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

Abstract

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

Estimating fishing and natural mortality rates for Pacific bluefin tuna (Thunnus orientalis) using electronic tagging data

“…In this paper we seek to use electronic tagging data to examine agegroup specific rates of natural mortality and seasonal movement patterns of PBFT. The use of a sequential Bayesian approach facilitates the flow of information from different data sources whereby the posterior from one analysis becomes the prior for the next (Michielsens et al, 2008). In the context of the current analysis, posterior probability density functions (pdfs) for seasonal movement rates from analysis of pop-up satellite archival tag (PAT) data were used as the prior for the archival tag model (Kurota et al, 2009).…”

Estimating spatial distribution of Atlantic salmon escapement using redd counts despite changes over time in counting procedure: application to the Allier River population

“…() or Michielsens et al. (). This model will use additional available information related to Atlantic salmon in the Allier catchment (e.g., 0 + sampling, juvenile stocking) to provide better estimates of the potential spawners but also estimates of the different parameters related to the life cycle of Atlantic salmon in the Allier catchment.…”

Biomass and mortality of chum salmon in the pelagic Bering Sea

“…The set of stock compositions used in a simulation was drawn randomly from the series of estimates produced by the MCMC stock composition analysis in order to incorporate the MCMC stock composition estimates into the MCMC return-at-age analysis as an informative prior (Michielsens et al 2008). Because the difference in genetic composition between Korean and Japanese rivers was insignificant (Sato et al 2004), we could not estimate reliable compositions of Korean and Japanese stocks.…”

“…For example, Michielsens et al (2008) assessed a mixed stock consisting of 4 wild Atlantic salmon stocks in the Baltic Sea using the Markov chain Monte Carlo (MCMC) analysis, combining the results of several previous studies. The objectives of the present study were to (1) use the MCMC return-at-age analysis to estimate the total biomass of immature chum salmon, one of the dominant pelagic planktivores in the Bering Sea basin; and (2) estimate demographic parameters such as the mortality, abundance-at-age, and catchability of a research trawl of chum salmon during their ocean life.…”

“…To estimate age-specific stock compositions simultaneously with a common set of baseline genetic compositions, the 'many-to-many' model was used for a mixed stock analysis of chum The age-specific abundance of immature chum salmon for Russian or North American stocks were estimated from the abundance of Japanese immatures estimated by the return-at-age analysis and composition estimates for stock mixtures in the ocean samples. The set of stock compositions used in a simulation was drawn randomly from the series of estimates produced by the MCMC stock composition analysis in order to incorporate the MCMC stock composition estimates into the MCMC return-at-age analysis as an informative prior (Michielsens et al 2008). Because the difference in genetic composition between Korean and Japanese rivers was insignificant (Sato et al 2004), we could not estimate reliable compositions of Korean and Japanese stocks.…”