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Limnol. Oceanogr., 54(1), 2009, 160–170
2009, by the American Society of Limnology and Oceanography, Inc.
E
Seasonal changes of bacterial and archaeal communities in the dark ocean:
Evidence from the Mediterranean Sea
Christian Winter,1,2 Marie-Emmanuelle Kerros, and Markus G. Weinbauer
Microbial Ecology and Biogeochemistry Group, CNRS, Laboratoire d’Océanographie de Villefranche, 06234 Villefranchesur-Mer CEDEX, France; Université Pierre et Marie Curie, Paris 6, Laboratoire d’Océanographie de Villefranche, BP 28,
06234 Villefranche-sur-Mer CEDEX, France
Abstract
The study site located in the northwestern Mediterranean Sea was visited nine times in 2005–2006 to collect
water samples from the epipelagic (5 m), mesopelagic (200 m, 600 m), and bathypelagic (1000 m, 2000 m) zones.
The relative abundance of Bacteria, Crenarchaea, and Euryarchaea was determined by catalyzed reporter
deposition fluorescence in situ hybridization (CARD-FISH). Apparent richness (total number of phylotypes
detected) and community composition (different phylotypes detected) of Bacteria and Archaea were assessed by
denaturing gradient gel electrophoresis (DGGE) analysis of polymerase chain reaction (PCR)-amplified
fragments of the 16S ribosomal ribonucleic acid (rRNA) gene followed by sequencing and phylogenetic analysis
of selected phylotypes. The relative abundance of Crenarchaea and Euryarchaea in the epipelagic zone increased
as stratification decreased. Apparent bacterial richness increased with decreasing stratification in the mesopelagic
and bathypelagic zones. Deep vertical mixing at the study site represented the beginning of a seasonal succession.
The effects of this succession were detectable throughout the water column and led to distinct prokaryotic
communities in different depth layers during the stratified period. The seasonal variability in the relative
abundance of Bacteria, as well as apparent prokaryotic richness and community composition, was comparable
between the different depth layers. This suggests that prokaryotic communities of the dark ocean can be as
dynamic as those found at the surface.
Our study site in the northwestern Mediterranean Sea is
characterized by unusually warm (,13uC) deep waters year
round and a uniform water column in winter with the
potential for deep vertical mixing. As early as 1990, the
deep water of the western Mediterranean Sea was shown to
have warmed by 0.12uC in the period from 1959 to 1988,
reflecting the increased surface temperatures in winter
(Béthoux et al. 1990). The study by Béthoux et al. (1990)
also suggests that deep vertical mixing appears to be very
effective in distributing changes occurring at the surface to
deeper waters within a relatively short period of time.
The mesopelagic (200–1000-m depth) and bathypelagic
(1000–4000-m depth) zones together comprise ,75% of the
volume of the global ocean and, thus, constitute the world’s
largest aquatic ecosystem. Yet our knowledge of the
microbial processes in the deep ocean is still limited. Only
recently, significant progress has been made in our
understanding of deep-water prokaryotic communities
and their roles in the carbon and nitrogen cycle (Herndl
et al. 2005; Ingalls et al. 2006; Wuchter et al. 2006). Owing
to logistical difficulties in sampling deep waters over the
course of months to years, few studies on potentially
occurring seasonal developments in the prokaryotic compartment of mesopelagic and bathypelagic waters exist
(Karner et al. 2001; Tanaka and Rassoulzadegan 2002;
Fuhrman et al. 2006).
The aim of this study was to collect data on seasonal
changes of prokaryotic communities in deep waters as
compared with the surface. A combination of methods was
used to be able to progressively home in on specific
prokaryotic groups or taxa. We used catalyzed reporter
deposition fluorescence in situ hybridization (CARDFISH) to enumerate Bacteria, Crenarchaea, and Euryarch-
Seasonality in surface waters of temperate oceanic
regions is a well characterized and annually reoccurring
succession of events (Longhurst 1998) that can be traced
through all levels of the pelagic food web including
bacterial communities (Fuhrman et al. 2006). Although
the specifics may vary depending on the study area,
seasonality in the surface layer is usually coordinated by
the buildup and erosion of a thermocline that limits the
exchange of nutrients between the epipelagic zone and
deeper waters during the stratified period. A thermocline
also allows for the accumulation of a substantial phytoplankton biomass by preventing single-celled photosynthetic organisms to sink out of the epipelagic zone.
1 Present address: University of British Columbia, Department
of Earth and Ocean Sciences, Oceanography, 1461-6270 University Boulevard, Vancouver, British Columbia V6T 1Z4, Canada.
2 Corresponding author ([email protected]).
Acknowledgments
We thank Jean-Claude Marty, Jacques Chiaverini, Floriane
Girard, and Stéphane Gouy for organizing the cruises to the
Dynamique des Flux de Matière en Mediterranée (DYFAMED)
site and for making room in the already tight schedule to
incorporate yet another hydrocast for this study. The captains and
crews of RV Tethys II are greatly acknowledged for their
assistance at sea. Maria-Luiza Pedrotti kindly provided the
epifluorescence microscope for analyzing the catalyzed reporter
deposition fluorescence in situ hybridization (CARD-FISH)
samples. We thank two anonymous reviewers for their helpful
comments. The European Commission was financially supporting
this study in the form of a Marie Curie postdoctoral fellowship to
C.W. (project ILVIROMAB, 007712). The cruises were financed
by Institut National des Sciences de l’Univers—Centre National
de la Recherche Scientifique (INSU-CNRS).
160
Prokaryotic communities in deep waters
161
September 2005 and June 2006 the sampling scheme was
amended to cover 5-m, 200-m, 600-m, 1000-m, and 2000-m
depth. From each depth a total volume of 2 liters was
retrieved and processed aboard ship immediately after
sampling as described below.
Fig. 1. Geographic map of the location of the Dynamique
des Flux de Matière en Mediterranée (DYFAMED) time series
station. Bathymetry outlines the basin of the Ligurian Sea and is
based on 5-min gridded elevation data (ETOPO5). The inset
shows southwestern Europe and highlights the area of the main
map for better orientation. The map was created using the online
map creation tool at http://www.aquarius.ifm-geomar.de/ and was
subsequently modified.
aea. Denaturing gradient gel electrophoresis (DGGE)
analysis of polymerase chain reaction (PCR)-amplified
portions of the 16S ribosomal ribonucleic acid (rRNA)
gene was used to assess the bacterial and archaeal
community composition followed by sequencing of selected
taxa.
Methods
Study site and sampling—The study was conducted at the
Dynamique des Flux de Matière en Mediterranée (DYFAMED) time series station, a former Joint Global Ocean
Flux Study (JGOFS) station. The DYFAMED time series
station is located in the center of the Ligurian Basin of the
Mediterranean Sea (43u259N, 07u529E) in ,2400 m of
water (Fig. 1). The central part of the Ligurian Sea is
composed of a homogenous waterbody isolated from direct
coastal inputs by the Liguro-Provençal current and is easily
accessible by ship. The site was visited nine times with RV
Tethys II in 2005–2006 (02 July 2005, 27 September 2005,
25 October 2005, 19 December 2005, 07 February 2006, 07
March 2006, 02 April 2006, 06 May 2006, 30 June 2006).
Sampling was performed using 12-liter Niskin bottles
mounted on a carousel sampler (SBE 32; Sea-Bird
Electronics) also holding the conductivity–temperature–
depth (CTD) sensors (SBE 911; Sea-Bird Electronics). In
July 2005, samples were retrieved from 5 m, 600 m, and
2000 m, whereas during the next eight visits between
CARD-FISH—Samples for CARD-FISH (10–50 mL depending on sampling depth) were fixed with formaldehyde
(2% final concentration) aboard ship and stored at 4uC for
up to 24 h. Subsequently, prokaryotic cells were collected
on 0.22-mm pore size membrane filters (Cyclopore
polycarbonate membrane, 25-mm diameter, cat.
No. 7060-2502; Whatman). The filters were air dried and
stored at 220uC until analysis. CARD-FISH was performed using the probes Eub338 (targeting Bacteria; 59GCT GCC TCC CGT AGG AGT-39; Amann et al. 1990),
Non338 (antisense probe to assess nonspecific background; 59-ACT CCT ACG GGA GGC AGC-39; Wallner
et al. 1993), Cren537 (targeting Crenarchaea; 59-TGA
CCA CTT GAG GTG CTG-39; Teira et al. 2004), and
Eury806 (targeting Euryarchaea; 59-CAC AGC GTT TAC
ACC TAG-39; Teira et al. 2004). Permeabilization,
hybridization, and mounting of filter slices on slides
followed the protocol of Teira et al. (2004). The slides were
examined under an Axioskop 2 microscope (Zeiss)
equipped with a 100 Watt mercury lamp and appropriate
filter sets for 49,6-diamidino-2-phenylindole (DAPI) and
Alexa488 (Invitrogen-Molecular Probes). More than 250
DAPI-stained cells in a minimum of 20 fields of view were
counted per sample.
Determination of bacterial and archaeal community
composition—Sample preparation and nucleic acid extraction: Samples (1 liter) were filtered through 0.22-mm pore
size filters (Cyclopore polycarbonate membrane, 47-mm
diameter, cat. No. 7060-4702; Whatman) to collect the
prokaryotic cells on the filters within 1 h of sampling.
Subsequently, filters were flash frozen in liquid nitrogen
and stored at 280uC until analysis.
Extraction of nucleic acids from the filters was performed using an Ultraclean Soil DNA Kit (cat. No. 12800100; MO BIO Laboratories). The filters were cut into small
pieces and transferred into the extraction tubes using
ethanol-flamed forceps and scissors. In order to minimize
shear damage to the nucleic acids, the alternative lysis
method as detailed in the manufacturer’s instructions
involving two heating steps to 70uC for 5 min was
performed. Nucleic acid extracts had a final volume of
50 mL in solution S5 (the solution contains no ethylenediaminetetraacetic acid [EDTA]; contents are proprietary
information by MO BIO Laboratories) and were directly
used as template in subsequent PCR reactions.
PCR amplification: The primer pair 341F (59-CCT ACG
GGA GGC AGC AG-39; Muyzer et al. 1995) and 907R
(59-CCG TCA ATT CMT TTG AGT TT-39; Schäfer et al.
2001) was used to amplify a 586-bp long fragment (E. coli
numbering positions 341–927) of the bacterial 16S rRNA
gene. A 591-bp long fragment (E. coli numbering positions
344–935) of the archaeal 16S rRNA gene was amplified
using primers 344F (59-ACG GGG YGC AGC AGG CGC
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Winter et al.
GA-39; Raskin et al. 1994) and 915R (59-GTG CTC CCC
CGC CAA TTC CT-39; Stahl and Amann 1991). A 40-bp
long GC clamp (59-CGC CCG CCG CGC CCC GCG
CCC GTC CCG CCG CCC CCG CCC G-39; Muyzer et al.
1996) was attached to the 59 end of the forward primers
341F and 344F to obtain PCR fragments suitable for
DGGE analysis. The resulting PCR fragments had a length
of 626 bp and 631 bp for the bacterial and archaeal
amplifications, respectively.
We used 1–2 mL of the nucleic acid extracts as template
in PCR reactions. PCR chemicals were from MBI
Fermentas, primers were purchased from MWG-Biotech,
and cycling was performed in a Mastercycler thermal cycler
(Eppendorf). Each 50-mL PCR reaction contained 5 mL of
103 Taq buffer (100 mmol L 21 Tris-HCl [pH 8.8],
500 mmol L 21 KCl, 0.8% Nonidet P40), 4 mL of
25 mmol L21 MgCl2, 1.25 mL of 10 mmol L21 dNTP
(deoxyribonucleotide triphosphate) mix, 0.5 mL of 100
mmol L21 primers, and 0.25 mL of 5 U mL21 Taq polymerase. Bacterial PCR reactions started with an initial
denaturation at 95uC for 1 min followed by 30 cycles with
denaturation at 95uC for 1 min, annealing at 56uC for
1 min, and elongation at 72uC for 1 min. A touchdown
protocol was used for archaeal PCR reactions (Casamayor
et al. 2000) with an initial denaturation at 95uC for 5 min,
followed by 20 cycles with 95uC for 1 min, 71uC for 1 min
decreasing every cycle by 0.5uC to 61uC, and 72uC for
3 min, followed by another 15 cycles at 95uC for 1 min,
61uC for 1 min, and 72uC for 3 min. The final elongation
step of bacterial and archaeal PCR reactions was performed at 72uC for 30 min in order to prevent the
formation of artificial double bands in subsequent DGGE
analysis (Janse et al. 2004). PCR fragments were cleaned
and concentrated using a QIAquick PCR purification kit
(Qiagen) according to the manufacturer’s instructions
resulting in a final volume of 28 mL in elution buffer
(Qiagen). Standard agarose gel electrophoresis was used to
size and quantify the PCR fragments.
DGGE analysis: DGGE analysis was performed on an
Ingeny phorU (Ingeny International) DGGE electrophoresis system. PCR products (200 ng per sample) were
loaded on 6% polyacrylamide gels containing linear
gradients (20–70%) of formamide and urea. Electrophoresis was performed at 100 V and 60uC for 16 h in 13 TAE
(tris-acetic acid-ethylenediamine-tetraacetic acid) buffer
(40 mmol L21 Tris, 20 mmol L21 acetic acid, 1 mmol L21
EDTA, pH 8.3). The gels were stained with SYBR gold
(1 : 10,000 dilution of stock solution; Invitrogen-Molecular
Probes) for 30 min before digitized gel images were
obtained using a gel doc EQ (Bio-Rad) gel documentation
system. The images were analyzed for the number of bands
per sample (presence vs. absence; Moeseneder et al. 1999)
serving as a measure of apparent bacterial and archaeal
richness. Additionally, the peak patterns were translated
into a binary data matrix for further statistical analysis.
Sequencing and phylogenetic analysis of selected DGGE
bands—In total 107 bands from 40 samples (93 bacterial
bands from 34 samples and 14 archaeal bands from 6
samples) were excised from the DGGE gels. The gel slices
were incubated in 200 mL of autoclaved Milli-Q water for
24 h at 4uC. Subsequently, 1 mL of the supernatant was
used to reamplify the extracted nucleic acids. The reamplified PCR fragments were separated by DGGE under
identical conditions as described above using the original
samples as standards. Reamplified PCR products were
sequenced by a commercial sequencing service (MWG
Biotech). Alignments and tree calculations were performed
using Geneious Pro software (version 3.5.6; Biomatters).
The nucleotide sequences were aligned against their closest
relatives from GenBank identified by basic local alignment
search tool (BLAST) searching (Altschul et al. 1990). A
consensus tree from 1000 replicates based on 475 nucleotide
positions was calculated by the unweighted pair group
method with arithmetic average (UPGMA) using the
Tamura–Nei genetic distance model (Tamura and Nei 1993).
Nucleotide sequence accession numbers—In total, 26
distinct (17 bacterial and 9 archaeal) partial 16S rRNA
gene sequences were obtained and deposited into GenBank
under the accession numbers EF368225–EF368241 and
EF382650–EF382658.
Statistical analysis—The mathematical software package
Mathematica (Wolfram Research, version 5.2.2) was used
for statistical analysis. Analysis of variance (ANOVA) and
Tukey’s post hoc test based on the Studentized range
distribution of average values was used to test for
significant differences between depth layers. Student t-tests
were used to test for significant differences between the
means of two populations based on the two-tailed tdistribution. Spearman rank correlation coefficients were
calculated to test relationships between the parameters.
Temporal variability of parameters is given as the
coefficient of variation. The DGGE banding patterns were
used to calculate detection frequencies of bands in each
depth layer for which nucleotide sequences were obtained
and were used to identify differences between depth layers.
The temporal variability in the binary presence–absence
matrices obtained by DGGE analyses for each depth layer
was determined as the percentage of variable bands. For
each depth layer, variable bands are defined as the
difference between the total number of bands and the
number of bands that were detected in all samples
throughout the sampling period. For selected statistics,
bootstrap analysis was performed to determine the
influence of stochastic effects on the statistics based on 1
3 105 bootstrap replicates. The results of the statistical tests
were assumed to be significant at p values #0.05.
Results
Seasonal and depth-related variability of parameters—
Physicochemical parameters: Salinity and potential density
(sT) were significantly lower in the epipelagic zone
compared with the mesopelagic and bathypelagic zones
(ANOVA, salinity, F ratio 5 4.53, p 5 0.0185; sT, F ratio
5 15.84, p , 0.0001; Table 1; Fig. 2). Temperature
averaged 16.98uC, 13.11uC, and 12.94uC in the epipelagic,
mesopelagic, and bathypelagic zones, respectively (Ta-
Prokaryotic communities in deep waters
163
Table 1. Parameters measured in the three depth layers (epipelagial, mesopelagial, and bathypelagial). The average (Avg), standard
deviation (SD), minimum, maximum, and number of samples (n) are given. Parameters are salinity; temperature in uC; potential density
(sT) in kg m23; the fraction of Bacteria, Crenarchaea, Euryarchaea; total probe-positive cells as percentage of DAPI-stained cells; and
bacterial and archaeal richness as the number of bands.
Avg
SD
Parameter
epi.
meso.
bathy.
Salinity
Temperature
sT
Bacteria
Crenarchaea
Euryarchaea
Probe positive
Richness Bacteria
Richness Archaea
38.14
16.98
27.87
25.2
1.8
0.9
27.9
26.7
10.3
38.51
13.11
29.09
13.0
11.4
1.8
26.2
27.2
10.3
38.48
12.94
29.10
13.4
7.4
0.6
21.5
28.2
9.7
epi.
Minimum
meso.
bathy.
0.70 0.02
3.97 0.13
1.29 0.02
18.1
7.4
2.4
8.9
1.4
1.8
18.8 11.8
3.4
4.3
0.6
0.7
0.02
0.08
0.01
8.9
5.2
0.6
11.6
2.7
0.7
ble 1), and was significantly higher in the epipelagic zone
compared with the mesopelagic and bathypelagic zones
(ANOVA, F ratio 5 17.48, p , 0.0001). Total variation of
temperature in the epipelagic zone was ,10uC, whereas in
the mesopelagic and bathypelagic zones variation was
smaller than 0.5uC (Table 1).
At the study site, the water column was stratified during
July–October 2005 and May–June 2006 with a mixed layer
depth of 30–50 m (Fig. 2). Water column stratification
epi. meso. bathy.
36.30
12.97
25.50
10.3
0.1
0.0
10.4
22
10
38.47
12.94
29.05
3.3
0.2
0.0
4.3
17
9
38.45
12.83
29.10
4.5
0.4
0.0
5.3
24
9
Maximum
n
epi.
meso.
bathy.
epi. meso. bathy.
38.5
22.44
29.10
56.7
7.0
4.2
62.0
31
11
38.54
13.30
29.11
27.4
30.7
6.6
44.9
31
11
38.51
13.09
29.11
35.8
19.6
2.3
47.5
32
11
9
9
9
8
8
8
8
9
3
17
17
17
15
15
15
15
17
15
17
17
17
17
17
17
17
17
15
started to deteriorate in December 2005 and was building
up in April 2006 (Fig. 2). The nonstratified period in
February–March 2006 was characterized by a uniform
salinity of ,38.5 and a temperature of ,13uC throughout
the water column (Table 1). sT was used as indicator for
seasonality because it takes the variation of both salinity
and temperature into account. With decreasing stratification sT increased to ,29.1 kg m23 (Table 1; Fig. 2)
throughout the water column in February–March 2006.
Fig. 2. Depth profiles of potential density (sT) at the DYFAMED station. Depth profiles of sT in kg m23 on (A) 02 July 2005, (B)
27 September 2005, (C) 25 October 2005, (D) 19 December 2005, (E) 07 February 2006, (F) 07 March 2006, (G) 02 April 2006, (H) 06
May 2006, (I) 30 June 2006. The inset show details for the upper 250 m of the water column.
164
Winter et al.
Fig. 3. Relative abundance of Bacteria, Crenarchaea, Euryarchaea, and total probe-positive
cells. The temporal development of the relative abundance of Bacteria, Crenarchaea,
Euryarchaea, and total probe-positive cells as percentage of 49,6-diamidino-2-phenylindole
(DAPI)-stained cells is shown for each sampling depth.
Relative abundance of Bacteria, Crenarchaea, and
Euryarchaea: Bacteria averaged 25.2%, 13.0%, and 13.4%
of DAPI-stained cells in the epipelagic, mesopelagic, and
bathypelagic zones, respectively (Table 1; Fig. 3), and the
proportion was significantly higher in the epipelagic zone
compared with the mesopelagic and bathypelagic zones
(ANOVA, F ratio 5 6.19, p 5 0.0058). On average,
Crenarchaea constituted 1.8%, 11.4%, and 7.4% of DAPIstained cells in the epipelagic, mesopelagic, and bathypelagic zones, respectively (Table 1; Fig. 3). Significantly
fewer Crenarchaea than Bacteria were found in the
epipelagic and bathypelagic zones (t-test, epipelagic, p 5
0.0078, degrees of freedom [df] 5 7; bathypelagic, p 5
0.0236, df 5 16), whereas in the mesopelagic zone the
relative abundance of both groups was similar (t-test, p 5
0.5909, df 5 14). The relative abundance of Crenarchaea
was significantly lower in the epipelagic compared with the
mesopelagic zone (ANOVA, F ratio 5 7.25, p 5 0.0028).
The fraction of Euryarchaea did not differ significantly
between depth layers (Table 1; Fig. 3) and averaged 1.1%
throughout the water column. The relative abundance of
Euryarchaea was significantly lower than Bacteria in all
depth layers and throughout the water column (t-test, in all
cases p , 0.05; epipelagic, df 5 7; mesopelagic, df 5 14;
bathypelagic, df 5 16). Significantly fewer Euryarchaea
than Crenarchaea were detected in the mesopelagic and
bathypelagic zones (t-test, in all cases p , 0.001;
mesopelagic, df 5 14; bathypelagic, df 5 16) but not in
the epipelagic zone (t-test, p 5 0.3630, df 5 7). On average,
the percentage of probe-positive cells (Eub338, Cren537,
Eury806) did not differ significantly between the depth
layers and varied between 10.4% and 62% in the epipelagic
zone, 4.3% and 45% in the mesopelagic zone, and 5.3% and
47.5% in the bathypelagic zone (Table 1; Fig. 3). Nonspecific background hybridization as tested using the antisense
probe Non338 was not detected.
Seasonal variability of the relative abundance of Bacteria
was similar in the three depth layers (coefficient of
variation, 55–64%; Fig. 4). However, the variability for
Crenarchaea and Euryarchaea was higher in the epipelagic
zone as compared with the mesopelagic and bathypelagic
zones (coefficient of variation, Crenarchaea, 68–127%;
Euryarchaea, 90–135%; Fig. 4). Seasonal variability of the
percentage of probe-positive cells was similar between the
Prokaryotic communities in deep waters
165
communities was small (Figs. 4 and 7). Thus, seasonal
variability in apparent bacterial and archaeal richness and
community composition was also detected for the mesopelagic and bathypelagic zones and was comparable to the
epipelagic zone.
Relationships between parameters—sT decreased with
increasing stratification particularly in the epipelagic zone
and increased as stratification broke down during the
winter months (Fig. 2). The relative abundance of both
Crenarchaea and Euryarchaea increased with increasing sT
in the epipelagic zone (Table 2). Throughout the water
column, apparent bacterial richness increased, whereas
apparent archaeal richness decreased with increasing sT
(Table 2). Furthermore, apparent bacterial richness correlated positively with sT in the mesopelagic and bathypelagic zones (Table 2). In the epipelagic zone, the relative
abundance of Crenarchaea and Euryarchaea decreased with
increasing temperature (Crenarchaea, r 5 20.71, p 5
0.0465; Euryarchaea, r 5 20.81, p 5 0.0149).
Fig. 4. Seasonal variability. Seasonal variability given as the
coefficient of variation (%) of the relative abundance of Bacteria,
Crenarchaea, Euryarchaea, total probe-positive cells, apparent
bacterial and archaeal richness in the epipelagic, mesopelagic, and
bathypelagic zones, respectively. The means and standard
deviations (error bars) of 1 3 105 bootstrap replicates are shown.
depth layers (coefficient of variation, 44–60%; Fig. 4).
Stochastic effects as determined by bootstrapping had little
influence on the relative abundance of Bacteria, Crenarchaea, and Euryarchaea, as well as total probe-positive cells
(Fig. 4).
Apparent bacterial and archaeal richness and community composition: Apparent bacterial richness did not differ
between depth layers and varied between 22 and 31 bands
(,1.43), 17 and 31 bands (,1.83), and 24 and 32 bands
(,1.33) in the epipelagic, mesopelagic, and bathypelagic
zones, respectively (Table 1). Apparent archaeal richness
was significantly higher in the mesopelagic compared with
the bathypelagic zone (ANOVA, F ratio 5 6.06, p 5
0.0080). Apparent archaeal richness ranged from 10 to 11
bands (,1.13), 9 to 11 bands (,1.23), and 9 to 11 bands
(,1.23) in the epipelagic, mesopelagic, and bathypelagic
zones, respectively (Table 1).
Seasonal variability of apparent bacterial and archaeal
richness was small and similar in the three depth layers
(coefficient of variation, bacterial, 9–15%; archaeal, 4–7%;
Fig. 4). However, seasonal variation in bacterial and
archaeal community composition based on the DGGE
banding patterns was much higher (Figs. 5–6). The
percentage of variable bacterial bands (i.e., bands only
detected during certain periods of time within a depth
layer) was 84%, 86%, and 80% in the epipelagic,
mesopelagic, and bathypelagic zones, respectively (Fig. 7).
Seasonal variability of the archaeal community composition was 31%, 38%, and 31% in the epipelagic, mesopelagic, and bathypelagic zones, respectively (Fig. 7). Based
on bootstrapping analysis, the influence of stochastic
effects on the seasonal variability of bacterial and archaeal
Phylogenetic analysis of prokaryotes and depth distribution—In total, 26 distinct (17 bacterial and 9 archaeal)
partial 16S rRNA gene nucleotide sequences were obtained.
The bacterial sequences varied in length between 486 and
532 bp and the archaeal sequences were between 428 and
513 bp long. Henceforth, the term ‘‘phylotype’’ will be used
when referring to the nucleotide sequences. Phylogenetic
analysis placed the bacterial phylotypes into six different
groups (Fig. 8). Seven phylotypes were related to dProteobacteria, five to a-Proteobacteria, two to c-Proteobacteria, and one phylotype each was related to Cyanobacteria, Actinobacteria, and Chloroflexi. All archaeal phylotypes clustered within the Marine Group II Euryarchaea
(Fig. 8). In no case did we detect 16S rRNA genes related
to phyoplankton plastids (Rappé et al. 1998), suggesting
that the influence of plastid signals on our results
particularly from the epipelagic zone was low.
Based on detection frequencies calculated for each depth
layer throughout the study period, the phylotypes DYFBac7 and DYFBac16 showed a preference for the
mesopelagic (DYFBac7, 71%; DYFBac16, 53%) and
bathypelagic zones (DYFBac7, 88%; DYFBac16, 76%)
and were rarely detected in the epipelagic zone (DYFBac7,
11%; DYFBac16, 22%; Fig. 5). On the other hand, the
phylotype DYFBac13 was frequently found in the epipelagic (67%) and mesopelagic zones (59%) and rarely in the
bathypelagic zone (35%; Fig. 5). The phylotype DYFArch3
was primarily found in the epipelagic (100%) and
mesopelagic zones (80%) as compared with the bathypelagic zone (40%; Fig. 6). DYFArch9 was most often found
in the epipelagic zone (67%), was found less frequently in
the mesopelagic zone (40%), and was never found in the
bathypelagic zone (Fig. 6).
Discussion
Variability of parameters within and between depth
layers—Our results on the depth distribution of Bacteria
and Crenarchaea support previous work from the Pacific
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Winter et al.
Fig. 5. Seasonal and depth-related variability of bacterial community composition.
Schematic representation of the DGGE banding patterns obtained for each cruise and sampling
depth. Presence and absence of bands is indicated by black and white squares, respectively.
Additionally, the figure indicates the position and designation of excised and subsequently
sequenced bands.
Fig. 6. Seasonal and depth-related variability of archaeal community composition.
Schematic representation of the DGGE banding patterns obtained for each cruise and sampling
depth. Presence and absence of bands is indicated by black and white squares, respectively.
Additionally, the figure indicates the position and designation of excised and subsequently
sequenced bands.
Prokaryotic communities in deep waters
Fig. 7. Temporal variability of the percentage of variable
bands for Bacteria and Archaea. Variable bands are defined as the
difference between the total number of bands detected by DGGE
analysis for each depth layer and the number of bands that were
detected in all samples throughout the sampling period. The
means and standard deviations (error bars) of 1 3 105 bootstrap
replicates are shown.
Ocean (Karner et al. 2001). However, on average the
detectability of DAPI-stained cells by CARD-FISH at the
study site (Table 1) was well below previously reported
values from the North Atlantic of ,70% using the same
method (Teira et al. 2004, 2006). Our low average detection
rates might be due to mismatches between the probes used
in CARD-FISH and the majority of prokaryotic cells at
our study site. However, at least the probe Eury806
perfectly matched the euryarchaeal nucleotide sequences
(Fig. 8) obtained from the study site. This analysis was not
possible with the probe Eub338 because the target sequence
was outside the amplified region and with Cren537 because
no crenarchaeal sequences were obtained. Also, although
average detectability of DAPI-stained cells was low,
seasonal variability was high throughout the water column
167
(Fig. 4), and the percentage of probe-positive cells reached
62% in April 2006 (Fig. 3).
The relative abundance of Bacteria, Crenarchaea, and
Euryarchaea varied by more than twofold (Fig. 4)
throughout the water column, suggesting that seasonality
is clearly a feature of surface as well as deep waters at the
study site. The DNA probes used in CARD-FISH target
the 16S rRNA molecule in ribosomes. Prokaryotic cells
are capable of adjusting the number of ribosomes
according to metabolic activity; thus, the intensity of
the signal in FISH is proportional to cellular activity
(Amann et al. 1995). If this assumption holds also for
CARD-FISH, our data suggest that prokaryotic metabolic activity as assessed by CARD-FISH varied substantially in surface and deep waters throughout our
study period. Such seasonality in bacterial productivity
has been shown for surface waters (down to 130 m) at our
study site (Lemée et al. 2002).
In contrast to previous studies conducted in the
Mediterranean Sea (Moeseneder et al. 2001), we did not
find any significant trend of apparent bacterial richness
with depth. Winter et al. (2008) also did not find differences
in apparent bacterial and archaeal richness between surface
and mesopelagic waters in the tropical Atlantic Ocean.
These data suggest that although specific bacterial communities exist in surface and deep waters they appear to be
composed of a similar number of detectable taxa. This was
also found in our study where certain bands were mainly
detected in specific depth layers (Fig. 5) while the number
of bands in different depth layers was similar (Table 1).
Also, although seasonal variability of apparent prokaryotic
richness was low, variability of prokaryotic community
composition was much higher (Figs. 5–7). These results
suggest that the number of presumably dominant phylotypes detectable by DGGE analysis appears to be more
rigorously controlled than the actual composition of the
prokaryotic communities in all depth layers. Thus, changing environmental conditions (e.g., availability of nutrients,
mortality due to grazers and viruses) led to the replacement
(in the sense of detectability) of specific phylotypes by
others with only slight changes in the total number of
dominant phylotypes. Even more interesting is that the
resulting seasonal variability in apparent prokaryotic
richness and community composition was similar in surface
and deep waters, further supporting the conclusion that
seasonality is a feature of deep waters at the study site.
Table 2. Spearman rank correlation coefficients. The Spearman rank correlation coefficients between potential density (sT) and
other parameters measured in this study are given for the entire water column and the three depth layers. Abbreviations as in Table 1.
Relevant (20.5 . r . 0.5) and statistically significant (p # 0.05) correlation coefficients are in bold.
Salinity
Temperature (uC)
Bacteria
Crenarchaea
Euryarchaea
Probe positive
Richness Bacteria
Richness Archaea
Total
Epipelagial
Mesopelagial
Bathypelagial
0.20
20.80
20.34
0.17
0.03
20.27
0.54
20.51
0.64
20.93
20.02
0.71
0.86
0.13
0.50
n.a.
20.45
20.90
20.06
20.40
20.13
20.44
0.67
20.28
0.12
0.07
20.38
20.37
20.24
20.43
0.88
20.37
168
Winter et al.
Fig. 8. Phylogenetic tree of the 16S rRNA gene sequences obtained in this study. Values at nodes represent bootstrap values, and the
scale bar represents substitutions per nucleotide position. See main text for further details.
Seasonality and the role of vertical mixing—Phylogenetic
analysis based on sequencing archaeal DGGE bands
revealed that all of the obtained nucleic acid sequences
represented members of the Marine Group II Euryarchaea
(Fig. 8). Thus, it appears that DGGE analysis of the
archaeal community using previously developed primers
and PCR conditions (Casamayor et al. 2000) is heavily
skewed toward Euryarchaea at the study site. However,
Euryarchaea comprised only a marginal fraction of the
prokaryotic community (Table 1; Fig. 3). It is interesting to
note that archaeal PCR products in the epipelagial could
only be obtained during periods with weak or no
stratification (December 2005 and February–March 2006;
Fig. 6) underlining the positive correlation between the
Prokaryotic communities in deep waters
relative abundance of Euryarchaea detected by CARDFISH and sT in the epipelagic zone (Table 2). Similarly, the
relative abundance of Crenarchaea increased with increasing sT in the epipelagic zone (Table 2). Together, these data
suggest that stratification in the epipelagic zone appears to
create a situation that is not favorable for archaeal growth
(e.g., bacterial competition, availability of nutrients). The
positive correlation between sT and apparent bacterial
richness in the mesopelagic and bathypelagic zones
(Table 2) is best explained by vertical mixing fueling deeper
waters with detectable bacterial taxa either by importing
them from the surface or by stimulating growth through
changing the availability of nutrients. In summary, our
data on the seasonal development of bacterial and archaeal
communities (Table 2) are compatible with deep vertical
mixing during the nonstratified period that led to identical
bacterial communities throughout the water column in
February–March 2006 (Fig. 5).
The most significant finding of this study is that
prokaryotic communities of deep waters exhibit seasonal
changes in terms of relative abundance and composition.
At the study site, deep vertical mixing during the
nonstratified period represents the beginning of an annual
succession. This succession is detectable throughout the
water column and leads to clearly different prokaryotic
communities in the epipelagic, mesopelagic, and bathypelagic zones during the stratified period. The basin-scale
vertical mixing of the water column suggests that the
Mediterranean Sea is especially vulnerable and/or adaptable to man-made alterations (e.g., climate change;
Béthoux et al. 1990) including the deliberate or accidental
release of hitherto in the study area unknown prokaryotic
taxa (e.g., genetically modified organisms, prokaryotes
transported in ships’ ballast water). Depending on the
strength of vertical mixing in winter and early spring,
alterations in surface waters will be relayed to deep waters
within a very short time. The seasonal variability in the
relative abundance of Bacteria as well as apparent
prokaryotic richness and community composition is
comparable between the different depth layers indicating
that the effects of seasonal changes on the prokaryotic
compartment do not diminish with depth. Thus, prokaryotic communities of the dark ocean can be as dynamic as
those found at the surface.
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Edited by: Peter G. Verity
Received: 1 June 2008
Accepted: 12 September 2008
Amended: 24 September 2008