Ecological drivers switch from bottom–up to top–down during model microbial community successions


Bottom–up selection has an important role in microbial community assembly but is unable to account for all observed variance. Other processes like top–down selection (e.g., predation) may be partially responsible for the unexplained variance. However, top–down processes and their interaction with bottom–up selective pressures often remain unexplored. We utilised an in situ marine biofilm model system to test the effects of bottom–up (i.e., substrate properties) and top–down (i.e., large predator exclusion via 100 µm mesh) selective pressures on community assembly over time (56 days). Prokaryotic and eukaryotic community compositions were monitored using 16 S and 18 S rRNA gene amplicon sequencing. Higher compositional variance was explained by growth substrate in early successional stages, but as biofilms mature, top–down predation becomes progressively more important. Wooden substrates promoted heterotrophic growth, whereas inert substrates’ (i.e., plastic, glass, tile) lack of degradable material selected for autotrophs. Early wood communities contained more mixotrophs and heterotrophs (e.g., the total abundance of Proteobacteria and Euglenozoa was 34% and 41% greater within wood compared to inert substrates). Inert substrates instead showed twice the autotrophic abundance (e.g., cyanobacteria and ochrophyta made up 37% and 10% more of the total abundance within inert substrates than in wood). Late native (non-enclosed) communities were mostly dominated by autotrophs across all substrates, whereas high heterotrophic abundance characterised enclosed communities. Late communities were primarily under top–down control, where large predators successively pruned heterotrophs. Integrating a top–down control increased explainable variance by 7–52%, leading to increased understanding of the underlying ecological processes guiding multitrophic community assembly and successional dynamics.

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Fig. 1: Two development stages identified through silhouette and ecotone analysis for prokaryotic (left) and eukaryotic (right) communities.
Fig. 2: Microbial biofilm beta-diversity by time.
Fig. 3: Prokaryotic and eukaryotic observed richness is enclosure specific in a stage dependent manner.
Fig. 4: Significant phylum changes in response to biofilm age.
Fig. 5: Microbial biofilm community assembly is divided into discrete stages associated with distinct compositions and selective pressures.

Data availability

The sequence data from this study have been deposited in NCBI under BioProject PRJNA630803. All data generated and/or analysed during the study is available within the GitHub repository,


  1. 1.

    Wade W. Unculturable bacteria - the uncharacterized organisms that cause oral infections. J R Soc Med. 2002;95:81–3.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Stewart EJ. Growing unculturable bacteria. J Bacteriol. 2012;194:4151–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Estes JA, Palmisano JF. Sea otters: their role in structuring nearshore communities. Science. 1974;185:1058–60.

  4. 4.

    Rajput R, Pokhriya P, Panwar P, Arunachalam A, Arunachalam K. Soil nutrients, microbial biomass, and crop response to organic amendments in rice cropping system in the Shiwaliks of Indian Himalayas. Int J Recycl Org Waste Agric. 2019;8:73–85.

    Article  Google Scholar 

  5. 5.

    Ofiţeru ID, Lunn M, Curtis TP, Wells GF, Criddle CS, Francis CA, et al. Combined niche and neutral effects in a microbial wastewater treatment community. Proc Natl Acad Sci USA. 2010;107:15345–50.

    PubMed  Article  Google Scholar 

  6. 6.

    Pereira e Silva MC, Dias ACF, van Elsas JD, Salles JF. Spatial and temporal variation of archaeal, bacterial and fungal communities in agricultural soils. PLoS ONE. 2012;7:e51554.

  7. 7.

    Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.

    PubMed  Article  Google Scholar 

  9. 9.

    Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002-17.

  10. 10.

    Dini-Andreote F, Stegen JC, Van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Natl Acad Sci USA. 2015;112:E1326–32.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Ju F, Xia Y, Guo F, Wang Z, Zhang T. Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants. Environ Microbiol. 2014;16:2421–32.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Wang J, Shen J, Wu Y, Tu C, Soininen J, Stegen JC, et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: Deterministic versus stochastic processes. ISME J. 2013;7:1310–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Price PB, Sowers T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc Natl Acad Sci USA. 2004;101:4631–6.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Bartlett DH. Pressure effects on in vivo microbial processes. Biochim Biophys Acta. 2002;1595:367–81.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Damore JA, Gore J. Understanding microbial cooperation. J Theor Biol. 2012;299:31–41.

    PubMed  Article  Google Scholar 

  16. 16.

    Knowlton N, Rohwer F. Multispecies microbial mutualisms on coral reefs: the host as a habitat. Am Nat. 2003;162:S51–62.

    PubMed  Article  Google Scholar 

  17. 17.

    Shoemaker KM, Duhamel S, Moisander PH. Copepods promote bacterial community changes in surrounding seawater through farming and nutrient enrichment. Environ Microbiol. 2019;21:3737–50.

  18. 18.

    Jürgens K, Matz C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie van Leeuwenhoek. 2002;81:413–34.

    PubMed  Article  Google Scholar 

  19. 19.

    Sherr EB, Sherr BF. Significance of predation by protists in aquatic microbial food webs. Antonie van Leeuwenhoek. 2002;81:293–308.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Azam F, Fenchel T, Field J, Gray J, Meyer-Reil L, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983;10:257–63.

    Article  Google Scholar 

  21. 21.

    Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Pacheco AR, Segrè D. A multidimensional perspective on microbial interactions. FEMS Microbiol Lett. 2019;366:fnz125.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Coleman ML, Chisholm SW. Ecosystem-specific selection pressures revealed through comparative population genomics. Proc Natl Acad Sci USA. 2010;107:18634–9.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Freestone AL, Carroll EW, Papacostas KJ, Ruiz GM, Torchin ME, Sewall BJ. Predation shapes invertebrate diversity in tropical but not temperate seagrass communities. J Anim Ecol. 20201;89:323–33.

  25. 25.

    Hutchinson GE. Homage to santa rosalia or why are there so many kinds of animals? Am Nat. 1959;93:145–59.

    Article  Google Scholar 

  26. 26.

    Holt AR, Davies ZG, Tyler C, Staddon S. Meta-analysis of the effects of predation on animal prey abundance: evidence from UK vertebrates. PLoS ONE. 2008;3:e2400.

  27. 27.

    del Giorgio PA, Bouvier TC. Linking the physiologic and phylogenetic successions in free-living bacterial communities along an estuarine salinity gradient. Limnol Oceanogr. 2002;47:471–86.

    Article  Google Scholar 

  28. 28.

    Lee CK, Laughlin DC, Bottos EM, Caruso T, Joy K, Barrett JE, et al. Biotic interactions are an unexpected yet critical control on the complexity of an abiotically driven polar ecosystem. Commun Biol. 2019;2:62.

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Gasol JM. A framework for the assessment of top-down vs bottom up control of heterotrophic nanoflagellate abundance. Mar Ecol Prog Ser. 1994;113:291–300.

    Article  Google Scholar 

  30. 30.

    Berdjeb L, Ghiglione JF, Jacquet S. Bottom-up versus top-down control of hypo-and epilimnion free-living bacterial community structures in two neighboring freshwater lakes. Appl Environ Microbiol. 2011;77:3591–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Grattepanche J-D, Juarez DL, Wood CC, McManus GB, Katz LA. Top-down and bottom-up effects on microbial eukaryotic diversity inferred from molecular analyses of microcosm experiments. PLoS ONE. 2019;14:e0215872.

  32. 32.

    Lami R, Ghiglione JF, Desdevises Y, West NJ, Lebaron P. Annual patterns of presence and activity of marine bacteria monitored by 16S rDNA-16S rRNA fingerprints in the coastal NW Mediterranean Sea. Aquat Micro Ecol. 2009;54:199–210.

    Article  Google Scholar 

  33. 33.

    Scepanovic P, Hodel F, Mondot S, Partula V, Byrd A, Hammer C, et al. A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals. Microbiome. 2019;7:130.

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2019. Available from:

  35. 35.

    Wickham H ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016. Available from:

  36. 36.

    Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Hugerth LW, Wefer HA, Lundin S, Jakobsson HE, Lindberg M, Rodin S, et al. DegePrime, a program for degenerate primer design for broad-taxonomic-range PCR in microbial ecology studies. Appl Environ Microbiol. 2014;80:5116–23.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature. 2002;420:806–10.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Thijs S, De Beeck MO, Beckers B, Truyens S, Stevens V, Van Hamme JD, et al. Comparative evaluation of four bacteria-specific primer pairs for 16S rRNA gene surveys. Front Microbiol. 2017;8:494.

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Apprill A, Mcnally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.

    Article  Google Scholar 

  41. 41.

    Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Kounosu A, Murase K, Yoshida A, Maruyama H, Kikuchi T. Improved 18S and 28S rDNA primer sets for NGS-based parasite detection. Sci Rep. 2019;9:1–12.

    CAS  Article  Google Scholar 

  43. 43.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Wickham H. Reshaping data with the reshape package. J Stat Softw. 2007;21:1–20.

    Article  Google Scholar 

  47. 47.

    Wickham H. The split-apply-combine strategy for data analysis. J Stat Softw. 2011;40:1–29.

    Google Scholar 

  48. 48.

    Dowle M, Srinivasan A. data.table: extension of ‘data.frame’. R Packag version 1126. 2019.

  49. 49.

    Gentle JE, Kaufman L, Rousseuw PJ. Finding droups in data: an introduction to cluster analysis. Biometrics. 1991;47:788.

    Article  Google Scholar 

  50. 50.

    Bagnaro A, Baltar F, Brownstein G, Lee WG, Morales SE, Pritchard DW, et al. Reducing the arbitrary: fuzzy detection of microbial ecotones and ecosystems – focus on the pelagic environment. Environ Microbiome. 2020;15:16.

    CAS  Article  Google Scholar 

  51. 51.

    Kassambara A. ggpubr: “ggplot2” Based Publication Ready Plots. 2019. Available from:

  52. 52.

    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2019; Available from:

  53. 53.

    Wickham H, François R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation. 2019. Available from:

  54. 54.

    Latombe G, McGeoch MA, Nipperess DA, Hui C. zetadiv: Functions to Compute Compositional Turnover Using Zeta Diversity. 2018. p. R package version 1.1.1.

  55. 55.

    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Wickham H. forcats: Tools for Working with Categorical Variables (Factors). 2019. Available from:

  57. 57.

    Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Bhatnagar M, Bhatnagar A. Diversity of polysaccharides in cyanobacteria. In: Microbial Diversity in Ecosystem Sustainability and Biotechnological Applications. Singapore: Springer Singapore; 2019 [cited 2019 Jul 25]. p. 447–96. Available from:

  59. 59.

    Sukenik A, Zohary T, Padisák J. Cyanoprokaryota and other prokaryotic algae. In: Encyclopedia of Inland Waters. Elsevier Inc.; 2009. p. 138–48.

  60. 60.

    Cavalier-Smith T. Kingdom Chromista and its eight phyla: a new synthesis emphasising periplastid protein targeting, cytoskeletal and periplastid evolution, and ancient divergences. Protoplasma. 2018;255:297–357.

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Wexler HM. Bacteroides: the good, the bad, and the nitty-gritty. Clin Microbiol Rev. 2007;20:593–621.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G. Environmental and gut bacteroidetes: the food connection. Front Microbiol. 2011;2:93.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Carere CR, Hards K, Houghton KM, Power JF, McDonald B, Collet C, et al. Mixotrophy drives niche expansion of verrucomicrobial methanotrophs. ISME J. 2017;11:2599–610.

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Taylor WD, Sanders RW. Protozoa. In: Ecology and Classification of North American Freshwater Invertebrates. Elsevier Inc.; 2010. p. 49–90.

  65. 65.

    Spring S, Bunk B, Spröer C, Schumann P, Rohde M, Tindall BJ, et al. Characterization of the first cultured representative of Verrucomicrobia subdivision 5 indicates the proposal of a novel phylum. ISME J. 2016;10:2801–16.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Thorp JH, Rogers DC. Introduction to the Phylum Arthropoda. In: Thorp and Covich’s Freshwater Invertebrates: Ecology and General Biology: Fourth Edition. Elsevier Inc.; 2014. p. 591–7.

  67. 67.

    Smith MW, Herfort L, Fortunato CS, Crump BC, Simon HM. Microbial players and processes involved in phytoplankton bloom utilization in the water column of a fast-flowing, river-dominated estuary. Microbiologyopen. 2017;6:e00467.

  68. 68.

    Fujio-Vejar S, Vasquez Y, Morales P, Magne F, Vera-Wolf P, Ugalde JA, et al. The gut microbiota of healthy Chilean subjects reveals a high abundance of the phylum Verrucomicrobia. Front Microbiol. 2017;8:1221.

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Majdi N, Traunspurger W. Free-living nematodes in the freshwater food web: a review. 2015;47:28–44.

  70. 70.

    Dong Y, Geng J, Liu J, Pang M, Awan F, Lu C, et al. Roles of three TonB systems in the iron utilization and virulence of the Aeromonas hydrophila Chinese epidemic strain NJ-35. Appl Microbiol Biotechnol. 2019;103:4203–15.

  71. 71.

    Lee KC, Webb RI, Janssen PH, Sangwan P, Romeo T, Staley JT, et al. Phylum Verrucomicrobia representatives share a compartmentalized cell plan with members of bacterial phylum Planctomycetes. BMC Microbiol. 2009;9:5.

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Yeates GW, Bongers T, De Goede RGM, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera-an outline for soil ecologists. J Nematol. 1993;25:315–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Suyama T, Shigematsu T, Suzuki T, Tokiwa Y, Kanagawa T, Nagashima KVP, et al. Photosynthetic apparatus in Roseateles depolymerans 61A is transcriptionally induced by carbon limitation. Appl Environ Microbiol. 2002;68:1665–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Schultz B, Koprivnikar J. Free-living parasite infectious stages promote zooplankton abundance under the risk of predation. Oecologia. 2019;191:411–9.

  75. 75.

    Kaufman L, Rousseeuw PJ. Partitioning Around Medoids (Program PAM). In: Kaufman L, Rousseeuw PJ, (editors.) Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 1990 [cited 2020 Mar 13]. p. 68–125. (Wiley Series in Probability and Statistics). Available from:

  76. 76.

    Ferrenberg S, O’neill SP, Knelman JE, Todd B, Duggan S, Bradley D, et al. Changes in assembly processes in soil bacterial communities following a wildfire disturbance. ISME J. 2013;7:1102–11.

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Antunes J, Leão P, Vasconcelos V. Marine biofilms: diversity of communities and of chemical cues. Environ Microbiol Rep. 2019;11:287–305.

    PubMed  Article  Google Scholar 

  78. 78.

    Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Dantas LCDM, Da Silva-Neto JP, Dantas TS, Naves LZ, Das Neves FD, Da, et al. Bacterial adhesion and surface roughness for different clinical techniques for acrylic polymethyl methacrylate. Int J Dent. 2016;2016:8685796.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. 80.

    Eginton PJ, Gibson H, Holah J, Handley PS, Gilbert P. The influence of substratum properties on the attachment of bacterial cells. Colloids Surf B Biointerfaces. 1995;5:153–9.

    CAS  Article  Google Scholar 

  81. 81.

    Characklis WG, McFeters GA, Marshall KC. Physiological ecology in biofilm systems. In: Characklis GW, Marshall KC, (editors.) Biofilms. New York: John Wiley & Sons; 1990. p. 341–94.

  82. 82.

    Bredon M, Dittmer J, Noël C, Moumen B, Bouchon D. Lignocellulose degradation at the holobiont level: teamwork in a keystone soil invertebrate. Microbiome. 2018;6:1–19.

    Article  Google Scholar 

  83. 83.

    Langenheder S, Lindström ES, Tranvik LJ. Structure and function of bacterial communities emerging from different sources under identical conditions. Appl Environ Microbiol. 2006;72:212–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Caruso T, Chan Y, Lacap DC, Lau MCY, McKay CP, Pointing SB. Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale. ISME J. 2011;5:1406–13.

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Johansen R, Albright M, Gallegos-Graves LV, Lopez D, Runde A, Yoshida T, et al. Tracking replicate divergence in microbial community composition and function in experimental microcosms. Micro Ecol. 2019;78:1035–9.

    Article  Google Scholar 

  86. 86.

    Castle SC, Nemergut DR, Grandy AS, Leff JW, Graham EB, Hood E, et al. Biogeochemical drivers of microbial community convergence across actively retreating glaciers. Soil Biol Biochem. 2016;101:74–84.

    CAS  Article  Google Scholar 

  87. 87.

    Bik HM, Alexiev A, Aulakh SK, Bharadwaj L, Flanagan J, Haggerty JM, et al. Microbial community succession and nutrient cycling responses following perturbations of experimental saltwater aquaria. mSphere. 2019;4:e00043–19.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Tanaka M, Nakayama J. Development of the gut microbiota in infancy and its impact on health in later life. Allergol Int. 2017;66:515–22.

    CAS  PubMed  Article  Google Scholar 

  89. 89.

    Pagaling E, Strathdee F, Spears BM, Cates ME, Allen RJ, Free A. Community history affects the predictability of microbial ecosystem development. ISME J. 2014;8:19–30.

    PubMed  Article  Google Scholar 

  90. 90.

    Dawson FH, Kern-Hansen U. The effect of natural and artificial shade on the macrophytes of lowland streams and the use of shade as a management technique. Int Rev Hydrobiol. 1979;64:437–55.

    Article  Google Scholar 

  91. 91.

    Rittman BE. The effect of shear stress on biofilm loss rate. Biotechnol Bioeng. 1982;24:501–506.

  92. 92.

    Rochex A, Godon JJ, Bernet N, Escudié R. Role of shear stress on composition, diversity and dynamics of biofilm bacterial communities. Water Res. 2008;42:4915–22.

    CAS  PubMed  Article  Google Scholar 

  93. 93.

    Holt RD, Lawton JH. The ecological consequences of shared natural enemies. Annu Rev Ecol Syst. 1994;25:495–520.

    Article  Google Scholar 

  94. 94.

    Schoener TW. & Spiller DA. Devastation prey diversity experimentally introduced predat field. Nature. 1996;381:691–4

  95. 95.

    Broglio E, Saiz E, Calbet A, Trepat I, Alcaraz M. Trophic impact and prey selection by crustacean zooplankton on the microbial communities of an oligotrophic coastal area (NW Mediterranean Sea). Aquat Micro Ecol. 2004;35:65–78.

    Article  Google Scholar 

  96. 96.

    Gómez F. Symbioses of Ciliates (Ciliophora) and Diatoms (Bacillariophyceae): taxonomy and host–symbiont interactions. Oceans. 2020;1:133–55.

    Article  Google Scholar 

  97. 97.

    Hessen DO, Elser JJ, Sterner RW, Urabe J. Ecological stoichiometry: an elementary approach using basic principles. Limnol Oceanogr. 2013;58:2219–36.

    CAS  Article  Google Scholar 

  98. 98.

    Ballantyne IVF, Menge DNL, Ostling A, Hosseini P. Nutrient recycling affects autotroph and ecosystem stoichiometry. Am Nat. 2008;171:511–23.

    PubMed  Article  Google Scholar 

  99. 99.

    Mitra A, Flynn KJ. Predator-prey interactions: is “ecological stoichiometry” sufficient when good food goes bad? J Plankton Res. 2005;27:393–9.

  100. 100.

    Flynn KJ, Davidson K. Predator-prey interactions between Isochrysis galbana and Oxyrrhis marina. II. Release of non-protein amines and faeces during predation of Isochrysis. J Plankton Res. 1993;15:893–905.

  101. 101.

    Yang JW, Wu W, Chung CC, Chiang KP, Gong GC, Hsieh CH. Predator and prey biodiversity relationship and its consequences on marine ecosystem functioning - Interplay between nanoflagellates and bacterioplankton. ISME J. 2018;12:1532–42.

    PubMed  PubMed Central  Article  Google Scholar 

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We thank Dave Wilson for his contribution to experimental set up.

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Tobias-Hünefeldt, S.P., Wenley, J., Baltar, F. et al. Ecological drivers switch from bottom–up to top–down during model microbial community successions. ISME J (2020).

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