Cooperative Research Report (CRR) number 328, 'Best practices for the provision of prior information for Bayesian stock assessment', focuses on the concept of prior information – that is, any relevant knowledge existing outside the primary input data used in stock assessment. Potential sources of prior information are primary data, literature, online databases and the knowledge of experts. This information can be effectively used in Bayesian analysis, which is a method of statistical inference formalizing learning from new data based on already existing knowledge. At the heart of the approach is the idea of presenting everything that is not known exactly as a probability distribution. This analysis has been successfully applied in a growing number of stock assessments.
In a world where scientific knowledge is rapdily expanding, the key to success is to learn from earlier studies and use that knowledge effectively in risk analysis and communication. According to the authors, using prior information is relevant in data-rich fisheries, but essential in data-limited cases related to, for example, bycatch species. Due to the application of the ecosystem approach to fishery management, there is an increasing need to expand risk-related advice to new species. With its rigorous specification of uncertainties, the Bayesian method offers an excellent framework for risk analysis.
The Bayesian CRR, edited by Atso Romakkaniemi, was produced by the ECOKNOWS project and made possible through the contributions of authors inspired by the research questions of various working groups such as the Baltic Fisheries Assessment Working Group (WGBFAS), the Assessment Working Group on Baltic Salmon and Trout (WGBAST), the Working Group on North Atlantic Salmon (WGNAS), and the Herring Assessment Working Group for the Area South of 62°N (HAWG).
Bayes' theorem; Photo: Matt Buck, Flickr