FEMS Microbiology Ecology, 92, 2016, fiw117
doi: 10.1093/femsec/fiw117
Advance Access Publication Date: 9 June 2016
Research Article
RESEARCH ARTICLE
Temperature effects on net greenhouse gas production
and bacterial communities in arctic thaw ponds
Karita Negandhi1,∗,† , Isabelle Laurion1 and Connie Lovejoy2
1
Institut national de la recherche Centr Eau Terre Enironnement (INRS-ETE) and Centre for Northern Studies
(CEN), Quebec, QC G1K 9A9 Canada and 2 Département de Biologie, Institut de Biologie Intégrative et des
Systèmes, and Centre for Northern Studies (CEN), Université Laval, Quebec, QC G1V 0A6 Canada
∗
Corresponding author: Department of Biology, Laval University, 269 Australian Hearing Hub, level 2, North Ryde, New South Wales, 2109 Australia.
Tel: +61 490 439 516; E-mail: [email protected]
†
Present address: Department of Earth and Planetary Sciences, Macquarie University, North Ryde, NSW, Australia.
One sentence summary: Greenhouse gas emissions from Arctic thaw pond sediment increased at warmer temperatures, consistent with changes in the
bacterial community after 16 days.
Editor: Dirk Wagner
ABSTRACT
One consequence of High Arctic permafrost thawing is the formation of small ponds, which release greenhouse gases
(GHG) from stored carbon through microbial activity. Under a climate with higher summer air temperatures and longer
ice-free seasons, sediments of shallow ponds are likely to become warmer, which could influence enzyme kinetics or select
for less cryophilic microbes. There is little data on the direct temperature effects on GHG production and consumption or
on microbial communities’ composition in Arctic ponds. We investigated GHG production over 16 days at 4◦ C and 9◦ C in
sediments collected from four thaw ponds. Consistent with an enzymatic response, production rates of CO2 and CH4 were
significantly greater at higher temperatures, with Q10 varying from 1.2 to 2.5. The bacterial community composition from
one pond was followed through the incubation by targeting the V6–V8 variable regions of the 16S rRNA gene and 16S rRNA.
Several rare taxa detected from rRNA accounted for significant community compositional changes. At the higher
temperature, the relative community contribution from Bacteroidetes decreased by 15% with compensating increases in
Betaproteobacteria, Alphaproteobacteria, Firmicutes, Acidobacteria, Verrucomicrobia and Actinobacteria. The increase in
experimental GHG production accompanied by changes in community indicates an additional factor to consider in
sediment environments when evaluating future climate scenarios.
Keywords: Arctic warming; organic carbon; permafrost; high-throughput tag sequencing; 16S rRNA; methane; carbon
dioxide
INTRODUCTION
The Arctic is warming more rapidly than in other parts of the
planet (Serreze and Barry 2011; Seddon et al. 2016), with predicted increases of 2◦ C to 9◦ C by the end of 21st century (Soloman et al. 2007). This is of particular concern since stored permafrost carbon (C) could be released to the atmosphere as
methane (CH4 ) and carbon dioxide (CO2 ) if this C pool is labile
and physically available to microbial communities (Schuur et al.
2009; Tarnocai 2009; Mann et al. 2015; Vonk et al. 2015). Overall a
greater understanding of the microbial community responses to
warming temperatures is needed to constrain estimates on future greenhouse gas (GHG) emissions from permafrost regions
(Riley et al. 2011). In particular, thawing permafrost can result in
the formation of thaw ponds and lakes (Zimov et al. 1997; Smith,
Sheng and MacDonald 2007; Zona et al. 2010) that are especially
Received: 30 October 2015; Accepted: 26 May 2016
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 8
efficient conduits of GHG to the atmosphere (Walter et al. 2006;
Laurion et al. 2010; Abnizova et al. 2012). Changes in snow and ice
cover and the input of colored dissolved organic matter (CDOM),
which traps solar radiation, act to increase water and lake sediment temperatures in shallow ponds, and directly expose microbial communities to these higher temperatures (Vonk et al.
2015 and references therein). This seasonally prolonged thermal
stratification, which promotes low-oxygen conditions at the bottom of thaw ponds and an aerobic environment near the surface,
results in conditions where the microbial communities control
the quantity of CH4 and CO2 released to the atmosphere (McCalley et al. 2014; Negandhi, Laurion and Lovejoy 2014; Crevecoeur et al. 2015). Treat et al. (2015) recently reviewed soil incubation studies in regions of permafrost thaw, and concluded
that changes in vegetation and hydrology linked to warmer conditions resulted in increased microbial anaerobic production of
CO2 and CH4 . Similar studies have not been carried out on sediments from High Arctic thaw ponds, which are expanding in
certain regions. Currently, community metabolic responses to
rising temperature and their resulting biogeochemical activity
are little known for aquatic environments, where microbes are
found under perennially cold temperatures (Hall, Neuhauser
and Cotner 2008; Sjöstedt, Hagström and Zweifel 2012; Galand
et al. 2013).
In addition to bulk measures of GHG production at different temperatures, knowing the taxonomic makeup of communities is useful for understanding the processes occurring in the
sediment. The gene coding for 16S rRNA (referred to as rDNA
hereafter) provides information on the taxonomic makeup of
the community that is present in a habitat at the time of sampling (Sogin et al. 2006). However, since bacterial clades and
groups may not respond similarly to increased temperatures,
identifying taxonomic groups that are most influenced can be
used to infer the taxa that will be favored at increased temperature. Although estimating growth rates via linear scaling is not
possible using rDNA across taxa (Blazewicz et al. 2013), bacteria identified from 16S rRNA in ribosomes (referred to as rRNA
hereafter) are generally thought to be involved in protein synthesis and therefore considered actively growing (Poulsen, Ballard and Stahl 1993). By examining both the rDNA and rRNA
from the same samples, a community bacterial response can be
inferred (Moeseneder, Arrieta and Herndl 2004). Distinguishing
presence versus activity is especially important in Arctic ecosystems, where a main survival strategy to cold temperatures is entering a dormant state (D’Amico et al. 2006).
To date, most rDNA and rRNA community comparisons testing the influence of temperature have used denaturing gel electrophoresis (DGGE) fingerprinting (Sharma et al. 2006; Yarwood
et al. 2013), phospholipid fatty acid analysis (Rinnan, Michelsen
and Bååth 2011; Schindlbacher et al. 2011) or a combination of
the above with qPCR (Yergeua and Kowalchuk 2008; Yarwood
et al. 2013). These methods do not provide information on the
rare taxa in the community, which are often responsible for key
biogeochemical processes. The use of high-throughput sequencing for rDNA and rRNA community comparison applied to temperate forest soils revealed that low-abundance species significantly affect soil decomposition processes (Baldrian et al. 2012).
Rare bacterial taxa can also be disproportionally active relative
to dominant bacterial taxa (Jones and Lennon 2010). A change
in the rare community could influence GHG emissions through,
for example, the recruitment of taxa able to better utilize stored
permafrost C (Davidson and Janssens 2006; Bradford et al. 2008).
The goal of the study was to measure GHG production rates
under increased temperatures in High Arctic thaw pond sediment and to identify the potential for changes in bacterial com-
munities. We experimentally investigated GHG production rates
at 4◦ C and 9◦ C from sediment sampled from four Arctic thaw
ponds, commonly found in continuous permafrost regions, and
incubated them under laboratory conditions. Both CO2 and CH4
were measured periodically during 16 days of incubation. Bacterial assemblages from one of the sampled ponds were also
followed over time, beginning with sediment collected in the
field and preserved immediately (in situ), after transport from
the field at the start of incubations (T1) and after 16 days of incubation (T16) at the 4◦ C and 9◦ C. The V6–V8 region of the 16S
rRNA gene was targeted to identify bacteria communities using
both rDNA and rRNA (converted to cDNA) as templates for highthroughput sequencing. Our working hypothesis was that temperature would have a significant effect on GHG production, and
that a subset of taxa would be more sensitive to the +5◦ C temperature.
METHODS
Study site
Samples were collected in the continuous permafrost region of
the C-79 glacier valley in Sirmilik National Park, Bylot Island,
Nunavut (73◦ 09 N, 79◦ 58 W). This valley is covered by organicrich sediments (up to 50% organic) made of accumulated peat
and wind-blown silt from the glacier-fed braided river. The active layer depth was between 40 and 60 cm at the time of sampling (D. Fortier, pers. comm.). The landscape is a network of
polygonal ice-wedges with hundreds of small shallow tundra
ponds. Two geomorphologic pond types were sampled; ‘polygonal ponds’ that form on top of low-centered polygons and are
associated with seasonal freeze–thaw cycles of the active layer;
and ‘trough ponds’ (termed runnel ponds in Negandhi et al. 2013;
Negandhi, Laurion and Lovejoy 2014) that form over melting icewedges and are associated with permafrost thawing and soil
erosion. These ponds are generally less than 1.5 m deep. Polygonal ponds are not prone to erosion and have thick cyanobacterial mats covering the bottom. Trough ponds are characterized by recent erosion due to permafrost slumping and thus can
be classified as thermokarstic (Grosse, Jones and Arp 2013). Because both polygonal and trough ponds are initially formed due
to permafrost thaw at the Bylot Island site, they are termed thaw
ponds here.
Physico-chemical and GHG data from 17 thaw ponds on Bylot Island collected in 2009 (Negandhi, Laurion and Lovejoy 2014)
were used to select two polygonal ponds (BYL1 and 22) and
two trough ponds (BYL24 and 38) for this experimental work.
Limnological characteristics of the ponds in 2010 are compared
to 2009 in Table 1. Surface concentrations of dissolved GHG
were measured during the day between 9:30 and 20:30, using
the headspace method (2 L of pond water in equilibrium with
20 ml of headspace). Methodological details are given in Laurion et al. (2010). In 2010, surface sediment samples from the
four selected ponds were collected for the incubation experiment to estimate GHG production rates (see below). Trough pond
BYL38 was further sampled for bacterial community changes
during the incubation experiment. This pond was originally chosen for pyrosequencing because of past particularly high in situ
GHG emissions (Laurion et al. 2010). This trough pond showed
typical thermo-erosional features that are likely to increase as
a direct consequence of global warming. While bacteria community sequencing was limited to this one trough pond, previous results indicated in situ communities clustered by pond
types, with trough ponds sharing 79% of OTUs (Negandhi,
Laurion and Lovejoy 2014).
3
Negandhi et al.
Table 1. Physiochemical properties of the thaw ponds sampled on Bylot Island between 14 and 31 July in 2009 and 2010. The four ponds
span a range of properties between polygonal (BYL1 and 22) and trough ponds (BYL24 and 38) with a few minor changes between years (Yr).
SRP—soluble reactive phosphorus; TP—total phosphorus; TN—total nitrogen; DOC—dissolved organic carbon; a320 —dissolved organic matter
absorption at wavelength 320 nm, as an index of the quantity of colored dissolved organic matter; S275–295 —spectral slope between 275 and 295
nm, an index of the molecular size of CDOM (larger values generally indicate small or more labile molecules).
Pond
Yr
SRP μg L−1
TP μg L−1
TN μg L−1
SO4 mg L−1
BYL 1
‘09
‘10
<0.2
0.5
15.6
19.0
0.36
0.5
1.5
1.06
BYL 22
‘09
‘10
<0.2
0.5
25.5
68.0
0.37
0.4
BYL 24
‘09
‘10
1.0
1.6
25.5
38.0
BYL 38
‘09
‘10
1.8
4.7
71.8
54.0
a320 m−1
S275-295 nm−1
CO2 μM
CH4 μM
8.4
8.7
13.1
13.2
0.0199
0.0193
6.3
18.7
1.0
0.7
0.85
1.62
8.1
6.2
19.6
20.8
0.0168
0.0191
25.0
36.2
1.9
1.3
0.4
0.4
0.67
1.81
11.5
9.6
37.0
36.3
0.0153
0.0139
18.0
69.8
2.6
0.8
0.87
0.5
3.1
1.74
12.2
12.1
72.9
67.3
0.0115
0.0122
182
91.8
4.7
2.9
Pond sediment collection
On 25 July 2010, 500 ml of sediment samples from the water interface to ∼10 cm deep were collected from the four ponds. Wet
sediment was collected into a sterilized plastic bag, and mixed
to produce a homogenized sample. A 50-ml sediment subsample from each pond was transferred from the bag into a sterR tube and poured through a funnel into acid-washed
ile Falcon
and pre-combusted 300-ml glass bottles. Bottom water from the
corresponding ponds was used to fill the glass bottles to the top,
leaving no headspace. The butyl rubber cap was pierced with
a needle to release the pressure and facilitate closure. Bottles
were stored in the dark. Trough ponds maintain thermal stratification for a large fraction of the summer leading to hypoxic
conditions in bottom waters (Bouchard et al. 2015, unpublished
results), and likely with anoxia in the sediments. Six bottles per
pond were collected for the incubation experiment (n = 24). At
this time, 3 ml of sediment from trough pond BYL38 was taken
from the same plastic bag mixture, and placed in a dry shipper
at the temperature of liquid nitrogen. Once back to our home
laboratory, samples were stored at −80◦ C until rDNA extraction
of the in situ community (see below).
Incubations
Since controlled temperature facilities were not available in
the field, all bottles were transported in the dark at 4◦ C to
our home laboratory. Incubations in the dark were started 3
days after collection. Half of the bottles were incubated at
4◦ C and half at 9◦ C. Temperatures were chosen to fall between the present summer average air temperature at Bylot of 4.5◦ C (http://www.cen.ulaval.ca/bylot/climate-descriptionbylotisland.htm), and the predicted increases of 2◦ C to 9◦ C by
the end of 21st century depending on model and forcing scenarios (Soloman et al. 2007). The 4◦ C incubation temperature
was maintained in an electronically monitored refrigerator. The
9◦ C incubation was carried out in a low temperature incubator (Fisher Scientific model 307) equipped with a thermistor
R Tidbit)
R to record temperature.
(Onset
The experimental setup was determined from preliminary
experiments carried out over 28 days in 2009. In the preliminary incubation, CO2 production reached a plateau after 18 days.
Therefore, 16 days was chosen for the 2010 experiment to focus on early GHG dynamics and avoid complete community replacement. CO2 and CH4 were measured on days 1, 5, 7, 9, 13
and 16 in the 24 bottles (three bottles at 4◦ C and three bottles
DOC mg L−1
at 9◦ C for each of the four ponds). The initial GC measurements
were taken on 28 July 2010 (T1) as follows. First, 50 ml of water was removed from each incubation bottle. The headspace of
each bottle was helium flushed for 5 min with an exit valve, after
∼30 min, 0.6 ml of gas was removed from the bottle headspace,
and 0.5 ml was injected in the GC using a 1.0 ml gas-tight syringe
(SG008130 Canadian Life Science, 1MF-CTC-GT-HS-5/0.63H). For
all other time points, no helium headspace flushing was applied
and 0.4 ml of gas was removed from each bottle, with 0.1 ml injected to the GC, which was sufficient to remain within the detection level of the instrument. At the end of the experiment,
sediment from the individual bottles was dried and weighed.
The dry weight average for each pond was used to normalize
GHG production rates.
High-throughput sequencing of trough pond BYL38
Nucleic acids were extracted from trough pond BYL38 sediments
on three occasions: in situ, at T1, and from both incubation end
points T16-4◦ C and T16-9◦ C. The T1, T16-4◦ C and T16-9◦ C sediment samples were collected from the incubation bottles, frozen
and stored at −80◦ C until extraction. An extra bottle was sacrificed at T1 for sequencing. Both DNA and RNA were extracted
R total RNA isolation kit no.
using a MO BIO Kit (RNA Powersoil
12866-25 and DNA elution accessory kit no. 12867-25). The reverse transcription of RNA to cDNA was performed using a High
Capacity Reverse Transcriptase Kit (Applied Biosystems) including an equal mixture (30 μL) of RNA and a RT mixture consisting
of 6 μL RT buffer, 2.4 μL dNTP, 6 μL of random primers, 3 μL of
Multi-Scribe Reverse Transcriptase and 12.6 μL of molecular biological grade H2 O. Amplification of DNA for high-throughput
16S rRNA gene and 16S rRNA from cDNA pyrosequencing was
performed with a 50-μL PCR reaction mixture consisting of 1X
HF buffer (New England Biolabs; NEB), 200-μM dNTP (Feldan
Bio), 0.4-mg mL−1 BSA (Fermentas), 1 U of Phusion High-Fidelity
DNA polymerase (NEB) and 0.2 μM of both primers. The forward
primer B969F (5 -ACGCGHNRAACCTTACC-3 ) was attached to
Roche multiplex identifiers (MIDs) adaptors. The reverse primer
used was BA1406R (5 -ACGGGCRGTGWGTRCAA-3 ; see Comeau
et al. 2011 for primer design). PCRs were carried out using three
separate rDNA template concentrations ranging from 0.01–0.1X
(37–94 ng) and two 0.5–1X (1.6–2.0 ng) rRNA template concentrations. Amplification cycles began with a 98◦ C denaturation step
for 30s, followed by 30 cycles at 98◦ C for 10s, annealing at 55◦ C
for 30s, extension at 72◦ C for 30s and a final extension at 72◦ C
for 5 min. For each sample, the triplicate reactions performed
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 8
for DNA and duplicate reactions for RNA were pooled for purification (QIAquick PCR purification kit; QIAGEN) and quantified spectrophotometrically (NanoDrop ND-1000). Equal quantities of the MID coded amplicons were mixed into one tube
for sequencing on a Roche 454 GS-FLX Titanium platform at
Université Laval Plate-forme d’analyses Génomiques. Resulting
reads were subjected to pyrotag pre-processing and quality control (Comeau et al. 2011). Low-quality reads were removed if
they contained non-assigned nucleotides (N’s), were <150 bp excluding the adaptor and sample MID tag-code, if they exceeded
the expected amplicon size, or if they had an incorrect Forward
primer sequence or bases present after the reverse primers were
trimmed. Next, reads were aligned using mothur version 1.19.4
(Schloss 2009) against SILVA (version 11) reference alignments,
and then manually checked to remove misaligned reads. After
processing, 2321 input reads per sample were randomly selected
to ensure the same number of reads from each sample. Furthestneighbor clustering at ≥97% similarity level was used to define
operational taxonomic units (OTUs) in the combined data using mothur version 1.19.4 (Schloss et al. 2009). OTUs that only
occurred once in the entire dataset were removed. OTUs were
then classified using a modified GreenGenes 2011 (GreenGenes
97; Werner et al. 2012, greengenes.lbl.gov/Download) database as
in Comeau et al. (2012). This database was curated by the removal
of some sequences that were unclassified at the phylum level
and the addition of missing bacterial genera from a consensus
between GreenGenes and the RDP database classifier tool (Cole
et al. 2009). Representative OTUs were selected using a 50% bootstrap value against the reference database trimmed to the V6–V8
region in mothur. Raw reads were deposited to NCBI Sequence
Read Sample (SRS) archive under accession number SRS853399,
and the corresponding Sequence Read Experiment (SRX) number SRX886511 for rDNA and SRX886502 for rRNA.
Calculations and statistics
The headspace CO2 and CH4 concentrations measured along the
incubations were multiplied by the headspace volume and normalized by the sediment dry weight of each bottle to calculate
production rates in mmol day−1 g−1 . Production rates were obtained from a linear regression on averaged GC concentrations
(n = 3) taken at each of the 6 sampling days, using ordinary least
square analysis. Temperature sensitivity for each pond was calculated and reported as temperature coefficient (Q10 ). A two-way
ANOVA was used to test for significant differences in GHG production rates between 4◦ C and 9◦ C and among individual ponds
or pond types. To test for differences in the amount of material
incubated, sediment dry weight at the end of the incubation was
also compared using a two-way ANOVA. Statistics were run using the software package PAleontological STatistics (PAST) version 3.01 (Hammer, Harper and Ryan 2001).
Descriptive statistics (OTU numbers, phylogenetic diversity
indices Chao 1, Shannon and Simpson) of bacterial communities
from BYL38 were performed in mothur version 1.19.4 (Schloss
et al. 2009). Analysis of similarity (ANOSIM) was used to test the
difference among starting (in situ DNA, in situ RNA, T1 DNA and
T1 RNA) and end (4◦ C DNA, 4◦ C RNA, 9◦ C DNA and 9◦ C RNA)
communities at the phyla level. R-values indicate the degree of
separation between communities: R-values >0.75 indicate wellseparated communities, R > 0.5 are for overlapping but clearly
different communities and R < 0.25 are for barely distinguishable communities (Clarke and Gorley 2001). A Bray–Curtis cluster analysis was used to group the eight bacterial communities.
A similarity percentage test (SIMPER; taxa % contribution to dis-
Table 2. Greenhouse gas production rates (mmol day−1 g−1 ) by the
sediment dry weight of four thaw ponds (polygonal ponds BYL1 and
BYL22; trough ponds BYL24 and 38) in response to increased temperature. The stars indicate the significance levels obtained from a
two-way ANOVA performed on the difference between temperature
treatments and between pond types. The sensitivity of each pond
production rate to increased temperature is reported by a Q10 value.
CO2 production rate1 , 2
CH4 production rate1
Pond
4◦ C
9◦ C
Q10
4◦ C
9◦ C
Q10
BYL1
BYL22
4.3
5.1
6.1
7.1
2.01
1.94
0.99
0.67
1.3
0.8
1.72
1.42
BYL24
BYL38
8.8
10.9
12.5
11.8
2.02
1.17
0.79
0.14
1.0
0.2
1.60
2.47
Significant difference (P < 0.005) between production rates by temperatures.
Significant difference (P < 0.005) between polygonal and trough pond production rates.
1
2
similarity) was used to identify which phyla contributed to a difference in the community at the two temperatures. To compare
the effect of assessing the communities at different taxonomic
levels (phylum, family or genus) and rDNA compared to rRNA,
percent similarities between all eight communities (four rDNA
and four rRNA) were calculated using Bray–Curtis similarity index. From there, a two-way ANOVA was performed to detect significant dissimilarities in community composition, followed by
Tukey post hoc tests. SIMPER was then used to identify the relative influence by bacteria genera.
RESULTS
Greenhouse gas production under increased
temperature
There was a smooth linear rise in GHG concentrations over time
in the bottle headspace, indicating positive production rates by
the pond sediments (Table 2) with linear regression r2 ≥0.942 and
P < 0.001. All four ponds had significantly higher CO2 production
rates at 9◦ C compared to 4◦ C (F1, 16 ≥24, P < 0.005). Trough pond,
BYL24, had the highest CO2 production rate and a high sensitivity of CO2 production to increased temperature (Q10 = 2.02), as
did polygonal pond, BYL1. The CO2 production rates of trough
ponds were significantly higher than for polygonal ponds (F1, 20
= 159, P < 0.005). Methane production rates followed a different
pattern, with BYL1 having the highest CH4 production rate, and
trough pond, BYL38, having the highest sensitivity to increased
temperature (Q10 = 2.47). Production rates of CH4 at 9◦ C were
significantly higher than at 4◦ C (F1, 16 = 21, P = 0.003), and between each individual pond (F3,16 = 66, P < 0.001), but not between pond types (Table 2). There was a significant difference in
the sediment dry weight between pond types (17.9 and 24.3 g in
polygonal and trough ponds, respectively; F1, 20 = 13, P = 0.002),
with no difference between the temperature treatments.
Sediment rDNA and rRNA communities in a trough
pond
At the Phylum level, the rDNA (Fig. 1) and rRNA (Fig. 2) communities from all sampling points (in situ, T1, and T16 at 4◦ C
and 9◦ C) appeared relatively similar. Bacteroidetes, Proteobacteria and Actinobacteria were dominant (each comprising >10%
of the total community), with Acidobacteria, Firmicutes, Verrumicrobia, Gemmatimonadetes and Chloroflexi accounting for
Negandhi et al.
5
Figure 1. Relative abundance (%) of bacterial phylum rDNA, in trough pond BYL38 sediment in situ, at the start of the incubation, 3 days after sample collection (T1),
and after 16 days of incubation at 4◦ C and 9◦ C.
Figure 2. Relative abundance (%) of bacterial phylum rRNA, in trough pond BYL38 sediment in situ, at start of the incubation 3 days after sample collection (T1), and
after 16 days of incubation at 4◦ C and 9◦ C. Phyla that were present in rRNA (Chlamydiae, Elusimicrobia and Lentisphaerae, OP3, SC3 and TM7; 0%–0.5%) but not in
rDNA are represented in black.
1%–10% of the reads. However, further investigations revealed
different communities at finer taxonomic levels between rRNA
and rDNA. The unclassified bacteria were more abundant (1.8%–
3.6%) in the rRNA communities (Fig. 2) compared to rDNA communities (≤ 0.7%; Fig. 1). Additionally, rRNA communities included more low-abundance (<1%) Phyla, with the occurrence
of Chlamydiae, Elusimicrobia, Lentisphaerea, OP3, SC3 and TM7,
making rRNA communities more diverse compared to the rDNA
(Table 3).
The in situ and T1 communities also differed; Bacteroidetes
reads decreased by 7.2% in rRNA, while Actinobacteria reads in-
creased by 10.6% in rRNA at T1 (Fig. 3). The in situ and T1 communities were significantly different from those after 16 days
of incubation (ANOSIM; R = 0.75, P= 0.027). Community cluster
analysis grouped the T1 and in situ rDNA communities together,
while the rRNA communities branched apart (Fig. 4). After 16
days of incubation, the rDNA and rRNA communities clustered
primarily by temperature. Both the 4◦ C rDNA and 4◦ C rRNA were
less diverse compared to the 9◦ C rDNA and 9◦ C rRNA communities, with the 9◦ C rRNA community branching apart (Table 3;
Figs 4 and 5). A Venn diagram of the in situ and T1 rDNA and
rRNA indicated 66 shared genera among the four communities,
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 8
Table 3. Descriptive statistics comparing rDNA and rRNA reads in
trough pond BYL38 sediment, where each time point was resampled
to 2321 reads.
All DNA
All RNA
OTUs
Chao1
Shannon
Simpson
1240
1768
1287
1815
6.95
7.26
0.0010
0.0008
DNA
in situ
T1
4◦ C
9◦ C
275
304
329
332
285
311
336
340
5.47
5.56
5.58
5.63
0.0043
0.0036
0.0042
0.0037
RNA
in situ
T1
4◦ C
9◦ C
449
424
448
447
454
434
454
459
5.91
5.85
5.84
5.92
0.0029
0.0033
0.0036
0.0028
with rRNA at in situ and T1 sharing the most (17; Fig. 5a). Comparatively, after 16 days of incubation there were slightly fewer
shared genera (50) among four final communities, with 9◦ C rDNA
and 9◦ C rRNA sharing the most genera (19; Fig. 5b). The 9◦ C rRNA
community also had the most unshared genera (60) among all
the communities (Fig. 5b).
To identify bacterial phyla and classes contributing to
community change between temperature treatments, a SIMPER
(a)
40
Proteo.
Bacterio.
D 24
N
A 16
50
Proteo.
32
Bacterio.
Act.
16
Aci.
8
F
G V
C
unc.
0
T1
24
Act.
8
(b)
40
in situ
32
0
Figure 4. Bray–Curtis cluster analysis of relative abundances in identified genera
for BYL38 sediment bacterial communities among experimental time points in
situ, at the start of incubation experiment (T1), and after 16 days of incubation
at 4◦ C and 9◦ C.
G
8
16
24
(c)
T16 at 4°C
32
Bacterio.
0
40 0
40
42
32
D 30
N
A 26
24
Aci.
F V
C
8
16
24
32
40
(d)
T16 at 9°C
Bacterio.
Proteo.
Proteo.
10
Aci.
F
G V
C
S
0 unc.
0
16
8
Act.
Act.
F
C G
S
unc.
10
20
30
RNA
40
0
50 0
Aci.
V
8
16
24
RNA
32
40
Figure 3. Comparison of rRNA versus rDNA abundant (>1%) phyla at all four sampling time points. Phyla above diagonal line indicate a higher rDNA percentage
than rRNA and vice versa. Size of circles indicates the extent in difference between rRNA and rDNA. (a) Community in situ;( b) community at the start of incubation
experiment (T1), 3 days after sample collection; (c) community after 16 days of incubation at 4◦ C; (d) community after 16 days of incubation at 9◦ C. Aci = Acidobacteria;
Act = Actinobacteria; Bacterio = Bacteroidetes; C = Chloroflexi; F = Firmicutes; G = Gemmatimonadetes; Proteo = Proteobacteria; S = Spirochaetes; V = Verrucomicrobia;
unc = unclassified bacteria.
Negandhi et al.
7
Figure 6. Phyla contributing to 86.5% of the 17% community change in BYL38
sediment after 16 days of incubation at 4◦ C and 9◦ C for (a) rDNA community and
(b) rRNA community.
Figure 5. Venn diagram illustrating the number and percent of shared genera
between rDNA and rRNA bacterial communities at those experimental times of
(a) in situ and start of the incubation (T1) and (b) after 16 days of incubation at
4◦ C and 9◦ C.
test was run with rDNA and rRNA combined by temperature.
An average of 17% dissimilarity by temperature was found for
communities at the phylum level, with the majority of this
dissimilarity (86.5%) attributed to Proteobacteria, Firmicutes,
Acidobacteria, Verrucomicrobia, Actinobacteria and Bacteroidetes. The proportion of these phyla in rRNA-derived
community was relatively greater at 4◦ C (71%) than at 9◦ C (65%;
Fig. 6). This difference was due to 15% fewer Bacteroidetes at
9◦ C, but 3.4% more Proteobacteria (Betaproteobacteria by 1.9%,
Alphaproteobacteria by 0.5%), 3% more Firmicutes, 2% more
Acidobacteria, and 1% more Verrucomicrobia and Actinobacteria. The main phylum contributing to the observed dissimilarity
between temperatures, Bacteroidetes, increased in both rDNA
and rRNA after 16 days of incubation at 4◦ C (Fig. 3). We note
that primers used here only detect 79% of Bacteria in the SILVA
database (Silva TestPrime v.1 results run 29 March 2016). Other
primer pairs would likely detect more bacterial groups and
our results should be considered conservative especially for
Actinobacteria, Betaproteobacteria and Firmicutes.
With rDNA and rRNA combined, the dissimilarity between
temperatures significantly increased from phylum (SIMPER,
17%,) to genus (31%; F2,15 = 11.98, P < 0.005). Bray–Curtis commu-
nity similarity indices over time differed significantly between
rRNA and rDNA, but only at the genus level (F1,15 = 15.58, P <
0.05). Each genus level taxon contributing ≥0.5% accounted for
only 20% of the dissimilarity between temperatures. Therefore,
a contribution of each genus level taxon at ≥0.1% was needed
to account for 70% of the dissimilarity between temperatures
(Fig. 7).
Among the 82 taxa identified at the genus level, only five contributed to ≥3% of the community composition (Fig. 7). Changes
among these genera included a decrease in Prolixobacter, unclassified Bacteroidetes, and Meniscus at 9◦ C, and an increase
in Geobacter and unclassified Actinomycetales. The other genera, each representing <3% individually, combined to 48%–53%
of both the rDNA and rRNA community (Fig. 7). Individual genera
response to temperature varied, even within the same phylum,
for example many Bacteroidetes genera, but not all, decreased at
9◦ C compared to 4◦ C (Table 1, Supporting Information). OTUs associated with methanotrophic taxa accounted for 3.1% of reads
from rDNA and 3.0% in rRNA (Table 2, Supporting Information).
DISCUSSION
Ice-wedge landscapes with polygon and trough ponds are
a common feature of low-lying continuous permafrost regions throughout the circumpolar Arctic (Smith, Sheng and
MacDonald 2007; Vincent, Hobbie and Laybourn-Parry 2008).
Here, we found that GHG production in sediments of both pond
geomorphologies increased with a modest 5◦ C temperature increase. A stimulation of respiration or cell growth produced Q10
values between 1.2 and 2.5, which are within the range reported
by Lupascu and Wadham (2012) for wetland permafrost soils.
Ice-wedge landscapes are presently facing accelerated warming,
with the active layer deepening and thermokarst processes mobilizing a large pool of old C (Vonk et al. 2015). Lakes and ponds
in lowland permafrost regions are currently a significant source
of CH4 to the atmosphere at the global scale (Wik et al. 2016), and
as summer temperatures rise in the Arctic it is critical to know
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 8
Figure 7. Comparison of rRNA versus rDNA community percentages at 4◦ C and 9◦ C at the taxonomic level of genus. Each color represents a phylum, with duplicates
indicating different genera within a specific phylum. Bottom squares are zoomed in representation of genera present at ≤3%.
how net fluxes and communities will react. While the experimental setup here, under dark anoxic conditions and with no
added C or nutrients, does not take into account the potential
for a photosynthetic sink of CO2 and methanotrophic oxidation
of CH4 , our results indicate a the potential for rapid response
in respiration rates under warmer temperatures. The bacterial
community, of the one trough pond we were able to investigate,
harbored specific genera able to take advantage of a rise of 5◦ C
within 16 days.
We previously measured the dissolved CH4 in pond surface
waters from a range of ponds in this region, including the experimental ponds sampled here. Over five summers between
2005 and 2011, concentrations were similar to those in 2009
and 2010 (Table 1). This larger data set collected in June and
July consisting of 5 to 12 measurements per pond, all sampled
during the day between 9:15 and 20:30, averaged 0.7, 0.8, 1.6
and 4.9 μM of CH4 in ponds BYL1, 22, 24 and 38, respectively
(Laurion et al. 2010, unpublished data), indicating that in situ
CH4 concentrations were greater in trough ponds and greatest
in BYL38. Since trough ponds were highly stratified, surface concentrations would likely rise during mixing events, but these seldom occur in summer (Bouchard et al. 2015). Moreover, diurnal
changes over 26 h in two polygonal ponds indicated that surface CH4 concentrations did not vary by more than 21% in July
(Negandhi et al. 2013). Interestingly, the in situ results did not reflect the experimental results, where BYL38 had the lowest CH4
production rate among the four ponds, which was 37% lower
than BYL1 at 4◦ C and 15% lower at 9◦ C. Trough pond, BYL24, had
the second highest in situ CH4 concentration, which was double
that of the two polygonal ponds, but again showed a lower experimental CH4 production rate compared to BYL1. The difference
between in situ and experimental results could be due to patchy
GHG production in pond sediments, as the 500 ml of sediment
in the experimental vessels may not capture natural variability.
Second, the sediment to water ratio varies among ponds of different morphologies and could influence the storage of GHG in
the water column over summer. Since the ratio was constant in
the incubation bottles, the experimental setup would reflect the
potential production of GHG, under dark anoxic conditions. The
in situ surface GHG concentrations likely indicate a more instantaneous estimate of net production rates in the presence of light
and oxygen.
Our experimental incubations suggest that in situ CH4 production should be greater in polygonal pond BYL1. The active
Negandhi et al.
benthic photosynthesis occurring in this pond (but not in trough
ponds, and possibly to a smaller extent in BYL22 that is much
shallower and smaller than BYL1) is likely to generate higher
concentrations of labile organic matter (King et al. 2002). This is
also apparent by the higher DOM absorption slope for polygonal
ponds (Table 1), which could support their higher experimental
CH4 production. Although primary production was stopped during the incubations, these compounds were likely present in the
water added to the bottles, and possibly in sufficient quantity to
last over the entire incubation. Photosynthetic activity by thick
cyanobacterial mats provide oxygen to the water column creating conditions for a larger fraction of CH4 to be oxidized in situ
and reducing efflux to the atmosphere, in addition to the photosynthetic uptake of CO2 leading to negative flux of this gas
(Laurion et al. 2010; Negandhi et al. 2013). In contrast, the stratified water column and hypoxic bottom waters in trough ponds
would inhibit CH4 oxidation (Christiansen et al. 2014), while humic substances and erosion would reduce light for planktonic
and benthic photosynthesis and O2 production.
The dark anoxic experimental conditions would have inhibited aerobic methane oxidation by methanotrophs. The relative percentage of aerobic methanotrophs found in trough pond
BYL38 at 4◦ C rRNA and 9◦ C rRNA (Table 2, Supporting Information) were similar and would not explain the differences in
net CH4 production rates for this pond under the two temperature treatments. Since we have no data on the methanotrophic
assemblages for the other three ponds, we cannot speculate
on whether community differences could explain the differences in CH4 production rates. The relative percentage of aerobic methanotrophs decreased from the start of the incubation
(T1) compared to day16 (Table 2, Supporting Information), and
in an earlier study, neither anaerobic methanotrophic Archaea
(ANMEs) nor bacteria (Methoxymirabilis) were recovered from in
situ sediment samples of the four studied ponds (Negandhi et al.
2013; Negandhi, Laurion and Lovejoy 2014). Although we attempted to amplify sediment Archaea using primers previously
successful on water samples (Negandhi et al. 2013), we failed
to obtain any amplicons. Targeted primers could well show the
presence of Methoxymirabilis, but for now our understanding of
community changes following warming is incomplete.
The CH4 :CO2 ratios were similar at 4◦ C and 9◦ C, yet clearly
lower in trough ponds (8% and 1.2%) compared to the polygonal ponds (19% and 12%; Fig. 8), even under the dark incubation
conditions with no photosynthetic activity. The lower CH4 :CO2
ratios in trough ponds could be explained by four potential
microbial interactions: (i) higher anaerobic microbial oxidation
of CH4 ; (ii) greater CO2 production coming from fermenters;
(iii) preference for acetoclastic over hydrogenotrophic methanogenesis; and (iv) larger proportion of bacteria outcompeting
methanogens for acetate substrates. For example, Geobacteraceae
can outcompete acetotrophic methanogens in the anaerobic oxidation of acetate to CO2 (Holmes et al. 2007). In the case of pond
BYL38 where we have data, Geobacter always contributed ≥79%
of the Deltaproteobacteria community, and was among the five
out of 82 genera that increased at 9◦ C and comprised ≥3% of
the total community (Fig. 7). Our experimental data supports the
fourth interaction for BYL38, and a previous study at the same
site (Negandhi et al. 2013) supports the third for all ponds, who
reported that genera associated with acetoclastic methanogenesis, were more common under in situ conditions. A preference
for acetoclastic methanogenesis at higher temperatures (>7◦ C)
was found for Arctic peat soils (Tveit et al. 2015), due to a syntrophic relationship between hydrogenotrophic Methanobacteriales and Firmicutes decreasing through the replacement of Fir-
9
Figure 8. Ratio of CH4 to CO2 over the 16 days of incubation at 4◦ C and 9◦ C for
(a) two polygonal ponds and (b) two trough ponds. Ratios are from an average of
three CH4 and CO2 replicates at each time point with standard deviations given.
micutes with Bacteriodetes. The decrease in most Bacteriodetes
found at 9◦ C during our incubation suggests a possible stimulation of hydrogenotrophic methanogenesis consistent with the
increasing CH4 :CO2 ratio at 9◦ C for this pond.
Alpine, temperate and agricultural regions show significant
increases in soil respiration under warmer temperatures but no
changes in the community structure (reviewed by Kuffner et al.
2012). Most, if not all, previous studies used rDNA as a template
and lacked any rRNA sequencing, which would detect more of
the short-term changes, given that rDNA can be preserved in
sediment over long periods (Young et al. 2007). We found considerable overlap between rDNA and rRNA taxa in pond BYL38
bacterial community, although relative percentages differed, especially at the genus level. The detection of six additional Phyla
in rRNA community at very low percentages (<0.2%; Fig. 2) would
be consitent with rare taxa being selected by the specific experimental conditions, and increasing their abundance or ribosomal activity (Moeseneder, Arrieta and Herndl 2004). While qPCR
would have helped distinguish between higher numbers and increased activity of bacterial groups this was not carried out due
10
FEMS Microbiology Ecology, 2016, Vol. 92, No. 8
to lack of available primers targeting the diverse groups and limited quantites of sediment for rDNA and rRNA extraction. Nevertheless, the rRNA overall provided an additional perspective on
the potential response of the bacterial community, indicating a
change at the genus level over a relatively short incubation.
The in situ and T1 clustering (Fig. 4) were similar to results
from other community comparisons from soils and sediment.
This clustering indicates a stable core community (rDNA) with
a subset of active (rRNA) members (Baldrian et al. 2012; Brettar, Christen and Höfle 2012). There were differences between
rDNA and rRNA at T1, which could indicate sensitivity to containerization and transport conditions (Fig. 3). Specifically, Bacteroidetes were negatively affected with a lower proportion of
reads in the rRNA than rDNA, and Actinobacteria positively affected, suggesting taxa specific responses within this 3-day period (Gattuso et al. 2002). BYL38 sediment sits atop a permafrost
ice wedge with sediment on average 1.8◦ C in July (unpublished
results) and community changes may have been a response to
the increase in temperature during transport (1.8◦ C–4◦ C).
The phyla contributing to the dissimilarity between temperature treatments showed a counter-intuitive response to warmer
temperatures, with a slightly higher percentage of dissimilar
taxa in rDNA at 9◦ C, but a lower rRNA percentage. A shift toward
a more diverse (both rDNA and rRNA; Fig. 6) yet less active community occurred along with an increase in CO2 and CH4 production at the higher temperature. The decline in the rRNA community was possibly due to a preference for internal reallocation of
C to respiration rather than to growth (López-Urrutia and Morán
2007). Alternatively, some bacterial groups maintain high numbers of ribosomes even in a dormant state, which enables them
to react quickly to changing conditions (Blazewicz et al. 2013),
and the change in rRNA community may have reflected previously dominant bacteria becoming active, and then declining
over the experiment. In a soil study, Zogg et al. (1997) reported
an increase in temperature resulted in higher respiration but a
decrease in the active microbial biomass in forest surface soils in
Michigan. Arctic bacterial communities are likely psychrophilic
and particularly sensitive to increased temperatures, and under
a long term warmer climate, community diversity and respiration rates could eventually be less sensitive to temperature (Bell
et al. 2005; Rinnan et al. 2007).
The sensitivity of GHG production rates to warming varied substantially, but remained within the range reported by
Lupascu and Wadham (2012) for wetland permafrost soils.
Methanogenesis is reported to be very sensitive to temperature
in a range of habitats, including aquatic ecosystems and wetlands (Yvon-Durocher et al. 2014). Importantly, methanogenesis
has the potential to be more temperature sensitive than aerobic
respiration and photosynthesis when substrates are not limiting (Olefeldt and Roulet 2012; Erhagen et al. 2013). Our incubation of the upper (10 cm) sediment showed that trough pond
BYL38 was the only pond with greater temperature sensitivity
for CH4 production compared to CO2 production (Q10 ; Table 2).
Trough pond, BYL24, has a stable shoreline colonized by brown
mosses, while BYL38 has active shoreline erosion, providing an
additional source of active layer permafrost C with an age >2000
years (Bouchard et al. 2015). This old C pool can be labile in
permafrost landscapes (Kleber et al. 2011; Schädel et al. 2014;
Mann et al. 2015), possibly because of shifts to communities that
are able to utilize different sources of C (Waldrop and Firestone
2004).
Much of the carbon stored within the Canadian Arctic permafrost may be enriched in degradation resistant polyphenolic
compounds produced by once living plants, mosses and Sphag-
num as shown for fens in the subarctic (Verhoeven and Toth
1995). Tveit et al. (2013) identified genes that code for enzymes
hydrolyzing plant polymers and degrading phenolic compounds
within Arctic peat soils that were attributed to the phyla Bacteriodetes, Actinobacteria and Verrucomicrobia. In the same
study, genes encoding for anaerobic respiration and fermentation were attributed to Actinobacteria. The decrease in some
Bacteriodetes genera and increase in several Actinobacteria and
Verrucomicrobia genera (Fig. 7; Table 1, Supporting Information),
here, suggest a need for studies specifically examining these
genes from environmental samples to provide a mechanistic
understanding of how emerging communities would be able to
utilize the permafrost C pool. The community shift detected in
BYL38 would not preclude continuing changes in the core community over a longer term, or even a return to something phylogenetically resembling the original core community after a series of warm summers (Rillig et al. 2015).
With the use of rRNA and rDNA high-throughput sequencing, we found that the sediment bacterial community of an arctic trough pond significantly responded to an increase in temperature over a relatively short time scale of 16 days. Under
warmer conditions, there was a community shift manifested by
an increase in rare OTUs and a decrease in the dominance of
Bacteriodetes. This bacterial community change was accompanied by greater CH4 production possibly linked to high C from
the thermokarstic erosion history in this pond. While polygonal
pond sediments also had a higher CH4 production at the warmer
temperature, the lower CH4 concentrations in situ suggest a regulation by methanotrophs in the environment, and a possible
CO2 sink associated with cyanobacterial mats.
The capacity of arctic thaw ponds to rapidly react to increased temperature and our evidence that microbial communities may be selected by temperature suggest that short-term
enzymatic adjustments may be aided by environmental selection of dormant species. We found that there was potential for
significantly higher CH4 and CO2 net emissions, especially in
sediments of ponds with poor light penetration limiting photosynthesis and with higher concentrations of organic C and nutrients. Here, we found distinct responses by pond type suggesting
that climate change affects must be studied in the context of
limnological characteristics. Given the diversity of aquatic systems across other permafrost landscapes (Vonk et al. 2015), there
is a need for similar studies of more pond types to accurately
predict the net effect of warming on global GHG emissions.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank P.-G. Rossi, C. Girard, L. Boutet, G. Deslongchamps and
S. Duval for their help in the field and laboratory, and A. Comeau
for advice on laboratory techniques and bioinformatics. We also
acknowledge G. Gauthier, the Centre for Northern Studies, the
Polar Continental Shelf Project and Parks Canada for logistic
support.
FUNDING
The study was funded in part by ArcticNet, a Canadian network
of centers of excellence, the Natural Sciences and Engineering
Research Council of Canada (NSERC) Discovery grants to CL and
Negandhi et al.
IL, the NSERC program EnviroNorth Collaborative Research and
Training Experience Program (CREATE) (to KN) and the Fonds de
Recherche du Québec—Nature et technologies (FQRNT) support
for Centre des Études Nordique. This study was a contribution
to the Canadian International Polar Year.
Conflict of interest. None declared.
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