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Zawartość zarchiwizowana w dniu 2024-05-07

Sustainable fisheries. How can the scientific basis for fish stock assessments and predictions be improved?


The objective of the project is to examine whether existing scientific knowledge can be better utilised for reducing the uncertainties and increasing the time horizon of fish stock assessments carried out by ICES and other stock assessment agencies/institutions, and evaluate implications for fishery management. The long-term goal is to develop stock assessment and prediction models, which utilise all relevant knowledge of importance for fish population dynamics, including species interactions and the effects of a fluctuating environment, and thereby increase both the time horizon and reliability of our predictions. The short-term goal is to identify key explaining variables to be used in predictive relationships and co-ordinate research to establish such relationships and gradually implement them in the assessment models.

The project includes the following sub-objectives:

1. Identify main causes for shortcomings in present fish stock assessments and predictions.
2. Consider the limits set by nature on predictability of fish stock development.
3. Review "the state of the art" within relevant research areas and identify the kind of scientific knowledge potentially useful in fish stock predictions, which exists without being effectively used at present. Discuss how such knowledge most effectively could be used and propose future co-ordinated research for expanding the scientific knowledge relevant for fish stock assessment.
4. Carry out case studies for selected areas and stocks with the aim of demonstrating the potential for improvements in quality and time horizon of fish stock predictions.
5. Evaluate how communication/co-operation between fish stock assessment experts and experts within various fields of basic marine sciences can be improved.

Assessing fish stocks and predicting their development is a complicated task for two main reasons: There are great methodological difficulties in estimating how many fish there are in the sea at any given time, and our knowledge of the dynamics of the stocks and key environmental driving forces is insufficient for making precise predictions of the development. A large research effort have been devoted in recent decades to the various aspects of stock estimation, population dynamics and environmental effects, but a concerted effort towards integrating the various aspect of scientific knowledge in an attempt to identifying key variables and relationships for improving our stock predictions is needed. However, present fish stock assessment practise is characterised by too sharp a de facto separation of fish stock assessment experts and experts within various fields of basic marine sciences. This hamper the optimal use of available scientific knowledge in the stock assessment process. Therefore, the project involves experts within the following three broad research areas (topics):

Topic 1: Variability of the marine physical environment and its effects on fish stocks
Topic 2: Population dynamics including species interactions
Topic 3: Population dynamics/stock assessment models, including multispecies models

It has also been attempted to involve experts from very different geographical areas. In north-south direction, the following three major areas are represented:

Area 1: Norwegian and Barents Seas and Icelandic waters.
Area 2: North Sea, Skagerrak/Kattegat and Baltic
Area 3: Areas west of the Iberian Peninsula and Mediterranean

The project will co-ordinate activities with the aim of conducting critical investigations of possible predictive relationships to be used in stock assessments. This will include scrutinising of published literature where such relationships are suggested, or where analyses are given which may be used to construct relationships. In addition to evaluating the soundness of proposed relationships from published material and discussions, the project will carry out its own investigations, using long time series of physical and biological data, either from published literature or from available data bases, as one main source for testing proposed relationships.

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