Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-1765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-1765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 May 2019
Development and technical paper |
| 06 May 2019
| 06 May 2019
Towards end-to-end (E2E) modelling in a consistent NPZD-F modelling framework (ECOSMO E2E_v1.0): application to the North Sea and Baltic Sea
Ute Daewel
CORRESPONDING AUTHOR
Helmholtz Centre Geesthacht, Institute of Coastal Research,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Corinna Schrum
Helmholtz Centre Geesthacht, Institute of Coastal Research,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Geophysical Institute, University of Bergen, Allegaten 41, 5007
Bergen, Norway
Jed I. Macdonald
Faculty of Life and Environmental Sciences, University of Iceland, 101
Reykjavík, Iceland
Oceanic Fisheries Programme, Pacific Community (SPC), Noumea BP D5
98848, New Caledonia
Related authors
David J. Amptmeijer, Elena Mikheeva, Ute Daewel, Johannes Bieser, and Corinna Schrum
Biogeosciences, 22, 7929–7960, https://doi.org/10.5194/bg-22-7929-2025, https://doi.org/10.5194/bg-22-7929-2025, 2025
Short summary
Short summary
We integrate bioaccumulation and biotic Hg transformations into a coupled ecosystem–mercury model to assess their effect on marine Hg cycling. Bioaccumulation increases methylmercury levels, especially in productive coastal waters, and alters Hg exchange between the Baltic and North Seas. These results highlight strong ecosystem feedbacks on marine Hg dynamics.
Feifei Liu, Ute Daewel, Jan Kossack, Kubilay Timur Demir, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 3699–3719, https://doi.org/10.5194/bg-22-3699-2025, https://doi.org/10.5194/bg-22-3699-2025, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) boosts oceanic CO₂ absorption, offering a climate solution. Using a regional model, we examined OAE in the North Sea, revealing that shallow coastal areas achieve higher CO₂ uptake than offshore where alkalinity is more susceptible to deep-ocean loss. Long-term carbon storage is limited, and pH shifts vary by location. Our findings guide OAE deployment to optimize carbon removal while minimizing ecological effects, supporting global climate mitigation efforts.
Kubilay Timur Demir, Moritz Mathis, Jan Kossack, Feifei Liu, Ute Daewel, Christoph Stegert, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 2569–2599, https://doi.org/10.5194/bg-22-2569-2025, https://doi.org/10.5194/bg-22-2569-2025, 2025
Short summary
Short summary
This study examines how variations in the ratios of carbon, nitrogen, and phosphorus in organic matter affect carbon cycling in the northwest European shelf seas. Traditional models with fixed ratios tend to underestimate biological carbon uptake. By integrating variable ratios into a regional model, we find that carbon dioxide uptake increases by 9 %–31 %. These results highlight the need to include variable ratios for accurate assessments of regional and global carbon cycles.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Lucas Porz, Wenyan Zhang, Nils Christiansen, Jan Kossack, Ute Daewel, and Corinna Schrum
Biogeosciences, 21, 2547–2570, https://doi.org/10.5194/bg-21-2547-2024, https://doi.org/10.5194/bg-21-2547-2024, 2024
Short summary
Short summary
Seafloor sediments store a large amount of carbon, helping to naturally regulate Earth's climate. If disturbed, some sediment particles can turn into CO2, but this effect is not well understood. Using computer simulations, we found that bottom-contacting fishing gears release about 1 million tons of CO2 per year in the North Sea, one of the most heavily fished regions globally. We show how protecting certain areas could reduce these emissions while also benefitting seafloor-living animals.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
Short summary
Short summary
High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
David J. Amptmeijer, Elena Mikheeva, Ute Daewel, Johannes Bieser, and Corinna Schrum
Biogeosciences, 22, 7929–7960, https://doi.org/10.5194/bg-22-7929-2025, https://doi.org/10.5194/bg-22-7929-2025, 2025
Short summary
Short summary
We integrate bioaccumulation and biotic Hg transformations into a coupled ecosystem–mercury model to assess their effect on marine Hg cycling. Bioaccumulation increases methylmercury levels, especially in productive coastal waters, and alters Hg exchange between the Baltic and North Seas. These results highlight strong ecosystem feedbacks on marine Hg dynamics.
Feifei Liu, Ute Daewel, Jan Kossack, Kubilay Timur Demir, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 3699–3719, https://doi.org/10.5194/bg-22-3699-2025, https://doi.org/10.5194/bg-22-3699-2025, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) boosts oceanic CO₂ absorption, offering a climate solution. Using a regional model, we examined OAE in the North Sea, revealing that shallow coastal areas achieve higher CO₂ uptake than offshore where alkalinity is more susceptible to deep-ocean loss. Long-term carbon storage is limited, and pH shifts vary by location. Our findings guide OAE deployment to optimize carbon removal while minimizing ecological effects, supporting global climate mitigation efforts.
Shilpa Lal, Sophie Cravatte, Christophe Menkes, Jed Macdonald, Romain LeGendre, Ines Mangolte, Cyril Dutheil, Neil Holbrook, and Simon Nicol
EGUsphere, https://doi.org/10.5194/egusphere-2025-3281, https://doi.org/10.5194/egusphere-2025-3281, 2025
Short summary
Short summary
This paper characterizes historical (1981–2023) marine heatwaves in the tropical southwestern Pacific, where they pose a challenge for marine resource dependent Islands. Heatwaves are distinguished as a function of their spatial extent, signature at the coast, and seasonality, to allow a better understanding of their impacts on ecosystems. Marine heatwaves are getting longer and more frequent, with greater spatial extents. Our results aim to inform the Pacific Islands on their vulnerability.
Kubilay Timur Demir, Moritz Mathis, Jan Kossack, Feifei Liu, Ute Daewel, Christoph Stegert, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 2569–2599, https://doi.org/10.5194/bg-22-2569-2025, https://doi.org/10.5194/bg-22-2569-2025, 2025
Short summary
Short summary
This study examines how variations in the ratios of carbon, nitrogen, and phosphorus in organic matter affect carbon cycling in the northwest European shelf seas. Traditional models with fixed ratios tend to underestimate biological carbon uptake. By integrating variable ratios into a regional model, we find that carbon dioxide uptake increases by 9 %–31 %. These results highlight the need to include variable ratios for accurate assessments of regional and global carbon cycles.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Lucas Porz, Wenyan Zhang, Nils Christiansen, Jan Kossack, Ute Daewel, and Corinna Schrum
Biogeosciences, 21, 2547–2570, https://doi.org/10.5194/bg-21-2547-2024, https://doi.org/10.5194/bg-21-2547-2024, 2024
Short summary
Short summary
Seafloor sediments store a large amount of carbon, helping to naturally regulate Earth's climate. If disturbed, some sediment particles can turn into CO2, but this effect is not well understood. Using computer simulations, we found that bottom-contacting fishing gears release about 1 million tons of CO2 per year in the North Sea, one of the most heavily fished regions globally. We show how protecting certain areas could reduce these emissions while also benefitting seafloor-living animals.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
Short summary
Short summary
High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Cited articles
Anders, K. and Möller, H.: Seasonal fluctuations in macrobenthic fauna of
the Fucus belt in Kiel Fjord (western Baltic Sea), Helgoländer Meeresun.,
36, 277–283, 1983.
Andersson, A., Hajdu, S., Haecky, P., Kuparinen, J., and Wikner, J.:
Succession and growth limitation of phytoplankton in the Gulf of Bothnia
(Baltic Sea), Mar. Biol., 126, 791–801, https://doi.org/10.1007/BF00351346, 1996.
Bagge, O., Thurow, F., Steffensen, E., and Bay, J.: The Baltic cod, Dana, 10,
1–28, 1994.
Barthel, K., Daewel, U., Pushpadas, D., Schrum, C., Årthun, M., and
Wehde, H.: Resolving frontal structures: On the computational costs and
pay-off using a less diffusive but computational more expensive advection
scheme, Ocean Dynam., 62, 1457–1470, https://doi.org/10.1007/s10236-012-0578-9, 2012.
Blackford, J. C., Allen, J. I., and Gilbert, F. J.: Ecosystem dynamics at six
contrasting sites?: a generic modelling study, J. Mar. Syst., 52, 191–215,
https://doi.org/10.1016/j.jmarsys.2004.02.004, 2004.
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y.,
Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin,
G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley,
S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine
biogeochemistry and the ecosystem dynamics of the lower trophic levels,
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016,
2016.
Callaway, R., Alsvag, J., de Boois, I., Cotter, J., Ford, A., Hinz, H.,
Jennings, S., Kröncke, I., Lancaster, J., Piet, G., Prince, P., and
Ehrich, S.: Diversity and community structure of epibenthic invertebrates and
fish in the North Sea, ICES J. Mar. Sci., 59, 1199–1214,
https://doi.org/10.1006/jmsc.2002.1288, 2002.
Casini, M., Hjelm, J., Molinero, J.-M., Lövgren, J., Cardinale, M.,
Bartolino, V., Belgrano, A., and Kornilovs, G.: Trophic cascades promote
threshold-like shifts, P. Natl. Acad. Sci. USA, 106, 197–202, 2009.
Charles, A. T.: Bio-Socio-Economic Fishery Models: Labour Dyanmics and
Multi-Objective Management, Can. J. Fish. Aquat. Sci., 46, 1313–1322, 1989.
Christensen, V. and Walters, C. J.: Ecopath with Ecosim: methods,
capabilities and limitations, Ecol. Modell., 172, 109–139,
https://doi.org/10.1016/j.ecolmodel.2003.09.003, 2004.
Clarke, A. and Johnston, N. M.: Scaling of metabolic rate with body mass and
temperature in teleost fish, J. Anim. Ecol., 68, 893–905,
https://doi.org/10.1046/j.1365-2656.1999.00337.x, 1999.
Colebrook, J. M., Environment, N., Place, P., and Hoe, T.: Continuous
plankton records: relationships between species of phytoplankton and
zooplankton in the seasonal cycle, Mar. Biol., 323, 313–323, 1984.
Coull, K., Jermyn, A., Newton, A., Henderson, G., and Hall, W.: Length/weight
Relationships for 88 Species of Fish Encountered in the North East Atlantic,
Scottish Fish. Res. Reports, 43, 82 pp., 1989.
Cury, P. and Shannon, L.: Regime shifts in upwelling ecosystems: observed
changes and possible mechanisms in the northern and southern Benguela, Prog.
Oceanogr., 60, 223–243, https://doi.org/10.1016/j.pocean.2004.02.007, 2004.
Cury, P., Shannon, L., and Shin, Y.-J.: The functioning of marine ecosystems,
in Responsible Fisheries in the Marine Ecosystem, edited by: Sinclair, M. and
Valdimarsson, G., FAO, Rome and CABI Publishing, Wallingford, 103–123, 2003.
Daan, N., Bromley, P. J., Hislop, J. R. G., and Nielson, N. A.: Ecology of
North Sea Fish, Neth. J. Sea Res., 26, 343–386, 1990.
Daewel, U. and Schrum, C.: Simulating long-term dynamics of the coupled North
Sea and Baltic Sea ecosystem with ECOSMO II: Model description and
validation, J. Mar. Syst., 119–120, 30–49,
https://doi.org/10.1016/j.jmarsys.2013.03.008, 2013.
Daewel, U., Peck, M. A., Kühn, W., St. John, M. A., Alekseeva, I., and
Schrum, C.: Coupling ecosystem and individual-based models to simulate the
influence of environmental variability on potential growth and survival of
larval sprat (Sprattus sprattus L.) in the North Sea, Fish. Oceanogr., 17,
333–351, https://doi.org/10.1111/j.1365-2419.2008.00482.x, 2008.
Daewel, U., Hjøllo, S. S., Huret, M., Ji, R., Maar, M., Niiranen, S.,
Travers-trolet, M., Peck, M. A., Wolfshaar, K. E., and van de Wolfshaar, K.
E.: Predation control of zooplankton dynamics: a review of observations and
models, ICES J. Mar. Sci., 71, 254–271, https://doi.org/10.1093/icesjms/fst125, 2014.
Daewel, U., Schrum, C., and Gupta, A. K.: The predictive potential of early
life stage individual-based models (IBMs): an example for Atlantic cod Gadus
morhua in the North Sea, Mar. Ecol. Prog. Ser., 534, 199–219,
https://doi.org/10.3354/meps11367, 2015.
Ekeroth, N., Blomqvist, S., and Hall, P. O. J.: Nutrient fluxes from reduced
Baltic Sea sediment: effects of oxygenation and macrobenthos, Mar. Ecol.
Prog. Ser., 544, 77–92, https://doi.org/10.3354/meps11592, 2016.
Elliott, M. and Hemingway, K.: Fishes in Estuaries, Fishes in Estuaries,
i–xx, https://doi.org/10.2134/jeq2003.3750, 2002.
Fennel, W.: Towards bridging biogeochemical and fish-production models, J.
Mar. Syst., 71, 171–194, https://doi.org/10.1016/j.jmarsys.2007.06.008, 2008.
Fennel, W.: Parameterizations of truncated food web models from the
perspective of an end-to-end model approach, J. Mar. Syst., 76, 171–185,
https://doi.org/10.1016/j.jmarsys.2008.05.005, 2009.
Fennel, W.: A nutrient to fish model for the example of the Baltic Sea, J.
Mar. Syst., 81, 184–195, https://doi.org/10.1016/j.jmarsys.2009.12.007, 2010.
Froese, R. and Pauly, D.: FishBase 2000: concepts, design and data sources,
ICLARM, Los Baños, Laguna, Philippines, 2000.
Froese, R., Thorson, J. T., and Reyes Jr., R. B.: A Bayesian approach for
estimating length-weight relationships in fishes, J. Appl. Ichthyol., 30,
78–85, https://doi.org/10.1111/jai.12299, 2014.
Fulton, E., Smith, A., and Punt, A.: Which ecological indicators can robustly
detect effects of fishing?, ICES J. Mar. Sci., 62, 540–551,
https://doi.org/10.1016/j.icesjms.2004.12.012, 2005.
Fulton, E. A.: Approaches to end-to-end ecosystem models, J. Mar. Syst., 81,
171–183, https://doi.org/10.1016/j.jmarsys.2009.12.012, 2010.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M., and Charnov, E.
L.: Effects of size and temperature on metabolic rate., Science, 293,
2248–2251, https://doi.org/10.1126/science.1061967, 2001.
Gogina, M. and Zettler, M. L.: Diversity and distribution of benthic
macrofauna in the Baltic Sea, Data inventory and its use for species
distribution modelling and prediction, J. Sea Res., 64, 313–321,
https://doi.org/10.1016/j.seares.2010.04.005, 2010.
Gogina, M., Glockzin, M., and Zettler, M. L.: Distribution of benthic
macrofaunal communities in the western Baltic Sea with regard to near-bottom
environmental parameters. 1. Causal analysis, J. Mar. Syst., 80, 57–70,
https://doi.org/10.1016/j.jmarsys.2009.10.001, 2010.
Greenstreet, S.: Seasonal variation in the consumption of food by fish in the
North Sea and implications for food web dynamics, ICES J. Mar. Sci., 54,
243–266, https://doi.org/10.1006/jmsc.1996.0183, 1997.
Greenstreet, S. P. R., Bryant, A. D., Broekhuizen, N., Hall, S. J., and
Heath, M. R.: Seasonal variation in the consumption of food by fish in the
North Sea and implications for food web dynamics, ICES J. Mar. Sci., 54,
243–266, https://doi.org/10.1006/jmsc.1996.0183, 1997.
Guerra, A. and Rocha, F.: The life history of Loligo vulgaris and Loligo
forbesi (Cephalopoda: Loliginidae) in Galician waters (NW Spain), Fish. Res.,
21, 43–69, https://doi.org/10.1016/0165-7836(94)90095-7, 1994.
Heath, M. R.: Ecosystem limits to food web fluxes and fisheries yields in the
North Sea simulated with an end-to-end food web model, Prog. Oceanogr., 102,
42–66, 2012.
Heip, C., Basford, D., Craeymeersch, J. A., Dewarumez, J. M., Dorjes, J., de
Wilde, P., Duineveld, G., Eleftheriou, A., Herman, P. M. J., Niermann, U.,
Kingston, P., Kunitzer, A., Rachor, E., Rumohr, H., Soetaert, K., and
Soltwedel, T.: Trends in biomass, density and diversity of North Sea
macrofauna, ICES J. Mar. Sci., 49, 13–22, https://doi.org/10.1093/icesjms/49.1.13, 1992.
Hinrichsen, H.-H., Lehmann, A., Petereit, C., Nissling, A., Ustups, D.,
Bergström, U., and Hüssy, K.: Spawning areas of eastern Baltic cod
revisited: Using hydrodynamic modelling to reveal spawning habitat
suitability, egg survival probability, and connectivity patterns, Prog.
Oceanogr., 143, 13–25, https://doi.org/10.1016/j.pocean.2016.02.004, 2016.
Huang, L., Wu, Y., Wan, R., and Zhang, J.: Carbon, nitrogen and phosphorus
stoichiometry in Japanese anchovy (Engraulis japonicus) from the Huanghai
Sea, China, Acta Oceanol. Sin., 31, 154–161, https://doi.org/10.1007/s13131-012-0229-5,
2012.
Hunter, E., Metcalfe, J. D., and Reynolds, J. D.: Migration route and
spawning area fidelity by North Sea plaice, Proc. Biol. Sci., 270,
2097–2103, https://doi.org/10.1098/rspb.2003.2473, 2003.
ICES: Manual for the International Bottom Trawl Surveys, Series of ICES
Survey Protocols, SISP 1-IBTS VIII, 68 pp., 2012.
ICES: Baltic Sea Ecoregion – Fisheries overview – November, 1–23,
https://doi.org/10.17895/ices.pub.4648, 2018a.
ICES: Greater North Sea Ecoregion – Fisheries overview – November, 1–31,
https://doi.org/10.17895/ices.pub.4647, 2018b.
Jennings, S., Alvsvåg, J., Cotter, A. J. R., Ehrich, S., Greenstreet, S.
P. R., Jarre-Teichmann, A., Mergardt, N., Rijnsdorp, A. D., and Smedstad, O.:
Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort,
diversity and community structure, III. International trawling effort in the
North Sea: An analysis of spatial and temporal trends, Fish. Res., 40,
125–134, https://doi.org/10.1016/S0165-7836(98)00208-2, 1999.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.
C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471,
https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Klaoudatos, D. S., Conides, A. J., Anastasopoulou, A., and Dulčić,
J.: Age, growth, mortality and sex ratio of the inshore population of the
edible crab, Cancer pagurus (Linnaeus 1758) in South Wales (UK), J. Appl.
Ichthyol., 29, 579–586, https://doi.org/10.1111/jai.12122, 2013.
Kröncke, I. and Bergfeld, C.: North sea benthos: a review, Senck. Marit.,
33, 205–268, https://doi.org/10.1007/BF03043049, 2003.
Maar, M., Friis, E., Larsen, J., Skovgaard, K., Wan, Z., She, J., Jonasson,
L., and Neumann, T.: Ecosystem modelling across a salinity gradient from the
North Sea to the Baltic Sea, Ecol. Modell., 222, 1696–1711,
https://doi.org/10.1016/j.ecolmodel.2011.03.006, 2011.
Maar, M., Rindorf, A., Møller, E. F., Christensen, A., Madsen, K. S., and
van Deurs, M.: Zooplankton mortality in 3D ecosystem modelling considering
variable spatial–temporal fish consumptions in the North Sea, Prog.
Oceanogr., 124, 78–91, https://doi.org/10.1016/j.pocean.2014.03.002, 2014.
Mackinson, S. and Daskalov, G.: An ecosystem model of the North Sea to
support an ecosystem approach to fisheries management: description and
parameterisation, Sci. Ser. Tech. Rep., 142, 196 pp., 2007.
McCully, S., Scott, F., and Ellis, J.: Lengths at maturity and conversion
factors for skate (Rajidae) around the British Isles, with an analysis of
data in the literature, ICES J. Mar. Sci., 69, 1812–1822,
https://doi.org/10.1093/icesjms/fst048, 2012.
Megrey, B. A., Rose, K. A., Klumb, R. A., Hay, D. E., Werner, F. E.,
Eslinger, D. L., and Smith, S. L.: A bioenergetics-based population dynamics
model of Pacific herring (Clupea harengus pallasi) coupled to a lower trophic
level nutrient – phytoplankton – zooplankton model?: Description,
calibration, and sensitivity analysis, Ecol. Modell., 202, 144–164,
https://doi.org/10.1016/j.ecolmodel.2006.08.020, 2007.
Michaelis, L. and Menten, M. L.: Die Kinetik der Invertinwirkung, Biochem.
Zeitschrift Beiträge zur chem. Physiol. u. Pathol., 49, 333–369,
availalbe at:
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/17273
(last access: 22 August 2012), 1913.
Möllmann, C., Kornilovs, G., and Sidrevics, L.: Long-term dynamics of
main mesozooplankton species in the central Baltic Sea, J. Plankton Res., 22,
2015–2038, 2000.
Monod, J.: Recherches sur la croissance des cultures bacteiriennes, Hermann
& cie, Paris, available at:
http://www.worldcat.org/title/recherches-sur-la-croissance-des-cultures-bacteriennes/oclc/6126763
(last access: 22 August 2012), 1942.
Müller-Navarra, S. H. and Lange, W.: Modelling tides in the Baltic Sea –
A short note on the harmonic analysis of a one-year water level time series,
in: Proceedings of the 6th HIROMB Scientific Workshop, St. Petersburg,
Russia, 8–10 September 2003, 16–20, 2004.
Neumann, T. and Schernewski, G.: Eutrophication in the Baltic Sea and shifts
in nitrogen fixation analyzed with a 3D ecosystem model, J. Mar. Syst., 74,
592–602, https://doi.org/10.1016/j.jmarsys.2008.05.003, 2008.
Nøttestad, L., Giske, J., Holst, J. C., and Huse, G.: A length-based
hypothesis for feeding migrations in pelagic fish, Can. J. Fish. Aquat. Sci.,
56, 26–34, https://doi.org/10.1139/f99-222, 2011.
Oguz, T., Salihoglu, B., and Fach, B.: A coupled plankton–anchovy population
dynamics model assessing nonlinear controls of anchovy and gelatinous biomass
in the Black Sea, Mar. Ecol. Prog. Ser., 369, 229–256, 2008.
Omstedt, A. and Hansson, D.: The Baltic Sea ocean climate system memory and
response to changes in the water and heat balance components, Cont. Shelf
Res., 26, 236–251, https://doi.org/10.1016/j.csr.2005.11.003, 2006.
Peck, M. A., Arvanitidis, C., Butenschön, M., Canu, D. M.,
Chatzinikolaou, E., Cucco, A., Domenici, P., Fernandes, J. A., Gasche, L.,
Huebert, K. B., Hufnagl, M., Jones, M. C., Kempf, A., Keyl, F., Maar, M.,
Mahévas, S., Marchal, P., Nicolas, D., Pinnegar, J. K., Rivot, E.,
Rochette, S., Sell, A. F., Sinerchia, M., Solidoro, C., Somerfield, P. J.,
Teal, L. R., Travers-Trolet, M., and van de Wolfshaar, K. E.: Projecting
changes in the distribution and productivity of living marine resources: A
critical review of the suite of modelling approaches used in the large
European project VECTORS, Estuar. Coast. Shelf Sci., 201, 40–55,
https://doi.org/10.1016/j.ecss.2016.05.019, 2015.
Persson, L.-E.: Temporal and spatial variation in coastal macrobenthic
community structure, Hanö bay (southern Baltic), J. Exp. Mar. Biol.
Ecol., 68, 277–293, 1983.
Pierce, G. J., Boyle, P. R., Hastie, L. C., and Key, L.: The life history of
Loligo forbesi (Cephalopoda: Loliginidae) in Scottish waters, Fish. Res., 21,
17–41, https://doi.org/10.1016/0165-7836(94)90094-9, 1994.
Pinto, C., Travers-Trolet, M., Macdonald, J., Rivot, E., and Vermard, Y.:
Combining multiple data sets to unravel the spatio-temporal dynamics of a
data-limited fish stock, Can. J. Fish. Aquat. Sci.,
https://doi.org/10.1139/cjfas-2018-0149, 2018.
Politikos, D. V., Curchitser, E. N., Rose, K. A., Checkley, D. M., and
Fiechter, J.: Climate variability and sardine recruitment in the California
Current: A mechanistic analysis of an ecosystem model, Fish. Oceanogr., 27,
602–622, https://doi.org/10.1111/fog.12381, 2018.
Radtke, H., Neumann, T., and Fennel, W.: A Eulerian nutrient to fish model of
the Baltic Sea – A feasibility-study, J. Mar. Syst., 125, 61–76,
https://doi.org/10.1016/j.jmarsys.2012.07.010, 2013.
Rees, H. L., Pendle, M. a, Waldock, R., Limpenny, D. S., and Boyd, S. E.: A
comparison of benthic biodiversity in the North Sea, English Channel, and
Celtic Seas, ICES J. Mar. Sci., 56, 228–246, https://doi.org/10.1006/jmsc.1998.0438,
1999.
Rees, H. L., Eggleton, J. D., Rachor, E. and Vanden Berghe, E. (Eds.):
Structure and dynamics of the North Sea benthos, ICES Cooperative Research
Report, ISBN: 87-7482-058-3, 258 pp., 2007.
Reiss, H. and Krönke, I.: Seasonal variability of epibenthic communities
in different areas of the southern North Sea, ICES J. Mar. Sci., 61,
882–905, https://doi.org/10.1016/j.icesjms.2004.06.020, 2004.
Reiss, H. and Krönke, I.: Seasonal variability of infaunal community
structures in three areas of the North Sea under different environmental
conditions, Estuar. Coast. Shelf Sci., 65, 253–274,
https://doi.org/10.1016/j.ecss.2005.06.008, 2005.
Rodhe, J., Tett, P., and Wulff, F.: The Baltic and North Seas: A Regional
Review of some important Physical-Chemical-Biological Interaction Processes,
in: The Sea, Vol. 14 B, edited by: Robinson, A. R. and Brink, K., Harvard
University Press, 1033–1075, 2006.
Rose, K. A., Fiechter, J., Curchitser, E. N., Hedstrom, K., Bernal, M.,
Creekmore, S., Haynie, A., Ito, S., Lluch-Cota, S., Megrey, B. A., Edwards,
C. A., Checkley, D., Koslow, T., McClatchie, S., Werner, F., MacCall, A., and
Agostini, V.: Demonstration of a Fully-Coupled End-to-End Model for Small
Pelagic Fish Using Sardine and Anchovy in the California Current, Prog.
Oceanogr., 138, 348–380, https://doi.org/10.1016/j.pocean.2015.01.012, 2015.
Schlüter, M., Hinkel, J., Bots, P. W. G., and Arlinghaus, R.: Application
of the SES framework for model-based analysis of the dynamics of
social-ecological systems, Ecol. Soc., 19, https://doi.org/10.5751/ES-05782-190136, 2014.
Schrum, C. and Backhaus, J. O.: Sensitivity of atmosphere-ocean heat exchange
and heat content in the North Sea and the Baltic Sea, Tellus A, 51, 526–549,
https://doi.org/10.1034/j.1600-0870.1992.00006.x, 1999.
Schrum, C., Alekseeva, I., and St. John, M.: Development of a coupled
physical–biological ecosystem model ECOSMO, J. Mar. Syst., 61, 79–99,
https://doi.org/10.1016/j.jmarsys.2006.01.005, 2006.
Seinä, A. and Palosuo, E.: The classification of the maximum annual
extent of ice cover in the Baltic Sea 1720–1995, Meri – Report Series of
the Finnish Institute of Marine Research, 20, 79–910, 1996.
Shin, Y.-J. and Cury, P.: Using an individual-based model of fish assemblages
to study the response of size spectra to changes in fishing, Can. J. Fish.
Aquat. Sci., 61, 414–431, https://doi.org/10.1139/f03-154, 2004.
Shin, Y.-J., Travers, M., and Maury, O.: Coupling low and high trophic levels
models: Towards a pathways-orientated approach for end-to-end models, Prog.
Oceanogr., 84, 105–112, 2010.
Shin, Y. and Cury, P.: Exploring fish community dynamics through
size-dependent trophic interactions using a spatialized individual-based
model, Aquat. Living Resour., 14, 65–80, https://doi.org/10.1016/S0990-7440(01)01106-8,
2001.
Skogen, M. D., Søiland, H., and Svendsen, E.: Effects of changing nutrient
loads to the North Sea, J. Mar. Syst., 46, 23–38,
https://doi.org/10.1016/j.jmarsys.2003.11.013, 2004.
Sparholt, H.: An estimate of the total biomass of fish in the North Sea, ICES
J. Mar. Sci., 46, 200–210, https://doi.org/10.1093/icesjms/46.2.200, 1990.
Sterner, R. W. and George, N. B.: Carbon , Nitrogen , and Phosphorus
Stoichiometry of Cyprinid Fishes, Ecology, 81, 127–140, 2000.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Templeman, W.: Length-weight Relationships & Morphometric Characteristics
and Thorniness of Thorny Skate (Raja radiata) from the Northwest Atlantic, J.
Northwest Atl. Fish. Sci., 7, 89–98, 1987.
Thurow, F.: Estimation of the total fish biomass in the Baltic Sea during the
20th century, ICES J. Mar. Sci., 54, 444–461, https://doi.org/10.1006/jmsc.1996.0195,
1997.
Timmermann, K., Norkko, J., Janas, U., Norkko, A., Gustafsson, B. G., and
Bonsdorff, E.: Modelling macrofaunal biomass in relation to hypoxia and
nutrient loading, J. Mar. Syst., 105–108, 60–69,
https://doi.org/10.1016/j.jmarsys.2012.06.001, 2012.
Tomczak, M. T., Niiranen, S., Hjerne, O., and Blenckner, T.: Ecosystem flow
dynamics in the Baltic Proper – Using a multi-trophic dataset as a basis for
food–web modelling, Ecol. Modell., 230, 123–147,
https://doi.org/10.1016/j.ecolmodel.2011.12.014, 2012.
Travers, M., Shin, Y.-J., Jennings, S., and Cury, P.: Towards end-to-end
models for investigating the effects of climate and fishing in marine
ecosystems, Prog. Oceanogr., 75, 751–770, https://doi.org/10.1016/j.pocean.2007.08.001,
2007.
Utne, K. R., Hjøllo, S. S., Huse, G., and Skogen, M.: Estimating the
consumption of Calanus finmarchicus by planktivorous fish in the Norwegian
Sea using a fully coupled 3D model system, Mar. Biol. Res., 8, 527–547,
https://doi.org/10.1080/17451000.2011.642804, 2012.
Vikebø, F., Jørgensen, C., Kristiansen, T., and Fiksen, Ø.: Drift,
growth, and survival of larval Northeast Arctic cod with simple rules of
behaviour, Mar. Ecol. Prog. Ser., 347, 207–219, https://doi.org/10.3354/meps06979, 2007.
Von Storch, H. and Zwiers, F. W.: Statistical Analysis in Climate Research,
Cambridge University Press, Cambridge, UK, 1999.
Watson, J. R., Stock, C. A., and Sarmiento, J. L.: Exploring the role of
movement in determining the global distribution of marine biomass using a
coupled hydrodynamic – Size-based ecosystem model, Prog. Oceanogr., 138,
521–532, https://doi.org/10.1016/j.pocean.2014.09.001, 2014.
Short summary
Here we propose a novel modelling approach that includes an extended food web in a functional-group-type marine ecosystem model (ECOSMO E2E) by formulating new groups for macrobenthos and fish. This enables the estimation of the dynamics of the higher-trophic-level production potential and constitutes a more consistent closure term for the lower-trophic-level ecosystem. Thus, the model allows for the study of the control mechanisms for marine ecosystems at a high spatial and temporal resolution.
Here we propose a novel modelling approach that includes an extended food web in a...
