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 motivated by a desire to incorporate more realism into stock assessment models, and much recent research effort has been devoted to the computational features of fitting state-space models for this purpose. Here, we extend the Bayesian application of nonlinear state-space modeling to sequential population 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 acquired, implementing a state-space model is a relatively simple task. Indeed, this application requires just 18 lines of code in its entirety and does not require the programmer to know the formulae for any prior density functions or likelihoods. Consequently, we suggest that this methodology may permit the implementation phase of nonlinear state-space modeling to be relegated, thereby allowing more effort to be devoted to the challenging issues of model checking, selection/averaging, sensitivity, and prior specification.