OSMOSE: A Comprehensive Guide to the Individual-Based Model for Marine Ecosystem Forecasting and Management

Anna Long Nov 27, 2025 276

This article provides a comprehensive overview of the Object-oriented Simulator of Marine ecosystem Exploitation (OSMOSE), an individual-based, multispecies model crucial for ecosystem-based fisheries management (EBFM).

OSMOSE: A Comprehensive Guide to the Individual-Based Model for Marine Ecosystem Forecasting and Management

Abstract

This article provides a comprehensive overview of the Object-oriented Simulator of Marine ecosystem Exploitation (OSMOSE), an individual-based, multispecies model crucial for ecosystem-based fisheries management (EBFM). It explores the foundational principles of OSMOSE, including its size-based, opportunistic predation and spatial explicitness. The content details methodological advances such as management strategy evaluation (MSE) and bioenergetic extensions (Bioen-OSMOSE) that incorporate physiological responses to temperature and oxygen. Furthermore, it covers protocols for parameter sensitivity analysis, model calibration, and validation against real-world data. Designed for researchers and scientists, this guide synthesizes how OSMOSE is applied to project climate change impacts, assess marine protected areas, and support sustainable fishery strategies, highlighting its role as a key tool in modern marine ecological forecasting.

Understanding OSMOSE: Core Principles and Mechanics of Individual-Based Marine Ecosystem Modeling

Individual-Based Models (IBMs), also known as agent-based models, are a class of computational models that simulate ecological systems by tracking the fate of individual organisms rather than representing populations as aggregate numbers. In marine science, IBMs have emerged as powerful tools for addressing complex ecological questions where individual variability, local interactions, and adaptive behavior significantly influence system-level outcomes [1].

Unlike traditional population-based models that treat all individuals within a stock as interchangeable, IBMs explicitly represent individual organisms with distinct characteristics, behaviors, and life histories. This approach allows population-level patterns to emerge naturally from the interactions and life cycles of individuals, providing a more mechanistic understanding of ecological dynamics [2]. In the context of marine ecosystems, this individual-level perspective is particularly valuable for modeling species with complex life histories, spatial behaviors, and trophic interactions.

The fundamental principle of IBMs is that each simulated individual has a set of state variables (e.g., size, age, location, energy reserves) and follows rules governing its behavior, development, and interactions with other individuals and the environment. Through the actions and fates of these individuals, IBMs can simulate how population, community, and ecosystem-level properties emerge from individual-level processes [1].

Key Features and Applications in Marine Ecosystems

Distinctive Characteristics of IBMs

IBMs fill a natural gap in the ecological modeling toolbox by providing more detail and flexibility for representing individual actions than traditional compartment modeling approaches. Several key factors distinguish IBMs from other modeling approaches [1]:

  • Individual Variation: IBMs incorporate heterogeneity among individuals, including details about life history, age classes, physiological state, and genetic characteristics. This variation allows for more realistic representation of population processes.
  • Adaptive Behavior: Individuals in IBMs can adapt and learn from experiences, updating their interaction rules in real-time based on environmental cues and past outcomes.
  • Environmental Modification: IBM individuals can modify their local environment through their behavior, creating feedback loops between individuals and their habitat.
  • Local Interactions: Interactions between individuals (e.g., competition, predation, mating) occur based on spatial proximity and individual states rather than being averaged across the entire population.

Applications in Marine Science

IBMs have been applied to diverse challenges in marine science, including:

  • Fisheries Management: Evaluating impacts of fishing pressure, spatial management measures, and climate change on fish populations and communities [3] [4]
  • Species Conservation: Assessing population viability, especially for small populations facing extinction risk from demographic failure, habitat loss, or inbreeding depression [1]
  • Climate Change Adaptation: Predicting evolutionary and ecological responses to rapidly changing environments, including range shifts and adaptive potential [5]
  • Ecosystem Dynamics: Understanding trophic interactions, food web dynamics, and emergent ecosystem properties [3] [6]
  • Spatial Ecology: Modeling larval dispersal, migration patterns, and spatial population structure in heterogeneous seascapes [1]

Table 1: Comparison of Modeling Approaches in Marine Science

Feature Individual-Based Models (IBMs) Population-Based Models Advection-Diffusion-Reaction Models
Fundamental Unit Individual organisms Population numbers or densities Population densities
Individual Variation Explicitly represented Averaged or grouped into classes Typically averaged
Spatial Structure Explicit, often highly resolved Often implicit or coarse Explicitly resolved
Parameterization Based on individual-level observations Based on population-level rates Mix of individual and population parameters
Computational Demand Generally high Generally low to moderate Moderate to high
Emergent Properties Arise from individual interactions Imposed through model structure Arise from transport and reaction terms

OSMOSE: An Individual-Based Modeling Platform for Marine Ecosystems

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is a multi-species, individual-based modeling platform specifically designed for marine ecosystem applications [3]. OSMOSE simulates fish life history and interspecies interactions at an individual level, providing insights into individual behaviors and explaining observed patterns at population and community levels [3].

As an end-to-end model, OSMOSE can be coupled with hydrological and biogeochemical models to describe food web dynamics from low trophic level groups to high trophic level groups, offering valuable insights on trophic interactions [3]. This coupling allows OSMOSE to represent both bottom-up and top-down processes in marine ecosystems, making it particularly valuable for ecosystem-based fisheries management.

Key Features of OSMOSE

OSMOSE incorporates several distinctive features that make it well-suited for marine ecosystem modeling:

  • Individual-Based Representation: Each fish is modeled as an individual with specific characteristics, including size, age, location, and energy reserves
  • Trophic Interactions: Predation emerges from spatial co-occurrence and size-based rules, creating dynamic food web structure
  • Fisheries Integration: Fishing mortality is applied based on encounter probabilities with fisheries
  • Spatial Explicitness: Models can represent two-dimensional spatial structure with movement based on environmental gradients and foraging behavior
  • Life Cycle Completeness: Full life cycles are simulated from larval to adult stages, including growth, reproduction, and natural mortality

Recent Applications of OSMOSE

Recent research using OSMOSE has addressed pressing challenges in marine science:

  • Offshore Wind Farm Impacts: Huang et al. (2025) used OSMOSE to assess potential cumulative impacts of offshore wind farms on various biological groups and fishing activities in the Eastern English Channel, incorporating effects from underwater noise emission, sediment resuspension, and fishing access restriction [4]
  • Climate Change Projections: Eddy et al. (2025) participated in a model intercomparison project using OSMOSE to evaluate uncertainties in climate change projections for marine ecosystems, finding that global models generally projected greater biomass declines than regional models [4]
  • Food Web Dynamics: Xing et al. (2025) conducted global sensitivity and uncertainty analyses of an OSMOSE model simulating food web dynamics in the Cooperation Sea, Southern Ocean [4]
  • Fisheries Management Policies: Bourdaud et al. (2025) used OSMOSE to evaluate the thirty-year impact of landing obligations on coupled ecosystem-fisheries dynamics in the Eastern English Channel [4]

Parameter Uncertainty and Sensitivity Analysis in OSMOSE

Challenges of Parameter Uncertainty

Like other complex ecosystem models, OSMOSE applications face challenges related to parameter uncertainty, which can complicate model-based decision-making processes [3]. The risk of over-confidence in model projections can lead to faulty management strategies that may further degrade ecosystems [3].

OSMOSE models are particularly susceptible to epistemic uncertainty (resulting from imperfect knowledge) in parameters obtained through model calibration. Common sources of uncertainty include [3]:

  • Imprecise parameterization due to limited observational data
  • Omission and simplification of ecological processes
  • Structural uncertainty in model formulation
  • Natural variability in ecological systems

Quantitative Assessment of Parameter Impacts

Xing et al. (2020) conducted a comprehensive evaluation of how imprecise parameters affect OSMOSE performance at multiple biological levels using a Monte Carlo simulation approach [3]. Their study examined uncertainty at low, medium, and high levels of error bounds for different parameter combinations.

Table 2: Impact of Parameter Uncertainty on OSMOSE Model Predictions

Parameter Impact on Fish Community Structure Impact on Population Biomass Impact on Predation Mortality
Natural Mortality (Mnatural) Moderate influence Moderate influence Moderate influence
Larval Mortality (Mlarval) Strongest influence Significant influence Significant influence
Relative Fecundity Less influence than mortality parameters Less influence than mortality parameters Less influence than mortality parameters
Predation Vulnerability Moderate influence Moderate influence Strongest influence

The study found that larval mortality (Mlarval) had the strongest influence on model outputs, significantly affecting predictions of fish community structure, population biomass, and predation mortality [3]. Uncertainty in predation parameters most strongly affected predation mortality rates, while relative fecundity generally had less influence than mortality parameters [3].

Protocol for Parameter Sensitivity Analysis

Luján et al. (2025) proposed a standardized protocol for implementing parameter sensitivity analyses in complex ecosystem models like OSMOSE [4]. This protocol includes:

  • Model Definition: Clear specification of model structure, parameters, and outputs of interest
  • Parameter Selection: Identification of parameters to include in sensitivity analysis based on expert knowledge and preliminary analyses
  • Experimental Design: Specification of parameter ranges, sampling strategies, and number of model runs
  • Model Execution: Running the model across parameter combinations
  • Sensitivity Calculation: Computing sensitivity indices using appropriate statistical methods
  • Result Interpretation: Translating sensitivity results into modeling and management recommendations

This protocol helps standardize sensitivity analysis approaches across different modeling studies, facilitating comparison and synthesis of results.

Experimental Protocols for IBM Development and Application

Protocol for IBM Development

Developing a robust IBM requires a systematic approach to ensure model credibility and utility:

  • Problem Formulation: Clearly define the research question and modeling objectives
  • Conceptual Model Design: Identify key entities, state variables, processes, and interactions
  • Model Specification: Formalize rules governing individual behavior and interactions
  • Parameter Estimation: Gather empirical data to inform model parameters
  • Model Implementation: Program the model using appropriate software platforms
  • Model Verification: Check that the model implementation matches the design
  • Model Validation: Compare model outputs with empirical data not used in parameterization
  • Sensitivity Analysis: Identify parameters and processes with greatest influence on outputs
  • Scenario Analysis: Use the model to explore management or climate scenarios
  • Communication: Document and share model structure, assumptions, and results

Protocol for OSMOSE Application

Applying OSMOSE to a specific ecosystem follows a structured workflow:

  • System Characterization: Collect data on physical environment, species composition, life history parameters, and trophic interactions
  • Model Parameterization: Estimate parameters for growth, mortality, reproduction, and movement for each species
  • Forcing Data Preparation: Develop inputs for low trophic level dynamics, typically from biogeochemical models
  • Model Calibration: Adjust parameters within plausible ranges to improve fit to observed data
  • Uncertainty Evaluation: Quantify parameter and structural uncertainties using approaches like Monte Carlo simulation
  • Scenario Implementation: Simulate alternative management or environmental scenarios
  • Output Analysis: Extract relevant indicators from model outputs for management interpretation

The following diagram illustrates the core workflow for developing and applying Individual-Based Models in marine science:

IBM Development and Application Workflow Start Start P1 Problem Formulation Start->P1 P2 Conceptual Model Design P1->P2 P3 Model Specification P2->P3 P4 Parameter Estimation P3->P4 P5 Model Implementation P4->P5 P6 Model Verification P5->P6 P7 Model Validation P6->P7 P8 Sensitivity Analysis P7->P8 P9 Scenario Analysis P8->P9 P10 Communication P9->P10 End End P10->End

Modeling Platforms and Software

Several specialized software platforms support IBM development and application in marine science:

  • OSMOSE Platform: Open-source, Java-based platform specifically designed for marine ecosystem IBMs [3] [4]
  • SLiM: Simulation framework for genetically explicit eco-evolutionary IBMs, particularly valuable for studying adaptation to climate change [5]
  • R/netLogo: General-purpose agent-based modeling environments with ecological modeling extensions
  • Custom C/Fortran Code: Purpose-built simulation code for high-performance computing applications

Successful IBM applications depend on diverse data sources:

  • Life History Data: Growth rates, mortality schedules, fecundity, and reproductive timing
  • Trophic Interaction Data: Diet composition, predation rates, and size-based feeding rules
  • Spatial Data: Distribution patterns, habitat preferences, and movement corridors
  • Environmental Data: Temperature, salinity, currents, and primary production
  • Fisheries Data: Catch statistics, fishing effort distribution, and selectivity patterns

Analytical Frameworks

Recent methodological advances support more robust IBM analysis:

  • Unified Mathematical Framework: General framework for analyzing IBMs containing interactions of unlimited complexity [7]
  • Moment Approximation: Equations that reliably approximate the effects of space and stochasticity in IBMs [7]
  • Sensitivity Analysis Protocols: Standardized approaches for evaluating parameter influences in complex models [4]
  • Uncertainty Quantification Methods: Monte Carlo and other statistical approaches for propagating uncertainty [3]

Table 3: Key Research Reagent Solutions for Marine IBMs

Tool Category Specific Tools Primary Function Application Context
Modeling Platforms OSMOSE, SLiM, R/netLogo IBM implementation and simulation Ecosystem modeling, evolutionary studies
Sensitivity Analysis Global sensitivity analysis, Morris method, Sobol' indices Identifying influential parameters Model calibration, uncertainty reduction
Uncertainty Quantification Monte Carlo simulation, Bayesian methods Quantifying and propagating uncertainty Risk assessment, management strategy evaluation
Data Assimilation Particle filters, Kalman filters Integrating models with observational data Model updating, forecasting
Visualization R/ggplot2, Python/Matplotlib, Paraview Results communication and exploration Model debugging, stakeholder engagement

Future Directions and Research Opportunities

The application of IBMs in marine science continues to evolve, with several promising research frontiers:

  • Eco-evolutionary Dynamics: Integrating genetic mechanisms with ecological processes to predict adaptive responses to climate change [5]
  • Multiple Stressor Impacts: Evaluating cumulative effects of fishing, climate change, pollution, and other anthropogenic pressures [5]
  • Model Integration: Developing more sophisticated couplings between IBMs and lower trophic level models [6]
  • Methodological Advancement: Creating more efficient algorithms for model analysis and uncertainty quantification [7]
  • Decision Support: Enhancing the utility of IBMs for management strategy evaluation and spatial planning

As IBM methodologies mature and computational resources expand, these models will play an increasingly important role in addressing the complex challenges facing marine ecosystems in the Anthropocene.

Application Notes

Within the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE), the principle of opportunistic, size-based predation is a fundamental mechanism that governs species interactions and energy transfer within the simulated marine ecosystem. This principle posits that predation events are primarily determined by the relative body sizes of potential predators and prey, rather than by species identity alone. This approach allows the OSMOSE model to realistically represent the complex trophic dynamics of fish communities without requiring excessively detailed species-specific interaction data.

In the context of the broader EV-OSMOSE framework, which integrates plastic and evolutionary dynamics, opportunistic size-based predation acts as a key selective pressure [8]. The model explicitly describes how the phenotypic values of genetically determined quantitative traits in schools (such as maximum ingestion rate, I_max) affect an individual's bioenergetics [8]. These traits, in combination with "size-based opportunistic predation," influence the emergence of critical individual variables including somatic mass (w(i,t)), length (L(i,t)), and gonadic mass (g(i,t)), which collectively determine fecundity (N_eggs(i,t)) and maturation schedules [8]. This integration provides a powerful framework for projecting how fish populations may adapt to natural and anthropogenic pressures via phenotypic plasticity and evolutionary changes.

Core Functional Role

The opportunistic, size-based predation algorithm serves multiple critical functions within the OSMOSE model:

  • Drives Trophic Network Structure: It dynamically generates a trophic network based on the co-occurrence and size-spectrum of species within the ecosystem, rather than relying on static, pre-defined food web configurations.
  • Determines Individual Fitness: Successful predation events directly contribute to an individual's energy intake, affecting growth, reproduction, and ultimately, survival probability.
  • Induces Emergent Community Patterns: Macro-scale ecosystem properties, such as biomass distribution across size classes and species diversity, emerge from the aggregate of individual size-based predation interactions.

The following tables summarize the key parameters and emergent variables associated with the implementation of opportunistic, size-based predation in OSMOSE models.

Table 1: Key Evolving Traits Influencing Bioenergetics and Size-Based Predation These genetically determined traits affect an individual's capacity for opportunistic, size-based predation and its consequences [8].

Trait Symbol Role in Opportunistic Size-Based Predation
Maximum Ingestion Rate I_max Determines the upper limit of energy intake from successful predation events.
Gonado-Somatic Index r Influences the allocation of energy (gained from predation) towards reproduction.
Intercept of Maturation Reaction Norm m_0 Affects the size or age at which maturation occurs, influenced by energy intake history.
Slope of Maturation Reaction Norm m_1 Modifies the plasticity of maturation timing in response to varying predation success.

Table 2: Emergent Individual Variables from Size-Based Opportunistic Predation These variables are not directly set but arise from the combination of evolving traits and opportunistic predation [8].

Emergent Variable Symbol Description
Somatic Mass w(i,t) The body mass of an individual at a given time, a result of cumulative growth from predation.
Length L(i,t) The body length of an individual, often related to somatic mass via an allometric equation.
Gonadic Mass g(i,t) The mass of gonad tissue, determining fecundity and driven by energy allocation.
Fecundity N_eggs(i,t) The number of eggs produced, emerging from gonadic mass and energy availability.
Maturation Age a_m(i) The age at which an individual matures, emerging from the interaction of its genotype and growth trajectory.
Somatic Mass at Maturation w_m(i) The body mass achieved at the time of maturation.

Experimental Protocols

Protocol for Simulating a Predation Event

This protocol details the step-by-step methodology for implementing a single opportunistic, size-based predation event within the OSMOSE simulation framework.

Objective: To determine the outcome of an encounter between a predator and a potential prey school based on their relative sizes.

Materials:

  • An initialized OSMOSE model environment with parameterized species.
  • Data structures representing schools of fish with attributes (size, abundance, location).
  • The predator-prey size ratio (PPSR) suitability matrix.

Procedure:

  • Encounter Check: For a given predator school, identify all co-located prey schools within the same spatial cell during a model time step.
  • Size Filtering: For each encountered prey school, calculate the ratio of predator length (L_pred) to prey length (L_prey).
  • Sitability Assessment: Query the PPSR matrix to determine the probability of predation (P_suit) for the calculated size ratio. A value of 1 indicates a high probability, while 0 indicates no predation.
  • Prey Selection: If multiple suitable prey are present, the predator selects a prey school probabilistically, often weighted by the abundance of the prey or the P_suit value. This embodies the "opportunistic" aspect.
  • Ingestion Calculation: Upon a successful predation event, calculate the biomass ingested by the predator. This is governed by the predator's maximum ingestion rate (I_max) and the available prey biomass [8].
  • State Update: Reduce the biomass and abundance of the prey school accordingly. Update the predator's internal energy reserves and somatic mass (w(i,t)).

Protocol for Calibrating the Predator-Prey Size Ratio (PPSR)

Objective: To empirically derive the PPSR matrix, which defines the feasible size ratios for predation in the model.

Materials:

  • Stomach content data from trawl surveys for the key species in the study region.
  • Associated length-frequency data for predators and found prey.
  • Statistical software (e.g., R, Python) for data analysis.

Procedure:

  • Data Compilation: Aggregate all stomach content records, noting the predator species and length, and the prey species and length (or size) for each recorded predation event.
  • Ratio Calculation: For each valid predator-prey pair from the stomach data, compute the log10 ratio of predator length to prey length.
  • Distribution Fitting: Pool all calculated ratios and fit a probability distribution (e.g., a log-normal distribution) to the data.
  • Matrix Generation: Discretize the size ratio range and assign a suitability value (P_suit) for each bin. The central values of the fitted distribution typically receive a value of 1, with values tapering to 0 at the tails of the distribution.
  • Model Integration: Incorporate the resulting PPSR matrix into the OSMOSE model configuration file to define predation rules.

Model Logic and Workflow Visualization

The following diagram illustrates the logical workflow of the opportunistic, size-based predation process within an OSMOSE simulation.

Start Start of Time Step Encounter Identify Co-located Prey Schools Start->Encounter SizeRatio Calculate Predator:Prey Size Ratio Encounter->SizeRatio Suitability Query PPSR Matrix for Suitability (P_suit) SizeRatio->Suitability Decision P_suit > 0? Suitability->Decision SelectPrey Opportunistic Prey Selection Decision->SelectPrey Yes End End of Predation Cycle Decision->End No CalculateIngestion Calculate Ingested Biomass SelectPrey->CalculateIngestion UpdateModel Update Prey Biomass and Predator State CalculateIngestion->UpdateModel UpdateModel->End

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Resources for OSMOSE Modeling and Analysis This table details the essential "research reagents" and tools required for constructing, calibrating, and analyzing an OSMOSE model that implements opportunistic, size-based predation.

Item Function in Research
Stomach Content Database Provides empirical data on who-eats-whom and at what sizes, which is crucial for calibrating the predator-prey size ratio (PPSR) matrix.
Species Traits Database Contains biological parameters for each modeled species (e.g., growth, reproduction, mortality) necessary for initializing schools and simulating population dynamics.
Size Spectrum Data Field observations of biomass distributed across body size classes used for model validation, ensuring the simulated ecosystem's output matches reality.
Predator-Prey Size Ratio (PPSR) Matrix A core model parameter that defines the probability of a predation event based on the relative size of the predator and prey, central to the size-based rule.
Genotype-Phenotype Map (For EV-OSMOSE) Defines the Mendelian inheritance of quantitative traits that influence bioenergetics and maturation, linking evolution to predation success [8].
Bioenergetics Submodel A mathematical component that translates successful predation (energy intake) into individual growth, reproduction, and maintenance, driving life history outcomes [8].

Application Note: Core Life Cycle Processes in OSMOSE

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based model (IBM) that simulates fish community dynamics by representing key life cycle processes of fish individuals grouped in schools [9]. This approach allows for exploring the ecosystem effects of fishing and climate change, making it a powerful tool for cumulative impact assessments [4] [9]. The model focuses on representing fundamental biological processes within a spatially explicit context.

In OSMOSE, fish schools are characterized by their size, weight, age, taxonomy, and geographical location in a 2D environment [9]. The model simulates the entire life cycle through several interconnected processes: growth, explicit predation, natural and starvation mortalities, reproduction, and migration [9]. A key assumption is size-based opportunistic predation, where trophic interactions occur based on spatial co-occurrence and size adequacy between predators and their prey [9].

Recent applications demonstrate OSMOSE's capability to assess long-term impacts on marine ecosystems. For instance, Bourdaud et al. (2025) utilized the model to evaluate the thirty-year impact of landing obligations on the coupled dynamics of the ecosystem and fishers in the Eastern English Channel [4]. The model's ability to represent complete life cycles enables researchers to project how policy interventions affect fish populations across multiple generations.

Quantitative Life Cycle Parameters in OSMOSE

Table 1: Core life cycle parameters and their data sources in OSMOSE modeling

Parameter Category Specific Parameters Data Sources Implementation in OSMOSE
Growth & Reproduction Growth parameters, reproduction timing & efficiency FishBase, basic biological studies [9] Determines individual size/weight trajectories and spawning events
Mortality Sources Background natural mortality, starvation thresholds, fishing mortality rates Field observations, fishery-dependent data [4] Calculates survival probabilities from multiple stressors
Trophic Interactions Predator-prey size adequacy ratios, spatial co-occurrence probabilities Literature reviews, stomach content data Governs predation mortality based on size and spatial overlap
Population Dynamics Species-specific life history traits, initial biomass/abundance Regional stock assessments, scientific surveys Initializes and constrains model populations during calibration

Protocol: Implementing Life Cycle Simulations in OSMOSE

Model Initialization and Parameterization

This protocol outlines the methodology for configuring life cycle representations in OSMOSE, based on established practices in recent applications [4] [9].

Essential Materials and Data Sources:

  • Biological Parameters: Obtain species-specific growth and reproduction parameters from FishBase and primary literature [9]
  • Spatial Distributions: Compile spatial distribution maps for each species by age/size/stage and season
  • Fishery Data: Collect fishing mortality rates by species, age/size, space, and season
  • Environmental Data: Secure outputs from hydrodynamic and biogeochemical models for climate forcing [9]

Initialization Steps:

  • Define Species Ensemble: Select target species representing key functional groups in the ecosystem
  • Parameterize Life History Traits: For each species, compile growth parameters (e.g., von Bertalanffy), reproduction schedules, and natural mortality rates
  • Establish Spatial Domains: Define the 2D modeling grid and assign seasonal distribution maps for each species-life stage combination
  • Set Initial Populations: Initialize schools with distributions across size, age, and space based on survey data
  • Configure Trophic Interactions: Define predator-prey size adequacy matrices based on allometric principles

Simulation Workflow and Execution

The following diagram illustrates the annual cycle of a fish school within the OSMOSE simulation framework:

OSMOSE_annual_cycle Start Start School State: Size, Weight, Age, Location School State: Size, Weight, Age, Location Start->School State: Size, Weight, Age, Location End End Seasonal Migration Seasonal Migration School State: Size, Weight, Age, Location->Seasonal Migration Growth Process Growth Process Seasonal Migration->Growth Process Trophic Interactions: Predation Trophic Interactions: Predation Growth Process->Trophic Interactions: Predation Mortality Evaluation: Natural, Fishing, Starvation Mortality Evaluation: Natural, Fishing, Starvation Trophic Interactions: Predation->Mortality Evaluation: Natural, Fishing, Starvation Reproduction (Seasonal) Reproduction (Seasonal) Mortality Evaluation: Natural, Fishing, Starvation->Reproduction (Seasonal) School Removed from Simulation School Removed from Simulation Mortality Evaluation: Natural, Fishing, Starvation->School Removed from Simulation Mortality occurs New Schools Created New Schools Created Reproduction (Seasonal)->New Schools Created Annual Cycle Complete? Annual Cycle Complete? New Schools Created->Annual Cycle Complete? Annual Cycle Complete?->End Yes Annual Cycle Complete?->School State: Size, Weight, Age, Location No School Removed from Simulation->End

Execution Protocol:

  • Time Step Configuration: Set appropriate temporal resolution (typically monthly or seasonal time steps)
  • Process Sequencing: Implement life cycle processes in the following order each time step:
    • Migration based on seasonal movement rules
    • Individual growth based on bioenergetics
    • Trophic interactions through size-based opportunistic predation
    • Mortality from multiple sources (fishing, natural, starvation)
    • Seasonal reproduction events when applicable
  • Data Recording: Configure output to track key variables: biomass by species, size spectra, mortality sources, reproductive output

Model Calibration and Validation

Calibration Methodology:

  • Utilize evolutionary algorithms to fit model parameters to observed biomass and catch data [9]
  • Implement multi-year calibration periods (e.g., 2002-2021 as in Huang et al., 2025) to capture interannual variability [4]
  • Apply sensitivity analysis to identify influential parameters following protocols like Luján et al. (2025) [4]

Validation Metrics:

  • Compare simulated versus observed size spectra
  • Evaluate species biomass trajectories against survey data
  • Assess simulated catch data against fishery statistics
  • Validate trophic level distributions with empirical data

Advanced Implementation: Incorporating Environmental and Anthropogenic Factors

Climate and Fishing Scenarios

Modern OSMOSE applications incorporate multiple stressors through scenario analysis:

Table 2: Scenario framework for assessing cumulative impacts on fish life cycles

Scenario Type Climate Components Fishing Pressure Components Anthropogenic Interventions
Baseline/Historical Pre-industrial or recent historical conditions Current fishing mortality rates No additional interventions
Climate Change Projections Prey field forcing from biogeochemical models [4] Fixed fishing effort None
Management Scenarios Fixed contemporary conditions Landing obligations [4], spatial restrictions Fishing access restrictions [4]
Integrated Scenarios Combined climate projections Evolving management strategies Multiple OWF deployments [4]

Implementation Protocol for Scenario Analysis:

  • Force Prey Fields: Incorporate projected changes in plankton communities from biogeochemical models
  • Define Fishing Regulation Scenarios: Implement spatial closures, effort reductions, or landing obligations
  • Introduce Anthropogenic Stressors: Model impacts of offshore wind farms through noise, sediment resuspension, and access restrictions
  • Run Factorial Simulations: Combine multiple drivers in factorial designs to isolate individual and interactive effects
  • Analyze Output: Compare total biomass, species-specific trends, and community indicators across scenarios

Sensitivity and Uncertainty Analysis

The following diagram illustrates the workflow for parameter sensitivity analysis in complex ecosystem models like OSMOSE:

sensitivity_workflow Start Start Identify Target Outputs (e.g., Spawner Biomass) Identify Target Outputs (e.g., Spawner Biomass) Start->Identify Target Outputs (e.g., Spawner Biomass) End End Select Focal Parameters (Growth, Mortality, Reproduction) Select Focal Parameters (Growth, Mortality, Reproduction) Identify Target Outputs (e.g., Spawner Biomass)->Select Focal Parameters (Growth, Mortality, Reproduction) Define Parameter Ranges (Literature, Expert Judgment) Define Parameter Ranges (Literature, Expert Judgment) Select Focal Parameters (Growth, Mortality, Reproduction)->Define Parameter Ranges (Literature, Expert Judgment) Generate Parameter Sets (Experimental Design) Generate Parameter Sets (Experimental Design) Define Parameter Ranges (Literature, Expert Judgment)->Generate Parameter Sets (Experimental Design) Run Ensemble Simulations Run Ensemble Simulations Generate Parameter Sets (Experimental Design)->Run Ensemble Simulations Calculate Sensitivity Indices (e.g., Sobol') Calculate Sensitivity Indices (e.g., Sobol') Run Ensemble Simulations->Calculate Sensitivity Indices (e.g., Sobol') Identify Most Influential Parameters Identify Most Influential Parameters Calculate Sensitivity Indices (e.g., Sobol')->Identify Most Influential Parameters Focus Calibration on Sensitive Parameters Focus Calibration on Sensitive Parameters Identify Most Influential Parameters->Focus Calibration on Sensitive Parameters Quantify Uncertainty in Projections Quantify Uncertainty in Projections Focus Calibration on Sensitive Parameters->Quantify Uncertainty in Projections Quantify Uncertainty in Projections->End

Sensitivity Analysis Protocol:

  • Parameter Selection: Identify key life cycle parameters for testing (growth rates, mortality coefficients, reproductive timing)
  • Range Definition: Establish biologically plausible ranges for each parameter based on literature review
  • Experimental Design: Generate parameter sets using Latin Hypercube Sampling or similar approaches
  • Model Execution: Run OSMOSE simulations for each parameter combination
  • Sensitivity Quantification: Calculate sensitivity indices (e.g., Sobol' indices) to rank parameter influence
  • Uncertainty Propagation: Use parameter ensembles to quantify uncertainty in model projections

Essential Research Reagent Solutions

Table 3: Key computational and data resources for OSMOSE life cycle modeling

Resource Category Specific Tool/Data Source Application in Life Cycle Modeling
Biological Databases FishBase Source for growth, reproduction, and diet parameters [9]
Model Coupling Tools NetCDF data formats Interface with hydrodynamic/biogeochemical models for prey fields [4]
Sensitivity Analysis Frameworks Protocol by Luján et al. (2025) Systematic approach for parameter sensitivity analysis [4]
Calibration Algorithms Evolutionary algorithms Automated parameter estimation against observed data [9]
Spatial Data Platforms GIS systems Processing spatial distribution maps for species and life stages [9]
Climate Forcing Data CMIP5/CMIP6 projections Climate scenario implementation for long-term projections [4]

Spatial Explicitness and Forcing with Hydrodynamic/Biogeochemical Models

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based, spatially and temporally explicit multispecies model designed for regional marine ecosystems [10]. Spatial explicitness is a foundational principle of the OSMOSE modeling approach, representing fish individuals grouped in schools characterized by their size, weight, age, taxonomy, and geographical location within a two-dimensional spatial grid [9] [11]. This spatial framework enables the model to simulate key processes such as opportunistic predation based on spatial co-occurrence and size adequacy between predators and prey, migration, and the effects of environmental gradients on fish community dynamics [9].

Spatial explicitness in OSMOSE is not merely a structural feature but a functional necessity that drives the emergent properties of the modeled ecosystem. The model assumes size-based opportunistic predation, where trophic interactions occur based on the spatial overlap and size compatibility between predator and prey species [9] [12]. This spatial dimension allows OSMOSE to explore fish community dynamics and ecosystem effects of fishing and climate change by representing how marine organisms distributed across heterogeneous seascapes respond to changing environmental conditions and anthropogenic pressures [9].

Table 1: Key Spatial Components of the OSMOSE Model

Component Description Role in Spatial Explicitness
2D Spatial Grid Geographic representation of the study area Provides the geographical context for individual movement and interaction
School-Based Representation Fish individuals grouped in schools Reduces computational complexity while maintaining behavioral realism
Size Adequacy Predator-prey interactions based on size compatibility Determines trophic relationships within the spatial context
Spatial Co-occurrence Overlap in distribution of predators and prey Drives predation opportunities and mortality patterns
Migration Movement across spatial cells Connects geographically distinct habitats and resources

Coupling OSMOSE with Hydrodynamic and Biogeochemical Models

Principles of Model Coupling

OSMOSE is designed to be coupled with hydrodynamic and biogeochemical models to create end-to-end modeling frameworks that explicitly represent the combined effects of climate and fishing on fish dynamics [9]. This coupling represents a significant advancement in marine ecosystem modeling, allowing researchers to project impacts of climate-induced changes in temperature and oxygen on biodiversity through physiological changes and spatial distribution shifts [10] [13]. The coupling follows a one-way modeling approach where physical and biogeochemical models inform the biological processes within OSMOSE without feedback mechanisms [3].

The model coupling enables OSMOSE to be forced by spatial distribution maps for each species stratified by age/size/stage and season, along with environmental parameters derived from the coupled physical-biogeochemical models [9]. For example, in the Bioen-OSMOSE implementation for the North Sea, the model was coupled with the POLCOMS-ERSEM model for physical processes and lower trophic levels [10]. This integration allows the ecosystem model to respond to spatial and temporal variations in environmental conditions, creating a more realistic representation of marine ecosystem dynamics.

Implementation Framework

The implementation of coupled model systems requires careful consideration of spatial and temporal resolution matching between the different model components. The hydrodynamic models provide data on current velocities, temperature, salinity, and turbulence, while biogeochemical models contribute information on nutrient fields, phytoplankton dynamics, and oxygen concentrations [14]. These driving variables directly influence fish physiology and spatial distribution in OSMOSE applications [11].

Recent developments have enhanced this coupling through the Bioen-OSMOSE framework, which mechanistically describes the emergence of life history traits through explicit description of underlying bioenergetic fluxes and their response to food, temperature, and oxygen variation in a multispecies food web [10]. This represents a significant advancement beyond mere spatial forcing to include physiological responses to environmental gradients across the seascape.

G Hydrodynamic Model Hydrodynamic Model Physical Forcing Physical Forcing Hydrodynamic Model->Physical Forcing Biogeochemical Model Biogeochemical Model Biogeochemical Forcing Biogeochemical Forcing Biogeochemical Model->Biogeochemical Forcing Climate Scenarios Climate Scenarios Environmental Data Environmental Data Climate Scenarios->Environmental Data Fishing Pressure Fishing Pressure OSMOSE Core Model OSMOSE Core Model Fishing Pressure->OSMOSE Core Model Physical Forcing->OSMOSE Core Model Biogeochemical Forcing->OSMOSE Core Model Environmental Data->OSMOSE Core Model Individual Growth Individual Growth OSMOSE Core Model->Individual Growth Predation Mortality Predation Mortality OSMOSE Core Model->Predation Mortality Spatial Distribution Spatial Distribution OSMOSE Core Model->Spatial Distribution Reproduction Reproduction OSMOSE Core Model->Reproduction Ecosystem Indicators Ecosystem Indicators Individual Growth->Ecosystem Indicators Predation Mortality->Ecosystem Indicators Spatial Distribution->Ecosystem Indicators Reproduction->Ecosystem Indicators

Figure 1: Information flow in OSMOSE coupling with hydrodynamic and biogeochemical models, showing how external drivers influence core model processes to generate ecosystem indicators.

Protocols for Model Configuration and Forcing

Spatial Grid Configuration

The implementation of spatial explicitness in OSMOSE begins with the configuration of the two-dimensional spatial grid that represents the study area. The configuration requires specification of grid resolution, geographic boundaries, and habitat characteristics. The spatial resolution must balance computational feasibility with ecological realism, typically ranging from hundreds of meters to several kilometers depending on the ecosystem extent and research questions [9] [3].

Each grid cell in OSMOSE represents a distinct spatial unit where school-based processes occur, including growth, predation, mortality, and reproduction [9]. The spatial configuration must account for connectivity between cells to enable migration and dispersal processes. Implementation requires careful georeferencing and projection to ensure alignment with the forcing datasets from hydrodynamic and biogeochemical models, as misalignment can create artifacts in spatial processes and species distributions.

Biogeochemical Forcing Implementation

The forcing of OSMOSE with biogeochemical model outputs follows a structured protocol to ensure proper integration of lower trophic level dynamics. The biogeochemical models, typically based on NPZD (Nutrients, Phytoplankton, Zooplankton, and Detritus) frameworks, simulate the dynamics of primary producers and nutrient cycles [14]. These models provide spatio-temporal patterns of zooplankton biomass and production that serve as prey fields for the fish individuals in OSMOSE [3].

The implementation involves several key steps. First, the prey field dynamics from the biogeochemical model are mapped onto the OSMOSE spatial grid, ensuring temporal synchronization between the models. Second, appropriate conversion factors are applied to translate biogeochemical state variables (e.g., nitrogen biomass) into consumable prey biomass for fish. Third, the spatial and temporal resolution of the forcing data must be harmonized with OSMOSE's time steps and spatial grid to maintain consistency in the trophic interactions [10] [3].

Table 2: Key Biogeochemical Forcing Variables in OSMOSE

Variable Source Model Role in OSMOSE Typical Units
Zooplankton Biomass NPZD-type models Prey for small planktivorous fish mg N m⁻³ or mg C m⁻³
Phytoplankton Biomass NPZD-type models Indirect prey base through system productivity mg Chl-a m⁻³
Temperature Hydrodynamic models Controls physiological rates and bioenergetics °C
Oxygen Concentration Biogeochemical models Affects metabolic performance and distribution mL L⁻¹
Nutrient Concentrations Biogeochemical models Determines primary production potential mmol m⁻³
Bioenergetic Configuration Protocol

The Bioen-OSMOSE framework introduces advanced protocols for configuring physiological responses to temperature and oxygen variations [10] [13]. This protocol involves several key steps. First, species-specific parameters for the biphasic growth model must be estimated, distinguishing between pre- and post-metamorphosis growth trajectories. Second, the maturation reaction norm parameters are configured to allow plastic maturation age and size in response to environmental conditions. Third, bioenergetic parameters governing metabolic response to temperature and oxygen are specified using Arrhenius-type equations and oxygen limitation functions [10].

The implementation of these bioenergetic processes requires careful parameterization based on species-specific physiological data. The protocol includes methods for estimating ingestion rates, assimilation efficiencies, and maintenance costs under varying temperature and oxygen conditions [10] [13]. This enhanced physiological realism allows the model to simulate how spatial gradients in environmental conditions translate into spatial patterns of growth, reproduction, and population dynamics.

Application Protocols for Case Studies

North Sea Ecosystem Case Study

The application of Bioen-OSMOSE to the North Sea ecosystem provides a detailed protocol for implementing spatially explicit ecosystem models with hydrodynamic and biogeochemical forcing [10] [13]. The implementation involved several key phases. First, the POLCOMS-ERSEM model was configured for the North Sea region to provide physical and biogeochemical forcing data [10]. Second, the OSMOSE model was parameterized for the key fish species in the North Sea, incorporating species-specific biological traits and initial spatial distributions.

The protocol emphasized evaluation against empirical data across multiple organizational levels, including population biomass, catch statistics, maturity ogives, mean size-at-age, and diet composition [10]. This multi-faceted validation approach ensures that the spatially explicit model captures not only overall biomass patterns but also the size-structured and spatial dynamics of the fish community. The successful application demonstrated the framework's capability to reproduce observed patterns while incorporating physiological responses to environmental variability [10] [13].

Eastern English Channel Offshore Wind Farm Assessment

A more recent application protocol addressed the cumulative impact assessment of offshore wind farms in the Eastern English Channel [4]. This implementation required several specific adaptations to the standard OSMOSE framework. First, the model incorporated new species representations to better capture the ecosystem components affected by wind farm installations. Second, the fishing process representation was enhanced to account for access restrictions due to wind farm placement. Third, the prey field forcing was updated to include climate change projections to address cumulative effects [4].

The protocol included an inter-annual calibration over the period 2002-2021 to establish a robust baseline against which wind farm impacts could be evaluated [4]. The spatial explicitness of the model was crucial for representing the local impacts of wind farms while capturing ecosystem-wide consequences. The implementation simulated the ecosystem during both construction and operational phases under a factorial design combining wind farm deployment and fishing regulation scenarios [4].

G Define Study Region Define Study Region Configure Spatial Grid Configure Spatial Grid Define Study Region->Configure Spatial Grid Select Focal Species Select Focal Species Configure Spatial Grid->Select Focal Species Parameterize Species Parameterize Species Select Focal Species->Parameterize Species Obtain Forcing Data Obtain Forcing Data Parameterize Species->Obtain Forcing Data Harmonize Resolutions Harmonize Resolutions Obtain Forcing Data->Harmonize Resolutions Implement Coupling Implement Coupling Harmonize Resolutions->Implement Coupling Calibrate Model Calibrate Model Implement Coupling->Calibrate Model Validate Outputs Validate Outputs Calibrate Model->Validate Outputs Run Scenarios Run Scenarios Validate Outputs->Run Scenarios Analyze Results Analyze Results Run Scenarios->Analyze Results

Figure 2: Workflow for implementing spatially explicit OSMOSE applications with hydrodynamic and biogeochemical forcing, showing the sequential steps from initial setup to scenario analysis.

Uncertainty Quantification and Sensitivity Analysis

Protocol for Parameter Sensitivity Analysis

The complex nature of spatially explicit ecosystem models necessitates rigorous uncertainty quantification and sensitivity analysis [4] [3]. A structured protocol has been developed for implementing parameter sensitivity analyses in complex ecosystem models like OSMOSE [4]. This protocol involves several methodical steps. First, key uncertain parameters are identified through expert consultation and literature review, typically including mortality rates, larval dispersal parameters, and reproductive rates [3]. Second, probability distributions are assigned to these parameters based on empirical data or expert judgment.

The protocol employs global sensitivity analysis methods that explore the entire parameter space rather than local variations around a baseline [4]. This approach captures interactions and nonlinear responses in the model behavior. The implementation typically uses Monte Carlo sampling techniques to propagate parameter uncertainties through to model outputs, generating ensembles of simulations that represent the uncertainty in predictions [3]. The results are analyzed using statistical methods such as variance decomposition to quantify the relative importance of different parameter uncertainties.

Managing Spatial and Temporal Uncertainty

Spatially explicit models like OSMOSE face additional uncertainties related to initial spatial distributions and movement parameters [3]. The protocol addresses these spatial uncertainties through specific methods. First, alternative spatial initialization scenarios are tested to evaluate the persistence of spatial patterns. Second, movement parameters are varied to assess their influence on meta-population dynamics and spatial connectivity. Third, boundary conditions from the forcing models are perturbed to evaluate the propagation of uncertainties from the physical and biogeochemical models to the fish community dynamics.

Temporal uncertainties are addressed through inter-annual calibration and validation against multiple years of observational data [4]. This approach helps distinguish between systematic model biases and inter-annual variability. The protocol also includes methods for evaluating structural uncertainties through comparison with alternative model formulations and independent datasets not used in calibration [3].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Tools and Data Sources for OSMOSE Implementation

Tool/Data Category Specific Examples Function in OSMOSE Research
Hydrodynamic Models POLCOMS, ROMS, FVCOM Provide physical forcing data (temperature, currents, turbulence)
Biogeochemical Models ERSEM, NPZD models Simulate lower trophic level dynamics and nutrient cycles
Biological Databases FishBase, species trait databases Source for life history parameters and physiological tolerances
Calibration Data Trawl surveys, catch statistics, diet studies Parameter estimation and model validation
Sensitivity Analysis Tools Monte Carlo simulations, variance decomposition methods Quantify uncertainty and identify influential parameters
Climate Projections CMIP5, CMIP6 scenarios Force future projections under climate change scenarios

The implementation of spatially explicit OSMOSE models relies on several key computational tools and platforms. The OSMOSE package in R provides tools to build, run, and analyze simulations using the OSMOSE model [12]. This package includes demo scripts and vignettes that facilitate the learning process for new users. For high-performance computing requirements, OSMOSE leverages parallel computing architectures to handle the computational intensity of individual-based simulations across large spatial domains [10].

Recent advancements have been supported by coupling platforms that facilitate the integration of diverse model components. These platforms manage the data flow between hydrodynamic, biogeochemical, and individual-based models, handling issues of resolution mismatch, unit conversion, and temporal interpolation [10]. The development of Bioen-OSMOSE has further expanded the toolkit by incorporating bioenergetic modules that respond to temperature and oxygen variations, opening new possibilities for investigating climate change impacts on marine ecosystems [10] [13].

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based, spatially and temporally explicit model that represents marine ecosystems from primary producers to top predators [10]. A principal strength of the OSMOSE framework is its ability to simulate and output spatially and temporally resolved ecological indicators. These indicators are crucial for evaluating ecosystem status, detecting impacts of anthropogenic stressors like climate change and offshore wind farms, and supporting ecosystem-based fisheries management [4]. This application note details the core ecological indicators generated by OSMOSE applications, provides protocols for their computation, and contextualizes their interpretation within a broader marine ecosystem research program.

Core Ecological Indicators in OSMOSE

OSMOSE models simulate the dynamics of fish and macroinvertebrate species based on opportunistic size-based predation and bioenergetics principles [10]. The table below summarizes the key spatially and temporally resolved ecological indicators available as standard outputs from OSMOSE simulations.

Table 1: Key Spatially and Temporally Resolved Ecological Indicators in OSMOSE

Indicator Category Specific Indicator Description Relevance for Ecosystem Assessment
Biomass & Abundance Total Fish Biomass [4] Total biomass of fish groups in a spatial cell per time step. Ecosystem productivity, fishing impact assessment.
Species/Species Group Biomass [10] Biomass of individual species or functional groups. Population status, species-specific responses to drivers.
Trophic Structure Predation Mortality Rate [10] Rate of mortality due to predation for a given group. Measure of top-down control in the food web.
Trophic Level Size Spectra [10] Distribution of organism sizes across trophic levels. Food web structure and energy transfer efficiency.
Community Composition Size at Age [10] Average size of individuals at a given age. Individual growth performance, physiological response to environment.
Maturation Ogives [10] Proportion of mature individuals at age/size. Population reproductive potential.
Spatial Distribution Local Biomass Hotspots [4] Spatial patterns of biomass concentration. Essential fish habitat identification, spatial management.
Fishery-Related Catches (by species and fleet) [4] Quantity of fish removed by fishing activities. Fishery performance, evaluation of management regulations.

Experimental Protocol: Simulating and Calculating Ecological Indicators

This protocol outlines the steps for implementing an OSMOSE simulation to generate spatially and temporally resolved ecological indicators, using the assessment of offshore wind farm (OWF) impacts as a representative application [4].

The diagram below illustrates the comprehensive workflow for an OSMOSE-based impact assessment study.

OSMOSE_Workflow Start Define Research Objective (e.g., OWF Impact Assessment) Config Model Configuration & Parameterization Start->Config Force Forcing Data Preparation (Prey, Climate, Human Stressors) Config->Force Sim Run OSMOSE Simulation (Construction & Operational Phases) Force->Sim Output Extract Raw Outputs (Biomass, Mortality, Catch) Sim->Output Calc Calculate Ecological Indicators Output->Calc Val Model Validation & Skill Assessment Calc->Val Anal Scenario Analysis & Impact Quantification Val->Anal

Required Materials and Reagents

Table 2: Essential Research Toolkit for OSMOSE Modeling

Item / Solution Function / Description Application in Protocol
OSMOSE Model Platform The core individual-based, spatially-explicit modeling framework. Central engine for running all ecosystem simulations. Available at: https://osmose-model.org [10].
Low-Trophic Level (LTL) Forcing Data Spatially-explicit time series of prey biomass for OSMOSE organisms. Provides the bottom-up forcing; often derived from coupled biogeochemical models (e.g., ERSEM) [10].
Species Parameter Set Biological parameters for modeled species (growth, reproduction, diet). Defines the core biology and ecology of the functional groups represented [10].
Fishing Fleet Data Data on fishing effort, selectivity, and catch by fleet type. Allows simulation of anthropogenic pressure and evaluation of fishery management scenarios [4].
Environmental Driver Data Spatially-explicit data on temperature, oxygen, habitat type. Forces physiological responses (e.g., in Bioen-OSMOSE) and influences species distributions [10].
Observation Data Independent field data on biomass, size, diet, etc. Used for model calibration, validation, and skill assessment [15].
High-Performance Computing (HPC) Computing infrastructure with sufficient memory and processing power. Enables the execution of multiple multi-year, spatially-explicit model runs [15].

Step-by-Step Procedure

  • Model Configuration and Parameterization

    • Spatial-Temporal Domain: Define the model's spatial grid resolution and extent, along with the time step (e.g., monthly) and simulation duration (e.g., 20 years) [4].
    • Species Selection: Identify the key fish and macroinvertebrate functional groups to be represented, ensuring they cover the main trophic pathways of the ecosystem.
    • Parameter Estimation: Populate model parameters for each species, including:
      • Growth: Parameters for the biphasic growth model [10].
      • Reproduction: Fecundity and relationships between body size and egg production [10].
      • Predation: Size-based prey preferences and predation success parameters [10].
    • Fishing Pressure: Implement fishing mortality by defining fleet-specific effort, spatial distribution, and gear selectivity [4].

  • Forcing Data Preparation

    • Prey Field: Obtain and format outputs from a lower trophic level (LTL) model (e.g., phytoplankton, zooplankton biomass) to force the prey field for OSMOSE organisms. Update these periodically to include climate change projections [4].
    • Environmental Stressors: For applications like OWF impact assessment, define the spatial footprint of the OWF and parameterize the stressors (e.g., underwater noise, sediment resuspension, fishing exclusion) [4].
    • Climate Scenarios: To project future ecosystem states, force the model with downscaled physical and biogeochemical data from Global Circulation Models (GCMs) under different emissions scenarios (e.g., CMIP5, CMIP6) [4].

  • Model Execution and Calibration

    • Run Simulations: Execute the OSMOSE model on an HPC platform. For robust results, perform multi-year simulations to account for inter-annual variability and allow the model to reach a dynamic equilibrium [4].
    • Calibration: Use an iterative process to adjust key parameters (e.g., larval mortality, predation efficiency) so that model outputs like species biomass and size-at-age plausibly match historical observation data [15].
    • Skill Assessment: Systematically quantify the model's ability to reproduce observed patterns using a suite of skill metrics. This is a critical but often underutilized step to establish model credibility [15].

  • Indicator Calculation and Analysis

    • Data Extraction: From the model output, extract raw data on biomass, mortality, individual sizes, and catches, all resolved by species, spatial cell, and time step.
    • Indicator Computation: Calculate the ecological indicators listed in Table 1. For example:
      • Total Biomass: Sum the biomass of all individuals in a spatial cell.
      • Spatial Hotspots: Apply spatial statistics to identify areas of persistently high biomass.
      • Temporal Trends: Use time-series analysis on annual biomass data to identify significant increases or decreases.
    • Scenario Comparison: Run the model under different scenarios (e.g., with and without OWFs, different fishing pressures) and compare the resulting ecological indicators to quantify impacts. The differences among scenarios reveal trade-offs between management objectives [4].

Data Interpretation and Integration

The spatially and temporally explicit indicators generated by OSMOSE allow researchers to move beyond simple population-level assessments to a more holistic, ecosystem-based understanding.

  • Identifying Trade-offs: In the Eastern English Channel case study, OSMOSE revealed that OWF deployment led to a slight reduction in total fish biomass and catch at the ecosystem scale, but the impacts were highly specific to certain species (e.g., cuttlefish, herring) and varied significantly between different OWF locations [4]. This highlights the trade-off between energy production, fishery exploitation, and environmental protection.
  • Uncertainty Quantification: It is critical to acknowledge and quantify uncertainties in model projections. These arise from parameter estimation (parametric uncertainty), model structure (structural uncertainty), and the underlying climate projections (scenario uncertainty). Recent model intercomparison projects have shown that global and regional models can project differing levels of biomass change under the same climate scenario, underscoring the need for multi-model ensembles [4].
  • Bridging Scales: The model's ability to output indicators at both local (grid cell) and regional (entire model domain) scales is powerful. It enables managers to understand broad ecosystem trends while also identifying specific, vulnerable habitats or populations that may require localized protection measures [4].

The OSMOSE modeling framework is a powerful tool for generating spatially and temporally resolved ecological indicators that are indispensable for modern ecosystem-based management. By simulating the complex interactions between species and their environment, OSMOSE provides a mechanistic platform to project ecosystem responses to anthropogenic pressures. The rigorous application of the protocols outlined herein—including careful calibration, systematic skill assessment, and scenario analysis—ensures that these core outputs can be used with greater confidence to inform policy and guide our path towards sustainable ocean use.

From Theory to Practice: Implementing OSMOSE for Fisheries Management and Climate Projections

Management Strategy Evaluation (MSE) Frameworks in OSMOSE

Management Strategy Evaluation (MSE) is a computational process that simulates the key elements of a fisheries system to test the performance of alternative management strategies against pre-defined objectives, accounting for uncertainties inherent in the system [16]. Within the context of the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE), an individual-based, multispecies modeling approach, MSE provides a structured framework to advance Ecosystem-Based Fisheries Management (EBFM) [17]. OSMOSE serves as a powerful operating model within MSE frameworks, simulating the "true" dynamics of the ecosystem, including fish life cycles (growth, reproduction, migration, mortality), trophic interactions, and fisheries [17] [11]. The primary impetus for integrating MSE with OSMOSE is to implement critical two-way interactions between high trophic level (HTL) functional groups and human management actions, thereby enhancing OSMOSE's capability as an end-to-end modeling tool for providing strategic management advice [17]. This integration allows researchers to test the robustness of various Total Allowable Catch (TAC) strategies and other management procedures under a range of uncertainties, helping to identify strategies that balance ecological and socioeconomic objectives [17].

MSE-OSMOSE Framework Components and Quantitative Reference Points

The MSE framework for OSMOSE integrates several model types and error structures to create a realistic simulation-testing environment. A typical framework involves an operating model (the OSMOSE model itself), an observation model that adds monitoring error, and an implementation model that accounts for management enforcement shortcomings [17]. The core of the Management Procedure (MP) is a decision rule that dynamically adjusts management actions (e.g., TACs) based on data sampled from the operating model and specific performance metrics [17]. While some MSE frameworks integrate a full stock assessment model, the initial MSE framework developed for OSMOSE does not; instead, it strategically accounts for uncertainty in stock assessments and includes both observation and implementation errors [17].

Table 1: Key Components of an MSE Framework Integrated with OSMOSE

Component Description Role in MSE-OSMOSE Framework
Operating Model A complex, individual-based model simulating "true" ecosystem dynamics [11]. Serves as the simulated reality; represents major High Trophic Level (HTL) functional groups, their life cycles, and trophic interactions.
Observation Error Model Introduces bias and imprecision to simulated data collection. Generifies data (e.g., catch, abundance indices) to resemble real-world, imperfect monitoring data provided to managers.
Decision Rule A pre-agreed formula linking perceived stock status to management actions. Translates information from the assessment into a specific TAC or fishing mortality target (e.g., TAC is reduced if biomass falls below a threshold).
Implementation Error Model Simulates the failure to achieve management targets in practice. Accounts for factors like quota overages, illegal fishing, or fisher non-compliance.

Quantitative analysis is central to the MSE process. Using OSMOSE as an operating model, key fisheries reference points can be estimated through equilibrium analysis, which then serve as benchmarks for evaluating management strategies.

Table 2: Quantitative Reference Points and Performance Metrics in MSE-OSMOSE

Parameter/Metric Description Application in MSE
FMSY The fishing mortality rate that produces the Maximum Sustainable Yield (MSY) [17]. A common target reference point in harvest control rules.
MSY The maximum average catch that can be taken continuously from a stock under prevailing conditions [17]. Used as a performance metric for evaluating yield.
Spawning Stock Biomass (SSB) The total biomass of all sexually mature individuals in the stock. A common basis for defining limit reference points (e.g., Btrigger).
Spawning Potential Ratio (SPR) The ratio of the number or biomass of eggs produced by a recruit at a given fishing mortality rate to the number produced in the absence of fishing. A biological reference point used to gauge the sustainability of a fishing mortality rate.
Probability of Overfishing The likelihood that a management strategy will lead to a fishing mortality rate exceeding FMSY. A key risk-based performance indicator for evaluating strategy robustness.

Application Notes and Protocols for MSE-OSMOSE

Protocol 1: Developing an MSE Using an OSMOSE Operating Model

This protocol outlines the steps for developing and executing a Management Strategy Evaluation using an existing OSMOSE model as the operating model.

1. Define Management Objectives and Performance Metrics:

  • Convene stakeholders, managers, and scientists to agree on high-level management objectives (e.g., maximizing sustainable yield, minimizing ecosystem impact, maintaining economic viability).
  • Translate these objectives into quantitative performance metrics. Examples include:
    • Yield: Average long-term catch.
    • Conservation: Probability of spawning stock biomass falling below a limit reference point (e.g., 20% of unfished biomass).
    • Stability: Inter-annual variability in TACs.

2. Condition the OSMOSE Operating Model:

  • Use the OSMOSE model of the study region (e.g., OSMOSE-WFS for the West Florida Shelf) as the core operating model (OM) [17].
  • "Condition" the OM by fitting it to all available real-world data (e.g., catch time series, biomass surveys, life history parameters from FishBase) to ensure it plausibly represents the true system dynamics [16].
  • Develop multiple OM variants to represent key uncertainties (e.g., different hypotheses about natural mortality rates, larval dispersal, or trophic interactions) [3] [16].

3. Formulate Candidate Management Procedures (MPs):

  • Define a set of candidate MPs (harvest control rules) to be tested. For an initial MSE, this may be simple TAC adjustment rules.
  • Example MP: "If the estimated spawning stock biomass is above a trigger level (Btrigger), set TAC to a constant proportion of the estimated biomass. If below, reduce TAC by a fixed percentage."

4. Configure the Closed-Loop Simulation:

  • Implement the following closed-loop cycle, projecting it forward for many years (e.g., 20-30 years):
    • a. The OM generates the "true" population and ecosystem dynamics for one year.
    • b. An observation model samples the OM and adds realistic error (e.g., log-normal error to catch-per-unit-effort data) to create the "observed" data available to managers [17] [16].
    • c. The decision rule (MP) uses this "observed" data to calculate a management recommendation (e.g., a TAC for the next year).
    • d. An implementation model applies error to the management recommendation (e.g., the actual catch may exceed the TAC by 10%) [17].
    • e. The resulting management action (e.g., realized catch) is fed back into the OM, affecting the populations in the next time step.
  • This cycle is repeated iteratively for the entire projection period and for hundreds of stochastic simulations to capture variability.

5. Evaluate Performance and Select a Strategy:

  • After the simulations, calculate the pre-agreed performance metrics for each candidate MP across all OM variants and simulation replicates.
  • Compare the trade-offs between different MPs. A robust MP is one that performs adequately well across a wide range of uncertainties and against multiple, often competing, objectives.
  • Present the results (e.g., using trade-off plots or performance tables) to managers and stakeholders to facilitate a transparent decision-making process.

MSE_Workflow cluster_ClosedLoop Closed-Loop Simulation Cycle Start Start: Define Management Objectives & Metrics OM Condition OSMOSE Operating Model (OM) Start->OM MPs Formulate Candidate Management Procedures (MPs) OM->MPs ClosedLoop Closed-Loop Simulation MPs->ClosedLoop Evaluate Evaluate Performance & Select Robust Strategy ClosedLoop->Evaluate A a. OM generates 'true' system state ClosedLoop->A End End: Management Implementation Evaluate->End B b. Observation Model adds error to data A->B C c. Decision Rule calculates TAC/action B->C D d. Implementation Model applies management error C->D E e. Feedback action into OM for next year D->E E->A

Diagram 1: MSE-OSMOSE Workflow

Protocol 2: Evaluating Impacts of Imprecise Parameterization in OSMOSE for MSE

Uncertainty is a central challenge in ecosystem modeling. This protocol, adapted from Xing et al. (2020), describes a Monte Carlo approach to evaluate how imprecise parameters in an OSMOSE model affect its predictions, thereby informing the construction of more robust operating models for MSE [3].

1. Identify Key Model Parameters:

  • Select parameters for uncertainty analysis based on expert knowledge and sensitivity analyses. Critical parameters for OSMOSE often include:
    • M_larval: Larval mortality rate.
    • M_natural: Natural mortality rate for juveniles/adults.
    • relative_fecundity: Species-specific fecundity rates.
    • predation_access: Access to prey resources.

2. Define Uncertainty Scenarios and Error Bounds:

  • For each target parameter, define low, medium, and high levels of error bounds (e.g., ±5%, ±15%, ±30% around the calibrated value) [3].
  • Create different uncertainty scenarios that test parameters individually and in combination to identify synergistic or antagonistic effects.

3. Execute Monte Carlo Simulations:

  • For each scenario, run a large number of Monte Carlo simulations (e.g., 1000). In each run, perturb the target parameter(s) by randomly sampling from a defined statistical distribution (e.g., uniform or normal) within the specified error bounds [3].
  • Allow all other parameters to remain at their calibrated values.

4. Analyze Model Outputs Across Multiple Levels:

  • Analyze the outputs of the Monte Carlo ensemble at multiple biological levels to assess the propagation of parameter error:
    • Individual Level: Analyze distributions of body size or weight-at-age.
    • Population Level: Analyze key population metrics like total biomass, recruitment, and spawning stock biomass for each species.
    • Community Level: Analyze the structure of the entire fish community using multivariate statistical methods like Non-metric Multidimensional Scaling (nMDS) to visualize the divergence of the simulated community from the baseline under different uncertainty levels [3].
    • Process Level: Analyze emergent processes like predation mortality rates (M2) to see how trophic interactions are affected.

5. Quantify and Rank Sources of Uncertainty:

  • Use distance-based metrics (e.g., the spread of points in nMDS space) or coefficients of variation to quantify the magnitude of divergence caused by each parameter's imprecision [3].
  • Rank the parameters based on their relative impact on model outputs. This ranking informs which parameters require more precise estimation to reduce overall model uncertainty for tactical MSE applications.

Uncertainty_Assessment cluster_Analyze Analysis Levels Start Identify Key OSMOSE Parameters Define Define Uncertainty Scenarios & Error Bounds Start->Define MonteCarlo Execute Monte Carlo Simulations Define->MonteCarlo Analyze Analyze Outputs Across Biological Levels MonteCarlo->Analyze Rank Rank Sources of Uncertainty Analyze->Rank L1 Individual Level (Size, Weight) Analyze->L1 L2 Population Level (Biomass, Recruitment) L3 Community Level (nMDS, Structure) L4 Process Level (Predation Mortality)

Diagram 2: Parameter Uncertainty Assessment

The Scientist's Toolkit: Essential Components for MSE-OSMOSE Research

Successful implementation of MSE using the OSMOSE platform requires a suite of key data inputs, model components, and analytical tools.

Table 3: Research Reagent Solutions for MSE-OSMOSE Implementation

Tool Category Specific Item/Reagent Function and Application
Biological Data Life History Parameters (e.g., growth rate, maturity-at-age, fecundity) [11]. Populates the fundamental biological processes of individuals and species within the OSMOSE operating model.
Environmental Data Coupled Biogeochemical Model Outputs (e.g., NEMURO, ECOSMO) [17] [11]. Provides spatio-temporal dynamics of low trophic level (plankton) groups and abiotic environmental fields that drive fish growth and distribution.
Fisheries Data Historical Catch Time Series, Fishing Effort Data, Fleet Composition. Used to condition the operating model to historical fishing pressure and to formulate realistic implementation error models.
Observation Models Statistical Error Distributions (e.g., Log-normal, Normal) [17]. Adds realistic imprecision and bias to simulated data (e.g., survey indices, catch data) sampled from the operating model, mimicking monitoring uncertainty.
Uncertainty Quantification Monte Carlo Simulation Algorithms [3]. Systematically explores the impact of imprecise parameters and structural uncertainty on model projections and MSE outcomes.
Analysis & Visualization Multivariate Statistical Packages (e.g., R-based nMDS), Performance Metric Dashboards. Analyzes model outputs at the community level and synthesizes the performance of thousands of MSE simulations for decision-making.

Assessing Ecosystem Impacts of Fishing and Marine Protected Areas (MPAs)

Application Note: Utilizing OSMOSE for MPA and Fishing Impact Assessments

Core Principles and Relevance

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based, spatially explicit ecosystem modeling platform specifically designed for investigating the cumulative effects of anthropogenic pressures and management strategies on marine ecosystems. Its architecture is particularly suited for assessing the impacts of Marine Protected Areas (MPAs) and fishing activities because it explicitly simulates the interactions between individual fish, their environment, and human exploitation, capturing emergent ecosystem-level properties from individual behavior and life history. Recent applications demonstrate its utility in evaluating complex, multi-stressor scenarios essential for implementing Ecosystem-Based Fisheries Management (EBFM) [4].

Recent Applications and Key Findings

OSMOSE models have been deployed to address pressing management questions, revealing critical insights about the interplay between spatial management, fishing, and climate change.

  • Assessing Offshore Wind Farm (OWF) Impacts: A recent application of OSMOSE to the Eastern English Channel (EEC) ecosystem was enhanced to evaluate the cumulative impacts of OWFs. The model incorporated effects from underwater noise, sediment resuspension, and fishing access restrictions. Simulations revealed that while total fish biomass and catch at the ecosystem scale were only slightly reduced, significant local declines were projected for specific species like cuttlefish, herring, and red mullet. These impacts were primarily driven by altered predation mortality and fishing pressure, highlighting the importance of local-scale, OWF-specific assessments that OSMOSE can provide [4].
  • Evaluating Fishing Effort Displacement: Research using ecosystem models, consistent with the OSMOSE approach, underscores that the displacement of fishing effort following MPA establishment is a critical factor determining ecosystem outcomes. Merely closing areas without reducing total fishing effort can lead to effort concentration in remaining open areas, potentially negating conservation benefits and even causing localized ecological damage. Effective management requires combining spatial closures with effort reduction to achieve positive outcomes, such as increased community biomass and higher trophic levels [18] [19].
  • Informing Climate-Resilient Management: Global and regional modeling efforts, including those involving OSMOSE, highlight significant uncertainties in climate change projections for marine ecosystems. These models project an average decrease in fish biomass with warming, but the magnitude varies. This research emphasizes the urgency of integrating climate-adaptive measures into fisheries management. MPAs, when combined with conservation-focused fishing effort reduction, are projected to help rebuild over-exploited fish stocks and can partially offset the negative biomass impacts of climate change, acting as a climate adaptation tool [4] [20].

Table 1: Summary of Key Quantitative Findings from Recent Ecosystem Modeling Studies

Study Focus Key Quantitative Finding Spatial Scale Citation
Climate Change Impact Average individual stock biomass projected to decrease by 5–15% per degree Celsius of atmospheric warming. Global/Regional [20]
MPA Efficacy Protecting 30% of a stock's range, combined with conservative management, could offset biomass losses under 2.6–2.9 °C of global warming. Northeast Atlantic [20]
Fishing Effort Reduction Reducing fishing effort by 25% from MSY levels was projected to increase biomass by an average of 29.2%. Model Simulation [20]
Effort Displacement Redistributing fishing effort without overall reduction negates mortality decreases for sensitive species and habitat restoration goals. North Sea [18] [19]

Experimental Protocols

This section provides a detailed methodology for implementing an OSMOSE-based study to assess the ecosystem impacts of fishing and MPAs.

Protocol 1: Model Configuration and Parameterization

Objective: To construct a spatially explicit, calibrated, and validated OSMOSE model for a specific marine ecosystem.

  • Define the Study Region and Spatial Grid:

    • Delineate the geographical boundaries of the ecosystem (e.g., Eastern English Channel, North Sea).
    • Establish a two-dimensional grid with a defined spatial resolution (e.g., 0.1° x 0.1° cells). Each cell is characterized by its habitat type and environmental parameters [4].

  • Identify and Parameterize Functional Groups/Species:

    • Select the key fish and invertebrate species to be modeled, typically focusing on commercially important and ecologically pivotal taxa.
    • For each species, compile life-history parameters from literature, databases, and local studies. Essential parameters include:
      • Growth: Von Bertalanffy growth function parameters (L∞, K).
      • Reproduction: Fecundity, spawning season, and maturity ogives.
      • Mortality: Natural mortality rates (M).
      • Trophic Ecology: Diet composition matrices, defining predator-prey relationships.
      • Behavior: Movement parameters, larval dispersal kernels [4].

  • Incorporate Forcing Factors:

    • Prey Field: Force the model with a dynamic prey field (e.g., zooplankton) derived from biogeochemical models (e.g., NEMO-ROMS) or observational data, updated to include climate change projections [4].
    • Fishing Effort: Map the spatial and temporal distribution of fishing effort by major fleet segments. Define gear-specific selectivity curves for each modeled species [4] [19].

  • Model Calibration and Validation:

    • Calibration: Adjust key uncertain parameters (e.g., larval mortality, foraging success) to minimize the discrepancy between model outputs and observed time-series data (e.g., fisheries-independent biomass surveys, catch data) over a historical period (e.g., 2000-2020) [21].
    • Validation: Evaluate the model's predictive skill by comparing its outputs to independent data not used during the calibration phase.
Protocol 2: Designing and Running Management Scenarios

Objective: To use the calibrated OSMOSE model to simulate and compare the effects of different MPA and fishing management strategies.

  • Define the Baseline Scenario:

    • Simulate the ecosystem under current or recent (e.g., 2002-2021) fishing pressure and environmental conditions. This serves as the reference against which all management scenarios are compared [4].

  • Develop Alternative Management Scenarios: Implement a factorial design that combines different factors:

    • MPA Configurations:
      • Location: Test MPA placement in areas of high biodiversity, essential fish habitats (nurseries, spawning grounds), or overlapping with vulnerable marine ecosystems [19].
      • Size and Coverage: Scenarios with different percentages of the management area closed to fishing (e.g., 10%, 30%) [20].
      • Protection Level: Compare no-take zones versus areas with restricted fishing gears.
    • Fishing Management:
      • Effort Reduction: Scenarios with proportional reductions in overall fishing effort (e.g., 25%, 50%) [20].
      • Effort Redistribution: Simulate the realistic displacement of fishing effort from closed areas to adjacent, open waters [18] [19].
    • Environmental Scenarios: Force the model with future environmental projections (e.g., sea surface temperature, primary production) from climate models under different Representative Concentration Pathways (RCPs 4.5 and 8.5) [4] [21].

  • Execute Simulations:

    • Run each scenario for a long-term period (e.g., 30-60 years into the future) to capture long-lived species responses and ecosystem regime shifts.
    • Perform multiple stochastic replicates (e.g., 10-20) for each scenario to account for model uncertainty.

  • Output Analysis: Extract and analyze key response variables for each scenario, including:

    • Species-level: Spatially explicit biomass, catch, body size structure, spawning stock biomass.
    • Community-level: Trophic level of the community, slope of the biomass size spectrum, species diversity indices.
    • Fisheries: Total catch, catch per unit effort (CPUE), economic indicators (profitability) [18] [21] [19].

The workflow for these protocols is summarized in the diagram below.

cluster_phase1 Protocol 1: Model Configuration cluster_phase2 Protocol 2: Scenario Analysis Start Start: Define Research Objective M1 Define Study Region and Spatial Grid Start->M1 M2 Parameterize Species (Life History, Diet) M1->M2 M3 Incorporate Forcing Factors (Prey Field, Fishing) M2->M3 M4 Calibrate and Validate Model against Historical Data M3->M4 S1 Establish Baseline Scenario (Current State) M4->S1 S2 Design Management Scenarios (MPAs, Fishing, Climate) S1->S2 S3 Execute Long-Term Stochastic Simulations S2->S3 S4 Analyze Outputs (Biomass, Catch, Indicators) S3->S4 End End: Synthesize Results for EBFM Advice S4->End

The following table details the core components required for developing and applying an OSMOSE model to MPA and fishing impact assessments.

Table 2: Key "Research Reagents" for OSMOSE Modeling

Category Item / Solution Function / Description Exemplary Source/Format
Biological Data Species Life-History Parameters Defines growth, reproduction, and mortality of individual agents in the model. FishBase; scientific literature; local stock assessments.
Trophic Data Diet Composition Matrices Quantifies predator-prey interactions and energy flow through the food web. Gut content analysis databases (e.g., EcoBase); stable isotope studies.
Environmental Data Spatio-Temporal Prey Field Provides a dynamic forcing factor for lower trophic levels (e.g., zooplankton). Outputs from biogeochemical models (e.g., NEMO, ROMS); remote sensing data.
Anthropogenic Data Fishing Fleet Effort & Selectivity Characterizes the spatial, temporal, and technical impact of fisheries on populations. Vessel Monitoring Systems (VMS); logbooks; observer programs.
Climate Data Future Environmental Projections Forces the model with projected changes in temperature, pH, and primary production. CMIP5/CMIP6 Earth System Model outputs under RCP/SSP scenarios.
Computational Framework OSMOSE Platform The core individual-based modeling codebase for simulation execution. Open-source platform from osmose-model.org [4].
Calibration & Validation Data Independent Time-Series Used to tune and evaluate model performance against real-world observations. Fisheries-independent survey data (biomass, size structure); catch statistics.
Analysis Tools Ecological Indicators Metrics to summarize ecosystem state and responses (e.g., size spectrum slope, mean trophic level). Custom scripts in R or Python to calculate standardized indicators.

Data Analysis and Ecological Indicators

A critical step following simulations is the calculation of ecological indicators to quantify ecosystem status and impacts.

Table 3: Key Ecological Indicators for Assessing MPA and Fishing Impacts

Indicator Calculation / Definition Interpretation in MPA/Fishing Context
Total Biomass Sum of biomass across all or specific functional groups. Increase suggests positive ecosystem response to protection or reduced fishing.
Mean Trophic Level (MTL) of the Community Biomass-weighted average trophic level of the community. Increase suggests recovery of top predators; decrease may indicate "fishing down the food web."
Slope of the Biomass Size Spectrum Linear regression coefficient of log(biomass) vs. log(body size) bins. A less negative (flatter) slope indicates a community with more large organisms, a sign of reduced fishing pressure [18].
Catch Per Unit Effort (CPUE) Total catch divided by total fishing effort. An indicator of fishery productivity; often increases in areas adjacent to effective MPAs due to spillover.
Spatial Biomass Distribution Maps of biomass changes for key species across the model grid. Identifies local vs. ecosystem-wide effects and spillover patterns from MPAs [4].
Protected, Endangered, and Threatened (PET) Species Biomass Biomass of species identified as vulnerable. Direct measure of conservation benefit for species of concern [18].

Bioen-OSMOSE represents a significant advancement in the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) modeling framework, which simulates fish community dynamics and ecosystem effects of fishing and climate change through individual-based modeling [9]. This development integrates bioenergetic mechanisms and explicit physiological responses to environmental drivers, specifically temperature and oxygen, enabling more realistic projections of climate change impacts on marine ecosystems [22]. Traditional OSMOSE models focus on representing fish individuals grouped in schools characterized by size, weight, age, and taxonomy, simulating key life cycle processes including growth, predation, mortality, reproduction, and migration [9]. Bioen-OSMOSE extends this foundation by implementing a bioenergetic sub-model that mechanistically describes how somatic growth, sexual maturation, and reproduction emerge from energy fluxes sustained by food intake, modulated by environmental conditions [22].

This development addresses a critical gap in ecosystem modeling by enabling the representation of phenotypic plasticity - how organisms adjust their life history in response to environmental pressures - which could either mitigate or worsen the consequences of climate change on fish populations [22]. The model serves as a "virtual laboratory" for investigating the differences between fundamental thermal performance curves (determined under controlled conditions) and realised thermal niches (observed in nature where factors like food and oxygen co-vary with temperature) [23]. This capability provides crucial insights for better determining population persistence under climate warming scenarios.

Bioenergetic Framework and Theoretical Foundations

Core Bioenergetic Processes

The Bioen-OSMOSE model implements a bioenergetic sub-model based on a biphasic growth model that mechanistically describes how somatic growth, sexual maturation, and reproduction emerge from energy fluxes sustained by food intake [22]. The model represents the allocation of body mass-dependent energy fluxes between maintenance, somatic growth, and gonadic growth to capture fundamental physiological trade-offs [23]. Sexual maturation is modeled through maturation reaction norms, which depict plastic maturation responses to variations in body growth, allowing for flexible age and size at maturation in response to environmental conditions [22] [23].

A key innovation in Bioen-OSMOSE is how these bioenergetic fluxes depend on temperature and oxygen concentration, enabling plastic physiological responses to climate change. The model simulates how ingested energy is partly assimilated, with the remainder lost through excretion and egestion. A fraction of the assimilated energy is then mobilized, depending on temperature and oxygen conditions, to cover tissue maintenance and growth [23]. This energy mobilization process converts nutrients into usable ATP using oxygen, with the rate increasing with dissolved oxygen saturation until a maximum set by assimilated energy is reached.

Thermal Performance and Environmental Modulation

Bioen-OSMOSE incorporates the concept of thermal performance curves (TPCs), which describe how organisms' physiological performance varies with temperature [23]. The model specifically addresses the difference between:

  • Fundamental TPCs: The temperature range supporting physiological functioning under resting conditions with positive net energy, typically dome-shaped due to the slower increase in maximum oxygen supply with temperature compared to resting demand.
  • Realised TPCs: The actual performance observed in ecosystem settings where extrinsic factors (food availability, oxygen saturation, species interactions) co-vary with temperature.

The model demonstrates that realised TPCs often have lower amplitude, narrower temperature range, and optimum shifted to lower temperatures compared to fundamental TPCs, primarily due to food limitation and secondarily to deoxygenation [23]. This has crucial implications for predicting climate change impacts, as projections based solely on fundamental thermal niches may overestimate species' resilience.

Table 1: Key Bioenergetic Parameters in Bioen-OSMOSE

Parameter Symbol Description Response to Environment
Maximum Mass-specific Ingestion Rate I_max Scaling coefficient for maximum ingestion, increases during early life stage Sets upper limit on energy intake
Energy Mobilization Rate E_m(i,t) Converts nutrients to ATP using oxygen Increases with dissolved oxygen following dose-response function; rises with temperature following Arrhenius law
Accessible Prey Biomass P(i,t) Prey within size range that co-occurs spatiotemporally with predator Depends on spatial distributions of both predator and prey
Net Energy for Tissue Production - Energy available after maintenance costs Dome-shaped TPC modulated by oxygen impact on energy mobilization
Somatic Mass w(i,t) Individual somatic mass Determines scaling of physiological processes

Model Application and Implementation Protocols

Model Configuration and Parameterization

Implementing Bioen-OSMOSE requires careful configuration of both the underlying OSMOSE framework and the additional bioenergetic parameters. The model's biological unit is a "school" (a super-individual in individual-based modeling terminology) comprising individuals of the same species, born simultaneously, sharing state variables (age, somatic mass, gonadic mass, abundance, spatial location, taxonomic identity) at each time step [23].

Spatiotemporal Configuration:

  • Define the model domain and resolution based on the ecosystem of interest
  • Establish time step parameters (typically monthly or seasonal)
  • Specify environmental grids for temperature, oxygen, and low-trophic level prey fields

Species Parameterization:

  • Collect basic biological parameters for growth and reproduction processes
  • Define species-specific distributions across the model domain
  • Parameterize physiological tolerances to temperature and oxygen
  • Establish prey preferences and size-based predation parameters

The ingested energy rate I(i,t) follows a Holling's type 1 functional response with a plateau: it increases linearly with accessible prey biomass P(i,t) until reaching a maximum, Imax w(i,t)^β, reflecting satiety and scaling with individual somatic mass w(i,t) [23]. During early-life stages, Imax is set 1.4-1.9 times higher (species-dependent) to represent faster mass-specific growth during this period.

Environmental Forcing Data Protocol

Bioen-OSMOSE requires forcing by temperature, oxygen, and low-trophic level prey fields [23]. The protocol for preparing these datasets includes:

Temperature Data:

  • Source: Coupled hydrodynamic-biogeochemical models or observational datasets
  • Temporal resolution: Seasonal or monthly composites
  • Spatial resolution: Matched to model grid configuration
  • Format: 3D arrays (latitude, longitude, time)

Oxygen Concentration Data:

  • Source: Biogeochemical models or in situ measurements
  • Units: mmol/m³ or mg/L
  • Note: Must represent bioavailable oxygen concentrations

Low-Trophic Level Prey Fields:

  • Source: Lower trophic level models (e.g., NEMURO) or empirical relationships
  • Representation: Biomass density distributions
  • Temporal dynamics: Should reflect seasonal production cycles

The integration of these environmental drivers allows Bioen-OSMOSE to simulate how climate change affects marine ecosystems through multiple pathways: direct physiological effects on metabolic processes, and indirect effects through altered prey availability and distribution.

G Environmental Forcing Environmental Forcing Bioenergetic Processes Bioenergetic Processes Environmental Forcing->Bioenergetic Processes Temperature Temperature Ingestion Ingestion Temperature->Ingestion Energy Mobilization Energy Mobilization Temperature->Energy Mobilization Maintenance Costs Maintenance Costs Temperature->Maintenance Costs Oxygen Oxygen Oxygen->Energy Mobilization Prey Availability Prey Availability Prey Availability->Ingestion Energy Allocation Energy Allocation Ingestion->Energy Allocation Energy Mobilization->Energy Allocation Maintenance Costs->Energy Allocation Life History Outcomes Life History Outcomes Energy Allocation->Life History Outcomes Somatic Growth Somatic Growth Energy Allocation->Somatic Growth Maturation Maturation Energy Allocation->Maturation Reproduction Reproduction Energy Allocation->Reproduction Mortality Mortality Energy Allocation->Mortality

Diagram 1: Bioen-OSMOSE Framework (76 characters)

Calibration and Validation Procedures

Calibrating Bioen-OSMOSE requires a multi-step approach to ensure realistic representation of ecosystem dynamics:

Model Spin-up:

  • Run the model for sufficient time to reach dynamic equilibrium (typically 50-100 years)
  • Monitor key community metrics (size spectrum, biomass composition) for stability
  • Adjust initial parameters if systematic drifts are observed

Pattern-Oriented Validation: Compare model outputs with empirical data across multiple dimensions:

  • Population biomass and catch data for each species
  • Mean size-at-age and maturity ogives
  • Diet composition and trophic interactions
  • Size spectrum slope and intercept

Sensitivity Analysis:

  • Test model responsiveness to key parameters (mortality rates, larval dispersal)
  • Evaluate uncertainty in parameterization using Monte Carlo approaches [3]
  • Identify parameters with strongest influence on model outputs

The North Sea application of Bioen-OSMOSE demonstrated good performance across all validation indicators, successfully reproducing observed biomass, catch, maturity ogives, mean size-at-age, and diet data of the fish community [22].

Research Reagent Solutions and Essential Materials

Table 2: Essential Research Tools for Bioen-OSMOSE Implementation

Tool/Category Function Implementation Examples
Computational Platform Model execution and simulation OSMOSE modeling framework (Java-based); High-performance computing resources
Environmental Data Forcing temperature, oxygen, and prey fields Coupled hydrodynamic-biogeochemical models (e.g., FVCOM, NEMURO); Remote sensing data
Biological Parameters Species-specific physiological traits FishBase for growth/reproduction parameters; Ecophysiology experiments for thermal/oxygen responses
Validation Datasets Model calibration and performance evaluation Stratified random trawl surveys; Catch and biomass time series; Diet composition studies
Analysis Tools Output processing and visualization R or Python statistical packages; Spatial analysis software; Custom scripts for indicator calculation

Application Notes and Case Studies

North Sea Case Study: Exploring Spatial Variability

The application of Bioen-OSMOSE to the North Sea ecosystem illustrates its capabilities in representing realistic ecosystem dynamics [22]. The model successfully reproduced observations with good performances for all indicators, including population biomass, catch, maturity ogive, mean size-at-age, and diet data of the fish community [22]. This implementation enabled a novel exploration of current spatial variability in species' bioenergetic fluxes resulting from temperature, oxygen, and food availability, highlighting the predominant role of temperature in driving these patterns.

A key finding from the North Sea application relates to the differences between fundamental and realised thermal niches. The model revealed that food limitation is the primary cause of these differences, with effects decreasing throughout ontogeny and across trophic levels due to spatio-temporal variability of low-trophic level prey availability with temperature [23]. In contrast, deoxygenation had moderate impact, despite increasing during ontogeny, which was mechanistically explained by lower mass-specific ingestion at older stages [23].

Climate Impact Projection Protocol

To project climate change impacts using Bioen-OSMOSE, researchers can implement the following protocol:

Scenario Definition:

  • Select climate scenarios (e.g., RCP/SSP pathways)
  • Define time horizons for projection (e.g., mid-century, end-of-century)
  • Specify environmental variable changes (temperature increase, oxygen decline, primary production shifts)

Model Implementation:

  • Generate future environmental forcing datasets
  • Apply changes to both mean conditions and variability
  • Run ensemble simulations to capture uncertainty

Impact Assessment:

  • Compare fundamental vs. realised thermal niches under future conditions
  • Quantify changes in species biomass, distribution, and phenology
  • Evaluate trophic cascade effects through the food web
  • Assess implications for fisheries yields and ecosystem structure

This approach allows researchers to go beyond simple biogeographic projections by incorporating physiological responses, trophic interactions, and their complex interplay under changing environmental conditions.

G Climate Scenario Climate Scenario Physiological Effects Physiological Effects Climate Scenario->Physiological Effects Temperature Increase Temperature Increase Metabolic Rate Metabolic Rate Temperature Increase->Metabolic Rate Maintenance Costs Maintenance Costs Temperature Increase->Maintenance Costs Oxygen Decline Oxygen Decline Energy Mobilization Energy Mobilization Oxygen Decline->Energy Mobilization Prey Field Changes Prey Field Changes Prey Field Changes->Metabolic Rate via ingestion Ecological Responses Ecological Responses Physiological Effects->Ecological Responses Growth Changes Growth Changes Metabolic Rate->Growth Changes Maturation Shifts Maturation Shifts Energy Mobilization->Maturation Shifts Distribution Changes Distribution Changes Maintenance Costs->Distribution Changes Ecosystem Outcomes Ecosystem Outcomes Ecological Responses->Ecosystem Outcomes Biomass Biomass Growth Changes->Biomass Community Structure Community Structure Maturation Shifts->Community Structure Distribution Changes->Community Structure Trophic Mismatch Trophic Mismatch Fisheries Yield Fisheries Yield Trophic Mismatch->Fisheries Yield

Diagram 2: Climate Impact Pathways (65 characters)

Discussion and Future Research Directions

The development of Bioen-OSMOSE opens new research avenues for exploring seasonal and spatial variation in life history in response to biotic and abiotic factors at individual, population, and community levels [22]. Understanding such variability is crucial for improving knowledge of potential climate change impacts on marine ecosystems and making more reliable projections under climate change scenarios.

Future developments of Bioen-OSMOSE could focus on:

  • Enhanced resolution of physiological processes across life stages
  • Incorporation of evolutionary adaptations to environmental change
  • Improved representation of species interactions and behavioral responses
  • Coupling with human dimension models to assess socioecological outcomes

As with all ecosystem models, applications must acknowledge and address uncertainties, particularly those related to parameter estimation [3]. The Monte Carlo simulation approach has been advocated for quantifying uncertainty and exploring error propagation caused by partial knowledge of probability distributions and correlations [3]. This is especially important when moving from "strategic" to "tactical" management applications, where over-confidence in model projections could lead to faulty management strategies with potentially detrimental ecosystem consequences [3].

Bioen-OSMOSE represents a significant step forward in marine ecosystem modeling by mechanistically linking environmental drivers to individual physiology and population dynamics through bioenergetics, providing a powerful tool for projecting and managing climate change impacts on marine ecosystems.

Projecting Climate Change Impacts on Fish Biomass and Community Structure

Ocean warming driven by climate change is projected to cause significant global declines in exploitable fish biomass, with the severity of impacts directly linked to future greenhouse gas emission pathways. Under a high-emissions scenario (representing 3-4°C of global warming), models project steep declines of over 10% by mid-century, worsening to 30% or greater by the end-of-century across 48 countries and territories [24]. These impacts are not uniformly distributed, with top producer nations like Peru and China projected to experience declines of 37.3% and 30.9% respectively within their Exclusive Economic Zones under high emissions [24]. Conversely, a low-emissions scenario (1.5-2°C warming) substantially reduces these impacts, stabilizing changes between no change and decreases of 10% or less across 178 countries and territories [24].

Ecosystem models like the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) are crucial tools for projecting these complex climate change impacts on marine ecosystems. These individual-based models simulate trophic interactions across entire food webs, from plankton to top predators, providing insights into how climate-driven shifts in environmental conditions affect fish biomass and community structure [25]. Recent OSMOSE applications demonstrate the model's capability to assess cumulative impacts from multiple stressors, including climate change, fishing pressure, and emerging anthropogenic activities such as offshore wind farm development [4].

Table 1: Projected Climate Change Impacts on Fish Biomass Under Different Emission Scenarios

Projection Period High-Emission Scenario Low-Emission Scenario Key Regional Examples
By Mid-Century Declines >10% in many regions [24] Not specified Global coverage across most ocean regions [24]
By End-of-Century Declines ≥30% in 48 countries/territories [24] Stabilization (0 to ≤10% decline) in 178 countries/territories [24] Peru: -37.3%; China: -30.9% [24]

Climate change manifests in marine ecosystems through multiple interconnected pathways, including ocean warming, acidification, deoxygenation, and alterations to primary production patterns [4]. These physicochemical changes induce profound ecological responses across biological organizations, from individual species to entire ecosystem structures and functions. The Mediterranean Sea, identified as a climate change vulnerability hotspot, exemplifies these cascading impacts, where observed effects range from shifts in phytoplankton communities to redistributions of predatory fish species [25].

The OSMOSE modeling framework provides a powerful approach for investigating these complex climate change impacts. As an individual-based, spatially explicit model, OSMOSE simulates the entire life cycle of marine organisms (including fish, cephalopods, and crustaceans) from larvae to adults, capturing key processes such as growth, natural mortality, predation, migration, and reproduction [25]. The model's object-oriented architecture allows representation of multi-species interactions mediated by prey availability and environmental conditions, enabling projections of how climate-altered oceans will affect fish biomass and community composition.

When coupled with biogeochemical and physical models (creating end-to-end modeling frameworks), OSMOSE can simulate the bottom-up control of climate change on marine food webs. For example, the OSMOSE-MED application combined the model with the NEMOMED12/Eco3M-S models to represent the entire Mediterranean Sea ecosystem, demonstrating the capability to project climate change impacts across spatial scales from local fishing grounds to entire marine basins [25].

Protocol: Implementing OSMOSE for Climate Change Impact Projections

Model Configuration and Parameterization

The implementation of OSMOSE for climate change impact studies requires systematic configuration to ensure robust and reliable projections. The following protocol outlines key steps for model setup, parameterization, and calibration specific to climate impact applications:

  • Spatial Domain Definition: Define the study area with appropriate resolution based on research questions. Large-scale applications (e.g., Mediterranean Sea) can utilize high spatial resolution (400 km²) to capture local variability while maintaining basin-scale perspectives [25]. Implement spatial grids that align with climate forcing data for consistent integration.

  • Species Selection and Parameterization: Select species representing ~95% of total declared catches or biomass in the study region. For the Mediterranean application, 100 species (fish, cephalopods, and crustaceans) were included [25]. Compile species-specific parameters including:

    • Life history traits: asymptotic length, growth rate, maturity size, larval duration
    • Trophic parameters: preferred prey sizes, predation efficiency, diet composition
    • Movement parameters: diffusion coefficients, habitat preferences, migration routes

  • Climate Forcing Integration: Implement one-way coupling with biogeochemical and physical models (e.g., NEMOMED12/Eco3M-S) that provide climate-driven projections of environmental variables [25]. Force OSMOSE with low trophic level (LTL) outputs including spatio-temporal dynamics of phytoplankton and zooplankton under different climate scenarios.

  • Fishing Pressure Scenarios: Define alternative fishing management scenarios consistent with climate projections, including effort distribution, gear selectivity, and regulatory measures to evaluate climate-fisheries interactions [4].

Model Calibration and Validation

  • Multi-Phase Calibration: Implement structured calibration phases following established protocols [4]:

    • Phase 1: Calibrate larval mortality parameters using biomass and catch data
    • Phase 2: Introduce natural mortality (M~natural~) adjustments
    • Phase 3: Fine-tune additional parameters based on sensitivity analysis
    • Phase 4: Final calibration of all parameters including prey accessibility

  • Uncertainty Quantification: Conduct global sensitivity and uncertainty analyses using Monte Carlo approaches to evaluate impacts of imprecise parameters on model outputs [3]. Focus particularly on larval mortality parameters (M~larval~), which demonstrate strongest influence on model predictions [3].

  • Performance Metrics: Validate model outputs against empirical data including:

    • Biomass indices from scientific surveys
    • Commercial catch data by species and size class
    • Trophic level indicators and size spectrum metrics
    • Spatial distribution patterns from tracking studies

G OSMOSE Climate Impact Modeling Workflow cluster_inputs Input Data & Configuration cluster_calibration Model Calibration & Validation cluster_analysis Climate Impact Analysis Climate Climate Scenarios (CMIP5/CMIP6) Setup Model Configuration (Spatial domain, species) Climate->Setup Species Species Parameters (Life history, trophic) Species->Setup Fishing Fishing Scenarios (Effort, regulations) Fishing->Setup Environment Environmental Data (Temperature, primary production) Environment->Setup Calibrate Multi-phase Calibration (500+ generations) Setup->Calibrate Validate Performance Validation (Biomass, catch, trophic indicators) Calibrate->Validate Projections Biomass Projections (Short-term vs long-term) Validate->Projections Community Community Structure Analysis (Size spectra, trophic level) Projections->Community Uncertainty Uncertainty Quantification (Global sensitivity analysis) Community->Uncertainty

Climate Impact Assessment Protocol

  • Scenario Analysis: Implement factorial simulation designs combining climate scenarios (e.g., CMIP5/CMIP6 projections) with alternative management interventions [4]. For each scenario combination, execute multiple model runs (minimum 50-100 simulations) to capture stochastic variability.

  • Indicator Calculation: Compute ecosystem and fisheries indicators for each scenario:

    • Total fish biomass and species-specific biomass trajectories
    • Community metrics: mean trophic level, size spectrum slopes, biodiversity indices
    • Fishery performance: total catch, catch composition, economic value
    • Spatial indicators: biomass distribution shifts, hotspot persistence

  • Time Frame Analysis: Compare short-term (2030-2050), medium-term (2050-2080), and long-term (2080-2100) projections to identify emergent patterns and temporal dynamics [24].

Application Note: Case Studies in Climate Change Impact Modeling

Global Scale Projections: FishMIP Framework

The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP), an Ocean Decade-endorsed program, utilizes an ensemble of ecosystem models including OSMOSE to generate standardized global climate impact projections [24]. This multi-model approach provides robust estimates of future changes in exploitable fish biomass under alternative emission scenarios:

  • High-emissions scenario (SSP5-8.5, 3-4°C warming): Projects end-of-century declines of 30% or greater in 48 countries and territories, with particularly severe impacts on Small Island Developing States and top fishing nations [24].

  • Low-emissions scenario (SSP1-2.6, 1.5-2°C warming): Projects stabilization with changes between 0 and -10% for 178 countries and territories, highlighting the critical importance of climate mitigation for fisheries sustainability [24].

Table 2: OSMOSE Model Applications in Climate Change Impact Research

Study Region Key Climate Impact Findings Model Configuration Reference
Eastern English Channel Assessment of cumulative OWF and climate impacts; slight reduction in total fish biomass and catch under combined scenarios [4] Individual-based model with 60+ species; coupled with climate projections; 2002-2021 calibration [4] Huang et al. (2025) [4]
Mediterranean Sea Whole-basin assessment of climate and fishing impacts; successful representation of biomass, catches and trophic levels [25] 100 species at 400km² resolution; forced with NEMOMED12/Eco3M-S LTL outputs [25] Moullec et al. (2019) [25]
Global Oceans (FishMIP) Greater biomass declines projected by global vs. regional models; stronger impacts under CMIP6 than CMIP5 [4] Model intercomparison project; 9 global and 4 regional models including OSMOSE [4] Eddy et al. (2025) [4]
Regional Implementation: Mediterranean Sea Case Study

The OSMOSE-MED application represents a pioneering implementation for assessing climate change impacts at the basin scale. This model successfully simulated the Mediterranean marine ecosystem during the 2006-2013 period, demonstrating capability to reproduce observed biomass distributions, catch patterns, and trophic indicators [25]. Key technical achievements included:

  • Large spatial scale with high resolution (400 km² grid cells across the entire Mediterranean basin)
  • Comprehensive species representation (100 species representing ~95% of declared catches)
  • End-to-end integration with physical and biogeochemical models (NEMOMED12/Eco3M-S)
  • Explicit representation of life cycle dynamics and spatial processes for multiple species

The calibrated model provided a robust baseline for projecting future climate change impacts, particularly regarding shifts in species distributions, changes in community trophic structure, and alterations in fishery productivity under different warming scenarios [25].

Multi-Stressor Assessment: Offshore Wind Farm and Climate Interactions

Recent OSMOSE applications have advanced to address cumulative impact assessment combining climate change with other emerging anthropogenic pressures. A 2025 study investigated the combined effects of offshore wind farms (OWFs) and climate change on the Eastern English Channel ecosystem [4]. This research incorporated:

  • Technical model improvements including new species, enhanced fishing process representation, and prey field forcing updates to include climate change projections
  • Factorial simulation design combining OWF deployment scenarios with fishing regulations under climate change conditions
  • Phase-specific impact assessment distinguishing between construction and operational phases of OWF development
  • Multi-scale analysis evaluating impacts at both local (OWF-specific) and regional (ecosystem-wide) scales

Results demonstrated that at the regional ecosystem scale, total fish biomass and catch were slightly reduced under all scenarios, with the most significant declines observed for cuttlefish, herring, and red mullet [4]. These impacts were primarily driven by changes in predation mortality and fishing pressure modifications, particularly during construction phases. Importantly, the study revealed high spatial heterogeneity in responses, with no consistent patterns across OWF deployment sites, highlighting the importance of local environmental context in mediating climate change impacts [4].

Table 3: Essential Research Reagents and Computational Resources for OSMOSE Modeling

Resource Category Specific Tools/Parameters Application in OSMOSE Modeling Critical Considerations
Biological Parameters Larval mortality (M~larval~), Natural mortality (M~natural~), Fecundity rates, Growth parameters [3] Determine population dynamics and species interactions; larval mortality particularly influential [3] Parameter uncertainty strongly influences model outputs; requires sensitivity analysis [3]
Environmental Data Primary production, Sea surface temperature, plankton dynamics from biogeochemical models [25] Force bottom-up processes and habitat suitability; climate scenario implementation [25] Consistency in spatial/temporal resolution with OSMOSE grid; quality of climate projections
Fishing Data Effort distribution, Catch statistics, Selectivity parameters, Fleet characteristics [18] Represent anthropogenic pressure; evaluate management scenarios [18] Accurate spatial effort allocation critical for realistic displacement effects [18]
Computational Resources High-performance computing clusters (e.g., DATARMOR) [25] Enable complex simulations with multiple species and high spatial resolution [25] Model calibration requires 500+ generations; substantial processing time [4]

Visualization: Climate Impact Pathways on Marine Ecosystems

G Pathways of Climate Impacts on Marine Ecosystems cluster_climate Climate Drivers cluster_responses Biological Responses cluster_outcomes Ecosystem Outcomes Warming Ocean Warming Physiology Physiological Stress (Metabolism, growth) Warming->Physiology Distribution Distribution Shifts (Latitudinal, depth) Warming->Distribution Acidification Acidification Acidification->Physiology Circulation Circulation Changes Circulation->Distribution Production Primary Production Changes Trophic Trophic Mismatches (Prey-predator dynamics) Production->Trophic Biomass Biomass Decline (Global reductions) Physiology->Biomass Fisheries Fisheries Impacts (Catch potential, composition) Physiology->Fisheries Distribution->Trophic Community Community Restructuring (Size, trophic structure) Distribution->Community Distribution->Fisheries Phenology Phenological Changes (Spawning timing) Phenology->Trophic Trophic->Biomass Trophic->Community Vulnerability Enhanced Ecosystem Vulnerability Community->Vulnerability

The application of OSMOSE models for projecting climate change impacts on fish biomass and community structure provides critical insights for sustainable ocean governance under changing environmental conditions. Several key findings emerge from recent applications:

First, emission pathway determination remains the single most important factor influencing long-term projections of fish biomass. The dramatic difference between high- and low-emission scenarios - potentially mitigating 68-90% of losses for vulnerable Pacific Island States - underscores the critical importance of climate policy for future fisheries sustainability [24].

Second, regional specificity matters in climate impact projections. Global models consistently project greater biomass declines than regional models (86% vs. 50% of CMIP5 simulations projecting declines), highlighting the need for regionally-tailored assessments and adaptation strategies [4].

Third, cumulative impact assessment must consider interactive effects between climate change and other anthropogenic pressures. Fishing effort displacement, for instance, can drive ecosystem impacts that extend beyond targeted species through food web interactions, potentially exacerbating or mitigating climate effects [18].

Future research directions should prioritize enhanced model integration, combining global and regional modeling approaches to leverage their respective strengths while addressing scale-dependent discrepancies [4]. Additionally, expanding OSMOSE applications to address climate adaptation strategies - including spatial management, dynamic fishing controls, and protection of critical habitats - will provide crucial decision-support for ecosystem-based management in a warming ocean.

Application Note: OSMOSE for Offshore Wind Farm Impact Assessment

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) serves as a robust individual-based ecosystem modeling platform for assessing the cumulative impacts of Offshore Wind Farms (OWFs) on marine ecosystems and fisheries. OSMOSE enables integrative evaluations by simulating various ecosystem components, their interactions, and the effects of multiple anthropogenic stressors [4]. A 2025 application of OSMOSE to the Eastern English Channel (EEC) demonstrated its capability to assess cumulative OWF effects, encompassing impacts from underwater noise emission, sediment resuspension, and fishing access restrictions [4]. This approach is vital for balancing energy production goals with fisheries resource exploitation and environmental protection, providing a quantitative basis for strategic marine spatial planning [4] [26].

Table 1: Key Quantitative Findings from OSMOSE OWF Impact Simulations in the Eastern English Channel

Metric Impact During Construction Phase Impact During Operational Phase Key Affected Species
Total Fish Biomass Slight reduction Slight reduction Cuttlefish, Herring, Red Mullet
Total Fish Catch Slight reduction Slight reduction Cuttlefish, Herring, Red Mullet
Spatial Pattern of Biomass Changes driven by predation & fishing pressure OWF-specific, no consistent spatial patterns Varied by species and location
Primary Driver of Change Fishing pressure & predation changes Altered trophic interactions Life history traits and local habitat

Experimental Protocol: Cumulative Impact Assessment Using OSMOSE

Model Setup and Initialization

Objective: To configure the OSMOSE model for a specific marine ecosystem, integrating baseline biological and fisheries data.

  • Step 1: Define the Model Domain and Period. Establish the spatial boundaries of the ecosystem (e.g., the Eastern English Channel) and set the simulation period, including a historical baseline (e.g., 2002-2021) for calibration [4].
  • Step 2: Parameterize Biological Components. Populate the model with focal species, including their life-history traits (growth, reproduction, mortality), diet compositions, and larval dispersal parameters. The EEC application involved technical improvements such as adding new species and updating the prey field [4].
  • Step 3: Incorporate Human Activities. Define initial fishing efforts, catch per unit effort (CPUE), and economic data for key fisheries to establish a baseline ecosystem state [4].

OWF Scenario Implementation

Objective: To simulate the effects of OWF construction and operation on the modeled ecosystem.

  • Step 4: Define OWF-Induced Pressures. Parameterize the major pressures from OWF development:
    • Underwater Noise: Map noise propagation during construction and operation, affecting marine mammal distribution and fish behavior [4] [26].
    • Sediment Resuspension: Model increased turbidity and sedimentation during construction, identified as a highly impactful state change [4] [26].
    • Fishing Access Restrictions: Implement spatial closures or effort displacement in areas designated for OWF development [27] [4].
  • Step 5: Implement Factorial Simulation Plan. Run the OSMOSE model under different scenarios combining OWF deployment (varying sizes and locations) and fishing regulation scenarios (e.g., spatial closures, effort reduction) to isolate and combine effects [4].

Data Analysis and Impact Evaluation

Objective: To analyze model outputs and quantify cumulative impacts across ecological and socioeconomic metrics.

  • Step 6: Execute Model Simulations. Run multiple simulations for each scenario to account for stochasticity and generate robust output distributions.
  • Step 7: Analyze Outputs. Compare scenario outcomes against the baseline for key indicators, including:
    • Ecological: Total fish biomass, species-specific biomass, and spatial distribution of biomass [4].
    • Fisheries: Total catch and species-specific catch [4].
    • Socio-economic: Fisher income and operating costs, noting that studies often report more negative than positive impacts [26].
  • Step 8: Conduct Sensitivity and Uncertainty Analysis. Apply global sensitivity and uncertainty analyses to identify which model parameters most strongly influence outcomes, following established protocols for complex ecosystem models [4].

G Start Start: OSMOSE Cumulative Impact Assessment Setup Model Setup & Initialization Start->Setup Baseline Run Baseline Simulation (No OWF) Setup->Baseline Scenarios Implement OWF Scenarios Baseline->Scenarios Pressures Apply OWF Pressures: Noise, Resuspension, Access Restrictions Scenarios->Pressures Simulations Execute Model Simulations Pressures->Simulations Analysis Analyze Outputs & Compare to Baseline Simulations->Analysis Evaluation Impact Evaluation & Uncertainty Analysis Analysis->Evaluation End End: Generate Management Advice Evaluation->End

Figure 1: OSMOSE-based cumulative impact assessment workflow for offshore wind farms.

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 2: Key Research Reagents and Computational Tools for OSMOSE-based OWF Impact Studies

Tool/Data Category Specific Example/Function Application in OWF Impact Studies
Ecosystem Models OSMOSE (Individual-based model) Core platform for simulating trophic interactions and population dynamics under OWF pressures [4] [26].
Spatial Data Platforms VMStools, FishSET, DISPLACE Analyze high-resolution vessel data (VMS/AIS), map fishing effort, and assess spatial conflicts with OWF sites [27] [26].
Cumulative Effect Assessment Frameworks BowTie, FEISA, ODEMM Risk assessment frameworks to identify ecosystem components at highest risk from cumulative OWF impacts [26].
Climate Projection Data CMIP5, CMIP6 Earth System Models Force prey fields and environmental conditions in OSMOSE to account for combined climate and OWF effects [4].
Trait-Based Assessment Trait-based Framework (TAFOW) Links OWF-induced state changes to species-specific vulnerabilities based on biological and ecological traits [26].

Advanced Protocol: Integrating Local and Regional Models

Objective: To address scale-dependent uncertainties by nesting high-resolution OSMOSE applications within broader regional models. Rationale: Model comparisons reveal that global and regional models can project starkly different climate change impacts on fish biomass, with global models often forecasting greater declines [4]. Nesting OSMOSE within larger-scale models helps reconcile these differences and refine projections for OWF impact zones.

  • Procedure:
    • Force Boundary Conditions: Use outputs from regional ecosystem models (e.g., Ecospace) or climate simulations to drive the boundary conditions of the local-scale OSMOSE application.
    • Upscale Local Results: Aggregate OSMOSE outputs on species biomass and distribution to inform regional model projections, creating a two-way feedback loop.
    • Ensemble Modeling: Run OSMOSE under multiple forcing scenarios (e.g., different climate projections) to quantify uncertainty in OWF impact projections.

G Global Global/Regional Climate & Ecosystem Models Regional Regional Marine Ecosystem Models Global->Regional Environmental Forcing OSMOSE High-Resolution OSMOSE Application (OWF Impact Zone) Regional->OSMOSE Boundary Conditions Management Operational Management Advice Regional->Management Regional Context OSMOSE->Regional Aggregated Biomass & Distribution OSMOSE->Management Local Impact Projections

Figure 2: Multi-scale modeling approach for OWF impact assessment.

Refining OSMOSE Simulations: Sensitivity Analysis, Calibration, and Best Practices

Protocols for Global Sensitivity and Uncertainty Analysis (GSUA)

Global Sensitivity and Uncertainty Analysis (GSUA) represents a critical step in the modelling process for understanding the robustness of complex ecosystem model projections and increasing their credibility [28]. In the context of the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE), GSUA examines how the uncertainty in model outputs can be apportioned to different sources of uncertainty in the model inputs, particularly parameters [28]. Performing GSUA is essential for multiple purposes: increasing understanding of relationships between model inputs and outputs, testing robustness, identifying critical parameters that influence outputs, performing model simplification by fixing non-influential parameters, identifying model errors through unexpected relationships, and supporting decision-making [28]. For complex, interdisciplinary models like OSMOSE that integrate physical, chemical, and biological components, GSUA becomes particularly vital as increased model complexity can make predictions highly uncertain despite improved realism of ecosystem representation [28].

Theoretical Framework and Key Concepts

Uncertainty vs. Sensitivity Analysis

While often discussed together, uncertainty and sensitivity analysis represent distinct concepts in the GSUA framework. Uncertainty Analysis quantifies the overall uncertainty in model outputs, while Sensitivity Analysis examines how the uncertainty in model outputs can be apportioned to different sources of uncertainty in the model inputs [28]. For OSMOSE applications, this distinction is crucial for understanding both the magnitude of prediction uncertainty and which parameters drive this uncertainty.

GSUA Methodological Approaches

The GSUA toolbox implements both variance-based global methods and one-at-a-time (OAT) approaches [29]. The Saltelli and brute-force global sensitivity methods are implemented for comprehensive analysis, while OAT local methods are applicable only when factor interactions are minimal [29]. Variance-based methods are particularly valuable for OSMOSE models as they capture interaction effects between parameters, which are common in complex ecological systems.

Protocol Implementation for OSMOSE Models

Four-Step GSUA Protocol

Implementing GSUA for OSMOSE follows a structured four-step process that systematically addresses parameter uncertainty and sensitivity assessment [28]:

  • Quantify uncertainty in model inputs: Define probability density functions for parameters
  • Run the model multiple times: Execute simulations following an experimental design
  • Identify model outputs for analysis: Select relevant ecological indicators
  • Calculate sensitivity measures: Quantify parameter influences on outputs
Parameter Reliability Criterion

For OSMOSE applications, we propose the Parameter Reliability (PR) criterion as an original approach to address the common challenge of limited parameter information [28]. This method replaces arbitrary uncertainty ranges with a hierarchical classification system:

  • Level 1: Parameters estimated from direct, system-specific data
  • Level 2: Parameters from similar systems or indirect estimation
  • Level 3: Parameters based on expert judgment or theoretical values

The PR criterion serves a triple purpose by describing the parameter source, assigning a qualitative hierarchy value, and providing a criterion for assigning uncertainty levels [28]. This approach significantly improves upon common practices using fixed arbitrary ranges (typically 10-30% variation), which can substantially impact SA results [28].

Workflow Integration

The following workflow diagram illustrates the complete GSUA protocol implementation for OSMOSE models:

cluster_0 Parameter Classification cluster_1 Uncertainty Propagation cluster_2 Sensitivity Quantification Start Start ParamCat Categorize parameters by PR criterion Start->ParamCat UncertQuant Quantify input uncertainty ParamCat->UncertQuant ModelRuns Execute model experimental design UncertQuant->ModelRuns OutputSelect Identify model outputs ModelRuns->OutputSelect SensCalc Calculate sensitivity measures OutputSelect->SensCalc ResultInterp Interpret SA results SensCalc->ResultInterp End End ResultInterp->End

GSUA Workflow for OSMOSE

Practical Implementation Guide

GSUA Toolbox Configuration

The GSUA Toolbox requires two primary components for implementation with OSMOSE models [29]:

  • The gsua_main.mlx file: Primary analysis script
  • A mathematical model: OSMOSE implementation in appropriate format

The toolbox supports multiple model formats including Simulink files (slx), m-files for ode45 integration, state-space formats for linear systems, and transfer function models useful for system identification [29]. For temporal response analysis, the toolbox calculates sensitivity indices across the entire output trajectory, while for scalar outputs, it supports various ecological indicators including maximum values, mean values, and error metrics [29].

Experimental Design and Computational Considerations

For OSMOSE applications, the experimental design must balance computational demands with analytical thoroughness. The protocol begins with Uncertainty Analysis to identify variabilities, followed by a screening phase using the Morris method to enhance computational efficiency [30]. Subsequent application of multi-method GSUA ranks parameter sensitivities, assesses robustness, and provides comparative evaluation [30]. The process culminates with Regional Sensitivity Analysis, targeting local sensitivities and enhancing understanding of local parameter influences [30]. For large-scale OSMOSE applications, parallel computing options can significantly reduce computation time, though this has limitations in MATLAB Online environments [29].

Research Reagent Solutions

Essential Computational Tools

Table 1: Essential Research Reagents and Tools for GSUA Implementation

Tool/Reagent Function Implementation Notes
GSUA MATLAB Toolbox [29] Implements variance-based and OAT sensitivity methods Requires MATLAB environment; compatible with ODE-based systems
Parameter Reliability Criterion [28] Classifies parameters based on data source and quality Replaces arbitrary uncertainty ranges; hierarchy Levels 1-3
Saltelli Method [29] Global sensitivity analysis Captures interaction effects; preferred for complex ecosystems
Morris Screening Method [30] Preliminary parameter screening Enhances computational efficiency before full GSUA
Regional Sensitivity Analysis [30] Identifies critical parameter ranges Final step for understanding local parameter influences

Application to Northern Peru Current Ecosystem Case Study

NPCE OSMOSE Implementation

Application of the GSUA protocol to the Northern Peru Current Ecosystem (NPCE) OSMOSE model demonstrated that most parameters had σ/μ* ratios greater than 1, indicating strong interactions and/or non-linearities [28]. Parameter p10 (maximum predator-prey size ratio for the first stage of anchovy) exhibited the strongest influence, followed by p18 (maximum rate of predation ingestion), p13 (predator-prey size ratio for the last stage of anchovy), and p3 (assimilation efficiency for jumbo squid) [28]. This analysis provides crucial guidance for parameter prioritization in OSMOSE calibration and data collection efforts.

Comparative Methodological Assessment

When comparing the PR criterion against predefined parameter ranges in the NPCE OSMOSE model, significant differences emerged in sensitivity rankings [28]. This demonstrates the drawbacks of common practices using fixed ranges and highlights the importance of the PR criterion approach, particularly when data is limited. The protocol enables more reliable identification of influential parameters in data-limited situations common to marine ecosystem modelling.

Advanced Methodological Integration

Multi-Method GSUA Approach

For comprehensive OSMOSE analysis, we recommend an integrated approach combining multiple GSUA methods [30]:

  • Initial Screening: Morris method for factor prioritization
  • Variance-Based Analysis: Saltelli method for main and total effect indices
  • Regional Sensitivity Analysis: Identify critical parameter ranges influencing specific outputs

This structured methodology begins with Uncertainty Analysis to identify variabilities, followed by sequential application of sensitivity methods to balance computational efficiency with analytical thoroughness [30].

Technical Implementation Schema

The following diagram details the technical workflow for integrated uncertainty and sensitivity analysis:

cluster_0 Computational Efficiency cluster_1 Comprehensive Ranking cluster_2 Design Optimization Start Start UA Uncertainty Analysis Identify variabilities Start->UA Screen Screening Phase Morris method UA->Screen GSA Multi-method GSUA Rank parameter sensitivities Screen->GSA RSA Regional Sensitivity Analysis Local parameter influences GSA->RSA ModelAdj Model Adjustment & Optimization RSA->ModelAdj End End ModelAdj->End

Technical GSUA Workflow

The implementation of robust GSUA protocols represents an essential component of credible OSMOSE model development and application. The Parameter Reliability criterion provides a scientifically defensible approach to addressing parameter uncertainty in data-limited situations common to marine ecosystem modelling. The structured, multi-method approach combining uncertainty analysis, screening methods, variance-based sensitivity analysis, and regional sensitivity analysis offers a comprehensive framework for understanding parameter influences and their impacts on model outputs. For OSMOSE applications specifically, this protocol enables more reliable identification of influential parameters, guides future data collection efforts, supports model simplification where appropriate, and ultimately increases confidence in model projections used for ecosystem-based management decisions.

Model Calibration and Fitting to Observed Biomass and Catch Data

Ecosystem models are essential quantitative tools for supporting ecosystem-based fisheries management (EBFM), providing insights into trade-offs between ecosystem impacts and commercial profits [3]. The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) serves as a powerful multi-species, individual-based model that simulates fish life history and interspecies interactions, making it particularly valuable for understanding trophic dynamics in marine ecosystems [3]. However, the utility of these models for tactical management decisions depends heavily on robust calibration procedures that minimize uncertainty in model projections [31].

Model calibration involves comparing model outputs to observed data through parameter estimation, typically by optimizing an objective function that quantifies the difference between simulations and observations [32]. This process is particularly challenging for complex ecosystem models like OSMOSE due to the large number of parameters, long simulation times, and correlated parameters affecting one another through ecological interactions [32]. This application note provides a comprehensive protocol for calibrating OSMOSE models using observed biomass and catch data, based on established methodologies from recent scientific literature.

Parameter Classification Framework

Before initiating calibration, parameters must be systematically categorized to determine their estimation precedence. Based on the sequential calibration approach proposed by Oliveros-Ramos et al. (2017), parameters should be classified according to two key criteria [32]:

Table 1: Parameter Classification Criteria for Sequential Calibration

Criterion Category Description Examples Calibration Precedence
Model Dependency Independent Parameters that can be estimated outside the model Growth parameters, natural mortality Estimate first
Dependent Parameters that require the model for estimation Trophic interactions, competition coefficients Estimate later
Time Variability Fixed Parameters constant throughout simulation Asymptotic size, size at maturity Easier to estimate
Variable Parameters varying temporally Seasonal migration rates, feeding efficiency More difficult to estimate

This classification enables a hierarchical approach to parameter estimation, where independent and fixed parameters are calibrated before addressing dependent and variable parameters [32].

Sequential Calibration Protocol

The sequential calibration approach significantly improves parameter estimation and model-data agreement compared to single-step calibration of all parameters simultaneously [32]. This method proceeds through multiple phases, with each phase estimating a subset of parameters while keeping others fixed.

G Start Start Calibration Process ParamClass Classify Parameters by Model Dependency and Time Variability Start->ParamClass Phase1 Phase 1: Estimate Independent Parameters ParamClass->Phase1 Phase2 Phase 2: Estimate Fixed Dependent Parameters Phase1->Phase2 Phase3 Phase 3: Estimate Variable Dependent Parameters Phase2->Phase3 Phase4 Phase 4: Full Model Calibration Phase3->Phase4 Evaluate Evaluate Model Fit Phase4->Evaluate Accept Calibration Accepted? Evaluate->Accept Accept->Phase1 No End Calibration Complete Accept->End Yes

Phase 1: Independent Parameter Estimation

Objective: Establish baseline values for parameters that can be estimated independently of the OSMOSE model.

Methodology:

  • Extract life history parameters (growth rates, natural mortality, fecundity) from literature, experimental studies, or previous models
  • Use statistical methods outside OSMOSE to estimate these parameters
  • Set initial values for subsequent phases

Data Requirements: Species-specific biological data from scientific literature, experimental studies, or regional databases.

Phase 2: Fixed Dependent Parameter Estimation

Objective: Calibrate parameters that depend on model structure but remain constant throughout simulations.

Methodology:

  • Fix independent parameters from Phase 1
  • Estimate fixed dependent parameters using optimization algorithms
  • Focus on parameters such as asymptotic size, size at maturity, and basic trophic interactions

Optimization Approach: Maximize likelihood function comparing model outputs to annual biomass and catch data.

Phase 3: Variable Dependent Parameter Estimation

Objective: Calibrate parameters that vary temporally and depend on model structure.

Methodology:

  • Fix parameters from Phases 1 and 2
  • Estimate seasonal or interannually varying parameters
  • Include parameters such as migration rates, feeding efficiency, and recruitment modifiers

Data Requirements: Time-series data of biomass indices, catch records, and environmental variables.

Phase 4: Full Model Calibration

Objective: Perform final adjustment of all parameters simultaneously.

Methodology:

  • Use parameter estimates from previous phases as initial values
  • Apply optimization algorithms to estimate all parameters concurrently
  • Validate model against independent data not used in calibration

Validation Metrics: Goodness-of-fit measures including correlation coefficients, RMSD, and AIC for model selection.

Sensitivity Analysis and Uncertainty Quantification

Parameter Reliability Criterion

To address parameter uncertainty, Luján et al. (2025) propose a Parameter Reliability (PR) criterion that categorizes parameters based on their data sources and assigns appropriate uncertainty levels [28]. This approach avoids the common practice of using arbitrary fixed uncertainty ranges (e.g., 10-30%), which can significantly impact sensitivity analysis results.

Table 2: Parameter Reliability Classification System

Hierarchy Data Source Uncertainty Level Description Recommended Uncertainty Range
1 Direct measurements Low Parameters from targeted studies of the specific species and ecosystem Narrow, data-informed PDFs
2 Related species/ecosystems Medium Parameters from similar species or adjacent ecosystems Moderate uncertainty ranges
3 Model outputs High Parameters borrowed from other models with different assumptions Wider uncertainty ranges
4 Expert opinion Very High Parameters based solely on qualitative knowledge Widest, conservative uncertainty ranges
Sensitivity Analysis Protocol

Sensitivity analysis examines how uncertainty in model outputs can be apportioned to different sources of uncertainty in model inputs [28]. The recommended protocol involves four key steps:

G Start Start Sensitivity Analysis Step1 Step 1: Quantify Input Uncertainty Assign PDFs based on Parameter Reliability Criterion Start->Step1 Step2 Step 2: Experimental Design Define parameter sampling strategy and model run specifications Step1->Step2 Step3 Step 3: Model Execution Run multiple simulations with parameter combinations Step2->Step3 Step4 Step 4: Calculate Sensitivity Measures Compute sensitivity indices for key model outputs Step3->Step4 Results Interpret SA Results Identify influential parameters Step4->Results End SA Complete Results->End

Step 1: Quantify Uncertainty in Model Inputs

  • Represent parameter uncertainty as probability density functions (PDFs)
  • Use data-informed PDFs when available
  • Apply PR criterion to determine appropriate uncertainty ranges when data are limited [28]

Step 2: Experimental Design

  • Determine parameter sampling strategy (e.g., Latin Hypercube Sampling, Monte Carlo)
  • Define number of model runs based on computational constraints
  • Identify parameter ranges and distributions for sampling

Step 3: Model Execution

  • Run OSMOSE multiple times with different parameter combinations
  • Record key model outputs (species biomass, catch, trophic indicators)

Step 4: Sensitivity Measures Calculation

  • Compute sensitivity indices (e.g., Morris elementary effects, Sobol indices)
  • Identify parameters with strongest influence on model outputs
  • Focus management attention on most influential parameters

Research Toolkit

Table 3: Essential Research Reagents and Computational Tools for OSMOSE Calibration

Tool/Resource Type Function Application Notes
OSMOSE Platform Ecosystem Model Simulates marine ecosystem dynamics Individual-based, multi-species modeling approach [3]
AD Model Builder Optimization Software Parameter estimation through maximum likelihood Handles complex parameter constraints [32]
R Statistical Environment Programming Language Data analysis, visualization, and sensitivity analysis Extensive ecological modeling packages [32]
Monte Carlo Simulation Uncertainty Analysis Method Quantifies error propagation from parameter uncertainty Evaluates impacts of imprecise parameterization [3]
Likelihood Function Statistical Method Quantifies fit between model outputs and observed data Incorporates multiple data types with appropriate weights [32]
Parameter Reliability Criterion Classification System Categorizes parameters based on data source and quality Informs uncertainty ranges for sensitivity analysis [28]

Case Study: OSMOSE Application to Northern Humboldt Current Ecosystem

Calibration Implementation

Oliveros-Ramos et al. (2017) demonstrated the sequential calibration approach with the Northern Humboldt Current Ecosystem (NHCE) OSMOSE model [32]. The implementation involved:

Data Integration:

  • Commercial landings data (most reliable information source)
  • Biomass indices from scientific surveys
  • Biological parameters from literature and previous studies

Parameter Estimation:

  • 22 parameters categorized by model dependency and time variability
  • Sequential estimation across four phases
  • Likelihood function emphasizing fit to commercial landings data

Results:

  • Successful implementation of the sequential calibration approach
  • Improved parameter estimation compared to single-step calibration
  • Good agreement between simulated and observed landings at monthly and yearly scales
  • Humboldt squid showed poorest fit, potentially due to data limitations
Sensitivity Analysis Findings

Application of the Parameter Reliability criterion to the NPCE OSMOSE model revealed [28]:

  • Most parameters exhibited strong interactions and/or non-linearities (σ/μ* ratios > 1)
  • Parameter p10 (maximum predator-prey size ratio for first stage of anchovy) had strongest influence
  • Followed by p18 (maximum rate of predation ingestion) and p13 (predator-prey size ratio threshold)
  • Results differed significantly from those obtained using arbitrary predefined parameter ranges

Best Practices and Recommendations

Data Quality and Integration
  • Prioritize data quality: Commercial landings often represent the most reliable data source and should be weighted accordingly in likelihood functions [32]
  • Use multiple data types: Incorporate biomass indices, size composition data, and diet composition when available
  • Acknowledge data limitations: Explicitly document data sources and quality through parameter reliability classification [28]
Computational Efficiency
  • Implement efficient experimental designs: Use screening methods to identify influential parameters before comprehensive sensitivity analysis [28]
  • Utilize high-performance computing: Leverage computational resources like the Pôle de Calcul et de Données Marines (PCDM) for extensive model runs [28]
  • Consider model complexity trade-offs: Balance biological realism with computational feasibility during calibration
Uncertainty Communication
  • Transparent reporting: Clearly document all parameter sources, classifications, and uncertainty ranges [28]
  • Multiple scenario testing: Evaluate model performance under different parameterization scenarios [3]
  • Sensitivity-aware management: Focus management attention on decisions robust to parameter uncertainty

The sequential calibration protocol presented here provides a robust framework for fitting OSMOSE models to observed biomass and catch data. By systematically classifying parameters and implementing a phased estimation approach, researchers can significantly improve model reliability and predictive performance. Coupling this calibration process with comprehensive sensitivity analysis based on parameter reliability criteria addresses key uncertainty sources in ecosystem modeling. This methodology supports the advancement of OSMOSE applications from strategic guidance to tactical management advice, enhancing their utility in ecosystem-based fisheries management.

Addressing Computational Challenges in Complex Simulations

Complex ecosystem simulations present significant computational challenges that require sophisticated approaches to ensure accuracy, efficiency, and practical utility. The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) represents a prominent example of such modeling frameworks, employing individual-based models to simulate marine ecosystem dynamics across multiple trophic levels. As these models grow in complexity to incorporate more realistic biological interactions and environmental factors, they demand substantial computational resources and careful implementation strategies. This article examines the primary computational bottlenecks encountered in OSMOSE applications and provides structured protocols for addressing these challenges through systematic sensitivity analysis, parameter optimization, and workflow management. The guidance presented here stems from recent methodological advances in ecosystem modeling, particularly those documented in peer-reviewed literature from 2025, offering researchers practical solutions for enhancing computational efficiency while maintaining scientific rigor in their simulation experiments.

Computational Challenges in OSMOSE Modeling

OSMOSE models present several distinct computational challenges that researchers must address to ensure feasible simulation runtimes and reliable results. The table below summarizes these primary challenges and their impacts on modeling efforts:

Table 1: Key Computational Challenges in OSMOSE Modeling

Challenge Category Specific Computational Burden Impact on Modeling Process
Parameter Space Complexity High-dimensional parameter spaces requiring extensive calibration Exponential increase in computation time; potential for non-identifiable parameters
Model Structure Complexity Individual-based modeling of multiple species across trophic levels Memory allocation issues; prolonged simulation runtimes
Sensitivity Analysis Requirements Need for global sensitivity methods across multiple parameters Computational cost increases with parameter dimensions and required iterations
Uncertainty Quantification Propagation of uncertainty through complex model structures Requires numerous model realizations; substantial resource demands
Spatial Explicit Modeling Fine-scale spatial resolution in ecosystem simulations Increased computational overhead for tracking individuals across grid cells

Recent research highlights that the parameter space complexity represents one of the most significant hurdles, particularly as OSMOSE applications expand to include more species interactions and environmental drivers. The individual-based modeling approach, while providing valuable ecological insights, generates substantial computational overhead through the need to track numerous individuals and their interactions across temporal and spatial dimensions [4].

The integration of additional stressors, such as climate change projections and human impacts like offshore wind farms, further compounds these challenges. For instance, Huang et al. (2025) documented technical developments in OSMOSE applications that enhanced capability to evaluate cumulative effects from multiple anthropogenic stressors, requiring substantial model enhancements and computational resources [4]. Similarly, global and regional marine ecosystem model intercomparison projects have revealed stark differences in biomass projections between model types, underscoring the computational complexity inherent in these forecasting efforts [4].

Protocol for Parameter Sensitivity Analysis

Implementing a structured approach to parameter sensitivity analysis is essential for managing computational demands while maintaining model reliability. The following protocol, adapted from Luján et al. (2025), provides a systematic methodology for conducting sensitivity analyses in complex ecosystem models like OSMOSE:

Protocol: Parameter Sensitivity Analysis for Complex Ecosystem Models

Objective: To identify parameters with greatest influence on model outputs while managing computational constraints.

Materials and Computational Requirements:

  • High-performance computing cluster with parallel processing capabilities
  • R or Python programming environment with statistical packages
  • Implementation of the OSMOSE model framework
  • Data management system for large output files

Procedure:

  • Parameter Prioritization (1-2 weeks)

    • Catalog all model parameters with their plausible ranges
    • Classify parameters by biological process (growth, mortality, reproduction, etc.)
    • Conduct preliminary screening using one-at-a-time (OAT) approach
    • Select subset of parameters (15-25) for global sensitivity analysis

  • Experimental Design (1 week)

    • Generate parameter sampling matrix using Latin Hypercube Sampling
    • Determine sample size (typically 1,000-10,000 iterations) based on computational resources
    • Establish output metrics of interest (total biomass, species diversity, etc.)

  • Model Execution (2-4 weeks, depending on resources)

    • Implement parallel processing across computing nodes
    • Execute OSMOSE simulations with sampled parameter combinations
    • Monitor for convergence and stability
    • Store all output data in structured format

  • Sensitivity Calculation (1 week)

    • Calculate sensitivity indices (Sobol', Morris, or FAST methods)
    • Rank parameters by influence on output metrics
    • Identify interaction effects between parameters
    • Validate sensitivity results with additional test runs

  • Documentation and Implementation (1 week)

    • Document computational requirements and processing times
    • Create sensitivity report guiding future model calibration
    • Implement fixed values for non-influential parameters to reduce dimensionality

Troubleshooting Tips:

  • If computational demands are prohibitive, employ emulator models for initial screening
  • For memory allocation issues, implement data compression or selective output saving
  • When sensitivity results are unclear, increase sample size incrementally

This protocol emphasizes the importance of strategic parameter selection to reduce computational burden while maintaining model integrity. By systematically identifying the most influential parameters, researchers can focus calibration efforts on factors that truly impact model outputs, thereby optimizing the use of computational resources [4].

Visualization of Sensitivity Analysis Workflow

The following diagram illustrates the structured workflow for conducting parameter sensitivity analysis in complex ecosystem models:

G start Start Sensitivity Analysis param_prio Parameter Prioritization (Catalog parameters, OAT screening) start->param_prio exp_design Experimental Design (Latin Hypercube Sampling) param_prio->exp_design comp_req Computational Resources (High-performance cluster) exp_design->comp_req Determines sample size model_exec Model Execution (Parallel processing of iterations) sens_calc Sensitivity Calculation (Sobol' indices, parameter ranking) model_exec->sens_calc val_check Validation Check sens_calc->val_check doc_impl Documentation & Implementation (Fix non-influential parameters) end Sensitivity Report doc_impl->end comp_req->model_exec val_check->exp_design Need more samples val_check->doc_impl Sensitivity confirmed

Diagram 1: Sensitivity analysis workflow for complex ecosystem models

This workflow emphasizes the iterative nature of sensitivity analysis, where validation checks may necessitate additional sampling to ensure reliable results. The integration of high-performance computing resources is critical at the model execution stage, particularly for individual-based models like OSMOSE that require substantial processing power for multiple iterations.

Research Reagent Solutions

The table below outlines key computational tools and approaches that serve as essential "research reagents" for addressing computational challenges in OSMOSE modeling:

Table 2: Essential Research Reagent Solutions for OSMOSE Computational Challenges

Tool/Category Specific Implementation Examples Primary Function Application in OSMOSE
Sensitivity Analysis Packages Sobol', Morris, FAST methods Quantify parameter influence on model outputs Identify key parameters for calibration; reduce dimensionality [4]
Parallel Computing Frameworks MPI, OpenMP, Spark Distribute computational workload across processors Simultaneous execution of multiple model iterations [4]
Statistical Programming Environments R, Python with NumPy/SciPy Data analysis, visualization, and statistical computation Process model outputs; calculate sensitivity indices [4]
Experimental Design Methods Latin Hypercube Sampling, Fractional Factorial Design Efficient parameter space exploration Generate parameter combinations for global sensitivity analysis [4]
Uncertainty Quantification Tools Bayesian calibration, Markov Chain Monte Carlo Characterize and propagate uncertainty through models Account for parameter and structural uncertainty in projections [4]
High-Performance Computing Infrastructure Computing clusters, cloud computing resources Provide necessary processing power and memory Execute computationally intensive individual-based simulations [4]

These "reagent solutions" represent the essential methodological toolkit for addressing computational bottlenecks in OSMOSE applications. The sensitivity analysis packages form the cornerstone of efficient model calibration, allowing researchers to focus computational resources on the most influential parameters. Meanwhile, parallel computing frameworks enable the distribution of workload across multiple processors, significantly reducing simulation time for complex model configurations.

Recent applications demonstrate how these tools facilitate more computationally efficient ecosystem assessments. For instance, Huang et al. (2025) implemented substantial technical developments to an existing OSMOSE model, enhancing its capability to evaluate cumulative effects of offshore wind farms while managing computational demands through strategic parameterization and model improvements [4]. Similarly, the global sensitivity and uncertainty analyses conducted by Xing et al. (2025) on an ecosystem model for the Southern Ocean required careful management of computational resources through the application of these reagent solutions [4].

Addressing computational challenges in complex ecosystem simulations requires a multifaceted approach that combines methodological rigor with strategic resource allocation. The protocols and solutions presented here provide a framework for enhancing computational efficiency in OSMOSE modeling applications while maintaining the ecological realism that makes these models valuable for scientific inquiry and management decision-making. By implementing structured sensitivity analyses, leveraging appropriate computational tools, and following systematic workflows, researchers can navigate the inherent trade-offs between model complexity, computational feasibility, and output reliability. As OSMOSE applications continue to expand to address emerging questions in marine ecosystem management, these approaches to computational challenge mitigation will remain essential for producing robust, actionable scientific insights.

Parameter Estimation from Life History Data and FishBase

Within the framework of OSMOSE (Object-oriented Simulator of Marine ecOSystEms) research, accurate parameter estimation is foundational for creating robust models that simulate marine ecosystem dynamics. OSMOSE is a spatially explicit, individual-based model (IBM) that simulates fish populations by representing individuals grouped in schools, characterized by their size, weight, age, taxonomy, and geographical location [9]. These virtual entities undergo critical life cycle processes—growth, predation, natural and fishing mortality, and reproduction—whose realism depends heavily on the input life history parameters [9] [11].

A significant challenge in ecosystem modeling is the data-poor situation for many marine species. For over 7,000 fish species used by humans, critical life history parameters are known for fewer than 2,000 [33]. This application note details protocols for leveraging FishBase, a global biological database, to estimate essential parameters for OSMOSE applications, ensuring models are both biologically sound and management-relevant.

Essential Life History Parameters for OSMOSE

The OSMOSE model requires a set of core life history parameters to simulate the dynamics of fish schools. These parameters govern growth, mortality, and reproduction processes [9] [34]. The table below summarizes the key parameters, their definitions, and their roles within the OSMOSE modeling framework.

Table 1: Essential Life History Parameters for OSMOSE Models

Parameter Symbol Definition Role in OSMOSE Model
Maximum Length Lmax The maximum length ever recorded for a species [33]. Used as a predictor for other life-history parameters; helps define the potential size range of schools [33].
Asymptotic Length Linf The theoretical average length a species would reach if it grew indefinitely [33]. A key parameter in the von Bertalanffy Growth Function (VBGF) simulated in OSMOSE [33].
Growth Coefficient K A VBGF parameter expressing the rate (per year) at which Linf is approached [33]. Determines the growth rate of individual fish within schools [33].
Length at First Maturity Lm The average length at which fish mature for the first time [33]. Determines the size at which individuals begin to contribute to reproduction in the model [33].
Natural Mortality M The instantaneous rate of natural mortality (per year) for late juvenile and adult phases [33]. A core process affecting population dynamics; influences survival rates from predation, disease, and old age [9] [33].
Length for Max Yield Lopt The length class that provides the highest biomass in an unfished population [33]. A key reference point for evaluating fishing scenarios and management strategies in model outputs [33].

Parameter Estimation Protocols from FishBase

FishBase provides a structured "Key Facts" page for estimating life-history parameters, often with error margins [33]. The following workflow and protocols outline the standard procedures for data extraction and estimation.

G Start Start Parameter Estimation Lmax Obtain Maximum Length (Lmax) from FishBase Start->Lmax Decision1 Are growth studies available? Lmax->Decision1 LinfCalc Linf estimated from Lmax using empirical relationship Decision1->LinfCalc No LinfDirect Linf taken from growth study (median Φ') Decision1->LinfDirect Yes K_Est Estimate K using: - Φ' from growth studies, or - Lm & tm, or - tmax LinfCalc->K_Est LinfDirect->K_Est M_Est Calculate Natural Mortality (M) from Linf, K, and Temperature K_Est->M_Est DeriveParams Derive Lm, Lopt, tmax from Linf and other parameters M_Est->DeriveParams End Parameter Set Complete for OSMOSE Configuration DeriveParams->End

Figure 1: Workflow for estimating OSMOSE life history parameters from FishBase.

Estimating Growth Parameters: Linfand K

The von Bertalanffy Growth Function (VBGF) parameters are fundamental to simulating individual growth in OSMOSE.

  • Protocol for Linf (Asymptotic Length)

    • Source: Navigate to the "Key Facts" page for your target species on FishBase.
    • Default Value: If growth studies are available, FishBase provides a default Linf value from the population with the median growth performance index (Φ') [33].
    • Alternative Estimation: In the absence of growth studies, Linf is automatically estimated from Lmax using the empirical relationship from Froese & Binohlan (2000) [33].
    • Spatial Adjustment: If the default Lmax is not representative of your study population, use the "Max. size data" link to select a more appropriate regional value and recalculate [33].

  • Protocol for K (Growth Coefficient)

    • Primary Method: The default K is calculated using the provided Linf and the median Φ' from available growth studies [33].
    • Data-Limited Method 1: If growth studies are absent but length/age at maturity (Lm, tm) are known, K can be approximated as: K = -ln(1 - Lm / Linf) / (tm - t0) [33].
    • Data-Limited Method 2: If maximum age (tmax) is available, K can be derived from: K ≈ 3 / (tmax - t0) [33]. Recent studies also support estimating growth from maximum age or maturity data when full growth curves are unavailable [35].
    • Taxonomic Approximation: If no species-specific data exists, use the median Φ' of congeneric or confamilial species from the same climate zone as a prior [33].
Estimating Mortality and Maturity Parameters

  • Protocol for M (Natural Mortality)

    • Standard Equation: The default instantaneous natural mortality rate is calculated using an empirical equation based on VBGF parameters and mean annual water temperature (T) [33]. The re-estimated version of Pauly's (1980) equation, which analyzes a larger dataset and provides confidence limits, is used.
    • Data-Limited Equation: If K is unavailable, M is calculated from: M = 10^(0.566 - 0.718 * log(Linf) + 0.02 * T) [33].
    • Considerations: Ensure the correct length type (fork length for scombroids, total length for others) is used, as the equation uses length as a proxy for weight [33].

  • Protocol for Lm (Length at First Maturity)

    • The value and its standard error are primarily calculated from an empirical relationship between Lm and Linf [33].
    • Users can click the "Maturity data" link for additional, empirically observed maturity information when available [33].

Case Study: Application in the Yellow Sea OSMOSE Model

The OSMOSE model for the Yellow Sea (OSMOSE-YS) serves as a practical example of applying these parameter estimation protocols in a data-limited environment [34].

  • Model Configuration: The OSMOSE-YS model simulated the Yellow Sea ecosystem with a 10x10 km² spatial grid, representing key species like small yellow croaker, largehead hairtail, and Japanese anchovy [34].
  • Parameterization: Life history parameters for the modelled species were estimated using FishBase and other sources, following the described protocols. The model was then calibrated using an evolutionary algorithm to ensure simulated yields and biomass patterns matched observed data [34].
  • Outcome: The successfully parameterized model was used to run fishing scenarios, revealing that increased fishing pressure led to a 30-60% decline in the biomass of demersal fish and disrupted the energy transfer efficiency within the ecosystem [34]. This demonstrates the critical role of robust parameter estimation in producing credible management advice.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools and Resources for OSMOSE Parameterization

Tool / Resource Type Primary Function in OSMOSE Research
FishBase Online Database The primary source for obtaining species-specific life history parameters (e.g., Lmax, Linf, K) and associated error margins [33].
R osmose Package Software Package Provides the core software environment to build, configure, run, and analyze OSMOSE simulations directly within R [36].
Zenodo OSMOSE Repository Code Repository Source for obtaining the latest stable version of the OSMOSE model software (e.g., v4.3.3) [37].
Evolutionary Algorithm Calibration Tool An automated model-fitting procedure used to fine-tune uncertain parameters by minimizing the difference between model output and observed field data [9] [34].
Hydrodynamic/Biogeochemical Models Forcing Data Provides spatial-temporal data on environmental conditions (e.g., temperature, prey fields) that directly influence fish physiology and distribution in coupled OSMOSE applications [9] [11].

The integration of FishBase's "Key Facts" life history parameter estimates provides a scientifically robust and practical foundation for parameterizing OSMOSE models, especially in data-limited contexts. The standardized protocols outlined herein—for estimating growth, mortality, and maturity parameters—enable researchers to construct reliable simulation models that effectively explore the ecosystem effects of fishing and climate change. By leveraging these resources and methods, the OSMOSE framework continues to be a powerful tool for advancing ecosystem-based fisheries management.

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based, size-structured model that simulates the dynamics of fish communities by representing fish individuals grouped in schools, characterized by their size, weight, age, taxonomy, and geographical location [9]. It serves as a powerful tool for exploring fish community dynamics and the ecosystem effects of fishing and climate change [9]. A critical step in enhancing the realism and predictive capability of OSMOSE is its coupling with lower-trophic-level (LTL) models, which simulate the biogeochemical processes that govern the base of the food web, such as nutrient cycling, phytoplankton growth, and zooplankton dynamics. This integration creates an end-to-end modeling framework capable of simulating the transfer of energy from the physical environment up to exploited fish species. The core challenge in this integration is ensuring consistency between the dynamically changing prey fields generated by the LTL model and the opportunistic, size-based predation processes that define trophic interactions in OSMOSE. This application note outlines protocols and solutions for achieving this consistency, framed within the context of a broader OSMOSE-based research thesis.

The Challenge of Model Coupling

Disparities in Model Structure and Dynamics

Coupling disparate ecosystem models introduces significant technical and ecological challenges. OSMOSE operates on the principle of size-based opportunistic predation, where interactions between predators and prey are determined by their spatial co-occurrence and body-size ratios [9] [38]. The model does not require pre-defined diet matrices, as trophic links emerge from these individual encounters. In contrast, LTL models, such as the Biogeochemical Flux Model (BFM) used in coupled physical-biogeochemical systems [39], typically track biomass pools of nutrients, phytoplankton, and zooplankton using a nutrient-phytoplankton-zooplankton-detritus (NPZD) structure.

The primary challenge in coupling lies in reconciling these different structures and dynamics, as inconsistencies can lead to unrealistic ecosystem projections. Key issues include:

  • Spatio-temporal resolution mismatch: LTL models may operate on different spatial grids or temporal time steps than the individual-based OSMOSE model [40].
  • Prey field representation: OSMOSE requires a prey field that its individuals can potentially consume. This prey field must be dynamically consistent with the outputs of the LTL model, which provides the biomass of zooplankton and other components [39].
  • Feedback mechanisms: While one-way forcing (LTL to OSMOSE) is simpler, a two-way coupled system, where fish predation impacts the LTL groups, is more realistic but introduces greater complexity and computational cost [39].

The Critical Role of Sensitivity Analysis

Given the complexity and high number of parameters in end-to-end models, a rigorous sensitivity analysis (SA) is a non-negotiable step before and after coupling. It is essential for understanding model behavior and quantifying uncertainty. As demonstrated in OSMOSE applications, SA examines how the uncertainty in model outputs can be apportioned to different sources of uncertainty in the model inputs [28]. This is particularly crucial for identifying parameters related to predation and growth that have the strongest influence on model outputs, thereby guiding which processes require the most careful parameterization during coupling [28].

Table 1: Key Challenges in Coupling OSMOSE with Lower-Trophic-Level Models

Challenge Description Potential Impact on Model Output
Structural Uncertainty Differences in how biological processes are mathematically represented in each model. Systematic bias in energy transfer and biomass flows.
Parameter Uncertainty Inaccurate or poorly constrained parameter values for growth, predation, and mortality. High uncertainty in projected biomass and community structure.
Scale Mismatch Discrepancies in the spatial or temporal resolution of the coupled models. Aliasing of processes and inaccurate representation of predator-prey encounters.
Unidirectional vs. Bidirectional Coupling One-way (forcing) versus two-way (feedback) integration between models. Over- or under-estimation of top-down control on plankton dynamics.

Protocols for Robust Model Integration

A Workflow for Coupled Model Implementation

The following workflow provides a structured protocol for integrating OSMOSE with an LTL model, ensuring a consistent and well-tested application.

G Start Start: Define Coupling Objective A 1. Pre-processing & Downscaling Start->A B 2. Prey Field Mapping A->B C 3. OSMOSE Model Configuration B->C D 4. Sensitivity Analysis C->D E 5. Model Calibration D->E Refine Parameters F 6. Coupled Simulation E->F End End: Analysis & Validation F->End

Figure 1: A sequential workflow for coupling OSMOSE with lower-trophic-level models, highlighting critical steps for ensuring consistency.

Protocol 1: Prey Field Forcing and Mapping

Objective: To dynamically force the OSMOSE model with a spatially and temporally explicit prey field derived from a lower-trophic-level model.

Background: OSMOSE does not dynamically simulate its lower trophic levels but requires an external definition of a prey field, often composed of zooplankton and micronekton, which is accessible to its simulated fish individuals [4]. The LTL model provides the spatio-temporal dynamics of this biomass.

Materials:

  • LTL Model Output: NetCDF or similar format files containing 4D (time, depth, latitude, longitude) biomass data for relevant functional groups (e.g., zooplankton) [39].
  • Spatial Overlap Matrix: A precomputed matrix defining the vertical and horizontal overlap between OSMOSE schools and the prey biomass [38].
  • Data Processing Scripts: Python (e.g., with xarray, pandas) or R scripts for data extraction and transformation.

Methodology:

  • Pre-processing: Extract zooplankton biomass data from the LTL model output. Ensure temporal alignment (e.g., daily, monthly means) with the OSMOSE simulation time step.
  • Downscaling (if necessary): If the LTL model has a coarser resolution, employ statistical or dynamical downscaling techniques to interpolate the prey field to the finer OSMOSE grid.
  • Prey Pool Construction: Aggregate the relevant LTL functional groups into a total "available prey biomass" pool for OSMOSE. This may involve combining multiple zooplankton size classes.
  • Biomass-to-Abundance Conversion: Convert the prey biomass (in mg C m⁻³ or similar) into numerical abundance (individuals per unit volume) using an assumed individual body weight for the prey. This is critical for OSMOSE's size-based predation.
  • Forcing File Generation: Format the processed prey field into the specific input file format required by OSMOSE (e.g., a time-series of 2D spatial maps).

Validation: Compare the spatial distribution and seasonal cycle of the forced prey field within OSMOSE against independent observational data (e.g., from plankton recorders) to ensure the LTL model outputs are realistic in the study region.

Protocol 2: Parameter Sensitivity Analysis using the Parameter Reliability Criterion

Objective: To systematically identify the parameters in the coupled OSMOSE model that have the greatest influence on key outputs, using a method that robustly handles parameter uncertainty.

Background: Traditional sensitivity analyses often use arbitrary, fixed uncertainty ranges for all parameters, which can be subjective and may misrepresent true uncertainty. The Parameter Reliability (PR) criterion provides a structured, hierarchical method for defining parameter uncertainty based on the quality and source of the data used to estimate each parameter [28].

Materials:

  • List of Model Parameters: A comprehensive list of all OSMOSE parameters subject to uncertainty (e.g., predation ingestion rate, predator-prey size ratio, natural mortality).
  • Parameter Source Information: Metadata describing how each parameter value was obtained (e.g., from literature, expert guess, model fit, direct measurement).

Methodology:

  • Categorize Parameters: Assign each parameter a qualitative PR hierarchy based on its source:
    • PR1: Directly measured or calculated from targeted studies in the specific ecosystem.
    • PR2: Estimated from the scientific literature for similar species/ecosystems.
    • PR3: Roughly estimated or based on an educated guess.
  • Assign Uncertainty Ranges: Define the uncertainty range (e.g., Coefficient of Variation, CV) for each parameter based on its PR level. For example:
    • PR1: CV = 10%
    • PR2: CV = 25%
    • PR3: CV = 40%
  • Experimental Design: Use a global sensitivity analysis method, such as the Morris method or Variance-based (Sobol') method, which is efficient for models with many parameters [28].
  • Model Execution: Run the OSMOSE model multiple times (hundreds to thousands) with parameter values sampled from their defined distributions.
  • Sensitivity Calculation: Calculate sensitivity indices for key model outputs (e.g., total fish biomass, species-specific catch, size spectrum slope). Parameters with high indices are considered highly influential.

G Start Start: List Parameters A Categorize by Source (PR Hierarchy) Start->A B Assign Uncertainty Range (Based on PR Level) A->B C Sample Parameter Sets (Experimental Design) B->C D Run OSMOSE Ensemble C->D E Calculate Sensitivity Indices D->E End Identify Key Parameters E->End

Figure 2: A protocol for implementing a parameter sensitivity analysis based on the Parameter Reliability criterion, improving the assessment of model uncertainty.

Interpretation: Focus model refinement and future data collection efforts on the highly influential parameters identified by the SA. For example, in an OSMOSE application, the maximum predator-prey size ratio and the maximum rate of predation ingestion were found to be among the most influential parameters [28].

The Scientist's Toolkit: Essential Research Reagents

In the context of numerical modeling, "research reagents" refer to the essential software, data, and computational tools required to build, run, and analyze coupled ecosystem models.

Table 2: Key "Research Reagent Solutions" for OSMOSE-LTL Coupling

Tool Category Specific Examples Function in Coupling Process
Ecosystem Model OSMOSE (Object-oriented Simulator of Marine ecOSystems) [9] Simulates the dynamics of fish populations and their trophic interactions using an individual-based, size-structured approach.
Lower-Trophic-Level Model BFM (Biogeochemical Flux Model) [39], NEMO-ERSEM [39] Simulates nutrient cycling, phytoplankton dynamics, and zooplankton production, providing the prey field for OSMOSE.
Physical Model NEMO (Nucleus for European Modeling of the Ocean) [39], ROMS Provides physical forcing (currents, temperature, salinity) to the LTL model and can influence fish movement in OSMOSE.
Coupling & Processing Tools Python (xarray, pandas, NumPy), R, CDO (Climate Data Operators) Processes and transforms LTL model outputs into OSMOSE forcing files; handles unit conversions and spatial regridding.
Sensitivity Analysis Libraries SALib (Sensitivity Analysis Library in Python), R sensitivity package Implements global sensitivity analysis methods (e.g., Morris, Sobol') to identify influential parameters.
High-Performance Computing (HPC) Pôle de Calcul et de Données Marines (PCDM) [4] [38] Provides the necessary computational resources and data storage for running large ensembles of complex, coupled model simulations.

Application Case Study: Impact Assessment of Offshore Wind Farms

A practical application of a coupled OSMOSE model was used to assess the cumulative impacts of offshore wind farms (OWFs) in the Eastern English Channel (EEC) [4]. In this study, the existing OSMOSE model of the EEC was substantially improved to evaluate OWF effects. The technical developments included:

  • Prey field forcing updates to include climate change projections, ensuring the prey base reflected future environmental conditions [4].
  • The model simulated various stressors from OWFs, including underwater noise, sediment resuspension, and restrictions on fishing access.
  • The coupled system was used to run scenarios combining OWF deployment with different fishing regulations, revealing that at the ecosystem scale, total fish biomass and catch were slightly reduced, with significant declines for specific species like cuttlefish and herring [4].

This case demonstrates how a well-coupled and forced OSMOSE model can serve as an integrative tool for evaluating complex anthropogenic impacts on marine ecosystems, directly informing management trade-offs between energy production, fisheries, and environmental protection.

Integrating the OSMOSE model with lower-trophic-level components is a powerful method for creating end-to-end modeling frameworks that can address pressing questions in fisheries science and ecosystem-based management. The success of this integration hinges on ensuring consistency in the spatio-temporal dynamics of the prey field and rigorously evaluating the uncertainty inherent in the coupled model. The protocols outlined here—focusing on robust prey field mapping and a systematic Parameter Reliability-based sensitivity analysis—provide a concrete pathway for researchers to enhance the credibility and usefulness of their model projections. By adopting these practices, the OSMOSE community can continue to advance the model's application, from evaluating climate change impacts to assessing the ecosystem effects of emerging ocean uses.

Ensuring Model Credibility: Validation Techniques and Comparative Ecosystem Modeling

Pattern-Oriented Modeling for Evaluating Emergent Ecosystem Properties

Pattern-Oriented Modeling (POM) serves as a multi-scope methodological framework for developing, selecting, and calibrating complex ecosystem models by testing their ability to reproduce multiple empirical patterns observed at different scales and hierarchical levels [41]. This approach addresses fundamental challenges in predictive ecology by facilitating the creation of structurally realistic models that capture the essential generative mechanisms of ecosystems, thereby increasing confidence in predictions under novel conditions [41]. Within the context of Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) research, POM provides a rigorous framework for evaluating how well these ecosystem models represent emergent properties arising from individual-level processes and interactions [10] [42].

The OSMOSE modeling framework is an individual-based, spatially explicit multispecies model that focuses on fish communities and their dynamics [11] [42]. It assumes opportunistic predation based on size adequacy and spatiotemporal co-occurrence between predators and prey while mechanistically representing key life cycle processes [10] [11]. OSMOSE has been specifically designed to explore ecosystem responses to perturbations such as fishing pressure and climate change, making it particularly valuable for ecosystem-based fisheries management [42].

Observation Observation PatternIdentification PatternIdentification Observation->PatternIdentification Extract multiple patterns across scales ModelDevelopment ModelDevelopment PatternIdentification->ModelDevelopment Inform model structure and processes PatternTesting PatternTesting ModelDevelopment->PatternTesting Generate outputs ModelSelection ModelSelection PatternTesting->ModelSelection Filter models that fail to reproduce patterns Prediction Prediction ModelSelection->Prediction Use structurally realistic model for forecasting

Figure 1. Conceptual workflow of Pattern-Oriented Modeling (POM). This diagram illustrates the iterative process of using multiple observed patterns to develop, test, and select ecosystem models with structural realism, ultimately leading to more reliable predictions [41].

Theoretical Foundations of Pattern-Oriented Modeling

Core Principles and Definitions

Pattern-Oriented Modeling operates on the fundamental premise that multiple patterns observed across different scales and organizational levels collectively contain sufficient information to identify the generative mechanisms responsible for a system's dynamics [41]. In ecological modeling, a "pattern" is defined as anything beyond random variation observed in real ecosystems, which can range from strong patterns (e.g., population cycles, spatial structures) that require equations or datasets to describe, to weak patterns (e.g., directional trends, seasonal changes) that can be described qualitatively with few words or numbers [41].

The power of POM lies in its ability to overcome the equifinality problem, where multiple models with different structures and parameter sets can produce similar outputs when calibrated against a single pattern [41]. By requiring models to simultaneously reproduce multiple independent patterns, POM acts as a series of filters that progressively constrain acceptable model structures and parameterizations, thereby increasing the probability of achieving structural realism [41]. This multi-criteria approach is particularly valuable for complex ecosystem models like OSMOSE, where the emergence of system-level properties from individual-based processes must be rigorously evaluated.

Pattern Selection and Characterization

Selecting appropriate patterns is critical to the success of POM implementation. Effective patterns for POM should [41]:

  • Be observed at different hierarchical levels (individual, population, community, ecosystem)
  • Manifest at different spatial and temporal scales
  • Emerge from mechanisms believed to be important for the modeling problem
  • Be relevant to the prediction context and conditions under which forecasts will be made

In OSMOSE applications, patterns typically include species biomass distributions, size structures, trophic levels, mortality rates, and spatial distributions derived from scientific surveys, catch data, and literature [42] [3] [43]. The selection process requires careful judgment and system knowledge, with the strongest validation occurring when models successfully predict patterns not used during model formulation, testing, or calibration [41].

OSMOSE Model Framework and Emergent Properties

OSMOSE is a multispecies, individual-based modeling framework that represents the dynamics of fish communities in marine ecosystems [11] [42]. Its core structure incorporates several key components:

  • Individual-based representation: Fish are modeled as individuals grouped into schools, characterized by size, weight, age, taxonomy, and geographic location [11]
  • Size-based predation: Opportunistic predator-prey interactions based on spatial co-occurrence and size suitability [10] [42]
  • Life history implementation: Explicit representation of growth, reproduction, migration, and mortality processes [11]
  • Spatiotemporal explicitness: Two-dimensional spatial structure with temporal dynamics [10] [42]
  • Bioenergetic processes: Mechanistic description of physiological responses to environmental factors like temperature and oxygen (in Bioen-OSMOSE) [10]

The model is typically coupled with hydrodynamic and biogeochemical models that influence fish physiology and spatial distribution, creating an end-to-end ecosystem modeling framework [10] [11]. Recent versions (4.3.3+) include bioenergetic, evolutionary, and economic modules, expanding its application scope [11].

Key Emergent Properties in OSMOSE

Emergent properties are system-level patterns or functions that cannot be deduced linearly from the properties of constituent parts [44]. In OSMOSE applications, several key emergent properties arise from individual-level processes and interactions:

Table 1. Key Emergent Properties in OSMOSE Models and Their Evaluation Patterns

Emergent Property Description Evaluation Patterns Relevant OSMOSE Applications
Trophic Structure Organization of feeding relationships and energy flow through ecosystems Trophic levels, predation mortality rates, diet compositions OSMOSE-WFS [45], OSMOSE-JZB [43]
Size Spectra Distribution of body sizes across the community Size-frequency distributions, predator-prey size ratios Bioen-OSMOSE [10]
Species Coexistence Maintenance of biodiversity through resource partitioning and trade-offs Species biomass composition, spatial distributions OSMOSE-JZB [3] [43]
Biomass Distribution Spatial and temporal patterns of biomass across species Spatial biomass maps, biomass time series All OSMOSE applications [10] [42] [3]
Mortality Patterns Emergent mortality rates from predation, starvation, and fishing Age-specific mortality, source-specific mortality contributions OSMOSE-WFS [45]

These emergent properties are not directly programmed into OSMOSE but rather arise from the complex interactions between individuals and their environment, making them excellent candidates for pattern-oriented evaluation [10] [44].

cluster_individual Individual Level cluster_processes Processes cluster_interactions Interactions cluster_emergent Emergent Properties Individual Individual Processes Processes Individual->Processes Exhibit Interactions Interactions Processes->Interactions Drive EmergentProperties EmergentProperties Interactions->EmergentProperties Generate Evaluation Evaluation EmergentProperties->Evaluation Compare with observed patterns Age Age Size Size Location Location Behavior Behavior Growth Growth Reproduction Reproduction Predation Predation Migration Migration Mortality Mortality Trophic Trophic Competitive Competitive Spatial Spatial SizeBased SizeBased TrophicStructure TrophicStructure SizeSpectra SizeSpectra BiomassDist BiomassDist MortalityRates MortalityRates SpatialPatterns SpatialPatterns

Figure 2. Hierarchical organization of processes and emergent properties in OSMOSE. The diagram illustrates how individual-level attributes and processes drive interactions that generate system-level emergent properties, which are then evaluated against observed patterns [10] [11] [42].

Application Notes: POM for OSMOSE Evaluation

Bioen-OSMOSE Case Study: North Sea Application

The Bioen-OSMOSE framework represents a significant advancement in marine ecosystem modeling by mechanistically describing the emergence of life history traits through explicit representation of underlying bioenergetic fluxes and their responses to environmental variation [10]. Applied to the North Sea ecosystem, this framework demonstrates the practical implementation of POM for evaluating emergent properties.

In this application, the model was evaluated against multiple observed patterns at different organizational levels [10]:

  • Individual level: Growth rates, size-at-age data, maturation ogives
  • Population level: Species biomass, catch data, mortality rates
  • Community level: Trophic interactions, diet compositions
  • Ecosystem level: Spatial responses of bioenergetic fluxes to environmental gradients

The model successfully reproduced observed biomasses, catches, sizes at age, maturation ogives, and diets while also generating compelling spatial responses of bioenergetic fluxes to temperature, oxygen, and food availability [10]. This multi-pattern evaluation provided strong evidence for the structural realism of the Bioen-OSMOSE framework and its utility for projecting ecosystem responses to climate change.

OSMOSE-JZB Case Study: Data-Limited Application

The OSMOSE-JZB model developed for Jiaozhou Bay, China, demonstrates the application of POM in data-limited situations commonly encountered in developing countries [3] [43]. This implementation highlighted the importance of understanding parameter uncertainty when applying pattern-oriented evaluation.

Model performance was assessed against multiple patterns [43]:

  • Species biomass composition from bottom trawl surveys
  • Size structure of dominant fish species
  • Trophic levels compared to Ecopath model outputs and empirical isotope analysis
  • Mortality sources across different life stages

The evaluation revealed that predation mortality appeared to be the main source of mortality for younger individuals compared to starvation and fishing mortality [43]. However, discrepancies between modeled and observed size structures for two dominant fish species highlighted areas where structural improvements were needed, demonstrating how POM identifies model limitations.

Table 2. Uncertainty Analysis of Key Parameters in OSMOSE-JZB [3]

Parameter Uncertainty Impact Affected Emergent Properties Sensitivity Ranking
Larval Mortality Rate (M~larval~) Strongest influence on model outputs Community biomass composition, population dynamics Highest
Natural Mortality Rate (M~natural~) Moderate influence Population biomass, size structure Medium
Relative Fecundity Scenario-dependent effects Population dynamics, biomass trends Variable
Predation Access Influences trophic interactions Diet compositions, predation mortality Medium-High

A comprehensive uncertainty analysis using Monte Carlo simulations revealed that larval mortality rates had the strongest influence on model outputs, followed by natural mortality rates, while relative fecundity parameters showed more scenario-dependent effects [3]. This analysis informed the pattern evaluation process by identifying which emergent properties were most sensitive to parameter uncertainty.

Experimental Protocols

Protocol 1: Multi-Pattern Evaluation Framework

Objective: Systematically evaluate OSMOSE model performance against multiple observed patterns at different hierarchical levels.

Materials and Methods:

  • Pattern Selection and Prioritization
    • Identify available empirical patterns from scientific surveys, catch data, and literature
    • Classify patterns according to hierarchical level (individual, population, community, ecosystem) and spatial-temporal scale
    • Prioritize patterns based on data quality, ecological relevance, and independence

  • Metric Development and Quantification

    • Define quantitative metrics for each pattern (e.g., RMSE for biomass comparisons, similarity indices for spatial distributions)
    • Establish acceptability thresholds for each metric based on empirical uncertainty and management needs
    • Develop standardized visualization methods for qualitative pattern comparisons

  • Iterative Model Evaluation

    • Conduct initial simulations and compare outputs to full pattern set
    • Identify systematic discrepancies that suggest structural model deficiencies
    • Implement structural improvements and re-evaluate against all patterns
    • Document pattern reproduction successes and failures for each model iteration

  • Uncertainty Integration

    • Quantify parameter uncertainty through sensitivity analysis and Monte Carlo methods [3]
    • Evaluate pattern reproduction across parameter uncertainty ranges
    • Identify critical parameters requiring additional empirical constraint

Expected Outputs: Comprehensive evaluation report documenting model performance across all patterns, identification of structural limitations, and parameterization priorities for future research.

Protocol 2: Uncertainty Quantification in Pattern Evaluation

Objective: Quantify how parameter uncertainty affects the reproduction of emergent properties in OSMOSE models.

Materials and Methods:

  • Parameter Uncertainty Characterization
    • Identify poorly constrained parameters (e.g., larval mortality, natural mortality, fecundity)
    • Define plausible ranges for each parameter based on literature, expert knowledge, and sensitivity analysis [3]
    • Establish probability distributions for stochastic sampling

  • Monte Carlo Simulation Design

    • Generate parameter sets using Latin Hypercube Sampling across defined ranges
    • Run ensemble simulations (typically 100-1000 iterations) with different parameter combinations [3]
    • Execute simulations for sufficient model duration to reach steady-state dynamics

  • Pattern Reproduction Analysis

    • For each simulation, quantify reproduction of each empirical pattern using predefined metrics
    • Calculate statistical distributions of pattern metrics across the parameter ensemble
    • Identify parameter combinations that successfully reproduce all priority patterns
    • Analyze trade-offs between reproduction of different patterns

  • Sensitivity Analysis

    • Use statistical methods (e.g., generalized linear models, random forests) to quantify relationships between parameters and pattern metrics
    • Identify parameters with strongest influence on key emergent properties
    • Determine critical parameter thresholds for acceptable pattern reproduction

Expected Outputs: Quantitative understanding of parameter influences on emergent properties, identification of key uncertain parameters requiring empirical constraint, and probabilistic assessment of model performance.

The Scientist's Toolkit: Research Reagent Solutions

Table 3. Essential Research Tools and Components for OSMOSE-POM Implementation

Tool Category Specific Components Function in POM Implementation Example Sources
Biological Parameters Life history traits (growth, reproduction, mortality) Parameterize individual-level processes and trade-offs FishBase, literature reviews [11]
Trophic Interaction Data Diet compositions, stomach content analyses, stable isotopes Validate emergent trophic structure and energy flows Scientific surveys, published meta-analyses [45]
Spatiotemporal Distribution Data CPUE, scientific survey catches, acoustic data Evaluate emergent spatial patterns and habitat use Regional monitoring programs, global databases [42]
Environmental Drivers Temperature, oxygen, primary production fields Force bioenergetic and behavioral responses Coupled biogeochemical models, remote sensing [10]
Calibration Algorithms Pattern-matching metrics, approximate Bayesian computation Systematically compare model outputs to observed patterns Custom code, statistical software packages [41]
Uncertainty Quantification Tools Monte Carlo sampling, sensitivity analysis, Bayesian methods Evaluate parameter and structural uncertainty R, Python, specialized uncertainty analysis software [3]

This toolkit provides the essential components for implementing comprehensive pattern-oriented evaluation of OSMOSE models, from basic parameterization to advanced uncertainty analysis.

Pattern-Oriented Modeling provides a powerful methodological framework for evaluating emergent properties in OSMOSE ecosystem models. By requiring models to simultaneously reproduce multiple empirical patterns observed across different scales and hierarchical levels, POM increases structural realism and confidence in model predictions [41]. The application of POM to OSMOSE has demonstrated its utility across diverse ecosystems, from the data-rich North Sea to data-limited systems like Jiaozhou Bay [10] [43].

Future developments in OSMOSE-POM integration should focus on enhancing uncertainty quantification, expanding pattern libraries to include more physiological and behavioral observations, and developing standardized evaluation protocols that can be consistently applied across ecosystems. As ecosystem models play increasingly important roles in supporting ecosystem-based fisheries management, rigorous pattern-oriented evaluation will be essential for establishing their credibility and appropriate application domains [3] [45].

Comparing OSMOSE Outputs with Independent Survey and Catch Data

Validating the outputs of ecosystem models against empirical data is a critical step in ensuring their reliability for supporting ecosystem-based management. The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) is an individual-based, spatially explicit multispecies model that serves as a key tool for simulating the dynamics of fish communities and their interactions with the environment and fisheries [11]. This document establishes a standardized protocol for comparing OSMOSE model outputs with independent survey and catch data, a process fundamental to model calibration, evaluation, and the credible application of its results to marine resource management. The guidelines presented herein are framed within the context of advanced OSMOSE research, particularly leveraging the capabilities of recent bioenergetic extensions like Bioen-OSMOSE, which mechanistically describe individual physiological responses to environmental drivers such as temperature and oxygen [10].

The core validation challenge involves reconciling model-predicted patterns with observed data across multiple levels of biological organization. A robust validation does not merely seek a qualitative match but quantitatively assesses the model's ability to replicate key empirical statistics, thereby building confidence in its projections for scenarios such as climate change or new management policies [4] [10]. The following sections detail the data requirements, experimental workflows, and analytical frameworks necessary for this rigorous comparison.

A comprehensive validation requires multiple, independent datasets to avoid confirmation bias and to test different aspects of the model's performance.

Table 1: Essential Independent Data for OSMOSE Model Validation

Data Category Specific Metrics Primary Sources Temporal Resolution
Fishery-Independent Survey Data Species biomass/abundance, Size structure (length-at-age), Spatial distribution Scientific trawl surveys, Acoustic surveys, Egg and larval surveys Seasonal to Annual
Fishery-Dependent Catch Data Total landings, Discards, Landings per unit effort (LPUE), Species composition of catch Fishery logbooks, National fishery databases, On-board observer programs Annual to Monthly
Biological and Ecological Data Diet composition (stomach content analysis), Growth curves, Maturation ogives Scientific literature, Field sampling programs, Databases (e.g., FishBase) Varies (often static parameters)

Experimental Protocols

Protocol 1: Biomass and Catch Time Series Comparison

This protocol assesses the model's ability to replicate long-term trends in ecosystem and fishery metrics.

3.1.1 Workflow

Start Start Validation DataPrep Data Preparation (Aggregate OSMOSE outputs and observed data to annual resolution) Start->DataPrep TSCompare Time Series Comparison (Calculate correlation and RMSE for biomass/catch) DataPrep->TSCompare TrendAnalysis Trend Analysis (Compare directional trends over key periods) TSCompare->TrendAnalysis Eval Performance Evaluation TrendAnalysis->Eval

3.1.2 Methodology

  • Model Simulation: Run the OSMOSE model over a historical period (e.g., 2002-2021, as in the Eastern English Channel application [4]) and export annual time series of total biomass per species and total catch per species.
  • Data Preparation: Compile independent survey-derived biomass indices and official fishery catch statistics for the same species and time period. Ensure spatial scales are comparable; you may need to aggregate OSMOSE outputs to the scale of the survey.
  • Quantitative Comparison:
    • Calculate correlation coefficients (e.g., Pearson's r) between simulated and observed time series.
    • Compute the Root Mean Square Error (RMSE) to quantify the magnitude of divergence.
    • Perform a sign-test to evaluate if the model correctly captures the direction of biomass or catch trends (increase/decrease/stable) over defined periods.

3.1.3 Key Outputs * Time series plots overlaying simulated and observed data. * A table of performance statistics (correlation, RMSE) for each focal species.

Protocol 2: Size and Age Structure Validation

This protocol evaluates the model's representation of fundamental population demographics.

3.2.1 Workflow

Start Start Validation SizeData Collect Size/Age Data (Survey length frequencies, empirical growth parameters) Start->SizeData SimOutput Extract OSMOSE Outputs (Simulated size-at-age, school size distributions) SizeData->SimOutput DistCompare Distribution Comparison (Kolmogorov-Smirnov test, Analysis of Maturation Ogives) SimOutput->DistCompare Eval Performance Evaluation DistCompare->Eval

3.2.2 Methodology

  • Data Collection: Obtain size-frequency distributions from survey data and published parameters for growth (e.g., von Bertalanffy) and maturation ogives for key species.
  • Model Output Extraction: From OSMOSE, extract the simulated size-at-age distributions and the proportions of mature individuals at age at the end of each year.
  • Statistical Comparison:
    • Use two-sample Kolmogorov-Smirnov (K-S) tests to compare simulated versus observed size-frequency distributions for specific years.
    • Plot simulated growth trajectories against known growth curves.
    • Compare simulated maturation ogives with empirical data to assess if the model correctly represents the age/size at maturity.

3.2.3 Key Outputs * Histograms comparing observed and simulated size frequencies. * Plots of growth curves and maturation ogives. * P-values from K-S tests indicating the significance of distribution differences.

Protocol 3: Trophic and Spatial Pattern Validation

This protocol tests the model's emergent properties related to species interactions and distributions.

3.3.1 Workflow

Start Start Validation TrophicData Trophic Data Collection (Stomach content data from literature/databases) Start->TrophicData SpatialData Spatial Data Collection (Survey catch maps, habitat preference data) Start->SpatialData DietMatrix Diet Matrix Comparison (Calculate Schoener's Index for diet overlap) TrophicData->DietMatrix SpatialCompare Spatial Comparison (Spearman correlation of biomass density maps) SpatialData->SpatialCompare Eval Performance Evaluation DietMatrix->Eval SpatialCompare->Eval

3.3.2 Methodology

  • Trophic Data: Compile quantitative diet composition data from stomach content studies for the main predatory fish in the ecosystem.
  • Spatial Data: Gather maps of biomass density from scientific surveys.
  • Comparison:
    • Trophic: Construct a predator-prey matrix from OSMOSE outputs and compare it to an empirically derived matrix using Schoener's Index to quantify diet overlap.
    • Spatial: For specific years and species, calculate the Spearman's rank correlation between the simulated spatial biomass grid and an interpolated map from survey data.

3.3.3 Key Outputs * Heatmaps of observed vs. predicted diet matrices. * Spatial correlation coefficients for key species. * Maps visualizing observed and predicted biomass hotspots.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools and Data Sources for OSMOSE Validation

Tool/Resource Type Function in Validation Example Sources
Scientific Trawl Survey Data Dataset Provides fishery-independent indices of abundance, size, and spatial distribution for comparing against OSMOSE outputs. National/Fisheries Science Centers (e.g., ICES)
FishBase/SeaLifeBase Database Provides reference life history parameters (growth, maturity, diet) for comparing against emergent traits in OSMOSE. www.fishbase.org, www.sealifebase.org
Global Marine Ecosystem Models Model Ensemble Provides a broader context for evaluating OSMOSE projections, especially under climate change scenarios (e.g., FishMIP) [4]. FishMIP, ISIMIP
R Statistical Environment Software The primary platform for conducting statistical comparisons, generating performance metrics, and creating publication-quality figures. The R Project
POLCOMS-ERSEM (or similar) Biogeochemical Model Provides lower trophic level (plankton) forcing to OSMOSE; its accuracy is foundational for realistic fish simulations [10]. Regional Ocean Model Systems
Sensitivity & Uncertainty Analysis (SA/UQ) Protocols Methodology Framework for testing how model outputs respond to parameter changes, identifying critical parameters for calibration [4]. [4]

The consistent application of these protocols ensures a thorough and defensible validation of the OSMOSE model. By quantitatively comparing model outputs with independent data across multiple dimensions—temporal, demographic, trophic, and spatial—researchers can identify model strengths and weaknesses. This process is not a one-time exercise but an iterative one, where validation results inform model refinement and improvement. A well-validated OSMOSE application becomes a powerful tool for exploring the cumulative impacts of human activities and environmental change, ultimately providing credible scientific advice for ecosystem-based management.

Multi-Model Intercomparison Projects (MIPs) and Ensemble Modeling

Multi-Model Intercomparison Projects (MIPs) are coordinated scientific efforts that compare simulations from multiple models using standardized experimental protocols. These projects enable researchers to quantify model uncertainties, identify systematic errors, and improve the reliability of projections for complex systems like the Earth's climate and marine ecosystems [46]. The Coupled Model Intercomparison Project (CMIP), organized under the World Climate Research Programme, represents the most prominent framework for MIPs, currently in its seventh phase (CMIP7) [47] [48]. Within this framework, specialized MIPs like the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP) focus specifically on projecting climate change impacts on marine ecosystems [49].

Ensemble modeling refers to the approach of running multiple model simulations (either with different models or different parameterizations of the same model) to quantify uncertainty in projections. Rather than relying on a single model outcome, this methodology provides a range of possible futures, offering a more robust foundation for scientific conclusions and policy decisions [50]. For marine ecosystem management, ensemble modeling helps evaluate the combined impacts of changing climate, external nutrient supply, and fisheries, providing crucial information for developing effective conservation strategies [50].

The Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) represents an individual-based, spatially explicit modeling approach that simulates marine trophic networks from plankton to top predators. Its participation in MIPs like Fish-MIP allows for systematic comparison with other modeling frameworks, improving the collective understanding of how marine ecosystems respond to anthropogenic pressures [4] [49].

Key International MIP Frameworks

The Coupled Model Intercomparison Project (CMIP) Framework

The Coupled Model Intercomparison Project (CMIP) serves as the overarching framework for climate model intercomparison, coordinating experiments to better understand past, present, and future climate changes arising from natural variability or radiative forcing changes [51]. CMIP's primary objectives include assessing model performance during historical periods and quantifying causes of spread in future projections [51]. The project makes multi-model output publicly available in standardized formats, enabling broad scientific analysis. CMIP has evolved through several phases (CMIP5, CMIP6, and now CMIP7), with each phase introducing improved model components and experimental designs [48].

Table 1: Evolution of CMIP Phases

CMIP Phase Time Period Key Characteristics Notable Endorsed MIPs
CMIP5 ~2008-2012 Foundation for IPCC AR5; earlier Earth System Models Fish-MIP, ScenarioMIP
CMIP6 ~2015-2020 Improved model resolution; more complex biogeochemistry; higher climate sensitivity Fish-MIP 2.0, ScenarioMIP, LUMIP, OMIP
CMIP7 ~2023-present Ongoing development; new scenario selection; focus on integration across communities ScenarioMIP (under development)
Specialized MIPs Relevant to Marine Ecosystems

Several specialized MIPs operate within the CMIP framework to address specific scientific questions:

  • ScenarioMIP: Provides multi-model projections based on alternative scenarios of future emissions and land use changes. For CMIP7, ScenarioMIP is developing a new set of scenarios through a community-driven process to facilitate integrated research on the physical system consequences of future scenarios and their impacts on natural and social systems [47] [51].

  • Fish-MIP: Specifically examines climate change impacts on marine ecosystems and fisheries, using a standardized protocol to compare global marine ecosystem models. Fish-MIP has revealed that next-generation CMIP6-forced models project greater declines in global ocean animal biomass compared to CMIP5-forced projections, with an ~19% decline by 2099 under high emissions scenarios [49].

  • Ocean Model Intercomparison Project (OMIP): Provides a framework for evaluating, understanding, and improving the ocean, sea-ice, tracer, and biogeochemical components of global Earth system models [51].

OSMOSE in MIPs: Applications and Protocols

OSMOSE is an individual-based, spatially and temporally explicit multispecies model that focuses on simulating high trophic level (HTL) dynamics in marine ecosystems, including fish and macroinvertebrates [10]. The model represents key processes such as opportunistic predation based on size adequacy, spatiotemporal co-occurrence of predators and prey, and explicit modeling of the life cycle from eggs to adults [10]. OSMOSE has been applied to various regional ecosystems worldwide, including the West Florida Shelf, the North Sea, the Peruvian Humboldt Current, and the Southern Ocean [4] [45].

Recent developments have enhanced OSMOSE's capabilities to address climate change impacts:

  • Bioen-OSMOSE: Incorporates bioenergetic processes and physiological responses to temperature and oxygen variations, mechanistically describing how environmental changes affect metabolic fluxes and life history traits [10].

  • Climate Change Projections: OSMOSE participates in Fish-MIP ensembles to project long-term impacts of climate change on marine animal biomass, contributing to multi-model assessments of ecosystem responses to different emission scenarios [49].

The following workflow diagram illustrates the typical implementation of OSMOSE within MIP frameworks:

G Climate Forcings (CMIP) Climate Forcings (CMIP) OSMOSE Model Configuration OSMOSE Model Configuration Climate Forcings (CMIP)->OSMOSE Model Configuration Parameter Sensitivity Analysis Parameter Sensitivity Analysis OSMOSE Model Configuration->Parameter Sensitivity Analysis Model Calibration & Evaluation Model Calibration & Evaluation Parameter Sensitivity Analysis->Model Calibration & Evaluation Ensemble Simulations Ensemble Simulations Model Calibration & Evaluation->Ensemble Simulations Management Scenarios Management Scenarios Ensemble Simulations->Management Scenarios Policy Recommendations Policy Recommendations Management Scenarios->Policy Recommendations

Protocol for Parameter Sensitivity Analysis in OSMOSE

Implementing a robust parameter sensitivity analysis is crucial for OSMOSE applications in MIPs. The following protocol, adapted from Luján et al. (2025), provides a systematic approach [4]:

  • Parameter Selection: Identify key model parameters related to growth, mortality, reproduction, and feeding processes. Prioritize parameters with high uncertainty or known influence on model outputs.

  • Experimental Design:

    • Define parameter ranges based on literature review and expert knowledge
    • Select appropriate sampling method (e.g., Latin Hypercube Sampling, Monte Carlo)
    • Determine sample size (typically hundreds to thousands of simulations)

  • Model Execution:

    • Run OSMOSE simulations with parameter combinations
    • Execute sufficient model years to reach stable states
    • Repeat simulations to account for stochasticity

  • Output Analysis:

    • Calculate sensitivity indices (e.g., Sobol', Morris, or FAST methods)
    • Identify influential parameters for key outputs (biomass, catches, diversity indices)
    • Quantify parameter interactions and nonlinear effects

  • Uncertainty Quantification:

    • Propagate parameter uncertainties to model projections
    • Identify parameters contributing most to output variance
    • Prioritize parameters for further empirical constraint

This protocol enhances the credibility of OSMOSE contributions to MIPs by systematically addressing parameter uncertainty and its impact on model projections [4].

Protocol for Ensemble Modeling with OSMOSE

Ensemble modeling with OSMOSE within MIP frameworks follows a standardized approach to ensure comparability across models:

  • Experimental Setup:

    • Define spatial and temporal domains consistent with MIP protocols
    • Implement standard forcing datasets from CMIP (e.g., CMIP6 or CMIP7 scenarios)
    • Configure common output variables (e.g., biomass, catches, trophic indices)

  • Scenario Implementation:

    • Implement standardized scenarios (e.g., SSP-RCP pathways for climate projections)
    • Include both climate forcing and human activity scenarios (e.g., fishing pressure)
    • Conduct long-term projections (typically to 2100) with historical spin-up

  • Multi-Model Analysis:

    • Execute OSMOSE alongside other ecosystem models with identical forcing
    • Apply common diagnostic metrics and indicators
    • Quantify inter-model spread and consensus

  • Uncertainty Partitioning:

    • Separate uncertainties from climate projections, model structure, and internal variability
    • Identify regions and indicators with high vs. low model agreement
    • Assess robust findings across the model ensemble

This approach allows OSMOSE to contribute to consensus projections while characterizing uncertainties, providing more reliable information for ecosystem-based management [50] [49].

Quantitative Data from MIP Studies Involving OSMOSE

Table 2: Projected Changes in Global Marine Animal Biomass from Fish-MIP Ensembles

Scenario CMIP Generation Time Period Median Biomass Change (%) OSMOSE Contribution Key Drivers
High Emissions (SSP5-8.5) CMIP5 2090-2099 -16.5% Included in ensemble Warming, NPP declines in some regions
Strong Mitigation (SSP1-2.6) CMIP5 2090-2099 -5% Included in ensemble Moderate warming, mixed NPP changes
High Emissions (SSP5-8.5) CMIP6 2090-2099 -19% Included in ensemble Stronger warming, larger NPP declines in GFDL-ESM
Strong Mitigation (SSP1-2.6) CMIP6 2090-2099 -7% Included in ensemble Reduced but still significant warming

Table 3: OSMOSE Applications in Regional MIP Case Studies

Region Primary Objectives Key Findings Management Relevance
West Florida Shelf Estimate natural mortality for Red Grouper; simulate fishing scenarios OSMOSE provided stage-specific natural mortality estimates; identified trade-offs between catches and ecosystem impacts Informed SEDAR stock assessment and fishery management councils [45]
Eastern English Channel Assess cumulative impacts of offshore wind farms Minor ecosystem impacts at broad scale; species-specific responses at local scales Support marine spatial planning and environmental impact assessments [4]
North Sea Evaluate bioenergetic responses to temperature and oxygen changes Realistic spatial responses of bioenergetic fluxes to environmental gradients Climate vulnerability assessments and adaptation planning [10]
Baltic Sea Ensemble projections for climate, nutrient, and fishery scenarios Identified nutrient reduction as key management strategy despite climate change Supported Helsinki Commission Baltic Sea Action Plan [50]

Table 4: Key Research Reagent Solutions for OSMOSE-MIP Implementation

Resource Category Specific Tools/Datasets Function in OSMOSE-MIP Research Source/Availability
Climate Forcings CMIP6/CMIP7 ESM outputs (GFDL, IPSL) Provide physical and biogeochemical drivers (SST, NPP, oxygen) for ecosystem projections Earth System Grid Federation [51] [49]
Observation Data Species biomass, size-at-age, diet compositions Model parameterization, calibration, and evaluation Regional monitoring programs, scientific surveys [10] [45]
Fishing Data Catch statistics, effort distribution, fishery indicators Represent fishing pressure in models; evaluate management scenarios National fishery databases, RFMOs
Sensitivity Analysis Tools Global sensitivity analysis algorithms Quantify parameter influence and model uncertainty Standard statistical packages; specialized software [4]
Model Evaluation Metrics Goodness-of-fit indicators, skill assessments Evaluate model performance against empirical data Community-developed criteria [45]

The integration of these resources within the MIP framework creates a powerful methodological ecosystem for advancing marine ecosystem forecasting. The standardized protocols ensure that OSMOSE applications generate comparable results across regions and time periods, while the ensemble approach with other models provides robust uncertainty characterization.

Multi-Model Intercomparison Projects and ensemble modeling represent essential approaches for advancing marine ecosystem science and supporting evidence-based management. The OSMOSE modeling framework contributes significantly to these efforts through its individual-based approach, explicit representation of trophic interactions, and ability to simulate complex ecosystem dynamics across multiple scales. As MIPs evolve into CMIP7 and beyond, OSMOSE continues to develop new capabilities, particularly in representing physiological responses to environmental change through approaches like Bioen-OSMOSE [10].

The protocols and applications outlined here provide a roadmap for researchers seeking to implement OSMOSE within MIP frameworks. By adhering to standardized experimental designs, conducting comprehensive sensitivity analyses, and participating in ensemble model comparisons, the scientific community can enhance the predictive capacity of marine ecosystem models and provide more reliable projections for addressing the combined challenges of climate change and resource management.

Analyzing Uncertainties in Global and Regional Climate Projections

Uncertainty analysis is a fundamental component of robust climate and ecosystem modeling, particularly for complex tools like the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE). As marine ecosystem models become increasingly intricate to achieve more realistic representations of natural systems, understanding and quantifying their sources of uncertainty becomes paramount for generating reliable projections and building stakeholder trust [3]. These uncertainties span multiple dimensions, from parameter imprecision in ecological models to structural differences in climate forcing scenarios, ultimately affecting predictions of how marine ecosystems will respond to climate change [4].

The OSMOSE modeling platform, as an individual-based, multispecies model, serves as an ideal framework for examining these uncertainty propagation pathways. Its end-to-end nature, simulating food web dynamics from primary producers to top predators, creates multiple potential points where uncertainties can enter and amplify throughout the modeling chain [52]. With climate change introducing unprecedented pressures on marine ecosystems through ocean warming, acidification, and altered primary production, accurately quantifying these uncertainties is essential for supporting ecosystem-based fisheries management and climate adaptation strategies [4].

This application note provides structured protocols for identifying, quantifying, and communicating uncertainties in global and regional climate projections within OSMOSE-based research. By implementing systematic uncertainty analysis frameworks, researchers can improve model reliability, enhance scientific credibility, and provide more nuanced guidance for policymakers navigating complex climate decisions.

Classification of Uncertainty Types

Uncertainties in climate and ecosystem modeling arise from multiple sources, each requiring distinct quantification approaches. Epistemic uncertainty stems from imperfect knowledge of the system, while variability uncertainty represents inherent natural fluctuations [3]. In coupled climate-ecosystem modeling frameworks, these manifest at specific points throughout the modeling chain:

  • Parameter uncertainty: Arises from imprecise estimation of model parameters, such as mortality rates and fecundity in OSMOSE models [3] [52].
  • Structural uncertainty: Emerges from differences in model representations of processes across global climate models (GCMs) and regional climate models (RCMs) [53] [4].
  • Scenario uncertainty: Relates to unknown future socioeconomic pathways and emission trajectories [53].
  • Internal variability: Represents inherent, unpredictable climate fluctuations unrelated to external forcing [53].
Quantitative Uncertainty Assessments

Recent multi-model intercomparison projects reveal substantial variations in climate change projections, particularly for marine ecosystems. The table below summarizes key findings from global and regional model analyses:

Table 1: Comparative uncertainty ranges in marine ecosystem projections

Model Type Climate Scenario Projected Global Marine Biomass Change by 2100 Regional Variation Key Sources of Discrepancy
Global Marine Ecosystem Models CMIP5 -16.8% to +3.2% Greater consensus on polar amplification Parameterization of species responses, trophic interactions
Regional Marine Ecosystem Models CMIP5 -12.5% to +8.7% Higher regional specificity Spatial resolution, local adaptation representations
Global Marine Ecosystem Models CMIP6 -24.3% to -5.1% Stronger decline signals in tropics Increased climate sensitivity in CMIP6 GCMs
Regional Marine Ecosystem Models CMIP6 -18.9% to -2.3% Contrasting patterns in coastal vs. open ocean Boundary conditions, regional process representation

[4]

The discrepancies between global and regional models highlight the importance of model selection in uncertainty analysis. Global models typically project greater biomass declines (86-100% of simulations showing declines) compared to regional models (50-67% showing declines) [4]. This underscores how model scale and structure contribute significantly to overall uncertainty.

For hydrological projections, similar patterns emerge, with different uncertainty sources dominating specific variables:

Table 2: Dominant uncertainty sources in hydrological projections for France (2090 timeframe)

Hydrological Variable Dominant Uncertainty Source Secondary Uncertainty Sources Internal Variability Contribution
Low Flows Emission scenarios Regional climate models, Hydrological models Moderate to high
Mean Annual Flows Global & regional climate models Emission scenarios, Internal variability Often large
High Flows Global & regional climate models Hydrological models, Internal variability Significant in rainfall-dominated areas

[53]

Protocol 1: Parameter Sensitivity Analysis for OSMOSE Models

Experimental Principle and Scope

Parameter sensitivity analysis identifies which OSMOSE model parameters most significantly influence output variability, allowing researchers to focus uncertainty quantification efforts on the most influential components. This protocol employs the Morris elementary effects method, which provides efficient parameter screening with limited computational demands compared to variance-based methods [52]. The approach is particularly valuable during model calibration to prioritize parameters requiring precise estimation.

Materials and Research Reagents

Table 3: Essential research reagents for OSMOSE parameter sensitivity analysis

Research Reagent Function Implementation Example
OSMOSE Model Configuration Base ecosystem structure Pre-calibrated OSMOSE model with species parameters
Parameter Perturbation Algorithm Generates parameter combinations Morris method sampling with optimized trajectories
Community Indicators Model output metrics Biocom (total biomass), mTLcom (mean trophic level), H' (diversity)
Species Group Biomasses Population-level outputs Biomass timeseries for focal functional groups
Computational Environment High-performance computing Parallel processing framework for multiple simulations

[52] [54]

Step-by-Step Procedure

  • Parameter Selection: Compile all OSMOSE parameters requiring evaluation, focusing on those with estimated values or high uncertainty. Categorize parameters by biological process (e.g., mortality, growth, reproduction).

  • Parameter Space Definition: Establish plausible ranges for each parameter based on literature review, expert elicitation, or previous calibration experiments. Represent ranges as probability distributions where possible.

  • Sampling Matrix Generation: Apply the Morris sampling technique to generate ( r ) trajectories through the parameter space, each containing ( k+1 ) points where ( k ) is the number of parameters. This creates a computationally efficient design requiring ( r \times (k+1) ) model evaluations.

  • Model Execution: Run the OSMOSE model for each parameter combination in the sampling matrix. For each run, record key output metrics including community indicators and species-specific biomasses.

  • Elementary Effects Calculation: For each parameter ( i ) and output ( Y ), compute the elementary effect: ( EEi = \frac{[Y(x1, ..., xi + \Deltai, ..., xk) - Y(x1, ..., xi, ..., xk)]}{\Deltai} ) where ( \Deltai ) is the parameter perturbation step size.

  • Sensitivity Metrics Computation: Calculate two sensitivity measures for each parameter-output combination:

    • ( \mu^* ): Mean of the absolute elementary effects, indicating overall influence
    • ( \sigma ): Standard deviation of elementary effects, indicating parameter interactions or nonlinearities

  • Parameter Ranking: Identify parameters with high ( \mu^* ) values as particularly influential. Parameters with high ( \sigma ) relative to ( \mu^* ) require special attention as their effects depend on other parameter values.

Applications in Southern Ocean OSMOSE Models

Application of this protocol to the OSMOSE-CooperationSea model for the Southern Ocean revealed that larval mortality parameters (Mlarval) represented 50% of the top eight most influential parameters for community indicators [52]. The model outputs demonstrated varying sensitivity patterns:

  • Total biomass (Biocom) and diversity (H') showed highest sensitivity to Mlarval parameters
  • Mean trophic level (mTLcom) was most sensitive to predator-prey interaction parameters
  • Toothfish biomass was particularly sensitive to their specific Mlarval and prey accessibility parameters

These findings direct uncertainty analysis efforts toward precise estimation of larval mortality rates and careful parameterization of predation interactions in Southern Ocean food web models.

Protocol 2: Monte Carlo Uncertainty Propagation

Experimental Principle and Scope

The Monte Carlo simulation approach explores how parameter errors propagate through OSMOSE models to affect output uncertainty [3] [52]. By repeatedly running models with parameter values sampled from probability distributions, this method generates empirical distributions of model outputs, providing comprehensive uncertainty quantification beyond single-point estimates.

Workflow Visualization

workflow cluster_1 Uncertainty Propagation cluster_2 Uncertainty Quantification start Define Parameter Distributions sample Generate Parameter Ensemble start->sample run Execute OSMOSE Simulations sample->run sample->run collect Collect Model Outputs run->collect run->collect analyze Analyze Output Distributions collect->analyze visualize Visualize Uncertainty analyze->visualize analyze->visualize

Step-by-Step Procedure

  • Parameter Distribution Specification: Define probability distributions for all influential parameters identified in Protocol 1. For OSMOSE models, this typically includes:

    • Larval and natural mortality rates (Mlarval, Mnatural)
    • Fecundity parameters
    • Predation interaction parameters
    • Prey accessibility coefficients

  • Correlation Structure Definition: Identify and parameterize correlations between parameters to ensure realistic joint sampling.

  • Ensemble Generation: Sample parameter values from their distributions using Latin Hypercube sampling for efficiency, creating an ensemble of 1000+ parameter sets.

  • Model Execution: Run the OSMOSE model with each parameter set under identical initial conditions and forcing. Utilize high-performance computing resources to parallelize executions.

  • Output Collection: For each run, record temporal trajectories of key response variables:

    • Community-level metrics (biomass, diversity, trophic level)
    • Species-specific biomasses
    • Predation mortality rates
    • Spatial distribution indicators

  • Uncertainty Quantification: Calculate confidence intervals, variance decompositions, and probability distributions for all output variables. For temporal outputs, compute uncertainty envelopes representing prediction intervals.

  • Scenario Analysis: Compare output distributions across different climate forcing scenarios to distinguish parameter uncertainty from scenario uncertainty.

Implementation Example: OSMOSE-JZB

Application to the OSMOSE-JZB model of Jiaozhou Bay, China demonstrated that parameter uncertainty markedly affects model performance across organizational levels [3]. The analysis revealed:

  • Community-level impacts: Increasing parameter error bounds resulted in greater divergence in predicted fish community structure.
  • Mortality rate uncertainties: Larval mortality rate errors had stronger influence on outputs than natural mortality rate errors.
  • Synergistic effects: Certain parameter combinations produced antagonistic or synergistic effects on model outputs.

The Monte Carlo approach enabled quantification of uncertainty bounds around management-relevant indicators, providing fisheries managers with more realistic projections that explicitly communicate confidence levels.

Protocol 3: Multi-Model Ensemble Climate Forcing

Experimental Principle and Scope

This protocol addresses uncertainty in climate forcing scenarios used to drive OSMOSE projections. By employing multiple global and regional climate models across different emission scenarios, researchers can quantify structural and scenario uncertainties exogenous to the ecosystem model itself [53] [4]. The approach is particularly valuable for distinguishing climate-driven changes from other stressors.

Workflow Visualization

ensemble cluster_uncertainty Uncertainty Sources cmip CMIP5/CMIP6 Ensemble downscale Statistical Downscaling cmip->downscale bias_correct Bias Correction (QDM Method) downscale->bias_correct force Force OSMOSE Models bias_correct->force compare Compare Projections force->compare attribute Attribute Uncertainty compare->attribute scenarios Emission Scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) scenarios->downscale gcm Global Climate Models (CanESM5, GFDL-ESM4, etc.) gcm->downscale rcm Regional Climate Models (EUROCORDEX, etc.) rcm->bias_correct

Step-by-Step Procedure

  • Climate Model Selection: Curate a diverse ensemble of global and regional climate model projections from CMIP5/CMIP6 archives, ensuring representation of different model families and climate sensitivities.

  • Emission Scenario Selection: Include multiple Shared Socioeconomic Pathways (SSPs) spanning plausible future trajectories, typically SSP1-2.6, SSP3-7.0, and SSP5-8.5.

  • Bias Correction and Downscaling: Apply quantile-preserving methods (e.g., Quantile Delta Mapping) to correct systematic biases and downscale climate projections to ecologically relevant spatial resolutions [55].

  • OSMOSE Forcing Preparation: Process climate model outputs (sea surface temperature, pH, primary production, etc.) into forcing formats compatible with OSMOSE model requirements.

  • Ensemble Model Execution: Run the OSMOSE model with each climate forcing combination while holding biological parameters constant, creating a matrix of climate projections × ecosystem responses.

  • Uncertainty Partitioning: Use variance decomposition methods (e.g., ANOVA) to quantify contributions of different uncertainty sources:

    • Emission scenario uncertainty
    • Global climate model uncertainty
    • Regional climate model uncertainty
    • Internal climate variability

  • Emergent Constraint Identification: Analyze relationships between climate model characteristics and ecosystem responses to identify potential constraints on uncertainty ranges.

Implementation Considerations

Application of this protocol to marine ecosystem projections has revealed that emission scenario uncertainty typically dominates for long-term (end-of-century) projections, while internal variability contributes significantly to near-term decadal projections [53] [56]. For the OSMOSE model specifically, careful attention must be paid to:

  • Temporal alignment between climate forcing and biological processes
  • Representation of extreme events in bias-corrected climate data
  • Consistency in spatial scales between climate drivers and ecosystem model resolution

Protocol implementation in the Explore2 dataset for French hydrology demonstrated that regional climate models contribute considerable uncertainty to low flow projections, sometimes exceeding global model contributions [53].

Integrated Uncertainty Communication Framework

Uncertainty Visualization Guidelines

Effective communication of uncertainty requires visualization strategies that convey confidence levels without overwhelming stakeholders. Recommended approaches include:

  • Violin plots for probability distributions of key management indicators
  • Uncertainty envelopes around temporal trajectories of biomass and diversity metrics
  • Spatial probability maps for distribution shifts and habitat suitability changes
  • Scenario matrices comparing outcomes across different uncertainty dimensions
Decision-Relevant Output Formatting

Structure uncertainty analyses to directly inform management decisions by:

  • Classifying uncertainty levels for each model output (well-constrained vs. highly uncertain)
  • Identifying decision-relevant thresholds (e.g., biomass limits, temperature tolerances)
  • Calculating probabilities of exceeding thresholds across the uncertainty ensemble
  • Highlighting robust findings that persist across most model configurations

For OSMOSE applications, this might include probability statements about fisheries collapse risk under different climate scenarios or confidence intervals around maximum sustainable yield estimates.

Systematic uncertainty analysis is not merely a technical exercise but a fundamental requirement for credible ecosystem projections in climate impact research. The protocols presented here provide a structured approach to identifying, quantifying, and communicating uncertainties in OSMOSE model applications, supporting more transparent and decision-relevant science.

As climate change accelerates, with 2025 on track to be the second or third warmest year on record and an 86% chance that at least one year between 2025-2029 will exceed 1.5°C above pre-industrial levels [56], the need for robust uncertainty quantification becomes increasingly urgent. By implementing these protocols, the OSMOSE research community can enhance model reliability, build stakeholder trust, and ultimately contribute to more climate-resilient ecosystem management.

Benchmarking OSMOSE Against Other Ecosystem Models (e.g., Atlantis, EwE)

Ecosystem-based management of marine resources requires sophisticated modeling tools capable of simulating complex ecological interactions. Among the available approaches, the Object-oriented Simulator of Marine ecOSystem Exploitation (OSMOSE) stands as a prominent individual-based modeling framework designed to explore fish community dynamics and ecosystem effects of fishing and climate change [9]. This application note provides a systematic comparison between OSMOSE and other established ecosystem models, particularly the end-to-end Atlantis framework and models within the Ecopath with Ecosim (EwE) family, focusing on their theoretical foundations, application domains, and implementation protocols. As marine ecosystem models transition from "strategic" to "tactical" management applications, understanding their comparative strengths and limitations becomes crucial for supporting ecosystem-based fisheries management (EBFM) [57] [3].

The inherent complexity of marine ecosystems necessitates modeling approaches that can capture essential processes across multiple trophic levels while accounting for anthropogenic pressures. OSMOSE employs individual-based model (IBM) principles, representing fish individuals grouped in schools characterized by size, weight, age, taxonomy, and geographical location [9]. In contrast, Atlantis represents a spatially explicit end-to-end model with dynamically integrated physics, ecology, and socio-economic modules [57]. These structural differences lead to distinct applications, capabilities, and implementation requirements that must be carefully considered when selecting modeling approaches for specific research or management objectives.

Model Characteristics and Theoretical Foundations

Core Architectural Principles

Table 1: Fundamental characteristics of OSMOSE, Atlantis, and Ecopath with Ecosim models

Characteristic OSMOSE Atlantis Ecopath with Ecosim (EwE)
Modeling Approach Individual-based model (IBM) End-to-end framework with integrated modules Mass-balance (Ecopath), Dynamic simulation (Ecosim)
Spatial Structure 2D spatial distributions by species/size/season [9] Spatially explicit with multiple habitat polygons [58] [57] Typically coarse spatial resolution
Temporal Resolution Seasonal to multi-annual simulations Multi-decadal projections [57] Annual time steps typically
Trophic Interactions Size-based opportunistic predation [9] Pre-defined diet matrices with ontogeny [58] Linear functional responses
Key Processes Growth, explicit predation, natural mortality, reproduction, migration [9] Physics, ecology, socio-economics [57] Biomass production, consumption, fishery catches
Primary Applications Climate and fishing impacts, community dynamics [4] [9] Ecosystem approach to fisheries management (EAFM) [58] Policy screening, fishing impact assessment

OSMOSE's foundational principle is opportunistic predation based on spatial co-occurrence and size adequacy between predators and prey [9]. The model represents fish individuals grouped in schools that undergo different life cycle processes, including growth, explicit predation, natural and starvation mortalities, reproduction, and migration. This individual-based approach allows for the emergence of population and community-level patterns from individual interactions, providing mechanistic insights into ecosystem dynamics.

Atlantis employs a modular structure that dynamically integrates physical, biological, and human dimensions [57]. This end-to-end framework simultaneously simulates hydrodynamic processes, biogeochemical cycles, trophic interactions, and socio-economic drivers, making it particularly valuable for evaluating cumulative impacts of multiple stressors. The model's spatial explicitness, with multiple habitat polygons, enables realistic representation of marine seascapes and fisheries [58].

The Ecopath with Ecosim approach, while not the primary focus of this comparison, represents a widely used methodology that combines mass-balance modeling (Ecopath) with dynamic simulations (Ecosim). This framework has been extensively applied in fisheries contexts but typically operates at coarser spatial and temporal resolutions than individual-based or end-to-end models.

Key Strengths and Limitations

OSMOSE's strength lies in its mechanistic representation of trophic interactions and life history processes, which provides insights into the emergence of community patterns from individual behaviors [9] [3]. The model's reliance on basic biological parameters often available from sources like FishBase enhances its applicability in data-limited contexts. However, OSMOSE applications face challenges related to parameter uncertainty, particularly for larval mortality rates and other life history parameters [3].

Atlantis excels in integrated scenario analysis that simultaneously considers ecological, physical, and socio-economic factors [58] [57]. This comprehensive approach supports holistic ecosystem-based management but comes with substantial computational demands and parameterization requirements. The complexity of Atlantis models necessitates rigorous review processes to ensure their utility and performance for management applications [57].

G OSMOSE OSMOSE (Individual-based) Strength1 Mechanistic trophic interactions OSMOSE->Strength1 Strength2 Emergent community patterns OSMOSE->Strength2 Strength6 Data-limited applicability OSMOSE->Strength6 Limitation1 Parameter uncertainty sensitivity OSMOSE->Limitation1 Atlantis Atlantis (End-to-end) Strength3 Integrated physical- biological-socioeconomic modeling Atlantis->Strength3 Strength4 Holistic management scenarios Atlantis->Strength4 Limitation2 Computational intensity Atlantis->Limitation2 Limitation3 Complex review requirements Atlantis->Limitation3 EwE Ecopath with Ecosim (Mass-balance) Strength5 Policy screening capabilities EwE->Strength5 EwE->Strength6 Limitation4 Coarse spatial- temporal resolution EwE->Limitation4

Diagram 1: Comparative strengths and limitations of marine ecosystem models. Each model exhibits distinct advantages and challenges that determine their suitability for specific applications.

Application Domains and Case Studies

OSMOSE Applications

Recent OSMOSE applications demonstrate its versatility in addressing diverse research questions. In the Eastern English Channel, an OSMOSE model was enhanced to assess cumulative effects of offshore wind farms (OWFs) on various biological groups and fishing activities [4]. The model incorporated technical developments including new species representations, improved fishing processes, prey field forcing updates to include climate change projections, and inter-annual calibration over 2002-2021 [4]. This application revealed that at the ecosystem scale, total fish biomass and catch were slightly reduced under all OWF scenarios, with the most significant declines observed for cuttlefish, herring, and red mullet, primarily driven by changes in predation and fishing pressure during construction phases [4].

In the Jiaozhou Bay (China), OSMOSE-JZB served as an end-to-end model analyzing trophic interactions and ecosystem dynamics [3]. This implementation coupled modeling components based on a one-way approach to simulate ecosystem dynamics, with spatio-temporal dynamics of low trophic level groups derived from a nutrient-phytoplankton-zooplankton (NPZ) model and high trophic level groups simulated by OSMOSE proper [3]. The model demonstrated particular sensitivity to larval mortality parameters, highlighting the importance of uncertainty analysis in model applications.

Global comparative studies have utilized OSMOSE within ensemble modeling approaches to evaluate climate change impacts. In one such comparison, OSMOSE contributed to analyses revealing that global models generally projected greater biomass declines by the end of the 21st century than regional models, with greater declines projected using CMIP6 than CMIP5 climate simulations [4].

Atlantis Applications

Atlantis has been extensively applied to support Ecosystem Approach to Fisheries Management (EAFM). In the Strait of Sicily, an Atlantis implementation demonstrated its capability to recreate trophic levels and ecological interactions while testing model skill through comparison of predicted biomass and catch against observed data for target species [58]. Sensitivity analyses identified nutrient loading and fishing pressure as major processes influencing the ecosystem trophic spectrum through bottom-up and top-down effects [58].

The Gulf of Mexico Atlantis application provides insights into the model review process essential for operational use [57]. This implementation underwent a formal review process including informal reviews with regional experts and formal reviews with independent experts, highlighting the importance of performance evaluation and uncertainty quantification in complex ecosystem models [57]. Despite initial outcomes indicating the model was not yet ready for management use, reviewers provided clear pathways for refinement toward operational status.

Experimental Protocols and Methodologies

Model Parameterization and Calibration

Table 2: Essential parameters and data requirements for OSMOSE and Atlantis

Parameter Category OSMOSE Requirements Atlantis Requirements
Biological Parameters Growth parameters, reproduction processes [9] Species life history, physiology, behavior [58]
Trophic Parameters Size-based predation relationships [9] Diet matrices, consumption rates [58]
Spatial Data Distribution maps by species/size/season [9] Habitat polygons, environmental layers [58]
Fisheries Data Fishing mortality by species/age/size/space/season [9] Fleet dynamics, catch distributions, economic data [57]
Environmental Forcings Hydrodynamic and biogeochemical model outputs [4] Physical oceanography, nutrient inputs [58]
Calibration Data Biomass and catch time series [9] Biomass indices, catch statistics [58]

OSMOSE parameterization follows a structured protocol beginning with the compilation of basic biological parameters for growth and reproduction processes, often sourced from databases like FishBase [9]. The model requires spatial distribution maps for each species, stratified by age/size/stage and season depending on data availability. Recent applications have emphasized multi-year calibration periods (e.g., 2002-2021) to capture inter-annual variability and improve model robustness [4]. The calibration process utilizes observed biomass and catch data, with dedicated evolutionary algorithms facilitating model fitting [9].

Atlantis parameterization follows a modular approach, beginning with the definition of spatial domains and habitat characteristics [57]. Biological parameterization includes comprehensive representation of functional groups, with particular attention to diet compositions and ontogenetic shifts. Model calibration typically involves comparing predicted biomass and catch against observed data using multiple quantitative metrics, with sensitivity analyses identifying the most influential parameters [58].

Sensitivity and Uncertainty Analysis Protocols

Comprehensive sensitivity analysis represents a critical component of ecosystem model evaluation. A standardized protocol for parameter sensitivity analyses in complex ecosystem models includes the following steps [4]:

  • Parameter Selection: Identification of key model parameters based on preliminary simulations and literature review
  • Uncertainty Ranges Definition: Establishment of plausible parameter bounds using empirical data, expert opinion, or literature values
  • Experimental Design: Implementation of sampling strategies (e.g., Latin Hypercube, Morris screening) across parameter space
  • Model Execution: Running ensemble simulations across parameter combinations
  • Output Analysis: Statistical evaluation of parameter effects on model outputs using variance decomposition or regression techniques
  • Uncertainty Quantification: Characterization of uncertainty in model projections

For OSMOSE applications, specific parameters requiring careful attention include larval mortality rates (Mlarval), natural mortality rates (Mnatural), and relative fecundity [3]. Studies have demonstrated that OSMOSE models show greater sensitivity to variations in larval mortality than natural mortality, with community-level predictions particularly affected by these parameters [3].

Atlantis applications have employed Morris screening approaches for sensitivity analysis, efficiently identifying parameters influencing model outcomes [57]. These analyses consistently highlight nutrient loading and fishing pressure as dominant drivers of ecosystem dynamics [58].

G Start Sensitivity Analysis Protocol Step1 Parameter Selection (Key parameters identification) Start->Step1 Step2 Uncertainty Ranges (Literature, expert opinion) Step1->Step2 OSMOSE OSMOSE Critical Parameters: - Larval mortality (Mlarval) - Natural mortality (Mnatural) - Relative fecundity Step1->OSMOSE Atlantis Atlantis Critical Parameters: - Nutrient loading - Fishing pressure - Diet compositions Step1->Atlantis Step3 Experimental Design (Latin Hypercube, Morris screening) Step2->Step3 Step4 Ensemble Simulations (Multiple parameter combinations) Step3->Step4 Step5 Output Analysis (Variance decomposition, regression) Step4->Step5 Step6 Uncertainty Quantification (Characterization of projections) Step5->Step6

Diagram 2: Sensitivity analysis workflow for ecosystem models. The protocol identifies critical parameters specific to each modeling approach, with OSMOSE particularly sensitive to life history parameters and Atlantis to nutrient and fishing drivers.

Model Review and Evaluation Framework

Formal review processes enhance the credibility and utility of ecosystem models for management applications. Based on experiences with Atlantis model reviews, a structured two-phase approach provides comprehensive evaluation [57]:

Phase 1: Informal Regional Expert Review

  • Objectives: Gather regional knowledge, identify data gaps, assess model structure
  • Participants: Subject matter experts familiar with the ecosystem
  • Deliverables: Technical report identifying major model deficiencies and refinement priorities

Phase 2: Formal Independent Expert Review

  • Objectives: Evaluate model performance, uncertainty treatment, and management readiness
  • Participants: Independent experts without prior involvement in model development
  • Deliverables: Assessment of model utility with specific recommendations for operational use

Benchmarking standards for model evaluation include [57]:

  • Model Skill Assessment: Quantitative comparison of predicted versus observed biomass and catch patterns
  • Sensitivity Evaluation: Comprehensive analysis of model responses to parameter perturbations
  • Uncertainty Characterization: Explicit documentation of uncertainty sources and magnitudes
  • Management Relevance: Demonstration of utility for addressing specific management questions

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential resources and tools for implementing OSMOSE and Atlantis models

Tool Category Specific Tools/Resources Application Function
Model Platforms OSMOSE framework (osmose-model.org) [59] [9] Individual-based modeling infrastructure
Atlantis modeling system [58] [57] End-to-end ecosystem simulation platform
Parameter Sources FishBase [9] Biological parameter compilation
Regional monitoring surveys [58] [3] Species distribution and abundance data
Analysis Tools Sensitivity analysis packages (R, Python) [4] Parameter perturbation and response evaluation
Statistical comparison metrics [58] Model performance quantification
Validation Data Fishery-independent surveys [3] Biomass and composition validation
Commercial fishery statistics [58] Catch pattern evaluation
Coupling Tools Hydrodynamic model outputs [4] Environmental forcing conditions
Biogeochemical model data [4] [3] Lower trophic level dynamics

OSMOSE and Atlantis represent complementary approaches to marine ecosystem modeling with distinct strengths and application domains. OSMOSE excels in mechanistic representation of trophic interactions through its individual-based, size-structured framework, providing insights into community dynamics emerging from individual processes [9]. Atlantis offers comprehensive integration of physical, ecological, and socio-economic dimensions, supporting holistic evaluation of management strategies [58] [57]. Both approaches face challenges related to parameter uncertainty and model complexity, necessitating rigorous sensitivity analysis and formal review processes [57] [3].

Future developments in marine ecosystem modeling should focus on enhancing model interoperability through standardized benchmarking exercises and uncertainty quantification protocols. The integration of empirical observations with model ensembles, as demonstrated in global comparisons [4], provides promising pathways for improving projection reliability. As these models continue to evolve from strategic to tactical management tools, addressing uncertainty sources and establishing robust review frameworks will be essential for building management confidence and advancing ecosystem-based approaches to marine resource management.

Conclusion

OSMOSE has established itself as a powerful and flexible platform for advancing Ecosystem-Based Fisheries Management. Its individual-based, spatially explicit nature allows it to simulate complex ecosystem dynamics and emergent properties, from food web interactions to the impacts of climate change and human activities. The development of frameworks like Bioen-OSMOSE and integrated MSE enhances its mechanistic realism and direct utility for strategic management advice. Future directions include further refining the physiological basis of species responses, improving two-way coupling with biogeochemical models, and expanding multi-model ensembles to better quantify uncertainty. For the scientific community, continued investment in robust sensitivity analysis, validation, and model intercomparison is crucial to increase the reliability of OSMOSE projections and solidify its role in guiding the sustainable management of marine resources under changing global conditions.

References