Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling

State space modelling and Bayesian analysis are both active areas of applie d research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state space models that may be non-linear and h ave non-normal errors, and hence it is particularly useful for modelling fi sheries dynamics. Here, this approach is demonstrated by fitting a non-line ar surplus production model to data on South Atlantic albacore tuna (Thunnu s alalunga). The state space approach allows for random variability in both the data (the measurement of relative biomass) and in annual biomass dynam ics of the tuna stock. Sampling from the joint posterior distribution of th e unobservables was achieved by using Metropolis-Hastings within-Gibbs samp ling.