ORIGINAL ARTICLE

Biogeography of the uncultured marinepicoeukaryote MAST-4: temperature-drivendistribution patterns

Raquel Rodrıguez-Martınez1, Gabrielle Rocap2, Guillem Salazar1 and Ramon Massana1

1Institut de Ciencies del Mar (CSIC). Passeig Marıtim de la Barceloneta, Barcelona, Spain and 2School ofOceanography, University of Washington, Seattle, WA, USA

The MAST-4 (marine stramenopile group 4) is a widespread uncultured picoeukaryote that makes upan important fraction of marine heterotrophic flagellates. This group has low genetic divergence andis composed of a small number of putative species. We combined ARISA (automated ribosomalintergenic spacer analysis) and ITS (Internal Transcribed Spacer) clone libraries to study thebiogeography of this marine protist, examining both spatial and temporal trends in MAST-4assemblages and associated environmental factors. The most represented MAST-4 clades appearedadapted to different temperature ranges, and their distributions did not suggest clear geographicalbarriers for dispersal. Distant samples sharing the same temperature had very similar assemblages,especially in cold temperatures, where only one clade, E1, dominated. The most highly representedclades, A and E1, showed very little differentiation between populations from distant geographicalregions. Within a single site, temporal variation also followed patterns governed by temperature. Ourresults contribute to the general discussion on microbial biogeography by showing strongenvironmental selection for some picoeukaryotes in the marine environment.The ISME Journal advance online publication, 18 April 2013; doi:10.1038/ismej.2013.53Subject Category: Microbial population and community ecologyKeywords: biogeography; ITS1; ARISA; temperature; fixation index; MAST-4

Introduction

Biogeography is the study of the distribution ofbiodiversity over space and time. Studies performedwith plants and animals have revealed geographi-cally restricted metapopulations due to deterministic(environmental) and stochastic (random dispersal)processes (MacArthur and Wilson, 1967). Whether ornot microorganisms follow similar patterns is an olddebate (Beijerinck, 1913) that has recently seen arenovated interest (O’Malley, 2008, Hanson et al.,2012). A long-held concept is that free-living organ-isms o1 mm (all prokaryotes and most protists) aresufficiently abundant to have worldwide distribu-tion owing to their dispersal ability (Finlay, 2002,Fenchel and Finlay, 2004). Microbial cells can bedispersed via wind, water or birds, and many specieshave a high ability to become dormant in harshenvironments (Whitfield, 2005). Consequently,microbial organisms are believed to occur whereverthe environment permits: ‘everything is everywhere,but the environment selects’ (Baas-Becking, 1934).

Implicit in this tenet is that free-living microbial taxaare not randomly distributed but exhibit biogeogra-phical patterns, and in some cases, these patternsmay be qualitatively similar to those observed formacroorganisms (Green et al., 2004, Horner-Devineet al., 2007). However, as indicated in the tenet, thesepatterns would be the result of local environmentalselection rather than dispersal limitation.

The current evidence confirms that environmentalselection is fundamental for the spatial variationobserved in microbial diversity (Martiny et al.,2006). The next frontier is to figure out whetherthese patterns are also influenced by geographicalbarriers that facilitate evolution and diversification.Because geographical distance is often correlatedwith specific environmental characteristics, disen-tangling the relative influence of these two factorson community divergence represents a major chal-lenge in elucidating whether or not microbes arelimited by dispersal. It is known that freshwaterdiatoms present dispersal limitations because ofdesiccation intolerances (Vyverman et al., 2007).Other highly specialized microorganisms, such asthe hyperthermophiles, are unlikely to make a longdispersal journey, so are easily isolated by geo-graphic barriers, resulting in the development ofa global population structure (Whitaker, 2003).However, marine systems are expected to be less

Correspondence: R Rodrıguez-Martınez or R Massana, MarineBiology and Oceanography Institut de Ciencies del Mar (CSIC),Passeig Marıtim de la Barceloneta, 37-49, Barcelona 08003, Spain.E-mail: raquelr@icm.csic.es or ramonm@icm.csic.esReceived 10 April 2012; revised 4 February 2013; accepted 2March 2013

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prone to dispersal limitation than terrestrial andfreshwater systems.

The oceans are an interconnected geophysicalfluid that potentially allows planktonic organisms todisperse globally. A global conveyor belt mixesoceanic waters at scales of thousand years(Broecker, 1991). Thus, tectonic and water massdispersal barriers are often weak and unable togeographically isolate pelagic planktonic popula-tions for extended periods of time (Sexton andNorris, 2008). The geographic distribution of marineplanktonic diatoms does not seem to be limited bydispersal; rather it is the environmental selectionwhich dominates diatom community structure(Cermeno and Falkowski, 2009). The general viewis that of a broad dispersal of marine planktonicmicrobes (Cermeno et al., 2010). The picoeukaryotesrepresent an optimal target to further investigatebiogeographical patterns of marine microbes.

In a previous study (Rodrıguez-Martınez et al.,2012), we investigated the diversity of an importantuncultured picoeukaryote, the MAST-4 (marinestramenopile group 4) lineage (Massana et al.,2004), which is widespread in surface marine waters(except polar systems) and represents approximately9% of heterotrophic flagellates (Massana et al., 2006,Rodrıguez-Martınez et al., 2009). We observed thatdespite its huge number of cells in the oceans,MAST-4 has a very low genetic divergence and iscomposed of only five main clades, each represent-ing at least one biological species. The small size(2 mm), high abundance (B100 cells ml�1 in theocean surface), worldwide distribution and lowgenetic diversity make MAST-4 a good example tostudy marine protist biogeography. In this work, wedetermined the MAST-4 community structure anddistribution by combining automated ribosomalintergenic spacer analysis (ARISA) (Fisher and

Triplett, 1999) and 18 S rDNA-ITS1 (InternalTranscribed Spacer 1) gene libraries. MAST-4 diver-sity was analyzed at 40 different locations, and wefound evidence for a strong environmental selectionand little dispersal limitation for the most repre-sented clades.

Materials and methods

Study sites and samplingSamples were selected from different oceanographiccruises performed at the North Atlantic Ocean(NOR, NAT, RG, BE and COC), the North PacificOcean (WE), the Mediterranean Sea (BL and AL) andthe southern hemisphere (IND and DH) (Figure 1).Details of these oligo- and mesotrophic environ-ments (up to 3.4 mg chlorophyll l� 1) have beenalready published: NOR (Not et al., 2005), NAT(Gonzalez et al., 2000), COC (Alonso-Saez et al.,2007), WE (del Giorgio et al., 2011), BL (Alonso-Saezet al., 2008), AL (Arin et al., 2002), IND (Not et al.,2008) and DH (Dıez et al., 2004). Seawater was takenwith Niskin bottles attached to a CTD (conductivity,temperature, depth recorder) rosette, prefiltered by a200-mm nylon mesh and then filtered in successionthrough a 3-mm pore-size polycarbonate filter anda 0.2-mm pore-size Sterivex unit (Durapore,Millipore, Billerica, MA, USA). DNA extraction fromthe picoplankton (0.2–3mm) was done using enzy-matic and sodium dodecyl sulfate digestion plusphenol purification (Massana et al., 2000). Thequality and quantity of extracted genomic DNA wasdetermined with a NanoDrop 1000 (Thermo FisherScientific Inc., Wilmington, DE, USA). Physico-chemical data (temperature, salinity) and chloro-phyll concentration from the samples were compiledfrom the previous studies. ARISA fingerprinting was

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done for the surface samples in all the stationsmarked in Figure 1, for additional depths in moststations and for a temporal survey at the Blanes BayMicrobial Observatory (BL). Surface samples fromstations marked with a star were used for clonelibraries.

Design of PCR primersSpecific primers were designed to amplify the endof the 18S rDNA (290 bp) by the ITS1 and thebeginning of the 5.8S (39 bp). The forward primerM4.18S-F (50-TGGGTAATCTTTGAACGTGAAT-30),located before the V9 region of the 18S rDNA, wasdesigned based on all MAST-4 sequences for thisregion available so far (52 unique clones). It had aperfect match (identical in nucleotide sequence) forall these clones, except two (ME1.29 and OLI11066)that had an extra nucleotide (likely a sequencingerror). It had 42 mismatches (except for onesequence with one mismatch and three sequenceswith two mismatches) to non-target sequences fromthe SILVA database (Pruesse et al., 2007). Thereverse primer M4.58S-R (50-GTTGCGAGAACCTA-GAC-30), located in the 5.8S rDNA, was designed tohave a perfect match with the 22 MAST-4 sequences(Rodrıguez-Martınez et al., 2012). This primer had atleast two mismatches with all stramenopilesequences extracted from GenBank, except for someLabyrinthulida sequences (3 with no mismatchesand 62 with one mismatch). Primers were checkedfor formation of primer dimers, GC (guanine-cyto-sine) content and theoretical melting temperature inthe website www.operon.com, using the OligoAnalysis and Plotting Tool. This primer set gave anamplicon size ranging from 500 to 650 bp.

Construction of clone librariesThe PCR mixture (30 ml) contained 15 ng of DNAtemplate, 0.5 mM of each primer, 200 mM of eachdNTP, 1 mM MgCl2, 1.5 units of a Taq DNA poly-merase (ThermoPrime, Thermo Scientific, Lafayette,CO, USA) and the enzyme buffer. PCR cycling,carried out in a Bio-Rad thermocycler (Bio-Rad,Hercules, CA, USA), was: initial denaturation at94 1C for 5 min; 30 cycles with denaturation at 94 1Cfor 1 min, annealing at 60 1C for 45 s and extension at72 1C for 1 min; and a final extension at 72 1C for10 min. We tested the MgCl2 concentration (from 0.5to 3 mM) and the annealing temperature (from 55 to66 1C) and chose the most stringent conditionsgiving the expected band. To check the specificityof the primer set, we confirmed the negative signalwith nine non-target cultures (diatoms, hapto-phytes, dinoflagellates and cyanobacteria). PCRproducts were purified with the QIAquick PCRPurification kit (QIAGEN, Valencia, CA, USA) andcloned using the TOPO-TA cloning kit (Invitrogen,Carlsbad, CA, USA) with the vector pCR4 followingthe manufacturer’s recommendations and a vector-

insert ratio of 1:5. Putative positive bacterialcolonies were picked and transferred to a new LB(Luria-Bertani) plate and finally into LB-glycerolsolution for frozen stocks (� 80 1C). Presence ofcorrect insert was checked by PCR reamplificationwith vector primers M13F and M13R using a smallaliquot of bacterial culture as template. Ampliconswith the right insert size (39–49 clones per library)were sequenced at the Macrogen sequencing servicein Korea. Chromatograms were examined with4Peaks (A. Griekspoor and T. Groothuis, mekentosj.com). Sequences have been deposited in GenBankunder accession numbers KC561142–KC561369.

Sequence analysisSequences from clone libraries, together withsequences from the ‘SSU-LSU’ dataset (Rodrıguez-Martınez et al., 2012), were aligned with MAFFTv6.853 (Katoh and Toh, 2008) with the E-INS-Ialgorithm, using a MAST-7 sequence as an outgroup.The alignment was inspected visually and modifiedusing secondary structure models folded in mFOLD(Zuker, 2003) as explained before (Rodrıguez-Martınez et al., 2012). A maximum likelihood (ML)phylogenetic tree was done with RAxML v7.0.4 MPIversion (Stamatakis, 2006), using the General TimeReversible model of nucleotide substitution and aGamma distributed rate of variation across sites. Theshape parameter (a) of the Gamma distribution wasestimated from the data set using default options.Phylogenies were done at the University of OsloBioportal (www.bioportal.uio.no). One thousandalternative ML trees were run, and the tree withthe best likelihood was selected and visualized inFigTree v1.3.1 (Rambaut, 2009). Bootstrap analyseswere run with 1000 pseudo-replicates and a con-sensus tree was constructed with RAxML. To inferintraspecific phylogenies and visualize alternativepotential evolutionary paths, we constructed med-ian-joining (MJ) networks (Bandelt et al., 1999) withthe Network 4.6.0.0 program (Fluxus Technology,Suffolk, UK). Rarefaction curves for each clonelibrary were constructed using Mothur (Schlosset al., 2009). The genetic differentiation betweenpopulations was estimated with DNAsp 5.10.1 usingthe fixation index (Fst), which ranges from 0 (nogenetic differentiation) to 1 (complete differentiation).

Generation of ARISA profilesEnvironmental DNA samples were PCR-amplified intriplicate for ARISA in a MJ Research cycler. PCRconditions were the same as described before exceptthat we used a volume of 25 ml with 10 ng of DNAtemplate, a different Taq DNA Polymerase (‘GeneChoice’) and the forward primer was fluorescentlylabeled (5-HEX). PCR products stored in the darkat 4 1C were purified with MultiScreen PCRm96 Platesand quantified using PicoGreen fluorescence(Invitrogen) in a SpectraMax M2 microplate reader

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(Molecular Devices Corp., Sunnyvale, CA, USA).Ten ng DNA were ethanol precipitated from tripli-cates or from pooled PCR products (when the yieldof the PCR was low), followed by resuspension with0.078 ml Tween, 9.67 ml water and 0.25 ml fluores-cently labeled internal size standard, CST ROX 60-1500 bp (http://www.bioventures.com/). Sampleswere run on a MegaBACE 1000 automated capillarysequencer (Molecular Dynamics, Sunnyvale, CA,USA). The electropherograms were then analyzedusing DAx software (v8.0; Van Mierlo SoftwareConsultancy, Eindhoven, the Netherlands). Onlypeaks exceeding four times the noise signal of theelectropherogram curve were considered.

Analysis of fingerprinting dataFrom DAx output tables, peak heights were binnedusing the ‘fixed window’ binning strategy to takeinto account the size-calling imprecision fromARISA fingerprints (Hewson and Fuhrman, 2006).In order to determine the best window size with ourdata, we applied the ‘automatic binning algorithm’(Ramette, 2009) developed in a R script (The RFoundation for Statistical Computing (http://cran.r-project.org/)); we chose 2 bp. To identify the bestwindow frame (out of the 20 possible starting with ashift value of 0.1), we used the ‘interactive binningalgorithm’ (Ramette, 2009). This algorithm binnedthe peaks for each frame, calculated the relativefluorescence intensity of each binned peak bydividing its height by the total peak height of thesample and omitted peaks with values o0.5%(considered as background). We added an optionin the script to compare frames considering onlytriplicate samples (instead of all the samples). Theframe with the best correlation among triplicateswas chosen; starting with 1.3 in our case. The finaloutput was a table with the relative intensity of eachbinned peak (each considered as a different opera-tional taxonomic unit (OTU)) in the size range of500–650 bp. We then performed a permutationalmultivariate analysis of variance (PERMANOVA)test using the sample as a grouping factor in order toestimate the variability due to the experimentalerror. If triplicates were identical, this test wouldexplain 100% of the variability. In our case, itexplained 92%, indicating that only 8% of thevariability of the samples was due to technicalimprecision.

A consensus OTU-table (relative intensity of allthe OTUs in all the samples) was obtained byaveraging the triplicates in each sample, as long asOTUs appeared at least in two of the threereplicates. A distance matrix from the consensusOTU-table was calculated with the Bray–Curtisdissimilarity index (community distance matrix).Patterns were explored using clustering analysis(along with the SIMPROF (similarity profile) sig-nificance test) and nonmetric multidimensionalscaling (NMDS) analysis. A stress value was

calculated based on the differences between Bray–Curtis distances and actual distances on the MDSplot, with lower values depicting a better represen-tation of sample distances in a two-dimensionalspace. Simple and partial Mantel tests (with thePearson correlation and based on 999 permutations)were done to compare the community distancematrix with a geographical distance matrix (usinggeographical coordinates in a perfect sphere) and atemperature Euclidean distance matrix. The rela-tionship between community composition (theOTU-table) and environmental factors was analyzedby a Constrained Correspondence Analysis. Auto-matic forward selection with significance tests ofMonte Carlo permutations were used to build theoptimal models. Additionally, we did a PERMA-NOVA test to check the importance of the environ-mental factors in the community composition. Wealso assessed the contribution of each OTU to theobserved similarity (or dissimilarity) betweengroups with a similarity percentage (SIMPER)analysis. All multivariate analyses were also donewith an OTU-table, including triplicates and withtransformed data (arcsine of the square root of therelative intensity) to reduce the skew; no differencesin the results were seen (data not shown). Statisticaltests and graphics were done using R packages (Gmt,Vegan, MASS), CANOCO 4.5 for Windows (ter Braakand Smilauer, 2002) and Primer v6.1.2 software.

Results

Validation of the different MAST-4 cladesIn a previous study, we showed that MAST-4 wascomposed of five main clades (Rodrıguez-Martınezet al., 2012). To verify the robustness of these clades,we obtained 228 additional sequences of 500–650 bpencompassing the V9 region of the 18S rDNA andthe ITS1 region from five different locations(Figure 1), including the locations from the previousstudy and one additional site. All sequencesobtained in clone libraries belonged to the MAST-4lineage, demonstrating the specificity of the pri-mers. The ML phylogenetic tree (Figure 2) revealedthat the new sequences were distributed in the fiveclades previously observed, sometimes forming newsubclades. Because we used the very variable ITS1region, bootstrap values for the deepest brancheswere low, but the purpose behind this locus choicewas the definitive assignment of sequences to cladesand subclades, rather than a robust intercladetopology. Sequences grouped in 12 different helixIII types for the ITS1 secondary structure asdescribed in Rodrıguez-Martınez et al. (2012),(Figure 2 and Supplementary Figure S1). Clade Aincluded sequences from the five different sites, allwith the same helix III motif. Clade B also had onehelix III motif but was divided in three phylogeneticsubclades. Clade C presented four helix III motifs,including several CBCs (compensatory base

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Figure 2 Maximum likelihood phylogenetic tree of MAST-4 built with 228 new sequences (end of 18S rDNA and complete ITS1) and 22clones from a previous study (indicated with letters). Clades and subclades are indicated with gray areas. Bootstraps values 450% areshown. The scale bar indicates 0.2 substitutions per position. MJ networks appear at the right of the tree, with ribotypes in a differentcolor depending the sample. Scale bars indicate 10 base changes between ribotypes (note different scale for each clade). At the top right,sequences for the conserved helix III stem (derived from ITS1 secondary structures) are listed.

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changes) that corresponded well with phylogeneticsubclades except for subclade C2 that had twomotifs differing by a hemiCBC. Subclades C2 andC3 included sequences from only one location.Clade D had only four sequences but representedtwo helix III motifs. Finally, clade E consisted offour helix III motifs, two of them forming well-differentiated lineages (E2 and E3), and the othertwo, with only one hemiCBC in the fifth base pair,included in the E1 subclade. Rarefaction curves ofthe five libraries did not reach a clear saturation at alevel of sequence similarity of 99%, indicatingundersampling of the community (data not shown).

Biogeographical patterns from sequence analysesTo examine biogeographical patterns from the fivesamples, we used the ITS1 sequences to constructMJ networks for each clade (Figure 2, right panels),which allow visualization of potential evolutionarypaths in data sets with large sample sizes and smallgenetic distances between individuals. These net-works revealed variation in genetic diversity in eachclade. According to this analysis, sequences withinclades E and A were very similar, whereas clade Bexhibited a larger diversity. In a second step, we usedthe ITS1 libraries to calculate Fst values a measure ofthe genetic differentiation among populations fromdifferent sites. This was done for each clade sepa-rately, as they form separate putative species.

Clade A included sequences from all the five sites,and there were few base changes between ribotypesas evidenced by short connecting lines in the MJnetwork. Fst values were generally low between thedifferent geographical populations of clade A(Figure 3). The Indian population (IND) appearedthe most isolated, with Fst values around 0.5 with

other locations, whereas the other four populationshad lower pairwise Fst values (o0.22). Interestingly,the two populations in the warm sites (Sargasso Sea(BE) and Mediterranean (BL)) showed very littledifferentiation, as occurred between the two popu-lations from the cold sites (North Pacific (WE) andNorth Atlantic (NOR)). In contrast to clade A, the MJnetwork for clades B and C showed a strong spatialstructuring, with sequences from the differentlocations typically forming separate subclades (Fstvalues 40.80 indicated well-differentiated popula-tions in each site). Subclade C1 stands as oneexception for this trend, as little differentiationwas seen between BE and IND populations(Fst¼ 0.18). Finally, sequences from subclade E1,originating from three different regions, were wellmixed in the network plot, and there were two casesof identical sequences from distant sites (Figure 2).The Fst values between these populations were low,particularly between WE and NOR sites (Figure 3).

Agreement between ARISA profiles and clone librariesIn order to interpret ARISA data, we first analyzedthe fingerprints from the same samples that origi-nated the clone libraries. For each sample, ARISAprofiles were similar to the size distribution ofclones in the corresponding library (SupplementaryFigure S2), suggesting that all peaks derived fromMAST-4 phylotypes (although ARISA peaks wereestimated at 5–9 bp larger than the actual fragmentsize). We then combined all clones from thedifferent libraries to obtain a taxonomic assignmentof ARISA peaks (Figure 4). Clones from clades A andC overlapped at the 511–537 bp region, althoughsome sizes were exclusive to a given clade, such as513, 525 and 528 (clade A) or 517 and 519 (clade C).

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Figure 3 Population differentiation of clades A and E1 among the five sites with clone libraries. Fst values for clades A (first value) andE1 (second value, when available) are shown above the lines, with line thickness indicating the similarity in population composition(low Fst values).

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Clones from clade B exhibited a wider sizerange, from 527 to 590 bp, and were the onlyones occupying the size spectra from 560 to 574 bp.Clade D, with only four sequences, had the largestclones (648 bp) and also appeared in the 580–594region intermixed with other clades. Clade E hadclones in a restricted region (580–584 bp), and themajority were 581 bp. This analysis enabled us toidentify or partially identify clades in subsequentARISA profiles.

Biogeographical patterns from ARISA fingerprintsWe investigated the biogeographical patterns ofMAST-4 assemblages by comparing the ARISAfingerprints from 107 samples obtained from 40separated sites. For most sites (25 out of 40), therewere several depths sampled (subsurface to 100 or250 m) and a subset of 23 samples derived from atemporal study in the Mediterranean site (BL).Samples were analyzed in triplicate and thenaveraged for further statistical analyses as they werehighly similar (see Materials and methods). Thecorrelation between community and geographicaldistance matrices done with a Mantel test(Figure 5a) was weak and significant (r¼ 0.28,P¼ 0.001), suggesting that geographical distancedid not have a strong relation with sample composi-tion. A partial Mantel test with geographicaldistance conditioned by temperature showed aweaker correlation (r¼ 0.14, P¼ 0.001) giving evenless weight to the relationship between communityand geographical distance, once the effect of themain environmental variable (see below) was

removed. Interestingly, a group of samples fromdistant sites (B15 000 km) were highly similar,whereas samples from the same geographical loca-tion could be both similar or different. A Mantel testcomparing the community and temperature dis-tances (Figure 5b) showed a high and significantcorrelation (r¼ 0.60, P¼ 0.001), highlighting tem-perature as an important driver of communitycomposition. Moreover, a partial Mantel test withtemperature distance conditioned by geographicaldistance did not bring down the correlation(r¼ 0.57, P¼ 0.001). Analysis of community compo-sition by NMDS revealed a clear grouping ofsamples based on temperature (Figure 5c) and notrend for geographical location (SupplementaryFigure S3). To create these groups, we used aseparate dendrogram (not shown) where cold sam-ples (from 2 to 9.4 1C) grouped in a cluster with 82%similarity, and the temperate (from 9.5–16.9 1C) andwarm (from 17–30 1C) groups also appeared, althoughwith some exceptions. The boundary between thetemperate and warm groups was obtained from theclustering of BL samples (see below).

We performed several analyses to identify andquantify the factors driving community changes. AConstrained Correspondence Analysis (Figure 5d;Table 1) confirmed that temperature was the factorexplaining most of the variance (51%), whereassampling depth (Z) explained 18% of the variance.The six deepest samples (200 and 250 m) collectedfrom the Indian Ocean and the Mediterranean Seaare grouped together in the top-left of the plot. Thesevariables were followed by salinity, bottom depth(Zmax, a proxy for coastal vs offshore waters) and

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chlorophyll concentration. The sum of these factorsexplained 91% of the variability. When sampleswithin each temperature group were analyzedseparately, temperature was less important thansalinity and sampling depth. A second analysiswas done after grouping the factors at discretelevels. A PERMANOVA test considering groupedfactors showed that temperature explained 36% ofthe variability in community composition, whereasall factors together explained 55% (Table 1).

We then searched for the OTUs driving thedifferences among cold, temperate and warm sam-ples. The SIMPER test identified five OTUs with adissimilarity contribution between groups 45%(Table 2). OTU 589 had the largest dissimilaritycontribution in all pairwise analyses, especiallywhen including cold samples. This was consistent

with the 99% similarity contribution of OTU 589 tothe cold group (Table 2) and its predominantpresence in cold waters (Figure 6). OTU 527was the most important in warm samples (22%similarity contribution) and contributed 7% to thedissimilarity between all comparisons with warmsamples. OTUs 529 and 531 appeared important intemperate and warm samples, whereas OTU 567was characteristic for temperate samples and absentin cold samples. Using the agreement betweenARISA and clone libraries peak profiles, we pre-dicted that OTU 589 corresponded to a clone of581 bp in size, belonging to clade E, whereas OTUs527, 529 and 531 (clone sizes between 519 and524 bp) could belong to either clades A or C andOTU 567 (clone size 560 bp) belonged to clade B.Therefore, clade E was best adapted to cold water,

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Figure 5 Analysis of the global data set of ARISA fingerprints for MAST-4 diversity. (a) Plot comparing community distances(calculated from the OTU-table) and geographic distances among samples, together with the Mantel test statistic r. (b) Plot comparingcommunity and environmental (temperature) distances, together with the Mantel test statistic r. (c) NMDS diagram displaying eachsample as a function of their community distances. Different symbols represent the temperature grouping. (d) ConstrainedCorrespondence Analysis diagram displaying each sample in function of their community distances and constrained for the threemost important factors: temperature (Temp), salinity (Sal) and sampling depth (Z). Arrows represent the direction and magnitude of theenvironmental factor gradient.

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clades A and/or C were typical of temperate andwarm waters while clade B was most characteristicof temperate waters.

Temporal changes at Blanes BayAt the Blanes Bay (BL) station in the NW Mediter-ranean Coast, we analyzed 23 different ARISAfingerprints covering a monthly seasonal samplingin 2003 and random dates from 2001 to 2006.Samples exhibited a large variability in the compo-sition of MAST-4 and grouped, in an associateddendrogram (not shown), in two clusters differen-tiated by seawater temperature (4 or o17 1C), ashighlighted in the NMDS plot (Figure 7). Thesimilarity of temperate samples was 458% andthe similarity of warm samples was 4 37%. Eachgroup contained samples from different years, high-lighting the importance of temperature in definingcommunity composition along this temporal scale.

The PERMANOVA test for this subset of samples,considering the same groups as above, showed thattemperature was also the factor explaining most ofthe community composition variability (46%), acontribution even higher than when considering allsamples. The SIMPER test revealed that OTU 589(clade E), which was predominant in temperate

waters, had again the largest dissimilarity contribu-tion (30%) between temperate and warm samples.Moreover, this analysis highlighted additional OTUsresponsible for community changes, such as OTU593 that was characteristic of warm samples andcontributed 8% of the dissimilarity between thetemperature groups.

Discussion

Despite the recent interest in microbial biogeogra-phy, the field has suffered from conceptual andmethodological limitations that confuse the emer-ging conclusions (Martiny et al., 2006). The firstproblem is the analytical strategy. Many studies arebased on a handful of isolated strains from separategeographical sites (Ki and Han, 2005; Kooistra et al.,2008), which can skew the conclusions reachedabout biogeography. Here rather than relying onculturing, we employ molecular markers easilyamplified from environmental DNA. Thus thesampled diversity is more representative of thenatural assemblage (by avoiding culturing bias). Inaddition, many more individuals (sequences) can beretrieved at once, allowing for more robust popula-tion comparisons.

Table 1 Separate statistical analysis (CCA, CCA per groups and PERMANOVA) to estimate the contribution (from R2 values) of fiveenvironmental factors in the ARISA fingerprinting variance

Variable CCA CCA (separate temperature groups) PERMANOVA

COLD TEMPERATE WARM

R2 Sign R2 Sign R2 Sign R2 Sign Grouped factors R2 Sign

Temperature (1C) 0.51 *** 0.01 0.12 ** 0.21 *** 2–9.5, 9.5–17, 17–30 0.36 ***Z (m) 0.18 *** 0.02 0.41 *** 0.13 *** 0–25, 25–75, 75–180, 180–250 0.08 ***Salinity 0.11 *** 0.06 ** 0.10 * 0.25 *** 32, 33, 34, 35, 36, 37, 38, 39 0.07 ***Zmax (m) 0.06 *** 0.01 0.06 0.08 ** 0–200, 200–1000, 1000–6000 0.02 *Chlorophyll (mg l�1) 0.05 * 0.01 0.04 0.15 *** 0–0.5, 0.5–1.5, 1.5–4 0.02 *Total 0.91 0.06 0.63 0.82 0.55

Abbreviations: ARISA, automated ribosomal intergenic spacer analysis; CCA, Constrained Correspondence Analysis; PERMANOVA,permutational multivariate analysis of variance; Z, sampling depth; Zmax, bottom depth.Significance codes: ***o0.001; **0.001–0.01; *0.01–0.05.Bold numbers represent the highest R2 of the particular analysis.

Table 2 Results of the SIMPER analysis to identify the contribution of the five most important OTUs to the dissimilarity and similarity ofthe groups defined by temperature

OTU Cladeassignment

Dissimilarity contribution % Similarity contribution %

TEMPERATE and COLD WARM and COLD TEMPERATE and WARM COLD TEMPERATE WARM

589 E 47.53 47.8 21.38 98.75 43.32 4.59527 A or C 3.74 7.09 7.37 0.04 6.17 21.89529 A or C 7.4 4.77 6.42 0.17 12.46 12.22531 A or C 5.25 3.75 3.98 0.08 8.47 13.06567 B 4.75 3.11 5.83 0 4.8 3.72

Abbreviation: OTU, operational taxonomic unit.Contributions 45% are represented in bold.

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Second, it is very important to choose a goodtaxonomic marker, because the view that no biogeo-graphical patterns exist in microorganisms (Finlayet al., 2006) could be caused by an ambiguous orincorrect identification of protist species (Lomolinoet al., 2006). Indeed many protist species appearto be widely distributed when identified via mor-phology, but these ‘morphological species’ mostlikely include cryptic species (Dolan, 2006). In our

study, we used the highly variable ITS1 region as adiversity marker, which provides an enhancedtaxonomic resolution as compared with the 18SrDNA gene (Brown and Fuhrman, 2005). ITS1 hasbeen used to assess the inter- and intraspecificdiversity of Pseudo-nitzschia populations (Orsiniet al., 2004) and for elucidating Pseudo-nitzschiacommunity structure using ARISA fingerprints(Hubbard et al., 2008). Our analysis using both theITS1 sequences and ARISA fingerprints confirmedthe ITS region as a good marker for studyingmicrobial biogeography.

Third, it is important to clearly determine thegenetic diversity of the studied taxa. We focus hereon the uncultured protist MAST-4, which exhibits alimited evolutionary diversification (Rodrıguez-Martınez et al., 2012). By designing specific primersamplifying the ITS1 region of MAST-4, we increasedthe currently available sequences for this group by10-fold. Even with this order of magnitude increasein sequence sampling, only two additional putativespecies were revealed within this group (based onthe conserved regions of ITS secondary structures(Coleman, 2009)). The evidence for not more than12 putative species confirms the low diversity ofMAST-4. One of the clades, clade A, still appears tobe a single species, with almost all clones having thesame helix III motif in the ITS1 secondary structure.This was consistent with our previous studyshowing low-sequence divergence, incongruent treetopologies and no polymorphisms in the criticalregions of the ITS1 and ITS2 secondary structuresfor sequences within this clade (Rodrıguez-Martınezet al., 2012). With the same criteria, subclade E1 also

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Figure 7 NMDS analysis of ARISA fingerprints in the temporalstudy at Blanes Bay. Samples are displayed with symbolsfor temperature as in Figure 5 and with the date of sampling(month–year). Points enclosed by dashed and solid lines cluster at37% and 58% similarity, respectively, in a separate dendrogram(not shown).

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forms a single species. The structuring of MAST-4 ina few genetically distinct species is similar to thatfound in other marine protists, such as the cosmo-politan diatom Skeletonema costatum (Kooistraet al., 2008).

Finally, a limitation of many studies is theinability to disentangle the relative contribution ofcontemporary environmental conditions and histor-ical contingencies in shaping the spatial variationsof microbial communities (Martiny et al., 2006). Ourstudy addresses this question by analyzing spatialchanges together with the associated environmentalfactors. In addition, as a single sampling event maynot cover the complete biodiversity of a givenlocation (Nolte et al., 2010), we also evaluatedtemporal changes over an annual cycle.

The analysis of MAST-4 assemblages using ARISAfingerprints showed that temperature was the mainfactor influencing the distribution patterns, as hasbeen observed in other marine microbes likeProchlorococcus (Martiny et al., 2009) and bacterialand archaeal assemblages (Winter et al., 2008).Distant samples sharing the same temperature couldhave similar MAST-4 composition, whereas sampleswith different temperatures contained very differentMAST-4 assemblages. This global pattern was alsoobservable in a temporal study at the same geogra-phical site (Blanes Bay). In this location with athermal seasonal cycle from 12 to 24 1C, samplesagain grouped by temperature, confirming it as animportant structuring factor. Moreover, the Fstanalysis revealed very little differentiation betweenpopulations geographically distant but with a similartemperature. It is worth mentioning that a similarpattern could have been obtained due to anotherdriving factor not measured here but strongly corre-lated with temperature (Martiny et al., 2009). Thus, thespatial distribution of MAST-4 seemed to be mainlycontrolled by contemporary environmental factorswith a null or low degree of provincialism. Otherabiotic factors that are known to be important indefining microbial community composition, such assalinity (Logares et al., 2009) and sampling depth(Winter et al., 2008), were important within each of thedefined temperature group.

In a more detailed analysis of clades, those whichlikely constitute a single species (A and E1)displayed well-mixed populations in the MJ net-works and exhibited low differentiation (Fst values)among populations, particularly in samples withsimilar temperature regardless of whether they camefrom distant places. By contrast, clades B and C,each probably composed of more than one species,exhibited some spatial structuring, with subcladesappearing in only one library. This intriguingfeature, perhaps indicating some dispersal restric-tion for clades B and C, could also be due toundersampling and deserves more attention infuture surveys. If confirmed by further results, thedispersal limitation of clades B and C would becomparable with that found with the cosmopolitan

marine planktonic diatom Pseudo-nitzschia pun-gens (Casteleyn et al., 2010).

Our data also highlighted specific MAST-4lineages adapted to different temperature regimes.Thus, clade B, and either or possibly both clades Aor C, were characteristic of temperate and warmwaters, whereas clade E1 (represented by OTU 589)was adapted to inhabit cold waters. The geneticstructure of MAST-4 with different lineages, someubiquitous in the oceans and with particular eco-logical properties, resembles that of other marinemicroorganisms. Thus, different Ostreococcus(Rodrıguez et al., 2005) and Synechococcus lineages(Ahlgren and Rocap, 2006) are adapted to differentlight levels, and a Micromonas pusilla clade adaptedto cold temperature has been reported (Lovejoyet al., 2006). It has been proposed that this ecotypicdifferentiation can partly explain the success ofthese microorganisms, allowing them to exploit thewhole spectrum of habitat variability.

The uncultured free-living protist MAST-4 iswidely abundant and very small and thus possessesthe properties for a worldwide distribution (Finlay,2002). Moreover, it lives in a marine habitat, wherewind, waves and currents produce mixing eventsthat facilitate the dispersion. However, we did notobserve all MAST-4 clades at all locations but sawbiogeographical patterns, stressing the importanceof the end of the tenet, ‘but the environment selects’.It is reasonable to hypothesize that MAST-4 has ahuge dispersal capacity and can arrive everywherewithin a marine habitat. For instance, there is onerecord of a MAST-4 sequence in the Arctic Ocean(Lovejoy and Potvin, 2011), showing the potential toarrive to such high latitudes probably dragged byPacific coastal currents. But then, depending on theenvironmental conditions, different organisms willthrive, resulting in different community patterns.Although microorganisms could spread across allsuitable habitats, local adaptations eventually maypromote speciation (Medlin, 2007). In conclusion,we did not see strong marine geographical barriersfor the dispersal of MAST-4, instead temperatureappeared as the main driver for community compo-sition. For MAST-4, we suggest that the environ-ment makes a taxonomic selection among cladeswith distinct physiological adaptations.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgements

Funding has been provided by FPI fellowships from theSpanish Ministry of Education and Science to RRM andGS and by projects FLAME (CGL2010-16304, MICINN,Spain) to RM, and ATOL (NSF, 0629521) to GR. We thankR.E. Collins for DAx software help, E. Sane for Primersoftware help, R. Logares for statistical advices andC. Williams for molecular help.

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