What is OSMOSE?

OSMOSE (Object-oriented Simulator of Marine Ecosystems) is an individual-based model (IBM) for simulating multispecies marine ecosystems. It was developed at IRD (Institut de Recherche pour le Developpement) and is designed to explore the combined effects of fishing, climate change, and species interactions on fish communities.

Unlike traditional stock-assessment models that treat each species independently, OSMOSE explicitly models predator-prey interactions among all species simultaneously, creating emergent food-web dynamics.

Core Principle: Size-Based Opportunistic Predation

OSMOSE is built on a single unifying hypothesis: predation is an opportunistic, size-structured process. A fish can eat any other organism that:

1. Co-occurs spatially — predator and prey occupy the same grid cell
2. Fits within the size window — the prey body size falls between the predator's minimum and maximum predator/prey size ratios

There are no fixed predator-prey links. The food web emerges from the spatial overlap and relative body sizes of all organisms in the system. This makes OSMOSE fundamentally different from models with pre-defined diet matrices.

Species Categories

OSMOSE distinguishes three types of organisms:

Focal Species (HTL — High Trophic Level)

The main modelled fish and invertebrate species. Each focal species is represented as a population of schools — groups of individuals sharing the same age, size, and location. Schools undergo the full life cycle: growth, reproduction, predation, natural mortality, starvation, fishing, and movement. Typically 5-15 focal species per application.

Resource Groups (LTL — Low Trophic Level)

Plankton and lower trophic level organisms that serve as prey for focal species but are not explicitly modelled as individuals. Their biomass is provided as external forcing (typically from a biogeochemical model such as ROMS-PISCES or NEMO-PISCES) in the form of spatiotemporal NetCDF fields. Resource groups have a fixed size range and trophic level, and their accessibility to fish predators is controlled by a coefficient (0-1).

Background Species

Species that interact with focal species through predation but are not the primary focus of the study. They have simplified dynamics compared to focal species — typically constant biomass distributions read from CSV maps. Background species fill ecological niches that would otherwise be empty in a model with only a few focal species.

Model Layers & Processes

OSMOSE simulates the following processes at each time step, applied sequentially to every school in the system:

1. Spatial Distribution & Movement

Each species has a set of distribution maps (CSV grids) defining where individuals can be found. Maps vary by:

• Season — different maps for different time steps within a year
• Age/size class — juveniles may occupy different habitats than adults (e.g., nursery grounds vs. spawning grounds)
• Year — maps can change over simulation years

At each time step, schools are distributed across grid cells according to the active map for their species, age, and season. This creates the spatial overlap patterns that drive predation.

2. Predation

The central process. For each predator school in each grid cell:

• All co-occurring organisms (schools + resource groups) within the predator's size window are identified as accessible prey
• Predation success depends on the predator's maximum ingestion rate and the relative biomass of available prey
• Prey biomass is removed proportionally — prey schools lose individuals to predation mortality

The predator/prey size ratio parameters (predation.predPrey.sizeRatio.min and .max) define the feeding window. Typical values are 1.0-5.0, meaning a predator eats prey 1x to 5x smaller than itself.

3. Starvation

If a school's predation efficiency (actual food intake / maximum possible intake) falls below a critical threshold (predation.efficiency.critical), the school suffers starvation mortality. This creates a feedback loop: when prey is scarce, predators starve, reducing predation pressure, which allows prey to recover.

4. Growth

Fish grow according to the von Bertalanffy growth equation:

L(t) = L∞ × (1 - exp(-K × (t - t₀)))

Where L∞ is the asymptotic length, K is the growth coefficient, and t₀ is the theoretical age at zero length. Weight is derived from length via the allometric relationship W = a × L^b.

Growth parameters are species-specific and set from literature (e.g., FishBase).

5. Natural Mortality

In addition to predation and starvation, schools experience additional mortality (also called "diverse" mortality) at a constant rate. This represents all other causes of death not explicitly modelled (disease, senescence, etc.). Separate rates can be set for larvae and post-larval stages.

6. Reproduction

Mature females (determined by size or age at maturity) produce eggs proportional to their body weight and a fecundity parameter (eggs per gram of mature female). Egg production follows a seasonal pattern defined by a CSV file specifying reproductive activity at each time step. New schools of age-0 fish are seeded into the population each time step based on total egg production.

7. Fishing Mortality

Fishing removes biomass at a specified rate per species per time step. Parameters include:

• Annual fishing mortality rate (F, year⁻¹)
• Age/size at recruitment to the fishery
• Seasonal patterns (optional, via fishery selectivity maps)
• Catchability and discards (in the extended fisheries module)

Multiple fisheries can target different species with different gears, selectivities, and spatial patterns.

The Spatial Grid

The simulation domain is a 2D regular or curvilinear grid covering the study area. Two grid types are supported:

• OriginalGrid — regular latitude/longitude grid defined by upper-left and lower-right corners plus number of cells (nx × ny)
• NcGrid — irregular grid read from a NetCDF file with explicit latitude/longitude coordinates per cell (used for curvilinear grids from ocean models)

A land/sea mask determines which cells are ocean (habitable) and which are land. The grid resolution typically ranges from 1/12° to 1/3° depending on the application area.

Time Stepping

OSMOSE operates on a fixed time step, typically bi-weekly or monthly (12 or 24 steps per year). At each step, all processes are applied in sequence:

1. Movement (distribute schools to grid cells)
2. Predation (size-based feeding interactions)
3. Starvation (mortality from insufficient feeding)
4. Natural mortality (background death rate)
5. Fishing mortality (harvest removal)
6. Growth (von Bertalanffy length/weight update)
7. Reproduction (egg production and seeding)

The order of processes within a time step is important — predation occurs before growth, so feeding success in a time step affects the energy available for growth.

End-to-End Coupling

OSMOSE is designed to be coupled with hydrodynamic and biogeochemical models (e.g., ROMS, NEMO) to form "end-to-end" ecosystem models:

Physical ocean model (ROMS/NEMO)
        ↓
Biogeochemical model (PISCES)  →  plankton biomass fields
        ↓
OSMOSE (fish + invertebrates)  →  emergent food web
        ↓
Fisheries & economic modules   →  catch, revenue, fleet dynamics

The biogeochemical model provides spatiotemporal plankton biomass (the LTL forcing), which feeds into OSMOSE as the base of the food web. OSMOSE then simulates all fish-to-fish and fish-to-plankton interactions. Optional economic modules simulate fleet behavior and market dynamics.

Model Extensions

Since version 4.3.3, OSMOSE includes several optional modules:

Bioen-OSMOSE (Bioenergetic Module)

Replaces the simple von Bertalanffy growth with a Dynamic Energy Budget (DEB) approach. Fish growth, reproduction, and maintenance are driven by food intake and temperature, allowing physiological responses to environmental change. Includes oxygen limitation effects on metabolism (Morell et al., 2023).

Ev-OSMOSE (Eco-Evolutionary Module)

Adds heritable trait variation to populations, enabling evolutionary dynamics. Traits such as growth rate, maturation size, and thermal tolerance can evolve over generations through natural selection, allowing exploration of fisheries-induced evolution and climate adaptation (Morell et al., 2023).

Fisheries Module

Extended fishing representation with:

• Multiple fleets with different gear selectivities
• Spatiotemporal effort allocation
• Catchability and discard rates per species per fleet
• Economic sub-model for revenue and cost calculations

Accessibility Matrix

Defines predation accessibility between species pairs beyond pure size-based rules. Values between 0 (no predation possible) and 1 (full accessibility) can encode habitat separation, diel vertical migration, or taxonomic avoidance patterns.

Calibration & Sensitivity Analysis

OSMOSE configurations require careful calibration due to the emergent nature of the food web. Common approaches:

• Evolutionary algorithms (NSGA-II) for multi-objective calibration against observed biomass, catch, and diet data
• Sobol sensitivity analysis to identify which parameters most influence model outputs
• Gaussian Process surrogates to accelerate calibration by reducing the number of full simulation runs needed
• Monte Carlo uncertainty analysis with parameter perturbation to quantify output uncertainty ranges

Key calibration targets typically include: species biomass time series, catch data, diet composition, mean trophic level of the community, and size spectrum slopes.

Regional Applications

OSMOSE has been applied to marine ecosystems worldwide:

Publications

Foundational Papers

• Shin Y-J & Cury P (2001). Exploring fish community dynamics through size-dependent trophic interactions using a spatialized individual-based model. Aquatic Living Resources, 14(2), 65-80.
• Shin Y-J & Cury P (2004). 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(3), 414-431.
• Shin Y-J et al. (2004). Simulation of the Southern Benguela ecosystem using OSMOSE. Afr. J. Mar. Sci., 26, 159-177.

End-to-End Coupling & Methodology

• Travers M et al. (2006). Simulating and testing the sensitivity of ecosystem-based indicators to fishing in the southern Benguela. Can. J. Fish. Aquat. Sci., 63, 943-956.
• Travers M et al. (2009). Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela. Ecological Modelling, 220, 3089-3099.
• Travers-Trolet M et al. (2014). An end-to-end coupled model ROMS-N2P2Z2D2-OSMOSE of the southern Benguela foodweb. Prog. Oceanogr., 134, 365-380.
• Rose KA et al. (2010). End-to-end models for the analysis of marine ecosystems: challenges, issues, and next steps. Mar. Coast. Fish., 2, 115-130.

Calibration & Uncertainty

• Oliveros-Ramos R et al. (2017). A multi-model approach for the Peruvian anchovy. Prog. Oceanogr., 134, 381-398.
• Lujan E et al. (2024). Key species and indicators revealed by an uncertainty analysis of OSMOSE. Mar. Ecol. Prog. Ser., 741, 29-46.
• Lujan E et al. (2025). A protocol for implementing parameter sensitivity analyses in complex ecosystem models. Ecological Modelling, 501, 110990.

Climate Change & Fisheries Management

• Fu C et al. (2012). Exploring model structure uncertainty using an end-to-end model of the Bay of Biscay. ICES J. Mar. Sci.
• Travers-Trolet M et al. (2020). The risky decrease of fishing reference points under climate change. Front. Mar. Sci., 7, 568232.
• Moullec F et al. (2019). An end-to-end model reveals losers and winners in a warming Mediterranean sea. Front. Mar. Sci., 6, 345.
• Moullec F et al. (2022). Using species distribution models only may underestimate climate change impacts on future marine biodiversity. Ecol. Model., 464, 109826.
• Eddy TD et al. (2025). Global and regional marine ecosystem models reveal key uncertainties in climate change projections. Earth's Future, 13(3), e2024EF005537.
• Bourdaud P et al. (2025). Thirty-year impact of a landing obligation on coupled ecosystem-fishers dynamics. Can. J. Fish. Aquat. Sci., 82, 0277.

Model Extensions

• Morell A et al. (2023). Bioen-OSMOSE: a bioenergetic marine ecosystem model with physiological response to temperature and oxygen. Prog. Oceanogr., 103064.
• Morell A et al. (2023). Ev-OSMOSE: an eco-genetic marine ecosystem model. bioRxiv, 2023-02.
• Morell A et al. (2024). Realised thermal niches in marine ectotherms are shaped by ontogeny and trophic interactions. Ecology Letters, 27(11), e70017.
• Kapur MS et al. (2026). Eco-evolutionary drivers of survey bias and consequences for fisheries stock assessment. ICES J. Mar. Sci., 83(3), fsag022.

Ecosystem Indicators & Reference Points

• Travers-Trolet M et al. (2019). Emergence of negative trophic level-size relationships from a size-based model. Ecol. Model., 410, 108800.
• Briton F et al. (2019). Reference levels of ecosystem indicators at multispecies MSY. ICES J. Mar. Sci., 76(7), 2070-2081.
• Guo C et al. (2019). Ecosystem-based reference points under varying plankton productivity states. ICES J. Mar. Sci., 76(7), 2045-2059.
• Ito S et al. (2023). Detection of fishing pressure using ecological network indicators. Ecol. Indic., 147, 110011.

For full documentation and the complete publication list visit osmose-model.org