Bayesian state-space modeling of age-structured data: fitting a model is just the beginning

Explicit modeling of process variability in the dynamics of fisheries is mo tivated by a desire to incorporate more realism into stock assessment model s, and much recent research effort has been devoted to the computational fe atures of fitting state-space models for this purpose. Here, we extend the Bayesian application of nonlinear state-space modeling to sequential popula tion analysis of age-structured data using a model formulation that allows for unreported catches and incidental fishing mortality. It is shown that, once a familiarity with the general-purpose Bayesian software BUGS is acqui red, implementing a state-space model is a relatively simple task. Indeed, this application requires just 18 lines of code in its entirety and does no t require the programmer to know the formulae for any prior density functio ns or likelihoods. Consequently, we suggest that this methodology may permi t the implementation phase of nonlinear state-space modeling to be relegate d, thereby allowing more effort to be devoted to the challenging issues of model checking, selection/averaging, sensitivity, and prior specification.