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Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater


Niche theory is a foundational ecological concept that explains the distribution of species in natural environments. Identifying the dimensions of any organism’s niche is challenging because numerous environmental factors can affect organism viability. We used serial invasion experiments to introduce Ruegeria pomeroyi DSS-3, a heterotrophic marine bacterium, into a coastal phytoplankton bloom on 14 dates. RNA-sequencing analysis of R. pomeroyi was conducted after 90 min to assess its niche dimensions in this dynamic ecosystem. We identified ~100 external conditions eliciting transcriptional responses, which included substrates, nutrients, metals and biotic interactions such as antagonism, resistance and cofactor synthesis. The peak bloom was characterized by favourable states for most of the substrate dimensions, but low inferred growth rates of R. pomeroyi at this stage indicated that its niche was narrowed by factors other than substrate availability, most probably negative biotic interactions with the bloom dinoflagellate. Our findings indicate chemical and biological features of the ocean environment that can constrain where heterotrophic bacteria survive.

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Fig. 1: Conditions and methods for invasion studies.
Fig. 2: R. pomeroyi gene expression patterns in Monterey Bay invasion studies.
Fig. 3: Time course of relative gene expression indicating R. pomeroyi‘s responses to niche dimensions.
Fig. 4: Characterization of R. pomeroyi niche boundaries.

Data availability

Data that support the findings of the present study have been deposited in the National Center for Biotechnology Information‘s Sequence Read Archive with BioProject nos. PRJNA641119 (RNA-seq) and PRJNA511156PRJNA511331 (16S and 18S rRNA data), and the Biological and Chemical Oceanography Data Management Office under at (environmental data). Source data are provided with this paper.


  1. 1.

    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).

    CAS  PubMed  Google Scholar 

  2. 2.

    Palovaara, J. et al. Stimulation of growth by proteorhodopsin phototrophy involves regulation of central metabolic pathways in marine planktonic bacteria. Proc. Natl Acad. Sci. USA 111, E3650–E3658 (2014).

    CAS  PubMed  Google Scholar 

  3. 3.

    Poretsky, R. S., Sun, S., Mou, X. & Moran, M. A. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ. Microbiol. 12, 616–627 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Church, M. J., Hutchins, D. A. & Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Appl. Environ. Microbiol. 66, 455–466 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Persson, O. P. et al. High abundance of virulence gene homologues in marine bacteria. Environ. Microbiol. 11, 1348–1357 (2009).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Yeung, L. Y. et al. Impact of diatom–diazotroph associations on carbon export in the Amazon River plume. Geophys. Res. Lett. 39, L18609 (2012).

    Google Scholar 

  7. 7.

    Colwell, R. K. & Fuentes, E. R. Experimental studies of the niche. Annu. Rev. Ecol. Syst. 6, 281–310 (1975).

    Google Scholar 

  8. 8.

    Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).

    Google Scholar 

  9. 9.

    Cohan, F. M. What are bacterial species? Annu. Rev. Microbiol. 56, 457–487 (2002).

    CAS  PubMed  Google Scholar 

  10. 10.

    Erguder, T. H., Boon, N., Wittebolle, L., Marzorati, M. & Verstraete, W. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol. Rev. 33, 855–869 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Meier, D. V. et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 11, 1545–1558 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Martens-Habbena, W., Berube, P. M., Urakawa, H., José, R. & Stahl, D. A. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461, 976–979 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gifford, S. M., Sharma, S., Booth, M. & Moran, M. A. Expression patterns reveal niche diversification in a marine microbial assemblage. ISME J. 7, 281–298 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Landa, M., Burns, A. S., Roth, S. J. & Moran, M. A. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 11, 2677–2690 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ottesen, E. A. et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl Acad. Sci. USA 110, E488–E497 (2013).

    CAS  PubMed  Google Scholar 

  16. 16.

    Galambos, D., Anderson, R. E., Reveillaud, J. & Huber, J. A. Genome-resolved metagenomics and metatranscriptomics reveal niche differentiation in functionally redundant microbial communities at deep-sea hydrothermal vents. Environ. Microbiol. 21, 4395–4410 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).

    CAS  PubMed  Google Scholar 

  18. 18.

    Shaiber, A. & Eren, A. M. Composite metagenome-assembled genomes reduce the quality of public genome repositories. mBio 10, e00725-19 (2019).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Cottrell, M. T. & Kirchman, D. L. Transcriptional control in marine copiotrophic and oligotrophic bacteria with streamlined genomes. Appl. Environ. Microbiol. 82, 6010–6018 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bell, T. Next-generation experiments linking community structure and ecosystem functioning. Environ. Microbiol. Rep. 11, 20–22 (2019).

    PubMed  Google Scholar 

  21. 21.

    Mallon, C. A., Van Elsas, J. D. & Salles, J. F. Microbial invasions: the process, patterns, and mechanisms. Trends Microbiol. 23, 719–729 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Kiene, R. P. et al. Unprecedented DMSP concentrations in a massive dinoflagellate bloom in Monterey Bay, CA. Geophys. Res. Lett. 46, 12279–12288 (2019).

    Google Scholar 

  23. 23.

    Anderson, S. R., Diou-Cass, Q. P. & Harvey, E. L. Short-term estimates of phytoplankton growth and mortality in a tidal estuary. Limnol. Oceanogr. 63, 2411–2422 (2018).

    Google Scholar 

  24. 24.

    Anderson, S. R. & Harvey, E. L. Seasonal variability and drivers of microzooplankton grazing and phytoplankton growth in a subtropical estuary. Front Mar. Sci. 6, 174–174 (2019).

    Google Scholar 

  25. 25.

    González, J. M. et al. Silicibacter pomeroyi sp. nov. and Roseovarius nubinhibens sp. nov., dimethylsulfoniopropionate-demethylating bacteria from marine environments. Int. J. Syst. Evol. Microbiol. 53, 1261–1269 (2003).

    PubMed  Google Scholar 

  26. 26.

    Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: the once and future niche. Proc. Natl Acad. Sci. USA 106, 19651–19658 (2009).

    CAS  PubMed  Google Scholar 

  28. 28.

    Holt, R. D. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc. Natl Acad. Sci. USA 106, 19659–19665 (2009).

    CAS  PubMed  Google Scholar 

  29. 29.

    Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun. Biol. 3, 119 (2020).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Baltar, F. et al. Towards integrating evolution, metabolism, and climate change studies of marine ecosystems. Trends Ecol. Evol. 34, 1022–1033 (2019).

    PubMed  Google Scholar 

  31. 31.

    Muller, E. E. Determining microbial niche breadth in the environment for better ecosystem fate predictions. mSystems 4, e00080-19 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Chan, L.-K. et al. Transcriptional changes underlying elemental stoichiometry shifts in a marine heterotrophic bacterium. Front. Microbiol. 3, 159 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kudela, R. M., Seeyave, S. & Cochlan, W. P. The role of nutrients in regulation and promotion of harmful algal blooms in upwelling systems. Prog. Oceanogr. 85, 122–135 (2010).

    Google Scholar 

  34. 34.

    Moran, M. A. et al. Genome sequence of Silicibacter pomeroyi reveals adaptations to the marine environment. Nature 432, 910–913 (2004).

    CAS  PubMed  Google Scholar 

  35. 35.

    Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).

    CAS  PubMed  Google Scholar 

  36. 36.

    Sharpe, G. C., Gifford, S. M. & Septer, A. N. A model Roseobacter employs a diffusible killing mechanism to eliminate competitors. mSystems 5, e00443-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Gil-Turnes, M. S., Hay, M. E. & Fenical, W. Symbiotic marine bacteria chemically defend crustacean embryos from a pathogenic fungus. Science 246, 116–118 (1989).

    CAS  PubMed  Google Scholar 

  38. 38.

    Lopanik, N., Lindquist, N. & Targett, N. Potent cytotoxins produced by a microbial symbiont protect host larvae from predation. Oecologia 139, 131–139 (2004).

    PubMed  Google Scholar 

  39. 39.

    Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).

    CAS  PubMed  Google Scholar 

  40. 40.

    Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Annu. Rev. Mar. Sci. 6, 339–367 (2014).

    Google Scholar 

  41. 41.

    Biers, E. J. et al. Occurrence and expression of gene transfer agent genes in marine bacterioplankton. Appl. Environ. Microbiol. 74, 2933–2939 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Gravel, D. et al. Experimental niche evolution alters the strength of the diversity–productivity relationship. Nature 469, 89–94 (2011).

    CAS  PubMed  Google Scholar 

  43. 43.

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

    CAS  PubMed  Google Scholar 

  44. 44.

    McDaniel, L. D. et al. High frequency of horizontal gene transfer in the oceans. Science 330, 50–50 (2010).

    CAS  PubMed  Google Scholar 

  45. 45.

    Nuss, A. M., Glaeser, J., Berghoff, B. A. & Klug, G. Overlapping alternative sigma factor regulons in the response to singlet oxygen in Rhodobacter sphaeroides. J. Bacteriol. 192, 2613–2623 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Berghoff, B. A. et al. Anoxygenic photosynthesis and photooxidative stress: a particular challenge for Roseobacter. Environ. Microbiol. 13, 775–791 (2011).

    CAS  PubMed  Google Scholar 

  47. 47.

    Zhao, K., Liu, M. & Burgess, R. R. The global transcriptional response of Escherichia coli to induced σ32 protein involves σ32 regulon activation followed by inactivation and degradation of σ32 in vivo. J. Biol. Chem. 280, 17758–17768 (2005).

    CAS  PubMed  Google Scholar 

  48. 48.

    Diaz, J. M. et al. Widespread production of extracellular superoxide by heterotrophic bacteria. Science 340, 1223–1226 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Wietz, M., Duncan, K., Patin, N. V. & Jensen, P. R. Antagonistic interactions mediated by marine bacteria: the role of small molecules. J. Chem. Ecol. 39, 879–891 (2013).

    CAS  PubMed  Google Scholar 

  50. 50.

    Maguire, B. A. Inhibition of bacterial ribosome assembly: a suitable drug target? Microbiol. Mol. Biol. Rev. 73, 22–35 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Wei, Y. et al. High-density microarray-mediated gene expression profiling of Escherichia coli. J. Bacteriol. 183, 545–556 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Wilson, D. N. & Nierhaus, K. H. The weird and wonderful world of bacterial ribosome regulation. Crit. Rev. Biochem. Mol. Biol. 42, 187–219 (2007).

    CAS  PubMed  Google Scholar 

  53. 53.

    Vinas, N. Relationships between Growth Rate and Gene Expression in Ruegeria pomeroyi DSS-3, a Model Marine Alphaproteobacterium. MSc thesis, Clemson Univ. (2015).

  54. 54.

    Ishihama, A. Functional modulation of Escherichia coli RNA polymerase. Annu. Rev. Microbiol. 54, 499–518 (2000).

    CAS  PubMed  Google Scholar 

  55. 55.

    González, J. M., Kiene, R. P. & Moran, M. A. Transformation of sulfur compounds by an abundant lineage of marine bacteria in the α-subclass of the class Proteobacteria. Appl. Environ. Microbiol. 65, 3810–3819 (1999).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Denger, K., Lehmann, S. & Cook, A. M. Molecular genetics and biochemistry of N-acetyltaurine degradation by Cupriavidus necator H16. Microbiology 157, 2983–2991 (2011).

    CAS  PubMed  Google Scholar 

  57. 57.

    Lidbury, I., Murrell, J. C. & Chen, Y. Trimethylamine N-oxide metabolism by abundant marine heterotrophic bacteria. Proc. Natl Acad. Sci. USA 111, 2710–2715 (2014).

    CAS  PubMed  Google Scholar 

  58. 58.

    Mou, X., Sun, S., Edwards, R. A., Hodson, R. E. & Moran, M. A. Bacterial carbon processing by generalist species in the coastal ocean. Nature 451, 708–711 (2008).

    CAS  PubMed  Google Scholar 

  59. 59.

    Schulz, A. et al. Feeding on compatible solutes: a substrate‐induced pathway for uptake and catabolism of ectoines and its genetic control by EnuR. Environ. Microbiol. 19, 926–946 (2017).

    CAS  PubMed  Google Scholar 

  60. 60.

    Weinitschke, S., Sharma, P. I., Stingl, U., Cook, A. M. & Smits, T. H. Gene clusters involved in isethionate degradation by terrestrial and marine bacteria. Appl. Environ. Microbiol. 76, 618–621 (2010).

    CAS  PubMed  Google Scholar 

  61. 61.

    Jessup, D. A., Miller, M. A., Ryan, J. P., Nevins, H. M. & Kerkering, H. A. Mass stranding of marine birds caused by a surfactant-producing red tide. PLoS ONE 4, 4550 (2009).

    Google Scholar 

  62. 62.

    Jones, T. et al. Mass mortality of marine birds in the Northeast Pacific caused by Akashiwo sanguinea. Mar. Ecol. Prog. Ser. 579, 111–127 (2017).

    Google Scholar 

  63. 63.

    Xu, N. et al. Acute toxicity of the cosmopolitan bloom-forming dinoflagellate Akashiwo sanguinea to finfish, shellfish, and zooplankton. Aquat. Microb. Ecol. 80, 209–222 (2017).

    Google Scholar 

  64. 64.

    Kiene, R. P. & Linn, L. J. Distribution and turnover of dissolved DMSP and its relationship with bacterial production and dimethylsulfide in the Gulf of Mexico. Limnol. Oceanogr. 45, 849–861 (2000).

    CAS  Google Scholar 

  65. 65.

    Motard-Côté, J., Kieber, D. J., Rellinger, A. & Kiene, R. P. Influence of the Mississippi River plume and non-bioavailable DMSP on dissolved DMSP turnover in the northern Gulf of Mexico. Environ. Chem. 13, 280–280 (2016).

    Google Scholar 

  66. 66.

    Lally, E. T., Hill, R. B., Kieba, I. R. & Korostoff, J. The interaction between RTX toxins and target cells. Trends Microbiol. 7, 356–361 (1999).

    CAS  PubMed  Google Scholar 

  67. 67.

    Billen, G. & Fontigny, A. Dynamics of a Phaeocystis-dominated spring bloom in Belgian coastal waters. II. Bacterioplankton dynamics. Mar. Ecol. Prog. Ser. 37, 249–257 (1987).

    Google Scholar 

  68. 68.

    Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).

    CAS  PubMed  Google Scholar 

  69. 69.

    Bunse, C. et al. Spatio-temporal interdependence of bacteria and phytoplankton during a Baltic Sea spring bloom. Front. Microbiol. 7, 517–517 (2016).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Pinhassi, J. et al. Changes in bacterioplankton composition under different phytoplankton regimens. Appl. Environ. Microbiol. 70, 6753–6766 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Morris, J. J., Johnson, Z. I., Szul, M. J., Keller, M. & Zinser, E. R. Dependence of the cyanobacterium Prochlorococcus on hydrogen peroxide scavenging microbes for growth at the ocean’s surface. PLoS ONE 6, e16805 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Stock, F. et al. N-acyl homoserine lactone derived tetramic acids impair photosynthesis in Phaeodactylum tricornutum. ACS Chem. Biol. 14, 198–203 (2019).

    CAS  PubMed  Google Scholar 

  74. 74.

    Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).

    Google Scholar 

  75. 75.

    Morris, J. J., Lenski, R. E. & Zinser, E. R. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Saupe, E. E. et al. Reconstructing ecological niche evolution when niches are incompletely characterized. Syst. Biol. 67, 428–438 (2018).

    PubMed  Google Scholar 

  78. 78.

    Fu, H., Uchimiya, M., Gore, J. & Moran, M. A. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc. Natl Acad. Sci. USA 117, 3656–3662 (2020).

    CAS  PubMed  Google Scholar 

  79. 79.

    González, J. M., Mayer, F., Moran, M. A., Hodson, R. E. & Whitman, W. B. Microbulbifer hydrolyticus gen. nov., sp. nov., and Marinobacterium georgiense gen. nov., sp. nov., two marine bacteria from a lignin-rich pulp mill waste enrichment community. Int. J. Syst. Evol. Microbiol. 47, 369–376 (1997).

    Google Scholar 

  80. 80.

    Nowinski, B. et al. Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom. Sci. Data 6, 129 (2019).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Anders, S., Pyl, P. T. & Huber, W. Genome analysis HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  83. 83.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).

    Google Scholar 

  84. 84.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550–550 (2014).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, 597–604 (2013).

    Google Scholar 

  87. 87.

    Bolyen, E. et al. Reproducible, interactive, scalable, and extensible microbiome data science using QIIME2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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We thank C. Preston, J. Birch, C. Sholin and the MBARI ESP team for providing field sampling infrastructure and expertise; S. Sharma for providing bioinformatic assistance; C. Smith, C. Thomas and K. Esson for assisting with field and laboratory techniques; R. Michisaki for providing expertise on microbial biomass estimates; and the University of Georgia Genomics and Bioinformatics Core for supplying sequencing services. This work was supported by the Simons Foundation (grant no. 542391 to M.A.M.) within the Principles of Microbial Ecosystems (PriME) Collaborative and by NSF (IOS-1656311). The rRNA amplicon sequencing was provided through the DOE Joint Genome Institute Community Sequencing Program.

Author information




B.N. and M.A.M. conceived of the study. B.N. collected the data. B.N. and M.A.M. analysed the data and wrote the paper.

Corresponding author

Correspondence to Mary Ann Moran.

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The authors declare no competing interests.

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Peer review information Nature Microbiology thanks Virginia Armbrust and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Protist and bacterial community composition during the 2016 Monterey Bay autumn bloom based on rRNA gene sequencing.

Each bar represents 1 replicate sample. a) 18S rRNA, order level. b) 18S rRNA, genus level. c) 16S rRNA, family level.

Source data

Extended Data Fig. 2

Salinity and temperature of seawater sampled by CTD at Monterey Bay Station MO in Fall, 2016.

Source data

Extended Data Fig. 3 Correlations of Z-score normalized R. pomeroyi gene expression module eigengenes with environmental data.

Expression was measured by RNAseq after incubation in seawater collected on 14 dates at Monterey Bay Station M0 in Fall, 2016. Dinoflagellate parasites are members of the Syndinales clade. Cells are colored by Pearson’s R parameter. Two stars indicate correlations at p < 0.01; one star indicates correlations at p < 0.05. d.f. = 12.

Source data

Extended Data Fig. 4 Correlations of representative R. pomeroyi genes for transport of ammonium (amtB) and urea (urtB) with Akashiwo biomass.

The extreme value of Akashiwo biomass is removed in the inset. **, significant Pearson’s R correlation, with p < 0.001 for amtB and urtB in the main figure, d.f. = 12; and p = 0.010 for amtB and p = 0.331 for umtB in the inset figure, d.f. = 11.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Source data

Source Data Fig. 1

Microbial abundance and Chl a, gene expression data for glucose catabolism genes.

Source Data Fig. 2

Gene expression data for PCA, 18S rRNA amplicon relative abundance and mean z-scores for gene modules.

Source Data Fig. 3

Gene expression data by date and replicate.

Source Data Fig. 4

Gene expression data for ribosomal proteins, rpoD and dmdA, genome accession nos. and DMSP concentrations.

Source Data Extended Data Fig. 1

The 18S rRNA ASV abundance at the order level, 18S rRNA ASV abundance at the genus level and 16S rRNA ASV abundance at the family level.

Source Data Extended Data Fig. 2

Temperature and salinity data.

Source Data Extended Data Fig. 3

Environmental data and module eigengenes.

Source Data Extended Data Fig. 4

Nitrogen gene expression data and A. sanguinea biomass data.

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Nowinski, B., Moran, M.A. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol 6, 524–532 (2021).

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