Nonlinear state-space modeling of fisheries biomass dynamics using Metropolis-Hastings within Gibbs sampling

State-space modeling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state-space models that may be nonlinear and have non-normal errors, and hence it is particularly useful for the modeling of fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus production model to data on South Atlantic albacore tuna (Thunnus alalunga), The state-space approach allows for random variability in both the data (measurement of relative biomass) and in annual biomass dynamics of the tuna stock. Sampling from the joint posterior distribution of the unobservables was achieved using Metropolis-Hastings within Gibbs sampling.