ORIGINAL ARTICLE

Seasonality and vertical structure ofmicrobial communities in an ocean gyre

Alexander H Treusch1, Kevin L Vergin1, Liam A Finlay1, Michael G Donatz2,Robert M Burton2, Craig A Carlson3 and Stephen J Giovannoni1

1Department of Microbiology, Oregon State University, Corvallis, OR, USA; 2Department of Mathematics,Oregon State University, Corvallis, OR, USA and 3Department of Ecology, Evolution and Marine Biology,University of California, Santa Barbara, CA, USA

Vertical, seasonal and geographical patterns in ocean microbial communities have been observed inmany studies, but the resolution of community dynamics has been limited by the scope of data sets,which are seldom up to the task of illuminating the highly structured and rhythmic patterns ofchange found in ocean ecosystems. We studied vertical and temporal patterns in the microbialcommunity composition in a set of 412 samples collected from the upper 300 m of the water columnin the northwestern Sargasso Sea, on cruises between 1991 and 2004. The region sampled spans theextent of deep winter mixing and the transition between the euphotic and the upper mesopelagiczones, where most carbon fixation and reoxidation occurs. A bioinformatic pipeline was developedto de-noise, normalize and align terminal restriction fragment length polymorphism (T-RFLP) datafrom three restriction enzymes and link T-RFLP peaks to microbial clades. Non-metric multi-dimensional scaling statistics resolved three microbial communities with distinctive compositionduring seasonal stratification: a surface community in the region of lowest nutrients, a deepchlorophyll maximum community and an upper mesopelagic community. A fourth microbialcommunity was associated with annual spring blooms of eukaryotic phytoplankton that occur in thenorthwestern Sargasso Sea as a consequence of winter convective mixing that entrains nutrients tothe surface. Many bacterial clades bloomed in seasonal patterns that shifted with the progression ofstratification. These richly detailed patterns of community change suggest that highly specializedadaptations and interactions govern the success of microbial populations in the oligotrophic ocean.The ISME Journal advance online publication, 4 June 2009; doi:10.1038/ismej.2009.60Subject Category: microbial population and community ecologyKeywords: marine bacteria; community dynamics; ocean stratification

Introduction

The global impact of ocean biology is heavilydetermined by its most populous members, themicrobial plankton. Study of these organismshas advanced rapidly, but there are still large gapsin basic ecological information about these organ-isms. A particularly fundamental issue is theirseasonal, vertical and biogeographical distributions.Advances in sequencing technology have made itpossible to gather very large amounts of sequencedata from a few samples, but the effectiveness ofthese data for resolving ecological processesdepends on the coverage of molecular diversitywithin each sample and the mesh of the samplingschemes, which inevitably are compromises that

subsample and underdetermine detailed patterns ofseasonality or vertical variation.

The vertical stratification of microbial commu-nities in the oceans was first apparent in the obviouscase of phytoplankton (Olson et al., 1990a, b; Mooreet al., 1998), but later studies that used molecularprobes as genetic markers for uncultivated microbialtaxa uncovered many more examples. Three newbacterial clades were reported from the aphoticzone—the SAR406/Marine Group A lineage, whichis related to the Fibrobacteres and Chlorobi phyla,the SAR202 clades, which are in the Chloroflexiphylum, and SAR324, in the Deltaproteobacteria(Giovannoni et al., 1996; Gordon and Giovannoni,1996; Fuhrman and Davis, 1997; Wright et al., 1997).All three were observed to peak in abundance in theupper mesopelagic zone, below the deep chloro-phyll maximum (DCM) in both the Pacific andAtlantic Oceans. Later studies corroborated thesereports, and SAR202 emerged as one of the mostabundant aphotic zone bacterioplankton groups,accounting for about 10% of the cells in theintegrated water column community (Morris et al.,

Received 22 January 2009; revised 21 April 2009; accepted 21April 2009

Correspondence: SJ Giovannoni, Department of Microbiology,Oregon State University, Nash Hall, Corvallis, OR 97331, USA.E-mail: steve.giovannoni@oregonstate.edu

The ISME Journal (2009), 1–16& 2009 International Society for Microbial Ecology All rights reserved 1751-7362/09 $32.00

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2004; Varela et al., 2008). Similarly, probe studiesshowed that marine Crenarchaeota are largelyconfined to the aphotic zone, where they are thesingle most abundant class of cells (Fuhrman andDavis, 1997). These findings were confirmed bymany subsequent studies (for example, DeLonget al., 1999; Karner et al., 2001). Recently, ametagenomic study at the Hawaii Ocean Time-seriesconfirmed some of the earlier findings (DeLonget al., 2006), but also reported anomalous observa-tions—almost no SAR11 16S rRNA genes,and evidence of phototrophs well below the photiczone.

Seasonal variation in ocean microbial surfacecommunities has been reported in several studies,although the logistical problems of periodicallycollecting samples makes ‘time-series’ studies lesscommon. Time-series have proven to be powerfultools in oceanography (for a review see Ducklowet al., 2009) and have recently been applied tomarine microbial ecology. Murray et al. used DNAhybridization methods to show seasonal changes ofArchaea in the Antarctic Ocean (Murray et al., 1998)and the Santa Barbara Channel (Murray et al., 1999).DuRand et al. (2001) used flow cytometry tomeasure seasonal and spatial patterns of Prochlor-ococcus and Synechococcus at the Bermuda Atlan-tic Time-series Study (BATS) site, and Heywoodet al. (2006) showed that the standing stocks of bothgroups undergo little interannual change. Morriset al. (2002) showed annual peaks in the relativeabundance of SAR11 by rRNA probe hybridization.A combination of fluorescence in situ hybridizationprobing and terminal restriction fragment lengthpolymorphism (T-RFLP) time-series data showedregular seasonal patterns of three ecotypes of SAR11in the surface 300 m at BATS (Carlson et al., 2009).To correlate marine archaeal abundance with nitri-fication, Wuchter et al. (2006) analyzed a one-yeartime-series from coastal North Sea water samples. Ina more global approach, Moeseneder et al. (1999)used spring and summer samples to study thebacterial community in the Aegean and Adriaticseas by T-RFLP. Bano and Hollibaugh (2002)analyzed the bacterioplankton assemblages in theArctic Ocean over three seasons using denaturinggradient gel electrophoresis and clone libraries.Studying the microbial community at BATS overseveral years using T-RFLP, Morris et al. (2005)demonstrated a response by specific bacteriallineages to the annual spring deep mixing eventswithin the mesopelagic zone at BATS. In a long-termstudy of the microbial community at the San PedroOcean Time-Series site, Fuhrman et al. (2006) usedautomated rRNA intergenic spacer analysis andwere able to link several of the operational taxo-nomic units to environmental factors such asnutrients. Recently, Nelson et al. (2008) reported acoastal two-year time-series from a 34-km transect inthe Mid-Atlantic Bight using T-RFLP. They showeddistinct temporal microbial community patterns,

mainly the separation of the terminal restrictionfragments observed into a summer and a wintergroup. Although many of these time-series wereshort-termed, those by Morris et al. (2005), Fuhrmanet al. (2006) and Carlson et al. (2009) relied onsamples collected over several years.

Here, we report a study that focuses on the 16SrRNA gene diversity in the surface layer (0–300 m) atBATS with vertical and relatively high temporalresolution, with the objective of defining ecologicalpatterns. About one-third of the CO2 that is photo-synthetically reduced on Earth is fixed in the oceansby photoautotrophs, but most of it is rapidlyreoxidized in the marine surface layer by hetero-trophic microorganisms (Field et al., 1998).Dynamic interactions between the organisms de-tected in this study lie at the heart of the carboncycle, although progress understanding these inter-actions has been slow to emerge because of thecomplexity of microbial community ecology.

The BATS program initiated in 1988, provides arich oceanographic and ecological context, includ-ing reports of seasonal patterns of biogeochemistry(Michaels et al., 1996; Steinberg et al., 2001), diatomabundance (Nelson and Brzezinski, 1997), photo-synthetic picoplankton abundance (Olson et al.,1990a), microbial size spectra (Gin et al., 1999) andheterotrophic bacterioplankton dynamics (Carlsonand Ducklow, 1996). We studied microbial commu-nity composition at BATS with the high-throughputcommunity profiling technique, T-RFLP, targetingthe small subunit rRNA gene (16S rRNA) (Liu et al.,1997). This approach enabled us to process largenumbers of samples reliably and reproducibly(Nocker et al., 2007), and relate the data to large16S rRNA gene sequence databases. The findingsspan a timeframe of 12 years, and show in rich detailthe highly structured patterns of microbial commu-nities within the surface 300 m of the oligotrophicSargasso Sea.

Materials and methods

Sample collection and nucleic acid extractionMonthly samples (1 m and 200 m) were collected atthe BATS site (311 400N, 641 100W) from August 1991to February 1994 and from September 1997 toJanuary 2004. In addition, depth profile samples(1, 40, 80, 120, 160, 200, 250 and 300 m) werecollected at 35 sampling dates (see overview inTable 1 and full table in Supplementary MaterialS1). Altogether, 412 samples were included in thisstudy. To collect the microbial biomass, seawaterwas filtered through 0.2-mm polyethersulfone mem-branes (Supor, Pall, East Hills, NY, USA) and wasstored at �80 1C until further use. Nucleic acidswere extracted and processed as described earlier(Giovannoni et al., 1996; Morris et al., 2005). Duringthe extractions, nucleic acids were separatedisopycnically by buoyant density centrifugation in

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cesium salts and the yields were measured byspectrophotometry.

In silico predictions of microbial diversityTo determine the number of enzymes needed toprovide good resolution for the T-RFLP analysis, wecreated a data set of 16S rDNA sequences fromGenbank (Benson et al., 2008) and unpublishedsequence data representing all major groups ofbacteria currently known to be present in marineenvironments. Altogether, more than 6500sequences were analyzed with ARB (Ludwig et al.,2004) and TRF-CUT (Ricke et al., 2005). Theenzymes BsuRI, Bsh1236I and MspI were predictedto provide the highest resolution of marine micro-bial communities. Each rRNA sequence or clade ofsequences gave a specific fingerprint of threefragment sizes for the three enzymes, which werefer to below as ‘triplets’. During the T-RFLPanalysis, we often observed a difference betweenthe apparent sizes of fragments estimated from theirelectrophoretic mobility and their in silico predictedsizes. This phenomenon (called T-RF drift) isdocumented in the literature and could be corre-lated with the purine content of DNA (Kaplan andKitts, 2003).

PCR, restriction digestion and T-RFLP analysisDNA was diluted to 5 ng ml–1 and 1 ml was amplifiedwith the primer pair 8F-FAM (5-carboxy-fluoresceinlabeled) and 519R (both Morris et al., 2005) in 50 mlreaction volume using the following conditions: 30cycles, denaturation at 94 1C for 15 s, annealing at55 1C for 30 s and elongation at 72 1C for 1 min.Acetamide was added at a final concentration of 5%to reduce GþC content-induced amplification bias(Reysenbach et al., 1992). PCR products wereanalyzed on gels and restricted in parallel withBsuRI, Bsh1236I and MspI (Fermentas, Hanover,MD, USA). To avoid overestimation of diversitybecause of partially digested fragments (Nockeret al., 2007), the restriction reactions were incubatedovernight with an excess of enzyme. Restrictionproducts were cleaned using a MultiScreen 96-well

filtration plate (Millipore, Billerica, MA, USA)containing Amersham Sephadex G-50 superfine(Amersham, Piscataway, NJ, USA). Terminal restric-tion fragments were resolved on an ABI 3100 geneticanalyzer (Applied Biosystems, Foster City, CA,USA). To size the fragments based on the internalstandard (Bioventures MapMarker 1000, Cambio,Cambridge, UK), the ABI Genotyper software wasused. The samples that failed to amplify consis-tently because of low DNA concentrations or thathad incomplete T-RFLP data were not considered forfurther analysis.

Clone librariesTo correlate observed peaks with species and toverify predicted triplets, clone libraries were con-structed from the samples BATS136, 160 m;BATS136, 250 m (both January 2000); BATS138,160 m (March 2000); BATS146, 250 m (November2000), BATS174A, 300 m (March 2003) andBATS180, 1 m (September 2003) as described inMorris et al. (2004). Samples were chosen on thebasis of the likelihood that they contained T-RFLPpeaks of interest. Ninety-six clones from each librarywere screened as described earlier (Vergin et al.,2001), except that clones were amplified andprocessed for T-RFLP analysis as described above.Plasmids from 175 clones exhibiting T-RFLP peaksof interest were isolated and sequenced using theprimer 519R. After initial phylogenetic analysis ofthe short sequences, seven full-length sequenceswere generated from clones and their phylogeneticorigins were reconstructed (see SupplementaryTable S2 for details). The sequences were depositedin GenBank under the accession numbers FJ960798to FJ960822.

De-noising, alignment and triplet determinationThe raw data sets for all three enzymes werestandardized by the method of Osborne et al. usinga variable percentage threshold (Osborne et al.,2006). To cope with minimal differences betweenindividual T-RFLP runs, we applied a clustering byreduction of intervals algorithm that was originallydeveloped for mass spectrometer data (Finlay et al.,2009). The resulting data set was analyzed bydifferent methods to (i) find triplets of correlatedfragments for the three different enzymes used, (ii)find seasonal and spatial patterns and (iii) makepredictions about the species represented by therespective triplets.

To find triplets, we developed a new algorithm(Burton et al., 2009). In brief, patterns for thepresence and absence of fragments were analyzedto find the most likely triplets. These predictionswere verified by comparing them to the databasegenerated during the initial computational analysisof diversity and clone libraries. Species wereassigned on the basis of comparisons of the

Table 1 Number of samples collected at specified depths for eachmonth spanning the years 1991–2003

Depth(m)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1 9 9 8 8 7 7 7 6 10 8 8 1040 3 5 4 1 3 0 3 3 2 0 5 080 3 6 5 1 3 1 3 3 2 0 4 0100 0 0 0 0 0 1 0 0 1 0 0 0120 3 7 5 1 3 1 3 4 2 0 5 0160 3 6 5 0 3 1 2 4 2 0 5 0200 8 9 8 7 7 7 6 8 9 9 8 9250 2 4 4 1 3 1 1 4 2 0 5 0300 2 4 3 0 3 1 1 3 2 0 4 0

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predicted triplets to the virtual T-RFLP resultsobtained from the TRF-CUT analysis of the marine16S rRNA gene database. These bioinformaticassignments were supported by comparisons toclones from the already published (Giovannoniet al., 1996; Morris et al., 2005) and newly generatedclone libraries. For this, clones were pre-screened byT-RFLP, and those producing triplets observed in theenvironmental data set were sequenced and theirphylogenetic origins were reconstructed. To plot themicrobial distributions shown in the figures, a‘triplet minimum’ was calculated for each datapoint. For each environmental sample, a tripletyields three relative abundance values, one for eachof the three restriction digestions. The lowest ofthese three values is the ‘triplet minimum’. Forexample, if the relative abundances of fragmentsfor a sample were 20% for BsuRI, 16% for Bsh1236Iand 18% for MspI for a given triplet, the tripletminimum would be 16%. This approach minimizesoverestimation of abundance caused by interferencebetween fragments originating from differentorganisms.

Statistical analysis of the T-RFLP data setAfter standardization with a variable percentagethreshold (Osborne et al., 2006), a non-metricmultidimensional scaling (NMS) analysis was car-ried out to reveal non-linear trends in communitystructure. For this purpose, the data from all threeenzymes were concatenated to build a master matrixincluding every sample. Before carrying out thestatistical analysis, fragments that were present infewer than five samples were removed. In addition,samples with an average distance more than threestandard deviations from the mean distance wereexcluded from the analysis. The resulting matrix(360 samples� 590 T-RFLP markers) was analyzedusing the PC-ORD 5 software package (McCune andGrace, 2002). Sørenson distance measurement andthe ‘slow and thorough’ autopilot setting (250ordinations with real data, 250 randomized MonteCarlo runs, 500 iterations) were chosen. Final stressof the stable, two-dimensional solution was 15.8with an instability of 0.00428. Probability of asimilar stress in the randomized data set was0.004. The plot was rotated to maximize overlapbetween axis 2 and the depth from which thesamples were collected. By overlaying the NMSplot with physical, chemical and biological mea-surements from BATS (depth, month of the year,temperature, salinity, several nitrogen species, dis-solved organic carbon, bacterial cell counts andpigments, including chlorophyll), correlationsbetween the T-RFLP data and these parameters weredetermined.

The analysis of similarities (ANOSIM) was carriedout using PRIMER v6 (Clarke, 1993). Groups weredefined as upper euphotic zone (UEZ), with surface,40 and 80 m samples, DCM, with samples from 120

and 160 m, and upper mesopelagic (UMP), withsamples from 200, 250 and 300 m. A Sørensonsimilarity matrix of the complete data set was usedfor the one-way ANOSIM with the number ofpermutations set to 999.

Annual deep convective mixing extends themixed layer depth into the mesopelagic zone everyyear at BATS and affects the surface ocean biogeo-chemistry at this site (see Steinberg et al., 2001 andcitations within). To examine the microbial commu-nity variability relative to the mixing event, wealigned each year’s data to the timing (month) of thedeep mixing event, and collapsed the decade oftriplet minima data to a single composite year. Thesedata were plotted for the final figures using OceanData View (Schlitzer, 2000). Seasonal and spatial(depth distribution) patterns were identified bychecking for the presence/absence of the fragmentsat every sampling date and depth using a MicrosoftExcel spreadsheet.

Richness, mixed layer depth, and primary productivityRichness was calculated as the average number ofthe observed T-RFLP markers for each enzyme anddepth. The results for the different enzymes werethen combined and averaged. Mixed layer depthwas calculated as the depth where sigma-t was equalto sea surface sigma-t, plus an increment in sigma-tequivalent to a 0.2 1C temperature decrease (Sprin-tall and Tomczak, 1992). Primary production datawere extracted from the public website of theBermuda Atlantic Time-series Study (http://bats.bios.edu/index.html).

Results

Sample collection and T-RFLP measurementsThe analysis we discuss here included 412 samplescollected over a period of 12 years and 9 depthsranging from the surface to 300 m (see Table 1 andSupplementary Material Table S1). We choseT-RFLP techniques to profile microbial communitystructure from a decade of nucleic acid samplesbecause they are high-throughput and T-RFLPfragments can be predicted in silico from 16S rRNAsequence databases. An initial computational ana-lysis of a marine microbial 16S rRNA gene data setusing ARB (Ludwig et al., 2004) and TRF-CUT(Ricke et al., 2005) was carried out to identify thenumber and types of restriction enzymes to be used.We settled for BsuRI, Bsh1236I and MspI becausethey were predicted to have the highest resolutionand the least redundancy. Most of the sequences inthe marine data set were cut by all three enzymes,whereas only a small percentage was cut by one ortwo of the enzymes. Using the three enzymes inindependent digests increased phylogenetic speci-ficity, and partially overcame the problem of multi-ple organisms contributing to individual fragment

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lengths (Marsh et al., 2000; Kent et al., 2003).Adopting this approach, we developed the tripletminimum method to reduce interference betweenphylogenetic groups in the estimation of microbialabundances. Of the 412 samples, 369 producedT-RFLP profiles for all the three restriction enzymesand were thus used in the analysis. Higher resolu-tion of the microbial community was obtained withthe enzymes BsuRI and MspI than with Bsh1236I,which was not predicted from the initial computa-tional analysis of the marine sequences from thedatabase. Altogether, 302 different fragment lengthswere detected for BsuRI, 248 for Bsh1236I and 289for MspI. On average, 42 and 38 fragments wereobserved in the surface samples and 55 and 56fragments in the 200 m samples, for BsuRI and MspI,respectively. In contrast, Bsh1236I digestionsyielded an average of only 30 fragments from surfacesamples and 35 fragments from 200 m samples.

Richness and RNA/DNA ratiosThe average richness of markers detected by thethree different restriction enzymes increased belowthe euphotic zone (Figure 1). Interestingly, the RNA/DNA ratios of the entire planktonic community,an indicator of metabolic activity, decreased with

increasing depth and were inversely related to theobserved richness of bacterial species (Figure 1).The greatest transition in the trends of the RNA/DNA ratio and richness occurred at B140 m (Fig-ure 1), the average depth of the 0.1% light level andthe beginning of the mesopelagic zone. As withspecies richness, seasonality in RNA/DNA ratioswas much less pronounced than vertical variation(data not shown).

Patterns in microbial community compositionThe northwestern Sargasso Sea undergoes annualcycles of productivity with spring phytoplanktonblooms corresponding to periods of convectivemixing, and productivity lows coincident withperiods marked by extensive oligotrophy in summerand autumn, when the water column becomeshighly stratified (Steinberg et al., 2001). NMSordination of the complete data set resolved fourmicrobial communities (Figure 2). During seasonalstratification, surface (UEZ; 0–80 m), deep chloro-phyll maximum (DCM; 120–160 m) and deep sam-ples (UMP; 200–300 m) clustered separately. The‘spring bloom’ community resolved separately in theordination, propelled in particular by eukaryoticphytoplankton, which emerged in the analysisbecause plastid 16S rRNA genes co-amplified withbacterial 16S rRNA genes.

We used the ANOSIM algorithm to providestatistical support for the microbial communitieswe report here. Defining ANOSIM analyses on thebasis of ordination results (Figure 2) would notprovide a reliable test because of circular reasoning.Therefore, we defined the communities for ANOSIManalyses based on depth horizons (SupplementaryFigure S3). The R-value received for the pair-wisecomparison UEZ–UMP was 0.78, showing that theyare fairly distinct communities. The DCM commu-nity had similarities with both the UEZ and UMPcommunities (DCM–UEZ: R¼ 0.39, DCM–UMP:R¼ 0.36) (see inset Supplementary Figure S3). Theunique characteristics of the spring bloom commu-nity have been discussed earlier by Morris et al.(2005).

Although the trends identified by ordination arethe dominant patterns of microbial communityvariation, many individual examples of spatial andtemporal patterns were discerned by examining thedistributions of individual fragments (Figure 3).Approximately half of the fragments showed seaso-nal patterns and half showed vertical patterns, withsome degree of overlap between the groups. Whenwe excluded fragments that showed little variationthroughout the year, 39% of all fragments exhibiteddistinct seasonal bloom patterns, whereas othersvaried with no discernable pattern. Similarly, afterexcluding fragments that were present at all depths,43% of all fragments showed stratified distributions.For a better comparison, the abundance of fragmentswith specific patterns were normalized to the

Figure 1 Average species richness, primary production andRNA/DNA ratios in the surface layer. Richness is indicated by thesolid line, primary production by the dashed line and RNA/DNAratios by the dotted line. The beginning of the mesopelagic zone(0.1% light level at 140 m) is marked.

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number of all fragments that showed non-randompatterns, including those present in all seasons or atall depths, and then averaged. This was possiblebecause all three enzymes exhibited the same trends(r2 between enzymes 0.59–0.84 for seasonal and0.9–0.92 for spatial patterns). Disregarding fragmentsthat were present year-round, the most commonpattern for fragments that displayed seasonal trends(Figure 3a) was high abundance in the spring(14.3%), as suggested by the NMS analysis, followedby those showing a summer/fall/winter maximum(12.4%), a spring/summer maximum (9.1%) and asummer only maximum (8.1%). Similarly, disre-garding fragments that were present throughout thewater column, most of the fragments with verticaltrends (Figure 3b) showed a 40–300 m distribution(25.8%), followed by fragments that were present inthe surface waters down to 160 m (20.1%) andfragments that were only observed in waters 200 mand deeper (15.6%).

We took a conservative approach to assign frag-ments to taxa (Figure 3). Generally, if a fragmentcould not be assigned to a triplet, we consideredthe phylogenetic origin of the fragment uncertainand did not analyze the fragment further. Asummary of patterns analyzed in this study orassociated with the spring bloom at BATS is shownin Figure 3c.

The relative abundance of fragments from theSAR11 clade of Alphaproteobacteria averaged 19%,

overshadowing all other organisms. A detailedanalysis that combined these T-RFLP data withSAR11 fluorescence in situ hybridization cell countsto model temporal and vertical distribution patternsin SAR11 ecotype populations has been published(Carlson et al., 2009).

The alphaproteobacterial clade OCS116 bloomedwith phytoplankton in the spring (Figure 4a). Morriset al. (2005) reported this earlier; however, ouranalysis showed a previously undetected populationof OCS116 in the region of the DCM during theperiod of water column stratification. An additionalgroup of organisms that as stratified in the DCMincluded members of the phylum Actinobacteriawhich are closely related to uncultured Microthrixspecies (Supplementary Figure S4). They werepresent in the DCM year-round, with occasionalblooms during the summer stratification period.

Another well-studied group of microorganisms atBATS is the marine picocyanobacteria clade. Ourresults with these organisms (Figure 4b) corroborateearlier findings, showing the validity of our ap-proach. Synechococcus and Prochlorococcus, whichare about 97% similar in 16S rRNA sequence,displayed the same triplet (BsuRI 291 bp, Bsh1236I95 bp, MspI 471 bp), a finding that the clone librariesconfirmed. Composite plots of the marine picocya-nobacteria clade triplet minima confirmed a highrelative abundance (up to 35% of the total fragmentfluorescence) of these organisms in the summer

Figure 2 Non-metric multidimensional scaling ordination of 360 samples in terminal restriction fragment length polymorphism markerspace consisting of 590 unique fragment lengths. Samples sharing similar markers cluster in this analysis, which takes into account themagnitudes of the markers. Sizes of dots are proportional to the depths from which the samples were taken (1, 40, 80, 120, 160, 200, 250and 300 m). Color shows the sampling month, indicating that seasonality is much less conspicuous than vertical stratification. Theordination was rotated to maximize overlap of axis 2 with depth. DCM, deep chlorophyll maximum; SBL, spring bloom; UEZ, uppereuphotic zone; UMP, upper mesopelagic.

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months in the euphotic zone (Figure 4b). Theprominent T-RFLP pattern of the marine cyanobac-teria resembles Prochlorococcus distributions re-ported earlier in the literature (Figure 4b) (Durandet al., 2001; Heywood et al., 2006). Although ouranalysis could not resolve Synechococcus fromProchlorococcus, those studies showed that Syne-chococcus is most abundant during spring deepmixing and Prochlorococcus populations reach theirmaximum abundances during summer and fall. In

addition, Durand et al. observed that Prochlorococ-cus was ten times more abundant and had a deeperpopulation maximum than Synechococcus (Durandet al., 2001). They also showed that, during thewinter months, these organisms are closer to thesurface, and when the maximum MLD extendsbelow the euphotic zone in late winter and earlyspring, their relative abundance is reduced (Durandet al., 2001; Steinberg et al., 2001).

Similar to earlier reports, we observed that theSAR202 clade of Chloroflexi was mainly found inthe aphotic zone (Morris et al., 2004). However, thebetter resolution afforded by this analysis allowedus to observe an earlier unseen summer bloom ofthis clade in the upper mesopelagic zone (Figure 5a).Although nearly absent from the euphotic zone,SAR202 blooms in the summer in the uppermesopelagic zone reached a relative abundance ofup to 2% in May through June (3 and 4 months afterdeep mixing), compared with an average yearlyupper mesopelagic value of 0.5–1%.

The deltaproteobacterial clade, SAR324/MarineGroup B, which was initially described by Fuhrmanand Davis (1997) and Wright et al. (1997), was alsoprevalent in our data set (Figure 5b and Supple-mentary Figure S5). The clade’s depth distributionincreased from 1.9% of the relative fragmentabundance at the surface to 14.4% at 300 m.Recently, Brown and Donachie (2007) delineatedthree species-level subclades (named I, II andSAR276) and estimated their abundances in differ-ent geographical locations on the basis of clonelibraries and database entries. We were able todistinguish between the subclades on the basis oftheir triplets, with subclade I contributing thegreatest average relative abundance (9%) in theupper mesopelagic zone. Subclade II, which wasonly detected below 160 m, reached 2% relativeabundance at 300 m. The SAR276 subclade waspresent throughout the upper 300 m of the watercolumn, with a mean relative abundance thatincreased from 1.6% at the surface to over 3% at

Figure 3 Seasonal (a) and vertical (b) patterns inferred fromindividual terminal restriction fragment length polymorphismpeaks and (c) organisms that contribute significantly to thedistinctness of the upper euphotic zone, deep chlorophyllmaximum, upper mesopelagic and spring bloom microbialcommunities. (a) The most common pattern was occurence onlyin the spring, followed by a summer/fall/winter pattern. Seasonswere defined as follows: December–February, winter; March–May, spring; June–Aug, summer; September–November, fall. Rarepatterns (those that were represented by o3% of all fragments)are not shown. Altogether, 6.9% of all fragments were in the ‘rarepattern’ category. (b) Fragments showing vertical distributionpatterns did not follow the euphotic/mesopelagic zone separationseen in the non-metric multidimensional scaling analysis, butrather had natural borders at 40 m and between 160 and 200 m.Rare patterns are not shown. Altogether, 8.1% of all fragmentswere in the ‘rare’ pattern category. (c) Microbial clades observedduring different times and in the different zones of the watercolumn.

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300 m. No seasonal patterns were detected amongthe SAR324 subclades (data not shown).

One striking pattern that emerged from thisanalysis was a seasonal bloom of the SAR86 cladein the mixed layer during the height of summerstratification (Figure 6a). The SAR86 clade belongsto the Gammaproteobacteria and is one of the mostcommon groups observed in clone libraries madefrom surface water samples. They attracted attentionwhen proteorhodopsin was first discovered in thefragment of a SAR86 genome (Beja et al., 2000a). Weused phylogenetic reconstruction, with confirmationby gene cloning and sequencing, to resolve thesubclades of SAR86 (Suzuki et al., 2004) and showthat they can be distinguished by the T-RFLP

methods we used, but not without some overlap(Figure 7; Supplementary Figures S6a and b). Thesummed relative abundances of all SAR86 fragmentsshowed that the SAR86 clade is present throughoutthe year, but increases in relative abundance fromApril to October (months 1–7 after DM), reaching apeak in relative abundance of 3% in June and July(months 3 and 4 after DM; Figure 6a). They werenearly absent from waters below 100 m, except inMay after spring convective overturn, when theywere observed as deep as 150 m. Surprisingly, all theSAR86 subclades exhibited similar distributionpatterns when analyzed separately (data not shown).In contrast to other studies (Suzuki et al., 2004), wedid not include the clades CHAB-I-7 and San Pedro

Figure 4 Averaged seasonal composites of (a) OCS116 and (b) the marine picocyanobacteria clade distributions in the surface 300 m atBermuda Atlantic Time-series Study. (a) The plot shows the spring bloom and summer deep chlorophyll maximum (DCM) occurrence ofOCS116 clade bacteria. After the nutrient intake by deep mixing and a subsequent spring bloom of OCS116 bacteria, these organismspersist in a depth horizon of 80–120 m, the area of the DCM, during the period of summer stratification. This might indicate anassociation of this clade with photosynthetic organisms. (b) The sum of the relative abundances of Prochlorococcus and Synechococcusare shown. DM, the month of the deep mixing event.

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Ocean Time-Series (SPOTS) in the SAR86 cladebecause they are phylogenetically distant.

The alphaproteobacterial clade SAR116 was alsoprevalent in the data set. These organisms had adistribution similar to that observed for SAR86;during June, July and August (months 3–5 after DM),a strong bloom was observed in the upper 50 m ofthe water column, reaching relative abundances ofabout 20% (Figure 6b). The SAR86 bloom beginswith the onset of water column stratification afterspring convective overturn. In a phylogenetic re-construction of selected full-length SAR116 se-quences from the databases, different subcladescould be delineated (Supplementary Figures S7and S8). Suzuki et al. (2001) described SAR116

subclades I, II and III and our phylogenetic analysis,supported by triplets, showed a further subgroupingof these subclades. Subgroups of SAR116 subclade Ishowed distinct triplets (Ia 192/96/178 bp; Ib 189/93/175 bp for BsuRI/Bsh1236I/MspI). Similarly,SAR116 subgroup II also exhibited distinct tripletpatterns. Subgroups IIa (226/196/149 bp) and IIc(289/93/439 bp) were predicted to have consistenttriplets; however, subgroup IIb, did not produce aconsistent triplet across all analyzed sequences.Therefore, subgroup IIb could not be traced in theT-RFLP data set. SAR116 subclade III resolved intotwo different phylogenetic subgroups (IIIa and IIIb)that did not have distinguishable triplets. Wedetected two additional subclades that have not

Figure 5 Averaged seasonal composite plots showing (a) the summer, upper mesopelagic bloom of SAR202 clade and (b) the year-roundupper mesopelagic occurrence of the SAR324 clade. (a) To represent a composite year in the context of deep mixing, each year of the12-year data record was first aligned to the month of each year’s maximal mixing and expressed as the month before and 10 months afterdeep mixing. The values plotted are triplet minima. (b) Similar to SAR202, the SAR324 clade is found in the mesopelagic zone. SAR324populations decrease in abundance during spring mixing, and generally extend to shallower depths than SAR202. DM indicates themonth of the deep mixing event.

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been reported earlier, IV and V, with triplets of 193/97/439 and 193/197/163 bp, respectively. Owing tooverlap between fragments for subclades III, IV andV, we were not able to distinguish between thesegroups.

Discussion

The Bermuda Atlantic Time-series Study providedan oceanographic context to study the patterns ofmicrobial community change in an ocean gyre.The surface layer of the BATS site differs from some

other oligotrophic gyres, for example, the HawaiiOcean Time-series site, in that it is seasonallyoligotrophic marked by periods of deep mixingfollowed by long periods of highly stratified lownutrient conditions. Each winter/spring, there aredeep convective overturn events at BATS where theMLD extends to as deep as 350 m, entrain nutrientsto the surface, and trigger annual phytoplanktonblooms that result in rapid nutrient drawdown andincreased organic particle flux (Steinberg et al.,2001). As the water column begins to stratify,dissolved organic matter accumulates in theeuphotic zone (Carlson et al., 1994; Hansell and

Figure 6 Averaged composite plot showing annual summer blooms in the upper euphotic zone of (a) the SAR86 clade and (b) theSAR116 clade at the Bermuda Atlantic Time-series Study site. (a) SAR86 relative abundance is highest in the summer mixed layer, whichextends to 10–20 m. This plot sums all of the SAR86 subclades shown in Figure 7. Identical seasonal and spatial distributions wereobserved for the subclades, despite their forming distinct phylogenetic groups that suggest ecotypes. These data indicate that both cladesare most abundant in the summer mixed layer; however, the scales are different, suggesting that either the SAR116 clade is relativelymore abundant than SAR86 throughout the year, or that it has more copies of the16S rRNA gene. DM indicates the month of the deepmixing event.

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Carlson, 2001). A broader assessment of the BATSbiogeochemical data is presented elsewhere (seeDeep Sea Research II volumes 43 No. 2–3 and 48:No. 8–9 for relevant studies).

RichnessSpecies richness—the total number of species in asample—is perhaps the broadest measure of com-munity diversity. Unexpectedly, we found thatrichness increased significantly below the euphoticzone (Figure 1). Although richness does not neces-sarily translate to phylogenetic diversity, a factor in

the lower diversity of the euphotic zone may be thatit is largely the domain of only two phyla, theCyanobacteria and Proteobacteria, whereas theupper mesopelagic zone harbors large populationsof bacteria from the Chloroflexi and Fibrobacteres/Chlorobi phyla, as well as Deltaproteobacteria thatare not common in the euphotic zone. The uppermesopelagic zone’s microbial community was alsoconsiderably more uniform in richness throughoutthe year (data not shown), and occupies a regionwhere fluctuations in light, temperature and nutri-ents are much less extreme. Variability in export fluxof both particulate and dissolved organic matter(Steinberg et al., 2001) in combination with solubi-lization of sinking particles (Azam and Long, 2001;Azam and Malfatti, 2007) within the mesopelagiczone potentially introduce heterogeneity of avail-able organic substrates in an otherwise nutrient-richthermally stable environment. All of these factorsmay contribute to the greater diversity we observedin the upper mesopelagic microbial community.Interestingly, Hewson et al. (2006) found higherrichness in 500 and 3000 m samples than in samplestaken at 1000 m, indicating additional factorscontrolling richness in the deeper meso- andbathypelagic. Other studies assessing the richnessof marine microbial communities focused on surfacesamples and showed a decrease in richnesswith an increase in latitude (Pommier et al., 2007;Fuhrman et al., 2008). Richness, latitude andtemperature were highly correlated, indicating thathigher temperatures support a richer communitystructure on the geographical scale. However, in ourdata set, temperature and richness were inverselyrelated, suggesting that factors other than tempera-ture control richness on a vertical scale in theoceans.

RNA/DNA ratiosWe found that RNA/DNA ratios were inverselyrelated to species richness, declining below theeuphotic zone (Figure 1). High RNA/DNA ratiosgenerally indicate greater metabolic activity becauseof the increased protein synthesis in rapidly growingcells (Dortch et al., 1983). However, across species,RNA/DNA ratios tend to increase with increasing cellsize and decrease with increasing genome size (Kempet al., 1993). Thus, although the high RNA/DNA ratiosobserved in the euphotic zone may reflect highrelative productivity and turnover rates, we suspectthat other factors are at work. In particular, relativelysmall genome sizes have been reported for someeuphotic zone bacteria (Giovannoni et al., 2005b,2008), as well as an increase in percentage of cellsshowing a higher DNA content with depth (Reinthaleret al., 2006). However, from the information available,it is not possible to identify trends in genome size andcell size that might affect RNA/DNA ratios along thevertical axis in ocean gyres.

Figure 7 16S rRNA evolutionary tree of the SAR86 clade ofGammaproteobacteria, showing the phylogenetic distribution oftriplets (BsuRI/Bsh1236I/MspI). Naming of the subclades followsSuzuki et al. (2001); however, we observed two subgroups withinSAR86 subclade III (IIIa and IIIb) that were not described earlier,but are consistent with terminal restriction fragment lengthpolymorphism triplets. Further, two of the subclades shown(CHAB-I-7 and San Pedro Ocean Time-Series (SPOTS)) are notincluded in the SAR86 clade in some studies. The sequence ofAlcanivorax borkumensis (AF062642) was used to set the root ofthe tree, which was calculated using the Neighbor-Joiningalgorithm and is based on 1423 nucleotides of sequence. Numbersindicate the percentage of bootstrap replicates that support thenearby branch. Dominant triplets are indicated for all subclades.Some sequences, including clones from this study, did not groupwith the major clades and might represent additional, lessabundant clades.

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Ordination indicates modes of communityorganizationFour modes of microbial community compositionemerged from the ordination analysis: a post-mixingspring bloom community, a stratified surface com-munity, a DCM community and a stratified uppermesopelagic community (Figures 2 and 3c). Apartfrom mixing events, aphotic and euphotic zones’communities at BATS separated very clearly in theNMS analysis. Some of these findings echo earlierreports, particularly those of Morris et al. (2005) andCarlson et al. (2009), who observed that springmixing events at BATS have a significant effect onmicrobial community composition, and the numer-ous reports which have shown euphotic zone andmesopelagic microbial communities are distinct(Giovannoni et al., 1996; Gordon and Giovannoni,1996; Fuhrman and Davis, 1997; Wright et al., 1997).However, the DCM community that emerged in theordination (Figure 2) has not been recognizedpreviously as a separate community with a distinc-tive composition, as we report here. As evident inthe ordination, the DCM community is related to thespring bloom community in its composition. This isdiscussed further below.

Inspection of individual T-RFLP fragment plotssupported the ordination results, but also under-scored that the four communities revealed byordination are generalizations from many individualdistribution patterns that are unique to organismswhose natural histories do not necessarily followthe general trends. For example, less than half (43%)of all fragments having stratified depth distribu-tions, and one-fourth of all fragments showing non-random patterns were present from 40 m down to300 m (Figure 3b). This perspective, illustrated inFigures 3a and b, can be reconciled with theordination results by considering the importance of‘keystone’ microbial groups, which affect ordina-tions in proportion to their abundance and therebycontribute to the distinctness of communities. SomeT-RFLP fragments could not be identified. Possibleexplanations, for example, episodic blooms andpseudo T-RFs (Egert and Friedrich, 2005) arediscussed in the Supplementary Material S9.

The spring bloom and the DCM microbial communityIn broad strokes the paragraphs above define keyfeatures of the BATS surface layer microbial ecosys-tem, but its dynamic nature is illustrated nowheremore aptly than in the post-mixing spring bloomand ensuing transition to stratification. Some ofthese dynamics are shown in Figures 4–6, but also inthe Morris et al.’s. (2005) study of seasonal mixingand the Carlson et al.’s (2009) study of SAR11seasonal dynamics.

The annual composite plot of OCS116 (Figure 4a),an uncultured alphaproteobacterial clade, revealeda spring bloom of these organisms, as previouslyreported by Morris et al. (2005), but also showed that

these organisms persist during the remainder of theyear as a population in the DCM microbial commu-nity. Other organisms follow a pattern similar to thatshown here for OCS116. For example, we observedthat members of a marine clade of Microthrix werealso abundant in the DCM community (Supplemen-tary Figure S4), and the SAR11 Ib ecotype follows asimilar pattern (Carlson et al., 2009).

Despite some common elements of microbialcommunity structure, the retreat of photoautotrophsto the DCM, brought about by declining nutrientconcentrations in the UEZ, is also accompanied bychanges in community composition. Figure 4bshows one of the strongest patterns to emerge fromthe data set—the average yearly distribution ofmarine Synechococcus and Prochlorococcus. Theseorganisms replace eukaryotic phytoplankton as thedominant phototrophs after the spring bloom.

Although above we draw attention to the mostprominent patterns of seasonal change, seasonalpatterns were also prominent in plots for manyindividual T-RFLP fragments. Indeed, 39% of allfragments followed readily discernable seasonalpatterns. In accord with the NMS statistical analy-sis, the most frequently observed seasonal patternwas the transient occurrence of many fragments inthe spring. Many of these were identified asfragments originating from plastid 16S rRNA genes,which amplify with the same primers used forbacterial 16S rRNA gene amplification. These willbe analyzed in detail in another publication. Others(SAR86 and SAR116), described below, becomedominant in the stratified surface waters of thesummer.

The stratified surface communityDuring stratified periods, the euphotic zone andparticularly the upper 40 m of the stratified watercolumn has the highest exposure to solar radiation,and is very low in macronutrients such as phos-phorous and combined nitrogen (NO3

�, NO2� and

NH3þ ), making it a truly extreme environment.Some features of community stratification that wefound, particularly those for SAR11 and photo-trophs, echo findings from earlier studies. However,the data also unveiled new features, notably highlystratified populations of SAR86 and SAR116 in thesummer euphotic zone (Figure 6).

The alphaproteobacterial SAR116 clade producedone of the most prominent T-RFLP patterns in thedata set, contributing an average of 12.6% of allfragments above 100 m, and consistently bloomingto 20–25% of the total 16S rRNA gene ampliconsignal during summer stratified periods (Figure 6b).Although SAR116 is a common bacterioplanktongroup, until now its association with the euphoticzone has been on the basis of clone library data,which are limited to few samples and lack robuststatistical support. Thus, this analysis provides thefirst overwhelming evidence that SAR116 is a

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euphotic zone clade. Even during the fall, winterand spring, SAR116 contributed 7–10% of theT-RFLP signal above 100 m. In contrast, in GlobalOcean Survey metagenomic data collected in thewinter and spring from the Sargasso Sea, only 2–3%of 16S genes were from SAR116.

Like SAR116, the SAR86 clade of Gammaproteo-bacteria emerged as a highly seasonal clade rele-gated to the extreme environment of the oligotrophicstratified euphotic zone (Figure 6a). This studyconfirms their euphotic zone distribution, whichpreviously was largely inferred from the frequentappearance of SAR86 clones in surface samples.

SAR86 and SAR116 clades, as well as the SAR11Ia and Ib ecotypes (Carlson et al., 2009), are mostpronounced in the stratified euphotic zone. Proteo-rhodopsin has now been observed in many marinemicrobial groups, including the SAR11 (Giovannoniet al., 2005a) and SAR86 clades (Beja et al.,2000a, b). It is thought to function as a light-dependent proton pump. Its discovery focusedattention on the potential importance of photoheter-otrophy in marine bacteria. The adaptations thatcontribute to the success of SAR86, SAR116 andSAR11 Ia and Ib ecotypes in these high-light lownutrient environments are not yet understood, inpart because cultures and genomes are not yetavailable for SAR86 and SAR116. Our findingsinvite further scrutiny of these organisms to under-stand the role of proteorhodopsin, and to identifyother factors that could contribute to the seasonalsurface blooms we observed.

The upper mesopelagic microbial communityThe surface and DCM communities describedabove overlay a community that is relatively highin mineral nutrients but has insufficient light tosupport a significant population of photosyntheticorganisms. However, it is poised in space to interactwith sinking particles and exported dissolvedorganic matter accumulating from primary produc-tion in the layers above.

Observations of the SAR202 clade, a diverse groupof uncultured organisms from the phylum Chloro-flexi, have been largely confined to the deep ocean(Giovannoni et al., 1996; Morris et al., 2004). TheSAR202 clade has a complex phylogenetic substruc-ture, suggesting that it may represent substantialphenotypic diversity. These organisms are estimatedto constitute about 10% of the total bacterialpopulation in the mesopelagic and bathypelagicrealms (Morris et al., 2004; Varela et al., 2008). Ouranalysis confirmed earlier findings, but alsorevealed annual blooms of SAR202 populations inthe upper mesopelagic zone several months afterdeep mixing (Figure 5a). The factors that cause theselarge populations to bloom in shallower regions ofthe mesopelagic during the summer stratificationare unknown.

Similar to SAR202, the SAR324 clade of Delta-proteobacteria produced a large T-RFLP signal(average 11.3%, see Figure 5b and SupplementaryFigure S5) in the upper mesopelagic zone, but weobserved no strong seasonal patterns in theirdistribution (Figure 5b). They were nearly absentfrom the euphotic zone (average 2.2%), consistentwith earlier reports showing that this unculturedmicrobial clade is distinctly a meso- and bathy-pelagic organism (Fuhrman and Davis, 1997; Wrightet al., 1997; Gonzalez et al., 2000). We foundSAR276, a subclade of SAR324, to be more abundantin the upper 120 m of the water column thansubclades I and II (Supplementary Figure S5). Below120 m, subclade I was much more abundant thanboth of the other two subclades. This findingcontrasts with the observations of Brown andDonachie (2007), who suggested that subclade II isthe most abundant in the water column. Their studywas on the basis of the analysis of 16S rRNA geneand ITS sequences from different sampling loca-tions, including BATS, and did not considerseasonal changes in abundance.

Sparse evidence for ecotypesEcotypes are closely related lineages of bacteriathat differ slightly in adaptive physiological traitsconferring niche specificity (Cohan, 2006). Differingspatial and temporal distribution patterns thatcorrelate with the fine structure of phylogenetictrees are often used to identify ecotypes. In mostcases, the traits that confer niche specificity toecotypes are unknown (Hunt et al., 2008;Koeppel et al., 2008). Two of the best examples ofmicrobial clades that are composed of multipleecotypes are SAR11 and Prochlorococcus. In thecase of Prochlorococcus, the distributions of eco-types and some traits that contribute to nichespecificity are both known (Moore et al., 1998).Several reports have documented ecotypes ofSAR11. Most recently, and thoroughly, Carlsonet al. (2009) used the same data we use in thisstudy to model the distributions of SAR11 ecotypepopulations at BATS.

The phylogenetic substructure of the SAR86 andSAR116 clades (Figure 7 and Supplementary FigureS7) suggests that, like SAR11 and Prochlorococcus,these microbial groups may consist of multipleecotypes. However, we were surprised that evidencefor neither SAR86 nor SAR116 ecotypes emergedfrom this analysis. The failure to resolve ecotypesfor these abundant clades is unlikely to be related tothe resolution of T-RFLP methods. We could resolvethe subclade structure of SAR86 reported in theliterature (Suzuki et al., 2001), and additionallydelineate several new subclades, which were re-flected in the triplets (Figure 7 and SupplementaryFigure S6a). Although the clade substructure ofSAR86 and SAR116 suggests different ecotypessimilar to what was shown for SAR11 (Carlson

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et al., 2009), there was no evidence to support theecotype hypothesis. If there are SAR86 and SAR116ecotypes at BATS, they are either sympatric, or thediffering patterns of their distributions were too fineto be resolved by our experimental design.

ConclusionThis large data set, and the comprehensive power ofT-RFLP analysis, showed many earlier unseenfeatures of microbial community populationdynamics in the northwestern Sargasso Sea. Someof these features, particularly those observed duringthe period of summer stratification, are likely to becommon features of stratified ocean gyres, whereasothers may be peculiar to systems that undergosimilar deep convective mixing events and ensuingphytoplankton blooms. A diversity of physical,chemical and biological measurements are taken atBATS on a monthly basis, and were available to usfor the NMS analysis to identify factors that mightcontrol the distribution of microbial populations.Although many correlations between routinemeasurements at BATS and microbial distributionswere analyzed, none of these emerged that over-wrote the dominant theme that seasonal mixing,surface warming, and stratification are the keydeterminants of transitions of microbial communitystructure.

Many oceanographic processes—and the naturalhistory of microbial plankton is no exception—areveiled by the vastness and complexity of the oceans.Time-series studies are designed to resolve seasonaland long-term patterns, sacrificing a geographicalperspective. All else being equal, a study of similarscope on the basis of random sampling would beexpected to observe the same variation, but notdiscern the patterns emerging from a time-series.Nonetheless, monthly time-series studies, such asBATS, will largely miss episodic and verticalfeatures that are too fine to be resolved by thisexperimental design. In an era of global change,time-series studies are a powerful approach todiscern long-term trends that might be obscured byyear-to-year variability. Future studies at BATS andother sites may use pyrosequencing, and methodssuch as quantitative PCR and fluorescent in situhybridization, to estimate the biomass of popula-tions, to quantify distributions of rare species, toidentify fine patterns in ecotype distributions and toidentify genomic adaptations that permit differentmicroorganisms to subdivide the ocean surface layerinto temporal and spatial niches.

Acknowledgements

We thank the officers and crew of the RV Weatherbird IIfor their assistance and support and the BATS chiefscientists and technicians for assistance in water collec-tion and accommodating wire time requests. We thank

Rachel Parsons for technical assistance and Robert Morrisfor his constructive input. This research was funded by agrant from the Marine Microbiology Initiative of theGordon and Betty Moore Foundation to SJG and NSFMicrobial Observatory grants MCB-0237728 & OCE-0801991 to CAC and SJG. AHT is supported by a FeodorLynen Fellowship of the Alexander von HumboldtFoundation.

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