FEMS Microbiology Ecology, 92, 2016, fiw002
doi: 10.1093/femsec/fiw002
Advance Access Publication Date: 5 January 2016
Research Article
RESEARCH ARTICLE
Biodiversity patterns of plankton assemblages at the
extremes of the Red Sea
J. K. Pearman∗ , S. Kürten, Y. V. B. Sarma, B. H. Jones and S. Carvalho
King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Biological and
Environmental Sciences & Engineering Division (BESE), Thuwal, 23955-6900, Saudi Arabia
∗
Corresponding author: KAUST, Thuwal, 23955-6900, Saudi Arabia. Tel: +966 128082539; E-mail: [email protected]
One sentence summary: Examining plankton distribution at the extremes of the Red Sea in relation to nutrient concentrations.
Editor: Julie Olson
ABSTRACT
The diversity of microbial plankton has received limited attention in the main basin of the Red Sea. This study investigates
changes in the community composition and structure of prokaryotes and eukaryotes at the extremes of the Red Sea along
cross-shelf gradients and between the surface and deep chlorophyll maximum. Using molecular methods to target both the
16S and 18S rRNA genes, it was observed that the dominant prokaryotic classes were Acidimicrobiia, Alphaproteobacteria
and Cyanobacteria, regardless of the region and depth. The eukaryotes Syndiniophyceae and Dinophyceae between them
dominated in the north, with Bacillariophyceae and Mamiellophyceae more prominent in the southern region. Significant
differences were observed for prokaryotes and eukaryotes for region, depth and distance from shore. Similarly, it was
noticed that communities became less similar with increasing distance from the shore. Canonical correspondence analysis
at the class level showed that Mamiellophyceae and Bacillariophyceae correlated with increased nutrients and chlorophyll
a found in the southern region, which is influenced by the input of Gulf of Aden Intermediate Water.
Keywords: microbial ecology; phytoplankton; amplicon sequencing; diatoms; Red Sea
INTRODUCTION
Marine plankton include organisms from all domains of life
(Bacteria, Archaea, Eukarya and viruses). This community has a
high diversity and undertakes a broad range of metabolic functions (Worden et al. 2015). The biomass of heterotrophic organisms typically exceeds that of autotrophs (Gasol, del Giorgio and
Duarte 1997). Phytoplankton perform the major part of primary
production in the marine system and account for nearly half of
the global primary production (Field et al. 1998). A combination of
viroplankton, bacterioplankton and protists are the main agents
involved in nutrient and organic matter recycling through the
microbial loop (Azam et al. 1983; Pomeroy et al. 2007). Due to
the importance of these organisms in marine biogeochemical
cycling, considerable effort has been made to understand their
patterns of diversity in the marine system (e.g. Short and Suttle
2002; Zwirglmaier et al. 2008; Lepère, Vaulot and Scanlan 2009;
Stoeck et al. 2009). Despite being crucial contributors to the biogeochemical processes (e.g. carbon fixation; Jardillier et al. 2010),
the eukaryotic component of plankton has received relatively little attention compared with its prokaryotic counterpart (Zinger,
Gobet and Pommier 2012).
With possible changes occurring in plankton dynamics due
to the effects of climate change, it is vital to gain a better understanding of the diversity and structure of planktonic assemblages (Behrenfeld 2011). The Red Sea, a narrow, semi-confined
sea, experiences high salinities as well as oligotrophic conditions (Raitsos et al. 2013). The seawater temperature is also high,
varying from 21 to 28◦ C in the north and from 26 to 32◦ C in the
south (Nandkeolyar et al. 2013). These characteristics are conducive to utilizing the Red Sea as a natural laboratory to study
community structure in warm, saline and oligotrophic waters.
Received: 13 August 2015; Accepted: 29 December 2015
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 3
The Red Sea experiences a continuous change from its southern entrance at the Gulf of Aden through the Strait of Bab al
Mandab, to its northern extent terminating in the Gulfs of Aqaba
and Suez. Cool (<18◦ C) and fresher (salinity < 36) water from
the Gulf of Aden enters the southern Red Sea where it is rapidly
heated in the southern part of the basin. As the water advects
northward, it increases in salinity through evaporation and mixing, reaching maximum salinities in excess of 40 in the northern
part of the basin. Gulf of Aden Intermediate Water (GAIW) carries significant concentrations of inorganic nutrients (Churchill
et al. 2014) and the southwest monsoon-driven upwelling in the
Gulf provides a supply of phytoplankton biomass to the southern Red Sea (Yao and Hoteit 2015).
While diversity studies of plankton in the Red Sea have been
limited, some research has been done in the Gulf of Aqaba at
the northern-most end of the Red Sea (Sommer et al. 2002; AlNajjar et al. 2007; Nassar et al. 2014). Notably, the distribution of
phytoplankton has been assessed near Jeddah in the central Red
Sea (Shaikh, Roff and Dowidar 1986; Touliabah et al. 2010) and a
transect of coastal stations along the Saudi Arabian coast was
analysed by Kürten et al. (2014). More recently, next generation
sequencing technologies have been used to investigate bacterial distributions in the north and central Red Sea (Ngugi et al.
2012; Ansari et al. 2015), as well as down a depth profile (Qian
et al. 2011). Next generation sequencing has also been used to
investigate the picoeukaryotic plankton at two stations in the
northern Red Sea (Acosta, Ngugi and Stingl 2013) and zooplankton distributions around three coral reef systems in the central
southern Red Sea (Pearman et al. 2014). Considering the extent
(approximately 2000 km) of the Red Sea, the amount of information available is still extremely limited.
The current study was designed to determine the effect of
nutrients on the distribution of the microbial plankton communities. By investigating the diversity of the prokaryotic and eukaryotic plankton across shelf gradients in the north and south
of the Saudi Arabian Red Sea, we were able to represent the
extreme environmental conditions that can be observed at the
scale of the Red Sea basin. A next generation technique was
used to target the 16S and 18S rRNA genes to get an indication of the diversity and structure of the planktonic community (prokaryotic and eukaryotic) in the photic zone of warm,
high salinity oligotrophic waters of the Red Sea. Changes in the
diversity patterns combined with environmental data enabled
an assessment of the main environmental drivers, determining
how prokaryotic and eukaryotic assemblages respond at the extremes of the Red Sea.
MATERIALS AND METHODS
Sample collection
Samples were collected during two research cruises between August and September 2014 aboard the RV Thuwal. A total of 56
stations were sampled in the north (35) and the south (21) of
the Red Sea. Overall, 94 samples were collected at either the
surface or the deep chlorophyll maximum (DCM). Station coordinates and ancillary information (sampling depth, temperature, salinity, chlorophyll a concentrations, nitrate, nitrite, silicate and phosphate concentrations) are presented in Supplementary Table S1 and displayed in Fig. 1.
Samples were collected using 10 L Niskin bottles with a
conductivity, temperature and density (CTD)–rosette profiler attached. Water samples were collected from 5 m (described as
surface) and from the DCM. The DCM was determined during the
downcast of the rosette and the Niskin bottles triggered during
the upcast at the determined depth.
For DNA analysis, samples were collected from both the surface and the DCM layers. Five litres of each water sample (with
no prefiltration) were filtered through 0.22 μm membrane filters (Millipore). Prior to analysis individual filters were stored at
−20◦ C in 15 mL tubes containing ∼5 mL of lysis buffer.
In order to investigate nutrient concentrations, water was
collected from the surface and DCM layers. Approximately 50
mL of water was filtered through 0.22 μm membrane filters (Millipore) for nutrient samples, which were then frozen at −20◦ C
until analysis. The samples were analysed using a Continuous
Flow Analyzer from Astoria-Pacific (www.astoria-pacific.com/
industrial/products/astoria-analyzer). Chlorophyll a samples
were collected at the same depth layers. For the analysis, 0.5–
3 L of water was filtered through GF/F filters, wrapped in aluminium foil, and frozen at −20◦ C on the research vessel and
then at −80◦ C (at the laboratory) until analysis. Chlorophyll a
was extracted using 90% acetone. Following extraction, the raw
fluorescence was measured with a Trilogy fluorometer (Turner
Designs).
DNA extraction and PCR amplification
The filters had 540 μL ATL buffer (Qiagen) and 60 μL proteinase
K (20 mg mL−1 ) added to them and were incubated at 55◦ C for 30
min. Approximately 15 mg of 0.1 mm zirconia/silica beads were
added to the cryotubes containing the filter/buffer and an equal
(to the sample) volume of phenol:chloroform:iso amyl alcohol
(IAA) (ratio of 25:24:1) was added to all samples. Cells were lysed
using Qiagen’s Tissue LyserII machine for 3 min at 30 s−1 .
Following lysis the aqueous layer was removed and a single
round of chloroform:IAA (ratio of 24:1) was undertaken before
the DNA was precipitated using 0.6 vol. isopropanol. Precipitated
DNA was washed in 70% ethanol and resuspended in DNase-free
water.
Approximately 5 ng DNA was used for each PCR. Primers targeting the 16S v3 and v4 region (Klindworth et al. 2013) were used
for prokaryotes and ones targeting the v4 region of the 18S rRNA
gene (Stoeck et al. 2010) were used for the eukaryotic fraction of
the community (Supplementary Table S2). PCR conditions were
as described by the respective authors, except that the second
round of amplification in the Stoeck protocol was undertaken at
49◦ C instead of at varying temperatures. PCR reactions were undertaken in duplicate and pooled. A no-addition negative was
also run.
Samples were cleaned and normalized using a SeqPrep Normalization plate and MiSeq library preparation was undertaken
following the Illumina 16S metagenomic sequencing library
preparation protocol. The samples were cleaned and normalized
a second time and the tagged samples were pooled for sequencing on a MiSeq sequencing platform at the King Abdullah University Core Laboratory. Raw reads were submitted to the NCBI
Single Read Archive (SRA) and can be found under the project
accession number SRP060785
Bioinformatics
Samples were automatically demultiplexed from the MiSeq sequencing machine. Forward and reverse reads were joined using
the join seqs.py script in QIIME (version 1.9.1) (Caporaso et al.
2010) before being quality filtered (quality = 25), and reads were
truncated at the reverse primer. Reads were clustered in a twostep process. Firstly, the CD-HIT (Li and Godzik 2006) algorithm
Pearman et al.
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Figure 1. Sampling points throughout the Red Sea. Sampling points in relation to (a) the whole basin and more specifically in (b) the northern region and (c) the
southern region. See Supplementary Table S1 for coordinates of sampling station. Produced using ArcGIS.
in QIIME was implemented using the trie function. Representative sequences were obtained for each operational taxonomic
unit (OTU) (the longest sequence was selected) and a second
round of clustering was undertaken using USEARCH (version
5.2.236) (Edgar 2010) at 97% similarity. Reference sequences were
obtained for the USEARCH OTUs and chimera checks against
the SILVA 119 database were undertaken using UCHIME (version 4.2) (Edgar et al. 2011). Taxonomic classifications for each
OTU were obtained using the RDP Classifier (with a minimum
confidence level of 0.5) (Wang et al. 2007) with the SILVA 119
database (Pruesse et al. 2007) as a reference for prokaryotes and
PR2 (Guillou et al. 2013) for the eukaryotic fraction. OTU tables
were constructed in QIIME having removed those OTUs that
were chimeras. In the prokaryotic database, OTUs were removed
that were classified as either eukaryotes or chloroplasts. In the
eukaryotic database, sequences whose lowest taxonomic classification was eukaryotes were removed, as well as those relating to metazoans, as these organisms are not representatively
sampled using Niskin bottles. Reference sequences were aligned
against the SILVA 119 database using muscle (Edgar 2004) with
the QIIME script align seqs.py.
Reads were rarefied to 5000 reads per sample for the eukaryotic samples (with the removal of 10 (2 northern surface,
3 southern surface and 5 southern DCM) out of the 94 sam-
ples) and 10 000 reads for the prokaryotic samples (with the
removal of 17 (9 southern surface and 8 southern DCM) out
of the 94 samples as they did not reach the required threshold). The number of samples in each region/depth/shore distance is depicted in Supplementary Table S3. The composition of each region was assessed by calculating the average
number of reads per OTU in each region, and then calculating the proportion of the community attributed to each class
using QIIME. Those classes contributing to more than 5% of
the community reads in at least one region were plotted using a custom script in R (R development core team 2015; package maps). Species accumulation plots were plotted in R using the package vegan (Oksanen et al. 2015) based on the
rarefied data. Shared OTUs between depths and regions were
calculated based on QIIME OTU tables and visualized with VennDiagram (Chen 2014) in R. Alpha diversity measures (Faith’s
phylogenetic distance (PD) and equitability) were calculated
in QIIME, and analysis of variance (ANOVA) statistics calculated on the results using the factors region, depth and
shelf (distance from shore). Weighted and unweighted UniFrac
(Lozupone and Knight 2005) distance matrices were constructed
and non-metric multidimensional scaling (NMDS) ordination
was undertaken with the package phyloseq (McMurdie and
Holmes 2013). A three-way PERMANOVA was carried out using
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Figure 2. Accumulation of OTUs with station. OTU accumulation for (a) prokaryotes and (b) eukaryotes for different regions and depths.
PERMANOVA in PRIMER v6 package (Clarke and Gorley 2006)
with the PERMANOVA+ add-on (Anderson, Gorley and Clarke
2008). This assessed the significance of the factors (factor ‘region’, orthogonal, two levels, north and south; factor ‘shelf’, orthogonal, three levels, near- (<10 km), mid- (10–50 km) and offshore (>50 km); factor ‘depth’, orthogonal, two levels, surface
and DCM). The statistical significance of multivariate variance
components was tested using 9999 permutations of residuals
under a reduced model, with a significance level of α = 0.05.
Only significant effects were further investigated through a series of pairwise comparisons using the appropriate terms in the
model. To produce distance decay plots, custom scripts were
used to plot 1 – unweighted UniFrac dissimilarity against linear distance between stations (calculated in geosphere (Hijmans
2015)). Forward selection (Adonis in vegan) of significant environmental variables was undertaken prior to the performance of a
constrained correspondence analysis (CCA) on the distribution
tables classified to the taxonomic level of class. The CCA model
was tested for significance using a permutational ANOVA for
CCA (permutations = 999). The major classes were then tested
for significant correlations against the forward selected environmental variables using linear regression.
RESULTS
Alpha- and beta-diversity patterns
A total of 5 445 534 and 7 914 709 reads were obtained, respectively, for the prokaryotic and eukaryotic components of
the plankton. After pre-processing to remove poor quality reads
(91 035 prokaryotic reads and 145 557 eukaryotic reads were removed), the cleaned reads were clustered in OTUs. Chimeras
were removed (1040 and 192 for prokaryotes and eukaryotes,
respectively) and OTUs were filtered taxonomically (eukaryotic
and chloroplast OTUs removed from the prokaryotic fraction
and prokaryotic and metazoan reads removed from the eukaryotic). After multiple resampling, a total of 2708 (prokaryotes)
and 813 (eukaryotes) OTUs were observed across all areas. Rarefaction curves showed that no area was sampled exhaustively
(Fig. 2) but both depths in the northern and southern regions
were approaching a plateau, especially in the eukaryotic fraction (Fig. 2b). A substantial proportion of OTUs for prokaryotes (831 OTUS; 30.6%) and eukaryotes (394 OTUS; 48.4%) were
shared between both regions and depths (Supplementary Fig.
S1a and b). Alpha diversity as measured by Faith’s definition
of phylogenetic diversity (PD) was analysed using univariate
ANOVA, which showed that for eukaryotes there was a higher
diversity in the northern than in the southern stations (F =
37.67, P < 0.001) and at the DCM compared with the surface
(F = 8.41, P = 0.005). Furthermore, there was a significant difference between distances from the shore, which was not consistent across the regions, i.e. there was a significant interaction between the factors region and shore (F = 6.54, P = 0.002;
Supplementary Table S4). The near- and mid-shore stations in
the south were significantly different from the near- and midshore stations in the north. Also, the south off-shore stations
were significantly different from the southern near- and midshore stations. For the prokaryotic fraction, there were significant differences in Faith’s PD for both region (F = 8.137, P =
0.006) and depth (F = 23.105, P < 0.001). There was no significant difference for the factor shore (F = 2.123, P = 0.128). In
terms of equitability, the prokaryotes showed significant differences between the surface and DCM (F = 31.43, P < 0.001) with
higher levels of equitability in the DCM. Also, there was a significant difference between the distances from shore (F = 6.09,
P = 0.004) with a positive relationship between distance from
shore and equitability. For equitability, however, the eukaryotic
Pearman et al.
5
Figure 3. Eukaryotic composition. Composition of the most significant classes (>5% of reads in at least one the region and depth and shore category) for eukaryotes.
fraction showed a significant interaction between all three factors (F = 3.21, P = 0.046) (Supplementary Table S4). In all areas,
Proteobacteria (comprising mainly the classes Alphaproteobacteria and Gammaproteobacteria) accounted for the highest
number of OTUs in the prokaryotic fraction. Other classes in several phyla well represented in terms of numbers of OTUs include
Actinobacteria (class Acidimicrobiia), Proteobacteria (Deltaproteobacteria), and Bacteroidetes (Flavobacteriia) (Supplementary
Table S5). For the eukaryotes, the phylum with the highest number of OTUs was Dinophyta (mainly Syndiniophyceae and Dinophyceae) and Ochrophyta (especially Bacillariophyceae).
To assess the differences in the composition of both the
prokaryotic and eukaryotic fraction of the community between depths and region, the average contribution to a region/
depth/shore distance for each OTU was calculated and summarized at the class level. In the eukaryotic fraction, Bacillariophyceae were predominantly observed in the southern region at
both depths. A similar pattern was observed for the chlorophyte
class Mamiellophyceae, also mainly observed in the southern region but especially around the DCM (Fig. 3). An increase in the
proportion of reads attributed to Mamiellophyceae at the DCM
was observed with increasing distance from shore with Bathycoccus and Ostreococcus being present at higher proportions at
the DCM in mid- and off-shore stations and Micromonas having higher proportions at all other southern stations. On the
other hand, the Alveolata class Syndiniophyceae had higher
number of reads in the northern region and was found at both
depths. Both regions had a substantial proportion of the reads
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Proportion of reads
Mid−Shore
Off−Shore
1.00
1.00
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
0.25
0.25
0.00
0.00
0.00
1.00
1.00
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
0.25
0.25
0.00
0.00
0.00
1.00
1.00
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
0.25
0.25
0.00
0.00
0.00
1.00
1.00
1.00
0.75
0.75
0.75
0.50
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0.50
0.25
0.25
0.25
Proportion of reads
Proportion of reads
Proportion of reads
DCM
South
Surface
DCM
North
Surface
Near−shore
0.00
0.00
0.00
Taxa
Taxa
Taxa
Taxa
Acidimicrobiia
Alphaproteobacteria
Cyanobacteria
Flavobacteriia
Gammaproteobacteria
Figure 4. Prokaryotic composition. Composition of the most significant classes (>5% of reads in at least one the region and depth and shore category) for prokaryotes.
attributed to the class Dinophyceae, with this group accounting
for a greater proportion of reads in surface waters compared to
the DCM (Fig. 3). Less differentiation in the distribution of classes
was observed in the prokaryotic fraction (Fig. 4). Acidimicrobiia,
Alphaproteobacteria and Cyanobacteria made substantial contributions in both regions and depths whilst Gammaproteobacteria and Flavobacteriia accounted for smaller amounts (Fig. 4).
Non-metric, multidimensional scaling analysis based on
unweighted (which favours rare OTUs) and weighted UniFrac
(which favours dominant OTUs) showed that both the composition and the structure of eukaryotes and bacteria differed between both region and depth (Fig. 5a–d). Significant interactions
were observed between the factors, indicating variations in the
trends. Pairwise comparisons (Supplementary Table S6) showed
that, in general, at both depths the community was different
in the north and south. For the eukaryotic fraction in the north
(weighted UniFrac), the near- and mid-shore areas showed significant differences between the surface and DCM, whilst in
the south, the near-shore showed no significant difference with
depth but the off-shore area did. For the unweighted UniFrac,
differences between the surface and DCM were observed at all
distances from shore for eukaryotes and prokaryotes. Overall,
the near- and off-shore stations were significantly different for
both eukaryotes and prokaryotes, with the mid-shore having a
composition more similar to the off-shore than the near-shore
especially for the prokaryotes (Supplementary Table S6).
Pearman et al.
7
Figure 5. NMDS ordination for prokaryotes and eukaryotes. NMDS ordination based on (a) prokaryotic unweighted UniFrac, (b) eukaryotic unweighted UniFrac, (c)
prokaryotic weighted UniFrac and (d) eukaryotic weighted UniFrac.
Distance decay (1 – unweighted UniFrac) plots were plotted to
assess whether stations, which were closer, had a higher similarity in composition than those with greater separation on
both a vertical and horizontal scale. Horizontally, using 1 – unweighted UniFrac as a similarity measure, it was observed that
there were small negative trends between similarity and distance for the eukaryotes at both the surface (Fig. 6b) and DCM
(Fig. 6d) and the prokaryotes at both depths (Fig. 6f and h). However, small r2 values were observed especially for the prokaryotic
fraction. On a vertical scale, similar trends were observed (with
higher r2 values) with similarity declining with distance down
the water column (Fig. 6a and c for the eukaryotes and Fig. 6e
and g for the prokaryotes). Similar trends were observed when
using 1 – weighted UniFrac (which is affected more by the dominant groups than rare groups) as a similarity measure, although
the negative relationship with horizontal distance had declined
to almost 0.
Relationship between environmental drivers and the
planktonic patterns
The southern region, when compared with the northern region,
was characterized by higher levels of nutrients and chlorophyll
a (Supplementary Fig. S2; CCA – Fig. 7). Using a permutation
test for CCA under a reduced model (permutations = 999)), the
prokaryotic CCA model was found to be significant (F = 4.02 P
< 0.001). However, only a very small proportion of the variation
was explained, with axis 1 explaining 5.7% and axis 2 explaining 1.9% of the variation. At the class level, none of the prokary-
otic classes (which accounted for >5% of the overall abundance)
were associated with any of the measured environmental variables (Fig. 7a). For the eukaryotic fraction the model (permutation test for CCA under a reduced model (permutations = 999))
was also significant (F = 7.34 P < 0.001). In this case, the first axis
explained a total of 29.4% of the variation whilst the second explained 16.0% of the variation. The Chlorophyta class Mamiellophyceae was associated with higher levels of NO3 and SiO2 .
Bacillariophyceae (Stramenopiles) were also linked with these
higher nutrient levels and greater concentrations of chlorophyll
a, typical of the stations in the southern region (Fig. 7b). The
Alveolata classes (including Dinophyceae and Syndiniophyceae)
were negatively associated with nutrients and the class Syndiniophyceae was associated with the higher salinity characteristic of the northern region (Fig. 7b). Linear regression analysis showed significant positive correlations between Mamiellophyceae and Bacillariophyceae, with the nutrients nitrate and
silicate as well as chlorophyll a (Supplementary Table S7). In contrast, Syndiniophyceae and Dinophyceae were negatively correlated with these nutrients but positively correlated with salinity
and temperature, corroborating the CCA results (Supplementary
Table S7). In some cases, however, r2 values, although significant,
were extremely low.
DISCUSSION
The present study showed that prokaryotic and eukaryotic
planktonic assemblages changed significantly in composition
and structure between the extremes of the Red Sea, from the
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Figure 6. Distance decay. Distance decay plots for (a) eukaryotes down the water column in the northern region, (b) eukaryotes at the surface along the Red Sea, (c)
eukaryotes down the water column in the southern region, (d) eukaryotes at the DCM along the Red Sea, (e) prokaryotes down the water column in the northern
region, (f) prokaryotes at the surface along the Red Sea, (g) prokaryotes down the water column in the southern region and (h) prokaryotes at the DCM along the Red
Sea. Similarity was measured as 1 – unweighted UniFrac dissimilarity.
surface to the DCM layer and from near-shore to off-shore in the
summer period. The Red Sea is known for its oligotrophic conditions, with the main input of nutrients into the system coming
from the Indian Ocean through the Gulf of Aden via the Gulf of
Aden Intermediate Water (GAIW) (Khimitsa and Bibik 1979; Souvermezoglou, Metzl and Poisson 1989). In the summer months,
southward winds over the entire Red Sea result in southward
surface transport and export through Bab al Mandab (Yao et al.
2014). During this period, the southwest monsoon creates upwelling in the Gulf of Aden that contributes to the inflow of the
cool, fresh and nutrient-rich (e.g. NO3 > 0.20 μmol l−1 ) GAIW into
the southern Red Sea (Poisson et al. 1984; Maillard and Soliman
1986; Souvermezoglou, Metzl and Poisson 1989; Sofianos and
Johns 2007). This water gradually advects northwards, is subjected to mixing, evaporation, and depletion of nutrients, and
finally becomes high-salinity surface water in the northern Red
Sea. This surface water undergoes convective overturning in the
north, contributing to the formation of Red Sea Deep Water (Yao
et al. 2014).
In terms of OTUs, the phylum Dinophyta (classes Dinophyceae and Syndiniophyceae) was dominant. However, care
has to be taken when assessing the diversity of eukaryotes based
on rRNA genes – especially in dinoflagellates – as rRNA gene
copies (both in number and similarity) can be different within
Pearman et al.
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Figure 7. CCA for (a) prokaryotes and (b) eukaryotes against selected environmental variables. Environmental parameters are: chla: chlorophyll a; Dep: depth; Ni:
nitrate; Nt: nitrite; Sa: salinity; Si: silicate; Temp: temperature. Class designations for (a) are: Ac: Acidimicrobiia; Al: Alphaproteobacteria; Cy: cyanobacteria; GM:
Gammaproteobacteria. For (b) the designations are: Bac: Bacillariophyceae; Dino: Dinophyceae; Mam: Mamiellophyceae; Syn: Syndiniophyceae.
species (Alverson and Kolnick 2005; Galluzzi et al. 2010). As such,
this would inflate the diversity observed. Prokopowich, Gregory
and Crease (2003) demonstrated that rRNA gene copy number
was positively correlated with genome size and this would have
an impact on the number of reads attributed to a species. However, there is limited information on the genome size and copy
number of planktonic eukaryotes. Thus, this bias could not be
accounted for in the current study.
Although many microbial species are globally distributed, an
increasing number of molecular studies show that many species
are spatially limited, similar to patterns exhibited by macrofauna (Pommier et al. 2007). Hewson et al. (2006) showed that
bacterioplankton assemblages are homogeneous on the scale of
meters to ∼5 km but heterogeneous on scales >50 km. Monier
et al. (2015) have shown that eukaryotic protists exhibit dis-
tance decay patterns, suggesting dispersal limitation (between
different water masses) and environmental filtering. The results
presented here show a significant decline in similarity with distance for both the surface and DCM samples, but no clear difference was detected across the shelf (Fig. 6). The southern region
is an area where GAIW inflow water has significant influence
on the nutrient loading, phytoplankton biomass and productivity during the summer and early fall. The northern region, in
contrast, represents the cumulative effect of transport, mixing,
nutrient depletion and increased salinity through evaporation
along the path from the southern Red Sea to the northern Red
Sea. The time taken for GAIW to be transported from its southern entry point to the northern Red Sea, where its nutrients
are deplete, is not well documented but may take longer than
1 year. Thus, the two regions could represent the effects of the
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 3
continuum from the southern Red Sea to the northern Red Sea.
However, to fully understand if the differences in community are
due to the effects of distance or other factors, further samples
in the central Red Sea would be required.
The current findings are in agreement with the expected nutrient and chlorophyll a gradients along the Red Sea (e.g. Racault
et al. 2015), with generally higher chlorophyll a concentrations
in the south compared with the north. Previous studies (e.g.
Jacquet et al. 2002; Duarte and Agusti 2004) have shown that
nutrient addition favours autotrophic groups compared with
heterotrophic groups. Whilst nutrients are only one of the factors which determine the distribution of trophic groups (autotrophs vs heterotrophs), the current study showed, especially
in the eukaryotic fraction, a higher fraction of the community
was autotrophic in the more nutrient enriched southern region.
These changes were due to an increase in the proportion of
reads attributed to diatoms and chlorophytes in the south and
a corresponding decline in the proportion of reads attributed to
Syndiniophyceae, which dominate the northern region (Fig. 3).
Diatoms have previously been shown to respond to increased
nutrient concentrations and Kürten et al. (2014), utilizing pigment fingerprinting approaches, found that diatoms accounted
for an increase in the proportion of eukaryotic phytoplankton
in the southern Red Sea compared with the northern reaches.
Sommer (2000) attributed the absence of diatoms from the
oligotrophic northern regions to nutrient limitation, including silicate, instead of growth limitation due to grazing. This
could explain the increased abundance of diatoms observed
in the southern regions where nutrient concentrations were
higher. The Chlorophyta genus Ostreococcus is more prevalent in
mesotrophic areas and coastal areas (Viprey et al. 2008; Cheung et
al. 2010) and has been positively correlated with nitrogen off the
coast of Chile (Collado-Fabbri, Vaulot and Ulloa 2011). Mamiellophyceae are typically very small, and thus have a large surface
area to volume ratio that provides for a more effective acquisition of nutrients, allowing them to potentially outcompete larger
species (Li et al. 2009) for the increased nutrients in the southern regions. In the northern regions, the eukaryotic community
has a large proportion attributed to the marine parasitic group
Syndiniophyceae, substantiating previous findings in this region
(Acosta, Ngugi and Stingl 2013; de Vargas et al. 2015). This group
appears to have a cosmopolitan distribution, being able to colonize all marine habitats and at least some are highly opportunistic, having the ability to infect a variety of hosts (Guillou et
al. 2008). The substantial contribution of Dinophyceae throughout the Red Sea corroborates the results from the Tara Ocean
cruise, which also found this group to contribute substantially
to the plankton community (de Vargas et al. 2015).
Overall, significant differences were observed between nearshore and off-shore stations for the eukaryotes and bacteria. In the surface samples, the number of reads attributed to
Mamiellophyceace showed a distinct decline moving toward offshore stations. The observation that Mamiellophyceae are more
prominent in the coastal regions is consistent with previous
studies (Not et al. 2004, 2008; Worden and Not 2008) that showed
that members of the Mamiellophyceae are more abundant in
coastal regions and rapidly decline in more oligotrophic areas.
At the DCM where nutrient levels increase further off-shore,
there is an increase in the proportion of reads attributed to
Mamiellophyceae. This suggests that the distribution of Mamiellophyceae is at least in part determined by the availability of
nutrients. The near-shore/off-shore gradient is also in agreement with studies undertaken in the Antarctic that showed
changes in the composition of eukaryotes across a similar gra-
dient (Garibotti et al. 2003; Luria, Ducklow and Amaral-Zettler
2014). Prokaryotes, however, did not respond as strongly across
the gradient (Luria, Ducklow and Amaral-Zettler 2014).
Differences in community assemblages corresponding with
depth occur on smaller scales compared with horizontal scales
in stratified waters (Moseneder, Winter and Herndl 2001). Low
levels of nutrients characterize the upper layers of stratified waters, with primary production dependent on diffusion across the
thermocline. This makes the bottom of the mixed layer a suitable environment in terms of nutrition. However, organisms inhabiting this region may be constrained by the reduced light levels and this results in a trade-off between nutrition and light
(Agusti and Duarte 1999). In the present study, further to the
regional changes, the prokaryotic and eukaryotic communities
changed with depth (Figs 4 and 6). A significant increase in alpha diversity (Faith’s PD) was observed at the DCM compared
with the surface for both eukaryotes and prokaryotes. The increase in alpha-diversity with depth has been previously reported in other environments including the Antarctic (Luria,
Ducklow and Amaral-Zettler 2014) and the Mediterranean (Pommier et al. 2010). Alpha diversity with depth, therefore, appears
to be a general pattern regardless of the characteristics of the
water bodies. Changes in the composition of eukaryotes with
depth have been reported in the Indian Ocean (Not et al. 2008)
and Beaufort Sea (Balzano et al. 2012), while for the prokaryotes, similar depth patterns have also been observed in the Atlantic (Treusch et al. 2009; Friedline et al. 2012). In the southern region, the predominantly Dinophyceae–Bacillariophyceae
community observed at the surface is replaced by green algae
(Mamiellophyceae comprising mainly Ostreococcus and Bathycoccus) as the dominant taxa at the DCM. Peaks in the abundance of
pico-prasinophytes (Micromonas, Ostreococcus and Bathycoccus) at
depth have been recorded in various regions including between
80–120 m in the Sargasso Sea (Treusch et al. 2012) and also in the
Beaufort Sea (Monier et al. 2015). In the current study, the surface
assemblage (and the near-shore DCM) of Mamiellophyceae had
higher proportions of Micromonas, whilst the mid- and off-shore
DCM assemblages showed increases in the proportions of Ostreococcus and Bathycoccus. The indication is that Micromonas may
be outcompeting Ostreococcus and Bathycoccus in the more nutrient limited conditions, but is replaced when more nutrients are
available.
In conclusion, this study has used molecular methods to expand knowledge of planktonic assemblage patterns in the photic
zone at the extreme ends of the main body of the Red Sea. Distinct prokaryotic and eukaryotic assemblages were observed between these extremes and also between the surface and DCM
with diatoms and Mamiellophyceae being more abundant in the
nutrient richer southern region and Syndiniophyceae accounting for a higher proportion of reads in the higher salinity northern region. The prokaryotic fraction showed less differentiation
at the class level, with Cyanobacteria, Alphaproteobacteria and
Acidimicrobiia the dominant component in the north and south.
This study forms the basis for further ecological and biogeochemical studies along the latitudinal extent of the Red Sea.
The molecular approach used proved to suitably assess the effects of the natural inputs of nutrients from the Gulf of Aden.
As the Red Sea is oligotrophic throughout its extension, variations in nutrients and chlorophyll a – which would be negligible
in other areas – can result in significant changes in the composition of plankton communities. Even though the links between microbial community composition and function may not
be straightforward (Luria, Ducklow and Amaral-Zettler 2014),
the patterns of variability observed for some of the key classes
Pearman et al.
(e.g. Maiellophyceae and Bacillariophyceae) seem to suggest a
correlation between nutrient inputs, primary productivity and
community composition. However, improvements in the classification of OTUs at lower taxonomic levels would be required to
fully tease apart the controlling factors of the dominant groups
observed in the Red Sea. Future studies should also address the
effect of seasonal variability on biological patterns to better understand the biogeochemical processes in this peculiar system.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
The authors would like to thank the crew of the RV Thuwal who
help during both research cruises. The authors would also like
to thank Riaan van der Merwe and Holger Anlauf who helped
with sample collection, Ute Langner for her help with the plotting of the map and Jennifer Otoadese for proof-reading the
manuscript. The authors would also like to thank the reviewers
and editor for the constructive comments they made to improve
the manuscript. This study has resulted from a collaboration between KAUST and Saudi Aramco within the framework of the
Saudi Aramco-KAUST Marine Environmental Research Center.
FUNDING
This work was supported by funding provided to BHJ by the King
Abdullah University of Science and Technology (KAUST)–Saudi
Aramco initiative.
Conflict of interest. None declared.
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