kox)2= 0.81. The gray shaded
area shows the 95 % confidence interval of the correlation.
Spearman rank order correlations coefficients
(ρS) of
kox vs. the number of sequences of pmoA, MOB and non-MOB
methylotrophs, and candidate OTUs. Candidate OTUs are OTUs that were differentially
more abundant in 10× and 200× incubated samples.
Total
Normalized1
Relative abundance
Inferred cell density2
pmoA
-0.86**3
n.d.
n.d.
n.d.
methylotrophs
-0.81**
-0.97***
-0.79**
-0.63.
MOB
-0.82**
-0.66*
-0.82**
-0.61.
non-MOB
-0.71*
-0.80**
-0.69*
-0.58.
candidate OTUs
-0.07ns
-0.23ns
-0.03ns
n.d.
1Normalized to total abundance of reads using the DESeq2
package. 2Inferred cell density was calculated from relative abundance and
flow cytometry cell counts, weighted for copy number of 16S for respective
OTUs. 3Significance levels: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 'ns' 1.
Cell-specific net oxidation rates in our study (3.2–7.5 fmol cell-1 h-1)
were relatively constant between treatments. They are 2 orders
of magnitude higher than reported for subarctic seawater (Steinle et al., 2016). Since the cell-specific rates only span
a narrow range, the ultimate control on the methane oxidation potential is
the number of MOB, as reported in previous studies (Crespo-Medina
et al., 2014; Kessler et al., 2011; Steinle et al., 2015).
Despite the long incubation time in our experiments and the fact that
methane was the only added source of carbon, the relative abundance of MOB
determined from 16S reads was low (< 2.5 %, Table 4). Other
studies of natural or man-made gas or oil spills, with dissolved methane
concentrations comparably high to our 10× and 200× treatments, reported
maximal values of 8 to 34 % of MOB (Crespo-Medina
et al., 2014; Kessler et al., 2011; Steinle et al., 2015, 2016).
Surprisingly, relative sequence abundances of MOB in the natural seawater
communities were higher than in the incubations except for the 200× treatment (Table 4). Inferred absolute MOB numbers were higher in 10× and
200× incubations than in situ (Fig. 5b). In contrast, absolute MOB numbers
in 0.2× and 2× incubations were very similar to in situ abundances,
indicating that either the provided methane concentration was too low or the
incubation time too short to stimulate MOB growth.
It is puzzling why the fraction of methane oxidizers in the bacterial
community did not increase above the observed low percentages although the
cell-specific oxidation rates were high and sufficient methane was
available, particularly in the 10× and 200× treatments. Oxygen and methane
can be ruled out as limiting factors, since both were abundant. Copper,
which is essential for expression of particulate methane monooxygenase, can
restrict MOB growth (Avdeeva and Gvozdev, 2017; Zhivotchenko et al., 1995). In the absence of copper, many MOB express a
copper-independent soluble methane monooxygenase (Hakemian
and Rosenzweig, 2007). Since we neither determined copper concentrations
nor the expression of particulate and soluble methane monooxygenase, we
cannot exclude that copper was limiting in our study. Further, the low
relative abundance of MOB sequences could be due to competition with other
bacterial taxa for other macro- or micronutrients. In the absence of other
added C substrates, these other taxa could have utilized the initial pool of
dissolved organic carbon (DOC). DOC concentration is about 68 µM
carbon in the southern Chukchi Sea (Tanaka et al., 2016), which
is in the same range as the amount of consumed methane carbon in the 200× treatments
and 2 orders of magnitude higher than the consumed carbon in
the 10× treatments.
As a result of the low MOB abundances, the potential of the microbial
community to mitigate release of dissolved methane to the atmosphere by
oxidation is small. For example, for methane concentrations in the Laptev
Sea area, the rates observed in this study would result in 0.2 %
consumption during the ice-covered period. This supports the results from a
previous study for the Beaufort Sea, where 1 to 2 % of dissolved
methane was calculated to be oxidized (Lorenson et al., 2016).
Structure of the methane degrading microbial community
This first study based on 16S MiSeq sequencing of methane-oxidizing seawater
communities in the Arctic provides a broader view of the community structure
than approaches with FISH and DGGE.
The dominance of γ-Proteobacteria MOB in our natural and incubated seawater samples agrees
with previous records of MOB diversity for polar and subpolar waters (Mau et al., 2013; Steinle et al.,
2015; Verdugo et al., 2016). In addition, non-methane-utilizing
methylotrophs were present in all of our samples. The relative read
abundance of non-MOB methylotrophs were, similar to MOB, tightly correlated
to kox, and the same correlation holds for the relative abundance of
total methylotrophs (MOB plus non-MOB). In contrast, the correlation between
OTUs that were differentially more abundant in the incubated samples and
kox was weak (Table 5). This points toward a possible link between the
MOB and non-MOB in this methane-oxidizing microbial community, in which
non-MOB methylotrophs might play a role for community methane oxidation,
whereas the OTUs that were differentially more abundant are not directly
linked to methane oxidation.
Methylophilaceae, the most abundant non-MOB methylotroph in our experiments,
have been found to be abundant in sediment methane-oxidizing communities in
lakes and marine systems (Beck et al., 2013;
Redmond et al., 2010). Possible cooperative behavior between methanotrophs
(Methylococcaceae) and non-MOB methylotrophs (Methylophilaceae) was
suggested (Beck et al., 2013), in which the latter
cross-feeds on intermediate metabolic products of the MOB, i.e., methanol,
and can even positively alter the metabolism of the MOB toward methane
assimilation (Krause et al., 2017).
To test if the non-methane MOB could be supported by the intermediate
substrates produced by MOB, we calculated a budget between the methane
carbon assimilated by the growing microbial population
(CCH4,assim), and the cell
carbon gained during growth (Ccell-growth) (Fig. 8).
We assumed (i) a cellular carbon content of 150 fg for exponentially
growing bacterial cells (Vrede et al., 2002) and (ii) that about one-third of
consumed CH4 carbon is assimilated, with the
remaining two-thirds respired to CO2 (Bastviken et al., 2003; Roslev et al., 1997).
CCH4,assim exceeds
MOB-Ccell-growth by a factor of 9 to 17, indicating
that some of the CCH4,assim
was available for secondary consumption by non-MOB. The entire
methylotrophic community (MOB + non-MOB methylotroph) growth can also be
explained solely by CCH4,assim, supporting the
possible link of non-MOB methylotrophs to methane consumption. In contrast,
only about 0.1 % of the total community growth could be supported by
CCH4,assim in the 10× treatment
and 15 % in the 200× treatment. The remaining cell growth, e.g.,
of the differentially more abundant OTUs, must have been supported by other
carbon sources, such as initially available DOC.
Ratio of methane-carbon assimilated (CH4-Cassim) to
cell-C gained during growth (cell-Cgrowth), based on flow cytometric
cell counts (total) or inferred cell numbers (Meth, MOB). The standard
deviation between replicates was 10 to 20 %. The vertical line
indicates a ratio of 1. Above 1, the entire cell gain can be explained by
the assimilated CH4.
MOB and methylotrophs in sea ice
The two sea-ice cores analyzed in this study give a first insight into the
possible role of methane oxidizers in sea ice. In contrast to seawater
samples, MOB found in sea ice samples were mostly α-Proteobacteria.
The relative sequence read abundance of MOB in the ice was very low (maximal
0.1 %), pointing to an overall low contribution of methane oxidation
inside sea ice. The highest relative abundances of MOB were found in the
top-most ice sections in both ice cores (Fig. 5a). This coincided with the
highest methane concentration in the case of IC2, whereas the top-most section
of IC1 had the second smallest concentration of methane in this ice core
(Fig. 2e). Relative abundances of MOB in the inner and bottom sections of
the ice cores were even lower, with 0 to 0.02 % only.
The top-most section of IC1 and the biologically rich bottom section of IC2
had the highest relative abundances of β-Proteobacteria
Methylophilaceae, a non-MOB methylotroph. Recently identified as DMS
degraders (Eyice et al., 2015), Methylophilaceae might use DMS,
a methylated compound abundant in sea ice, as substrate (Kirst et al., 1991).