Fisheries systems – management and modelling

• Describe the development and increasing complexity of fisheries and ecosystem-based management, including advisory processes, quantitative methods, and institutional frameworks.
• Implement single-species stock assessment models used in ICES advisory processes to evaluate stock status, population dynamics, and associated uncertainty.
• Analyse biological interactions, including species-specific natural mortality, adapting quantitative multi-species models.
• Apply quantitative mixed fisheries models to analyse technical interactions, including fleet- and stock-specific fishing patterns across target and by-catch species.
• Evaluate spatially explicit bio-economic and vessel-based management strategies to model the impacts of maritime spatial planning on fleets, fish stocks, and seabed habitats.
• Interpret the results from advanced ecosystem models to support strategic fisheries advice, including the integrated impacts of fishing, environmental drivers, and species interactions, and socio-economic dimensions on resource dynamics.
• Predict future stock dynamics and fishery yields under alternative harvest control rules and management scenarios according MSY-based targets using Management Strategy Evaluation (MSE) across single-species, multi-species, mixed fisheries and ecosystem contexts.
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The course introduces principles and practice in modern fisheries management, with focus on quantitative evaluation methods and their role in scientific management advice. It begins with an overview of international fisheries and ecosystem-based management systems including their foundations, institutional frameworks, data sources, advisory processes, governance structures, and decision-making under multiple ecological and socio-economic objectives and risks. The remaining course is structured into the following three main parts:

Part 1: Single-species management advice
Hands-on training in application of two advanced single-stock assessment models used by the ICES advisory framework: a stochastic surplus production model (SPiCT) and a stochastic age-structured stochastic model (SAM). Students learn to estimate stock size, fishing mortality, and stock status relative to maximum sustainable yield (MSY) reference points using time series of catch and survey data. Model diagnostics, uncertainty quantification, and best practices for validating assessments are key in the training, and will conclude with an application of MSE also used in the next course parts.

Part 2: Multi-species and mixed fisheries management advice
Introduction to biological interactions between stocks involving a stochastic multi-species assessment model (SMS) and broader MSY reference point ranges accounting for trade-offs in exploitation of several species in the same fisheries. It also introduce simple and advanced methods (TEMAS, FCUBE) integrating the complexity of technical interactions in mixed fisheries and multi-fleet dynamics. Students will define and analyse interactions between target and bycatch species fishing mortality by fleet using data on effort, selectivity, and behavior also covering recreational fisheries.

Part 3: Holistic fisheries system and ecosystem management advice
Practical training in spatially explicit bio-economic and vessel-based MSE tools (e.g. VMS-Logbook-Tools, DISPLACE) combining logbook and VMS (satellite tracking) data for tactic management advice for several fisheries. These simulation models assess impacts of maritime spatial planning scenarios, e.g. windfarm closures, on fishing fleets, target stocks, and the seabed according to ecological and economic sustainability. Further, students evaluate output from advanced ecosystem-models integrating biological interactions, environmental forcing (e.g., climate change, eutrophication), and fisheries dynamics enabling strategic long-term management advice. This allows to explore broader ecological and socio-economic implications of management strategies before implementation.

Students work with both simple and advanced models in data-rich and data-poor contexts, developing the ability to critically evaluate trade-offs, risks, and sustainability of alternative management strategies.

25318/25345/25349, are recommended but not required. Prior experience with R is recommended. Preparatory materials will be made available in advance of the course to allow students to assess and, if necessary, improve their R skills.

Lectures as well as group and individual assignments and exercises including modeling.

This course provides students with competences relevant to UN SDGs, particularly #8 (Decent work and economic growth), #12 (Responsible consumption and production), #13 (Climate action), and #14 (Life below water).
The intention is to allow the critical and creative use of generative AI in the teaching process both by students and teachers.