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RESEARCH ARTICLE
Transient Changes in Bacterioplankton
Communities Induced by the Submarine
Volcanic Eruption of El Hierro (Canary
Islands)
Isabel Ferrera
1
*, Javier Arístegui
2
, José M. González
3
, María F. Montero
2
,
Eugenio Fraile-Nuez
4
, Josep M. Gasol
1
1Departament de Biologia Marina i Oceanografia, Institut de Ciències del Mar, CSIC, Barcelona, Spain,
2Instituto de Oceanografía y Cambio Global, Universidad de Las Palmas de Gran Canaria, Las Palmas,
Spain, 3Department of Microbiology, University of La Laguna, La Laguna, Spain, 4Instituto Español de
Oceanografía, Centro Oceanográfico de Canarias, Santa Cruz de Tenerife, Spain
*iferrera@icm.csic.es
Abstract
The submarine volcanic eruption occurring near El Hierro (Canary Islands) in October 2011
provided a unique opportunity to determine the effects of such events on the microbial popu-
lations of the surrounding waters. The birth of a new underwater volcano produced a large
plume of vent material detectable from space that led to abrupt changes in the physical-
chemical properties of the water column. We combined flow cytometry and 454-
pyrosequencing of 16S rRNA gene amplicons (V1–V3 regions for Bacteria and V3–V5 for
Archaea) to monitor the area around the volcano through the eruptive and post-eruptive
phases (November 2011 to April 2012). Flow cytometric analyses revealed higher abun-
dance and relative activity (expressed as a percentage of high-nucleic acid content cells) of
heterotrophic prokaryotes during the eruptive process as compared to post-eruptive stages.
Changes observed in populations detectable by flow cytometry were more evident at depths
closer to the volcano (~70–200 m), coinciding also with oxygen depletion. Alpha-diversity
analyses revealed that species richness (Chao1 index) decreased during the eruptive
phase; however, no dramatic changes in community composition were observed. The most
abundant taxa during the eruptive phase were similar to those in the post-eruptive stages
and to those typically prevalent in oceanic bacterioplankton communities (i.e. the alphapro-
teobacterial SAR11 group, the Flavobacteriia class of the Bacteroidetes and certain groups
of Gammaproteobacteria). Yet, although at low abundance, we also detected the presence
of taxa not typically found in bacterioplankton communities such as the Epsilonproteobac-
teria and members of the candidate division ZB3, particularly during the eruptive stage.
These groups are often associated with deep-sea hydrothermal vents or sulfur-rich springs.
Both cytometric and sequence analyses showed that once the eruption ceased, evidences
of the volcano-induced changes were no longer observed.
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 1/16
OPEN ACCESS
Citation: Ferrera I, Arístegui J, González JM,
Montero MF, Fraile-Nuez E, Gasol JM (2015)
Transient Changes in Bacterioplankton Communities
Induced by the Submarine Volcanic Eruption of El
Hierro (Canary Islands). PLoS ONE 10(2): e0118136.
doi:10.1371/journal.pone.0118136
Academic Editor: Fabiano Thompson, Universidade
Federal do Rio de Janeiro, BRAZIL
Received: July 3, 2014
Accepted: January 8, 2015
Published: February 11, 2015
Copyright: © 2015 Ferrera et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Sequence data has
been deposited in the MG-RAST public database
(http://metagenomics.anl.gov/) under ID numbers
4600697–4600744 (Project Name: Hierro submarine
volcano).
Funding: This work was partly supported by projects
VULCANO (CTM2012-36317) to E. Fraile-Nuez and
J. Arístegui, MarineGems (CTM2010-20361) to J.M.
González and STORM (CTM2009-09352/MAR) to J.
M. Gasol and I. Ferrera, all funded by the suppressed
Spanish Ministry of Science and Innovation. J.M.
González received additional funding from the
Introduction
Submarine volcanic activity results in the release of dissolved and particulate substances, as
well as heat into the ocean that can be discharged either continuously (chronic plumes) or oc-
casionally (event plumes) [1] potentially leading to abrupt changes in the physical-chemical
properties of seawater and strongly affecting the marine biota. Microorganisms are recognized
to play key roles in such environments, yet few studies have characterized the microbial com-
munities that inhabit geologically active marine environments, partly because of the difficulty
associated with sample collection. While most of the knowledge on the biogeochemistry of un-
derwater volcanic activity comes from the study of highly evolved deep-sea hydrothermal
vents continuously releasing high-temperature reduced hydrothermal fluids (“black smokers”)
[2–10], there is little information on event plumes because direct observations of submarine
eruptions are rare since they are difficult to predict and monitor and usually occur in
remote locations.
El Hierro, the youngest of the Canary Islands, is located in the Northeastern Atlantic Ocean
above the presumed location of the Canary Island hot spot, a mantle plume that feeds upwell-
ing magma just under the surface. Seismic and volcanic activity has been continuously docu-
mented since 1990 when geophysical monitoring of the island started [11]. In summer 2011, El
Hierro began an intense episode of seismic activity that caused more than 12000 earthquakes.
As a result, an eruption took place in October 2011 and gave rise to a new shallow submarine
volcano of ca. 650 m located 1.8 km south of the island [12]. Initial geophysical surveys of the
volcanic eruption were followed by a series of hydrographic cruises, which allowed the study of
the changes in the seawater’s physical and chemical parameters as well as their effects on the
marine ecosystem. The discharge of high temperature hydrothermal fluids, magmatic gases
and volcanic particles during October and November produced warming of the water column
and dramatic changes in seawater chemistry, including a significant decrease in pH and oxygen
and an increase in iron and nutrients near the volcano [12–13]. These physical-chemical
anomalies had strong effects on some pelagic communities. Dead fish were observed floating
on surface waters, no fish schools were acoustically detected within the affected area [12] and
the diel vertical migration of zooplankton was disrupted [14]. Furthermore, preliminary results
reported that the activity of the local microbial communities was also significantly altered.
Small picophytoplankton, i.e., Prochlorococcus and Synechococcus, showed a significant decline
in abundance at depths >75m compared to far-field unaffected stations [12]. Conversely, het-
erotrophic prokaryotes seemed to increase with depth at stations affected by the volcanic emis-
sions [12]. In order to further characterize the effects of the eruption on the bacterioplankton
communities, we combined flow cytometry and 454-pyrosequencing of 16S rRNA gene ampli-
cons to monitor the area around the volcano through the eruptive and post-eruptive phases
(November 2011 to April 2012). The effects of this disturbance on prokaryote abundance, ac-
tivity, diversity and community structure are presented here.
Materials and Methods
Sampling
The samples were collected in seven oceanographic surveys starting three weeks after the onset
of the eruption until the complete cessation of the volcanic unrest (Bimbache (BBC) cruises
BBC3, 4–9 Nov 2011; BBC5, 16–20 Nov 2011; BBC8, 13–15 Jan 2012; BBC10, 9–12 Feb 2012;
BBC12, 24–26 Feb 2012; and Guayota (GYT) cruises GYT2, 17 Mar 2012; GYT3, 28 Apr
2012). Cruises were carried out by the Spanish Institute of Oceanography with the authoriza-
tion of the Spanish Government. BBC cruises were performed from aboard R/V Ramon
Effects of the El Hierro Submarine Eruption on Bacterioplankton
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 2/16
Ministry of Economy and Competitiveness (project
EcoBGM, CTM2013-48292-C3-3-R). The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
Margalef whereas the GYT cruises were performed from aboard the R/V Atlantic Explorer. The
environmental variables measured include temperature, oxygen, salinity and transmittance
and have been published elsewhere [12–14]. Based on satellite and CTD profile data, samples
were collected in stations located in the area most affected by the plume and in a control station
located in a less affected area east of the island (Station 1) (Fig. 1). Temperature and dissolved
oxygen depth profiles along the Bimbache cruises in Station 3 (above volcano) and Station 1
(control) are presented in S1 Fig. Samples for flow cytometric determination of prokaryote
abundance were collected in all cruises. Samples (1.6 ml) were preserved with paraformalde-
hyde (2% final concentration), left 10 min in the dark to fix, deep frozen in liquid nitrogen and
Fig 1. RAPIDEYE© color composite image acquired on October 26, 2011 showing the island of El
Hierro. The location of the volcano (yellow star) and of the stations sampled during leg BBC3 (red dots) is
indicated. The inset map shows chlorophyll concentration on November 06, 2011 and was acquired by NASA
Terra MODIS and processed by the Marinemet project.
doi:10.1371/journal.pone.0118136.g001
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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stored at—80°C. For DNA analyses, samples could only be collected during cruises BBC3,
BBC10, BBC12 and GYT3. About 10 l of seawater were sequentially filtered through a 3-μm
pore-size polycarbonate filter (Poretics) and a 0.2-μm Sterivex filter (Millipore) using a peri-
staltic pump. The Sterivex units were flash frozen in liquid nitrogen and kept at—80°C until ex-
traction was performed.
Flow-cytometric analyses
Once in the lab, fixed samples were thawed, stained with Syto13 (Molecular Probes) in the
dark for a few minutes, and run through a BD FACSCalibur cytometer with a laser emitting at
488 nm. High and Low Nucleic Acid content prokaryotes (HNA, LNA) were identified in bi-
variate scatter plots of side scatter (SSC-H) versus green fluorescence (FL1-H). Picocyanobac-
teria were discriminated in plots of orange fluorescence (FL2) versus red fluorescence (FL3)
and were subtracted from HNA prokaryote counts. For statistical analyses, the data were
grouped depending on three factors: time of sampling, sample location and depth. Multifactor
analysis of variance for abundance of prokaryotes and HNA cells with these three factors with
Tukey-Kramer post hoc comparison at the 5% significance level was performed in R (http://
www.R-project.org). Data was also plotted using R.
Nucleic acid extraction and sequencing
The Sterivex units (Millipore) were filled with 1.8 ml of lysis buffer (50 mM Tris-HCl pH 8.3,
40 mM EDTA pH 8.0, 0.75 M sucrose) treated with lysozyme, proteinase K and sodium dode-
cyl sulfate. Nucleic acids were extracted with phenol and concentrated in an Amicon 100
(Millipore) as described in Massana et al. [15]. DNA was quantified spectrophotometrically
(Nanodrop, Thermo Scientific) and a subsample was used for pyrosequencing at the Research
and Testing Laboratory (Lubbock, TX, USA; http://www.medicalbiofilm.org) using the bTE-
FAP method by 454 GL FLX technology as described previously [16]. Primers 28F (5’-
GAGTTTGATCNTGGCTCAG-3’) and 519R (5’- GTNTTACNGCGGCKGCTG-3’) generated
amplicons spanning the V1 to V3 regions of the bacterial 16S rRNA gene (*500 bp), and
primers 341F (50-GYGCASCAGKCGMGAAW-30) and 958R (50-GGACTACVSGGGTATC-
TAAT-30) were used to amplify archaeal fragments spanning the V3 to V5 regions (*600 bp).
The generated pyrosequencing data were processed using the QIIME (Quantitative Insights
Into Microbial Ecology) pipeline [17] as described in Sánchez et al. [18]. After an ID was as-
signed to each sample using a bar code, a sequence filtration step was performed before denois-
ing. Sequences were removed from the subsequent analyses if they were shorter than 150 bp,
had an average quality score <25 calculated in sliding windows of 50 bp, or had an uncorrect-
able barcode or >3 ambiguous bases. The remaining sequences were run through Denoiser to
reduce the impact of pyrosequencing errors [19]. Curated sequences were then grouped into
operational taxonomic units (OTUs) or phylotypes using UCLUST [20] with a minimum iden-
tity of 97%. A representative sequence from each phylotype was chosen by selecting the most
abundant sequence in each cluster. The resulting representative sequences were checked for
chimeras using ChimeraSlayer [21] in mothur [22]. The identity of 16S rRNA phylotypes was
determined using the RDP Classifier [23] implemented in QIIME. BLAST was also used for
certain unclassified OTUs as some lineages were not correctly classified by RDP. OTUs repre-
sented by one single tag (singletons) were discarded to avoid potential artifacts in diversity esti-
mates. Likewise, OTUs assigned to chloroplasts or mitochondria were removed. Venn
Diagram Plotter (http://omics.pnl.gov/software/VennDiagramPlotter.php) was used to gener-
ate area-proportional Venn Diagrams. Chao1 diversity metrics and rarefaction curves were
computed in QIIME and plotted in Kaleidagraph (v.4.1). Non-metric multidimensional scaling
Effects of the El Hierro Submarine Eruption on Bacterioplankton
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 4/16
(nMDS) plots were performed and plotted in R (Vegan package) [24]. Phylogenetic trees were
constructed with RAxML [25] using the GTR substitution matrix (implemented as
GTRGAMMA) and an alignment made with MUSCLE [26] that was previously trimmed using
the Gblocks software [27] to eliminate highly diverged regions. Sequence data has been depos-
ited in the MG-RAST public database (http://metagenomics.anl.gov/) under ID numbers
4600697–4600744 (Project Name: Hierro submarine volcano).
Results and Discussion
Background information
The submarine eruption off the island of El Hierro started on October 10
th
, 2011. Geophysical
surveys determined that on October 23
rd
the active volcano was located at a depth of 350 m at
27°37’07”N—17°59’28”W. In January 2012, the cone had risen to a depth of 130 m and in Feb-
ruary it reached its maximum elevation of 88 m below sea level [28]. In order to oversee the ef-
fects of the eruption on the surrounding waters, physical-chemical data and biological samples
were collected from the volcanic unrest until the eruption had ceased. The first hydrographic
cruise took place three weeks after the eruption (Leg BBC3) when the strongest bubbling epi-
sode occurred. Additional samples were collected in late November (BBC5), January (BBC8),
February (BBC10, BBC12) March (GYT2) and April (GYT3).
Immediately after the eruption, changes in sea surface reflectance (SSR) due to the
discharge of hydrothermal fluids, magmatic gases and volcanic particles, were observed by sat-
ellite [13–14,29]. Despite some activity being recorded through March 5
th
, the waters along the
south bay of the island were significantly cleaner in early February [14]. Furthermore, based on
physical-chemical profiles [13–14] and the measured microbiological parameters (see below),
we observed that by January the situation seemed significantly restored. Thus, and from here
on, we refer to samples collected in November (BBB3 and BBC5) as the eruptive phase and to
samples collected from January to April (BBC8, BBC10, BBC12, GYT2 and GYT3) as the post-
eruptive phase.
During the eruptive phase, scientists observed warming of the water column and dramatic
changes in seawater chemistry, including a significant decrease in pH and oxygen and an in-
crease in iron that was more pronounced towards the southwest of the island [13]. The CTD
profiles revealed a strong thermocline around 80–90 m depth and a clear deoxygenation from
~75 to ~175 m that was particularly pronounced in stations near the volcano [12](S1 Fig.). Re-
duced species of sulfur, iron and manganese from volcanic fluid are oxidized quickly when
mixing with seawater, which results in oxygen depletion as well as acidification [13]. The ther-
mocline weakened in the post-eruptive phase coinciding with the winter period as typically oc-
curs in the region as a result of surface cooling [30]. No anomalies in oxygen profiles were
observed in the post-eruptive stages. Overall, physical-chemical data indicates that about
2 months after the eruption the oceanographic conditions returned to normal.
Effects on abundance and activity of bacterioplankton
The effects of the eruption on the abundance of bacterioplankton in the surrounding waters
were monitored by flow cytometry in samples collected from the volcanic eruption until it had
ceased. Furthermore, we measured nucleic acid content as a single cell-based proxy of cell ac-
tivity. Previous investigations have shown that the cells with a high nucleic acid (HNA) content
tend to be more active cells [31–32]. Yet, they also represent versatile bacteria with larger and
more flexible genomes [33]. A total of 536 samples were analyzed including stations in the af-
fected zone and waters outside the main influence of the eruption (e.g. Station 1) (Fig. 1,S1
Table). Samples were collected at different depths from surface to bathypelagic waters. Based
Effects of the El Hierro Submarine Eruption on Bacterioplankton
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 5/16
on physical-chemical (temperature, salinity, density, oxygen) and biological (bacterial abun-
dance) parameters the samples have been grouped into three depth categories: subsurface wa-
ters (0–70 m), oxygen depleted waters (70–200 m) and deep waters (200–1900 m). Analysis of
variance including three factors revealed that there were significant differences (p<0.001) in
the abundance of heterotrophic prokaryotes between sampling periods (eruption and post-
eruption), sampled area (control, affected and volcano) and between depths (subsurface, oxy-
gen depleted and deep waters). In general, the number of prokaryotes was higher during the
eruption than in the post-eruption stages (Fig. 2). Differences were more evident when
Fig 2. Distribution of prokaryotic abundace (cells ml
-1
in log units) and the percentage of high-nucleic acid content cells (% HNA) in samples
collected during the eruptive phase (left pannels) and the post-eruptive phase (right pannels). The samples are grouped in different categories by
depth (SF: subsurface, 0–70 m; OD: oxygen depleted waters, 70–200 m; DE: deep waters, 200–1900) and location (Control: stations in the control zone,
Affected: stations in all affected areas, Volcano: affected stations in the vicinity of the volcano; see S1 Table).
doi:10.1371/journal.pone.0118136.g002
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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comparing only the control station with stations in the vicinity of the volcano (Stations 3, 4, 21,
22, 23, 24) (Fig. 2). Oxygen depleted and deep waters were in general more affected than sub-
surface waters. Likewise, significant differences were found in the percentage of presumably
more active cells (HNA cells) between sampling periods and between depths, but no significant
differences were found between areas. Nevertheless, the percentage of HNA cells was higher
during the eruptive phase in the affected zone and near the volcano, particularly at medium
depths and deep waters (Fig. 2). Tukey-Kramer post hoc comparisons indeed revealed differ-
ences in the fraction of HNA cells between the control and the volcano stations. Despite Station
1 being sampled as a control station, a certain influence of the eruption was observed in the ox-
ygen profiles (S1 Fig.). Additionally, Ariza et al. [14] found by analyzing satellite reflectance
that the control zone was affected by small turbidity pulses during the strongest eruptive epi-
sodes. Yet, Station 1, placed outside the main influence of the eruption, is considered to be the
control zone for reference.
Flow cytometric analyses also revealed the presence of two types of particles that were dis-
tinct from the HNA or LNA prokaryotic populations typically observed in bacterioplankton
cytograms. One is characterized by particles with high SSC and relatively low fluorescence,
likely representing inorganic particles (Fig. 3A). This population was observed associated with
the discharge of vent material and appeared mostly in stations closer to the volcano (Stations 3,
4, 21, 22, 23, 24) as compared to the rest of stations (p <0.001). Another distinct population
appeared with high SSC and relatively high FL1 (Fig. 3B) that could represent cells attached to
these particles. The particles would confer high scatter signal to the prokaryotes, and these
were detected in significantly higher amounts in the volcano zone (p <0.0001). Fig. 3 also illus-
trates the difference in relative nucleic acid content of the whole of the prokaryotes nearest the
volcano (Fig. 3A) as compared to elsewhere (Fig. 3B).
Fig 3. Two examples of Syto13-stained bacterioplankton samples (A: cruise BBC3–St.3, 0 m; B: cruise BBC3–St.17, 70 m) as seen by the flow
cytometer in plots of nucleic-acid-based green fluorescence (FL1) versus particle side scatter (a surrogate of particle size). The typical prokaryotic
signals (Prok) and the reference beads (b) are accompanied by likely inorganic vent-derived particles (part) and HNA cells likely attached to particles (Prok +
part).
doi:10.1371/journal.pone.0118136.g003
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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The eruption likely promoted an increase in the abundance and activity of heterotrophic
prokaryotes during the eruptive phase, notably in depths closer to the volcano (70–200 m) and
in deeper waters (200–1900 m). The values returned to normal levels in the post-eruptive peri-
od. Santana-Casiano et al. [13] reported a striking enrichment of Fe(II) and nutrients at sta-
tions over the volcano during the eruptive phase that could explain the higher values in
prokaryotic abundance and activity observed. We do not have data from before the volcanic
eruption to compare, but Baltar et al. [34] had reported values of around 24–46% of HNA in
epi- and mesopelagic waters of the subtropical northeast Atlantic Ocean near the Canary Is-
lands. These values are within the range of values observed in the post-eruptive stages. Differ-
ences between eruption and post-eruption periods could also be attributed partly to
seasonality. However, the fact that the HNA values were significantly higher near the volcano
supports the hypothesis that the observed changes in the percentage of HNA cells were to a
large extent a consequence of the eruption. In fact, extraordinarily high mean HNA values
were observed in these waters during the eruptive phase in depths closer to the volcano and
deeper waters (79% and 91%, respectively), and these values were significantly different from
those of the control zone (Fig. 2).
Effects on diversity of bacterioplankton
Twenty-four samples collected during 4 of the 7 hydrographic cruises were selected for pyrose-
quencing. Samples correspond to different stations and depths within the epipelagic (0–200 m)
layer in the vicinity of the volcano when it was active (Leg BBC3) and in the following months
when its activity had decreased (Legs BBC10, BBC12, and GYT2). Samples were also obtained
in the far-field Station 1 in legs BBC10 and BBC12. After a rigorous quality control (see Materi-
al and Methods), a total of 213994 bacterial (average 8916 per sample, range 2502–22216) and
80610 archaeal (average per sample 3359, range 365–8761) 16S rRNA high-quality tags were
kept and analyzed. Pyrosequencing of all bacterial and most archaeal amplicons was successful,
but unfortunately two archaeal samples (BBC3_St.3_0m, BBC10_St.1_800m) resulted in a low
number of reads. Clustering of reads into OTUs resulted in a total of 2521 different observed
bacterial OTUs ranging between 285 and 1191 per sample (average 572). Overall, bacterial di-
versity was greater than archaeal diversity. For Archaea, a total of 566 OTUs were distinguished
with an average of 158 per sample (range 36–362) but the number of archaeal reads was also
lower than for Bacteria. The OTU diversity estimate is a function of the sampling effort and,
in fact, we did find a correlation between the observed richness and the sequencing depth
(R
2
= 0.69, p = 0.03). For that reason, we normalized each dataset for comparative purposes.
When alpha-diversity was computed at the minimum sequencing depth for the Bacteria
dataset (2500), we observed that samples collected during the eruption contained overall less
bacterial richness than samples collected in the following months. Mesopelagic (800 m) sam-
ples collected in the control station contained higher bacterial richness than epipelagic
(0–200 m) samples of the same stations (Fig. 4). Archaeal richness in eruption and post-
eruption samples was within the same range (average 181 and 178 respectively) but much
more variability was observed in this dataset (S2 Fig.). Contrarily to Bacteria, Archaea in the
far-field deep station were less rich than in epipelagic samples collected at the same time. How-
ever, only one deep sample could be included in the comparison (samples that resulted in a low
number of reads were excluded from this comparison). Rarefaction curves were asymptotic in-
dicating that we retrieved most of the diversity present (S3 Fig.) but this trend may have been
influenced by the removal of all singletons. A large proportion of diversity in the environment
corresponds to the low-abundant organisms of the rare biosphere [35] often appearing as sin-
gletons. By removing them we might have underestimated diversity, but as a tradeoff we
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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reduced the potential artificial inflation of diversity estimates by spurious OTUs associated
with pyrosequencing errors [36]. Yet, we must point out that we did not perform ultra-deep se-
quencing since the main goal of our study was to determine whether the volcanic eruption led
to changes in diversity, rather than accurately describing the rare biosphere. However, we can-
not rule out that we might have underestimated the effect of the eruption on diversity by the
limited depth of sequencing.
Effects on bacterioplankton community structure
Differences in microbial composition (beta-diversity) were assessed using OTU-based metrics.
Bray-Curtis dissimilarity matrices (bacterial and archaeal) were constructed based on the
square root transformed relative abundance of each OTU. The distance between samples was
visualized using non-metric multidimensional scaling (nMDS). Visualization of the bacterial
Bray-Curtis dissimilarity matrix revealed the presence of three distinct groups of samples ac-
cording to sampling time and depth (Fig. 5). The first group included all the epipelagic samples
from the eruption time, the second group included all epipelagic samples collected in post-
eruption cruises, including the far-field sample from Station 1, and the third group clustered
the two mesopelagic samples (800 m) collected as reference. Unfortunately, samples from the
less affected area during the eruptive phase are not available and thus we cannot discount the
possibility that the grouping of samples might partially be influenced by seasonality or other
factors as well [37]. However, the presence of certain groups typically associated with hydro-
thermal systems supports the hypothesis that, as for abundance and activity, the volcano in-
duced some changes in bacterial community structure. For Archaea, deep ocean samples were
different from subsurface samples but no clear clustering between eruptive and post-eruptive
Fig 4. Bacteria richness estimates (Chao1) by type of samples: epipelagic samples from eruption
(eruption), epipelagic samples from post-eruption (post-eruption) and mesopelagic samples (deep).
doi:10.1371/journal.pone.0118136.g004
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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periods was observed (S4 Fig.). The lower number of sequences analyzed for Archaea could ex-
plain this observed lack of effect, yet previous studies have shown than around 1000 sequences
per sample and between 30–300 OTUs are sufficient to accurately reveal patterns of beta-
diversity in aquatic systems [38–39]. We had sequence numbers below these values only for
two of the archaeal samples and nearly identical results were obtained when these were re-
moved from the analyses (data not shown). Therefore, we can conclude that geochemical
changes in seawater did result in changes in bacterial community structure but no effect was
observed for archaeal communities. Venn diagrams confirmed that a higher proportion of
OTUs were shared between samples for Archaea than for Bacteria (Fig. 6). Our data also
Fig 5. Non-metrical multidimensional (nMDS) plot based on the OTU distributions of the bacterial dataset. The position of samples reflects how
different bacterial assemblages are from each other based on their distance in a two-dimensional plot. Distance is derived from the Bray-Curtis similarity
coefficients calculated from the square root transformed relative abundance of each OTU. Samples are indicated by station numberand depth. Color code
indicates the cruise.
doi:10.1371/journal.pone.0118136.g005
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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suggest that, as for abundance and activity, bacterioplankton communities in the area sur-
rounding the volcano were restored in terms of community structure shortly after the eruption.
In addition, although the eruption resulted in changes in bacterial communities, the induced
differences were less dramatic than, for example, the natural differences found between subsur-
face and deep waters (Fig. 5).
Fig 6. Venn diagrams of shared 16S rRNA gene based OTUs between the three groups of samples:
epipelagic during eruption, epipelagic during post-eruption and mesopelagic during post-eruption for
Bacteria (top) and Archaea (bottom). The number of OTUs and of samples (n) in each group is indicated.
doi:10.1371/journal.pone.0118136.g006
Effects of the El Hierro Submarine Eruption on Bacterioplankton
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Effects on bacterioplankton community composition
While beta-diversity analyses allow depicting differences in community structure, assigning an
identity to each OTU provides insights on how these communities differ taxonomically. Taking
into account the whole Bacteria dataset, we found that most sequences were related to the
phyla Proteobacteria (68.4%), Cyanobacteria (15.8%) and Bacteroidetes (5.9%). Within the
Proteobacteria, the most abundant classes were the Alpha- (54.8%) and Gammaproteobacteria
(10.0%), but the Beta- (0.1%), Delta- (2.6%), Epsilon- (0.3%) and Zetaproteobacteria (<0.1%)
were also present. Overall the most abundant group was the Rickettsiales (SAR11 group,
Alphaproteobacteria) making up 52.0% of reads with a total of 611 different OTUs out of the
2521 total OTUs. The Bacteroidetes were largely represented by members of the Flavobacteriia
(70% of total Bacteroidetes) and less by the Sphingobacteria (10%). Other groups present with
abundances >1% were the Actinobacteria (2.1%) and uncultured group SAR406 (2%). Chloro-
flexi, Planctomycetes, Gemmatimonadetes, Lentisphaerae, Nitrospirae and SAR202 were also
detected at low abundances (<1%).
Despite the majority of samples being dominated by Proteobacteria, interesting differences
were observed at lower phylogenetic levels and when grouping samples by sampling period
(S2 Table). The percentage of Alphaproteobacteria was significantly lower in samples collected
in November (average 39%) than in samples collected months later (average 56%) (ANOVA,
p<0.05). Among these, the relative abundance of the SAR11 group was also lower. Likewise, the
relative abundance of Bacteroidetes decreased on average two-fold in samples collected during
the eruption compared to months afterwards. The Epsilonproteobacteria made up almost 2% of
bacterioplankton communities in November but were hardly present in either epi- or mesope-
lagic samples collected months after the eruption. Interestingly, the few OTUs found in the
post-eruption period were different than those present during the eruption (see Fig. 7). Mem-
bers of this proteobacterial class are not commonly found in marine planktonic communities
but typically dominate deep-sea hydrothermal environments and are known to play significant
roles in carbon and sulfur cycling in such ecosystems [40]. Likewise, members of the candidate
division ZB3 often associated to oxygen minimum zones and sulfidic environments [41–42]
Fig 7. Maximum-likelihood tree of Epsilonprotebacteria 16S rRNA gene sequences retrieved from the bacterial dataset. Each sequence from this
study (labeled as OTU number) is representative of clustered sequences at 97% cutoff. The group of samples (eruption, post-eruption or deep samples)
where it was detected is shown in parentheses. Reference sequences from GenBank database are indicated by their accession number in parenthesis. The
sequence of Wolinella succinogenes served as outgroup (GenBank accession number M88159) and is not shown. The scale bar indicates substitutions
per site.
doi:10.1371/journal.pone.0118136.g007
Effects of the El Hierro Submarine Eruption on Bacterioplankton
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 12 / 16
were also detected in the water column at very low abundances. Sequences related to the Zeta-
proteobacteria (iron-oxidizers associated with seamounts) [43] were detected in the water col-
umn at very low abundances in the post-eruption period. Compounds released during the
eruption included inorganic sulfur, hydrogen, reduced iron, manganese and ammonium, all of
which can serve as a source of energy for some of these organisms, which were likely accompa-
nying the volcanic emissions and thrived in the water column during and after the eruption.
Two phyla, the Thaumarchaeota (58.5%) and Euryarchaeota (41.5%) dominated the Ar-
chaeal dataset. Among the Thaumarchaeota, 94% of reads where closely related to the genus
Nitrosopumilus. Most Euryarchaeota OTUs were classified within the class Thermoplasmata,
mainly within Marine Group II, although between <1 and 12% of reads, depending on the
sample, were classified as Marine Group III. Overall, no clear differences were observed in the
archaeal lineages detected within the different stations and sampling periods in the nearby of
the volcano. The larger differences were observed between epi- and mesopelagic samples. The
archaeal groups that typically dominate hydrothermal systems (i.e. Nanoarchaea, Archeaoglo-
bales, Thermococcales, Thermoplasmatales) were not detected in our sequences; however, we
cannot discount their presence since we did not perform deep sequencing of Archaea and they
may have been present at very low abundances. Unlike the bacterial groups associated with
eruptive processes like the Epsilonproteobacteria that can grow at mesophilic temperatures,
most archaeal groups from hydrothermal systems are from thermophilic to hyperthermophilic
[44–45]. We hypothesize that if they had been released with the vent material, they could likely
not develop in the water column.
Conclusions
Monitoring of the waters surrounding the volcano revealed that the eruption promoted an in-
crease in the abundance and activity of prokaryotes, probably as a consequence of warming of
the water column and the dramatic changes in seawater chemistry, including an increase in Fe
(II) and other nutrients, near the volcano [12–13]. The birth of the volcano resulted also in a
decrease in bacterial diversity and in minor changes in bacterioplankton composition but, in
contrast, no effects were detected in the archaeal community. Nonetheless, the changes pro-
duced by the eruption were temporal and all the microbial parameters analyzed indicate that
between January and February the microbial community returned to normal levels. The moni-
toring of this eruption from the initial unrest represents a unique natural ecosystem scale ex-
periment, which allowed us to determine for the first time the effects of volcanic eruptions on
planktonic microbial abundance, activity and diversity.
Supporting Information
S1 Table. Sampling period, geographic location of stations (latitude, longitude) and sam-
pling depths (in m) of samples used for the flow-cytometric analyses included in this study
from Bimbache (BBC) and Guayota (GYT) cruises. For comparative analyses, samples were
grouped in three categories depending on the location: stations in the control zone (Control),
stations in the vicinity of the volcano (Volcano) and stations in other affected areas (Affected).
Stations indicated with letter R represent those sampled over several cruises.
(PDF)
S2 Table. Average relative abundance (percentage) of different taxa in each of the three
types of samples clustered together in the nMDS plot (see Fig. 5): eruption, post-eruption
and deep samples.
(PDF)
Effects of the El Hierro Submarine Eruption on Bacterioplankton
PLOS ONE | DOI:10.1371/journal.pone.0118136 February 11, 2015 13 / 16
S1 Fig. Temperature and oxygen profiles from Station 3R (Volcano) and Station 1R (Con-
trol) throughout the Bimbache cruises (BBC3, 4–9 Nov 2011; BBC5, 16–20 Nov 2011;
BBC8, 13–15 Jan 2012; BBC10, 9–12 Feb 2012; BBC12, 24–26 Feb 2012).
(PDF)
S2 Fig. Archaea richness estimates (Chao1) by groups of samples: epipelagic samples from
eruption (eruption), epipelagic samples from post-eruption (post-eruption) and mesope-
lagic samples (deep).
(PDF)
S3 Fig. Rarefaction analyses of the bacterial 16R rRNA gene sequences clustered at 97%
similarity. Operational taxonomic units represented by one tag only (singletons) were dis-
carded from the dataset to avoid potential artifacts in diversity estimates. BBC: Bimbache and
GYT: Guayota cruises. St. Station. See Fig. 1 and S1 Table for sample information.
(PDF)
S4 Fig. Non-metrical multidimensional (nMDS) analysis based on the OTU distribution of
the archaeal dataset. The position of samples reflects how different archaeal assemblages are
from each other based on their distance in a two-dimensional plot. Distance is derived from
Bray-Curtis similarity coefficients calculated from the square root transformed relative abun-
dance of each OTU.
(PDF)
Acknowledgments
We thank the Instituto Español de Oceanografía for inviting us to participate in the Bimbache
cruises (on board R/V Ramon Margalef) and to the Universidad de Las Palmas de Gran Cana-
ria for funding the Guayota cruises (on board R/V Atlantic Explorer). Thanks also to the crew
and scientists involved in field sampling, particularly to Minerva Espino, Iván Alonso, Nauzet
Hernández, Yeray Santana and Isabel Baños. We thank Marta Royo-Llonch for help extracting
the DNA and Guillem Salazar for helping with statistical analyses.
Author Contributions
Conceived and designed the experiments: IF JA EFN J. M. Gasol. Performed the experiments:
IF JA MFM J. M. González J. M. Gasol. Analyzed the data: IF JA J. M. González J. M. Gasol.
Contributed reagents/materials/analysis tools: IF JA J. M. González MFM EFN J. M. Gasol.
Wrote the paper: IF JA J. M. González EFN J. M. Gasol.
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Effects of the El Hierro Submarine Eruption on Bacterioplankton
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Table S1. Sampling period, geographic location of stations (latitude, longitude) and sampling depths (in
m) of samples used for the flow-cytometric analyses included in this study from Bimbache (BBC) and
Guayota (GYT) cruises. For comparative analyses, samples were grouped in three categories depending
on the location: stations in the control zone (Control), stations in the vicinity of the volcano (Volcano) and
stations in other affected areas (Affected). Stations indicated with letter R represent those sampled over
several cruises.
Period
LSTg_Station
Latitude
Longitude
Depths (m)
Category
Eruption
4-9 Nov 2011
BBC3_ST01R
27.65502
-17.91484
5, 75, 900, 400, 1926
Control
BBC3_ST03R
27.61808
-17.99314
0
Volcano
BBC3_ST04R
27.62924
-18.00648
5, 25, 50, 75, 150
Volcano
BBC3_ST05R
27.65880
-18.02904
5, 25, 50, 75, 100, 125
Affected
BBC3_ST06
27.65528
-18.06668
5, 25, 75
Affected
BBC3_ST08
27.73048
-18.21604
5, 25, 50, 75
Affected
BBC3_ST10
27.65512
-18.14060
5, 25, 50, 65, 150
Affected
BBC3_ST11
27.62102
-18.14046
5, 25, 63, 76, 150
Affected
BBC3_ST12
27.58118
-18.06668
5, 25, 70
Affected
BBC3_ST14
27.54400
-17.99032
5, 25, 50, 150, 400, 900
Affected
BBC3_ST15
27.58110
-17.98878
5, 25, 50, 64, 150, 400, 758
Affected
BBC3_ST17
27.54330
-18.06252
5, 25, 50, 70, 150, 900
Affected
BBC3_ST18
27.65512
-18.21464
5, 25, 50, 83, 105, 400, 900
Affected
BBC3_ST20
27.54616
-18.14120
5, 25, 50, 78, 167
Affected
BBC3_ST22R
27.62546
-17.98922
20, 75, 90
Volcano
BBC3_ST23R
27.62476
-17.99452
10, 75, 90, 100
Volcano
BBC3_ST24R
27.62934
-18.00766
5, 25, 50, 75, 250
Volcano
BBC3_ST914
27.05712
-18.48694
5, 25, 50, 75, 470, 870
Affected
16-20 Nov 2011
BBC5_ST01
27.75956
-18.18516
5, 25, 50, 75, 150, 400
Affected
BBC5_ST02
27.77882
-18.22348
5, 25, 58, 75, 100
Affected
BBC5_ST02
27.77882
-18.22348
5, 25, 58, 75, 100
Affected
BBC5_ST03
27.82024
-18.20372
5, 25, 50, 75, 100
Affected
BBC5_ST03R
27.61804
-17.99532
5, 25, 50, 100, 125, 160, 305
Volcano
BBC5_ST04
27.79504
-18.17132
5, 25, 50, 75, 100
Affected
BBC5_ST04R
27.62832
-18.00642
5, 25, 50, 75, 125
Volcano
BBC5_ST05
27.77308
-18.10917
5, 25, 55, 75, 100
Affected
BBC5_ST05R
27.65920
-18.03010
5, 25, 50, 75, 100, 125, 509
Affected
BBC5_ST06
27.79882
-18.12584
5, 25, 50, 75, 100
Affected
BBC5_ST07
27.82291
-18.16480
5, 25, 50, 88, 105
Affected
BBC5_ST08
27.82448
-18.11716
5, 25, 50, 80
Affected
BBC5_ST09
27.79866
-18.09206
5, 25, 50, 75, 100
Affected
BBC5_ST10
27.78872
-18.06280
5, 25, 50, 75
Affected
BBC5_ST11
27.77924
-18.03965
5, 25, 50, 75, 100
Affected
BBC5_ST12
27.82092
-18.04384
5, 25, 50, 75, 100
Affected
BBC5_ST13
27.82720
-18.07732
5, 25, 50, 75, 100
Affected
BBC5_ST14
27.85314
-18.09638
5, 25, 50, 75, 100
Affected
BBC5_ST15
27.88246
-18.03948
5, 25, 50, 75, 100
Affected
BBC5_ST16
27.85118
-18.03480
5, 25, 50, 75, 100
Affected
BBC5_ST17
27.82640
-18.01242
5, 25, 50, 75, 100
Affected
BBC5_ST18
27.85000
-17.98460
5, 25, 50, 75, 100
Affected
BBC5_ST19
27.87785
-17.99131
5, 25, 50, 73, 100
Affected
Posteruption
13-15 Jan 2012
BBC8_ST01
27.76958
-17.89514
5, 44, 75, 100, 150, 300
Affected
BBC8_ST01R
27.65582
-17.91504
5, 50, 75, 100, 150, 300
Control
BBC8_ST02
27.73426
-17.91492
5, 25, 75, 100, 150, 300
Affected
BBC8_ST02R
27.61848
-17.91458
5, 25, 75, 100, 150, 300
Affected
BBC8_ST03
27.65600
-17.95796
5, 25, 75, 100, 150, 300
Affected
BBC8_ST03R
27.61835
-17.98932
5, 25, 50, 100
Volcano
BBC8_ST04
27.61924
-17.95676
5, 50, 75, 100, 150
Affected
BBC8_ST04R
27.62860
-18.00580
5, 50, 75, 150, 310
Volcano
BBC8_ST05
27.68310
-17.95870
5, 50, 75, 100, 150, 200, 300
Affected
BBC8_ST05R
27.65910
-18.02830
5, 50, 75, 86, 150, 300
Affected
BBC8_ST06
27.67977
-17.91742
5, 50, 75, 100, 150, 300
Affected
BBC8_ST07
27.70700
-17.91792
5, 50, 85, 100, 150, 300
Affected
BBC8_ST08
27.70596
-17.94732
5, 50, 75, 100, 150, 300
Affected
BBC8_ST09
27.74858
-17.88124
5, 50, 75, 100, 150, 300
Affected
BBC8_ST21R
27.61062
-17.99748
5, 50, 75, 100, 150, 300
Volcano
BBC8_ST23R
27.62478
-17.99454
5, 57, 75, 100, 150
Volcano
9-12 Feb 2012
BBC10_ST01
27.68432
-18.06760
5, 25, 50, 75, 100, 150, 200, 400, 800
Affected
BBC10_ST02
27.65778
-18.06673
5, 50, 150, 200, 400, 500
Affected
BBC10_ST03
27.62938
-18.06676
5, 25, 50, 75, 100, 150, 200, 400, 500
Affected
BBC10_ST03R
27.62008
-17.99550
5, 25, 50, 75, 100, 125, 150, 160
Volcano
BBC10_ST04
27.61194
-18.06758
5, 50, 150, 200, 400, 500
Affected
BBC10_ST04R
27.62844
-18.00556
5, 50, 75, 100, 150, 200
Volcano
BBC10_ST05
27.59048
-18.06532
5, 50, 150, 200, 400, 500
Affected
BBC10_ST05R
27.65796
-18.02892
5, 25, 50, 75, 100, 150
Affected
BBC10_ST06
27.62838
-18.02784
5, 50, 165, 200, 400, 500
Affected
BBC10_ST07
27.60978
-18.02658
25, 5, 100, 200, 400, 500
Affected
BBC10_ST08
27.58518
-18.02578
5, 56, 150, 200, 400, 500
Affected
BBC10_ST21R
27.61130
-17.99658
5, 25, 50, 75, 100, 150
Affected
BBC10_ST23R
27.62516
-17.99524
5, 150, 215
Volcano
24-26 Feb 2012
BBC12_ST01
27.62830
-18.02660
5, 50, 100, 200, 300, 400
Affected
BBC12_ST01R
27.65724
-17.91360
5, 75, 125, 400, 600, 800
Control
BBC12_ST02
27.60914
-18.02592
5, 50, 100, 200, 300, 400
Affected
BBC12_ST03
27.58776
-17.99796
5, 50, 100, 200, 300, 400
Affected
BBC12_ST03R
27.61988
-17.99266
5, 20, 25, 30, 50, 70, 75, 86, 100, 165, 185
Volcano
BBC12_ST04
27.58530
-18.02660
5, 50, 100, 200, 300, 400
Affected
BBC12_ST04R
27.62880
-18.00540
5, 25, 40, 100, 200, 300
Volcano
BBC12_ST05
27.61137
-18.00320
5, 25, 50, 75, 100, 127
Affected
BBC12_ST05R
27.65883
-18.02874
5, 25, 50, 100, 200, 300
Affected
BBC12_ST06
27.62010
-18.00296
5, 50, 100, 200, 300, 423
Affected
BBC12_ST07
27.62082
-17.98715
5, 25, 50, 75, 100, 150
Affected
BBC12_ST08
27.63324
-17.98424
5, 25, 38
Affected
BBC12_ST21R
27.61020
-17.99784
5, 50, 100, 200, 300, 400
Volcano
17 Mar 2012
GYT2_ST04R
27.62850
-18.00567
5, 25, 60, 100, 125, 250
Volcano
GYT2_ST23R
27.62467
-17.99467
5, 35, 75, 100
Volcano
GYT3_ST03R
27.61833
-17.98917
5, 25, 50, 75, 100, 115, 150, 250
Volcano
28 Apr 2012
GYT3_ST21R
27.61050
-17.99733
5, 50, 75, 100, 200, 300
Volcano
GYT3_ST23R
27.62467
-17.99467
5, 50, 75, 100, 150, 180
Volcano
Table S2. Average relative abundance (percentage) of different taxa in each of the three types of
samples clustered together in the nMDS plot (see Figure 5): eruption, post-eruption and deep samples.
Figure S1. Temperature and oxygen profiles from Station 3R (Volcano) and Station 1R (Control)
throughout the Bimbache cruises (BBC3, 4-9 Nov 2011; BBC5, 16-20 Nov 2011; BBC8, 13-15 Jan 2012;
BBC10, 9-12 Feb 2012; BBC12, 24-26 Feb 2012).
Figure S2. Archaea richness estimates (Chao1) by groups of samples: epipelagic samples from eruption
(eruption), epipelagic samples from post-eruption (post-eruption) and mesopelagic samples (deep).
Figure S3. Rarefaction analyses of the bacterial 16R rRNA gene sequences clustered at 97% similarity.
Operational taxonomic units represented by one tag only (singletons) were discarded from the dataset to
avoid potential artifacts in diversity estimates. BBC: Bimbache and GYT: Guayota cruises. St. Station.
See Figure 1 and Supplementary Table SM1 for sample information.
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Figure S4. Non-metrical multidimensional (nMDS) analysis based on the OTU distribution of the
archaeal dataset. The position of samples reflects how different archaeal assemblages are from each
other based on their distance in a two-dimensional plot. Distance is derived from Bray–Curtis similarity
coefficients calculated from the square root transformed relative abundance of each OTU.
