INTRODUCTION TO ECOLOGICAL MODELLING

• Conceptual foundations: systems thinking, model purpose, scope, and hierarchy

• Model structure: selecting state variables, external drivers, and key biophysical processes; mapping trophic and non-trophic interactions

• Mathematical formulation: translating ecological concepts into differential and difference equations, mass-balance constraints, and parameter sets

• Software practice: step-by-step projects in STELLA® (diagram-based dynamical systems), Ecopath with Ecosim (trophic mass-balance and time-dynamic food-web modelling), and R (custom-coded simulation and visualisation)

• Calibration and validation: parameter estimation, sensitivity and uncertainty analysis, and benchmarking against empirical data

• Scenario analysis and communication: running “what-if” experiments, evaluating management options, and presenting model insights to scientific and stakeholder audiences

Learning outcomes

By the end of the week you will be able to:

• Formulate clear ecological questions and translate them into appropriate model structures.

• Build and document dynamic models in STELLA, Ecopath with Ecosim, and R, selecting the platform that best fits the problem.

• Evaluate model behaviour through calibration, sensitivity testing, and uncertainty appraisal.

• Design and run scenario simulations that inform conservation, fisheries, and climate-adaptation decisions.

• Critically interpret and communicate modelling results, acknowledging assumptions and limitations, to support evidence-based marine ecosystem management.

TYPES OF MODELS AND Applications

• The course will unpack a spectrum of modelling frameworks—scope-for-growth, population-dynamics, Dynamic Energy Budget (DEB), and coupled hydrodynamic models—clarifying their assumptions, strengths, and real-world applications.

• A specific focus of this edition will be on food web models and linear inverse modelling with 2 days of dedicated lecturers delivered by Nathalie Niquil, Research Director at UMR BOREA, University of Caen Normandy (ORCID: 0000-0002-0772-754X).

• Food web models and linear inverse modeling (LIM) are powerful tools for unravelling the complexity of marine ecosystems.

• A food web model represents “who eats whom” in an ecosystem, often as a network of trophic interactions. In these models, each link is typically a weighted connection denoting a flow of organic matter or energy from prey to predator. By capturing these trophic connections, food web models help scientists visualize and quantify the interactions among species.

• Linear inverse modelling (LIM) , on the other hand, is a quantitative approach used to estimate the magnitudes of those energy or nutrient flows through the food web, especially when many flows cannot be measured directly.

• Together, these approaches bridge the gap between raw ecological data and a global understanding of ecosystem structure.