Combining multiple Bayesian data analyses in a sequential framework for quantitative fisheries stock assessment

• DOI: 10.1139/f08-015
• Publication Date: 2008-04-17T13:44:46Z
• Authors: Catherine G.J. Mi...; Murdoch K McAllister; Sakari Kuikka; Samu Mäntyniemi; Atso Romakkaniemi; Tapani Pakarinen; Lars Karlsson; Laura Uusitalo
• Keywords: Stock assessment; Stock (firearms); Fish stock

This paper presents a sequential Bayesian framework for quantitative fisheries stock assessment that relies on wide range of fisheries-dependent and -independent data information. The presented methodology combines information from multiple analyses through the incorporation joint posterior probability density functions (pdfs) in subsequent analyses, either as informative prior pdfs or additional likelihood contributions. Different practical strategies are minimising any loss between analyses. Using this methodology, final model used provision management advice can be kept relatively simple, despite dependence large variety other is illustrated mixed-stock fishery four wild Atlantic salmon (Salmo salar) stocks northern Baltic Sea. different results considerable update previously available smolt abundance production capacity estimates by substantially reducing associated uncertainty. also allows, first time, estimation stock–recruit stocks.