ORIGINAL ARTICLE

Protistan microbial observatory in the CariacoBasin, Caribbean. I. Pyrosequencing vs Sangerinsights into species richness

Virginia Edgcomb1, William Orsi2, John Bunge3, Sunok Jeon4, Richard Christen5,Chesley Leslin2, Mark Holder6, Gordon T Taylor7, Paula Suarez8, Ramon Varela9

and Slava Epstein2,10

1Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA;2Department of Biology, Northeastern University, Boston, MA, USA; 3Department of Statistical Science,Cornell University, Ithaca, NY, USA; 4Department of Environmental Science, Kangwon National University,Kangwon-Do, South Korea; 5Universite de Nice et CNRS UMR 6543, Laboratoire de Biologie Virtuelle, Centrede Biochimie, Parc Valose, Nice, France; 6Department of Ecology and Evolutionary Biology, University ofKansas, Lawrence, KS, USA; 7School of Marine and Atmospheric Sciences, Stony Brook University, StonyBrook, NY, USA; 8Departamento de Biologıa de Organismos, Universidad Simon Bolıvar, Sartenejas, Baruta,Venezuela; 9Estacion de Investigaciones Marinas de Margarita, Fundacion la Salle de Ciencias Naturales,Punta de Piedras, Venezuela and 10Marine Sciences Center, Northeastern University, Nahant, MA, USA

Microbial diversity and distribution are topics of intensive research. In two companion papers in thisissue, we describe the results of the Cariaco Microbial Observatory (Caribbean Sea, Venezuela). TheBasin contains the largest body of marine anoxic water, and presents an opportunity to studyprotistan communities across biogeochemical gradients. In the first paper, we survey 18S ribosomalRNA (rRNA) gene sequence diversity using both Sanger- and pyrosequencing-based approaches,employing multiple PCR primers, and state-of-the-art statistical analyses to estimate microbialrichness missed by the survey. Sampling the Basin at three stations, in two seasons, and at fourdepths with distinct biogeochemical regimes, we obtained the largest, and arguably the leastbiased collection of over 6000 nearly full-length protistan rRNA gene sequences from a givenoceanographic regime to date, and over 80 000 pyrosequencing tags. These represent all major andmany minor protistan taxa, at frequencies globally similar between the two sequence collections.This large data set provided, via the recently developed parametric modeling, the first statisticallysound prediction of the total size of protistan richness in a large and varied environment, such asthe Cariaco Basin: over 36 000 species, defined as almost full-length 18S rRNA gene sequenceclusters sharing over 99% sequence homology. This richness is a small fraction of the grand total ofknown protists (over 100 000–500 000 species), suggesting a degree of protistan endemism.The ISME Journal advance online publication, 10 March 2011; doi:10.1038/ismej.2011.6Subject Category: microbial ecology and functional diversity of natural habitatsKeywords: protists; diversity; species richness; anoxic; pyrosequencing; 18S rRNA approach

Introduction

The history of research on protistan diversity andtaxonomy spans almost two centuries. Protistsattained their status as a separate kingdom evenbefore the role of the nucleus was first proposed(and so protists included bacteria as a result; Owen,1860; Haeckel, 1866). Centuries later, it is stillessentially unknown whether all the major protistan

groups have or have not been discovered, whether ornot protistan species are globally distributed (Finlayand Fenchel, 1999; Foissner, 1999, 2006; Finlay,2002; Baldauf, 2003; Cavalier-Smith, 2004), or howmany species are present in a given environment(Jeon et al., 2006).

One of the main reasons for such a lamentablestate of affairs is the extent of protistan diversity.The number of protistan life forms appears tooverwhelm our current abilities to completelyinventory all species in a single sample from mostenvironments, let alone get a complete representa-tion of the community present in the ecosystemfrom which this sample was drawn. Over the past 10years, numerous studies have applied ribosomalRNA (rRNA) gene sequencing in attempts to

Received 31 May 2010; revised 10 December 2010; accepted 14December 2010

Correspondence: S Epstein, Department of Biology, NortheasternUniversity, 360 Huntington Avenue, 313 Mugar Life SciencesBuilding, Boston, MA 02115, USA.E-mail: slava.epstein@gmail.com

The ISME Journal (2011), 1–13& 2011 International Society for Microbial Ecology All rights reserved 1751-7362/11

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describe the surprising diversity of microbial eukar-yotes (for example, Diez et al., 2001; Lopez-Garciaet al., 2001; Moon-van der Staay et al., 2001; Moreiraand Lopez-Garcia, 2002). Most of these studies werelimited to o1000 clones sequenced (Christen, 2008),forcing researchers to focus on single samples. Asshown by the preponderance of sequences regis-tered only once in the library, and by statisticalmodeling (Hong et al., 2006; Jeon et al., 2006, 2008),current practices have universally resulted in onlypartial recovery of any single sample’s diversity andrichness, have provided little to no sample replica-tion within a particular environment, and haveproved insufficient to settle biogeographic argu-ments regarding the global scale of microbialeukaryotic diversity, their degree of endemicity,and even the species richness of a single sample.High-throughput pyrosequencing technology pro-mised to address methodological shortcomings byrecovering uncommon, perhaps even exceedinglyrare species (Sogin et al., 2006; Huber et al., 2007),but the short read lengths of 454 sequences made itnecessary to rely on the existing long rRNA genesequences to establish taxonomic identities. Also,concerns remain about the role that sequencingerrors may play in producing an artifactual pictureof the sample’s richness (Kunin et al., 2010). Tocomplicate matters further, the design of PCRprimers used for both Sanger and pyrosequencingapproaches may be significantly biased in theirrecovery of protists, possibly creating a distortedview of the extant richness and diversity. Underthese circumstances, comparing two species lists isdifficult and these problems are compounded byuncertainties about nonparametric statistical meth-ods commonly used to estimate the size of thespecies pool.

In these companion papers, we attempt to addressat least some of the limitations of the studiespublished to date. Our experimental plan was to(i) focus on an environment rather than on a sample,(ii) scale up sequencing efforts proportionally tothe large number of samples required to surveythis environment, (iii) employ a dual sequencing(Sanger/pyrosequencing) strategy, (iv) minimize thebiases of both approaches (v) and analyze themolecular diversity data using statistical tools wedeveloped for the purpose of estimating microbialrichness. With these goals, we established in 2004 amulti-year Microbial Observatory in the CariacoBasin off the coast of Venezuela. The Cariaco Basinis the world’s largest, truly marine anoxic systemand it offers a large number of contrasting biogeo-chemical habitats that can be sampled in a singlehydrographic cast. We sampled the Basin exten-sively at three stations, in two contrasting seasons,each time at four depths and across strong biogeo-chemical gradients spanning fully oxygenated layersto deep, highly sulfidic habitats. We believe thatthese samples represent fairly broad coverage of theBasin’s geochemistry, its protists, their spatial

heterogeneity and seasonal dynamics. In this firstpaper, we present the largest, to date, set of protistangene sequences from an environment 4800-nt-longeach (16 000 total, 6489 protistan) for the purpose ofproviding reliable statistical estimates of total proti-stan richness within our samples, with meaningfulstandard error. To minimize bias, we employed amultiple PCR primer strategy, and also complemen-ted our Sanger clone libraries with B82 000 qualityprotistan sequences using massively parallel pyro-sequencing of amplicons from the V9 region. Thisdual sequencing approach enabled the first directcomparison of environmental 18S rRNA genesequence diversity obtained by these two methods.The two sequence technologies showed substantialsimilarity in the types of protists recovered, and intheir relative proportions. This tentatively indicatedthat both approaches represented the target diversityreasonably well.

Materials and methods

For more details see Supplementary Materials andmethods.

Sample collectionSamples were collected from three stations inthe Cariaco Basin, Venezuela: Station A (10.501N,64.661W), Station B (10.401N, 64.461W) and StationC (10.401N, 65.351W) (Figures 1 and 2, Supplemen-tary Table S1). Samples for DNA extractions werecollected at depths corresponding to 40 m abovethe oxic/anoxic interface, the oxic/anoxic interface,40 m below the interface and at 900 m. The oxic/anoxic interface was defined as the depth at whichoxygen became undetectable. This depth usuallycorresponded to a particle density maximum detec-ted by transmissometry. Samples were withdrawn

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Figure 1 Map of study site. Station A represents the site of theUS–Venezuelan Cariaco biogeochemical time series programStation A in the eastern sub-basin. Station B is a shallowerstation south of the Tortuga channel, the source of incomingCaribbean waters. Station C is centered in the less productivewestern sub-basin.

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from Niskin bottles under N2 pressure into sterile,intravenous bags immediately after retrieval andstored at in situ temperatures. Samples werethen filtered onto 47 mm Durapore membranes(Millipore, Billerica, MA, USA) (0.45 mM pore size)onboard, with little to no exposure of the sample tothe atmosphere. Depending on the cell concentra-tion in each sample, 2.5–2.8 l of seawater were

filtered per sample under gentle vacuum(o25 cm Hg) until no passage of water throughfilters was observed. The variable filtration volume(filtering until passage of water through the filterceased) does not completely control for differencesin population abundances between our samples, butit likely reduces biases that may be introduced bypotentially unequal sampling efforts. Filtration was

Figure 2 Vertical profiles of oxygen, hydrogen sulfide and bacterial and flagellate abundances at the time of sampling for protistan DNAin (A) January and (B) May 2005; modified from Taylor et al. (2001) and Scranton et al. (2006).

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conducted under a gentle vacuum (o25 cm Hg).Membranes were stored in cryovials containingDNA extraction buffer (Stoeck et al., 2003) at�20 1C until further processing.

DNA preparation and PCR amplification of 18Srecombinant DNAHigh-molecular-weight DNA was prepared usingpreviously described methods (Stoeck et al., 2003,2007). DNA quality was checked by agarose gelelectrophoresis (1%), and the DNA yield wasquantified using a Nanodrop ND-1000 UV–Vis spec-trophotometer (Nanodrop Technologies, Wilmington,DE, USA). The DNA yield from a minimum of twofilters per depth at each station was combined beforePCR. The 18S rRNA gene was amplified using fourdifferent primer combinations; Euk528F (50-CGGTAATTCCAGCTCC-30) paired with U1492R (50-GGTTACCTTGTTACGACTT-30), U1391R (50-GGGCGGTGTGTACAARGR-30) or U1517R (50-ACGGCTACCTTGTTACGACTT-30) and Euk360F (50-CGGAGARGGMGCMTGAGA-30) paired with U1492R. All primercombinations were applied to all samples, and PCRproducts from a minimum of three reactions perprimer pair were pooled.

Clone library construction and sequencingTo constrain cost, PCR products for stations B and Cwere pooled from the same depths before clonelibrary construction. This admittedly caused loss ofsome information on distribution of protists, butallowed a more in-depth sequencing of samples,better covering the Basin. Separate clone librarieswere constructed for each of four depths from theeight pooled B/C samples (four from January andfour from May 2005) and from the eight stationA samples, producing 64 different clone libraries.Inserts were sequenced bi-directionally using anApplied Biosystems (Carlsbad, CA, USA) 3730XLcapillary sequencer. Processing of the data usedPHRED, PHRAP (Ewing and Green, 1998; Ewinget al., 1998) and a pipeline script to call bases fromchromatograms and perform quality control proce-dures. The sequences were checked for chimerasusing the Bellerophon Chimera Check and the CheckChimera utilities (Ribosomal Database Project) (Coleet al., 2003). After removal of short sequences(o800 bp), putative chimeras, bacterial, archaeal andmetazoan sequences, remaining sequences weregrouped into operational taxonomic units (OTUs)based on 90%, 95%, 98% and 99% rRNA genesequence similarity levels. This was achieved by firstmaking all possible pair-wise sequence alignments byusing ClustalW (Thompson et al., 1994), calculating% sequence identities, followed by clustering thesequences by using the unweighted pair groupmethod with arithmetic mean as implemented inthe OC clustering program (http://www.compbio.dundee.ac.uk/Software/OC/oc.html). The number ofOTUs and their frequencies at each cutoff value

became the subject of statistical analyses. Allprotistan sequences have been deposited in Gen-Bank under the accession numbers GU819239–GU825728.

Statistical analyses of clone library dataFor each sample, we obtained ‘frequency count’ dataat the 90%, 95%, 98% and 99% sequence identitylevels, that is, the number of OTUs registered in thecorresponding clone library only once (the ‘single-tons’), or twice (the ‘doubletons’), and so on. On thebasis of these frequency count data, we estimatedthe total number of OTUs at each % identity level,representing the sum of seen (empirically registered)and unseen OTUs (present but undetected dueto limited sequencing effort). To do this, we usedthe program CatchAll (Bunge, 2011) to computeeach of five nonparametric (Good-Turing, Chao1,ACE, ACE1 and Chao-Bunge) and eight para-metric (Poisson; negative binomial; inverse Gaus-sian, Pareto and lognormal-mixed Poisson; andmixtures of one, two, or three geometrics) estima-tors, at every right-truncation point of the data(that is, eliminating outliers at every possible cutoffpoint in the data), as described previously (Honget al., 2006, 2009; Jeon et al., 2006, 2008). For thenonparametric analyses, we used a fixed right-truncation point (maximum frequency) equal to 10,and selected optimal analyses as recommended inthe statistical literature (we also compared nonpara-metric results at higher truncation points, but theseare typically nonsensical; Bunge and Barger, 2008).For the parametric analyses, we first eliminated allresults with an asymptotically corrected goodness-of-fit hypothesis test P-value less than 0.01. We thenselected the analysis at each right-truncation pointwith a minimum value of AICc (Akaike’s informa-tion criterion, corrected for sample size). Finally, weeliminated analyses with a standard error greaterthan 1/2 of the total richness estimate. From theremaining results, we selected the one at the highestright-truncation point (so as to use the maximumamount of data) such that an uncorrected w2 good-ness-of-fit hypothesis test P-value exceeded 0.01 (orif this was not possible, the maximum correspond-ing P-value). This yielded one nonparametric andone parametric estimate of total richness for eachsample at each % identity level.

Massively parallel 454 pyrosequencingThe PCR primers we used for 454 sequencing the V9hypervariable region were 1,391F (50-GTACACACCGCCCGTC-30) and EukB (reverse) (50-TGATCCTTCTGCAGGTTCACCTAC-30). For each individualenvironmental DNA extract (each station, depthand season combination), we ran three independentPCR reactions, which were pooled and cleanedusing the MinElute PCR purification kit (Qiagen,Valencia, CA, USA). Pyrosequencing (Roche GS FLX

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Sequencing) was performed by MWG Biotech,Huntsville, AL, USA. In total, we recovered 251 648sequence reads that were subjected to quality control,leaving us with 82 484 protistan sequences for furtherconsideration. Tag sequences have been deposited inthe National Center for Biotechnology Information(NCBI) Short Read Archive under the accessionnumber SRP003469. Annotated tag sequences areavailable from the authors.

Bioinformatic analysis of tag sequencesWe proceeded with our analyses by following apipeline developed earlier for inspecting and qual-ity checking pyrosequencing reads (Stoeck et al.,2009). Sequences shorter than 100 nucleotides, aswell as those with at least one ambiguous position(N), were removed from consideration. To maketaxonomic assignments, we first built a referencedatabase containing all small subunit rRNA genesequences in public databases longer than 500nucleotides. Each sequence tag was compared withthese reference sequences using similarity searcheswith the program BLASTN and requesting up to 30best hits, using the following BLAST parameters:-m 7 -r 3 -q -2 -G 6 -E 6 (Stoeck et al., 2010). We thenparsed the BLAST output to extract taxonomicassignments at a series of thresholds for sequencesimilarity (70%, 75%, 80%, 85%, 90%, 92%, 95%,96%, 97%, 98%, 99% and 100%). Sequencesimilarity was calculated as the sum of identitiesfor non-overlapping (if any) high scoring pairs (seethe BLAST documentation) divided by the length ofthe query sequence. For each query, we used onlythe most similar target sequence for which a goodtaxonomic assignment was provided (that is, longerdescription in the OC field in the EMBL entries). Toenable direct comparison between taxonomic as-signments of 454- and Sanger-produced sequences,the above BLAST analyses were repeated for nearlyfull-length 18S rRNA gene sequences, and sepa-rately for their V9 domains.

Results

Overall protistan diversity in the Cariaco BasinWe examined the extent of protistan diversity in theBasin in two ways. First, we amplified the 18S rRNAgene from community DNA, followed by cloningand sequencing the amplicons. From each of the 64libraries, we sequenced an average of 250 clones,resulting in over 16 000 rRNA gene sequences, orat a minimum, 672 sequences for each of the16 samples. Culling of confirmed or suspectedmetazoan, chimeric and short (o800 bp) sequencesleft 6498 good quality target 18S protistan sequencesfor further analyses. Supplementary Table S2 pre-sents the final number of sequences, grouped intoOTUs sharing over 99% of gene sequence identity,and categorized by PCR primer pair, season, depth

and site. Second, we used the same DNA extracts astemplates for 16 separate PCR reactions followed bypyrosequencing of the V9 region of the 18S rRNAgene. This produced a collection of over quarter of amillion reads, with 173 723 reads meeting ourquality control criteria, 52% of which were metazo-an and other non-target sequences.

It is important to demonstrate the agreementbetween the taxonomic assignments establishedfrom the nearly full-length rRNA gene sequencesvs the V9 tags contained therein. We achieved thisby extracting the V9 region from 3107 of our clonesequences that contained this region, and comparingthe taxonomic assignments made by BLAST. Sup-plementary Table S3 shows a general global agree-ment, as well as several minor differences. Theproportion of sequences falling into the unassigned/unclassified categories is larger for the V9 tags andthis effect appears stronger in Alveolata. We notethat for taxonomic assignment of the pyrosequen-cing tags, we chose the X70% sequence similaritythreshold because at this level fewer tags remainedunassigned than at higher thresholds.

At the highest level of taxonomic assignment,approximately corresponding to kingdoms andphyla, the frequencies of different rRNA genesequences appear similar between the conventionalclone library/Sanger- and pyrosequencing-basedmethods (Figure 3). Both collections were domi-nated by Alveolata, Rhizaria, Stramenopiles andunclassified 18S rRNA gene sequences, and in thesame order of dominance, followed in both cases byFungi and Euglenozoa. The most visible differencebetween Sanger- and pyrosequencing-based meth-ods at this level of taxonomic assignment is thenumber of rarer taxa constituting o1% of respectivesequence collections: 9 vs 14, respectively.

These gross similarities in the overall recovery ofprotists by the two methods were observed at lowerlevels of taxonomic assignment as well. For exam-ple, in both sequence collections, the Alveolata weredominated by the ciliate subphylum Intramacro-nucleata and four dinoflagellate orders (Gymnodi-niales, Prorocetrales, Syndiniales and Gonyaula-cales), as well as unclassified alveolates (Figure 4).Similarly, Stramenopiles were dominated by thesame taxa regardless of the method used to detectthem (data not shown). Expectedly, pyrosequencingdetected more of the rare taxa (again defined asthose comprising o1% of the entire collection): 11vs 8 rare stramenopile taxa registered using clonelibraries/Sanger sequencing. Classification to orderwas the lowest taxonomic level possible for themajority of our tag sequences. At this level, Sangersequencing and pyrosequencing again recoveredsimilar groups at similar frequencies (Robertsonand Burke, 1989). For example, within the dino-flagellate class Dinophyceae, almost three quartersof all sequences were represented by three orderspresent at essentially the same proportions inour Sanger- and pyrosequencing-based data sets

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(Gymnodiniales, Peridiniales and Prorocentrales).The other abundantly represented orders werealso in the same proportions (Syndiniales andGonyaulacales) (Figure 5). We note that these grosssimilarities between the two libraries appear inde-pendent of the cutoff level of query/hit similarityused to assign V9 sequences to taxa by BLAST, asraising this cutoff to 80% did not affect the overallpattern (data not shown).

Biases of the rRNA gene sequence collectionTo generate conventional 18S rRNA gene clonelibraries, we used four different PCR primer setsapplied to each DNA extract obtained. Table 1

provides a representative subset of these datasummarizing the number of occurrences of ciliatesby primer set. We chose to present data for this groupbecause we recovered roughly the same number ofrRNA gene sequences (E100) for each of the four PCRprimer sets, which simplified the comparison of theirrespective performances. Also, the relatively large sizeof the ciliate 18S rRNA gene sequence collectionminimized the influence of stochastic variation on thepattern of recovery. Differential recovery of severalciliate clades illustrates the substantial biases of thePCR primer pairs used, such as an apparent discri-mination against Euplotida by the 360F/1492Rpair, or discrimination for Grossglockneriidae andChoreotrichida by the 528F/1391R pair.

Figure 3 Phylum- and kingdom-level assignments of the 18S rRNA gene sequence collections obtained by conventional cloning/Sangersequencing and 454 pyrosequencing approaches.

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Total protistan richness in the Cariaco BasinWe clustered protistan 18S rRNA gene sequencesrecovered from our clone libraries into OTUs atdifferent levels of sequence identity. The 6498individual sequences fell into 2099, 1496, 767 and392 OTUs at 99%, 98%, 95% and 90% identitylevels, respectively. We then reconstructed the OTUfrequency distributions (Figure 6), and modeledthese distributions to estimate the size of microbialrichness that must exist in our samples, and there-fore in the Basin to account for the empirical datacollected, using the program CatchAll (Bunge,2011). In accordance with previous experience(Bunge and Barger, 2008; Jeon et al., 2008; Honget al., 2009), the best-fitting models are finitemixtures of one, two or three geometric distribu-tions, while the Poisson, negative binomial, inverseGaussian-, Pareto- and lognormal-mixed Poissonprovided inferior fits or presented other statisticalanomalies (data not shown). Typically, higher ordermixtures are required to produce acceptable rich-ness estimates and goodness-of-fit assessments thatsatisfy our criteria (see Materials and methods). Forpurely comparative purposes, Table 2 also presents

the richness predictions based on nonparametricrichness lower bound Chao1 and the nonparametricestimator ACE1; these are shown only for compar-ison with the parametric estimates.

The estimates of the total protistan richness in oursamples based on two- and three-mixed exponentialdistributions show that they may harbor thousandsof protistan genera (defined as OTUs formed at90–95% gene sequence similarity), and up to tens ofthousands of protistan species (defined as OTUsformed at 98–99% gene sequence similarity), in-dicating that our empirical collection sampled about20–30% of all the genera, and about 5–10% of all thespecies in our samples. By extension, these areminimal estimates of protistan richness in the entireBasin.

Discussion

The first of our companion papers offers a generaldescription of the Cariaco Basin, the overall view ofprotistan diversity we detected, and a comparison ofthe 18S rRNA gene sequence collections produced

Figure 4 Subphylum and class level assignments of the alveolate 18S rRNA gene sequence collections obtained by conventionalcloning/Sanger sequencing and 454 pyrosequencing approaches.

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by the tandem approach of two alternative sequen-cing technologies (Sanger and pyrosequencing).The Cariaco Basin is an excellent model environ-ment to study protistan species richness, phyloge-netic diversity, habitat specialization, communitystructure, its dynamics and global uniqueness. Thisis because the Basin is a mosaic of dramaticallydifferent biogeochemical niches, and has existed assuch for millions of years (Robertson and Burke,1989), though it likely went through periods ofoxidation (Peterson et al., 2000). The Cariaco watercolumn progresses from fully oxic to sulfidic acrossa temporally varying boundary between 250 and

350 m. Within the redox transition zone lie stronggradients in O2, NO3

�, H2S, NH4þ , NO2

�, PO43� and

CH4, and enrichments in S2O32�, SO3

2�, S0, Mn2þ andFe2þ that select for specific bacterial and archaealphylotypes (Taylor et al., 2001; Scranton et al., 2006;Li et al., 2008; Lin et al., 2008). This transition zoneexhibits a peak in prokaryotic metabolic activity andcell numbers, which often coincides with peaks inprotist cell numbers.

Protists are important members of aquatic micro-bial communities because of their autotrophic andheterotrophic activities. Their grazing of prokaryoticand other eukaryotic microbes regenerates nutrients,

Figure 5 Order assignments of the Dinophyceae 18S rRNA gene sequence collections obtained by conventional cloning/Sangersequencing and 454 pyrosequencing approaches.

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modifies or re-mineralizes organic matter (for ex-ample, Taylor, 1982; Taylor et al., 1985; Sherr andSherr, 2002), and can affect the quantity, activity andphysiological state of their prey (Madsen et al.,1991; Frias-Lopez et al., 2009). Grazing releasesinorganic nutrients that often limit primary produc-tion, and makes organic carbon available to highertrophic levels (Berman et al., 1987). In our compa-nion paper (Orsi et al., 2011), we identify many newclades at species to class levels, some of whichappear restricted to specific layers of the watercolumn and have a significantly nonrandom dis-tribution. We also use multivariate communityanalyses coupled with parametric richness esti-mates for selected regions of the Basin to demon-strate that distinct communities of protists existwithin close proximity (40 m) to each other. Thesefindings suggest many pelagic protists are specia-lized to specific habitats, and likely diversify, at

least in part due to separation by geochemicalbarriers. For a discussion of the influence ofbiogeochemical factors on protistan communitystructure, see Orsi et al. (2011).

Overall protistan diversity in the Cariaco BasinOwing to the scale of sequencing efforts, weanticipated and demonstrated recovery of the ex-pectedly large diversity that contains representa-tives of all major clades (Figure 3). The remarkablesimilarity between the results produced by the twosequencing approaches employed came as a sur-prise. Indeed, at all three hierarchical levels exam-ined, both sequence collections were dominated bythe same taxa, often present at essentially the samefrequencies (Figures 3–5). Such comparisons arerare, and, to the best of our knowledge, ours is thefirst published report of such a comparison forprotists. Although similarity at the highest level oftaxonomic hierarchy (Figures 3, 4) is not counter-intuitive, we find it remarkable that the overallsimilarity between compositions of the dominantclades holds in many cases down to the level ofprotistan orders (Figure 5). For example, threedinoflagellate orders, Gymnodiniales, Peridinialesand Prorocentrales comprised 72% of either seq-uence collection, at essentially the same frequen-cies. We did not extend these analyses to the levelsof family or genus because this level of assignment isnot currently reliable for pyrosequencing data. Thedifferences between the two sequence collections donot appear until we consider relatively rare taxawith frequencies in single percentage points of thetotal respective databases. Phyla represented by lessthan 1% of the total number of sequences are morenumerous in the collection produced by pyrose-quencing than Sanger sequencing (Figure 3), whichwe attribute to the sequencing depth afforded by theformer method. We note, however, that the impact ofpyrosequencing errors on recovered diversity hasnot yet been fully established (Kunin et al., 2010),and we interpret the rare taxa with caution.

Biases of the rRNA gene approach and theirminimizationOne of our goals was to produce a minimally biasedmolecular survey of protists, and we attempted tominimize the PCR primer biases (Suzuki andGiovannoni, 1996; Polz and Cavanaugh, 1998; Jeonet al., 2008; Hong et al., 2009) by using a multiple-primer approach (Stoeck et al., 2006). Recently, ourgroup showed statistically that no tested pair ofconserved primers could provide amplification ofall templates present in the DNA extract, and that acombination of PCR reactions each using differentprimers led to qualitatively richer clone libraries forboth protists (Jeon et al., 2008) and bacteria (Honget al., 2009). The difference in performance of thefour primer pairs used here is illustrated in Table 1,

Table 1 Differential recovery of ciliate taxa by four different PCRprimer sets

3F14R 5F13R 5F14R 5F15R

Choreotrichida 0 1 0 0Colpodidae 0 11 0 0Cryptocaryon 1 0 0 0Epalxellidae 1 0 0 0Euplotida 0 4 7 10Furgasoniidae 0 1 0 0Grossglockneriidae 9 74 3 46Halteriidae 1 0 0 0Hausmanniellidae 0 12 0 0Nassulidae 1 0 1 0Parameciidae 0 1 0 0Philasterida 0 3 0 4Pleuronematida 8 0 31 11Prorodontidae 0 0 0 1Stichotrichida 79 0 58 32Strombidiidae 4 0 5 13Tintinnida 0 0 1 0Total 104 107 106 117

Figure 6 The fit of the three-mixed exponential distribution(solid line) to the empirical data points (filled circles, OTUsclustered at 99% 18S rRNA gene sequence identity).

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which gives examples of organisms completelymissed from the clone libraries created by all butone PCR primer set, or organisms universallydetected by all but one primer pair. Note that forTable 1 we chose to present representatives of thephylum Ciliophora. Such a choice is conservativebecause PCR primers have been designed using asignificant number of ciliate sequences, potentiallyleading to less discrimination against these organ-isms compared with less studied groups.

We tentatively conclude that the multiple PCRprimer approach provided a more balanced view ofmicrobial composition in the Cariaco Basin, and thatthis study is less biased than the majority ofprevious protistan surveys (including, for example,our own earlier study of the Cariaco Basin (Stoecket al., 2003). Coupled with the size of the data set,we are well-positioned to model distributions inorder to estimate the total number of protists in theBasin, including those missed by our libraries.

Total protistan richness in the Cariaco BasinWe used a new statistical approach (Hong et al.,2006) to model OTU frequency distributions and toprovide estimates of the total protistan richness inour samples, and thus conservatively in the entireBasin (The estimate of total taxonomic richness forour samples is an estimate of the total richness thatwould be observed if sampling were carried outusing the same procedures, but with infinite effort,that is, exhaustively. It is also an approximation ofthe total richness of the Basin, under the assump-tion that the conditions occurring in the indivi-dual samples are ‘representative’, that is, that the

conditions of the individual samples representan approximately random sample within a givenbiogeochemical regime extant in the Basin. Thelocations for sample collection were carefullyselected based on a decade of previous Cariacotime series data showing the three sites and fourdepths we sampled to be representative of the rangeof biogeochemical conditions in the Basin. It is,however, possible that distributions of OTUs in theBasin are even patchier than we detected because ofthe additional variations in conditions that we didnot sample. This would cause an unknown numberof OTUs to be missed in our samples. If this is thecase, our estimates of the total diversity are likely tobe conservative). Our group developed this methodas a general tool for richness estimation (Hong et al.,2006) and later applied it to microbial eukaryotes(Jeon et al., 2006; Stoeck et al., 2007; Edgcomb et al.,2009). Here we produced a sequence collection largeenough to be modeled by our parametric statisticalmethod, with an appropriate goodness-of-fit, andmost importantly to obtain a standard error suffi-ciently low as to not render the estimate uninforma-tive. We compared essentially all known parametricand nonparametric estimates for the total richness,along with associated statistics, including standarderrors and goodness-of-fit diagnostics. Our dataalso illustrated a potential underestimation of totalrichness by nonparametric estimators. Table 2shows that, at the level of OTUs based on 99% genesequence identity, the OTUs empirically observed inour samples constitute about 10% of all predicted tobe present, and the difference between richnesspredictions based on parametric vs nonparametricapproaches is substantial. As OTUs become more

Table 2 Predicted richness of protistan assemblages in the Cariaco Basin, with associated statistics

% Identity ofsequences within OTU

Statistic Best parametric model Nonparametric estimators

X99 Three-mixed exponential Chao1 ACE1Estimated total number of OTUs 35968 8960 17335

s.e. 13123 613 1490CI 18373–72596 7860–10269 14684–20545

GOF 0.1396 NA NAX98 Three-mixed exponential

Estimated total number of OTUs 12480 4351 7198s.e. 3489 282 601CI 7480–21666 3850–4959 6137–8503

GOF 0.8204 NA NAX95 Two-mixed exponential

Estimated total number of OTUs 4039 1951 2799s.e. 798 156 280CI 2815–6015 1685–2300 2323–3427

GOF 0.0598 NA NAX90 Three-mixed exponential

Estimated total number of OTUs 1143 748 1029s.e. 162 74 130CI 887–1532 630–924 821–1338

GOF 0.6788 NA NA

Abbreviations: CI, confidence interval; GOF, goodness-of-fit; NA, not applicable; OTU, operational taxonomic unit.GOF (P-value for the corrected Pearson w2 goodness-of-fit test).

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inclusive, and the portion of the sampled richnessincreases, such differences become smaller; forOTUs based on 90% gene sequence identity, theparametric modeling- and nonparametric estima-tors-based richness predictions converge on essen-tially same values.

Our approach enabled us to predict how manyOTUs must be present in our samples, and thusconservatively in the Basin: 35 968±13 123 (s.e.)and 4039±798 (s.e.) for OTUs comprised of 18SrRNA gene sequences sharing 499% and 495%identity, respectively. These OTU cutoffs areroughly comparable to species and genera. We arguethat this is the first estimate of total protistanrichness that is relatively unbiased, and withrealistic standard errors. We note that, althoughindividual clone libraries sampled the respectiveenvironments to varying degrees (that is, similarly-sized libraries cover diversity differently if the targethabitats have different richness), this has no bearingon our estimates. Indeed, the sample size does notinfluence the value of the sample’s predictedrichness, only the standard error of its estimate.Some bias in the estimators of total richness may bepresent for small samples, but this is not a majorfactor here. Furthermore, differences in samplingintensity may have been minimized by the variablefiltration volumes we applied (filtering until filtersclogged—see above).

It is instructive to compare our results to theprevious richness estimates obtained using a single3-l sample from the Cariaco Basin (395±153 (s.e.)and 164±40 (s.e.) at 99% and 95% OTU clustering,respectively; Jeon et al., 2006). Samples in this studycovered temporal, spatial and biogeochemical varia-tions in the Basin, and resulted in estimates ofdiversity that are 2–3 orders of magnitude higher.This indicates a very patchy distribution of speciesover the volume of the Basin, and tentativelysuggests the possibility of a high degree of habitatspecialization.

The debate over the degree to which protistcommunities exhibit global distribution has notbeen fully resolved (Foissner, 1999, 2006; Lawleyet al., 2004; Boenigk et al., 2006; Bass et al., 2007;Caron, 2009). We would like to emphasize that allexisting knowledge of microbial diversity comesfrom samples, and, strictly speaking, is limited tothe collection of samples analyzed so far—ratherthan environments from which they were obtained.Under these circumstances, a reasonable analysiswould be to compare a proxy for global richness,such as the total number of protistan species knownfrom samples collected to date, to a proxy forrichness in a large and varied environment, suchas a collection of samples covering its multitude ofhabitats. This collection should be reasonably largebecause, as Caron (2009) and Ramette and Tiedje(2006) argue, a species could be globally distributedeven if it is not found in each and every sample. Theexpress purpose of our study was to overcome a

common limitation of microbial diversity studies,typically based on one to a few samples perenvironment. We did so by focusing on a largesystem, and revisiting multiple locations in differentseasons, and sampling across its principal biogeo-chemical regimes, without sacrificing the per sam-ple depth of sequence coverage. It should be notedthat not all protist populations were sampled in thisstudy because we intentionally excluded the photiczone. There is an additional important caveat: thetotal richness, whether in a single sample orcollection of samples, can only be estimated statis-tically, as even these pyrosequencing efforts do notallow for a complete coverage of sample(s) richness.The statistical tools typically used in microbialdiversity studies do not necessarily work well forthe purpose (Chao and Bunge, 2002). The state-of-the-art statistical tool kit we developed, coupledwith a large-scale survey of an environment as largeand chemically diverse as the Cariaco Basin, meetthe above requirements, and provide us with what isneeded for a proxy for total protistan richness in theaphotic zone of the Cariaco Basin. Our analysesshow that this proxy, or the number of species ofmicrobial eukaryotes predicted to exist in thecollection of our samples, appears to be a substantialfigure, but it falls far short of the total number ofprotistan species described to date (100 000–500 000species (Corliss, 1982; Adl et al., 2007)) by being 3 to15 times lower, suggesting a degree of endemism inthis environment.

Conclusions

As part of the Cariaco Microbial Observatory, wecollected the largest, and possibly the least biased,collection of protistan molecular signatures to datefrom a single environment. Two sequencing ap-proaches produced globally similar pictures ofprotistan diversity, which contain representativesof all major protistan clades known today. Para-metric statistical modeling predicted the totalnumber of species in samples spanning differentlocations, seasons and a range of diverse habitatswithin the Cariaco Basin. This number is far lowerthan the global known richness, indicating a degreeof endemicity in the distribution of the majority ofmicrobial eukaryotes.

Acknowledgements

We thank the captain and crew of the B/O Hermano Ginesand the staff of the Fundacion La Salle de CienciasNaturales, Margarita Island for their assistance during ourfield work in Venezuela, particularly Yrene Astor. We aregrateful to the dedicated researchers of the CARIACObiogeochemical time series program, without whom thiswork would not have been possible. We would like tothank Valentin Ilyin (Northeastern University), DavidMark Welch (MBL) and Jed Goldstone (WHOI) for helpful

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discussions on bioinformatics approaches, Hilary Morrisonand Rich Fox (MBL) for the use of their pipeline scripts forsequence data processing, and the Academic ResearchComputing User Group at Northeastern University forpermitting us to use the Opportunity computer cluster. Wethank Linda Woodard for supervising the statisticalcomputations. Comments from two anonymous reviewerssignificantly improved this manuscript. This research wasconducted using the resources of the Cornell UniversityCenter for Advanced Computing, which receives fundingfrom Cornell University, New York State, the NationalScience Foundation, and other leading public agencies,foundations and corporations. This research was sup-ported by grants from NSF (MCB-0348341 and DEB-0816840 to SSE, DEB-0816638 to JB, MCB-0348407 to VEand OCE 03-26175 and MCB-03-47811 to GTT), from ANR-Biodiversite project Aquaparadox and the ERA-net projectBiodivERsA BioMarKs program to RC, and from VenezuelaFONACIT (nos. 96280221 and 2000001702 to RV). This isa contribution # 268 from the Marine Science Center,Northeastern University, Nahant, MA, USA.

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