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  • Perspective
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More realistic plankton simulation models will improve projections of ocean ecosystem responses to global change

Abstract

Plankton models form the core of marine ecosystem simulators, with uses from regional resource and ecosystem management to climate change projections. In this Perspective, we suggest that stronger alignment of models with empirical knowledge about plankton physiology, diversity and trophic roles will improve model utility and the reliability of their outputs regarding biodiversity, ecophysiology, trophic dynamics and biogeochemistry. We recommend key steps to resolve the disconnect between empirical research and simulation models accounting for well-established plankton processes with an aim to increase the utility of such models for applied uses. A central challenge is characterizing the complexity of plankton diversity and activity in ways that are amenable to model incorporation. We argue that experts in empirical science are best placed to advise the development of next-generation models to address these challenges, and we propose a series of actions to achieve that engagement, including involvement of these experts in the design and exploitation of plankton digital twins.

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Fig. 1: Proposed route to enhancing the representation of plankton in marine ecosystem simulators.

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References

  1. Worden, A. Z. et al. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347, 1257594 (2015).

    Article  PubMed  Google Scholar 

  2. Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Sommer, U., Paul, C. & Moustaka-Gouni, M. Warming and ocean acidification effects on phytoplankton—from species shifts to size shifts within species in a mesocosm experiment. PLoS ONE 10, e0125239 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Trimborn, S. et al. Iron sources alter the response of Southern Ocean phytoplankton to ocean acidification. Mar. Ecol. Prog. Ser. 578, 35–50 (2017).

    Article  CAS  Google Scholar 

  5. Kléparski, L. et al. Ocean climate and hydrodynamics drive decadal shifts in northeast Atlantic dinoflagellates. Glob. Change Biol. 30, e17163 (2024).

    Article  Google Scholar 

  6. Henson, S. A. et al. Uncertain response of ocean biological carbon export in a changing world. Nat. Geosci. 15, 248–254 (2022).

    Article  CAS  Google Scholar 

  7. Martin, P. et al. Iron fertilization enhanced net community production but not downward particle flux during the Southern Ocean iron fertilization experiment LOHAFEX. Glob. Biogeochem. Cycles 27, 871–881 (2013).

    Article  CAS  Google Scholar 

  8. Seifert, M. et al. Interaction matters: bottom‐up driver interdependencies alter the projected response of phytoplankton communities to climate change. Glob. Change Biol. 29, 4234–4258 (2023).

    Article  CAS  Google Scholar 

  9. Asch, R. G., Stock, C. A. & Sarmiento, J. L. Climate change impacts on mismatches between phytoplankton blooms and fish spawning phenology. Glob. Change Biol. 25, 2544–2559 (2019).

    Article  Google Scholar 

  10. Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).

    Article  Google Scholar 

  11. Tagliabue, A. et al. Persistent uncertainties in ocean net primary production climate change projections at regional scales raise challenges for assessing impacts on ecosystem services. Front. Clim. 3, 738224 (2021).

    Article  Google Scholar 

  12. Li, M. et al. A three-dimensional mixotrophic model of Karlodinium veneficum blooms for a eutrophic estuary. Harmful Algae 113, 102203 (2022).

    Article  PubMed  Google Scholar 

  13. Flynn, K. J., Torres, R., Irigoien, X. & Blackford, J. C. Plankton digital twins—a new research tool. J. Plankton Res. 44, 805 (2022).

    Article  Google Scholar 

  14. Flynn, K. J. Simulating plankton—getting it right in the era of Digital Twins of the Ocean; projection introduction and executive discussion. Zenodo https://doi.org/10.5281/zenodo.10953377 (2024).

  15. Flynn, K. J. et al. Simulating plankton—getting it right in the era of Digital Twins of the Ocean; building and challenging perceptions. Zenodo https://doi.org/10.5281/zenodo.10952555 (2024).

  16. Lennon, J. T. et al. Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction. mBio, e00455-24 (2024).

  17. Skogen, M. D. et al. Bridging the gap: integrating models and observations for better ecosystem understanding. Mar. Ecol. Prog. Ser. 739, 257–268 (2024).

    Article  Google Scholar 

  18. Friedrichs, M. A. et al. Assessment of skill and portability in regional marine biogeochemical models: role of multiple planktonic groups. J. Geophys. Res. Oceans 112 (2007).

  19. Tagliabue, A. Climate forecasts: bring marine microbes into the equation. Nature 623, 250–252 (2023).

    Article  CAS  PubMed  Google Scholar 

  20. Mitra, A., Flynn, K. J. & Fasham, M. J. R. Accounting correctly for grazing dynamics in nutrient–phytoplankton–zooplankton models. Limnol. Oceanogr. 52, 649–661 (2007).

    Article  CAS  Google Scholar 

  21. Sett, S., Schulz, K. G., Bach, L. T. & Riebesell, U. Shift towards larger diatoms in a natural phytoplankton assemblage under combined high-CO2 and warming conditions. J. Plankton Res. 40, 391–406 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ratnarajah, L. et al. Monitoring and modelling marine zooplankton in a changing climate. Nat. Commun. 14, 564 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. 12, 5483 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Schmidt, K. et al. Essential omega‐3 fatty acids are depleted in sea ice and pelagic algae of the central Arctic Ocean. Glob. Change Biol. 30, e17090 (2024).

    Article  Google Scholar 

  26. Aumont, O., Éthé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model Dev. Discuss. 8, 1375–1509 (2015).

    Google Scholar 

  27. Wright, R. M., Le Quéré, C., Buitenhuis, E., Pitois, S. & Gibbons, M. J. Role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model. Biogeosciences 18, 1291–1320 (2021).

    Article  CAS  Google Scholar 

  28. Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394 (1949).

    Article  CAS  Google Scholar 

  29. Droop, M. R. Vitamin B12 and marine ecology. IV. The kinetics of uptake, growth, and inhibition in Monochrysis lutheri. J. Mar. Biol. Assoc. UK 48, 689–733 (1968).

    Article  CAS  Google Scholar 

  30. Holling, C. S. The functional response of predators to prey density and its role in mimicry and population regulation. Mem. Entomol. Soc. Can. 97, 5–60 (1965).

    Article  Google Scholar 

  31. Shuter, B. A model of physiological adaptation in unicellular algae. J. Theor. Biol. 78, 519–532 (1979).

    Article  CAS  PubMed  Google Scholar 

  32. Fasham, M. J. R., Ducklow, H. W. & Mckelvie, S. M. A nitrogen based model of plankton dynamics in the oceanic mixed layer. J. Mar. Res. 48, 591–639 (1990).

    Article  CAS  Google Scholar 

  33. Faktorová, D. et al. Genetic tool development in marine protists: emerging model organisms for experimental cell biology. Nat. Methods 17, 481–494 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pomeroy, L. R. The ocean’s food web, a changing paradigm. Bioscience 24, 499–504 (1974).

    Article  Google Scholar 

  35. Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).

    Article  Google Scholar 

  36. Suttle, C. A. Marine viruses—major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593–599 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Stoecker, D. K. Conceptual models of mixotrophy in planktonic protists and some ecological and evolutionary implications. Eur. J. Protistol. 34, 281–290 (1998).

    Article  Google Scholar 

  40. Glibert, P. M. & Mitra, A. From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean. Limnol. Oceanogr. 67, 585–597 (2022).

    Article  Google Scholar 

  41. Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton Univ. Press, 2002).

  42. Flynn, K. J. Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modellers. J. Plankton Res. 27, 1205–1210 (2005).

    Article  CAS  Google Scholar 

  43. Yamazaki, H. & Smith, S. L. Advances in plankton modeling and biodiversity evaluation. J. Plankton Res. 38, 944–945 (2016).

    Article  Google Scholar 

  44. Pahlow, M. & Oschlies, A. Chain model of phytoplankton P, N and light colimitation. Mar. Ecol. Prog. Ser. 376, 69–83 (2009).

    Article  CAS  Google Scholar 

  45. Follows, M. J. & Dutkiewicz, S. Modeling diverse communities of marine microbes. Annu. Rev. Mar. Sci. 3, 427–451 (2011).

    Article  Google Scholar 

  46. Polimene, L. et al. Decrease in diatom palatability contributes to bloom formation in the western English Channel. Prog. Oceanogr. 137, 484–497 (2015).

    Article  Google Scholar 

  47. Martin, A. P. et al. When to add a new process to a model—and when not: a marine biogeochemical perspective. Ecol. Modell. 498, 110870 (2024).

    Article  CAS  Google Scholar 

  48. Stukel, M. R., Décima, M. & Landry, M. R. Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach. Biogeosciences 19, 3595–3624 (2022).

    Article  CAS  Google Scholar 

  49. Nowicki, M., DeVries, T. & Siegel, D. A. Quantifying the carbon export and sequestration pathways of the ocean’s biological carbon pump. Glob. Biogeochem. Cycles 36, e2021GB007083 (2022).

    Article  CAS  Google Scholar 

  50. Wilson, J. D. et al. The biological carbon pump in CMIP6 models: 21st century trends and uncertainties. Proc. Natl Acad. Sci. USA 119, e2204369119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Pinti, J. et al. Model estimates of metazoans’ contributions to the biological carbon pump. Biogeosciences 20, 997–1009 (2023).

    Article  CAS  Google Scholar 

  52. Mitra, A. et al. Bridging the gap between marine biogeochemical and fisheries sciences: configuring the zooplankton link. Prog. Oceanogr. 129, 176–199 (2014).

    Article  Google Scholar 

  53. Rohr, T. et al. Zooplankton grazing is the largest source of uncertainty for marine carbon cycling in CMIP6 models. Commun. Earth Environ. 4, 212 (2023).

    Article  Google Scholar 

  54. Mateus, M. D. Bridging the gap between knowing and modelling viruses in marine systems—an upcoming frontier. Front. Mar. Sci. 3, 284 (2017).

    Article  Google Scholar 

  55. Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Zhao, Z., Amano, C., Reinthaler, T., Orellana, M. V. & Herndl, G. J. Substrate uptake patterns shape niche separation in marine prokaryotic microbiome. Sci. Adv. 10, eadn5143 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Flynn, K. J., Speirs, D. C., Heath, M. R. & Mitra, A. Subtle differences in the representation of consumer dynamics have large effects in marine food web models. Front. Mar. Sci. 8, 638892 (2021).

    Article  Google Scholar 

  58. Hansen, B., Bjørnsen, P. K. & Hansen, P. J. The size ratio between planktonic predators and their prey. Limnol. Oceanogr. 39, 395–403 (1994).

    Article  Google Scholar 

  59. Kiørboe, T., Saiz, E. & Viitasalo, M. Prey switching behaviour in the planktonic copepod Acartia tonsa. Mar. Ecol. Prog. Ser. 143, 65–75 (1996).

    Article  Google Scholar 

  60. Helenius, L. K. & Saiz, E. Feeding behaviour of the nauplii of the marine calanoid copepod Paracartia grani Sars: functional response, prey size spectrum, and effects of the presence of alternative prey. PLoS ONE 12, e0172902 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Follett, C. L. et al. Trophic interactions with heterotrophic bacteria limit the range of Prochlorococcus. Proc. Natl Acad. Sci. USA 119, e2110993118 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Mitchell, J. F., Lowe, J., Wood, R. A. & Vellinga, M. Extreme events due to human-induced climate change. Philos. Trans. R. Soc. A 364, 2117–2133 (2006).

    Article  Google Scholar 

  63. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  64. Babcock, R. C. et al. Severe continental-scale impacts of climate change are happening now: extreme climate events impact marine habitat forming communities along 45% of Australia’s coast. Front. Mar. Sci. 6, 466674 (2019).

    Google Scholar 

  65. Murphy, G. E., Romanuk, T. N. & Worm, B. Cascading effects of climate change on plankton community structure. Ecol. Evol. 10, 2170–2181 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Cheung, W. W. et al. Marine high temperature extremes amplify the impacts of climate change on fish and fisheries. Sci. Adv. 7, eabh0895 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bork, P. et al. Tara Oceans studies plankton at planetary scale. Science 348, 873 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. De Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).

    Article  PubMed  Google Scholar 

  69. Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).

    Article  CAS  PubMed  Google Scholar 

  70. Flynn, K. J. Dynamic Ecology—an Introduction to the Art of Simulating Trophic Dynamics (Swansea Univ., 2018).

  71. Fennel, K. et al. Ocean biogeochemical modelling. Nat. Rev. Methods Prim. 2, 76 (2022).

    Article  CAS  Google Scholar 

  72. Allen, J. I. & Polimene, L. Linking physiology to ecology: towards a new generation of plankton models. J. Plankton Res. 33, 989–997 (2011).

    Article  Google Scholar 

  73. Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).

    Article  CAS  Google Scholar 

  74. Lan, S. S. et al. Flexible phytoplankton functional type (FlexPFT) model: size-scaling of traits and optimal growth. J. Plankton Res. 38, 977–992 (2016).

    Article  Google Scholar 

  75. Anderson, T. R., Hessen, D. O., Mitra, A., Mayor, D. J. & Yool, A. Sensitivity of secondary production and export flux to choice of trophic transfer formulation in marine ecosystem models. J. Mar. Syst. 125, 41–53 (2013).

    Article  Google Scholar 

  76. Atkinson, A. et al. Steeper size spectra with decreasing phytoplankton indicate strong trophic amplification of future marine biomass declines. Nat. Commun. https://doi.org/10.1038/s41467-023-44406-5 (2024).

  77. Flynn, K. J., Skibinski, D. O. & Lindemann, C. Effects of growth rate, cell size, motion, and elemental stoichiometry on nutrient transport kinetics. PLoS Comput. Biol. 14, e1006118 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Tillmann, U., Mitra, A., Flynn, K. J. & Larsson, M. E. Mucus-trap-assisted feeding is a common strategy of the small mixoplanktonic Prorocentrum pervagatum and P. cordatum (Prorocentrales, Dinophyceae). Microorganisms 11, 1730 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Mitra, A., Flynn, K. J., Stoecker, D. K. & Raven, J. A. Trait trade-offs in phagotrophic microalgae: the mixoplankton conundrum. Eur. J. Phycol. 59, 51–70 (2024).

    Article  CAS  Google Scholar 

  80. Strzepek, R. F. et al. The ongoing need for rates: can physiology and omics come together to co-design the measurements needed to understand complex ocean biogeochemistry? J. Plankton Res. 44, 485–495 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Mock, T. et al. Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes. Glob. Change Biol. 22, 61–75 (2016).

    Article  Google Scholar 

  82. Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).

    Article  CAS  PubMed  Google Scholar 

  83. Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).

    Article  Google Scholar 

  84. Jin, P. et al. Increased genetic diversity loss and genetic differentiation in a model marine diatom adapted to ocean warming compared to high CO2. ISME J. 16, 2587–2598 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Flynn, K. J. Toxin production in migrating dinoflagellates: a modelling study of PSP producing Alexandrium. Harmful Algae 1, 147–155 (2002).

    Article  CAS  Google Scholar 

  86. Hofmann, E. E. et al. Understanding controls on Margalefidinium polykrikoides blooms in the lower Chesapeake Bay. Harmful Algae 107, 102064 (2021).

    Article  CAS  PubMed  Google Scholar 

  87. Xiong, J. et al. Biophysical interactions control the progression of harmful algal blooms in Chesapeake Bay: a novel Lagrangian particle tracking model with mixotrophic growth and vertical migration. Limnol. Oceanogr. Lett. 8, 498–508 (2023).

    Article  Google Scholar 

  88. Menden-Deuer, S., Rowlett, J., Nursultanov, M., Collins, S. & Rynearson, T. Biodiversity of marine microbes is safeguarded by phenotypic heterogeneity in ecological traits. PLoS ONE 16, e0254799 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. de Lorenzo, V. The principle of uncertainty in biology: will machine learning/artificial intelligence lead to the end of mechanistic studies? PLoS Biol. 22, e3002495 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Glibert, P. M. Phytoplankton Whispering: An Introduction to the Physiology and Ecology of Microalgae (Springer Nature, 2024).

  91. Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).

    Google Scholar 

  92. Lacour, T., Larivière, J. & Babin, M. Growth, Chl a content, photosynthesis, and elemental composition in polar and temperate microalgae. Limnol. Oceanogr. 62, 43–58 (2017).

    Article  Google Scholar 

  93. Wang, Q. et al. Predicting temperature impacts on aquatic productivity: questioning the metabolic theory of ecology’s ‘canonical’ activation energies. Limnol. Oceanogr. 64, 1172–1185 (2019).

    Article  Google Scholar 

  94. Kreft, J. U. et al. From genes to ecosystems in microbiology: modelling approaches and the importance of individuality. Front. Microbiol. 8, 2299 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Panahi, B., Farhadian, M. & Hejazi, M. A. Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after transition from autotrophic to heterotrophic growth condition in microalgae. PLoS ONE 15, e0225677 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Ralston, D. K. & Moore, S. K. Modeling harmful algal blooms in a changing climate. Harmful Algae 91, 101729 (2020).

    Article  PubMed  Google Scholar 

  97. Harper, A. B. et al. Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4. 6 with new plant functional types. Geosci. Model Dev. 11, 2857–2873 (2018).

    Article  Google Scholar 

  98. Weithoff, G. The concepts of ‘plant functional types’ and ‘functional diversity’ in lake phytoplankton—a new understanding of phytoplankton ecology? Freshw. Biol. 48, 1669–1675 (2003).

    Article  Google Scholar 

  99. Kwiatkowski, L. et al. iMarNet: an ocean biogeochemistry model intercomparison project within a common physical ocean modelling framework. Biogeosciences 11, 7291–7304 (2014).

    Article  Google Scholar 

  100. Intergovernmental Oceanographic Commission. The Science we Need for the Ocean we Want: The United Nations Decade of Ocean Science for Sustainable Development (2021-2030) (2020).

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Acknowledgements

This work was funded by UKRI Natural Environment Research Council (NE/X010783/1; ‘Simulating plankton—getting it right in the era of Digital Twins of the Ocean’), under the direction of K.J.F.

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Flynn, K.J., Atkinson, A., Beardall, J. et al. More realistic plankton simulation models will improve projections of ocean ecosystem responses to global change. Nat Ecol Evol 9, 1562–1570 (2025). https://doi.org/10.1038/s41559-025-02788-3

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