Articles | Volume 22, issue 22
https://doi.org/10.5194/bg-22-7233-2025
© 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/bg-22-7233-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Including different mesozooplankton feeding strategies in a biogeochemical ocean model impacts global ocean biomass and carbon cycle
Lisa Di Matteo
CORRESPONDING AUTHOR
Sorbonne Université, MNHN, CNRS, IRD, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN, 75005 Paris, France
Fabio Benedetti
Institute of Plant Sciences, University of Bern, Bern, Switzerland
Sakina-Dorothée Ayata
Sorbonne Université, MNHN, CNRS, IRD, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN, 75005 Paris, France
Institut universitaire de France (IUF), Paris, France
Olivier Aumont
Sorbonne Université, MNHN, CNRS, IRD, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN, 75005 Paris, France
Université Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France
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This study presents a long-term monitoring dataset (2010–2023) from the Iroise Marine Natural Park, a French marine protected area in the NE Atlantic. It includes environmental parameters (temperature, salinity), microscopy-based phytoplankton counts, and zooplankton abundances and biovolumes estimated from imaging data. Two transects and three coastal stations were sampled seasonally, capturing spatial–temporal dynamics. This datasets offers new opportunities to study plankton diversity.
Quentin Hyvernat, Alexandre Mignot, Elodie Gutknecht, Giovanni Ruggiero, Coralie Perruche, Guillaume Samson, Raphaëlle Sauzède, Olivier Aumont, Hervé Claustre, and Fabrizio D'Ortenzio
EGUsphere, https://doi.org/10.5194/egusphere-2025-4369, https://doi.org/10.5194/egusphere-2025-4369, 2025
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We introduce an iterative Importance Sampling (iIS) framework to optimize the PISCES model using BGC-Argo data. Using these data, 20 metrics are applied to better constrain parameter values. Three parameter selection strategies are compared: 29 main effects parameters, 66 parameters including interaction effects, and all 95 parameters. All yield statistically indistinguishable but significant skill gains, reducing NRMSE by 54–56% in median across assimilated metrics in the productive layer.
Madhavan Girijakumari Keerthi, Olivier Aumont, Lester Kwiatkowski, and Marina Levy
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We assessed how well climate models replicate sub-seasonal changes in ocean chlorophyll observed by satellites. Models struggle to capture these variations accurately. Some overestimate fluctuations and their impact on annual chlorophyll variability, while others underestimate them. The underestimation is likely due to limited model resolution, while the overestimation may come from internal model oscillations.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
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Gelatinous zooplankton play a key role in the ocean carbon cycle. In particular, pelagic tunicates, which feed on a wide size range of prey, produce rapidly sinking detritus. Thus, they efficiently transfer carbon from the surface to the depths. Consequently, we added these organisms to a marine biogeochemical model (PISCES-v2) and evaluated their impact on the global carbon cycle. We found that they contribute significantly to carbon export and that this contribution increases with depth.
Laurent Bopp, Olivier Aumont, Lester Kwiatkowski, Corentin Clerc, Léonard Dupont, Christian Ethé, Thomas Gorgues, Roland Séférian, and Alessandro Tagliabue
Biogeosciences, 19, 4267–4285, https://doi.org/10.5194/bg-19-4267-2022, https://doi.org/10.5194/bg-19-4267-2022, 2022
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Martí Galí, Marcus Falls, Hervé Claustre, Olivier Aumont, and Raffaele Bernardello
Biogeosciences, 19, 1245–1275, https://doi.org/10.5194/bg-19-1245-2022, https://doi.org/10.5194/bg-19-1245-2022, 2022
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Part of the organic matter produced by plankton in the upper ocean is exported to the deep ocean. This process, known as the biological carbon pump, is key for the regulation of atmospheric carbon dioxide and global climate. However, the dynamics of organic particles below the upper ocean layer are not well understood. Here we compared the measurements acquired by autonomous robots in the top 1000 m of the ocean to a numerical model, which can help improve future climate projections.
Julien Jouanno, Rachid Benshila, Léo Berline, Antonin Soulié, Marie-Hélène Radenac, Guillaume Morvan, Frédéric Diaz, Julio Sheinbaum, Cristele Chevalier, Thierry Thibaut, Thomas Changeux, Frédéric Menard, Sarah Berthet, Olivier Aumont, Christian Ethé, Pierre Nabat, and Marc Mallet
Geosci. Model Dev., 14, 4069–4086, https://doi.org/10.5194/gmd-14-4069-2021, https://doi.org/10.5194/gmd-14-4069-2021, 2021
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The tropical Atlantic has been facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. We developed a modeling framework based on the NEMO ocean model, which integrates transport by currents and waves, and physiology of Sargassum with varying internal nutrient quota, and considers stranding at the coast. Results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.
Cited articles
Alcaraz, M. and Strickler, J. R.: Locomotion in copepods: pattern of movements and energetics of Cyclops, Hydrobiologia, 167, 409–414, https://doi.org/10.1007/BF00026333, 1988. a, b
Almeda, R., van Someren Gréve, H., and Kiørboe, T.: Behavior is a major determinant of predation risk in zooplankton, Ecosphere, 8, e01668, https://doi.org/10.1002/ecs2.1668, 2017. a
Atkinson, A., Polimene, L., Fileman, E. S., Widdicombe, C. E., McEvoy, A. J., Smyth, T. J., Djeghri, N., Sailley, S. F., and Cornwell, L. E.: Comment. What drives plankton seasonality in a stratifying shelf sea? Some competing and complementary theories, Limnol. Oceanogr., 63, 2877–2884, https://doi.org/10.1002/lno.11036, 2018. a
Aumont, O., Maury, O., Lefort, S., and Bopp, L.: Evaluating the potential impacts of the diurnal vertical migration by marine organisms on marine biogeochemistry, Global Biogeochem. Cy., 32, 1622–1643, https://doi.org/10.1029/2018GB005886, 2018. a, b, c
Barton, A. D., Pershing, A. J., Litchman, E., Record, N. R., Edwards, K. F., Finkel, Z. V., Kiørboe, T., and Ward, B. A.: The biogeography of marine plankton traits, Ecol. Lett., 16, 522–534, https://doi.org/10.1111/ele.12063, 2013. a, b
Batchelder, H. P., Edwards, C. A., and Powell, T. M.: Individual-based models of copepod populations in coastal upwelling regions: implications of physiologically and environmentally influenced diel vertical migration on demographic success and nearshore retention, Prog. Oceanogr., 2–4, 307–333, 2002. a
Becker, É. C., Mazzocchi, M. G., de Macedo-Soares, L. C. P., Costa Brandão, M., and Santarosa Freire, A.: Latitudinal gradient of copepod functional diversity in the South Atlantic Ocean, Prog. Oceanogr., 199, 102710, https://doi.org/10.1016/j.pocean.2021.102710, 2021. a
Benedetti, F., Gasparini, S., and Ayata, S.-D.: Identifying copepod functional groups from species functional traits, J. Plankton Res., 38, 159–166, https://doi.org/10.1093/plankt/fbv096, 2016. a
Bressac, M., Laurenceau-Cornec, E. C., Kennedy, F., Santoro, A. E., Paul, N. L., Briggs, N., Carvalho, F., and Boyd, P. W.: Decoding drivers of carbon flux attenuation in the oceanic biological pump, Nature, 633, 587–593, https://doi.org/10.1038/s41586-024-07850-x, 2024. a
Brun, P., Payne, M. R., and Kiørboe, T.: Trait biogeography of marine copepods – an analysis across scales, Ecol. Lett., 19, 1403–1413, https://doi.org/10.1111/ele.12688, 2016. a, b
Bucklin, A., Peijnenburg, K. T. C. A., Kosobokova, K. N., O'Brien, T. D., Blanco-Bercial, L., Cornils, A., Falkenhaug, T., Hopcroft, R. R., Hosia, A., Laakmann, S., Li, C., Martell, L., Questel, J. M., Wall-Palmer, D., Wang, M., Wiebe, P. H., and Weydmann-Zwolicka, A.: Toward a global reference database of COI barcodes for marine zooplankton, Mar. Biol., 168, 78, https://doi.org/10.1007/s00227-021-03887-y, 2021. a
Buesseler, K. and Boyd, P.: Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean, Limnol. Oceanogr., 54, https://doi.org/10.4319/lo.2009.54.4.1210, 2009. a
Buitenhuis, E. T., Rivkin, R. B., Sailley, S., and Le Quéré, C.: Biogeochemical fluxes through microzooplankton, Global Biogeochem. Cy., 24, https://doi.org/10.1029/2009GB003601, 2010. a
Buitenhuis, E. T., Vogt, M., Moriarty, R., Bednaršek, N., Doney, S. C., Leblanc, K., Le Quéré, C., Luo, Y.-W., O'Brien, C., O'Brien, T., Peloquin, J., Schiebel, R., and Swan, C.: MAREDAT: towards a world atlas of MARine Ecosystem DATa, Earth Syst. Sci. Data, 5, 227–239, https://doi.org/10.5194/essd-5-227-2013, 2013. a, b
Cadier, M., Andersen, K. H., Visser, A. W., and Kiørboe, T.: Competition–defense tradeoff increases the diversity of microbial plankton communities and dampens trophic cascades, Oikos, 128, 1027–1040, https://doi.org/10.1111/oik.06101, 2019. a
Calbet, A.: Mesozooplankton grazing effect on primary production: a global comparative analysis in marine ecosystems, Limnol. Oceanogr., 46, 1824–1830, https://doi.org/10.4319/lo.2001.46.7.1824, 2001. a
Chenillat, F., Rivière, P., and Ohman, M. D.: On the sensitivity of plankton ecosystem models to the formulation of zooplankton grazing, PLoS One, 16, e0252033, https://doi.org/10.1371/journal.pone.0252033, 2021. a, b, c
Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model–data comparison and impact on the ocean carbon cycle , Biogeosciences, 20, 869–895, https://doi.org/10.5194/bg-20-869-2023, 2023. a, b
Clerc, C.: Supplementary materials for “Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model”, Zenodo [data set], https://doi.org/10.5281/zenodo.12166409, 2024. a
Clerc, C., Bopp, L., Benedetti, F., Knecht, N., Vogt, M., and Aumont, O.: Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model, Global Biogeochem. Cy., 38, e2024GB008153, https://doi.org/10.1029/2024GB008153, 2024. a, b, c
Di Matteo, L., Ayata, S.-D., Aumont, O., and Benedetti, F.: Supplementary material for “Including different mesozooplankton feeding strategies in a biogeochemical ocean model impacts global ocean biomass and carbon cycle”, Zenodo [data set], https://doi.org/10.5281/zenodo.15065240, 2025. a
DeVries, T. and Weber, T.: The export and fate of organic matter in the ocean: new constraints from combining satellite and oceanographic tracer observations, Global Biogeochem. Cy., 31, 535–555, https://doi.org/10.1002/2016GB005551, 2017. a
Drago, L., Panaíotis, T., Irisson, J.-O., Babin, M., Biard, T., Carlotti, F., Coppola, L., Guidi, L., Hauss, H., Karp-Boss, L., Lombard, F., McDonnell, A. M. P., Picheral, M., Rogge, A., Waite, A. M., Stemmann, L., and Kiko, R.: Global distribution of zooplankton biomass estimated by in situ imaging and machine learning, Frontiers in Marine Science, 9, 894372, https://doi.org/10.3389/fmars.2022.894372, 2022. a, b
Evans, L. E., Hirst, A. G., Kratina, P., and Beaugrand, G.: Temperature-mediated changes in zooplankton body size: large scale temporal and spatial analysis, Ecography, 43, 581–590, https://doi.org/10.1111/ecog.04631, 2020. a
Fennel, K., Mattern, J. P., Doney, S. C., Bopp, L., Moore, A. M., Wang, B., and Yu, L.: Ocean biogeochemical modelling, Nature Reviews Methods Primers, 2, 76, https://doi.org/10.1038/s43586-022-00154-2, 2022. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Zweng, M. M., Baranova, O. K., and Johnson, D. R.: World Ocean Atlas 2009, Vol. 4: Nutrients (Phosphate, Nitrate, Silicate), edited by: Levitus, S., NOAA Atlas NESDIS 71, U.S. Government Printing Office, Washington, D.C., 398 pp., 2010. a, b
Garcia, H., Locarnini, R., Boyer, T., Antonov, J., Mishonov, A., Baranova, O., Zweng, M., Reagan, J., and Johnson, D.: World Ocean Atlas 2009, Vol. 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, 70, 2013. a
Gentleman, W.: A chronology of plankton dynamics in silico: how computer models have been used to study marine ecosystems, Hydrobiologia, 480, 69–85, https://doi.org/10.1023/A:1021289119442, 2002. a
Gentleman, W., Leising, A., Frost, B., Strom, S., and Murray, J.: Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics, Deep-Sea Res. Pt. II, 50, 2847–2875, https://doi.org/10.1016/j.dsr2.2003.07.001, 2003. a, b
Henson, S., Sanders, R., and Madsen, E.: Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean, Global Biogeochem. Cy., 26, 1028, https://doi.org/10.1029/2011GB004099, 2012. a
Henson, S. A., Laufkötter, C., Leung, S., Giering, S. L. C., Palevsky, H. I., and Cavan, E. L.: Uncertain response of ocean biological carbon export in a changing world, Nat. Geosci., 15, 248–254, https://doi.org/10.1038/s41561-022-00927-0, 2022. a
Hernández-León, S. and Ikeda, T.: A global assessment of mesozooplankton respiration in the ocean, J. Plankton Res., 27, 153–158, https://doi.org/10.1093/plankt/fbh166, 2005. a
Hébert, M.-P., Beisner, B. E., and Maranger, R.: A meta-analysis of zooplankton functional traits influencing ecosystem function, Ecology, 97, 1069–1080, https://doi.org/10.1890/15-1084.1, 2016. a
Jackson, G. A.: Flux feeding as a mechanism for zooplankton grazing and its implications for vertical particulate flux, Limnol. Oceanogr., 38, 1328–1331, https://doi.org/10.4319/lo.1993.38.6.1328, 1993. a, b
Jónasdóttir, S. H., Visser, A. W., Richardson, K., and Heath, M. R.: Seasonal copepod lipid pump promotes carbon sequestration in the deep North Atlantic, P. Natl. Acad. Sci. USA, 112, 12122–12126, https://doi.org/10.1073/pnas.1512110112, 2015. a
Karsenti, E., Acinas, S. G., Bork, P., Bowler, C., Vargas, C. D., Raes, J., Sullivan, M., Arendt, D., Benzoni, F., Claverie, J.-M., Follows, M., Gorsky, G., Hingamp, P., Iudicone, D., Jaillon, O., Kandels-Lewis, S., Krzic, U., Not, F., Ogata, H., Pesant, S., Reynaud, E. G., Sardet, C., Sieracki, M. E., Speich, S., Velayoudon, D., Weissenbach, J., Wincker, P., and the Tara Oceans Consortium: A Holistic Approach to Marine Eco-Systems Biology, PLoS Biol., 9, e1001177, https://doi.org/10.1371/journal.pbio.1001177, 2011. a
Kelly, T. B., Davison, P. C., Goericke, R., Landry, M. R., Ohman, M. D., and Stukel, M. R.: The importance of mesozooplankton diel vertical migration for sustaining a mesopelagic food web, Frontiers in Marine Science, 6, https://doi.org/10.3389/fmars.2019.00508, 2019. a, b
Kenitz, K. M., Visser, A. W., Mariani, P., and Andersen, K. H.: Seasonal succession in zooplankton feeding traits reveals trophic trait coupling, Limnol. Oceanogr., 62, 1184–1197, https://doi.org/10.1002/lno.10494, 2017. a, b
Key, R. M., Kozyr, A., Sabine, C. L., Lee, K., Wanninkhof, R., Bullister, J. L., Feely, R. A., Millero, F. J., Mordy, C., and Peng, T.-H.: A global ocean carbon climatology: results from Global Data Analysis Project (GLODAP), Global Biogeochem. Cy., 18, https://doi.org/10.1029/2004GB002247, 2004. a
Kiørboe, T.: Optimal swimming strategies in mate-searching pelagic copepods, Oecologia, 155, 179–192, https://doi.org/10.1007/s00442-007-0893-x, 2008. a
Kiørboe, T.: Foraging mode and prey size spectra of suspension-feeding copepods and other zooplankton, Mar. Ecol. Prog. Ser., 558, 15–20, https://doi.org/10.3354/meps11877, 2016. a
Kiørboe, T.: Organismal trade offs and the pace of planktonic life, Biological Reviews, brv.13108, https://doi.org/10.1111/brv.13108, 2024. a
Kiørboe, T. and Hirst, A. G.: Shifts in mass scaling of respiration, feeding, and growth rates across life-form transitions in marine pelagic organisms, Am. Nat., 183, E118–E130, https://doi.org/10.1086/675241, 2014. a
Kiørboe, T., Saiz, E., and Viitasalo, M.: Prey switching behaviour in the planktonic copepod Acartia tonsa, Mar. Ecol. Prog. Ser., 143, 65–75, https://doi.org/10.3354/meps143065, 1996. a
Kiørboe, T., Jiang, H., and Colin, S. P.: Danger of zooplankton feeding: the fluid signal generated by ambush-feeding copepods, P. Roy. Soc. B-Biol. Sci., 277, 3229–3237, https://doi.org/10.1098/rspb.2010.0629, 2010. a
Kiørboe, T., Visser, A., and Andersen, K. H.: A trait-based approach to ocean ecology, ICES J. Mar. Sci., 75, 1849–1863, https://doi.org/10.1093/icesjms/fsy090, 2018b. 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, Glob. Change Biol., 11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005. a, b
Le Quéré, C., Buitenhuis, E. T., Moriarty, R., Alvain, S., Aumont, O., Bopp, L., Chollet, S., Enright, C., Franklin, D. J., Geider, R. J., Harrison, S. P., Hirst, A. G., Larsen, S., Legendre, L., Platt, T., Prentice, I. C., Rivkin, R. B., Sailley, S., Sathyendranath, S., Stephens, N., Vogt, M., and Vallina, S. M.: Role of zooplankton dynamics for Southern Ocean phytoplankton biomass and global biogeochemical cycles, Biogeosciences, 13, 4111–4133, https://doi.org/10.5194/bg-13-4111-2016, 2016. a
Litchman, E., Ohman, M. D., and Kiørboe, T.: Trait-based approaches to zooplankton communities, J. Plankton Res., 35, 473–484, https://doi.org/10.1093/plankt/fbt019, 2013. a, b, c, d
Liu, K., Xu, Z., Liu, X., Huang, B., Liu, H., and Chen, B.: Modelling global mesozooplankton biomass using machine learning, Prog. Oceanogr., 229, 103371, https://doi.org/10.1016/j.pocean.2024.103371, 2024. a
Madec, G., Bell, M., Blaker, A., Bricaud, C., Bruciaferri, D., Castrillo, M., Calvert, D., Chanut, J., Clementi, E., Coward, A., Epicoco, I., Éthé, C., Ganderton, J., Harle, J., Hutchinson, K., Iovino, D., Lea, D., Lovato, T., Martin, M., Martin, N., Mele, F., Martins, D., Masson, S., Mathiot, P., Mele, F., Mocavero, S., Müller, S., Nurser, A. J. G., Paronuzzi, S., Peltier, M., Person, R., Rousset, C., Rynders, S., Samson, G., Téchené, S., Vancoppenolle, M., and Wilson, C.: NEMO Ocean Engine Reference Manual, Zenodo [code], https://doi.org/10.5281/zenodo.8167700, 2023. a, b
Mariani, P., Andersen, K. H., Visser, A. W., Barton, A. D., and Kiørboe, T.: Control of plankton seasonal succession by adaptive grazing, Limnol. Oceanogr., 58, 173–184, https://doi.org/10.4319/lo.2013.58.1.0173, 2013. a
Martini, S., Larras, F., Boyé, A., Faure, E., Aberle, N., Archambault, P., Bacouillard, L., Beisner, B. E., Bittner, L., Castella, E., Danger, M., Gauthier, O., Karp-Boss, L., Lombard, F., Maps, F., Stemmann, L., Thiébaut, E., Usseglio-Polatera, P., Vogt, M., Laviale, M., and Ayata, S.-D.: Functional trait-based approaches as a common framework for aquatic ecologists, Limnol. Oceanogr., 66, 965–994, https://doi.org/10.1002/lno.11655, 2021. a, b, c
Mitra, A., Castellani, C., Gentleman, W. C., Jónasdóttir, S. H., Flynn, K. J., Bode, A., Halsband, C., Kuhn, P., Licandro, P., Agersted, M. D., Calbet, A., Lindeque, P. K., Koppelmann, R., Møller, E. F., Gislason, A., Nielsen, T. G., and St. John, M.: Bridging the gap between marine biogeochemical and fisheries sciences; configuring the zooplankton link, Prog. Oceanogr., 129, 176–199, https://doi.org/10.1016/j.pocean.2014.04.025, 2014. a
Moriarty, R. and O'Brien, T. D.: Distribution of mesozooplankton biomass in the global ocean, Earth Syst. Sci. Data, 5, 45–55, https://doi.org/10.5194/essd-5-45-2013, 2013. a
Morris, M. J., Gust, G., and Torres, J. J.: Propulsion efficiency and cost of transport for copepods: a hydromechanical model of crustacean swimming, Mar. Biol., 86, 283–295, https://doi.org/10.1007/BF00397515, 1985. a, b
Negrete-García, G., Luo, J. Y., Long, M. C., Lindsay, K., Levy, M., and Barton, A. D.: Plankton energy flows using a global size-structured and trait-based model, Prog. Oceanogr., 209, 102898, https://doi.org/10.1016/j.pocean.2022.102898, 2022. a
Nguyen, T. T. H., Zakem, E. J., Ebrahimi, A., Schwartzman, J., Caglar, T., Amarnath, K., Alcolombri, U., Peaudecerf, F. J., Hwa, T., Stocker, R., Cordero, O. X., and Levine, N. M.: Microbes contribute to setting the ocean carbon flux by altering the fate of sinking particulates, Nat. Commun., 13, 1657, https://doi.org/10.1038/s41467-022-29297-2, 2022. a
Nowicki, M., DeVries, T., and Siegel, D. A.: Quantifying the carbon export and sequestration pathways of the ocean's biological carbon pump, Global Biogeochem. Cy., 36, e2021GB007083, https://doi.org/10.1029/2021GB007083, 2022. a
Ohman, M. D.: Behavioral responses of zooplankton to predation, B. Mar. Sci., 43, 530–550, 1988. a
Ohman, M. D.: The demographic benefits of diel vertical migration by zooplankton, Ecol. Monogr., 60, 257–281, https://doi.org/10.2307/1943058, 1990. a
Ohman, M. D.: A sea of tentacles: optically discernible traits resolved from planktonic organisms in situ, ICES J. Mar. Sci., 76, 1959–1972, https://doi.org/10.1093/icesjms/fsz184, 2019. a, b
Ohman, M. D. and Romagnan, J.-B.: Nonlinear effects of body size and optical attenuation on Diel Vertical Migration by zooplankton, Limnol. Oceanogr., 61, 765–770, https://doi.org/10.1002/lno.10251, 2016. a
Orenstein, E. C., Ayata, S.-D., Maps, F., Becker, É. C., Benedetti, F., Biard, T., de Garidel-Thoron, T., Ellen, J. S., Ferrario, F., Giering, S. L. C., Guy-Haim, T., Hoebeke, L., Iversen, M. H., Kiørboe, T., Lalonde, J.-F., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K. O., Niehoff, B., Ohman, M. D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H. M., Stemmann, L. S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A. M., and Irisson, J.-O.: Machine learning techniques to characterize functional traits of plankton from image data, Limnol. Oceanogr., 67, 1647–1669, https://doi.org/10.1002/lno.12101, 2022. a
Parra, S. M., Greer, A. T., Book, J. W., Deary, A. L., Soto, I. M., Culpepper, C., Hernandez, F. J., and Miles, T. N.: Acoustic detection of zooplankton diel vertical migration behaviors on the northern Gulf of Mexico shelf, Limnol. Oceanogr., 64, 2092–2113, https://doi.org/10.1002/lno.11171, 2019. a
Picheral, M., Catalano, C., Brousseau, D., Claustre, H., Coppola, L., Leymarie, E., Coindat, J., Dias, F., Fevre, S., Guidi, L., Irisson, J. O., Legendre, L., Lombard, F., Mortier, L., Penkerch, C., Rogge, A., Schmechtig, C., Thibault, S., Tixier, T., Waite, A., and Stemmann, L.: The Underwater Vision Profiler 6: an imaging sensor of particle size spectra and plankton, for autonomous and cabled platforms, Limnol. Oceanogr.-Meth., 20, 115–129, https://doi.org/10.1002/lom3.10475, 2022. a
Pinti, J., DeVries, T., Norin, T., Serra-Pompei, C., Proud, R., Siegel, D. A., Kiørboe, T., Petrik, C. M., Andersen, K. H., Brierley, A. S., and Visser, A. W.: Model estimates of metazoans' contributions to the biological carbon pump, Biogeosciences, 20, 997–1009, https://doi.org/10.5194/bg-20-997-2023, 2023. a
Ratnarajah, L., Abu-Alhaija, R., Atkinson, A., Batten, S., Bax, N. J., Bernard, K. S., Canonico, G., Cornils, A., Everett, J. D., Grigoratou, M., Ishak, N. H. A., Johns, D., Lombard, F., Muxagata, E., Ostle, C., Pitois, S., Richardson, A. J., Schmidt, K., Stemmann, L., Swadling, K. M., Yang, G., and Yebra, L.: Monitoring and modelling marine zooplankton in a changing climate, Nat. Commun., 14, 564, https://doi.org/10.1038/s41467-023-36241-5, 2023. a, b, c
Rohr, T., Richardson, A. J., Lenton, A., Chamberlain, M. A., and Shadwick, E. H.: Zooplankton grazing is the largest source of uncertainty for marine carbon cycling in CMIP6 models, Communications Earth and Environment, 4, 1–22, https://doi.org/10.1038/s43247-023-00871-w, 2023. a, b, c
Sathyendranath, S., Brewin, R., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F., Moore, T., Müller, D., Regner, P., Roy, S., Steele, C., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V., Feng, H., Feldman, G., Franz, B., Frouin, R., Gould, R., Hooker, S., Kahru, M., Kratzer, S., Mitchell, B., Muller-Karger, F., Sosik, H., Voss, K., Werdell, J., and Platt, T.: An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019. a
Schmoker, C., Hernández-León, S., and Calbet, A.: Microzooplankton grazing in the oceans: impacts, data variability, knowledge gaps and future directions, J. Plankton Res., 35, 691–706, https://doi.org/10.1093/plankt/fbt023, 2013. a
Serra-Pompei, C., Ward, B. A., Pinti, J., Visser, A. W., Kiørboe, T., and Andersen, K. H.: Linking plankton size spectra and community composition to carbon export and its efficiency, Global Biogeochem. Cy., 36, e2021GB007275, https://doi.org/10.1029/2021GB007275, 2022. a, b
Sieburth, J. M., Smetacek, V., and Lenz, J.: Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions 1, Limnol. Oceanogr., 23, 1256–1263, https://doi.org/10.4319/lo.1978.23.6.1256, 1978. a
Stamieszkin, K., Pershing, A. J., Record, N. R., Pilskaln, C. H., Dam, H. G., and Feinberg, L. R.: Size as the master trait in modeled copepod fecal pellet carbon flux, Limnol. Oceanogr., 60, 2090–2107, https://doi.org/10.1002/lno.10156, 2015. a, b
Stemmann, L., Jackson, G., and Ianson, D.: A vertical model of particle size distributions and fluxes in the midwater column that includes biological and physical processes – Part I: Model formulation, Deep-Sea Res. Pt. I, 51, 865–884, https://doi.org/10.1016/j.dsr.2004.03.001, 2004. a
Strömberg, K. H. P., Smyth, T. J., Allen, J. I., Pitois, S., and O'Brien, T. D.: Estimation of global zooplankton biomass from satellite ocean colour, J. Marine Syst., 78, 18–27, https://doi.org/10.1016/j.jmarsys.2009.02.004, 2009. a
Stukel, M. R., Ohman, M. D., Kelly, T. B., and Biard, T.: The roles of suspension-feeding and flux-feeding zooplankton as gatekeepers of particle flux into the mesopelagic ocean in the Northeast Pacific, Frontiers in Marine Science, 6, 397, https://doi.org/10.3389/fmars.2019.00397, 2019. a, b, c, d, e, f, g, h
Tilman, D.: Constraints and tradeoffs: toward a predictive theory of competition and succession, Oikos, 58, 3–15, https://doi.org/10.2307/3565355, 1990. a
Tiselius, P. and Jonsson, P. R.: Foraging behaviour of six calanoid copepods: observations and hydrodynamic analysis, Mar. Ecol. Prog. Ser., 66, 23–33, 1990. a
Tiselius, P., Jonsson, P. R., Kuurtvedt, S., Olsen, E. M., and Jørstud, T.: Effects of copepod foraging behavior on predation risk: an experimental study of the predatory copepod Pareuchaeta norvegica feeding on Acartia clausi and A. tonsa (Copepoda), Limnol. Oceanogr., 42, 164–170, https://doi.org/10.4319/lo.1997.42.1.0164, 1997. a
Turner, J. T.: Zooplankton fecal pellets, marine snow and sinking phytoplankton blooms, Aquat. Microb. Ecol., 27, 57–102, https://doi.org/10.3354/ame027057, 2002. a
Uye, S.-i. and Kaname, K.: Relations between fecal pellet volume and body size for major zooplankters of the Inland Sea of Japan, Journal of Oceanography, 50, 43–49, https://doi.org/10.1007/BF02233855, 1994. a
van Someren Gréve, H., Kiørboe, T., and Almeda, R.: Bottom-up behaviourally mediated trophic cascades in plankton food webs, P. Roy. Soc. B-Biol. Sci., 286, 20181664, https://doi.org/10.1098/rspb.2018.1664, 2019. a
Vancoppenolle, M., Rousset, C., Blockley, E., Aksenov, Y., Feltham, D., Fichefet, T., Garric, G., Guémas, V., Iovino, D., Keeley, S., Madec, G., Massonnet, F., Ridley, J., Schroeder, D., and Tietsche, S.: SI3, the NEMO Sea Ice Engine, Zenodo [code], https://doi.org/10.5281/zenodo.7534900, 2023. a
Verity, P. and Smetacek, V.: Organism life cycles, predation, and the structure of marine pelagic ecosystems, Mar. Ecol. Prog. Ser., – MAR ECOL-PROGR SER, 130, 277–293, https://doi.org/10.3354/meps130277, 1996. a
Violle, C., Navas, M.-L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., and Garnier, E.: Let the concept of trait be functional!, Oikos, 116, 882–892, https://doi.org/10.1111/j.0030-1299.2007.15559.x, 2007. a
Visser, A. W., Mariani, P., and Pigolotti, S.: Swimming in turbulence: zooplankton fitness in terms of foraging efficiency and predation risk, J. Plankton Res., 31, 121–133, https://doi.org/10.1093/plankt/fbn109, 2008. a, b
Vlymen, W. J.: Energy expenditure of swimming copepods, Limnol. Oceanogr., 15, 348–356, https://doi.org/10.4319/lo.1970.15.3.0348, 1970. a, b
Werner, E. E. and Anholt, B. R.: Ecological consequences of the trade-off between growth and mortality rates mediated by foraging activity, Am. Nat., 142, 242–272, https://doi.org/10.1086/285537, 1993. a, b
Co-editor-in-chief
Mesozooplankton are a size group of zooplankton (0.2 mm to 2 cm) that is often lumped together with smaller, microzooplankton taxa, and treated as a functionally homogeneous group in oceanic surveys and biogeochemical models. Di Matteo used a trait-based modeling approach that accounts for the strong functional differences among mesozooplankton taxa, here grouped as cruisers, ambushers, and flux-feeders. Integration of model results with observational data revealed clear biogeographical distribution patterns that suggest distinct roles of major mesozooplankton groups with respect to their contributions to oceanic food webs and the export of carbon to the deep ocean.
Mesozooplankton are a size group of zooplankton (0.2 mm to 2 cm) that is often lumped together...
Short summary
Mesozooplankton gather small current-drifting animals. They are very diverse and play key roles in the functioning of marine ecosystem and carbon cycle, especially through the production of rapidly sinking particles. Usually represented as one compartment, here we add three feeding strategies in an ocean biogeochemical model and investigate their impact on carbon cycle at global scale. We find distinct distributions between mesozooplankton types and diverse contributions to carbon export.
Mesozooplankton gather small current-drifting animals. They are very diverse and play key roles...
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