Evaluating uncertainty in harvest control law catches using Bayesian Markov chain Monte Carlo virtual population analysis with adaptive rejection sampling and including structural uncertainty

A new method is developed for calculating Bayes posterior distributions of future catches that conform to a specified harvest control law while incorporating uncertainty in biological reference points, natural mortality, and some aspects of model structure in addition to the usual stochastic noise. A Markov chain Monte Carlo approach is used to calculate Bayesian posterior distributions for critical parameters of a Norwegian spring-spawning herring (Clupea harengus) stock assessment using an assessment model that incorporates catch-at-age, survey, and tag release and recapture observations. Exceptionally, the approach allows prior uncertainty in model structure (e.g., whether survey observation errors should be treated as normal, lognormal, or gamma variates; whether Ricker or Beverton-Holt forms are used to model recruitment). This modelling approach is a useful tool that allows management advice to be provided that takes into account uncertainty in model structures and in some parameters that, by conven...