FEMS Microbiology Ecology, 91, 2015
doi: 10.1093/femsec/fiu022
Advance Access Publication Date: 8 December 2014
Research Article
RESEARCH ARTICLE
Ice formation and growth shape bacterial community
structure in Baltic Sea drift ice
Eeva Eronen-Rasimus1,2,∗ , Christina Lyra3 , Janne-Markus Rintala2,4 ,
Klaus Jürgens5 , Vilma Ikonen1,† and Hermanni Kaartokallio1
1
Marine Research Centre, Finnish Environment Institute (SYKE), Erik Palménin aukio 1, PO Box 140, Helsinki
00251, Finland, 2 Tvärminne Zoological Station, University of Helsinki, J.A. Palménin tie 260, FI-10900 Hanko,
Finland, 3 Department of Food and Environmental Sciences, PO Box 56, Viikinkaari 9, FI-00014 University of
Helsinki, Finland, 4 Department of Environmental Sciences, PO Box 65, Viikinkaari 1, FI-00014 University of
Helsinki, Finland and 5 Leibniz Institute for Baltic Sea Research Biological Oceanography, Seestr. 15,
18119 Rostock, Germany
∗ Corresponding author: Marine Research Centre, Finnish Environment Institute (SYKE), Erik Palménin aukio 1, PO Box 140, Helsinki 00251, Finland.
Tel. +358 (0) 40-182-31-72; Fax. +358 (0)9-54-90-22-90; E-mail [email protected]
†
Present address: Roal Oy, Tykkimäentie 15, FI-05200 Rajamäki, Finland.
One sentence summary: Clear changes in the bacterial communities were observed in Baltic Sea drift-ice samples from open water to thick ice, leading
to bacterial communities similar to those of polar sea ice.
Editor: Patricia Sobecky
ABSTRACT
Drift ice, open water and under-ice water bacterial communities covering several developmental stages from open water to
thick ice were studied in the northern Baltic Sea. The bacterial communities were assessed with 16S rRNA gene
terminal-restriction fragment length polymorphism and cloning, together with bacterial abundance and production
measurements. In the early stages, open water and pancake ice were dominated by Alphaproteobacteria and
Actinobacteria, which are common bacterial groups in Baltic Sea wintertime surface waters. The pancake ice bacterial
communities were similar to the open-water communities, suggesting that the parent water determines the sea-ice
bacterial community in the early stages of sea-ice formation. In consolidated young and thick ice, the bacterial
communities were significantly different from water bacterial communities as well as from each other, indicating
community development in Baltic Sea drift ice along with ice-type changes. The thick ice was dominated by typical sea-ice
genera from classes Flavobacteria and Gammaproteobacteria, similar to those in polar sea-ice bacterial communities. Since
the thick ice bacterial community was remarkably different from that of the parent seawater, results indicate that thick ice
bacterial communities were recruited from the rarer members of the seawater bacterial community.
Key words: bacteria; sea ice; 16S rRNA; T-RFLP; cloning
INTRODUCTION
The Baltic Sea, a brackish subpolar semi-enclosed sea, is annually covered with seasonal ice to varying extent (Granskog,
Kaartokallio and Kuosa 2010). Coastal areas are covered by fast
ice, meaning that ice forms and melts almost at the same place,
whereas in offshore areas, depending on water depth and ice
thickness, ice is moved by winds and currents and is called
drift ice. Dynamic ice growth conditions, such as rafting and
Received: 4 July 2014; Accepted: 30 November 2014
C FEMS 2014. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
ridging, are common in Baltic drift ice, and the pancake ice cycle
is important in turbulent waters similar to that in the Antarctic
(Granskog et al., 2006). Pancake ice floes are round-shaped ice
discs that form through accretion of ice crystals that move by
waves and ocean swell. By bumping and grinding against one
another, they can grow from centimetres to decimetres in size
and eventually congeal into larger floes and finally to a continuous solid ice sheet (Petrich and Eicken 2010). Ice floes can slide
one on the other, resulting in rafted ice. As a consequence of
ridging, ice floes can pile against one another, forming pack ice
fields.
Baltic Sea ice is structurally similar to polar sea ice, despite
its brackish nature (Granskog et al., 2010). However, brine channels are smaller and the brine salinity is lower compared to the
polar counterparts (Granskog et al., 2006). When sea ice forms,
inorganic and organic components dissolved in seawater are
concentrated into brine that moves within channels and pores
in the sea-ice matrix (Petrich and Eicken 2010). Sea ice serves
as a habitat for diverse assemblages of auto- and heterotrophic
organisms, including sea-ice bacteria (Thomas and Dieckmann
2002; Mock and Thomas 2005; Arrigo, Mock and Lizotte 2010;
Caron and Gast 2010; Deming 2010). Bacterial growth in sea ice
is regulated by multiple simultaneously acting environmental
factors, such as salinity, temperature, nutrients (Pomeroy and
Wiebe 2001; Kuosa and Kaartokallio 2006), as well as food web
interactions, such as protistan grazing (Kaartokallio 2004; Riedel,
Michel and Gosselin 2007). Similar to the open ocean, bacteria in
sea ice participate in decomposing of particulate organic matter
and recycling and production of dissolved organic matter (DOM).
Bacteria are entrained from seawater into the forming seaice matrix and apparently suffer a transient reduction in activity, but the activity is recovered after the ice has consolidated (Grossmann and Gleitz 1993; Grossmann 1994; Grossmann
and Dieckmann 1994; Helmke and Weyland 1995; Kaartokallio
2004; Kaartokallio et al., 2008; Deming 2010). In consolidated sea
ice, psychrophilic bacteria replace psychrotrophic bacteria along
with maturation of the ice cover (Helmke and Weyland 1995;
Bowman et al., 1997a). Succession and development of bacterial communities in sea ice have been associated with high substrate concentrations and abundant attachment sites, such as
particles and algae (Helmke and Weyland 1995; Bowman et al.,
1997a; Junge et al., 2002; Junge, Eicken and Deming 2004; EronenRasimus et al., 2014). However, detailed description of bacterial
succession at the community level in sea ice has been done
only twice. In temperate Baltic Sea fast ice, the bacterial community changed throughout the winter (Kaartokallio et al., 2008)
whereas in extremely cold Arctic drift ice, no change in bacterial community composition was observed during the lowproductive winter period (Collins, Rocap and Deming 2010). Despite the knowledge on succession in thick sea ice, bacterial
communities in the various developmental stages of drift ice
have not been studied.
Bacterial communities in the Baltic Sea consist of both freshwater (e.g. Betaproteobacteria, Actinobacteria and Verrucomicrobia) and marine (Alphaproteobacteria, Bacteroidetes) bacteria adapted to ambient brackish water conditions (Hagström,
Pinhassi and Zwiefel 2000; Pinhassi and Hagström 2000; Sipura
et al., 2005; Riemann et al., 2008; Andersson, Riemann and
Bertilsson 2010; Herlemann et al., 2011; Laas et al., 2014). Baltic
Sea wintertime surface-water bacterial communities are dominated by Alphaproteobacteria and Actinobacteria (Laas et al.,
2014). In general, the same bacterial groups are found in both
water and ice (Kaartokallio, Laamanen and Sivonen 2005, 2008;
Andersson et al., 2010; Herlemann et al., 2011; Laas et al., 2014).
The phylum Bacteroidetes (e.g. class Flavobacteria) and classes
Alpha- and Gammaproteobacteria dominate sea-ice bacterial
communities, but less abundant groups such as Betaproteobacteria, Gram-positive bacteria and Archaea have also been found
(Bowman et al., 1997a; Bowman, Brown and Nichols 1997b; Junge
et al., 1998, 2002, 2004; Staley and Gosink 1999; Petri and Imhoff
2001; Brown and Bowman 2001; Brinkmeyer et al., 2003, 2004;
Kaartokallio et al., 2005, 2008; Mock and Thomas 2005; Collins
et al., 2010; Bowman et al., 2012). The same bacterial classes
are described both in Baltic and polar sea ice, but their relative
abundance varies, depending on sampling location and time.
The phylum Bacteroidetes in sea ice is possibly favoured by decreasing temperature (Junge et al., 2004) and is apparently abundant in both Arctic and Baltic Sea ice during the cold winter
months (Junge et al., 2004; Kaartokallio et al., 2008). In contrast,
Gammaproteobacteria are most abundant in spring/summer
sea ice and thus presumably favour high availability of organic
substrates originating from ice algal growth (Junge et al., 2002;
Brinkmeyer et al., 2003; Kaartokallio et al., 2008; Deming 2010;
Bowman et al., 2012). Betaproteobacteria occur more often in
Baltic and Arctic sea ice than in Antarctic ice, suggesting terrestrial influence on sea-ice bacterial communities (Brinkmeyer
et al., 2003, 2004; Kaartokallio et al., 2008).
Our aim was to investigate the bacterial community structure in various developmental stages of drift ice from open water
to consolidated sea ice. The study was conducted aboard the RV
Maria S. Merian in the Gulf of Bothnia, Baltic Sea in February–
March 2006. The bacterial communities were studied with 16S
rRNA gene terminal-restriction fragment length polymorphism
(T-RFLP) and cloning together with bacterial abundance and production measurements.
MATERIALS AND METHODS
Study site and sampling
The samples were collected from the Bothnian Sea and Bothnian
Bay, Baltic Sea, aboard the RV Maria S. Merian in February–March
2006 (Fig. 1). The overall ice conditions during the cruise and
sea-ice structures are described in detail in Rintala, Piiparinen
and Uusikivi (2010). Open-water samples were retrieved with
a conductivity–temperature–depth /Rosette sampler from three
different depths: 2–5, 70–80 and 100–230 m at stations 22–24.
Sea ice was sampled from stations 25–29 and 31 from a range of
ice types including new ice, pancake ice, consolidated ice with
two different developmental stages categorized as young ice and
thick ice, as well as pack ice (see Rintala et al., 2010). Ice at stations 26 and 29 were rafted (see Rintala et al., 2010). For bacterial community analysis, new ice and pancake ice were collected
from one station, young ice from two different stations and thick
ice from three different stations (Table 1). Two replicates were
collected from each station, except for pancake ice which had
three replicates.
New ice, young ice, thick ice and three out of five pack ice
samples were drilled with a US Army Cold Regions Research
and Engineering Laboratory-type power core auger (9 cm Ø, Kovacs Enterprise, Roseburg, OR, USA). The ice cores were cut with
a handsaw into three sections so that the surface and bottom
ice were approximately 10 cm in length and the middle part
of the ice varied from 13 to 30 cm, except at station 25, where
the ice was cut in only two pieces (Table 1). The remaining two
pack ice cores were cut with a handsaw from the surface layer of
the pack ice field. Three approximately 0.5-m-diameter pancake
ice floes were collected from the ice edge, using a metal basket.
Eronen-Rasimus et al.
3
Bacterial abundance
St. 26
65
SWEDEN
To determine the bacterial abundance, 20 ml of melted ice
or water was fixed with 25% electron microscopy grade glutaraldehyde (1% final concentration; Sigma-Aldrich, St. Louis,
MO, USA) and stored in a cool, dark place prior to analysis.
Sample of 5 ml was filtered onto 0.2-μm-polycarbonate filters
(Millipore, Darmstadt, Germany) and stained with 0.02% acridine orange (Hobbie, Daley and Jasper 1977). The total bacterial numbers were counted using a Leitz Aristoplan (Leica
Microsystems, Bensheim, Germany) epifluorescence microscope
equipped with Leitz a I3 filter and PL Fluotar 100X 12.5/20X oil
immersion objective giving a total magnification of ×1000. At
least 200 recorded cells in a minimum of 20 random fields were
counted using a New Porton E11 counting grid (Graticules Ltd,
Edenbridge, Kent, UK).
St. 25
St. 28
St. 25
St. 31
64
Latitude ° N
St. 29
63
Pancake ice
St. 24
FINLAND
62
St. 23
61
St. 22
60
THE
BALTIC
SEA
59
58
18
20
22
24
26
Longtitude ° E
Bacterial production
28
30
Figure 1. Map of the northern Baltic Sea, showing sampling stations during the
cruise aboard RV Maria S. Merian during 28 February–9 March 2006. Open water
was collected from stations 22 to 24, new ice and pack ice from station 28, young
ice from station 25 and 31 and thick ice from station 26, 27 and 29. Pancake ice
had no station number and is thus marked directly on the map.
The ice samples were allowed to thaw in autoclaved plastic containers in darkness at 4◦ C (direct melting, Helmke and Weyland
1995; Kaartokallio 2004) and filtered subsequently after becoming fully melted. Under-ice water samples were collected directly
from the drill holes into autoclaved plastic bottles. Slush was removed from the hole before sampling and the bottles were kept
in darkness until further processing.
For the DNA extractions, 470–1500 (mode 500) ml of melted
sea ice and 1000 ml of under-ice water and open water were filtered through sterile 0.22 μm membrane filters (Ø 47 mm; Whatman, GE Healthcare, Little Chalfont, Kent, UK) and frozen immediately at −80◦ C.
Bacterial net biomass production was measured using the 3 Hlabelled thymidine incorporation method (Fuhrman and Azam
1982). The samples were processed based on the description in Kaartokallio (2004): a piece of each ice section was
crushed with a spike. Approximately, 7.5 g of crushed ice
was weighed in a scintillation vial, after which 2 ml of 0.2μm-filtered seawater was added to better simulate the brine
pocket salinity and ensure even distribution of labelled substrate. All the work was done in a climate room at 4◦ C.
Three aliquots and a formaldehyde-killed absorption blank
were amended with [methyl-3 H]-thymidine (PerkinElmer, specific activity 20 Ci mmol L−1 ) to a final concentration of 18 nM.
The samples were incubated in the dark at 4◦ C for 17–22 h.
The incubations were stopped by adding formaldehyde (final
concentration of 5%). The standard cold trichloroacetic acid extraction method (Fuhrman and Azam 1980, 1982) was used for
removal of unincorporated 3 H-thymidine. The samples were filtered onto 0.2 μm polycarbonate filters (Millipore, Darmstadt,
Germany), after which the filters were placed into scintillation
vials, scintillation cocktail (Irgasafe plus PerkinElmer Life and
Analytical Sciences, Groningen, Netherlands) added and measured with a scintillation counter (PerkinElmer Life and Analytical Sciences, Groningen, Netherlands). The empirical conversion factor of 2 × 1018 cells per mol incorporated thymidine
L−1 was used to estimate bacterial cell production (Smits and
Riemann 1988).
Environmental parameters
DNA extraction and PCR amplification
Inorganic nutrients including phosphate (PO4 3− ), ammonium
(NH4 + ), nitrite (NO2 − ), nitrate (NO3 − ), as well as temperature,
salinity and brine volume were measured (Table 1) and the structures of the sea ice cores were determined from one replicate
from each station, except for that in pancake ice, nutrients were
measured from all three replicates. The methods employed are
described in Rintala et al. (2010). Elevated PO4 3− , NH4 + and NO3 −
concentrations, high bulk salinity and brine volume percentage
were detected in the snow ice (0–16 cm) layer at station 26, indicating incorporation of nutrients from atmospheric deposition
into the ice via snow ice formation (see Rintala et al., 2010).
In addition to the environmental parameters described
above, also chlorophyll a (chl-a), algal, protozoan and metazoan
biomasses were determined from the same sample set. The
methods and results were reported and discussed earlier in Rintala et al. (2010).
R DNA Isolation
DNA was extracted from filters with a PowerSoil
Kit (Mobio Laboratories, Inc., Carlsbad, CA, USA) as described by
Eronen-Rasimus et al. (2014). The extracted DNA was used as a
template (∼50 ng) in triplicate PCR reactions to amplify the 16S
rRNA genes for the T-RFLP and clone libraries. For the T-RFLP,
the primers FAM27f, FAM-GAGTTTGATCMTGGCTCAG with 6carboxyfluorescein label [Sait et al., 2003; high-performance liquid chromatography (HPLC)-purified, Oligomer Oy, Helsinki, Finland], and unlabelled 1406r, ACGGGCGGTGTGTRC, (Lane et al.,
1985; HPLC-purified; Oligomer Oy) were used, whereas for the
clone libraries both primers were unlabelled. The PCR reactions
and purifications were performed, as described by Sinkko et al.
(2011) with the following exceptions: three parallel PCR reactions from each sample were done in a reaction volume of 25 μl
and DyNazymeTM EXT DNA polymerase was used (Finnzymes,
Thermo Fisher Scientific, Vantaa, Finland).
0–0.10
0.10–0.40a
0.40–0.63
NA
0–0.12
0.12–0.25
0.25–0.46
NA
0–0.10
0.10–0.40
0.40–0.59
NA
0–0.12
0.12–0.27a
0.27–0.34
NA
0–0.12
0.12–0.25
NA
0.12
0.12
0.19
0.08
0.20
0.17
0.17
0.14
0.55
0.33
0.11
0.07
0.28
0.15
0.89
0.10
0.07
0.11
0.06
0.16c
0.14
0.07
–
–
–
8.89
3.76
2.21
1.38
8.86
32.14
9.60
0.78
9.28
11.48
1.45
3.28
8.94
7.62
4.14
9.31
1.77c
11.28
1.73
–
–
–
–
–
–
–
–
–
NO3 −
0.04
0.03
0.03
0.03
–
–
–
0.02
–
–
–
0.02
0.03
0.01
0.01
0.03
–
–
0.09
0.08c
0.01
0.03
–
–
–
–
–
–
–
–
–
NO2 −
NA = not applicable
a
indicates samples from which 16S rRNA gene clone libraries were derived
b
mean value from 2 replicates
c
mean value from 3 replicates
under-ice water
29
thick ice
under-ice water
27
thick ice
under-ice water
26
thick ice
under-ice watera
31
young ice
under-ice water
25
young ice
pancake icea
0–0.09
–
–
–
2a
70
210
24
open water
28
new ice
under-ice water
–
–
–
3
80
100
23
open water
–
–
–
PO4 3−
5
70
230
Depth from
surface (m)
22
open water
Station number
and sample type
2.02
0.89
1.06
1.47
1.48
1.38
1.44
1.25
5.71
2.51
0.65
0.06
2.87
1.08
3.03
0.12
0.08
0.07
0.63
1.06c
1.17
–
–
–
–
–
–
–
–
–
–
NH4 +
6.50
7.58
4.05
31.76
6.29
3.30
3.32
29.95
8.14
4.97
2.89
24.78
7.05
1.94
5.58
33.52
–
3.62
32.21
4.48c
15.63
33.13
–
–
–
–
–
–
–
–
–
SiO4 −
0.70
0.90
0.45
2.40
1.10
0.35
0.45
2.20
1.30
0.77
0.40
2.10
0.30
0.50
0.60
2.50
0.60
0.38
3.45
–
1.60
3.50
5.42
5.57
6.39
5.46
6.04
6.44
5.39
6.45
6.99
Salinity
0.33
0.64
1.39
–
1.18
0.94
0.97
–
1.12
0.58
0.98
–
0.52
0.98
2.02
–
−5.45
−3.81
−2.00
–
−4.80
−2.67
−2.30
–
−6.80
−4.47
−1.32
–
0.83
3.48
–
–
1.17
–
NA
NA
NA
−4.35
−3.07
−1.60
–
−3.50
−0.53
–
–
−3.50
–
0.31
1.54
3.49
NA
NA
NA
NA
NA
NA
−0.14
2.61
4.29
0.57
2.84
3.47
Brine volume
(%)
Temperature
(◦ C)
4.80
3.40
2.81
7.36
2.32
2.28
2.64
7.36
2.59
3.05
2.44
6.60
3.27
4.50
3.87
7.84
1.87
3.38
9.59
4.61c
6.28b
–
–
–
–
–
–
–
–
–
Bacterial abundance
(x108 cells L−1 )
0.30
2.74
2.58
–
4.97
4.76
1.14
–
2.22
6.80
8.43
–
0.27
0.21
0.72
–
–
–
–
–
0.44
–
–
–
–
–
–
–
–
–
Tdr incorporation
(x107 cells L−1 h−1 )
Table 1. Dissolved inorganic nutrient concentrations (μM), bulk salinities, temperatures, total bacterial abundance and bacterial cell production measured by thymidine (Tdr) incorporation from
northern Baltic Sea water and sea-ice stations. Ice was rafted at stations 26 and 29 (see Rintala et al., 2010).
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
Eronen-Rasimus et al.
T-RFLP
The bacterial community dynamics and composition were
determined with T-RFLP (Liu et al., 1997) and cloning. Restriction enzyme digestions and T-RFLP were performed at the
Institute of Biotechnology, University of Helsinki, Finland, as
described by Sinkko et al. (2011) with three different restriction enzymes: BsuRI, MspI and RsaI (Fermentas, Thermo Fisher
Scientific, Burlington, ON, Canada). The peaks were cleared of
background noise and normalized with the statistical methods
developed by Abdo et al. (2006), written in Perl and R languages
(R Development Core Team 2011). With BsuRI, fragments from
26.5 to 1000 base pairs (bp) and with MspI and RsaI from 49.5 to
1000 bp were included in normalization.
Cloning and identification of terminal restriction
fragments (T-RFs)
Five clone libraries were constructed to identify the T-RFs and to
determine the taxonomic classification for bacterial communities in different sample types. Cloning of the 16S rRNA gene sequences, plasmid extractions and sequencing were performed
at the Institute of Biotechnology, University of Helsinki, Finland.
Approximately, 950 bp from the 5 terminus of each 16S rRNA
gene were sequenced and the sequences were corrected manually with the Staden Package 1.6.0 Gap v. 4.10 (Staden, Beal
and Bonfield 1998; Staden, Judge and Bonfield 2003). Putative
chimeras were checked with the Chimera Uchime algorithm
in Mothur v.1.31.2. (Schloss et al., 2009). Taxonomic classification of the 16S rRNA genes was done with the naı̈ve Bayesian
Classifier (v. 2.6, RDP training set 9, Wang et al., 2007) by applying an 80% confidence threshold, and the Seqmatch tool
(release 11.1, v. 3, default options) of the Ribosomal Database
Project (RDP, Cole et al., 2009). Since almost all of the sequence
matches were affiliated with unknown bacterial sequences, Seqmatch was also carried out with default options, excluding
uncultured sequences. Representative sequences were chosen
by (1) lowest rank and (2) highest S ab score. In addition, the
National Center for Biotechnology Information nucleotide Basic Local Alignment Search Tool (blastn), megablast, 2.2.29+,
(Morgulis et al., 2008) was used to obtain the identity percentage
for the identified matches from RDP Seqmatch. The 16S rRNA
gene sequences were deposited in the European Molecular Biology Laboratory Nucleotide Sequence Database under accession
numbers from LM651929 to LM652337.
Shared richness based on the observed OTUs with cutoff level 0.03 was calculated using venn command in Mothur
(Schloss et al., 2009). The chloroplast sequences were omitted
from the clone library analyses. The T-RFs were identified with in
silico-digested 16S rRNA clone libraries using the Restriction Enzyme Database (REBASE 7.11, version 1.20080403) virtual digest
program (http://insilico.ehu.es/restriction/main/; Roberts et al.,
2010) and verified in vitro. For the in vitro digestions, the 16S rRNA
gene PCR amplifications, digestions and analyses from clones
were performed as described above.
Phylogenetic analysis of the 16S rRNA genes
A phylogenetic neighbour-joining (NJ) tree was constructed to
visualize the diversity in the clone libraries obtained. Approximately 950-bp-long sequences were aligned with RDPipeline
aligner [INFERence of RNA Alignment (INFERNAL) version 1.1rc4,
Nawrocki, Kolbe and Eddy 2009] of the RDP (Cole et al., 2009). A
sequence from the archaeon Sulfolobus tokodaii (AB022438) was
5
used as an outgroup in the alignment. The bootstrapped (1000)
NJ tree was constructed using PHYLogeny Interference Package
(Phylip) 3.695 (Felsenstein 2005), with the Jukes–Kantor evolution model. The tree was visualized with the Interactive Tree Of
Life online tool (Letunic and Bork 2007). Reference sequences for
the phylogenetic tree were chosen based on Seqmatch results as
described above.
Statistical analysis
A priori groups were determined, based on sample types.
Generalized discriminant analysis based on distance (Canonical Analysis of Principal coordinates routine, Anderson and
Robinson 2003) was performed to test whether the bacterial
communities could be discriminated between a priori groups.
Discriminant analysis was performed on the Bray–Curtis similarity matrix derived from square-root-transformed T-RF data.
Square-root transformation was applied to reduce the contribution of dominant species, because the Bray–Curtis resemblance measure does not scale individual peaks by its total or
maximum across all samples and our samples showed large
differences between their relative abundances. P-values were
calculated with 9999 permutations. For discriminant analysis,
Plymouth Routines In Multivariate Ecological Research (PRIMER)
v. 6 software (Clarke and Gorley 2006) with the add-on package
PERmutational ANOVA/MANOVA+ (PERMANOVA+) (Anderson,
Gorley and Clarke 2008) was used.
Pairwise comparisons between clone library sequences were
done with Mothur LIbrary SHUFFling (LIBSHUFF) (Schloss, Larget
and Handelsman 2004) applying Bonferroni correction leading to
a 0.005 significance level.
RESULTS
Bacterial abundance and production
Bacterial abundance and production rates are presented in
Table 1. The bacterial abundance was one-fourth higher in new
ice than in pancake ice implying that bacteria suffered reduction in cell numbers immediately after ice formation. After consolidation of sea ice, the bacterial abundance did not notably
change.
Bacterial production was negligible in the early stages of ice
formation but was clearly higher in thick ice, indicating active
bacterial growth and biomass turnover in thick ice.
Bacterial community composition and dynamics
Based on LIBSHUFF analysis, all clone libraries differed significantly, except that for pancake ice, which did not differ from
the water samples (Table 2, Bonferroni corrected P < 0.005).
The sequences obtained were defined with the RDP Classifier
and Seqmatch tools. The open-water, under-ice water and pancake ice bacterial communities were dominated by classes Alphaproteobacteria (21, 22 and 19% respectively) and Actinobacteria (15, 29 and 28% respectively; Table 3, Fig. 2). Most of
the actinobacterial sequences in all three clone libraries had
closest sequence match to Ilumatobacter coccineus (identity 95%;
AP012057) extracted from Kumagawa River estuary sediment,
Japan (Matsumoto et al., 2009) and most of the Alphaproteobacteria to Candidatus Pelagibacter sp. (SAR11, group III) (identity
99%; CP002511) from Arctic Ocean surface water (Oh et al., 2011).
The young ice bacterial community was dominated
by the class Gammaproteobacteria (53%; Table 3). The
6
FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
Table 2. P-values of pairwise comparisons from LIBSHUFF analysis
between five constructed 16S rRNA gene clone libraries from the
northern Baltic Sea water and ice samples. The open-water clone library was constructed from the surface water from station 24, underice water from station 31, pancake ice from replicate three, middle
part of the young ice from station 31 and middle part of columnar
ice from station 29 (Table 1). Significant values applying Bonferroni
correction (P < 0.005) are shaded in grey.
gammaproteobacterial sequences showed closest sequence
matching to Serratia marcescens (identity 99%; GQ889261) from
soil and cyanobacteria-associated Acinetobacter johnsonii (identity 99%; DQ911549). In addition, Alphaproteobacteria (15%) and
Planctomycetes (14%) were common in young ice (Table 3, Fig. 2).
Similar to the water samples, most of the Alphaproteobacteria
showed closest sequence matching to Candidatus Pelagibacter
sp. (identity 99%; CP002511) (59). Planctomycetes showed closest
sequence matching to uncultured Planctomycetaceae sequences
(identity 99%; HM856408) from Yellowstone Lake, WY, USA (Clingenpeel et al., 2011).
Overall, the bacterial community in thick ice differed from
all other bacterial communities at the genus level (Tables 3
and 4). The thick ice bacterial community was dominated
by the class Flavobacteria (36%) and most of the sequences
showed closest sequence matching to Flavobacterium degerlachei
(identity 99%; AJ557886) isolated from the Ace Lake microbial
mat, Antarctica (Van Trappen et al., 2004). All flavobacterial
sequences were affiliated with polar regions covering Flavobacterium spp. sequences from both Arctic and Antarctic habitats
(Brinkmeyer et al., 2003; Van Trappen et al., 2004; Harding
et al., 2011; Zhou et al., 2013; Prasad et al., 2014). The second
most common classes in thick ice were Gammaproteobacteria
(14%) and Betaproteobacteria (14%; Table 3, Fig. 2) with closest
sequence matches to Psychromonas ingrahamii (Gammaproteobacteria; identity 99%; CP000510), Shewanella denitrificans
(Gammaproteobacteria; identity 99%; AY771743) from the
Table 3. A summary of bacterial taxa in 16S rRNA gene clone libraries derived from northern Baltic Sea water and sea-ice samples. Taxonomic
classification was done with naı̈ve Bayesian Classifier (v. 2.6, RDP training set 9, Wang et al., 2007) of the RDP (Cole et al., 2009) with an 80%
confidence threshold level.
Phylum, class and lowest rank (>80%)
Proteobacteria:
Alphaproteobacteria:
Rhodobacter
Thalassospira
Loktanella
unclassified Rhodobacteraceae
unclassified Rhodospirillaceae
unclassified Sphingomonadaceae
unclassified Acetobacteraceae
unclassified Alphaproteobacteria
Betaproteobacteria:
Albidiferax
Limnohabitans
Methylotenera
Georgfuchsia
Acidovorax
unclassified Comamonadaceae
unclassified Methylophilaceae
unclassified Oxalobacteraceae
unclassified Burkholderiales
unclassified Betaproteobacteria
Gammaproteobacteria:
Psychrobacter
Psychromonas
Acinetobacter
Serratia
Thalassolituus
Rheinheimera
Pseudomonas
Haliea
Shewanella
Legionella
unclassified Enterobacteriaceae
unclassified Gammaproteobacteria
Deltaproteobacteria:
unclassified Deltaproteobacteria
unclassified Proteobacteria
Open water
Under-ice water
Pancake ice
Young ice
Thick ice
ND
ND
1
2
1
1
2
3
ND
ND
ND
ND
ND
ND
ND
16
1
ND
ND
ND
ND
ND
ND
6
1
ND
ND
1
ND
1
ND
12
1
1
ND
ND
ND
ND
ND
ND
ND
1
1
ND
ND
1
ND
ND
ND
ND
ND
ND
1
1
ND
ND
1
ND
ND
1
ND
ND
1
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
1
ND
ND
1
ND
ND
2
ND
ND
ND
ND
ND
ND
ND
1
2
ND
ND
ND
ND
ND
ND
ND
ND
ND
1
ND
2
2
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
5
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
35
11
2
ND
2
ND
ND
ND
1
1
ND
1
ND
ND
1
ND
ND
1
1
ND
ND
1
1
4
ND
4
ND
2
ND
1
ND
ND
Eronen-Rasimus et al.
7
Table 3. continued.
Phylum, class and lowest rank (>80%)
Open water
Under-ice water
Pancake ice
Young ice
Thick ice
7
ND
ND
ND
ND
ND
ND
7
1
ND
10
2
ND
1
5
ND
1
3
1
ND
ND
2
ND
ND
6
ND
1
ND
ND
ND
ND
1
ND
ND
ND
ND
ND
1
2
1
ND
ND
ND
ND
1
1
ND
ND
ND
1
2
ND
ND
ND
ND
10
2
ND
1
ND
ND
3
ND
ND
ND
ND
1
1
ND
ND
2
10
2
14
4
1
ND
1
ND
1
ND
ND
ND
ND
ND
ND
ND
ND
1
ND
Chloroflexi:
Caldilineae:
Caldilinea
1
1
1
ND
ND
Nitrospira:
Nitrospira:
Nitrospira
ND
ND
ND
ND
1
1
ND
ND
ND
ND
ND
1
ND
ND
2
ND
ND
ND
ND
ND
Firmicutes:
unclassified Firmicutes
1
ND
ND
ND
ND
Cyanobacteria:
GpIIa
2
1
ND
ND
ND
ND
ND
3
22
70
2
1
1
2
75
ND
ND
6
44
80
ND
ND
2
13
112
ND
ND
4
36
72
Actinobacteria:
Actinobacteria:
Ilumatobacter
Conexibacter
Citricoccus
unclassified Actinomycetales
unclassified Micrococcineae
unclassified Corynebacterineae
unclassified Acidimicrobineae
Bacteroidetes:
Flavobacteria:
Flavobacterium
Polaribacter
Wandonia
unclassified Flavobacteriales
unclassified Cryomorphaceae
Sphingobacteria:
Algoriphagus
unclassified Bacteroidetes
Planctomycetes:
Phycisphaerae:
Phycisphaera
Planctomyces:
Planctomyces
Schlesneria
unclassified Planctomycetaceae
Lentisphaerae:
Lentisphaeria:
unclassified Lentisphaeria
Verrucomicrobia:
Verrucomicrobiae:
unclassified Verrucomicrobia
unclassified Verrucomicrobiaceae
Other:
OD1 genera incertae sedis
TM7 genera incertae sedis
unclassified Bacteria
Chloroplast
Total number of sequences
Arctic and to Albidiferax ferrireducens (Betaproteobacteria;
identity 97%; CP000267).
A phylogenetic NJ tree was constructed to visualize diversity
in different sample types (Fig. 3). In general, the various sample
types were intermixed in the tree, but certain genera, such as
Flavobacterium and Acinetobacter, also formed their own clades
specific for only sea ice.
The clone library sequences were also digested in vitro to
identify T-RFs in the various sample types (Fig. 4). In general,
the same bacterial groups that were identified with cloning were
also identified with T-RFLP. Actinobacteria was the most common class in seawater samples, whereas Flavobacteria was the
most common class in sea-ice samples. Alphaproteobacteria
were more abundant in water than in sea ice. Planctomycetes were
8
FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
100 %
90 %
Alphaproteobacteria
Betaproteobacteria
80 %
Gammaproteobacteria
Deltaproteobacteria
70 %
Flavobacteria
Sphingobacteria
60 %
Actinobacteria
Phycisphaerae
50 %
Planctomycetacia
Verrucomicrobiae
40 %
Lentisphaeria
Caldilineae
30 %
Nitrospira
OD1 genera incertae sedis
20 %
TM7 genera incertae sedis
Cyanobacteria
10 %
Unclassified
0%
Open water
Under-ice
water
Pancake ice
Young ice
Thick ice
Figure 2. Bacterial diversity of 16S rRNA gene clones from the northern Baltic Sea. The open-water clone library was constructed from the surface water from station
24, under-ice water from station 31, pancake ice from replicate three, middle part of the young ice from station 31 and middle part of thick ice from station 29
(Table 1). Taxonomic classification was done at class level with naı̈ve Bayesian Classifier (v. 2.6, RDP training set 9) (Wang et al., 2007) of the RDP (Cole et al., 2009) with
an 80% confidence threshold level.
Table 4. Total and shared number of bacterial 16S rRNA gene sequence OTUs between different sample types collected from northern Baltic
Sea water and drift ice samples. OTUs are defined by 97% sequence similarity.
most abundant in the under-ice water and young ice samples.
In general, Gammaproteobacteria and Betaproteobacteria were
equally distributed among seawater and ice samples. However,
the pack ice at station 28 and young ice at station 31 were dominated by Gammaproteobacteria.
Generalized discriminant analysis (Anderson and Robinson
2003) yielded results similar to those of the clone libraries, showing that bacterial communities were discriminated by the sample types (Fig. 5). Generalized discriminant analysis showed
significant differences between a priori groups with all three restriction enzymes (P = 0.0001 with 9999 permutations). With the
BsuRI T-RF data, the first eight axes (choice of m = 8) classified a
priori groups 86.9% correctly and explained 78% of the total variability in the T-RF data. Since new ice (station 28) had formed in
a refrozen ship lead (see Rintala et al., 2010), the new ice bacterial community resembled the under-ice water community, indicating no changes in community composition shortly after ice
formation.
DISCUSSION
Clear changes in the bacterial communities were observed, both
at the class and genus levels in Baltic Sea drift-ice samples from
open water to thick ice. The results suggest that the bacterial
communities in Baltic Sea drift ice developed over time, leading to bacterial communities similar to those of polar sea ice, although distinctive features in the communities were also found.
The bacterial communities apparently went through reduction in activity in the beginning of ice formation as bacterial
abundance decreased from new ice to pancake ice and bacterial
production was very low. After consolidation of ice, the bacterial production was still low in young ice and increased only in
thick ice. Reduction in bacterial activity during the ice formation
has been also observed earlier in sea ice (Grossmann and Gleitz
1993; Grossmann 1994; Kaartokallio 2004, 2008). However, in experimental conditions activity was rapidly restored in the presence of abundant substrate (Eronen-Rasimus et al., 2014). The
low bacterial production followed the chl-a concentration in ice
9
Eronen-Rasimus et al.
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Figure 3. Phylogenetic NJ tree of 16S rRNA gene sequences (∼900 bp) derived from the northern Baltic Sea water and drift-ice samples. Bootstrap values >50% are
shown in grey circles. The S. tokodaii (AB022438) sequence was used as an outgroup in the alignment.
(see results in Rintala et al., 2010) and therefore recovery after
ice consolidation presumably followed the growth and adjacent
introduction of new substrate from the ice algal community.
The patterns in activity were concomitant with observed
community changes in the different sample types. The openwater bacterial community differed significantly from all other
bacterial communities, except that of pancake ice, indicating
that parent water determines the early stages of sea-ice bacterial
communities. In the open water, under-ice water and pancake
ice, the classes Alphaproteobacteria and Actinobacteria dominated bacterial communities, which is in accordance with previous study of Baltic Sea wintertime seawater communities (Laas
et al., 2014). Actinobacteria are often associated with soil and
freshwater environments, but they are also common members
of the Baltic Sea bacterioplankton community (Riemann et al.,
2008; Holmfeldt et al., 2009; Andersson et al., 2010; Herlemann
et al., 2011; Laas et al., 2014). Although few actinobacterial sequences/isolates have been found in both Arctic sea ice and Arctic melt ponds (Brinkmeyer et al., 2003, 2004; Collins et al., 2010;
Bowman et al., 2012), as well as Antarctic (Bowman et al., 1997a,b;
Junge et al., 1998; Brinkmeyer et al., 2003) and Baltic Sea ice (Kaartokallio et al., 2005), it seems that Actinobacteria are more common in less saline habitats.
Most of the Alphaproteobacteria in water and pancake ice
samples showed closest sequence identity to the Arctic Candidatus Pelagibacter sp., which is a member of the SAR11 clade
(group III) (Giovannoni et al., 1990; Oh et al., 2011). SAR11 is a
ubiquitous member of marine bacterial communities (Morris
24
25
31
26
27
28
29
28
25
31
26
27
29
81
112
169
182
187
230
250-252
271
279
290
299
321
448
591-592
612-614
907
new ice
pancake ice
young ice (surface)
young ice (bottom)
young ice (surface)
young ice (middle)
young ice (bottom)
thick ice (surface)
thick ice (middle)
thick ice (bottom)
thick ice (surface)
thick ice (middle)
thick ice (bottom)
thick ice (surface)
thick ice (middle)
thick ice (bottom)
packed ice (drilled)
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
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10
Figure 4. Distribution and relative abundance of in vitro (digested) identified bacterial taxa from the northern Baltic Sea water and ice samples. The size of the symbol
relates to the relative abundance of 16S rRNA gene T-RF. Taxonomic classification was done with naı̈ve Bayesian rRNA Classifier (v. 2.6, RDP training set 9, Wang et al.,
2007) of the RDP (Cole et al., 2009) with an 80% confidence threshold level. The accession numbers indicate the identity of digested clone.
Figure 5. Generalized discriminant analysis plot of BsuI-digested 16S rRNA gene
sequence T-RFs, showing changes in the bacterial community according to different sample types from open water to thick ice. Only results with BsuI are
shown, since it yielded the best taxonomic separation.
et al., 2002), including Baltic Sea waters (Herlemann et al., 2011;
Laas et al., 2014) and winter ice (Collins et al., 2010). Although
SAR11 seems to persist in Arctic thick ice (Collins et al., 2010),
in the Baltic Sea they appear to be most competitive in aquatic
habitats.
After consolidation of sea ice, the bacterial community
in young ice was significantly different from other sample
types. Young ice was dominated by Gammaproteobacteria.
Overwhelming gammaproteobacterial dominance in the initial
phases of ice formation was also seen previously in an experimental study where the mesocosms were enriched with DOM
(Eronen-Rasimus et al., 2014), indicating that Gammaproteobacteria are capable of opportunistic growth in sea ice. Gammaproteobacterial sequences were affiliated with the genera Acinetobacter and Serratia. Acinetobacter has been previously found in sea
ice (Brinkmeyer et al., 2003; Deming 2010) and from the Baltic Sea
water column (Hagström et al., 2000; Labrenz, Jost and Jürgens
2007) whereas psychrotolerant Serratia has been found in Baltic
Sea sediment (Edlund and Jansson 2006) but not in sea ice. Although the bacterial production in young ice was very low, it
is possible that Gammaproteobacteria were temporarily active
in the initial phases of the ice formation. As also chl-a concentration was low (see results in Rintala et al., 2010), Gammaproteobacteria would have been presumably utilizing some other
than algal-derived substrate. However, it is also possible that
these bacteria had been transported into growing sea ice e.g.
from sediment or other external source with water currents
and thus are not actively growing members of sea-ice bacterial
communities.
In addition to Gammaproteobacteria, young ice also had high
numbers of Planctomycetes sequences, reported previously only
once from a melt pond (Brinkmeyer et al., 2004) but not to our
knowledge from sea ice. The sequences were affiliated with
the class Phycisphaera, a recently accepted class in Planctomycetes (Fukunaga et al., 2009). High numbers of planctomycete
sequences and T-RFs indicate their possible ecological importance in Baltic Sea ice. Planctomycetes are often associated with
aggregates and algal blooms in marine environments (DeLong,
Franks and Alldredge 1993; Pizzetti et al., 2011) and co-occur
with Gammaproteobacteria in Baltic Sea waters (Riemann et al.,
Eronen-Rasimus et al.
2008). Thus, Plactomycetes probably have an ecology similar to
that of Gammaproteobacteria, which occur in the sea ice in
spring/summer presumably subsequent to an ice algal bloom
(Junge et al., 2002; Brinkmeyer et al., 2003; Kaartokallio et al., 2008;
Deming 2010; Bowman et al., 2012) and are possibly also permanent members of the Baltic Sea ice communities.
Thick ice, which differed from all other ice and water types,
was dominated by the class Flavobacteria, with sequences most
closely related to Flavobacterium from both the Arctic and Antarctic. The class Flavobacteria, of the phylum Bacteroidetes, is the
other major bacterial group inhabiting sea ice along with the Proteobacteria (Bowman et al., 1997a,2012; Staley and Gosink 1999;
Brown and Bowman 2001; Junge et al., 2002, 2004; Brinkmeyer
et al., 2003; Kaartokallio et al., 2005, 2008). The second most common group in thick ice was Gammaproteobacteria and the sequences were classified to the genera Psychromonas and Shewanella. Both genera are typical sea-ice bacteria (Bowman et al.,
1997a,b, 2012; Brown and Bowman 2001; Junge et al., 2002;
Brinkmeyer et al., 2003; Deming 2010) and their prevalence together with Flavobacteria indicates a shift in bacterial community composition from seawater communities to sea-ice communities. The betaproteobacterial sequences were as abundant
as those of Gammaproteobacteria and thus Betaproteobacteria
are likely permanent members of sea-ice bacterial communities,
as also previously shown in Baltic and Arctic sea ice (Brinkmeyer
et al., 2003; Kaartokallio et al., 2008; Collins et al., 2010) as well
as in melt ponds (Brinkmeyer et al., 2004). In general, the water
and thick ice communities differed at the genus level, suggesting that most of the community members in the thick ice represented the rarer members of the open-water bacterial communities. The result is an example of the rare biosphere concept (Sogin et al., 2006; Pedros-Alio 2006, 2012) showing how bacterial communities have potential to respond to changing environmental conditions implying ability of minor community
members to become dominant when environmental conditions
change.
Overall, the bacterial communities within each sample
type were very similar to each other, despite the long geographical distance between sampling stations, suggesting that
selection pressure caused by ice formation and ice maturation
is a stronger community-shaping factor than geographical
distance. Thus, based on this and previous studies,
plausible explanations for the differences observed in
bacterial communities from open water to thick ice
include formation and maturation of ice and subsequent
selection of psychrotolerant and psychrotrophic bacteria,
substrate availability by sea-ice algae, as well as resource
competition. Measured bacterial production and observed community changes together indicate an active bacterial growth
and biomass turnover in thick ice. Substrate availability and
maturation of sea ice are known to shape bacterial communities
(Helmke and Weyland 1995; Pomeroy and Wiebe 2001; Kaartokallio et al., 2008; Eronen-Rasimus et al., 2014). For example,
the role of sea-ice maturation was associated with changes in
bacterial community composition when the communities were
observed throughout the ice-covered season (January–March) in
Baltic Sea fast ice (Kaartokallio et al., 2008). In cold Arctic drift
ice, the bacterial communities did not change in contrast to
those of the parent seawater, indicating that selection during
freezing processes is relatively minor (Collins et al., 2010). However, the authors speculated that copiotrophic spring/summer
sea-ice bacteria, such as Gammaproteobacteria, appear in sea
ice as a result of competitive outgrowth of oligotrophic bacteria,
similar to our conclusions.
11
In conclusion, the parent water bacterial community probably determines the sea-ice community at the early stages of
sea-ice development, despite the change in temperature and
salinity during sea-ice formation. Along with ice consolidation,
the sea-ice bacterial communities changed remarkably together
with increasing bacterial production, pointing to a temporal development in Baltic Sea drift-ice bacterial communities. In addition, the results suggest that sea-ice communities are possibly formed by taxa that were present in very low numbers in
the parent water community and not readily detected with the
methods employed in this study. The consolidated sea-ice bacterial community was dominated by typical sea-ice-associated
flavobacterial and gammaproteobacterial genera, pointing to a
similar community structure in polar sea ice, despite the brackish nature of Baltic Sea water. However, the classes Betaproteobacteria and Planctomycetes, which seldom occur in polar sea
ice, are probably permanent members of the Baltic Sea ice bacterial communities, showing that the Baltic Sea also has unique
features of its own, probably influenced by the low salinity in
its northern parts. In all, sea-ice formation is a phenomenon
where a natural marine environment is fundamentally changed
by physical forcing and sea-ice bacteria show substantial adaptive capacity to respond this environmental change. Thus, we
suggest that sea-ice bacteria are recruited from the rare members of the parent water, and that sea ice provides an excellent
example of the importance of the total existing biodiversity in
marine bacterial community formation.
ACKNOWLEDGEMENTS
This work was supported by the Walter and Andree de Nottbeck Foundation. The authors thank the crew of the RV Maria S.
Merian for technical support during the cruise, and Erika Trost
and Dr Falk Pollehne for performing nutrient analyses from sea
ice samples. Also, we want to thank Prof. Kaarina Sivonen for
providing facilities for the molecular work and Riitta Autio for
support in various stages during the study.
Conflict of interest statement. None declared.
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