Go to main content

Ecosystem models

ecosystem illustration

Ecosystem models are used to describe larger or smaller parts of the ecosystems and interactions between these and the surrounding environment. The models may include one or more types of nutrient salts, plankton, fish and marine mammals, all of which may be affected by external forces such as climate change, fishing or other human activity.


Process-based ecosystem models (frequently referred to as dynamic models) synthesize existing observational and experimental knowledge into a numerical framework. They quantify processes that are difficult or impossible to measure, reveal ecosystem functions and complex food web interactions (e.g. trophic cascading effects) and evaluate responses of the local or wider ecosystem components to pressures from human activities and natural drivers. For predictions about the future ocean, models are the only tool to study long-term ecosystem responses to marine stressors.

At Institute of Marine Research we do mainly use these ecosystem models:

NORWECOM.E2E is a merger of several models; an Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model for nutrient cycling and the lower trophic levels and different Individual Based Models (IBMs) developed initially for fish  and zooplankton. NORWECOM.E2E is  one of very few bottom-up models world-wide where IBMs for different trophic levels are two-way coupled and used to simulate food web dynamics of a large regional sea, and the only model system to run for the Norwegian Sea. At present, IBMs for adult pelagic fish (mackerel, herring, blue whiting), Calanus finmarchicus, Calanus hyperboreus, Calanus glacialis and fishing vessels are included. The model system also has modules for ocean acidification and contaminants. Through NORWECOM.E2E, all these models are now being integrated into a fully coupled model system using physical fields (salinity, temperature, velocities, etc.) from the an ocean model. The model system is used to study natural variations and effects of, for example, climate change, ocean acidification or fishing in our ecosystems, but also as a tool for assessing monitoring strategies and observation patterns.

The IBMs for zooplankton and fish are three-dimensional in time and space, taking into account the entire life cycle and the most important life history features such as growth, mortality, movement and reproduction, as well as adaptive properties that control the interaction with the environment. The models are connected so that each IBM receives input about the other species. For example, zooplankton will receive information on the amount of phytoplankton and zooplankton densities from the NPZD model. The zooplankton then feed on the plankton and the local plankton quantity is continuously updated in the model. All zooplankton biomass that is removed (eg by grazing pelagic fish) becomes part of the mortality in the Calanus population so that mass balance is achieved. The three species of pelagic fish included in the model are herring, blue whiting and mackerel, which are dominant in terms of biomass in the region. The pelagic fish feeds on Calanus, and the spatial distribution of C. finmarchicus will therefore be affected by the fish in the model. The two-way connection provides unique opportunities to study the combined effects of pressures.

Cited literature

Aksnes, D.L., Ulvestad, K.B., Baliño, B.M., Berntsen, J., Egge, J.K., Svendsen, E. (1995) Ecological modeling in coastal waters - Towards predictive physical-chemical-biological simulation-models. Ophelia, 41, 5-36.

Green, N.W., et al (2011). Tilførselsprogrammet 2010. Overvåking av tilførsler og miljøtilstand I Nordsjøen. Technical report TA 2810/2011 (p. 101pp+106app). Oslo, Norway: KLIF.

Grimm, V., Railsback, S. (2005). Individual-based Modeling and Ecology: Princeton University Press.

Hjøllo, S.S., Huse, G., Skogen, M.D., Melle, W. (2012) Modelling secondary production in the Norwegian Sea with a fully coupled physical/primary production/individual-based Calanus finmarchicus model system. Marine Biology Research, 8, 508-526.

Huse, G. (2005) Artificial evolution of Calanus' life history strategies under different predation levels. GLOBEC Newsletter, 11, 19 Huse, G., Ellingsen, I. (2008) Capelin migrations and climate change - a modeling analysis. Climatic Change, 87, 177-197.

Huse, G., Giske, J. (1998) Ecology in Mare Pentium: an individual based spatio-temporal model for fish with adapted behaviour. Fisheries Research (Amsterdam), 37, 163-178.

Huse, G., et al. 2012. Effects of interactions between fish populations on ecosystem dynamics in the Norwegian Sea - results of the INFERNO project Preface. Marine Biology Research, 8: 415-419.

Huse, G., Johansen, G.O., Bogstad, L., Gjøsæter, H. (2004) Studying spatial and trophic interactions between capelin and cod using individual-based modeling. ICES Journal of Marine Science, 61, 1201-1213.

Samuelsen, A., Huse, G., Hansen, C. (2009) Shelf recruitment of Calanus finmarchicus off the west coast of Norway: role of physical processes and timing of diapause termination. Marine Ecology Progress Series, 386, 163-180.

Shchepetkin, A.F., McWilliams, J.C. (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9, 347-404.

Skaret, G., Dalpadado, P., Hjøllo, S.S., Skogen, M.D.,& Strand, E.(2014). Calanus finmarchicus abundance, production and population dynamics in the Barents Sea in a future climate. Prog. Oceanogr.doi:dx.doi.org/10.1016/j.pocean.2014.04.008

Skogen, M.D., Budgell, W.P., Rey, F. (2007) Interannual variability in Nordic seas primary production. ICES Journal of Marine Science, 64, 889-898.

Skogen, M.D., Olsen, A., Børsheim, K.Y., B., S.A., Skjelvan, I. (2014) Modelling ocean acidification in the Nordic and Barents Seas in present and future climate. Journal of Marine Systems.

Skogen, M.D., Svendsen, E., Berntsen, J., Aksnes, D., Ulvestad, K.B. (1995) Modeling the primary production in the north-sea using a coupled 3-dimensional physical-chemical-biological ocean model. Estuarine Coastal and Shelf Science, 41, 545-565.

Strand, E., Huse, G., Giske, J. (2002) Artificial evolution of life history and behavior. American Naturalist, 159, 624-644.

Utne K.R., Hjøllo S.S., Huse G., Skogen M. 2012. Estimating the consumption of Calanus finmarchicus by planktivorous fish in the Norwegian Sea using a fully coupled 3D model system. Marine Biology Research, 8:5-6, 527-547

Atlantis (Fulton et al. 2010; Audzijonyte, 2017) is a so-called end-to-end model, which means that it includes more or less ‘everything’, from physics to fisheries. It has been implemented in numerous regions around the world , with varying number of ecosystem components and size of model domain.

The Nordic and Barents Seas (NoBa) Atlantis model is among the largest Atlantis models in the world, covering 4 million km2 by 60 polygons and seven depth levels (Figure 1). The polygons are defined to be as homogeneous as possible, with respect to hydrography, topography and ecology. The spatial grid of the model should, to the largest degree possible, have natural boundaries. NoBa are among few Atlantis models that experience extreme variantions in daylight, and which also include sea-ice.

Atlantismodell figur1
Figure 1: Polygons in NoBa marked in dark grey, colors indicate bottom depth.


Currently, NoBa includes 57 species and functional groups (Hansen et al., 2016) including four ice-related components (Figure 2).  All ecosystem models are simplifications of the real world, and although some species in NoBa are represented as a separate component (e.g. Northeast Atlantic cod, mackerel, Norwegian Spring Spawning herring), some had to be represented in functional groups, both due to lack of data, but also to reduce the complexity of the NoBa Atlantis model. Gathering species in functional groups has been performed with the aim that the species included in a group should eat similar prey, have similar longevities and be in the same size class. It is not a good idea to group together prey and predators, or species that live for 2 years with one that lives for 40 years.

Diettmatrisen i NoBa, med utvalgte grupper representert med skygger. Illustrasjon.
Figure 2: Diet matrix in NoBa, with representative groups in shadows. Illustration by Ina Nilsen.

In addition to the biology, NoBa includes fisheries. There are multiple possibilities of adding fisheries in an Atlantis model, but so far the fisheries in NoBa has been represented by using fisheries mortalities calculated from fisheries assessment working groups (ICES Arctic fisheries working group, ICES working group for widely distributed species). Some species have harvest control rules implemented (e.g. Kaplan et al., 2020), while others are harvested at constant rates. Atlantis has recently been coupled with R, with the aim of online management strategy evaluations (Perryman et al., in Prep) applying multiple different tools (including the built-in MSE routines incorporated in the code.

Atlantis provides great opportunities to run “what-if” scenarios, exploring the cumulative impact of climate, fisheries and other stressors. NoBa has so far been exploring impacts of climate change and fisheries (Hansen et al., 2019a), balanced harvest in the Nordic and Barents Seas (Nilsen et al., 2020), ecosystem based harvest control rules (Kaplan et al., 2020), ecosystem responses to mass mortality events (Olsen et al., 2019) and sensitivity analysis (Hansen et al., 2019b). There are multiple projects applying NoBa, including projects exploring the effect of invading species (snowcrab), ocean acidification, ecosystem responses to cumulative effects, multiple with the overarching aim of how ecosystem models can contribute to managing the ecosystem in a sustainable way.


Fulton EA, Smith ADM and Smith DC. 2007. Alternative Management Strategies for Southeast Australian Commonwealth Fisheries: Stage 2: Quantitative Management Strategy Evaluation. Australian Fisheries Management Authority Report.