Articles | Volume 21, issue 4
https://doi.org/10.5194/os-21-1787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-21-1787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2025
Research article |
| 25 Aug 2025
| 25 Aug 2025
Multiscale phytoplankton dynamics in a coastal system of the eastern English Channel: the Boulogne-sur-Mer coastal area
Kévin Robache
CORRESPONDING AUTHOR
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Zéline Hubert
CORRESPONDING AUTHOR
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Clémentine Gallot
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Mediterranean Institute of Oceanography (MIO), Campus de Luminy, 163 Av. de Luminy, 13288 Marseille CEDEX 9, France
Alexandre Epinoux
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Arnaud P. Louchart
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, the Netherlands
Jean-Valéry Facq
Ifremer, Laboratoire d'Hydrodynamique Marine, 62200 Boulogne-sur-Mer, France
Alain Lefebvre
Ifremer, COAST, Laboratoire Environnement et Ressources, 62200 Boulogne-sur-Mer, France
Michel Répécaud
Ifremer, Laboratoire Détection, Capteurs et Mesures, 29280 Plouzané, France
Vincent Cornille
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Florine Verhaeghe
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Yann Audinet
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Laurent Brutier
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
François G. Schmitt
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
Luis Felipe Artigas
CORRESPONDING AUTHOR
CNRS, IRD, UMR 8187 LOG, Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, 62930 Wimereux, France
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Cited articles
Agawin, N. S. R., Duarte, C. M., and Agustí, S.: Nutrient and Temperature Control of the Contribution of Picoplankton to Phytoplankton Biomass and Production, Limnol. Oceanogr., 45, 591–600, https://doi.org/10.4319/lo.2000.45.3.0591, 2000. a
André, J.-M., Navarette, C., Blanchot, J., and Radenac, M.-H.: Picophytoplankton Dynamics in the Equatorial Pacific: Growth and Grazing Rates from Cytometric Counts, J. Geophys. Res.-Oceans, 104, 3369–3380, https://doi.org/10.1029/1998JC900005, 1999. a
Bar-On, Y. M. and Milo, R.: The Biomass Composition of the Oceans: A Blueprint of Our Blue Planet, Cell, 179, 1451–1454, https://doi.org/10.1016/j.cell.2019.11.018, 2019. a
Bar-On, Y. M., Phillips, R., and Milo, R.: The Biomass Distribution on Earth, P. Natl. Acad. Sci. USA, 115, 6506–6511, https://doi.org/10.1073/pnas.1711842115, 2018. a
Barrillon, S., Fuchs, R., Petrenko, A. A., Comby, C., Bosse, A., Yohia, C., Fuda, J.-L., Bhairy, N., Cyr, F., Doglioli, A. M., Grégori, G., Tzortzis, R., d'Ovidio, F., and Thyssen, M.: Phytoplankton Reaction to an Intense Storm in the North-Western Mediterranean Sea, Biogeosciences, 20, 141–161, https://doi.org/10.5194/bg-20-141-2023, 2023. a
Bertin, S., Sentchev, A., and Alekseenko, E.: Fusion of Lagrangian Drifter Data and Numerical Model Outputs for Improved Assessment of Turbulent Dispersion, Ocean Sci., 20, 965–980, https://doi.org/10.5194/os-20-965-2024, 2024. a
Blauw, A. N., Benincà, E., Laane, R. W. P. M., Greenwood, N., and Huisman, J.: Dancing with the Tides: Fluctuations of Coastal Phytoplankton Orchestrated by Different Oscillatory Modes of the Tidal Cycle, PLOS ONE, 7, e49319, https://doi.org/10.1371/journal.pone.0049319, 2012. a
Bonato, S., Christaki, U., Lefebvre, A., Lizon, F., Thyssen, M., and Artigas, L. F.: High Spatial Variability of Phytoplankton Assessed by Flow Cytometry, in a Dynamic Productive Coastal Area, in Spring: The Eastern English Channel, Estuar. Coast. Shelf Sci., 154, 214–223, https://doi.org/10.1016/j.ecss.2014.12.037, 2015. a, b, c
Bonato, S., Breton, E., Didry, M., Lizon, F., Cornille, V., Lécuyer, E., Christaki, U., and Artigas, L. F.: Spatio-Temporal Patterns in Phytoplankton Assemblages in Inshore–Offshore Gradients Using Flow Cytometry: A Case Study in the Eastern English Channel, J. Mar. Syst., 156, 76–85, https://doi.org/10.1016/j.jmarsys.2015.11.009, 2016. a
Breton, E., Brunet, C., Sautour, B., and Brylinski, J.-M.: Annual Variations of Phytoplankton Biomass in the Eastern English Channel: Comparison by Pigment Signatures and Microscopic Counts, J. Plankt. Res., 22, 1423–1440, https://doi.org/10.1093/plankt/22.8.1423, 2000. a, b, c
Breton, E., Christaki, U., Sautour, B., Demonio, O., Skouroliakou, D.-I., Beaugrand, G., Seuront, L., Kléparski, L., Poquet, A., Nowaczyk, A., Crouvoisier, M., Ferreira, S., Pecqueur, D., Salmeron, C., Brylinski, J.-M., Lheureux, A., and Goberville, E.: Seasonal Variations in the Biodiversity, Ecological Strategy, and Specialization of Diatoms and Copepods in a Coastal System With Phaeocystis Blooms: The Key Role of Trait Trade-Offs, Front. Mar. Sci., 8, 656300, https://doi.org/10.3389/fmars.2021.656300, 2021. a
Breton, E., Goberville, E., Sautour, B., Ouadi, A., Skouroliakou, D.-I., Seuront, L., Beaugrand, G., Kléparski, L., Crouvoisier, M., Pecqueur, D., Salmeron, C., Cauvin, A., Poquet, A., Garcia, N., Gohin, F., and Christaki, U.: Multiple Phytoplankton Community Responses to Environmental Change in a Temperate Coastal System: A Trait-Based Approach, Front. Mar. Sci., 9, 914475, https://doi.org/10.3389/fmars.2022.914475, 2022. a, b, c
Brylinski, J.-M., Lagadeuc, Y., Gentilhomme, V., Dupont, J.-P., Lafite, R., Dupeuble, P.-A., Huault, M.-F., Auger, Y., Puskaric, E., Wartel, M., and Cabioch, L.: Le “Fleuve Cotier”: Un Phénomène Hydrologique Important En Manche Orientale. Exemple Du Pas-de-Calais, Oceanolog, Acta, Special issue, https://archimer.ifremer.fr/doc/00268/37874/ (last access: 20 August 2025), 1991. a, b
Camuffo, D., Becherini, F., and della Valle, A.: Relationship between Selected Percentiles and Return Periods of Extreme Events, Acta Geophys., 68, 1201–1211, https://doi.org/10.1007/s11600-020-00452-x, 2020. a
Chen, B., Liu, H., Huang, B., and Wang, J.: Temperature Effects on the Growth Rate of Marine Picoplankton, Mar. Ecol.-Prog. Ser., 505, 37–47, https://doi.org/10.3354/meps10773, 2014. a
Chiswell, S. M., Calil, P. H., and Boyd, P. W.: Spring Blooms and Annual Cycles of Phytoplankton: A Unified Perspective, J. Plankt. Res., 37, 500–508, https://doi.org/10.1093/plankt/fbv021, 2015. a
Cloern, J. E., Foster, S. Q., and Kleckner, A. E.: Phytoplankton Primary Production in the World's Estuarine-Coastal Ecosystems, Biogeosciences, 11, 2477–2501, https://doi.org/10.5194/bg-11-2477-2014, 2014. a
Crossland, C. J., Baird, D., Ducrotoy, J.-P., Lindeboom, H., Buddemeier, R. W., Dennison, W. C., Maxwell, B. A., Smith, S. V., and Swaney, D. P.: The Coastal Zone – a Domain of Global Interactions, in: Coastal Fluxes in the Anthropocene: The Land-Ocean Interactions in the Coastal Zone Project of the International Geosphere-Biosphere Programme, Global Change – The IGBP Series, edited by: Crossland, C. J., Kremer, H. H., Lindeboom, H. J., Marshall Crossland, J. I., and Le Tissier, M. D. A., Springer, Berlin, Heidelberg, 1–37, ISBN 978-3-540-27851-1, 2005. a, b
Cunningham, A. and Buonnacorsi, G. A.: Narrow-Angle Forward Light Scattering from Individual Algal Cells: Implications for Size and Shape Discrimination in Flow Cytometry, J. Plankt. Res., 14, 223–234, https://doi.org/10.1093/plankt/14.2.223, 1992. a
Delegrange, A., Lefebvre, A., Gohin, F., Courcot, L., and Vincent, D.: Pseudo-Nitzschia Sp. Diversity and Seasonality in the Southern North Sea, Domoic Acid Levels and Associated Phytoplankton Communities, Estuar. Coast. Shelf Sci., 214, 194–206, https://doi.org/10.1016/j.ecss.2018.09.030, 2018. a
Dubelaar, G. B. J. and Gerritzen, P. L.: CytoBuoy: A Step Forward towards Using Flow Cytometry in Operational Oceanography, Scientia Marina, 64, 255–265, https://doi.org/10.3989/scimar.2000.64n2255, 2000. a
Dubelaar, G. B. J. and Jonker, R. R.: Flow Cytometry as a Tool for the Study of Phytoplankton, Scientia Marina, 64, 135–156, https://doi.org/10.3989/scimar.2000.64n2135, 2000. a
Dubelaar, G. B. J., Geerders, P. J. F., and Jonker, R. R.: High Frequency Monitoring Reveals Phytoplankton Dynamics, J. Environ. Monit., 6, 946–952, https://doi.org/10.1039/B409350J, 2004. a, b, c
Dugenne, M., Thyssen, M., Nerini, D., and Grégori, G. J.: Consequence of a Sudden Wind Event on the Dynamics of a Coastal Phytoplankton Community: An Insight into Specific Population Growth Rates Using a Single Cell High Frequency Approach, Front. Microbiol., 5, 485, https://doi.org/10.3389/fmicb.2014.00485, 2014. a
Falkowski, P. G., Barber, R. T., and Smetacek, V.: Biogeochemical Controls and Feedbacks on Ocean Primary Production, Science, 281, 200–206, https://doi.org/10.1126/science.281.5374.200, 1998. a
Falkowski, P. G., Laws, E. A., Barber, R. T., and Murray, J. W.: Phytoplankton and Their Role in Primary, New, and Export Production, in: Ocean Biogeochemistry: The Role of the Ocean Carbon Cycle in Global Change, Global Change – The IGBP Series (Closed), edited by: Fasham, M. J. R., Springer, Berlin, Heidelberg, 99–121, ISBN 978-3-642-55844-3, 2003. a
Field, C. B., Behrenfeld, M. J., Randerson, J. T., and Falkowski, P.: Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components, Science, 281, 237–240, https://doi.org/10.1126/science.281.5374.237, 1998. a
Flandrin, P. and Gonçalvès, P.: Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions, Int. J. Wavelets Multiresolut. Inf. Process., 02, 477–496, https://doi.org/10.1142/S0219691304000561, 2004. a
Flandrin, P., Rilling, G., and Goncalves, P.: Empirical Mode Decomposition as a Filter Bank, IEEE Sig. Process. Lett., 11, 112–114, https://doi.org/10.1109/LSP.2003.821662, 2004. a
Fontana, S. and Pomati, F.: Opportunities and Challenges in Deriving Phytoplankton Diversity Measures from Individual Trait-Based Data Obtained by Scanning Flow-Cytometry, Front. Microbiol., 5, 324, https://doi.org/10.3389/fmicb.2014.00324, 2014. a, b, c
Fragoso, G. M., Poulton, A. J., Pratt, N. J., Johnsen, G., and Purdie, D. A.: Trait-Based Analysis of Subpolar North Atlantic Phytoplankton and Plastidic Ciliate Communities Using Automated Flow Cytometer, Limnol. Oceanogr., 64, 1763–1778, https://doi.org/10.1002/lno.11189, 2019. a, b, c
Fuchs, R., Thyssen, M., Creach, V., Dugenne, M., Izard, L., Latimier, M., Louchart, A., Marrec, P., Rijkeboer, M., Grégori, G., and Pommeret, D.: Automatic Recognition of Flow Cytometric Phytoplankton Functional Groups Using Convolutional Neural Networks, Limnol. Oceanogr.: Meth., 20, 387–399, https://doi.org/10.1002/lom3.10493, 2022. a
Genitsaris, S., Monchy, S., Viscogliosi, E., Sime-Ngando, T., Ferreira, S., and Christaki, U.: Seasonal Variations of Marine Protist Community Structure Based on Taxon-Specific Traits Using the Eastern English Channel as a Model Coastal System, FEMS Microbiol. Ecol., 91, fiv034, https://doi.org/10.1093/femsec/fiv034, 2015. a
Gentilhomme, V. and Lizon, F.: Seasonal Cycle of Nitrogen and Phytoplankton Biomass in a Well-Mixed Coastal System (Eastern English Channel), Hydrobiologia, 361, 191–199, https://doi.org/10.1023/A:1003134617808, 1997. a, b
Gómez, F. and Souissi, S.: The distribution and life cycle of the dinoflagellate Spatulodinium pseudonoctiluca (Dinophyceae, Noctilucales) in the northeastern English Channel, Comptes Rendus. Biologies, 330, 231–236, https://doi.org/10.1016/j.crvi.2007.02.002, 2007. a
Grattepanche, J. D., Vincent, D., Breton, E., and Christaki, U.: Microzooplankton Herbivory during the Diatom–Phaeocystis Spring Succession in the Eastern English Channel, J. Exp. Mar. Biol. Ecol., 404, 87–97, https://doi.org/10.1016/j.jembe.2011.04.004, 2011. a
Halawi Ghosn, R., Poisson-Caillault, É., Charria, G., Bonnat, A., Repecaud, M., Facq, J.-V., Quéméner, L., Duquesne, V., Blondel, C., and Lefebvre, A.: MAREL Carnot Data and Metadata from the Coriolis Data Center, Earth Syst. Sci. Data, 15, 4205–4218, https://doi.org/10.5194/essd-15-4205-2023, 2023. a, b, c
Hemming, M., Roughan, M., and Schaeffer, A.: Exploring Multi-Decadal Time Series of Temperature Extremes in Australian Coastal Waters, Earth Syst. Sci. Data, 16, 887–901, https://doi.org/10.5194/essd-16-887-2024, 2024. a
Hernández-Fariñas, T., Soudant, D., Barillé, L., Belin, C., Lefebvre, A., and Bacher, C.: Temporal Changes in the Phytoplankton Community along the French Coast of the Eastern English Channel and the Southern Bight of the North Sea, ICES J. Mar. Sci., 71, 821–833, https://doi.org/10.1093/icesjms/fst192, 2014. a
Hill, M. O.: Diversity and Evenness: A Unifying Notation and Its Consequences, Ecology, 54, 427–432, https://doi.org/10.2307/1934352, 1973. a, b
Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., and Wernberg, T.: A Hierarchical Approach to Defining Marine Heatwaves, Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014, 2016. a
Holland, M., Louchart, A., Artigas, L. F., and Mcquatters-Gollop, A.: Changes in Phytoplankton and Zooplankton Communities Common Indicator Assessment Changes in Phytoplankton and Zooplankton Communities, in: OSPAR, 2023: The 2023 Quality Status Report for the Northeast Atlantic, OSPAR Commission, p. 39, https://hal.science/hal-04404131 (last access: 20 August 2025), 2023. a
Houliez, E., Lizon, F., Lefebvre, S., Artigas, L. F., and Schmitt, F. G.: Short-Term Variability and Control of Phytoplankton Photosynthetic Activity in a Macrotidal Ecosystem (the Strait of Dover, Eastern English Channel), Mar. Biol., 160, 1661–1679, https://doi.org/10.1007/s00227-013-2218-4, 2013. a
Houliez, E., Schmitt, F. G., Breton, E., Skouroliakou, D.-I., and Christaki, U.: On the Conditions Promoting Pseudo-nitzschia Spp. Blooms in the Eastern English Channel and Southern North Sea, Harmful Algae, 125, 102424, https://doi.org/10.1016/j.hal.2023.102424, 2023. a
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H.: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, P. Roy. Soc. Lond. A, 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998. a, b, c
Huang, Y., Schmitt, F. G., Lu, Z., and Liu, Y.: Analysis of Daily River Flow Fluctuations Using Empirical Mode Decomposition and Arbitrary Order Hilbert Spectral Analysis, J. Hydrol., 373, 103–111, https://doi.org/10.1016/j.jhydrol.2009.04.015, 2009. a
Huang, Y. X., Schmitt, F. G., Lu, Z. M., and Liu, Y. L.: An Amplitude-Frequency Study of Turbulent Scaling Intermittency Using Empirical Mode Decomposition and Hilbert Spectral Analysis, Europhys. Lett., 84, 40010, https://doi.org/10.1209/0295-5075/84/40010, 2008. a
Hubert, Z., Artigas, L. F., Li, L.-L., Dédécker, C., and Monchy, S.: Exploring the Regional Diversity of Eukaryotic Phytoplankton in the English Channel by Combining High-Throughput Approaches, Authorea, 1–32, in review, https://doi.org/10.22541/au.174523338.82769110/v1, 2025a. a
Hubert, Z., Louchart, A., Robache, K., Epinoux, A., Gallot, C., Cornille, V., Crouvoisier, M., Monchy, S., and Artigas, L. F.: Decadal Changes in Phytoplankton Functional Composition in the Eastern English Channel: possible upcoming major effects of climate change, Ocean Science, 21, 679–700, https://doi.org/10.5194/os-21-679-2025, 2025b. a
Hunter-Cevera, K. R., Neubert, M. G., Olson, R. J., Shalapyonok, A., Solow, A. R., and Sosik, H. M.: Seasons of Syn, Limnol. Oceanogr., 65, 1085–1102, https://doi.org/10.1002/lno.11374, 2020. a
Hynes, A. M., Winter, J., Berthiaume, C. T., Shimabukuro, E., Cain, K., White, A., Armbrust, E. V., and Ribalet, F.: High-Frequency Sampling Captures Variability in Phytoplankton Population-Specific Periodicity, Growth, and Productivity, Limnol and Oceanogr.,69, 2516–2531, https://doi.org/10.1002/lno.12683, 2024. a
Jamous, M. and Marsooli, R.: A Multidecadal Assessment of Mean and Extreme Wave Climate Observed at Buoys off the U.S. East, Gulf, and West Coasts, J. Mar. Sci. Eng., 11, 916, https://doi.org/10.3390/jmse11050916, 2023. a
Jouanneau, N., Sentchev, A., and Dumas, F.: Numerical Modelling of Circulation and Dispersion Processes in Boulogne-sur-Mer Harbour (Eastern English Channel): Sensitivity to Physical Forcing and Harbour Design, Ocean Dynam., 63, 1321–1340, https://doi.org/10.1007/s10236-013-0659-4, 2013. a, b, c
Karasiewicz, S., Breton, E., Lefebvre, A., Hernández Fariñas, T., and Lefebvre, S.: Realized Niche Analysis of Phytoplankton Communities Involving HAB: Phaeocystis Spp. as a Case Study, Harmful Algae, 72, 1–13, https://doi.org/10.1016/j.hal.2017.12.005, 2018. a
Kbaier Ben Ismail, D., Lazure, P., and Puillat, I.: Statistical Properties and Time-Frequency Analysis of Temperature, Salinity and Turbidity Measured by the MAREL Carnot Station in the Coastal Waters of Boulogne-sur-Mer (France), J. Mar. Syst., 162, 137–153, https://doi.org/10.1016/j.jmarsys.2016.03.010, 2016. a
Kim, D. and Oh, H.-S.: EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, R J., 1, 40–46, https://doi.org/10.32614/RJ-2009-002, 2009. a
Kim, D. and Oh, H.-S.: EMD: Empirical Mode Decomposition and Hilbert Spectral Analysis, version 1.5.9, CRAN [code], https://doi.org/10.32614/CRAN.package.EMD, 2022. a
Lancelot, C., Thieu, V., Polard, A., Garnier, J., Billen, G., Hecq, W., and Gypens, N.: Cost Assessment and Ecological Effectiveness of Nutrient Reduction Options for Mitigating Phaeocystis Colony Blooms in the Southern North Sea: An Integrated Modeling Approach, Sci. Total Environ., 409, 2179–2191, https://doi.org/10.1016/j.scitotenv.2011.02.023, 2011. a, b
Lefebvre, A. and Dezécache, C.: Trajectories of Changes in Phytoplankton Biomass, Phaeocystis Globosa and Diatom (Incl. Pseudo-nitzschia Sp.) Abundances Related to Nutrient Pressures in the Eastern English Channel, Southern North Sea, J. Mar. Sci. Eng., 8, 401, https://doi.org/10.3390/jmse8060401, 2020. a, b, c
Lefebvre, A., Guiselin, N., Barbet, F., and Artigas, F. L.: Long-Term Hydrological and Phytoplankton Monitoring (1992–2007) of Three Potentially Eutrophic Systems in the Eastern English Channel and the Southern Bight of the North Sea, ICES J. Mar. Sci., 68, 2029–2043, https://doi.org/10.1093/icesjms/fsr149, 2011. a, b, c, d, e
Lefebvre, A., Blondel, C., Duquesne, V., Hebert, P., Cordier, R., Belin, C., Huguet, A., Durand, G., Soudant, D., and Devreker, D.: SRN Dataset – Regional Observation and Monitoring Program for Phytoplankton and Hydrology in the Eastern English Channel, SEANOE [data set], https://doi.org/10.17882/50832, 2024. a, b, c, d
Legendre, L. and Rassoulzadegan, F.: Plankton and Nutrient Dynamics in Marine Waters, Ophelia, 41, 153–172, https://doi.org/10.1080/00785236.1995.10422042, 1995. a
Le Quéré, C., Harrison, S. P., Colin Prentice, I., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.: Ecosystem Dynamics Based on Plankton Functional Types for Global Ocean Biogeochemistry Models, Global Change Biol., 11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005. a
Levoy, F., Anthony, E. J., Monfort, O., and Larsonneur, C.: The Morphodynamics of Megatidal Beaches in Normandy, France, Mar. Geol., 171, 39–59, https://doi.org/10.1016/S0025-3227(00)00110-9, 2000. a
Lheureux, A., David, V., Del Amo, Y., Soudant, D., Auby, I., Bozec, Y., Conan, P., Ganthy, F., Grégori, G., Lefebvre, A., Leynart, A., Rimmelin-Maury, P., Souchu, P., Vantrepote, V., Blondel, C., Cariou, T., Crispi, O., Cordier, M.-A., Crouvoisier, M., Duquesne, V., Ferreira, S., Garcia, N., Gouriou, L., Grosteffan, E., Le Merrer, Y., Meteigner, C., Retho, M., Tournaire, M.-P., and Savoye, N.: Trajectories of Nutrients Concentrations and Ratios in the French Coastal Ecosystems: 20 ears of Changes in Relation with Large-Scale and Local Drivers, Sci. Total Environ., 857, 159619, https://doi.org/10.1016/j.scitotenv.2022.159619, 2023. a
Li, W. K. W.: Cytometric Diversity in Marine Ultraphytoplankton, Limnol. Oceanogr., 42, 874–880, https://doi.org/10.4319/lo.1997.42.5.0874, 1997. a
Li, W. K. W.: Annual Average Abundance of Heterotrophic Bacteria and Synechococcus in Surface Ocean Waters, Limnol. Oceanogr., 43, 1746–1753, https://doi.org/10.4319/lo.1998.43.7.1746, 1998. a
Li, W. K. W.: Macroecological Patterns of Phytoplankton in the Northwestern North Atlantic Ocean, Nature, 419, 154–157, https://doi.org/10.1038/nature00994, 2002. a
Litchman, E., Klausmeier, C. A., Schofield, O. M., and Falkowski, P. G.: The Role of Functional Traits and Trade-Offs in Structuring Phytoplankton Communities: Scaling from Cellular to Ecosystem Level, Ecol. Lett., 10, 1170–1181, https://doi.org/10.1111/j.1461-0248.2007.01117.x, 2007. a
Lomb, N. R.: Least-Squares Frequency Analysis of Unequally Spaced Data, Astrophys. Space Sci., 39, 447–462, https://doi.org/10.1007/BF00648343, 1976. a
Louchart, A., Lizon, F., Lefebvre, A., Didry, M., Schmitt, F. G., and Artigas, L. F.: Phytoplankton Distribution from Western to Central English Channel, Revealed by Automated Flow Cytometry during the Summer-Fall Transition, Cont. Shelf Res., 195, 104056, https://doi.org/10.1016/j.csr.2020.104056, 2020. a, b
Louchart, A., Holland, M., Mcquatters-Gollop, A., and Artigas, L. F.: Changes in Phytoplankton Biomass and Zooplankton Abundance Common Indicator Assessment Changes in Phytoplankton Biomass and Zooplankton Abundance, in: OSPAR, 2023: The 2023 Quality Status Report for the Northeast Atlantic, OSPAR Commission, p. 34, 2023. https://hal.science/hal-04404153 (last access: August 2025), 2025. a
Louchart, A., Lizon, F., Debusschere, E., Mortelmans, J., Rijkeboer, M., Crouvoisier, M., Lebourg, E., Deneudt, K., Schmitt, F. G., and Artigas, L. F.: The Importance of Niches in Defining Phytoplankton Functional Beta Diversity during a Spring Bloom, Mar. Biol., 171, 26 https://doi.org/10.1007/s00227-023-04346-6, 2024. a, b
MAREL Carnot: High Frequency Measurement of the Coastal Environment in the Eastern English Channel. Data from MAREL CARNOT – COAST-HF (Coastal Ocean Observing System – High Frequency) Monitoring Programme within the Research Infrastructure ILICO, SEAMOE [data set], https://doi.org/10.17882/39754, 2024. a, b
Masquelier, S., Foulon, E., Jouenne, F., Ferréol, M., Brussaard, C. P. D., and Vaulot, D.: Distribution of Eukaryotic Plankton in the English Channel and the North Sea in Summer, J. Sea Res., 66, 111–122, https://doi.org/10.1016/j.seares.2011.05.004, 2011. a
Menge, D. N. L. and Weitz, J. S.: Dangerous Nutrients: Evolution of Phytoplankton Resource Uptake Subject to Virus Attack, J. Theor. Biol., 257, 104–115, https://doi.org/10.1016/j.jtbi.2008.10.032, 2009. a
Not, F., Latasa, M., Marie, D., Cariou, T., Vaulot, D., and Simon, N.: A Single Species, Micromonas Pusilla (Prasinophyceae), Dominates the Eukaryotic Picoplankton in the Western English Channel, Appl. Environ. Microbiol., 70, 4064–4072, https://doi.org/10.1128/AEM.70.7.4064-4072.2004, 2004. a
Olson, R. J., Shalapyonok, A., and Sosik, H. M.: An Automated Submersible Flow Cytometer for Analyzing Pico- and Nanophytoplankton: FlowCytobot, Deep-Sea Res. Pt. I, 50, 301–315, https://doi.org/10.1016/S0967-0637(03)00003-7, 2003. a
Pal, R. and Choudhury, A. K.: An Introduction to Phytoplanktons: Diversity and Ecology, Springer India, New Delhi, ISBN 978-81-322-1837-1, https://doi.org/10.1007/978-81-322-1838-8, 2014. a
Peperzak, L., Duin, R., Colijn, F., and Gieskes, W.: Growth and Mortality of Flagellates and Non-Flagellate Cells of Phaeocystis Globosa (Prymnesiophyceae), J. Plankt. Res., 22, 107–120, https://doi.org/10.1093/plankt/22.1.107, 2000. a
Pereira, G. C., Figueiredo, A. R., and Ebecken, N. F. F.: Using in Situ Flow Cytometry Images of Ciliates and Dinoflagellates for Aquatic System Monitoring, Brazil. J. Biol., 78, 240–247, https://doi.org/10.1590/1519-6984.05016, 2017. a
Pomati, F., Kraft, N. J. B., Posch, T., Eugster, B., Jokela, J., and Ibelings, B. W.: Individual Cell Based Traits Obtained by Scanning Flow-Cytometry Show Selection by Biotic and Abiotic Environmental Factors during a Phytoplankton Spring Bloom, PLOS ONE, 8, e71677, https://doi.org/10.1371/journal.pone.0071677, 2013. a
Rantajärvi, E., Olsonen, R., Hällfors, S., Leppänen, J.-M., and Raateoja, M.: Effect of Sampling Frequency on Detection of Natural Variability in Phytoplankton: Unattended High-Frequency Measurements on Board Ferries in the Baltic Sea, ICES J. Mar. Sci., 55, 697–704, https://doi.org/10.1006/jmsc.1998.0384, 1998. a
Robache, K., Hubert, Z., Gallot, C., Epinoux, A., Louchart, A. P., Facq, J.-V., Lefebvre, A., Répécaud, M., Cornille, V., Verhaeghe, F., Audinet, Y., Brutier, L., Schmitt, F. G., and Artigas, L. F.: High-Frequency Monitoring of Phytoplankton Functional Groups Using an Automated Flow Cytometer during Two Deployments (2021, 2022) at the MAREL CARNOT Station (Boulogne-sur-Mer, France) in the Eastern English Channel, SEANOE [data set], https://doi.org/10.17882/104948, 2025. a
Röthig, T., Trevathan-Tackett, S. M., Voolstra, C. R., Ross, C., Chaffron, S., Durack, P. J., Warmuth, L. M., and Sweet, M.: Human-Induced Salinity Changes Impact Marine Organisms and Ecosystems, Global Change Biol., 29, 4731–4749, https://doi.org/10.1111/gcb.16859, 2023. a
Rousseau, V., Vaulot, D., Casotti, R., Cariou, V., Lenz, J., Gunkel, J., and Baumann, M.: The Life Cycle of Phaeocystis (Prymnesiophycaea): Evidence and Hypotheses, J. Mar. Syst., 5, 23–39, https://doi.org/10.1016/0924-7963(94)90014-0, 1994. a
Ruf, T.: The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series, Biol. Rhythm Res., 30, 178–201, https://doi.org/10.1076/brhm.30.2.178.1422, 1999. a, b
Ruf, T.: lomb: Lomb-Scargle Periodogram, version 2.5.0, CRAN [code], https://doi.org/10.32614/CRAN.package.lomb, 2024. a
Rutten, T. P. A., Sandee, B., and Hofman, A. R. T.: Phytoplankton Monitoring by High Performance Flow Cytometry: A Successful Approach?, Cytometry A, 64, 16–26, https://doi.org/10.1002/cyto.a.20106, 2005. a
Sazhin, A. F., Artigas, L. F., Nejstgaard, J. C., and Frischer, M. E.: The Colonization of Two Phaeocystis Species (Prymnesiophyceae) by Pennate Diatoms and Other Protists: A Significant Contribution to Colony Biomass, in: Phaeocystis, Major Link in the Biogeochemical Cycling of Climate-Relevant Elements, edited by: van Leeuwe, M. A., Stefels, J., Belviso, S., Lancelot, C., Verity, P. G., and Gieskes, W. W. C., Springer Netherlands, Dordrecht, 137–145, ISBN 978-1-4020-6214-8, 2007. a, b
Scargle, J. D.: Studies in Astronomical Time Series Analysis. II – Statistical Aspects of Spectral Analysis of Unevenly Spaced Data, Astrophys. J., 263, 835–853, https://doi.org/10.1086/160554, 1982. a
Schapira, M., Vincent, D., Gentilhomme, V., and Seuront, L.: Temporal Patterns of Phytoplankton Assemblages, Size Spectra and Diversity during the Wane of a Phaeocystis Globosa Spring Bloom in Hydrologically Contrasted Coastal Waters, J. Mar. Biol. Assoc. UK, 88, 649–662, https://doi.org/10.1017/S0025315408001306, 2008. a
Schmitt, F. G., Anneville, O., and Souissi, S.: Dynamique Intermittente Du Plancton: Analyse de La Dynamique Multi-Échelle En Utilisant La Décomposition Modale Empirique, in: Comptes-Rendus de La 16e Rencontre Du Non-Linéaire, edited by: Falcon, É.,Josserand, C., Lefranc, M., Pétrélis, F., and Pham, C.-T., Non-Linéaire Publications, Paris, 149–154, ISBN: 978-2-9538596-2-1, 2013. a
Sentchev, A. and Yaremchuk, M.: Monitoring Tidal Currents with a Towed ADCP System, Ocean Dynam., 66, 119–132, https://doi.org/10.1007/s10236-015-0913-z, 2016. a, b
Serre-Fredj, L., Jacqueline, F., Navon, M., Izabel, G., Chasselin, L., Jolly, O., Repecaud, M., and Claquin, P.: Coupling High Frequency Monitoring and Bioassay Experiments to Investigate a Harmful Algal Bloom in the Bay of Seine (French-English Channel), Mar. Pollut. Bull., 168, 112387, https://doi.org/10.1016/j.marpolbul.2021.112387, 2021. a
Seuront, L.: Hydrodynamic and Tidal Controls of Small-Scale Phytoplankton Patchiness, Mar. Ecol.-Prog. Ser., 302, 93–101, https://doi.org/10.3354/meps302093, 2005. a
Seuront, L., Schmitt, F., Lagadeuc, Y., Schertzer, D., Lovejoy, S., and Frontier, S.: Multifractal Analysis of Phytoplankton Biomass and Temperature in the Ocean, Geophys. Res. Lett., 23, 3591–3594, https://doi.org/10.1029/96GL03473, 1996. a
Shannon, C. E.: A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948. a
SHOM: MNT Topo-Bathymétrique Côtier Du Port de Boulogne-sur-Mer et de Ses Abords à 10 m (Projet TANDEM), SHOM [data set], https://doi.org/10.17183/MNT_COTIER_PORT_BSM_TANDEM_10m_WGS84, 2015. a
Simon, A., Poppeschi, C., Plecha, S., Charria, G., and Russo, A.: Coastal and Regional Marine Heatwaves and Cold Spells in the Northeastern Atlantic, Ocean Sci., 19, 1339–1355, https://doi.org/10.5194/os-19-1339-2023, 2023. a
Smith, W. O. and Lancelot, C.: Bottom-up versus Top-down Control in Phytoplankton of the Southern Ocean, Antarct. Sci., 16, 531–539, https://doi.org/10.1017/S0954102004002305, 2004. a
Sosik, H. M., Olson, R. J., Neubert, M. G., Shalapyonok, A., and Solow, A. R.: Growth Rates of Coastal Phytoplankton from Time-Series Measurements with a Submersible Flow Cytometer, Limnol. Oceanogr., 48, 1756–1765, https://doi.org/10.4319/lo.2003.48.5.1756, 2003. a
Sosik, H. M., Olson, R. J., and Armbrust, E. V.: Flow Cytometry in Phytoplankton Research, in: Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications, edited by: Suggett, D. J., Prášil, O., and Borowitzka, M. A., Developments in Applied Phycology, Springer Netherlands, Dordrecht, 171–185, ISBN 978-90-481-9268-7, 2010. a
Sun, X. and Wang, W.: The Impact of Environmental Parameters on Phytoplankton Functional Groups in Northeastern China, Ecol. Eng., 164, 106209, https://doi.org/10.1016/j.ecoleng.2021.106209, 2021. a, b
Thyssen, M., Grégori, G. J., Grisoni, J.-M., Pedrotti, M. L., Mousseau, L., Artigas, L. F., Marro, S., Garcia, N., Passafiume, O., and Denis, M. J.: Onset of the Spring Bloom in the Northwestern Mediterranean Sea: Influence of Environmental Pulse Events on the in Situ Hourly-Scale Dynamics of the Phytoplankton Community Structure, Front. Microbiol., 5, 387, https://doi.org/10.3389/fmicb.2014.00387, 2014. a
Thyssen, M., Grégori, G., Créach, V., Lahbib, S., Dugenne, M., Aardema, H. M., Artigas, L.-F., Huang, B., Barani, A., Beaugeard, L., Bellaaj-Zouari, A., Beran, A., Casotti, R., Del Amo, Y., Denis, M., Dubelaar, G. B. J., Endres, S., Haraguchi, L., Karlson, B., Lambert, C., Louchart, A., Marie, D., Moncoiffé, G., Pecqueur, D., Ribalet, F., Rijkeboer, M., Silovic, T., Silva, R., Marro, S., Sosik, H. M., Sourisseau, M., Tarran, G., Van Oostende, N., Zhao, L., and Zheng, S.: Interoperable Vocabulary for Marine Microbial Flow Cytometry, Front. Mar. Sci., 9, 975877, https://doi.org/10.3389/fmars.2022.975877, 2022. a, b
Tinker, J., Palmer, M. D., Harrison, B. J., O'Dea, E., Sexton, D. M. H., Yamazaki, K., and Rostron, J. W.: Twenty-First Century Marine Climate Projections for the NW European Shelf Seas Based on a Perturbed Parameter Ensemble, Ocean Sci., 20, 835–885, https://doi.org/10.5194/os-20-835-2024, 2024. a
Wickham, H. and RStudio: tidyverse: Easily Install and Load the “Tidyverse”, version 2.0.0, CRAN [code], https://doi.org/10.32614/CRAN.package.tidyverse, 2023. a
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H.: Welcome to the Tidyverse, J. Open Sour. Softw., 4, 1686, https://doi.org/10.21105/joss.01686, 2019. a
Wickham, H. , Chang W., Henry L., Pedersen T. L., Takahashi, K., Wilke, C., Woo K., Yutani, H., Dunnington, D., van den Brand, T., and Posit: PBC ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics, version 3.5.1, CRAN [code], https://doi.org/10.32614/CRAN.package.ggplot2, 2025. a
Worden, A. Z., Nolan, J. K., and Palenik, B.: Assessing the Dynamics and Ecology of Marine Picophytoplankton: The Importance of the Eukaryotic Component, Limnol. Oceanogr., 49, 168–179, https://doi.org/10.4319/lo.2004.49.1.0168, 2004. a
Wyatt, T.: Margalef's Mandala and Phytoplankton Bloom Strategies, Deep-Sea Res. Pt. II, 101, 32–49, https://doi.org/10.1016/j.dsr2.2012.12.006, 2014. a
Xiu-ren, N. and Vaulot, D.: Simultaneous Estimates of Synechococcus Spp. Growth and Grazing Mortality Rates in the English Channel, Chinese J. Oceanol. Limnol., 14, 8–16, https://doi.org/10.1007/BF02850534, 1996. a, b, c
Short summary
By deploying an automated flow cytometer at a coastal monitoring station in France, we tracked phytoplankton changes every 2 h during spring (2021 and 2022) and summer (2022). Our study revealed distinct seasonal shifts, e.g., with diatoms and haptophytes in spring. Rare weather events rapidly altered community composition. We found that most variability occurred on short timescales, underscoring the importance of high-frequency monitoring for understanding marine phytoplankton dynamics.
By deploying an automated flow cytometer at a coastal monitoring station in France, we tracked...
