ORIGINAL ARTICLE

Marine bacterial, archaeal and protistan associationnetworks reveal ecological linkages

Joshua A Steele, Peter D Countway, Li Xia, Patrick D Vigil, J Michael Beman, Diane Y Kim,Cheryl-Emiliane T Chow, Rohan Sachdeva, Adriane C Jones, Michael S Schwalbach,Julie M Rose, Ian Hewson, Anand Patel, Fengzhu Sun, David A Caron and Jed A FuhrmanDepartment of Biological Sciences and Wrigley Institute for Environmental Studies, University of SouthernCalifornia, Los Angeles, CA, USA

Microbes have central roles in ocean food webs and global biogeochemical processes, yet specificecological relationships among these taxa are largely unknown. This is in part due to the dilute,microscopic nature of the planktonic microbial community, which prevents direct observation oftheir interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigatetime-dependent variations among taxa from all three domains of life in a marine microbialcommunity. We investigated the community composition of bacteria, archaea and protists throughcultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among theseorganisms, we calculated time-dependent rank correlations (that is, local similarity correlations)among relative abundances of bacteria, archaea, protists, total abundance of bacteria and virusesand physico-chemical parameters. We used a network generated from these statistical correlationsto visualize and identify time-dependent associations among ecologically important taxa, forexample, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizingarchaea. Negative correlations, perhaps suggesting competition or predation, were also common.The analysis revealed a progression of microbial communities through time, and also a group ofunknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbiosesor parasitism. Possible ‘keystone’ species were evident. The network has statistical features similarto previously described ecological networks, and in network parlance has non-random, small worldproperties (that is, highly interconnected nodes). This approach provides new insights into thenatural history of microbes.The ISME Journal (2011) 5, 1414–1425; doi:10.1038/ismej.2011.24; published online 24 March 2011Subject Category: microbial population and community ecologyKeywords: co-occurrence patterns; stramenopiles; dinoflagellates; SAR11; cyanobacteria; time series

Introduction

The past two decades have seen a revolution incharacterizing microbial communities that performcentral roles in ocean food webs (Sherr and Sherr,2008; Caron, 2009; Fuhrman, 2009) and biogeo-chemical processes (Ducklow, 2000) while makingup the majority of global biomass (Pace, 1997; Diezet al., 2001; Fuhrman, 2009). Translating thatinformation into understanding of the actions andinteractions of these microbes in complex systemshas been difficult. Cultivation (Giovannoni andStingl, 2007), gene chips (DeSantis et al., 2007)and metagenomic or transcriptomic efforts (Rusch

et al., 2007; DeLong, 2009) have provided remark-able information on the potential ecological roles ofmicroorganisms, but these fundamentally reduc-tionist approaches do not readily describe theinteractions among microbes within a communityor with their environment. Unlike the situation withanimals and plants, only rarely can we observeinteractions among microbes, such as grazing byprotists (Sherr and Sherr, 2008) or localized syn-trophy (Orphan et al., 2001); characterizing wholecommunities of microbes poses major challenges.We recently began investigating the use of networkanalysis based upon natural environmental co-occurrence patterns to examine the complex inter-actions among microbes and their environment,initially looking at bacteria and environmentalparameters only (Ruan et al., 2006; Fuhrman andSteele, 2008; Fuhrman, 2009). Other recent reportshave also started to apply similar approaches, suchas a meta-analysis of public 16S rRNA databases todemonstrate co-occurrence networks of microbes,

Received 2 November 2010; revised 4 February 2011; accepted 4February 2011; published online 24 March 2011

Correspondence: JA Steele, Division of Geological and PlanetarySciences, California Institute of Technology, Mail Code 100-23,Pasadena, CA 91125, USA.E-mail: josh.a.steele@gmail.com

The ISME Journal (2011) 5, 1414–1425& 2011 International Society for Microbial Ecology All rights reserved 1751-7362/11

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including possible symbiotic associations and con-nections with environmental metadata (Chaffronet al., 2010). A related approach has been used toshow patterns from changes in functional geneexpression in soil bacteria during a CO2 additionexperiment (Zhou et al., 2010). These studiespublished to date have included only bacteria andsome basic environmental parameters, but littleinformation on the rest of the microbial community(for example, archaea and protists) that are them-selves inherently of great interest and also no doubtinfluence the bacteria. To fully examine the factorsthat control microbial ecological processes, we havein this study expanded our co-occurrence networkanalysis to include a broad variety of bacteria,archaea and protists, as well as environmentalparameters.

We investigated linkages within a microbialplankton community in response to changing en-vironmental conditions by sampling a biologicalfeature, the subsurface chlorophyll maximum layer,off the southern California coast monthly fromAugust 2000 to March 2004. Co-occurrence patternsof all variables over time, with and without timelags, were calculated and the results were used tocreate visual networks of parameters. These showwhich microbes co-occur or do not co-occur, and theenvironmental conditions that correlate positivelyor negatively with these relationships. These net-works reveal elements of the natural history ofvarious microbes without isolation, enrichment ormanipulation, and our application of this approachdiscovered numerous interesting features of thissystem, including possible ‘keystone’ species.

Materials and methods

Site and sample collectionMonthly samples were collected from the deepchlorophyll maximum depth in the University ofSouthern California Microbial Observatory at theSan Pedro Ocean Time Series site at 331 330 N, 1181240 W offshore from Los Angeles, CA, USA. This siteand the bacterial, eukaryotic and archaeal commu-nities have been previously characterized (Fuhrmanet al., 2006; Countway and Caron, 2006; Bemanet al., 2010; Countway et al., 2010).

Characterization of the microbial communityTo identify and estimate changes in the relativeabundance of hundreds of different types of bacter-ia, eukarya and archaea, we employed molecularfingerprinting techniques and quantitative PCRcoupled with rRNA clone libraries. Briefly, thebacterial community composition was estimated byautomated ribosomal intergenic spacer analysis(ARISA) using 16S-internal transcribed spacer-23SrRNA gene clone libraries to identify the ARISApeaks, as described previously (Fuhrman et al.,

2006; Fisher and Triplett, 1999; Brown et al., 2005).The protistan community composition was esti-mated by terminal restriction fragment length poly-morphism (TRFLP) using 18S rRNA gene clonelibraries to identify fragments as described pre-viously (Countway et al., 2005, 2010; Caron et al.,2009; Vigil et al., 2009). The archaeal populationswere estimated by quantitative PCR of 16S rRNAgene and amoA genes as described in Beman et al.(2010). For the ARISA and TRFLP analyses, frag-ment lengths were used as genetic signatures thatdistinguish specific organisms, although not allfragments can be linked to specific microbial taxaat this time. DNA quantities were standardizedbefore amplification with the ARISA and TRFLPprimers and standardized again before beinganalyzed on the ABI-377XL sequencer (AppliedBiosystems, Foster City, CA, USA; ARISA) and BeckmanCEQ8000 sequencer (Beckman Coulter, Fullerton,CA, USA; TRFLP). Internal size standards wereincluded with each sample and the ARISA andTRFLP fluorescent peaks were conservativelybinned in the manner described in Fuhrman et al.(2006). Changes in relative abundance were exam-ined by ranking only within taxa, not between them,to obviate PCR quantitation artifacts (Fuhrmanand Steele, 2008; Fuhrman, 2009). We measuredtemperature, salinity, density, sample depth, nutri-ents (NO2, NO3, SiO3 and PO4), dissolved oxygen,pigments (chlorophyll a and phaeophytin), totalabundance of bacteria and viruses, and estimatedthe bacterial heterotrophic growth rates using3H-leucine and 3H-thymidine incorporation follow-ing previous studies (for example, Fuhrman et al.,2006; Countway et al., 2010).

Statistical and network analysesTo examine associations between the microbialpopulations and their environment, we analyzedthe correlations, of the microbial operational taxo-nomic units (OTUs) with each other and with bioticand abiotic parameters over time using localsimilarity analysis (Ruan et al., 2006). Local simi-larity analysis calculates contemporaneous andtime-lagged correlations based on normalizedranked data and produces correlation coefficients,referred to in the text as local similarity correlations(ls), that are analogous to a Spearman’s rankedcorrelation (Ruan et al., 2006). Any parameters(OTUs or physico-chemical parameters) whichoccurred in o6 months were excluded from theanalysis. We included 212 variables in the analysis:96 bacterial OTUs, 97 eukaryotic OTUs and 4archaeal groups defined by quantitative PCR (TotalArchaea, Crenarchaea, Euryarchaea and amoA-con-taining archaea), 9 physico-chemical measurements(temperature, salinity, density, depth, nutrients anddissolved oxygen) and 6 biotic measurements(bacterial abundance, viral abundance, hetero-trophic bacterial growth rates and pigments). To

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sort through, condense and visualize the correla-tions generated by the analysis, we used Cytoscape(Shannon et al., 2003) to create networks showingthe 212 variables with 1005 significant local simi-larity correlations (Pp0.01) with a false discoveryrate (that is, q-value) o0.062 (Storey, 2002). Falsediscovery rates are calculated from the observedP-value distribution and vary with the P-value (forexample, for the connections with P¼ 0.01, 0.005,0.001 and q¼ 0.062, 0.043, 0.014, respectively).These correlation networks include nodes thatconsisted of OTUs—as a proxy for ‘species’ (Brownet al., 2005; Countway et al., 2005; Vigil et al., 2009;Beman et al., 2010)—defined by molecular finger-printing, and the biotic and physico-chemicalenvironmental parameters; the local similarityscores (rank correlations that may include a 1-monthtime lag) among the taxa and the environmentalparameters constituted the edges, that is, connec-tions between nodes.

Three network topology characteristics for ournetwork and for a random network of identical sizewere calculated using Network Analyzer in Cyto-scape (Assenov et al., 2008): Clustering coefficient(Cl), the average fraction of pairs of species one linkaway from a species that are also linked to oneanother (Watts and Strogatz, 1998; Albert andBarabasi, 2002); characteristic path length (L), theaverage shortest path between all pairs of species(Watts and Strogatz, 1998); and degree distribution,the nodes which have k edges and the probabilityP(k) that a node will have k edges (Watts andStrogatz, 1998; Dunne et al., 2002; Oleson et al.,2006). We treated edges as undirected when calcu-lating path length and clustering, because therelationships are correlative and could imply inter-actions in either direction. Best fit estimates for thedegree distributions were performed using MATLAB(7.10.0, Mathworks, Natick, MA, USA). Randomnetwork characteristics were calculated from net-works, which had the same number of nodes andedges as the empirical networks randomly as-sembled using the Erdos–Reyni model networksusing the default settings on the Random Networksroutines in Cytoscape (Erdos and Renyi, 1960;Shannon et al., 2003). As a measure of standardeffect size to compare topological characteristicsfrom ecological, biological and social networks withsimilarly sized random networks, we calculated thelog response ratio according to the procedures inGurevitch and Hedges (2001). The log response ratiowas used for such comparisons as unknownvariances in studies chosen for comparison doesnot invalidate the measure (Gurevitch and Hedges,1999; Hedges et al., 1999).

Results and discussion

A fundamental unknown in microbial ecology(especially among morphologically non-descript

microorganisms) is whether a particular OTU defi-nition allows us to discern distinct niches (West andScanlan, 1999; Achtman and Wagner, 2008;Fuhrman and Steele, 2008; Koeppel et al., 2008).Networks that reveal connections among particularOTUs are one way to approach that question(Fuhrman and Steele 2008; Fuhrman, 2009). Evenamong phylogenetically related microbes whichserve as ‘hubs’ in the subnetworks (that is, theOTU groups around which the subnetworks werebuilt, such as SAR11 OTUs or stramenopiles;Figure 1), there are unique combinations that tendto co-occur (Figures 1–4). Niche separation based onphysico-chemical conditions has previously beenshown for Prochlorococcus (West and Scanlan,1999; Rocap et al., 2002), SAR11 (Carlson et al.,2009) and the eukaryotic alga Ostreococcus(Rodriguez et al., 2005), but we extend the nicheparameters to include co-occurring organisms, andwe have been able to distinguish many distinctSAR11 OTUs (Figure 1a; Table 1).

It is appropriate to consider limitations andpossible biases in the molecular fingerprintingtechniques that generate the relative abundancedata used to describe the bacterial and protistancommunities in this study. As PCR-based techni-ques, molecular fingerprints are subject to all of thebiases inherent to that technique including unevenand non-specific amplification efficiencies (forexample, Suzuki and Giovannoni, 1996; Polz andCavanaugh, 1998; Suzuki et al., 1998) and variationin DNA or in rRNA gene copy number per cell (Fogelet al., 1999; Crosby and Criddle, 2003). However, ithas been suggested that within an environment anda study, these PCR bias effects will have a smallinfluence between samples as they should applyequally to all samples (Yannarell and Triplett, 2005;Schutte et al., 2008). In this study, careful standar-dization of the amount of template DNA used inPCRs and fingerprint analysis should furtherminimize these variations and allow for compar-isons of relative abundance within these commu-nities. Previous work at this microbial ocean timeseries demonstrated a strong correlation (r2¼ 0.86)between the relative fluorescence of ProchlorococcusOTUs identified by ARISA and independentestimates of abundance by flow cytometry (Brownet al., 2005). Another issue is the detection limit ofthese fingerprinting techniques. We have calculatedour detection limit with ARISA and TRFLP to beB0.1% of the bacteria and protistan community inthis system; and extensive 16S and 18S clonelibraries from these communities have boosted ourconfidence both in identifying OTUs and in ourlevel of sampling of the community (Brown andFuhrman, 2005; Brown et al., 2005; Countway et al.,2005, 2010; Hewson and Fuhrman, 2006; Vigil et al.,2009). At this level, we are sampling the moredominant members of the community and are wellaware that there are ecological roles being performedby rare or sometimes rare members as has been

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shown with deep sequencing projects (for example,Huber et al., 2007). The detection limit must also bekept in mind, as there are OTUs on the edge of thislimit that will come in and out of detectionrandomly. We attempted to account for this random-ness by restricting our analysis to OTUs that werepresent on at least B20% of the sampling dates, aswell as through permutation testing in the local

similarity analysis program to determine the validityof the local similarity correlations. We feel thatwe have taken a conservative approach and areconfident that we are able to compare changes inrelative abundance over time and that we canstatistically relate these changes to environmentaldata to describe the ecology of the microbes in thissystem.

Figure 1 Subnetworks organized around 10 SAR11 OTUs (abbreviated S11, a) and 7 stramenopile OTUs (abbreviated Dia: Diatom, Str:stramenopile, b). Abbreviated names are followed by OTU fragment size for bacteria and eukaryotes. Circles are bacteria, diamonds areeukaryotes, triangles are archaea, squares are biotic environmental variables and hexagons are abiotic environmental variables. Sizes ofthe bacterial and eukaryotic nodes indicate the average abundance of the OTUs as measured by ARISA and TRFLP, respectively. Solidlines show a positive correlation, dashed lines show a negative correlation, arrows indicate a 1-month shift in the correlation.Abbreviations for other nodes are translated in Table 1.

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Comparing relative abundance by fragment analysishas important consequences to bear in mind. Werestricted our comparisons to the OTUs that wereamenable to detection by these methods, which isnaturally a subset of the community. Also, webinned and standardized the fluorescent peaks inorder to create fair comparisons between all sam-ples. This meant we estimated changes in relativeabundance of each OTU reflecting a shift in theproportion of that particular OTU within thecommunity, and not necessarily a change in itsabsolute abundance. We interpret the local similar-ity rank correlations in these proportions as indica-tive of similarities in overall environmentalresponses over time, for example, OTUs that go upor down together over time in relative proportions(or are both unchanging) are mathematically res-ponding (or ‘behaving’) like each other; in contrast,OTUs whose proportions change unrelated to eachother do not appear to ‘behave’ like each other.

Organisms associated with the phylogeneticallyrelated ‘hubs’ in the subnetworks reveal complexcommunity interactions. The alveolate (for example,dinoflagellates) subnetwork (Figure 2a) includes ahighly interconnected group of 15 unknown eukar-yotic taxa that correlate with both Alveolate-562 andDinoflagellate-198. Local similarity correlationsamong all 15 taxa range from 0.51 to 0.90(Figure 2b). We speculate that the highest correla-tions (such as those between Alveolate-562 and

EOTU-642 (ls¼ 0.90, Po0.001), EOTU-622 (ls¼ 0.92,Po0.001) and EOTU-522 (ls¼ 0.88, Po0.001)) mayimply either multiple rRNA operons from a singletaxon or direct symbiotic dependence such asmutualism or parasitism (Chambouvet et al., 2008).Thus, these 15 nodes may not all be unique orindependent organisms, however, it is equally likelythat these are distinct organisms that are closelybound ecologically.

Correlations with environmental factors provideawareness of the conditions that favor or disfavorparticular collections of organisms. In general,correlations between microbes dominated the net-work, rather than those between microbes andabiotic or biotic environmental parameters (Supple-mentary Figure S1). This may relate to the relativelystable environment in the deep chlorophyll max-imum layer, hence changes in community composi-tion were driven more by biological interactionsthan significant changes in the physico-chemicalenvironment. It is also possible that there wereinfluential environmental parameters which werenot measured but could have explained instances ofa microbe’s occurrence or shifts in the community.Correlations with biotic parameters generatehypotheses to help explain microbial niches. Forexample, in the SAR11 subnetwork, we observed anegative correlation (ls¼�0.576, Po0.001) betweenthe relative abundance of SAR11 Surface group3-719 and total bacterial counts (Figure 1a). This may

Table 1 Abbreviations and identifications for organisms and physico-chemical variables included in the microbial association network

Abbreviation Translation Abbreviation Translation

Aca Acantharea Lin LinglulodiniumAct Actinobacterium NO2 Dissolved nitriteAlp a-Proteobacterium NO3 Dissolved nitrateAlt Alteromonas O2 Dissolved oxygenAlv Alveolate Ost OstreococcusAmoA amoA gene abundance OTU Unidentified bacterial taxonArch Tot Archaea abundance Phaeo Phaeophytin concentrationArt Arthropod Plst PlastidBac Bacteroidetes PO4 Dissolved phosphateBact Tot Bacteria abundance Pro ProchlorococcusBet b-Proteobacterium B Prod (leu) Heterotrophic bacterial production (leucine incorporation)Cfl Choanoflagellate B Prod (thy) Heterotrophic bacterial production (thymidine incorporation)CHB CHAB1-7 Rho RhodophyteChl Chlorophyte Ros RoseobacterChlor a Chlorophyll a concentration S11 SAR11Cil Ciliate S116 SAR116Cnd Cnidarian S406 SAR406Cnt Centroheliozoa S86 SAR86Crc Cercozoid S92 SAR92Cren Crenarchaea abundance Sal Water salinityCrp Cryptophyte SiO3 Dissolved silicateDel d-Proteobacteria Sph SphingobacterDen Water density Str StramenopileDep Depth of chlorophyll maximum layer Syn SynechococcusDia Diatom Tel TelonemaDin Dinoflagellate Temp Water temperatureEOTU Unidentified eukaryote taxon Ver VerrucomicrobiumEury Euryarchaea abundance Vir Tot Virus abundanceFla FlavobacteriumGam g-ProteobacteriumHap Haptophyte

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reflect superior competition for growth under lowabundance conditions, or may be the result of betterresistance to losses by grazing or viral lysis com-pared with other taxa. In contrast, g-proteobacteriumSAR92-749 (Figure 3) more likely is a weedy oropportunistic species, as the relative abundance ofSAR92-749 positively correlated with bacterial

production measured by leucine and thymidineincorporation (ls¼ 0.54, P¼ 0.003 and ls¼ 0.495,P¼ 0.005, respectively).

Correlations with abiotic parameters revealedputative associations between the connected taxaand particular environmental measurements. Thepositive correlation between Stramenopile-490 and

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Din 568

EOTU 319

EOTU 599

EOTU 450

Str 492Bac 609

Alv 456

Hap/Alv 179

EOTU 131

EOTU 482

Crp/Art 177OTU 804

EOTU 566EOTU 592

EOTU 382

EOTU 302

Ost 322

Cil 482

CHA 402B Prod (leu)S406 624

Act 420

Alt 642 EOTU 222

Alv 333

Alt 549

Alv/Hapt 335Tel 331

Din 198

EOTU 522EOTU 402

EOTU 103

EOTU 422

OTU 889

Din 241

EOTU 123

EOTU 83

Alv 327

Cil/Hap 194 EOTU130

OTU 606

Din 126

Din 340

Art 495

Cil 197OTU 627

EOTU 379

Fla 854

Sph 639

Cil/Cfl 182

OTU 543

S116 657

Cr/Alv 300

EOTU 536S11 663 Din 233

S86/Pla 537

OTU 510

EOTU 189

Act 426

OTU 884

EOTU 622

EOTU 462

EOTU 362

EOTU 73

EOTU 142

EOTU 422

EOTU 582

EOTU 542

EOTU 162

EOTU 642

EOTU 93

EOTU 192

S11 699

Cil 196

Alv 562OTU 531

Ros 489

Figure 2 Alveolate subnetwork and highly connected eukaryotic cluster. The subnetwork is organized around 17 alveolates(abbreviated Alv: alveolate, Cil: ciliate, Din: dinoflagellate) as central nodes (a). Fifteen eukaryotic OTUs are highly correlated with Alv562, Din 198 and Din 241. When visualized separately, these OTUs form a highly inter-connected cluster (b). This cluster accounts for theeukaryotes with the highest connectedness in the overall network.

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density (ls¼ 0.54, P¼ 0.001) associates this strame-nopile with colder, saltier water (Figure 1b), perhapsindicating an association with a deeper chlorophyllmaximum layer, physical forces affecting tempera-ture or salinity (for example, upwelling), or winter-time southward flow of the Southern Californiacurrent system (Fuhrman et al., 2006). SAR11Surface group 3-719 was negatively correlated withdensity at a 1-month time lag (ls¼�0.5, P¼ 0.004),that is, a month after the water becomes warmer andless saline, the relative abundance of SAR11 Surfacegroup 3-719 increases (Figure 1a). Although a directconnection is not identified by the analysis, thismay suggest that SAR11 Surface group 3-719 andStramenopile-492 were part of an ecological pro-gression tied to seasonal changes in the water or tophysical disruption.

Correlations between domains were common inthe network. Although the subnetworks with bacter-ia at the center (Figures 1a, 3 and 4a) tended to havemore connections to bacteria, and those witheukaryotes at the center (Figures 1b, 2a and 4b)tended to have more connections to eukaryotes,each subnetwork had multiple instances wherebacteria and eukaryotes were connected. Specificbacteria and eukaryotes were also connectedto archaea (for example, Euryarchaea, Figures 1band 4a; Crenarchaea, Figure 3). Some of theseco-occurrences may represent guilds of organisms

performing similar or complementary functions toeach other, while others may co-occur because ofshared, preferred environmental conditions. Whenattempting to describe these collections, it is tempt-ing to look for positive correlations among thephylogenetically related ‘hubs’ in these subnet-works that may indicate OTUs that are performingsimilar ecological roles, for example, the correlationbetween SAR11 Surface group 3-719 and SAR11Surface group 1-681 (ls¼ 0.54, P¼ 0.003; Figure 1a)and between Stramenopile-492 and Stramenopile-490 (ls¼ 0.64, Po0.001; Figure 1b). It is possiblethat some co-occurring organisms are in differentniches within our samples but that those nichestend to co-occur. Note that if these organisms weretruly redundant (that is, ecologically identical),other taxa should have responded to both similarly,creating many shared neighbors. While the Strame-nopile-490 and Stramenopile-492 shared four neigh-bors, SAR11 Surface group 3-719 and SAR11Surface group 1-681 did not share neighbors.Furthermore, there were distinct, unshared neigh-bors in each of these cases: six neighbors in SAR11example (Figure 1a) and eight neighbors in Strame-nopile example (Figure 1b). While functionalredundancy presumably occurs among microbes, itis unclear to what extent the collective suite offunctions of particular microorganisms overlap—that is, the extent of distinctiveness of individual

Figure 3 Subnetwork built around g-proteobacteria OTUs as central nodes (abbreviated Alt: alteromonas, CHB: CHABI-7, Gam:g-proteobacterium, S86: SAR86, S92: SAR92). This subnetwork identifies 12 g-proteobacterial OTUs. g-proteobacteria OTUs correlatewith eukaryotes and Crenarchaea (Cren), as well as environmental parameters and bacterial production.

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microbial niches (Achtman and Wagner, 2008). In anearlier study, Fuhrman et al. (2006) concluded thatthe predictability of most bacterial OTUs at thislocation suggested little functional redundancyamong them. It is interesting to note that whileSAR11 Surface 3-719 correlates with many physico-chemical parameters and fewer OTUs, SAR11 Sur-face 1-681 correlates with no physico-chemicalparameters. In this case, it may be that SAR11Surface 3-719 is serving as a signal or proxy forthe combination of environmental parameters which

would correlate with SAR11 Surface 1-681. Simi-larly, the correlation between Stramenopile-490 andStramenopile-492 may also be due to these OTUsstanding in as a representative correlation for thecollection of OTUs to which they are connected(that is, a proxy correlation). Previous studies haveshown that combinations of environmental variablesare more predictive of the microbial communitychanges over time compared with single environ-mental variables (Fuhrman et al., 2006; Vigil et al.,2009).

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Din 340

OTU 606 6

Lin/Alv/Cnd 337

OTU 859

Din 241

Cil/Hap 194

OTU 477

EOTU 79

EOTU 586

Cil/Cfl 182

Sal

Fla 854

B Prod (leu) EOTU 582

Alv 562

Ost 322

Syn 1040

Cil 196

Din 233

EOTU 482

Cil/Cfl 4855

OTU 531

Dia 488

Str 490

Act 42 3

EOTU 462

EOTU 522

EOTU 422

EOTU 422

Cil 482

EOTU 592EOTU 103

EOTU 83Ros 489

S11 693

CHA 402

EOTU 402

Hap/Alv 179

EOTU 192EOTU 542

EOTU 379

Cil 197

OTU 889

Sph 639Cr/Alv 300

S116 657

OTU 627

OTU 543

Art 495

EOTU 536Din 126

−0.59

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S92 759

Pro 828

S86 534

Bac 594

S86 525

OTU 606

S11 675

Del/Bac 824

EOTU 79

Eury

S86/Pla 537

Bac 609

EOTU 344S11 666

OTU 964Bac 651

Cil/Cfl 485

DepBac 789

Bac 612OTU 477

OTU 474

Act 423

Act 420OTU 471

Pro 944

Ver 734

PO4

CHA 402

Syn 1051

Ost 322

OTU 884

Pla 570

Syn 1040

Str 227

Bac 799Act 426

Figure 4 Subnetworks built around four cyanobacteria OTUs (abbreviated Pro: Prochlorococcus and Syn: Synechococcus, a) and builtaround three ciliate OTUs and two OTUs identified as ciliates or choanoflagellates OTU (abbreviated Cil: ciliate, Cfl: choanoflagellate,b). Correlations among physico-chemical variables and archaea, bacteria and eukarya show the potential for this network visualization toprovide information about ecological relationships of the targeted OTUs.

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Negative correlations may indicate competition orpredation among the taxa. The negative correlationbetween Ciliate/Choanoflagellate-485 with actino-bacter-423 (ls¼�0.55, Po0.001) and SAR11 693(ls¼�0.52, P¼ 0.003) or between Ciliate-197 withflavobacter-854 (ls¼�0.54, P¼ 0.003; Figure 4b)with no time delay could point to a predator–preyrelationship, especially considering that these pro-tists are phagotrophic. A negative correlation(ls¼�0.51, P¼ 0.003) with no time delay betweenSynechococcus-1051 and Ostreococcus-322 (aphototrophic prokaryote and a phototrophic eukar-yote of similar size; Figure 4a) may reflect competi-tion. A positive correlation with a time delay mayreflect a change in the bacterial community as theenvironment changes. Prochlorococcus grpII-944(a low light-adapted group) and CHABI-7-402exhibited a positive correlation with a 1-month timelag (ls¼ 0.51, P¼ 0.002), and ProchlorococcusgrpI-828 (a high light-adapted group) correlatedpositively with CHABI-7-402 (ls¼ 0.63, Po0.001;Figure 4a) with no time delay, suggesting a progres-sion where CHABI-7-402 and ProchlorococcusgrpI-828 give way to the low light-adapted Pro-chlorococcus grpII-944. We suggest that positivecorrelations without delay indicate organisms thatthrive under similar conditions, while delayedpositive correlations reflect a shift of dominanceduring a monthly progression. These possibilitiesraise many hypotheses for further examinationusing approaches targeting particular taxa.

Observing the network as a whole, it is interestingto note that many of the physico-chemical and bioticparameters were not as highly connected as theOTUs, shown clearly when they are sorted by theirnumber of connections (Supplementary Figure S1).In fact, relatively invariant biotic parameters, suchas total virus counts, and pigments did not have asmany significant correlations as seasonally variableparameters such as depth of the chlorophyllmaximum layer, NO3 and salinity. Interestingly,heterotrophic bacterial production by thymidineincorporation had fewer connections than produc-tion measured by leucine incorporation (Supple-mentary Figure S1). This may be due to leucine, butnot thymidine, incorporation from nanomolar con-centrations by cyanobacteria and pico-eukaryotes inaddition to heterotrophic bacteria in surface oceanwaters (Zubkov et al., 2003; Michelou et al., 2007),with nanomolar thymidine believed to be taken upeffectively only by heterotrophic bacteria (Fuhrmanand Azam, 1982). Some connections to productionare intriguing and call for further study, for example,the positive correlation lagged 1 month between twodifferent ciliates (482 and 485) and bacterialproduction measured by leucine incorporation(Figure 4a). We also note that our pairwise analysiscould miss interactions where individual para-meters do not relate consistently to changes inmicrobial community composition even thoughparticular combinations of parameters might.

Properties of the whole microbial associationnetwork can reveal patterns that can be used toplace the microbial network in context with otherecological, biological and non-biological networks.We calculated the clustering coefficient, the char-acteristic path length and the node degree distribu-tion (Watts and Strogatz, 1998; Albert and Barabasi,2002; Dunne et al., 2002; Oleson et al., 2006) of theentire network (microbial and environmental nodes)and just the microbial nodes and compared thestructure of the association network with a randomnetwork of the same size as well as networks fromprevious studies (Table 2). As restricting theseanalyses to just the microbial parameters did notfundamentally change the network metrics we willdiscuss the more inclusive values. For the wholenetwork, the observed characteristic path length of2.99 and clustering coefficient of 0.27 were bothgreater than the random characteristic path length of2.66 and random clustering coefficient of 0.044(Table 2). The observed:random network clusteringcoefficient ratio of 6.1 (log response ratio of 1.81)shows that the association network has ‘smallworld’ properties (that is, nodes are more connectedthan a random network of similar size; Watts andStrogatz, 1998), although the effect is much smallerthan the social networks (log response ratio 5.19–5.7for the internet and 7.98 for actors; Watts andStrogatz, 1998; Albert and Barabasi, 2002; Dunneet al., 2002; Oleson et al., 2006). The observedclustering coefficient of 0.27 is not as high asclustering coefficients for pollination (Olesonet al., 2006), social networks (Watts and Strogatz,1998) or the global microbial 16S/genome databasenetwork (Chaffron et al., 2010). However, it is withinthe range of biological networks (for example,Escherichia coli substrate and Caenorhabditiselegans neural networks; Watts and Strogatz, 1998;Albert and Barabasi, 2002), ecological networks (forexample, food webs, Albert et al., 2000; Montoyaet al., 2006) and the functional gene network fromsoil bacteria (Table 2; Zhou et al., 2010). It isinteresting to note that the larger metadata focusedmicrobial study which spanned multiple environ-ments (Chaffron et al., 2010) found higher clusteringcompared with the Zhou et al. (2010) study of thebacterial community in soil plots and this study at anocean time series. While our slightly lower clusteringcoefficient indicates our three-domain study includedless highly correlated microbes compared with theclusters in the global 16S bacterial network, theoverall comparisons with other biological/ecologicalnetworks suggest that the higher than randomclustering we report here is not unreasonable andmay be more characteristic of bacterial/archaeal/protistan microbial communities in general. In anycase, it suggests that further investigation into themicrobial community in the ocean and elsewhere willreveal more potential interactions.

Node connectivity distribution can be used tocompare hierarchical structure between systems.

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For the entire microbial network it did not resemblethe Poisson shape of the random network (best fit bya Gaussian curve, r2¼ 0.91; Supplementary FigureS2), but was best described by a truncated powerlaw function (P(k)¼ k�ae�bkþ c; SupplementaryFigure S2); fitting this function yielded a¼ 0.014and b¼ 0.004 (r2¼ 0.8), which is characteristic ofsome ecological networks (for example, plants inplant–frugivore interactions and some food webs)and like other ecological networks these coefficientvalues are much smaller than the power lawexponents of 2–3 found in large social or proteininteraction networks (Watts and Strogatz, 1998;Albert and Barabasi, 2002; Dunne et al., 2002;Montoya et al., 2006). These structural similaritiesto many other ecological, microbial and self-organizing networks (Watts and Strogatz, 1998;Albert and Barabasi, 2002; Dunne et al., 2002;Montoya et al., 2006; Oleson et al., 2006), allowus to infer that we are observing meaningful, non-random relationships over time. The small worldpattern of few highly connected nodes, as opposedto an even distribution of connectivity, makesthe network more robust to change (Albert et al.,

2000; Montoya et al., 2006) with an importantcaveat: if highly connected nodes are lost, thenetwork would change dramatically. These highlyconnected nodes might be analogous to microbial‘keystone species.’

Although we cannot claim that we have acomprehensive view of interactions within marinemicrobial communities, our correlation networkprovides information on the natural history ofbacteria, eukaryotes and archaea, and their interac-tions with the environment. Future studies compar-ing networks for different communities will allowfor exploration of the resilience and resistance ofthose communities (Albert et al., 2000; Montoyaet al., 2006) and yield insight into the effects offuture environmental changes. We feel that theseanalyses are useful not only for this type ofcommunity analysis but are able to extend to otherdatasets (for example, new studies based on deepDNA sequencing across all three domains of life) forfurther exploration and generation of testablehypotheses and, someday, predictive models ofmicrobial diversity that will deepen our under-standing of microbial interactions in the ocean.

Table 2 Clustering coefficient and average shortest path length from the microbial association network and comparisons to other realand random networks

Cl a Clrandom Cl/Clrandom L Lrandom lrCl lrL

Ecological networksMicrobial association (this study)

All nodes (n¼212) 0.27 (±0.02) 0.044 (±0.003) 6.14 2.99 (±0.03) 2.62 (±0.01) 1.81 0.13Microbial nodes (n¼197) 0.26 (±0.02) 0.047 (±0.003) 5.53 3.05 (±0.03) 2.64 (±0.012) 1.71 0.14

Food websb,c,d 0.02 to 0.43 0.03 to 0.33 0.30 to 3.80 1.33 to 3.74 1.41 to 3.73 �1.20 to 1.34 0.34 to 1.32Pollinator-plant networksc,d 0.72 to 1.00 0.08 to 1.0 1.0 to 10.9 1.0 to 2.31 ND 0.0 to 2.39 NDMicrobial database networke 0.501 ND ND 6.30 ND ND NDFunctional microbialnetworksf

0.10 to 0.22 0.028 to 0.099 2.22 to 3.57 3.09 to 4.21 3.00 to 3.84 0.79 to 1.28 0.030 to 0.091

Biological networksCaenorhabditis elegans,neural networkg,h

0.28 0.05 5.6 2.65 2.25 1.71 0.16

Escherichia coli,metabolic networkh,i

0.32 to 0.59 0.026 to 0.09 6.56 to 12.3 2.62 to 2.90 1.98 to 3.04 1.88 to 2.51 �0.05 to 0.27

Social networksPower gridg,h 0.08 0.005 16 18.7 12.4 2.77 0.41Actorsg,h 0.79 0.00027 2925 3.65 2.99 7.98 0.20Internet, domain levelh,j,k,l 0.18 to 0.3 0.001 180 to 300 3.70 to 3.76 6.36 to 6.18 5.19 to 5.70 �0.50 to �0.54

aCl is the average clustering coefficient, Clrandom is the clustering coefficient from an identically sized random network, Cl/Clrandom is the ratio ofthe clustering coefficient from the real network to the clustering coefficient of the random network, L is the average shortest path length, Lrandom isthe average shortest path length from the random network, lrCL and lrL are the log response ratio for the average clustering coefficient and averageshortest path length between the observed and random networks; values in parentheses are standard error; ND indicates missing data.bDunne et al. (2002).cOleson et al. (2006).dMontoya et al. (2006).eChaffron et al. (2010).fZhou et al. (2010).gWatts and Strogatz (1998).hAlbert and Barabasi (2002).iWagner and Fell (2000).jAlbert et al. (2000).kYook et al. (2002).lPastor-Satorras et al. (2001).

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Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We thank the captain and crew of the R/V Seawatch,M Neumann and R Smith for oxygen, salinity andchlorophyll data; Z Zheng for nutrient data. We thankD Capone, W Ziebis, J Devinny D Keifer and E Berlow forhelpful comments on earlier versions of this manuscript,and anonymous reviewers for their careful reading andexcellent comments that improved this paper. The USNational Science Foundation Microbial Observatories andBiological Oceanography programs funded this research,grants 0703159, 0623575 and 0648581.

References

Achtman M, Wagner M. (2008). Microbial diversity andthe genetic nature of microbial species. Nat RevMicrobiol 6: 431–440.

Albert R, Barabasi AL. (2002). Statistical mechanics ofcomplex networks. Rev Mod Phys 74: 47–97.

Albert R, Jeong H, Barabasi AL. (2000). Error and attacktolerance of complex networks. Nature 406: 378–382.

Assenov Y, Ramırez F, Schelhorn SE, Lengauer T, Albrecht M.(2008). Computing topological parameters of biologicalnetworks. Bioinformatics 24: 282–284.

Beman JM, Sachdeva R, Fuhrman JA. (2010). Populationecology of nitrifying archaea and bacteria in theSouthern California Bight. Environ Microbiol 12:1282–1292.

Brown MV, Fuhrman JA. (2005). Marine bacterial micro-diversity as revealed by internal transcribed spaceranalysis. Aquat Microb Ecol 41: 15–23.

Brown MV, Schwalbach MS, Hewson I, Fuhrman JA.(2005). Coupling 16S-ITS rDNA clone libraries andautomated ribosomal intergenic spacer analysis toshow marine microbial diversity: development andapplication to a time series. Environ Microbiol 7:1466–1479.

Carlson CA, Morris R, Parsons R, Treusch AH, Giovannoni SJet al. (2009). Seasonal dynamics of SAR11 populationsin the euphotic and mesopelagic zones of thenorthwestern Sargasso Sea. ISME J 3: 283–295.

Caron DA. (2009). New accomplishments and approachesfor assessing protistan diversity and ecology in naturalecosystems. Bioscience 59: 287–299.

Caron DA, Countway PD, Savai P, Gast RJ, Schnetzer A,Moorthi SD et al. (2009). Defining DNA-basedoperational taxonomic units for microbial-eukaryoteecology. Appl Environ Microbiol 75: 5797–5808.

Chaffron S, Rehrauer H, Pernthaler J, Von Mering C.(2010). A global network of coexisting microbes fromenvironmental and whole-genome sequence data.Genome Res 20: 947–959.

Chambouvet A, Morin P, Marie D, Guillou L. (2008).Control of toxic marine dinoflagellate blooms by serialparasitic killers. Science 322: 1254–1257.

Countway PD, Caron DA. (2006). Abundance anddistribution of Ostreococcus sp. in the San PedroChannel, California, as revealed by quantitative PCR.Appl Environ Microbiol 72: 2496–2506.

Countway PD, Gast RJ, Savai P, Caron DA. (2005).Protistan diversity estimates based on 18S rDNA fromseawater incubations in the western North Atlantic.J Eukaryot Microbiol 52: 95–106.

Countway PD, Vigil PD, Schnetzer A, Moorthi S,Caron DA. (2010). Seasonal analysis of protistancommunity structure and diversity at the USC Micro-bial Observatory (San Pedro Channel, North PacificOcean). Limnol Oceanogr 55: 2381–2396.

Crosby LD, Criddle CS. (2003). Understanding bias in microbialcommunity analysis techniques due to rrn operon copynumber heterogeneity. Biotechniques 34: 790–802.

DeLong EF. (2009). The microbial ocean from genomes tobiomes. Nature 459: 200–206.

DeSantis TZ, Brodie EL, Moberg JP, Zubieta IX, Piceno YMet al. (2007). High-density universal 16S rRNA micro-array analysis reveals broader diversity than typicalclone library when sampling the environment. MicrobEcol 53: 371.

Diez B, Pedros-Alio C, Massana R. (2001). Study of geneticdiversity of eukaryotic picoplankton in different ocea-nic regions by small-subunit rRNA gene cloning andsequencing. Appl Environ Microbiol 67: 2932–2941.

Ducklow HW. (2000). Bacterial production and biomassin the oceans in microbial ecology of the oceans.Kirchman DL (ed.). Wiley-Liss: New York. pp, 85–120.

Dunne JA, Williams RJ, Martinez ND. (2002). Food-webstructure and network theory: the role of connectanceand size. Proc Natl Acad Sci USA 99: 12917–12922.

Erdos P, Renyi A. (1960). On the evolution of randomgraphs. Publ Math Inst Hung Acad Sci 5: 17–61.

Fisher MM, Triplett EW. (1999). Automated approach forribosomal intergenic spacer analysis of microbialdiversity and its application to freshwater bacterialcommunities. Appl Environ Microbiol 65: 4630–4636.

Fogel GB, Collins CR, Li J, Brunk CF. (1999). Prokaryoticgenome size and SSU rDNA copy number: estimationof microbial relative abundance from a mixedpopulation. Microb Ecol 38: 93–113.

Fuhrman JA. (2009). Microbial community structure andits functional implications. Nature 459: 193–199.

Fuhrman JA, Azam F. (1982). Thymidine incorporation asa measure of heterotrophic bacterioplankton produc-tion in marine surface waters—evaluation and fieldresults. Mar Biol 66: 109–120.

Fuhrman JA, Hewson I, Schwalbach MS, Steele JA,Brown MV et al. (2006). Annually reoccurring bacterialcommunities are predictable from ocean conditions.Proc Natl Acad Sci USA 103: 13104–13109.

Fuhrman JA, Steele JA. (2008). Community structure ofmarine bacterioplankton: patterns, networks, andrelationships to function. Aquat Microb Ecol 53: 69–81.

Giovannoni S, Stingl U. (2007). The importance ofculturing bacterioplankton in the ‘omics’ age. NatRev Microbiol 5: 820–826.

Gurevitch J, Hedges LV. (1999). Statistical issues inecological meta-analysis. Ecology 80: 1142–1149.

Gurevitch J, Hedges LV. (2001). Meta-analysis: combiningthe results of independent experiments in design andanalysis of ecological experiments. Scheiner SM andGurevitch J (eds). Oxford University Press: New York.

Hedges LV, Gurevitch J, Curtis P. (1999). The meta-analysisof response ratios in experimental ecology. Ecology 80:1150–1156.

Hewson I, Fuhrman JA. (2006). Improved strategy forcomparing microbial assemblage fingerprints. MicrobEcol 51: 147–153.

Marine microbial correlation networkJA Steele et al

1424

The ISME Journal

Huber JA, Mark Welch D, Morrison HG, Huse SM,Neal PR, Butterfield DA, Sogin ML. (2007). Microbialpopulation structures in the deep marine biosphere.Science 318: 97–100.

Koeppel A, Perry EB, Sikorski J, Krizanc D, Warner A et al.(2008). Identifying the fundamental units of bacterialdiversity: a paradigm shift to incorporate ecology intobacterial systematics. Proc Natl Acad Sci USA 105:2504–2509.

Michelou VK, Cottrell MT, Kirchman DL. (2007).Light-stimulated bacterial production and aminoacidassimilation by cyanobacteria and other microbesin the North Atlantic Ocean. Appl Environ Microbiol73: 5539–5546.

Montoya JM, Pimm SL, Sole RV. (2006). Ecologicalnetworks and their fragility. Nature 442: 259–264.

Oleson JM, Bascompte J, Dupont YL, Jordano P. (2006).The smallest of all worlds: pollination networks.J Theor Biol 240: 270–276.

Orphan VJ, House CH, Hinrichs KU, McKeegan KD,DeLong EF. (2001). Methane-consuming archaearevealed by directly coupled isotopic and phyloge-netic analysis. Science 293: 484–487.

Pace NR. (1997). A molecular view of microbial diversityand the biosphere. Science 276: 734–740.

Pastor-Satorras R, Vazquez A, Vespignani A. (2001).Dynamical and correlation properties of the Internet,Phys. Rev Lett 87: 258701.

Polz MF, Cavanaugh CM. (1998). Bias in template-to-product ratios in multitemplate PCR. Appl EnvironMicrobiol 64: 3724–3730.

Rocap G, Distel DL, Waterbury JB, Chisolm SW. (2002).Resolution of Prochlorococcus and Synechococcusecotypes by using 16S-23S ribosomal DNA internaltranscribed spacer sequences. Appl Environ Microbiol68: 1180–1191.

Rodriguez F, Derelle E, Guillou L, Le Gall F, Vaulot D et al.(2005). Ecotype diversity in the marine picoeukaryoteOstreococcus (Chlorophyta, Prasinophyceae). EnvironMicrobiol 7: 853–859.

Ruan QS, Dutta D, Schwalbach MS, Steele JA, Fuhrman JAet al. (2006). Local similarity analysis reveals uniqueassociations among marine bacterioplankton species andenvironmental factors. Bioinformatics 22: 2532–2538.

Rusch DB, Halpern AL, Sutton G, Heidelberg KB,Williamson S et al. (2007). The Sorcerer II Global OceanSampling expedition: Northwest Atlantic throughEastern Tropical Pacific. PLoS Biol 5: 398–431.

Schutte UM, Abdo EZ, Bent SJ, Shyu C, Williams CJ,Pierson JD, Forney LJ. (2008). Advances in the use

of terminal restriction fragment length polymorphism(T-RFLP) analysis of 16S rRNA genes to characterizemicrobial communities. Appl Microbiol Biotechnol80: 365–380.

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al.(2003). Cytoscape: a software environment forintegrated models of biomolecular interactionnetworks. Genome Res 13: 2498–2503.

Sherr EB, Sherr BF. (2008). Understanding roles ofmicrobes in marine pelagic food webs: a brief historyin microbial ecology of the oceans, Kirchman DL (ed.).Wiley-Liss: New York. pp, 27–44.

Storey JD. (2002). A direct approach to false discoveryrates. J Roy Stat Soc B 64: 479–498.

Suzuki MT, Giovannoni SJ. (1996). Bias caused bytemplate annealing in the amplification of mixturesof 16S rRNA genes by PCR. Appl Environ Microbiol 62:625–630.

Suzuki MT, Rappe M, Giovannoni SJ. (1998). Kinetic biasin estimates of picoplankton community structureobtained by measurements of small-subunit rRNAgene PCR amplicon length heterogeneity. Appl Envir-on Microbiol 64: 4522–4529.

Vigil P, Countway PD, Rose J, Lonsdale DJ, Gobler CJ et al.(2009). Rapid shifts in dominant taxa among microbialeukaryotes in estuarine ecosystems. Aquat MicrobEcol 54: 83–100.

Wagner A, Fell D. (2000) Technical Report No. 00-07-041,Santa Fe Institute.

Watts DJ, Strogatz SH. (1998). Collective dynamics of‘small-world’ networks. Nature 393: 440–442.

West NJ, Scanlan DJ. (1999). Niche-partitioning ofProchlorococcus populations in a stratified watercolumn in the eastern North Atlantic Ocean. ApplEnviron Microbiol 65: 2585–2591.

Yannarell AC, Triplett EW. (2005). Geographic andenvironmental sources of variation in lake bacterialcommunity composition. Appl Environ Microbiol 71:227–239.

Yook SH, Jeong H, Barabasi AL. (2002). Modeling theInternet’s large-scale topology. Proc Natl Acad SciUSA 99: 13382–13386.

Zhou J, Deng Y, Luo F, He Z, Tu Q et al. (2010). Functionalmolecular ecological networks. MBio 1: e00169–e00110.

Zubkov MV, Fuchs BM, Tarran GA, Burkill PH, Amann R.(2003). High rate of uptake of organic nitrogencompounds by Prochlorococcus cyanobacteria as akey to their dominance in oligotrophic oceanic waters.Appl Environ Microbiol 69: 1299–1304.

Supplementary Information accompanies the paper on The ISME Journal website (http://www.nature.com/ismej)

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