ORIGINAL ARTICLE

Reproducibility of Vibrionaceae population structurein coastal bacterioplankton

Gitta Szabo1,5, Sarah P Preheim1, Kathryn M Kauffman1, Lawrence A David2,Jesse Shapiro3, Eric J Alm1,3,4 and Martin F Polz1

1Department of Civil and Environmental Engineering, Massachusetts Institute of Technology,Cambridge, MA, USA; 2Society of Fellows, Harvard University, Cambridge, MA, USA;3Broad Institute, Cambridge, MA, USA and 4Department of Biological Engineering, MassachusettsInstitute of Technology, Cambridge, MA, USA

How reproducibly microbial populations assemble in the wild remains poorly understood. Here, weassess evidence for ecological specialization and predictability of fine-scale population structureand habitat association in coastal ocean Vibrionaceae across years. We compare Vibrionaceaelifestyles in the bacterioplankton (combinations of free-living, particle, or zooplankton associations)measured using the same sampling scheme in 2006 and 2009 to assess whether the same groupsshow the same environmental association year after year. This reveals complex dynamics withpopulations falling primarily into two categories: (i) nearly equally represented in each of the twosamplings and (ii) highly skewed, often to an extent that they appear exclusive to one or the othersampling times. Importantly, populations recovered at the same abundance in both samplingsoccupied highly similar habitats suggesting predictable and robust environmental association whileskewed abundances of some populations may be triggered by shifts in ecological conditions. Thelatter is supported by difference in the composition of large eukaryotic plankton between years, withsamples in 2006 being dominated by copepods, and those in 2009 by diatoms. Overall, thecomparison supports highly predictable population-habitat linkage but highlights the fact thatcomplex, and often unmeasured, environmental dynamics in habitat occurrence may have strongeffects on population dynamics.The ISME Journal advance online publication, 22 November 2012; doi:10.1038/ismej.2012.134Subject Category: microbial population and community ecologyKeywords: population structure and dynamics; community assembly; niche

Introduction

Surveys of microbial diversity have consistentlyhighlighted the vast number of genetically differentmicrobes in environmental samples. In ocean water,for example, thousands of distinct ribotypes (cellscharacterized by different rRNA sequences) can co-occur, a considerable fraction of which at very lowfrequency (for example, Acinas et al., 2004; Soginet al., 2006; and Rusch et al., 2007). Moreover,metagenomic comparison has revealed similarmetabolic functions in phylogenetically distantmicrobes (Venter et al., 2004; Howard et al., 2008;Mou et al., 2008) suggesting that these might be, to ahigh degree, functionally interchangeable. Taken

together, these results pose serious challenges forinterpretation of community structure and dynamics(Polz et al., 2006). A key question is to what extentmicrobes are optimized for, and therefore linked to,specific ecological opportunities and to what extentthey tend to act as ecological generalists. If microbesdisplay a high degree of ecological specialization,then community assembly may be largely determi-nistic, and, importantly, structure–function relation-ships would become predictable at the level ofbacterial taxonomy. This is because taxa, recogniz-able by some type of marker genes, would representpopulations that have been competitively optimizedto occupy a defined niche. Alternatively, commu-nities may assemble from pools of generalists,strongly influenced by stochastic processes (disper-sal, founder effects, bottlenecks) and allowing forfrequent invasions. In this case, predictabilitywould be much more challenging and only possibleat the level of genetic diversity representing meta-bolic function.

Although numerous studies have establisheddifferential distribution of microbes among samples,what types of community assembly mechanisms

Correspondence: Professor MF Polz, Department of Civil andEnvironmental Engineering, Massachusetts Institute of Techno-logy, 77 Massachusetts Avenue, 48-417, Cambridge, MA 02140,USA.E-mail: mpolz@mit.edu5Current address: Department of Microbial Ecology, University ofVienna, Vienna, Austria.Received 29 March 2012; revised 3 September 2012; accepted3 September 2012

The ISME Journal (2012), 1–11& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12

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cause observed differences in organismal composi-tion remains poorly understood (Hughes-Martinyet al., 2006; Green et al., 2008). In fact, several recentstudies have reached different conclusions. Forocean water and alpine soils, repeated samplinghas revealed temporally and spatially recurrentpatterns (that is, autocorrelations) of microbial taxathat were statistically significant and predictablefrom biotic and abiotic factors (Fuhrman et al., 2006;King et al., 2010; Gilbert et al., 2012). Such findingscan be interpreted as supporting low ecologicalredundancy and high niche fidelity. Similar conclu-sions were reached by following genotypic clustersin the development of acid mine drainage biofilms(Denef et al., 2010). However, experimental additionof model organic compounds to marine samplestriggered a taxonomically widespread response,which was taken as evidence for dominance ofgeneralist taxa (Mou et al., 2008). Consistent withthis observation, some modeling-based studies havede-emphasized niche-based processes (Sloan et al.,2006; Ofiteru et al., 2010). Instead, they explainedcommunity assembly by (near) neutral processes,which explain the composition of communities bystochastic birth-death and immigration processes.

One of the central questions in this debate oncommunity assembly mechanisms is how conservedhabitat specialization is for closely related geno-types. To address this question, it is necessary todistinguish microbial habitats within samples andassess the phylogenetic bounds of associated ecolo-gically differentiated populations, both of whichremain difficult. Microbial diversity is typicallyassessed using the conserved rRNA markergenes, for which universal cutoffs are chosen todelineate taxa, and samples are collected at scales,which comprise many different microbial habitats.As a consequence, ecological associations are mea-sured as correlations of operational taxonomic unitswith macroecological features, such as nutrientconcentrations and temperature. However, compara-tive genomics has revealed large gene contentdiversity even within identical ribotypes (Tettelinet al., 2008), questioning whether fine-scaleecological associations beyond combinations ofmacroecological features can evolve. This is becausehorizontal gene transfer can spread ecologicallyadaptive genes and alleles among otherwise unrelatedgenomes (Doolittle and Papke, 2006; Retchless andLawrence, 2007), and recent analysis has shown thatthis process can happen on ecological time scales(Boucher et al., 2011; Smillie et al., 2011). Hence, wereason that this debate on community assemblymechanisms will benefit from a system that, akin tomacroecology, allows assessment of whether ecologi-cally coherent populations exist that reproduciblyassociate with physically defined habitats.

Over the past years, we have developed Vibriona-ceae bacteria in the coastal ocean as a model forpopulation biology and ecology. Our approach hasbeen to (i) sample hundreds of individuals from

fine-scale environmental fractions, (ii) establishhigh-resolution phylogenetic relationships by multi-locus sequence analysis and (iii) test for ecologicalassociation using a mathematical model (AdaptML)(Hunt et al., 2008a; Preheim et al., 2011a, b).Populations are identified as groups of relatedstrains sharing a characteristic distribution amongenvironmental samples; these distributions arereferred to as ‘projected habitats’ (or ‘habitats’ forshort) based on the rationale that they allowdifferentiation of the true habitats/niches if theseare differentially apportioned among samples (Huntet al., 2008a). To date, we have shown that bacteriaof the family Vibrionaceae partition resources in thecoastal ocean by differential distribution among thefree-living and associated (with suspended organicparticles and zooplankton) fractions of bacterio-plankton (Hunt et al., 2008a; Preheim et al., 2011a,b). Sampling during different seasons has revealedstrong temporal differentiation with the same ‘habi-tat’ type often occupied by season-specific popula-tions (Hunt et al., 2008a; Preheim et al., 2011a).Moreover, two studies carried out 1 year apart buttargeting different types of samples from coastalwater have suggested population re-occurrence(Preheim et al., 2011b); however, the evidenceremains indirect since no sampling scheme hasbeen reproduced in a manner to ascertain to whatextent populations occupy the same ‘habitats’.

Here, we ask to what extent fine-scale populationstructure and habitat association of Vibrionaceaein coastal ocean water is reproduced in the sameseason (and presumably overall similar ecologicalconditions) in different years, as evidence for eco-logical specialization and predictability. In 2009, werepeated a sampling scheme first carried out in 2006to differentiate Vibrionaceae lifestyles in the bacter-ioplankton (combinations of free-living, particle, orzooplankton associations) (Hunt et al., 2008a).Overall, the comparison supports highly predictablepopulation-habitat linkage but highlights that oftenunmeasured, fine-scale temporal and spatialdynamics, such as shifts in eukaryotic plankton,may have strong effects on population dynamics.

Materials and methods

Environmental sampling and strain isolationTo compare the population structure of Vibriona-ceae, we repeated in the fall of 2009 (October 2 and6) a comprehensive survey carried out 3 years earlier(6 September 2006) (Hunt et al., 2008a). In bothyears, water samples were collected at high tidefrom the mouth of the Plum Island Estuary, Ipswich,MA, USA, and thus represent coastal ocean ratherthan estuarine water. Water temperature was 16 1Cin 2006 and 13.5 1C on both days in 2009.

To differentiate free-living and particle/organism-associated bacterial lifestyles, we isolatedstrains from four size fractions of sequentially

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filtered seawater. The largest size fraction (X63 mm)captures bacterial cells primarily associated withlarger phytoplankton and zooplankton; however,this size fraction can also contain some detritalparticles. The fraction between 63 and 5mm isenriched in different types of organic particles andsmaller phytoplankton and protozoa (for simplicityhereafter ‘large particle fraction’ since bacterialattachment to these organism is expected to beminor compared with organic particles), while thefraction between 5 and 1 mm may contain largebacterial cells or those attached to small particles(‘small particle fraction’); however, this fraction alsoprovides a firm buffer between obviously attachedand free-living cells, which were obtained from the1- to 0.2-mm fraction.

For the largest size fraction, 100 l samples werepassed through a 63-mm mesh, in two and fourreplicates in 2006 and 2009, respectively. The meshwas rinsed with sterile seawater and the contentswashed into 50 ml conical tubes. All other sizefractions were derived from 63mm pre-filtered water,collected in four replicate 4 l Nalgene bottles. Thesewere transported in a cooler to the laboratory wherefurther processing commenced within an hour ofcollection.

In the laboratory, all X63mm samples werehomogenized using a tissue grinder (VWR Scienti-fic, Radnor, PA, USA), vortexed for 20 min at lowspeed. The homogenates were diluted 10-fold to10 000-fold and cells concentrated onto 0.2 mm poresize filters. The o63-mm water samples weresequentially filtered through 5, 1 and 0.2 mm poresize filters, where the 63–5 and 5–1 mm size fractionswere collected using gravity filtration to avoidbreakdown of fragile particles. For these, filtrationwas repeated with sterile seawater to further removecells unattached to particles. Subsequently, allfilters were placed into 50 ml conical tubes contain-ing 45 ml sterile seawater and vortexed for 20 minat low speed to break up particles and resuspendbacterial cells. Supernatants were used for isolation ofVibrionaceae by concentrating serial dilutions onto0.2mm Supor-200 filters (Pall, Prot Washington, NY,USA) using gentle vacuum pressure. These filterswere then placed onto Agar plates containing Vibrioselective Thiosulfate Citrate Bile Salts Sucrose media(BD Difco, Franklin Lakes, NJ, USA) with 2% NaCl(marine TCBS) and incubated at room temperature for1–3 days. Single colonies were picked and re-streakedthree times alternating Tryptic Soy Broth (TSB) (BDBacto, Franklin Lakes, NJ, USA) with 2% NaCl andmarine TCBS media to obtain pure strains.

Isolate characterization by gene sequencingFor preparation of DNA for gene sequencing,isolates were grown in Marine Broth 2216 (BDDifco) for 2–3 days at room temperature withshaking. DNA was released from liquid cultureswith Lyse-N-Go (Thermo Fisher Scientific,

Rockford, IL, USA). A 600-bp fragment of thehsp60 genes was amplified using H279 and H280primers (Goh et al., 1996) using the following PCRconditions: 94 1C for 3 min; 30 cycles of 94 1C for1 min, 37 1C for 1 min, 72 1C for 1 min; 72 1C for5 min. All sequencing was by the Sanger method atthe Josephine Bay Paul Center of the MarineBiological Laboratory in Woods Hole, MA, USA.Raw sequences were trimmed and verified usingSequencher (Gene Codes Corp., Ann Arbor, MI,USA). Sequences (541 bp in length) were alignedwith ClustalW (Jeanmougin et al., 1998). Thealignment was manually curated using MacClade(Sinauer Associates, Sunderland, MA, USA).

Hsp60 sequences were assigned to populationsidentified in 2006 based on multilocus sequenceanalysis as described in Preheim et al. (2011b) andSupplementary Table 1. Taxonomic placement ofunassigned populations was done by phylogeneticanalysis of 16S rRNA gene sequences. Genes werePCR amplified from representative strains using the27f and 1492r primers (Lane, 1991). Sequencing wasperformed with the same primer pair at the Bay PaulCenter at the Marine Biological Laboratory. A sequencesimilarity search was performed against type strainsequences in the Ribosomal Database Project (RDP)employing the Seqmatch tool (Cole et al., 2009).All sequences were aligned with CLUSTAL W(Jeanmougin et al., 1998) and non-overlappingsequences concatenated (final length B1200 bp). Thealignment was manually refined and served as inputfor calculation of a maximum likelihood tree usingMEGA 5 (Tamura et al., 2011). Percentages of sequencesimilarity shared with the nearest relatives on the treewere calculated by the EMBOSS Needle tool (availableat http://www.ebi.ac.uk/Tools/) and are given inSupplementary Table 1.

Phylogenetic analysisA maximum likelihood gene tree for all hsp60 genesequences was constructed using PhyML v.2.4.5(Guindon and Gascuel, 2003) with the follow-ing parameter settings: a likelihood ratio testsupported the use of the GTR substitution model(Preheim et al., 2011a); PhyML estimated thetransition/transversion ratio and the proportion ofinvariable nucleotide sites; four gamma categorieswere used; a BIONJ tree served as starting tree; treetopology, branch lengths and rate parameters wereoptimized by PhyML. Strains with identical hsp60loci and isolated from the same sample wereincluded only once in the phylogeny to preventAdaptML (see below) from identifying clonal expan-sions as likely habitats.

Population prediction and identification of ‘projectedhabitat’We applied an empirical model, AdaptML (Huntet al., 2008a), to estimate population structure of

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Vibrionaceae. AdaptML is a maximum likelihoodmethod that employs a hidden Markov model tolearn ‘projected habitats’ (distribution patterns amongenvironmental categories) and ecologically cohesive‘populations’ (groups of related strains sharing thesame projected habitat). In this case, the ecologicaldata were the size fractions from which the strainswere isolated and phylogenetic information wasprovided by the hsp60 gene tree with E. coli as anoutgroup. A squared distance threshold of 0.025was used to merge habitats with similar environ-mental distribution patterns during AdaptML’s itera-tive model fitting step. Although AdaptML predictspopulation membership for each isolate, we restrictedour analysis to populations that (i) passed a post hocempirical significance threshold (P-valueo0.01)(Hunt et al., 2008a) and (ii) consisted of at least 20isolates The analysis was re-run 100 times with thesame parameters to verify habitat predictions wererobust. Model predictions and ecological data forstrains were visualized with iTOL (Letunic and Bork,2007; Figure 1a).

For visualization of the distribution among sizefractions of all significant populations in Figure 1b,one representative strain was selected from eachpopulation to create an ultrametric tree using theAnalysis of Phylogenetics and Evolution (Paradiset al., 2004) package in R programming language(http://www.r-project.org/). Because slightly differ-ent numbers of strains were obtained from thedifferent size fractions, the distributions werenormalized to the average number of strains persize fraction (349) (Figure 1b).

We did not provide sampling year information toAdaptML, but we manually curated the phyloge-netic results where this temporal information wasclearly relevant. Two deep-branching clusters weregrouped into a single population by AdaptML, butwere treated separately (populations #1 and #2)because of their distinct phylogeny and the fact that#1 was overlapping between years, whereas #2 wasobserved in 2006 only. We also note that, previously,populations #6, #9, #11 and #14 were listed asmembers of V. splendidus (Hunt et al., 2008a);

Figure 1 Phylogeny and ecological associations of Vibrionaceae populations inferred by AdaptML. (a) Maximum likelihood tree basedon partial sequences of hsp60 genes. Inner and outer rings show the size fraction and year of isolation, respectively. Populations, whichcontained at least 10 strains in one of the years and passed a post hoc empirical significance threshold (P-valueo0.01), are shaded whereochre¼nearly equal among years and cross-hatched black and blue¼ skewed toward 2006 and 2009, respectively. Projected habitats aredisplayed by colored circles whose colors reflect trends in distributions among the different size fractions. Taxonomic assignment tonumbered populations are as follows: #1, Vibrio aestuarianus; #2, V. ordalii; #3, Enterovibrio calviensis; #4, Enterovibrio norvegicus; #5,V. breoganii; #6, V. crassostreae; #7, Vibrio sp. F10; #8, Vibrio sp. F12; #9, V. splendidus; #10, V. kanaloae; #11 and #12, V. tasmaniensis;#13, V. gigantis; #14, V. cyclotrophicus. (b) Ultrametric tree summarizing the distribution of each population among size fractions.(c) Characteristic distribution patterns over size fractions (‘projected habitats’) inferred by AdaptML.

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however, since then taxonomic revision of popula-tions predicted in our studies has shown that onlypopulation #9 is congruent with V. splendidus(though we note that there is disagreement amongtaxonomists; Le Roux et al., 2009; Preheim et al.,2011b; Supplementary Table 1).

Similarity of ecological associationsIn addition to AdaptML analysis, we tested whetherbacterial populations reproducibly associated withthe same size fractions using Fisher’s exact test. Weevaluated the null hypothesis that strains associatedwith congruent populations from 2006 and 2009possess the same distribution patterns amongenvironmental categories in the 2 years. Onlypopulations that included at least 10 strains in eachsampling were compared, and their distributionswere normalized to the average size of sharedpopulations per size fraction in each year (56 in2006 and 94.5 in 2009). A 2� 4 (years�habitats)contingency table was created for each populationbased on the normalized data. Fisher’s exact test wascomputed in the R programming package (http://www.r-project.org/). The criterion for significancewas set to Po0.05.

Population-specific genes for V. cyclotrophicusIn the previous analysis of population structure ofplanktonic Vibrionaceae, two nascent populationsof V. cyclotrophicus were identified based on theirdistinct ecological associations (group II.A and II.Bof population #15 in Hunt et al., 2008a). A set of fiveloci was shown to be present in sub-population II.Abut absent in II.B: MSHA biogenesis proteins mshNand mshF (flex1 and flex2); a probable maltoseO-acetyltransferase (flex3); a probable glycosyltrans-ferase (flex4); and a putative intercellular adhesionprotein (flex5) (Shapiro et al., 2012). Representativesof the two V. cyclotrophicus sub-populations recov-ered in this study were tested for the presenceand absence of these five genes, using diagnosticPCR tests with specific primers according toShapiro et al. (2012) (Table 1). Phusion High-Fidelity DNA Polymerase (New England Biolobs,Ipswich, MA, USA) was applied with the followingPCR conditions: 95 1C for 3 min; 30 cycles of 95 1Cfor 30 s, 50 1C for 30 s, 72 1C for 15 s.

Analysis of eukaryotic planktonThe composition of larger eukaryote plankton wasanalyzed by PCR amplification, cloning and sequen-cing of the mitochondrial cytochrome c oxidasesubunit I gene (cox1). DNA was extracted usingPuregene DNA Isolation Kit (Qiagen, Valencia, CA,USA) from 0.2 mm filters, which were obtained whenconcentrating the X63 mm size seawater fractions of2006 and 2009. The filters were kept frozen until thetime of DNA extraction. Concentrations of DNA

were measured using a NanoDrop spectrophot-ometer (Nanodrop Technologies, Wilmington, DE,USA). Equal amounts of template DNA from eachreplicate sample in each year were pooled. AnB710-bp fragment of cox1 gene was amplified withuniversal eukaryote primers LCO1490 and HCO2198(Folmer et al., 1994) using Phusion High-FidelityDNA Polymerase (New England Biolabs). PCR wasperformed with the following parameters: 95 1C for3 min, and 30 cycles of 95 1C for 30 s, 37 1C for 30 s,72 1C for 15 s. Single DNA bands were excised from1.5% agarose gels and were extracted with NucleoS-pin Extract II Kit (Macherey-Nagel, Bethlehem, PA,USA). Purified PCR products were ligated into thepJET1.2/blunt cloning vector using CloneJET PCRCloning Kit (Thermo Scientific, Rockford, IL, USA)according to manufacturer’s protocol. Ligation pro-ducts were transformed into E. coli competent cells,which were then plated on LB-ampicillin mediaand were grown overnight at 37 1C. Single colonieswere randomly picked into LB-ampicillin liquidbroth and were grown with shaking overnight.Liquid cultures were heat shocked (95 1C for10 min) to release template DNA. The cox1 geneinserts were reamplified employing vector-specificprimers. Sequencing was performed with LCO1490and HCO2198 primers at the Bay Paul Center at theMarine Biological Laboratory. Roughly 100 genesequences were analyzed from each year. Taxonomicidentification was according to data in the NCBI nrdatabase (www.ncbi.nlm.nih.gov) using the BLASTalgorithm (Altschul et al., 1990). A maximum likeli-hood gene tree was constructed as described for thephylogenetic analysis of Vibrionaceae strains.

Results and discussion

The combined analysis of 1396 Vibrionaceae iso-lates from samples obtained in the fall of 2006 and2009 resulted in 14 significant populations (Figures1a and b), whose environmental associationscan be summarized as four distinct characteristicdistributions (‘projected habitats’ and thereafter‘habitat’ for simplicity) (Figure 1c). Importantly,

Table 1 Primers used to screen for presence of genes in theflexible genome of V. cyclotrophicus (sub)populations II.Aand II.B

Name Direction Annealingtemperature (1C)

Sequence (50-30)

Flex1 F 54.6 GTCAAAACACGCTCTCCAR 54.6 TCGATCTCGCACAAAACT

Flex2 F 48.2 TTTTGTTATTTGGAGTGTGR 48.2 CTTTTTCGAGCAATATATC

Flex3 F 51.2 GCATGATCACCTCCACGAR 51.2 ACCTACCCCACAAATACT

Flex4 F 52.2 ACACCTTACATCGGATCAR 52.2 GTACAACCACACTTTTCCT

Flex5 F 55.0 CGCTTCGCACTCTTTAACAR 55.0 CCACGCAGCATATCCAAT

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most populations are represented in both 2006 and2009. The five populations occupying habitat HA arepredicted to be predominantly free-living in thewater column: Vibrio aesturianius (#1), V. ordalii(#2), Enterovibrio calviensis-like (#3), V. tasmanien-sis (#12) and V. gigantis (#13) are all highly enrichedin the smallest size fraction. All other ‘habitats’show a large portion of strains associated withdifferent size classes of particles (Figure 1c). HabitatHB appears nearly equally distributed in all sizefractions except the largest (X63mm), zooplankton(and larger phytoplankton) enriched fraction. Thethree populations (E. norvegicus, #4; V. splendidus,#9; V. tasmaniensis, #11) occupying habitat HB maytherefore be free-living or associated with organicparticles and/or phytoplankton that pass through63 mm filters. Whether these populations are inter-preted as predominantly free-living or particle-attached depends on whether the bacterial cellsisolated from the 1–5 mm fraction are activelygrowing (and therefore large) or attached to verysmall particles. On the other hand, lifestyles thatunambiguously include attached lifestyles are dis-played by habitats HC and HD, which are predomi-nantly associated with large organic particles orzooplankton and phytoplankton. For example, forpopulation Vibrio sp. F-10 (#7), which has pre-viously been shown (by isolation from handpickedspecimens) to be predominantly associated withliving zooplankton (Preheim et al., 2011a), theanalysis confirms strong bias toward the largestfraction. Although populations summarized bythese two habitats are clearly attached, the observedrelative frequencies among size fractions differsubstantially among populations (Figure 1b). Thisindicates that these populations colonize differenttypes of organisms and particles (with differentdistributions among the size fractions). Overall, thesimilarity in predicted habitats for the 2006 and2009 samples give confidence in similar ecology ofthese Vibrionaceae populations.

The 14 populations fall mainly into two cate-gories: relative abundance nearly equal between2006 and 2009 samples, or skewed toward either2006 or 2009. Five populations were in the firstcategory while in the second, two populations (V.breoganii, #5 and V. cyclotrophicus, #14) hadmoderately differing (3.1- to 6.4-fold) but highabundances in both samplings and the remainingseven were nearly absent from either one of the

samplings (that is, o10 isolates) (Figures 1a and 2).By this combined analysis of the data, 11 and 10populations were sufficiently abundant to be con-sidered represented in 2006 and 2009 samples,respectively; however, the overall number of pre-dicted populations is an underestimate of the actualnumber because of our restriction to populationswith 410 members in at least one of the years.

The most striking feature of the populationsappearing at similar abundance in both years is thattheir environmental associations also appear highlysimilar. Inspection of the relative habitat frequencydistribution of isolates of V. aesturianius (#1), V.crassostreae (#6), Vibrio sp. F-10 (#7), V. splendidus(#9) and V. tasmaniensis (#11) suggests matchingassociation with the different size fractions in bothyears (Figure 2). This visual impression is confirmedby a Fisher’s exact test with the null hypothesis thatthe relative proportion of populations among sizefractions is independent of sample year. This nullhypothesis cannot be rejected for all populationsshared at similar abundances and is hence consis-tent with the visual impression of indistinguishabledistributions (Table 2). Although V. breoganii (#5),one of the two populations with moderately skewedabundances showed significantly different distribu-tion among size fractions in the two years (Table 2),its ecology may nonetheless be similar. A moderateassociation with the 1–5 mm fraction was not seen in2006, but in both years the plankton-enriched andlarge particle fractions were dominant, and theparticle-associated fraction reaches nearly equalrepresentation if the 1–5 mm fraction is interpretedas cells attached to small particles (Figure 2).Regardless, V. breoganii pursued a predominantlyattached lifestyle in both years so that overall, six ofthe seven shared populations show robust andpredictable ‘habitat’ associations.

V. cyclotrophicus (#14) was the only populationpresent in both years with qualitatively differentdistribution among the size fractions between years(Figures 1 and 2). In 2006, it occupied primarily thelargest size fraction, indicating a possible associa-tion with larger organisms such as zooplankton(Hunt et al., 2008a). In contrast, the populationrecovered in 2009 behaved like a generalist, occupy-ing all size fractions with near equal frequency(Figure 2). Because 46-fold more strains wererecovered in 2009, V. cyclotrophicus may haveresponded to a shift in ecological conditions.

Figure 2 Distributions patterns among size fractions of populations recovered in both 2006 and 2009. Population numbers and colorlegend as in Figure 1.

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To shed more light on these differences inV. cyclotrophicus occurrence, we determinedwhether there was a concurrent shift in the large,eukaryotic plankton community between years. Weamplified, sequenced and taxonomically binned themitochondrial marker gene cox1 and compared therelative frequency of different sequence types in thelargest size fraction. This showed that the 2006sequences were exclusively composed of the cala-noid copepod Acartia tonsa while in 2009 only fewsuch sequences were recovered (Figure 3). Instead,the samples were dominated by a centric diatomrelated at B90% nucleotide identity to the large(80–130 mm) species Ditylum brightwelli. Hence, thehabitat shift and population expansion of V. cyclo-trophicus coincided with a shift in relative compo-sition of eukaryotic plankton.

Based on these observations, we hypothesize thatV. cyclotrophicus might undergo a life cycle ofattachment to larger organisms during less favorableconditions and then detaching to engage in activegrowth in the water column in response to increasedcarbon availability during algal (for example, dia-tom) blooms. In support, we have recently observedthat V. cyclotrophicus isolates are unusual amongVibrio populations in that they are chemotactictoward diatom exudates (Chien, Ahmed, Stockerand Polz, unpublished). Unicellular algae canexcrete large amounts of photosynthate (Bertilssonand Jones, 2003) creating a microzone of enrichedcarbon substrates around the cell. Chemotaxistoward this ‘phycosphere’ (Bell and Mitchell,1972) may enable bacteria to take advantage ofnutrient enrichment around algal cells (Stockeret al., 2008). Recent analysis of monthly samplesover a 6-year observation period in the Englishchannel have revealed a very large bloom event of anunidentified Vibrio sp. following a diatom bloom(Gilbert et al., 2012; Larsen et al., 2012). Moreover, V.cholerae, which is believed to be primarily attachedto zooplankton (Huq et al., 1990), was also shown torespond to algal blooms by active growth in the free-living state (Worden et al., 2006). Additionally, V.cyclotrophicus may use algal exudates as a cue to

hone in on diatoms for attachment, which might bemediated by chitin contained in the diatom cell wall(Durkin et al., 2009). Vibrios, in general, possessthe ability to metabolize chitin (Hunt et al., 2008b),and V. cyclotrophicus, in particular, encodes in itsflexible genome an MSHA pilus (Shapiro et al.,2012), previously implicated in attachment tochitinous surfaces in V. cholerae (Watnick et al.,1999). Although this pilus might also suggest anassociation with zooplankton (such as copepods),we note that members of V. cyclotrophicus werenearly absent on hand-picked copepod specimens ina recent study (Preheim et al., 2011b). It willtherefore be interesting to explore whether thesebacteria discriminate among different types ofzooplankton and phytoplankton for attachment.

A further line of evidence for high predictabilityof population occurrence is that two very recentlydiverged sister populations of V. cyclotrophicuswere also recovered as ecologically distinct popula-tions in this study. This population split wasoriginally described in Hunt et al. (2008a) as groupII.A (large particle associated) and II.B (smallparticle) of population #15 and was recentlycharacterized by sequencing of 20 genomes (13 and7 isolates, respectively) to reconstruct microevolu-tionary events accompanying ecological specializa-tion (Shapiro et al., 2012). This showed that thepopulations remain so closely related that they areindistinguishable in 16S rRNA loci and share 499%average nucleotide identity across the core genomemaking them one of the most closely related butecologically differentiated groups of bacteria yetreported (Shapiro et al., 2012). Here, we alsodetected the sister population II.B of V. cyclotrophi-cus II.A (population #14 in Figure 1) in initialAdaptML runs with a habitat prediction for popula-tion II.B matching the one originally reported (Huntet al., 2008a). However, because our procedure forconservative estimation of habitat associationsincludes removal of isolates with identical hsp60gene sequences to avoid prediction of association bychance sampling of clonal expansions, the size ofpopulation II.B dropped below AdaptML’s thresholdfor population prediction. To nonetheless testwhether the two sister populations of V. cyclotro-phicus detected in 2006 also occurred in thesame habitats in 2009, we developed a PCRassay relying on 5 genes that were shown bygenomic comparison among 20 isolates from the2006 samples to be highly enriched in the flexiblegenome of population II.A but nearly absent inpopulation II.B. The results confirm a similarpattern in the populations recovered in 2009 withthe genes nearly universally present and absent inthe corresponding populations II.A and II.B, respec-tively (Table 3). This therefore suggests that even forthese extremely closely related groups of strains,ecological differentiation is reproducible and thathabitats are occupied by genetically similarpopulations.

Table 2 Statistical comparison of habitat distribution patterns forpopulations in samples obtained in 2006 and 2009 demonstratinglack of dependence on sampling

Population P-valuea

1 0.7725 0.0106 0.6137 0.4179 0.70311 0.79914 0.019

aFisher’s exact test was used to evaluate differences in distributionpatterns among years; values 40.05 indicate no evidence ofdifference. Population numbers correspond to Figures 1 and 2.

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For the seven populations whose abundance wasso skewed that they were nearly exclusive to eitherone of the samples, the dynamics remain elusive.This is because sampling of relatively few isolatesfrom microbial communities containing high diver-sity of lognormally distributed taxa, can add astochastic element to observations of populationstructure. It is, however, noteworthy that four of

these skewed populations were assigned a free-living ‘habitat’ (Figure 1). They may thus have, assuggested for V. cyclotrophicus, more than onealternate habitat and bloom by expanding into thefree-living state under certain conditions. Whetherthis was due to shifts in eukaryotic plankton or otherwater parameters remains unknown. Indeed, che-mical parameters differed considerably among

Figure 3 Phylogenetic tree representing the composition of larger eukaryote plankton based on cox1 sequence data in samples obtainedin 2006 and 2009.

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samples and the water temperature was 16 1C in2006 and 13.5 1C on both days in 2009(Supplementary Table 2), and Vibrio abundancecan be strongly correlated with temperature(Heidelberg et al., 2002; Randa et al., 2004;Thompson et al., 2004; Hsieh et al., 2008).

Overall, our results show that when populationsare recovered at similar relative abundances inconsecutive years, the habitat predictions are robustand reproducible, suggesting that similar stages ofdefined population cycles or lifestyles weresampled (for example, bloom versus attachment toa specific host). On the other hand, a considerablefraction of populations was at low frequency orundetectable in one of the samples. This mayindicate that these populations occasionally bloom,but are otherwise rare as has been shown for otherbacterioplankton populations, which can experi-ence population expansions on relatively shorttimescales (Rehnstam et al., 1993; Rieman et al.,2000; Piccini et al., 2006; Gilbert et al., 2012). Suchblooms of specific populations while the remainderof the community remains constant are likelytriggered by specific physical, chemical and/orbiotic conditions and are thus consistent with fine-scale structuring and predictable association withwhat we here term ‘habitats’ (that is, combinationsof specific parameters). Alternatively, observation of

sporadic population occurrence may simply be dueto the stochastic nature of sampling but may also bedriven by dispersal rather than specific growthwithin the sampled environment. The latter conclu-sion was recently reached for vibrios associatedwith different body parts (respiratory and gastro-intestinal tract) of mussels and crabs (Preheim et al.,2011a). Notably, many of the same populations thatappear specifically associated in the water columnassemble neutrally in these animals due to feedingdynamics. To clarify the relative importance ofdifferent factors in population assembly, morefrequent temporal sampling will be needed todetermine how finely structured and predictablebiological interdependencies might be.

The considerations above highlight the impor-tance of sampling at relevant environmental scalesas has also been pointed out for analysis of plantcommunities where both phylogenetic and spatialscale of observation can influence the interpretationof community assembly (Cavender-Bares et al.,2009). At fine-graded spatial and phylogeneticscales, communities appear assembled accordingto niche partitioning while decreasing resolutionchanges interpretation of community assemblymechanisms first to neutrality and finally to habitatfiltering (Cavender-Bares et al., 2009). The latterpredicts that communities contain more closelyrelated species than expected by chance (phyloge-netic clustering) because their close relationshipensures that they share many traits that mightenhance their survival in a given environment(Horner-Devine and Bohannan, 2006). These con-siderations are relevant for microbial communitiessince they are typically sampled at the ‘bucket’scale, corresponding more to ecosystem rather thanto habitat scales, and with phylogenetic markers(for example, 16S rRNA genes) that have relativelycoarse genotypic resolution. The latter is particu-larly important for microbes since horizontalgene transfer can spread adaptive genes rapidlyso that slowly evolving marker genes may notprovide sufficient resolution to differentiate at thepopulation level (Doolittle and Papke, 2006;Polz et al., 2006). We therefore suggest that finespatial and, in particular for aquatic microbes,temporal scale comparative sampling will beimportant to clarify whether bacterioplankton gen-erally assembles with high niche fidelity and thusaccording to ‘rules.’

Acknowledgements

This work was supported by grants from the NationalScience Foundation Evolutionary Ecology program(Evolutionary Processes), and the National ScienceFoundation and National Institutes of Health co-spon-sored Woods Hole Center for Oceans and Human Health,the Moore Foundation and the Department of Energy.GS was supported by a stipend from the RosztoczyFoundation.

Table 3 PCR screen to test for distribution of genes in the flexiblegenome of V. cyclotrophicus (sub)populations II.A and II.Bobtained in 2009 demonstrating clear genotypic differentiation

Population Isolate Genea

hsp60b flex1 flex2 flex3 flex4 flex5

II.A 1F_A1634 þ � þ � � �1F_A33 þ þ þ þ � �1F_A1560 þ þ þ þ þ þ1F_A1433 þ þ þ þ þ þ1F_A1553 þ � � � � þ5F_A1948 þ þ þ þ þ þ5F_A1931 þ þ þ þ þ þ5F_A1944 þ þ þ þ þ þ5F_A56 þ � � � � �5F_A93 þ þ þ þ þ þFF_A1147 þ þ þ þ þ þFF_A1115 þ þ þ þ þ þFF_A1149 þ � þ � � �FF_A1066 þ þ þ þ þ þFF_A1245 þ þ þ þ þ þZF_A738 þ þ þ þ þ þZF_A453 þ þ þ þ þ þZF_A773 þ þ þ þ þ þZF_A812 þ þ þ þ þ þZF_A588 þ þ þ þ þ þ

II.B FF_A1219 þ � � � � �FF_A1319 þ � � � � þ /�1F_A1380 þ � þ � � �1F_A1444 þ � � � � þ /�5F_A1731 þ � � � � �

aPrimers are shown in Table 1; þ , � and þ /� indicate stronglypositive, negative and weak bands in PCR screens.bAmplification of the hsp60 gene served as positive control.

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