MICROBIOLOGY OFAQUATIC SYSTEMS

Winter–Summer Succession of Unicellular Eukaryotesin a Meso-eutrophic Coastal System

Urania Christaki & Konstantinos A. Kormas & Savvas Genitsaris &

Clément Georges & Télesphore Sime-Ngando &

Eric Viscogliosi & Sébastien Monchy

Received: 13 May 2013 /Accepted: 3 September 2013 /Published online: 1 October 2013# Springer Science+Business Media New York 2013

Abstract The objective of this study was to explore the suc-cession of planktonic unicellular eukaryotes by means of 18SrRNA gene tag pyrosequencing in the eastern English Channel(EEC) during the winter to summer transition. The 59 mostrepresentative (>0.1 %, representing altogether 95 % of totalreads), unique operational taxonomic units (OTUs) from allsamples belonged to 18 known high-level taxonomic groupsand 1 unaffiliated clade. The five most abundant OTUs(69.2 % of total reads) belonged to Dinophyceae, Cercozoa,Haptophyceae, marine alveolate group I, and Fungi. Clusterand network analysis between samples distinguished the win-ter, the pre-bloom, the Phaeocystis globosa bloom and thepost-bloom early summer conditions. The OTUs-based net-work revealed that P. globosa showed a relatively low numberof connections—most of them negative—with all other OTUs.

Fungi were linked to all major taxonomic groups, exceptDinophyceae. Cercozoa mostly co-occurred with the Fungi,the Bacillariophyceae and several of the miscellaneous OTUs.This study provided a more detailed exploration into theplanktonic succession pattern of the EEC due to its increaseddepth of taxonomic sampling over previous efforts based onclassical monitoring observations. Data analysis implied thatthe food web concept in a coastal system based on predator–prey (e.g. grazer–phytoplankton) relationships is just a part ofthe ecological picture; and those organisms exploiting a varietyof strategies, such as saprotrophy and parasitism, are persistentand abundant members of the community.

Introduction

The development and use of molecular approaches in ocean-ography has considerably increased our understanding ofdiversity and biogeography, particularly among the prokary-otes. Biological diversity surveys have benefited from theadvent of next-generation sequencing (NGS) methods suchas the tag pyrosequencing of the hypervariable small subunitribosomal RNA (SSU rRNA) region. Unicellular eukaryotes,including phototrophs, heterotrophs, mixotrophs and para-sites, have become more identifiable through molecular anal-ysis, which today potentially allows us to recognise major andnovel phylogenetic groups (e.g. [1–10]), to mention just a few.Even though, as a whole, attempts to improve our knowledgeconcerning the genomics of individual groups and speciescontinue to increase, only a small proportion of the molecularanalysis effort has been aimed at investigating temporal com-munity structures and their relationship to environmental fac-tors. Among these studies, the majority has focused on pro-karyotes (e.g. [11–14]); the remaining on eukaryotes [15–19].

Estimation of species richness is a basic tool for macro-ecological analysis and is the first step in measuring diversity

Electronic supplementary material The online version of this article(doi:10.1007/s00248-013-0290-4) contains supplementary material,which is available to authorized users.

U. Christaki (*) : S. Genitsaris :C. Georges : S. MonchyLaboratoire d’Océanologie et Géosciences (LOG), UMR CNRS8187, Université du Littoral Côte d’Opale (ULCO), 32 av. Foch,62930 Wimereux, Francee-mail: urania.christaki@univ-littoral.fr

K. A. KormasDepartment of Ichthyology and Aquatic Environment, University ofThessaly, 384 46 Nea Ionia, Greece

T. Sime-NgandoLaboratoire Microorganismes: Génome et Environnement (LMGE),UMR CNRS 6023, Clermont Université Blaise Pascal, BP 80026,63171 Aubière Cedex, France

E. ViscogliosiCenter for Infection and Immunity of Lille (CIIL), Institut Pasteur ofLille, Inserm U1019, CNRS UMR 8204, Biology and Diversity ofEmerging Eukaryotic Pathogens, EA4547, Université Lille Nord deFrance, Lille, France

Microb Ecol (2014) 67:13–23DOI 10.1007/s00248-013-0290-4

in natural and engineered ecosystems. In the manner thatmicrobial ecology has lent on macro-ecology to advanceits concepts [20–22], NGS, such as tag pyrosequencing,can be combined with well-established working tools inmacro-ecology, like network and modelling analysis. Inthis way, a sharper and more comprehensive picture ofthe complexity of natural microbial communities can beprovided [23]. The improvement in NGS methods, togeth-er with lower costs, has removed one of the major limi-tations in microbial ecology—the lack of repetitive sam-pling in terms of time (i.e. succession) and/or space.

Additionally, ecological interactions, which shape theabundance of species, are much more complicated than rela-tions usually named simply as ‘trophic interactions’ [23]. Aprerequisite for this is to integrate in the classical food websthe novel and/or previously unobserved groups—which areunveiled by NGS—many of which may be of ecologicalimportance (e.g. [24]). For example, many of these organisms,not previously included in succession studies and food webmodels, may well be exploiting a variety of strategies, such assaprotrophy [25, 26] and parasitism [27, 28].

The eastern English Channel (EEC) is an ideal ecosys-tem to study fluctuations in planktonic community struc-tures and trophic relationships. It is a productive meso-eutrophic marine ecosystem, characterised by springblooms of the haptophyte Phaeocystis globosa , precededand followed by communities of colonial diatoms ([29–31]and references herein). The massive P. globosa prolifera-tion is related to the excess of nitrogen and silicate lim-itation at the end of the winter period [32]. Existingevidence, based on grazing experiments and microscopy,indicates unicellular microzooplankton, in particular dino-flagellates, to be major consumers of diatoms and to feedon smaller colonies of P. globosa [30, 31, 33, 34]. How-ever, despite all these early studies, the succession ofplanktonic communities is not yet sufficiently understood.

For this reason, unlike previous studies, which were basedon conventional monitoring observations in the region, theobjective of this study was to explore the succession of plank-tonic microscopic/unicellular eukaryotes in greater detail bymeans of 18S rRNA gene tag pyrosequencing. Expectationswere that tag pyrosequencing would extend currently knownplanktonic diversity and include novel taxa in the plank-tonic succession pattern. The survey focused on thewinter to summer succession, with the sampling periodchosen to include (a) the end of the winter diatom-dominated community, (b) P. globosa growth and se-nescence and (c) the transition to summer conditions.Specifically, the two main questions were as follows:(1) are there any new ‘players’ in the ‘successionscene’? and (2) does the molecular approach provideinsights into current perceptions of general planktonsuccession patterns?

Methods

Sample Collection

The sampling site was located at the SOMLIT (French Net-work of Coastal Observatories) station (50°40′75″ N, 1°31′17″ E; 20–25 mwater depth) in the EEC. This site was chosenas the physical and hydrological properties encountered hereare representative of the coastal water masses of the EEC. TheEEC is characterised by its tidal range, between 3 and 9m, anda residual circulation parallel to the coast. This so-calledcoastal flow is separated from offshore waters by a tidallymaintained frontal area separating the sampling site fromoffshore waters [35]. Sampling was carried out at high tideat a depth of 3 m, from February 25 to June 9, 2009.

Seawater samples of 2.5 L for tag pyrosequencing werecollected in sterile polyethylene bottles, kept at in situ temper-ature in the dark and filtered within 2 h. They were thenscreened with a 150-μm mesh (to retain larger metazoa).Following this, sequential filtration through 10, 3 and0.6 μm nucleopore filters (47 mm diameter) was performedusing a very low filtration pressure peristaltic pump (15 rpm).This method was used in order to avoid filter clumping and tominimise organism disruption [36]. The filters were thenimmediately frozen in liquid nitrogen and stored at −80 °Cuntil analysis. DNA extractions were carried out after poolingcollectively the 10, 3 and 0.6 μm filters.

DNA Extraction

Whole filters with planktonic microorganisms were incubatedovernight at 30 °C with 500 μL of a buffer containing 400 Uof lyticase enzyme (Sigma, Castle Hill, NSW, Australia), in asorbitol-based buffer [37] containing 0.1 M sorbitol, 100 mMTris–HCl (pH 8.0), 100 mM EDTA and 14 mM β-mercaptoethanol. Proteinase K (0.1 mg mL−1) and sodiumdodecyl sulphate (1 % final concentration) were added to thesample and incubated for 1 h at 37 °C to improve extractionfrom organisms having a thick cell wall (e.g. [36]). The DNAwas subsequently purified with the NucleoSpin® Plant DNAExtraction Kit (Macherey-Nagel, Düren, Germany).

PCR and Tag Pyrosequencing

The DNA samples were amplified using the two universaleukaryote primers 18S-82F (5′-GAAACTGCGAATGGCTC-3′) [38] and Euk-516r (5′-ACCAGACTTGCCCTCC-3′) [39].These primers have been designed to amplify a domainaround 470–480 bp corresponding to the variable V2 andV3 eukaryote 18S rDNA regions.

There is no one single primer set that is adequate to coverthe entire existing diversity of a sample (e.g. [40]). Aftervalidation on the entire SILVA 108 database, this primer set

14 U. Christaki et al.

was shown to theoretically amplify the 18S rDNA region fromall major taxonomic groups (see also [41]).

A 10-bp TAG sequence specific to each sample, a 4-bpTCAG key and a 26-bp adapter for the GS FLX technologywere added to the primers. Polymerase chain reaction (PCR)was carried out according to standard conditions for PlatinumTaq High-Fidelity DNA Polymerase (Invitrogen, Carlsbad,CA, USA), with 10 ng of environmental DNA as template.After the denaturation step at 95 °C for 5 min, 25 cycles ofamplification were performed with the GeneAmp PCR Sys-tem Apparatus (Applied Biosystems, Foster City, CA, USA)as follows: 30 s at 95 °C, 30 s at 50 °C and 1 min at 72 °C. Tagpyrosequencing was carried out by the company GenoScreen(Lille, France). The library was prepared following the proce-dures described by Roche (Basel, Switzerland) and used in a1/4 plate run on a 454 GS FLX Titanium sequencer.Pyrosequences were submitted to GenBank-SRA under theaccession number SRP018834.

Tag Pyrosequencing Quality Filtering

A total of 237,795 raw sequences (February 25, 26,979;March 2, 36,226;March 11, 22,320;March 16, 29,574;March30, 26,557; April 7, 27,077; April 27, 22,656; May 20,22,030; and June 9, 24,376) were obtained, which had a meanlength of 363 bp. Downstream sequence processing wasperformed using the mothur 1.28.0 software following thestandard operating procedure, including data normalisation[42, 43]. Firstly, the flowgrams from each of the nine sampleswere extracted and separated according to their TAG. Theresulting nine flowgrams were denoised using the mothur1.28.0 implementation of PyroNoise [44]. Then, primer se-quences, TAG and key fragments were subsequently re-moved, and only reads above 200 bp longwith homopolymersshorter than 8 bp were kept. The data set was then dereplicatedto the unique reads and aligned against the SILVA 108database containing 62,587 eukaryotic SSU rRNA se-quences. Around 9 % of the unique reads suspected ofbeing most likely chimeras were removed using theUCHIME software [45]. Next, the remaining reads wereclustered into operational taxonomic units (OTUs) at 97 %similarity threshold. Finally, single singletons (uniqueamplicons that occurred only once in the whole data set)were removed from downstream analyses, as these aremost likely erroneous sequencing products [6, 46, 47].

Data Analysis

After tag pyrosequencing quality filtering, a total of 501 uniqueOTUs were identified considering all samples. Taxonomicclassification was assigned based on the SILVA 108 SSUrRNA database using BLASTN [48]. Taxonomic clusteringof 59 out of the 501 unique OTUs, comprising each >0.1 % of

the sequences and representing altogether 95 % of the se-quences, was also performed. These 59 OTUs were comparedwith the BLAST function of GenBank (http://www.ncbi.nlm.nih.gov/BLAST/) for the detection of the closest cultured oruncultured relatives, and their taxonomic affiliation wasverified against the PR2 database [49]. Multiple alignmentswere performed using the program ClustalW2 [50]. Differentanalyses (neighbour joining, minimum evolution, andmaximum likelihood) were performed using the MEGAversion 5 software [51], in order to construct topologicaltrees, showing the taxonomic affiliations of the 59 OTUs. Allthree methods provided very similar tree topologies (data notshown). The tree presented in this paper was based onmaximum likelihood analysis using the Jukes–Cantorsubstitution model, since it is well suited to the analysis ofdistantly related sequences, as is the case in this study.Bootstrapping under parsimony criteria was performed with1,000 replicates.

Protistan assemblages from the different sampling dateswere compared using the Plymouth routines in the multivariateecological research (PRIMER v.6) software package [52]. TheBray–Curtis similarity coefficients were calculated to build thematrix based on OTUs presence/absence in order to investigatethe qualitative diversity and the different community structures.The similarity profile (SIMPROF) permutation test wasconducted in PRIMER v.6 to establish the significance ofdendrogram branches resulting from cluster analysis. Temporalsuccession of the prevailing OTUs was also assessed by net-work analysis based on the correlations of the 59 OTUs,representing >0.1 % in abundance. The Spearman correlationcoefficient was estimated on the presence/absence of theseOTUs for the nine sampling dates using the PAST 2.17csoftware [53].

An additional network showing the connectivity of theOTUs log-transformed relative abundances was alsoconstructed. Only the statistically significant (P <0.05) corre-lations were displayed in the network matrix. Network visu-alisation was performed with Cytoscape 2.8.3 software [54].Positive correlations correspond to the co-occurrence ofOTUs and/or when their relative abundance patterns are sim-ilar between two samples.

Rarefaction curves and alpha diversity estimators withinparticular samples (richness estimator SChao1; the heteroge-neity of the diversity; the Shannon, Simpson and Berger–Parker indices) were calculated with the PAST 2.17c soft-ware [53]. The SChao1 approach uses the numbers ofsingletons and doubletons to estimate the number ofexpected species. According to SChao1, ‘missing’ speciesinformation is mostly concentrated on those of low-frequency counts. The Simpson index measures the ‘even-ness’ of the community and ranges from 0 (one taxondominates the community) to 1 (all taxa are representedequally). Berger–Parker dominance is the number of reads

Diversity and Succession of Unicellular Eukaryotes 15

of the dominant OTU relative to the total number of readsin the sample (for more details, see [55]).

Results

After quality filtering and normalisation, the 63,648 remainingreads represented 501 unique OTUs at 97% similarity for all 9samples. Rarefaction curves calculated for all sampling datesapproached a plateau in most cases when ≥97 % levels ofsequence similarities were applied (Fig. 1). The ratio of ob-served to expected (SChao1) OTUs in the samples was 80±8 %(mean±SD) for all of the dates. The lowest Simpson, Shan-non, and equitability indices and the highest dominance andBerger–Parker indices were recorded for the March 30 sam-ple, while the highest values of the Simpson, Shannon, andequitability indices and the lowest values of dominance andBerger–Parker indices were calculated for the May 20 sample(Table 1).

After removal of metazoan OTUs, the overall relativeabundance of the major high-level taxonomic groups wasshown as a series of pie charts for each of the nine samples(Fig. 2). Alveolata, including Apicomplexa, Ciliophora,Dinophyceae and marine alveolate group I (MALV-I), repre-sented the largest percentage of total reads in the samples,except in the 7 and 27 of April samples where they accountedfor only ∼2 and 4 % of the reads, respectively (Fig. 2). AmongAlveolata, Dinophyceae were clearly dominant, except in theFebruary 25 sample where MALV-I accounted for 35 % of thereads. Cercozoa and Fungi became the most quantitativelyimportant groups in the April 7 sample, while Haptophyta,which accounted for 0 to 2 % until April 7, became dominant(68 %) on April 27. Their relative abundance then droppedagain to 2 and 0.5 % in the May and June samples, respec-tively (Fig. 2).

Fifty-nine of the 501 unique OTUs representing each>0.1 % and altogether 95 % of the total reads were used tobuild a maximum likelihood topological tree (Fig. 3; Suppl.Table 1). After clustering, the 59 OTUs were found to belongto 18 known taxonomic groups and 1 unaffiliated clade(Fig. 3). The highest numbers of unique OTUs were groupedwithin the Dinophyceae (13 OTUs) and the Cercozoa(12 OTUs), including the most abundant OTUs in thewhole data set (i.e. EEC09_1183 and EEC09_1001).These were followed in abundance by the Fungi (sevenOTUs), Ciliophora (four OTUs), Bacillariophyceae (fourOTUs) and MALV-I (three OTUs), while Haptophyceaewere represented by two OTUs (Fig. 3).

Five OTUs were assigned as Alveolata: Dinophyceae,Cercozoa, Haptophyceae (P. globosa ), MALV-I and Fungi,together representing ∼69.2 % of the total reads in the wholedata set (48.7, 6.9, 6.9, 4.9 and 1.8 %, respectively). Thedinoflagellate-related OTU (EEC09_1183) was always pres-ent in the samples; it was very abundant until March 30 andbecame abundant again in the June sample (Fig. 4a). Cercozoa(EEC09_1001) and P. globosa (EEC09_1141) OTUs domi-nated in the April 7 and 27, samples, respectively (Fig. 4a, b).The MALV-I OTU (EEC09_1168) represented 31 % of thereads in the February sample (Fig. 4b) and was particularlyabundant outside of the period of the P. globosa bloom(Fig. 4a). Finally, the Fungi OTU (EEC09_1174) was presentin each sample and was most abundant during the P. globosabloom on April 27 (Fig. 4a).

The plot of the cumulative number of the OTUs (Fig. 5)revealed an influx of newly introduced OTUs clearly notice-able in the March 11 sample (100 new OTUs) and the May 20sample (136 new OTUs), while the influx of OTUs deceler-ated at the onset of the P. globosa bloom between March 16and April 7 (Fig. 5).

The Bray–Curtis similarity analysis (Fig. 6a) indicat-ed four major clusters: February 25 and March 2 (57 %similarity), March 11, 16 and 30 (50 % similarity),April 7 and 27 (44 % similarity) and May 20 and June9 (38 % similarity). The SIMPROF significance testindicated significant differences (P <0.05) between thesefour groups and also showed that, within the ‘March’group, the March 30 sample was significantly differentfrom the 11 and 16 samples (Fig. 6a). The stress valueof the 2-D MDS plot was low (P =0.05, plot notshown), which indicated clear-cut relationships.

Network analysis between dates showed that theMarch 11 and 16 samples had the highest number ofconnections (five each). Additionally, these two datesshared the highest number of common OTUs (from 56to 113) with the other dates. The least connected dateswere May 20 and June 9 (Fig. 6b).

The OTU-based network (Fig. 7) revealed that 44 ofthe 59 most abundant OTUs were connected to each

Fig. 1 Rarefaction curves representing the numbers of OTUs against thenumber of high-quality reads. The OTUs were determined using theprogrammothur [42], with a cutoff value set to 0.03 (OTUs were groupedwhen their level of sequence similarity was ≥97 %) for the analysis

16 U. Christaki et al.

other. The Fungi (five OTUs) were correlated mostly with allmajor taxonomic groups, except Dinophyceae. The Cercozoa(ten OTUs) were mostly positively connected with the Fungi,the Bacillariophyceae (three OTUs) and several of the variousOTUs. Among these various OTUs (19 OTUs in total), themost represented were the MALV-I (3 OTUs), marinestramenopiles (MAST) (3 OTUs) and Ciliophora (3 OTUs)groups.

Discussion

Are There Any New ‘Players’ in the ‘Succession Scene’?

According to previous microscopical counts in the area, twomajor morphospecies, Gyrodinium spirale and Gyrodinium

fusiforme , dominated the dinoflagellate community [30, 31].Evidenced by both microscopy and tag pyrosequencing data,Gyrodinium was the most abundant dinoflagellate genus. It isalsoworth noting that the temporal dynamics ofGyrodinium andP. globosa observed by tag pyrosequencing were in agreementwith the microscopical evidence [31]. Other members of themicrobial foodweb community grouped inmicroscopical studiesunder the name of ‘heterotrophic nanoflagellates’ could belongto a wide variety of groups (e.g. Cercozoa, fungal zoospores,MAST, etc.), according to tag pyrosequencing data. For example,in the present study, the April 7 sample was dominated byCercozoa and Fungi (Fig. 2), which were missed in the previ-ouslymentionedmicroscopical studies. Furthermore, three of thefive most abundant OTUs belonged to taxa not previouslyconsidered in planktonic succession patterns (Figs. 3 and 4).The Cryothecomonas-like OTU (EEC09_1001; Fig. 3; Suppl.

Table 1 Number of OTUs, the richness estimator (SChao1) and the heterogeneity of the diversity indices (dominance, Simpson and Shannon) for eachsampling date

February 25 March 2 March 11 March 16 March 30 April 7 April 27 May 20 June 9

Nb OTUs 118 121 167 142 83 82 154 201 119

SChao1 130 128 218 188 101 107 210 275 154

Dominance (D) 0.18 0.42 0.55 0.67 0.77 0.40 0.34 0.13 0.49

Simpson (1−D) 0.82 0.58 0.45 0.33 0.24 0.60 0.66 0.87 0.51

Shannon (H) 2.58 1.92 1.60 1.13 0.78 1.64 2.22 2.97 1.57

Equitability 0.54 0.40 0.31 0.23 0.18 0.37 0.44 0.56 0.33

Berger–Parker 0.31 0.64 0.74 0.82 0.87 0.62 0.58 0.31 0.70

Note that equitability (H /Hmax) is more informative than Shannon (H) index sinceHmax corresponds to the Shannon index expected when all OTUs areequally represented in the sample. All the indices are calculated based on normalised data

Fig. 2 Relative abundance of major high-level taxonomic groups ofprotists in each of the nine samples assigned based on the SILVA 108SSU rRNA database using BLASTN and complemented with the PR2

database. To facilitate reading, the percentage of reads assigned to aspecific group is given in the pie charts when above 0.1 %

Diversity and Succession of Unicellular Eukaryotes 17

Table 1), undetectable untilMarch 30, became dominant onApril7 and then sharply decreased in the following samples (down toonly one read in the June sample; Fig. 4a). This pattern seemedconsistent with the parasitic behaviour of many cercozoans. Forexample, Cryothecomonas longipes , a widely distributedcercozoan [56, 57], is known to parasitise on diatoms [58, 59].According to our data, three of the four diatom OTUs related toChaetoceros , Minidiscus and Stephanodiscus were present in

the winter/early spring samples just before the outburst of thecercozoan OTU (Fig. 3). Therefore, although our taxonomicaffiliation should be interpreted cautiously, the temporal dynam-ics of this specific OTU has provided some indication of itsparasitic nature on diatoms and its possible role in ecologicalsuccession. Indeed, the abundance of the three out of fourdiatom-related OTUs seemed to be positively associated mostlywith the Cercozoa (Fig. 7). However, since correlations are not

Fig. 3 Maximum likelihood topological tree of the 59 most abundantOTUs found in the EEC (in bold), and representative relatives afterBLAST searches from GenBank (accession number given betweenbrackets ) and PR2 databases. Bootstrap values were estimated from1,000 replicates. Numbers in parentheses indicate the relative abundance

of the fivemost abundant OTUs considering all samples. The ribbon aftereach OTU represents the relative abundance in each of the nine samplingdates (25_Feb, 02_Mar, 11_Mar, 16_Mar, 30_Mar, 07_Apr, 27_Apr,20_May and 09_June ). The figure was truncated into two parts tofacilitate reading

18 U. Christaki et al.

necessarily causative agents of the observed biological relations,at this point, it is not safe to state whether the observed correla-tions reflected a true synergistic or antagonistic occurrence, sincea parasite might increase or decrease in abundance along withtheir host during its life cycle.

Another abundant OTU was a marine alveolate belongingto MALV-I (EEC09_1168; Figs. 3 and 4). MALV-I andMALV-II groups appearing in virtually all marine surveysbelong to marine alveolates [4]. Recent phylogenetic analysesplace them closer to dinoflagellates ([4], see also Suppl.Table 1). The MALV-I OTU (EEC09_1168) was absent dur-ing the P. globosa bloom, and as the cercozoan describedbefore, it showed a single sharp peak. Members of theseenigmatic marine alveolate groups were found to be mostlikely intracellular symbionts or parasites (e.g. [60–62]).Moreover, their considerable clonal abundance and diversitysuggests specific interaction with different hosts, and as aconsequence, it has been proposed that the whole assemblageis composed of marine parasites [4].

EEC09_1174, belonging to Fungi, was present in all thesamples. The limited number of available studies on the mo-lecular phylogeny of Fungi from coastal systems (for a review,see [63]) has shown that members of this group are present incoastal waters [64]. Fungi are possibly related to the degrada-tion of the freshly produced organic material by primary pro-ducers [65, 66]. This was also reflected in the network analysis(Fig. 7), where two out of the three abundant diatom-relatedOTUs were connected to Fungi. It is known for diatoms that

polysaccharides are the main exudates ([67] and referencestherein), and these sugars could promote the presence of Fungi.

The parasitic assemblage of the EEC also included oneLabyrinthulomycetes, two Oomycetes, one Apicomplexa andtwo Ichthyosporea-related OTUs. The closest relative of thelabyrinthulid (99% identity), a member of the Aplanochytriumgenus, was recently proposed to be involved either in predator–prey or commensalistic relationships with zooplankton ratherthan being solely saprophytic [68]. Marine Oomycetes appearto be a diverse group, including an increasing number ofcultured species [69, 70], which are specific parasites ofmarineorganisms [71]. The Apicomplexa is a phylum entirely repre-sented by widely distributed parasites with a large range ofhosts [72], including commercially important fish and bivalves[73, 74]. Finally, one of the Ichthyosporea OTU is closelyrelated to Sphaeroforma arctica (99 % identity), a plausibleparasite [75], isolated from the arctic marine amphipodGammarus setosus [76], while the second OTU is related toa Mytilus sp. parasite (99 % identity) (Fig. 3; Suppl. Table 1).

Within the 59 most abundant OTUs, 1 unaffiliated eukary-otic clade was found (Fig. 3), including the OTUEEC09_1037,clusteringwith an unknown representative. This OTU is closelyrelated to a novel sequence reported from deep-water corals inthe Norwegian Sea [77]. One OTU belonged to the widelydistributed novel group of Picobiliphyta [78]. A recent studybased on single-cell genomics [79] reported that these organ-isms bear no plastid-related DNA or proteins, rendering themunlikely obligate photoautotrophs.

Does the Molecular Approach Provide Insights into ourPerception of General Plankton Succession Patterns?

According to microscopic observations on planktonic succes-sion in the area, dinoflagellate biomass, mainly belonging to thespecies putatively identified as G. spirale , closely tracks thebiomass of diatoms, as well as colonies of Phaeocystis ,suggesting that G. spirale may be the major consumer ofphytoplankton [30, 31]. Based on microscopy data from Febru-ary 25 to March 16, the phytoplankton community was domi-nated by large colony-forming diatoms and the highest Chl aconcentration was recorded (22.9 μg Chl aL−1; Suppl. Table 2)

Fig. 4 Temporal variation of the five most abundant OTUs representing69.2 % of the total reads of normalised data. EEC09_1183 Dinophyceae,EEC09_1001 Cercozoa, EEC09_1141 P. globosa, EEC09_1168 MALV-

I and EEC09_1174 Fungi-related OTUs (see also Fig. 3) as number ofreads (a) and as percentage of number of reads in each sample (b)

Fig. 5 Plot of the total number of OTUs present at each sampling date(black circles) and the cumulative number of the OTUs (grey circles) basedon the new arriving OTUs between two consecutive dates (open circles)

Diversity and Succession of Unicellular Eukaryotes 19

[31]. Afterwards, on April 27, the P. globosa dominated thephytoplankton community, accounting for a ten times increasein phytoplankton biomass [31] (Suppl. Table 2). The combina-tion of the high biomass of P. globosa and its relatively lownumber of connections—five out of seven in total being nega-tive correlations (Fig. 7)—has provided some further evidencethat the system is taken over by this organism during its bloom.This is also evidenced by the hampered rate of newly introduced

OTUs during its bloom, based on accumulation curves (Fig. 5),compared to pre-bloom and post-bloom periods.

In the present study, numerous taxa such as Cercozoa,Fungi, Labyrinthulomycetes, Telonemia, Ichthyosporea,MALV-I and MAST (Fig. 3) were included in the data setand the subsequent cluster and network analysis for the firsttime for the EEC (Fig. 6). The cluster analysis, based on the501 OTUs’ normalised presence/absence data, fashioned intofour significantly different clusters (P <0.05). These repre-sented the ‘winter’ conditions (February 25 and March 2),the ‘pre-bloom’ (March 11, 16 and 30, with March 30 furtherbeing significantly different from the two other dates of thecluster), the ‘P. globosa bloom’ (April 7 and 27) and the ‘post-bloom’ period (May 20 and June 9). These clusters based ontag pyrosequencing data were in accordance with the previ-ously mentioned microscopical observations, and they alsoreflect satisfactorily the physicochemical and biological pa-rameters obtained during our study (Suppl. Table 2). Thehighest nitrogen concentrations recorded in ‘winter’ in theFebruary 25 and March 2 samples (∼15 μM for NO3+NO2),along with high chlorophyll values (up to 22.9 μg Chl a L−1),were due to diatoms. The massive phytoplankton proliferationin these coastal waters is related to the disequilibrium ofavailable nutrients including an excess of nitrogen and silicatelimitation [32]. Observations over the last decade at theSOMLIT station in the EEC have shown that the N/P ratiocan reach values >100 at the end of the winter and dropsdramatically with the P. globosa bloom. The N/P ratio reachedits highest values in the March 11, 16 and 30 samples,highlighting the pre-bloom conditions (Suppl. Table 2). Thecluster, defined as the ‘P. globosa bloom’, was associatedwith

Fig. 6 Cluster diagram based on Bray–Curtis similarities calculated fromnormalised presence/absence data of OTUs. Asterisks at nodes in thedendrogram indicate significant differences between bifurcations (P<0.05). Similar symbols at the end of each branch indicate statisticallyundistinguishable protistan assemblages (a) and correlation-based net-work visualisation of the presence/absence of all the OTUs found in each

sampling date (b). The size of each node is proportional to the number oftotal OTUs found in each sampling date (minimum, 82OTUs; maximum,201 OTUs; cf. Table 1). Thickness of edges (lines) is proportional to thenumber of shared OTUs between the connected samplings (minimum, 41OTUs; maximum, 114 OTUs).*P<0.05, **P <0.01, ***P<0.001; sta-tistically significant correlations. All connections are positive

Fig. 7 Network diagram of Spearman’s correlations (edges) between theOTUs (nodes) that showed statistically significant (P<0.05) correlationsof their relative abundances, across all nine sampling dates. All OTUswere taxonomically assigned based on their position on the topology tree(see Fig. 3). Blue and red colours indicate positive and negative connec-tions, respectively

20 U. Christaki et al.

the decrease in the N/P ratio and a ten times increase inautotrophic biomass (Suppl. Table 2). Finally, during thepost-bloom period (May 20 to June 9), the N/P ratio droppeddrastically (Suppl. Table 2), indicating that P. globosa hadconsumed most of the available nitrogen for its growth.

The network analysis of dates based on the same data set gavea complementary view of the succession. The March 11 and 16samples (defined as the ‘pre-bloom’ transition period betweenthe winter conditions and the P. globosa bloom) had the highestnumber of connections and shared OTUs with all the previousdates. The post-bloom May and June samples represented com-munities dissociated from the previous ones (Fig. 6b).

Tag pyrosequencing is recognised as a powerful method forSSU rDNA sequencing (e.g. [6, 7, 80]); however, this methodis often merely applied to provide a snapshot of the ecosystembiodiversity (for a review, see [81]). The top 5 most abundantOTUs observed in this study (Fig. 4) were also found in aprevious study related to the P. globosa bloom [36], whichindicates their persistence in this ecosystem. Further studies onthe roles of these organisms need be carried out. A difficult butfascinating challenge for contemporary biologists is to interpretsequencing data in the context of ecosystem function. Repli-cated in time and space, observations in metagenomics andrelated approaches, will contribute to acquiring solid results onthe ‘who, what, when, where, why and how’ of ecosystems[81], while the new analysis approaches of co-occurrence andcorrelation patterns, such as network analysis, can open thepath to global models of ecosystem dynamics [23].

Acknowledgements This study was supported by the INSU/CNRS/EC2CO-IMMERSE (EC2CO_2011), the ‘Nord-Pas de Calais’ Regionand FRB-DEMO (FRB_2013), the ANR-ROME (ANR 12 BSV7 001902), the BQR-ULCO 2012 projects and the SOMLIT network. We aregrateful to Eric Lecuyer and JDGrattepanche for the sampling assistance.www.englisheditor.webs.com is acknowledged for the paper’s Englishproofing. Finally, we are grateful to four anonymous reviewers for theirconstructive comments, which improved our work.

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