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New SAR11 isolate genomes and global marine metagenomes resolve ecologically relevant units within the Pelagibacterales

Abstract

The bacterial order Pelagibacterales (SAR11) is widely distributed across the global surface ocean, where its activities are integral to the marine carbon cycle. High-quality genomes from isolates that can be propagated and phenotyped are needed to unify perspectives on the ecology and evolution of this complex group. Here, we increase the number of complete SAR11 isolate genomes threefold by describing 81 new SAR11 strains from coastal and offshore surface seawater of the tropical Pacific Ocean. Our analyses of the genomes and their spatiotemporal distributions support the existence of 29 monophyletic, discrete Pelagibacterales ecotypes that we define as genera. The spatiotemporal distributions of genomes within genera were correlated at fine scales with variation in ecologically-relevant gene content, supporting generic assignments and providing indications of speciation. We provide a hierarchical system of classification for SAR11 populations that is meaningfully correlated with evolution and ecology, providing a valid and utilitarian systematic nomenclature for this clade.

Data availability

We have deposited the assembled sequence data for newly sequenced genomes, including raw sequencing reads, under NCBI BioProject ID PRJNA1170004. Ribosomal RNA gene sequence data were published previously under NCBI BioProject ID PRJNA673898. The Supplementary Data includes all remaining data, including accession numbers for all previously sequenced genomes and metagenomes. The URL https://seqco.de/r:r4auejub serves as the SeqCode registry for all taxon names defined in this study.

Code availability

All custom R, BASH, and Python scripts used for data analyses in this study are publicly available on GitHub at https://github.com/kcfreel/SAR11-genomes-from-the-tropical-Pacific and archived with Zenodo (https://doi.org/10.5281/zenodo.17614008). Additionally, a fully reproducible bioinformatics workflow for the analysis of SAR11 genomes is available at https://merenlab.org/data/sar11-phylogenomics/, enabling the reproduction of our phylogenomic tree and its extension with new genomes.

References

  1. Grote, J. et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio 3, e00252–12 (2012).

  2. Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).

    Google Scholar 

  3. Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295 (2009).

    Google Scholar 

  4. Schattenhofer, M. et al. Latitudinal distribution of prokaryotic picoplankton populations in the Atlantic Ocean. Environ. Microbiol. 11, 2078–2093 (2009).

    Google Scholar 

  5. Eiler, A., Hayakawa, D. H., Church, M. J., Karl, D. M. & Rappé, M. S. Dynamics of the SAR11 bacterioplankton lineage in relation to environmental conditions in the oligotrophic North Pacific subtropical gyre. Environ. Microbiol. 11, 2291–2300 (2009).

    Google Scholar 

  6. Becker, J. W., Hogle, S. L., Rosendo, K. & Chisholm, S. W. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. 13, 1506–1519 (2019).

    Google Scholar 

  7. Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).

    Google Scholar 

  8. Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. Elife 8, e46497 (2019).

  9. Tucker, S. J. et al. Spatial and temporal dynamics of SAR11 marine bacteria across a nearshore to offshore transect in the tropical Pacific Ocean. PeerJ 9, e12274 (2021).

    Google Scholar 

  10. Haro-Moreno, J. M. et al. Ecogenomics of the SAR11 clade. Environ. Microbiol. 22, 1748–1763 (2020).

    Google Scholar 

  11. Tucker, S. J., Freel, K. C., Eren, A. M. & Rappé, M. S. Habitat-specificity in SAR11 is associated with a few genes under high selection. ISME J. 19, wraf216 https://doi.org/10.1093/ismejo/wraf216 (2025).

  12. Giovannoni, S. J., Britschgi, T. B., Moyer, C. L. & Field, K. G. Genetic diversity in Sargasso Sea bacterioplankton. Nature 345, 60–63 (1990).

    Google Scholar 

  13. Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

    Google Scholar 

  14. Paoli, L. et al. Biosynthetic potential of the global ocean microbiome. Nature 607, 111–118 (2022).

    Google Scholar 

  15. Tsementzi, D. et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature 536, 179–183 (2016).

    Google Scholar 

  16. Kiefl, E. et al. Structure-informed microbial population genetics elucidates selective pressures that shape protein evolution. Sci. Adv. 9, eabq4632 (2023).

    Google Scholar 

  17. López-Pérez, M., Haro-Moreno, J. M., Coutinho, F. H., Martinez-Garcia, M. & Rodriguez-Valera, F. The evolutionary success of the marine bacterium SAR11 analyzed through a metagenomic perspective. mSystems 5, e00605–20 (2020).

  18. Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).

    Google Scholar 

  19. Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).

    Google Scholar 

  20. Chang, T., Gavelis, G. S., Brown, J. M. & Stepanauskas, R. Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton. Microbiome 12, 126 (2024).

    Google Scholar 

  21. Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).

    Google Scholar 

  22. Thrash, J. C. et al. Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Sci. Rep. 1, 13 (2011).

    Google Scholar 

  23. Muñoz-Gómez, S. A. et al. An updated phylogeny of the Alphaproteobacteria reveals that the parasitic Rickettsiales and Holosporales have independent origins. Elife 8, e42535 (2019).

  24. Vergin, K. L. et al. High intraspecific recombination rate in a native population of Candidatus pelagibacter ubique (SAR11). Environ. Microbiol. 9, 2430–2440 (2007).

    Google Scholar 

  25. Wilhelm, L. J., Tripp, H. J., Givan, S. A., Smith, D. P. & Giovannoni, S. J. Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data. Biol. Direct 2, 27 (2007).

    Google Scholar 

  26. Carini, P., Steindler, L., Beszteri, S. & Giovannoni, S. J. Nutrient requirements for growth of the extreme oligotroph ‘Candidatus Pelagibacter ubique’ HTCC1062 on a defined medium. ISME J. 7, 592–602 (2013).

    Google Scholar 

  27. Sun, J. et al. The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsulfoniopropionate to the gases dimethyl sulfide and methanethiol. Nat. Microbiol. 1, 16065 (2016).

    Google Scholar 

  28. Tripp, H. J. et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452, 741–744 (2008).

    Google Scholar 

  29. Rappé, M. S., Connon, S. A., Vergin, K. L. & Giovannoni, S. J. Cultivation of the ubiquitous SAR11 marine bacterioplankton clade. Nature 418, 630–633 (2002).

    Google Scholar 

  30. Giovannoni, S. J. et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science 309, 1242–1245 (2005).

    Google Scholar 

  31. Schwalbach, M. S., Tripp, H. J., Steindler, L., Smith, D. P. & Giovannoni, S. J. The presence of the glycolysis operon in SAR11 genomes is positively correlated with ocean productivity. Environ. Microbiol. 12, 490–500 (2010).

    Google Scholar 

  32. Sun, J. et al. One-carbon metabolism in SAR11 pelagic marine bacteria. PLoS ONE 6, e23973 (2011).

    Google Scholar 

  33. Giovannoni, S. J. SAR11 Bacteria: The Most Abundant Plankton in the Oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).

    Google Scholar 

  34. Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).

    Google Scholar 

  35. Viklund, J., Ettema, T. J. G. & Andersson, S. G. E. Independent genome reduction and phylogenetic reclassification of the oceanic SAR11 clade. Mol. Biol. Evol. 29, 599–615 (2012).

    Google Scholar 

  36. Carini, P. et al. Discovery of a SAR11 growth requirement for thiamin’s pyrimidine precursor and its distribution in the Sargasso Sea. ISME J. 8, 1727–1738 (2014).

    Google Scholar 

  37. Brandon, M. L. High-Throughput Isolation of Pelagic Marine Bacteria from the Coastal Subtropical Pacific Ocean Master’s thesis (University of Hawaiʻi at Mānoa, Department of Oceanography, 2006).

  38. Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332 (2013).

    Google Scholar 

  39. Thrash, J. C. et al. Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. 8, 1440–1451 (2014).

    Google Scholar 

  40. Wang, Z. & Wu, M. A phylum-level bacterial phylogenetic marker database. Mol. Biol. Evol. 30, 1258–1262 (2013).

    Google Scholar 

  41. Suzuki, M. T., Béjà, O., Taylor, L. T. & Delong, E. F. Phylogenetic analysis of ribosomal RNA operons from uncultivated coastal marine bacterioplankton. Environ. Microbiol. 3, 323–331 (2001).

    Google Scholar 

  42. Getz, E. W. et al. The AEGEAN-169 clade of bacterioplankton is synonymous with SAR11 subclade V (HIMB59) and metabolically distinct. mSystems 8, e0017923 (2023).

    Google Scholar 

  43. Viklund, J., Martijn, J., Ettema, T. J. G. & Andersson, S. G. E. Comparative and phylogenomic evidence that the alphaproteobacterium HIMB59 is not a member of the oceanic SAR11 clade. PLoS ONE 8, e78858 (2013).

    Google Scholar 

  44. Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).

    Google Scholar 

  45. Muñoz-Gómez, S. A. et al. Site-and-branch-heterogeneous analyses of an expanded dataset favour mitochondria as sister to known Alphaproteobacteria. Nat. Ecol. Evol. 6, 253–262 (2022).

    Google Scholar 

  46. Evans, J. T. & Denef, V. J. To dereplicate or not to dereplicate? mSphere 5, e00971–19 (2020).

  47. Zhao, J. et al. Promiscuous and genome-wide recombination underlies the sequence-discrete species of the SAR11 lineage in the deep ocean. ISME J. 19, wraf072 (2025).

  48. Hellweger, F. L., van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science 345, 1346–1349 (2014).

    Google Scholar 

  49. Villarreal-Chiu, J. F., Quinn, J. P. & McGrath, J. W. The genes and enzymes of phosphonate metabolism by bacteria, and their distribution in the marine environment. Front. Microbiol. 3, 19 (2012).

    Google Scholar 

  50. Carini, P., White, A. E., Campbell, E. O. & Giovannoni, S. J. Methane production by phosphate-starved SAR11 chemoheterotrophic marine bacteria. Nat. Commun. 5, 4346 (2014).

    Google Scholar 

  51. Sosa, O. A., Repeta, D. J., DeLong, E. F., Ashkezari, M. D. & Karl, D. M. Phosphate-limited ocean regions select for bacterial populations enriched in the carbon-phosphorus lyase pathway for phosphonate degradation. Environ. Microbiol. 21, 2402–2414 (2019).

    Google Scholar 

  52. Acker, M. et al. Phosphonate production by marine microbes: exploring new sources and potential function. Proc. Natl Acad. Sci. USA. 119, e2113386119 (2022).

    Google Scholar 

  53. Zhao, X. et al. Three-dimensional structure of the ultraoligotrophic marine bacterium ‘Candidatus Pelagibacter ubique’. Appl. Environ. Microbiol. 83, e02807–16 (2017).

  54. Craig, L. & Li, J. Type IV pili: paradoxes in form and function. Curr. Opin. Struct. Biol. 18, 267–277 (2008).

    Google Scholar 

  55. Braakman, R. et al. Global niche partitioning of purine and pyrimidine cross-feeding among ocean microbes. Sci. Adv. 11, eadp1949 (2025).

  56. Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).

    Google Scholar 

  57. Brown, M. V. et al. Global biogeography of SAR11 marine bacteria. Mol. Syst. Biol. 8, 595 (2012).

    Google Scholar 

  58. Davies, T. J. Evolutionary ecology: when relatives cannot live together. Curr. Biol. 16, R645–R647 (2006).

    Google Scholar 

  59. Ramfelt, O., Freel, K. C., Tucker, S. J., Nigro, O. D. & Rappé, M. S. Isolate-anchored comparisons reveal evolutionary and functional differentiation across SAR86 marine bacteria. ISME J. 18, wrae227 (2024).

  60. Tucker, S. J. et al. Seasonal and spatial transitions in phytoplankton assemblages spanning estuarine to open ocean waters of the tropical Pacific. Limnol. Oceanogr. 70, 1693–1708 (2025).

    Google Scholar 

  61. Ramfelt, O., Tucker, S. J., Freel, K. C., Eren, A. M. & Rappe, M. S. Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean. Preprint at https://doi.org/10.1101/2025.06.17.660167 (2025).

  62. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Google Scholar 

  63. Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 5, e00731–19 (2020).

  64. Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).

    Google Scholar 

  65. Waite, D. W. et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int. J. Syst. Evol. Microbiol. 70, 5972–6016 (2020).

    Google Scholar 

  66. Sanford, R. A., Lloyd, K. G., Konstantinidis, K. T. & Löffler, F. E. Microbial taxonomy run amok. Trends Microbiol. 29, 394–404 (2021).

    Google Scholar 

  67. Monaghan, E. A., Freel, K. C. & Rappé, M. S. Isolation of SAR11 Marine Bacteria from Cryopreserved Seawater. mSystems 5, e00954–20 (2020).

  68. Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).

    Google Scholar 

  69. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Google Scholar 

  70. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Google Scholar 

  71. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Google Scholar 

  72. Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).

    Google Scholar 

  73. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Google Scholar 

  74. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

  75. Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).

    Google Scholar 

  76. Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).

    Google Scholar 

  77. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Google Scholar 

  78. Milne, I. et al. Using tablet for visual exploration of second-generation sequencing data. Brief. Bioinform. 14, 193–202 (2013).

    Google Scholar 

  79. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Google Scholar 

  80. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).

    Google Scholar 

  81. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Google Scholar 

  82. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    Google Scholar 

  83. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the Genomic Era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Google Scholar 

  84. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    Google Scholar 

  85. Revell, L. J. phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ 12, e16505 (2024).

    Google Scholar 

  86. R Development Core Team, R. R: A Language and Environment for Statistical Computing https://doi.org/10.1007/978-3-540-74686-7 (2011).

  87. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Google Scholar 

  88. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).

    Google Scholar 

  89. Winter, K. B. et al. Collaborative Research To Inform Adaptive Comanagement: A Framework For The Heʻeia National Estuarine Research Reserve. Ecol. Soc. 25, 15 (2020).

  90. Kūlana Noiʻi Working Group. Kūlana Noiʻi version 2. [online] URL: https://seagrant.soest.hawaii.edu/wp-content/uploads/2021/09/Kulana-Noii-2.0_LowRes.pdf (Hawaiʻi Sea Grant, 2021).

  91. Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Google Scholar 

  92. Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).

    Google Scholar 

  93. Biller, S. J. et al. Marine microbial metagenomes sampled across space and time. Sci. Data 5, 180176 (2018).

    Google Scholar 

  94. Kudo, T. et al. Seasonal changes in the abundance of bacterial genes related to dimethylsulfoniopropionate catabolism in seawater from Ofunato Bay revealed by metagenomic analysis. Gene 665, 174–184 (2018).

    Google Scholar 

  95. Yoshitake, K. et al. Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan. Sci. Rep. 11, 12222 (2021).

    Google Scholar 

  96. Mueller, R. S. et al. Metagenome sequencing of a coastal marine microbial community from Monterey Bay, California. Genome Announc. 3, e00341–15 (2015).

  97. Kopf, A. et al. The Ocean Sampling Day Consortium. Gigascience 4, 27 (2015).

    Google Scholar 

  98. Shaiber, A. et al. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biol. 21, 292 (2020).

    Google Scholar 

  99. Köster, J. & Rahmann, S. Building and documenting workflows with Python-based snakemake. GCB 49, 56 (2012).

    Google Scholar 

  100. Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high-quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).

    Google Scholar 

  101. Utter, D. R. et al. Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity. Genome Biol. 21, 293 (2020).

    Google Scholar 

  102. Community Ecology Package [R package vegan version 2.7-1]. Comprehensive R Archive Network (CRAN) https://cran.r-project.org/web/packages/vegan/index.html (2025).

  103. Delmont, T. O. & Eren, A. M. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ https://doi.org/10.7717/peerj.4320 (2018).

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Acknowledgements

We thank Kumu Hula, Kawaikapuokalani Hewett and Aimee Sato for their generous guidance in using appropriate ʻŌlelo Hawaiʻi (Hawaiian Language) words to create species names for isolates cultivated on Moku o Loʻe. We also thank K. Luttrell for help with Latin grammar, R. Malmstrom and N. Nath for sequencing the genomes of isolates HIMB109 and HIMB123, R. Ouye for assistance with HTC experiments, O. Ramfelt for bioinformatic support, and C. Foley for her generous help with creating the map used in Supplementary Fig. 1b. We also thank F. Trigodet for their help with the high-performance computing at the University of Oldenburg. Finally, we sincerely thank Luis Miguel Rodriquez-R, Marike Palmer, and the entire SeqCode team for their expert grammatical and taxonomic guidance. This research was supported by funding from the National Science Foundation grants OCE-1538628 (MSR), DEB-2224832 (MSR), and OCE-2149128 (MSR) as well as the Simons Postdoctoral Fellowship in Marine Microbial Ecology (LS-FMME-00989028) (SJT). This is HIMB publication 2025 and SOEST publication 12043.

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K.C.F., S.J.T., A.M.E. and M.S.R. conceived the study, developed methodology and led the investigation and visualization for the study. A.M.E. and M.S.R. supervised the study. KCF wrote the original draft. K.C.F., S.J.T., E.B.F., U.S., S.J.G., A.M.E. and M.S.R. reviewed and edited the manuscript.

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Michael S. Rappé.

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Freel, K.C., Tucker, S.J., Freel, E.B. et al. New SAR11 isolate genomes and global marine metagenomes resolve ecologically relevant units within the Pelagibacterales.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67043-6

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