Dr. Darcy Webber1,2
1QUANTIFISH
2SAFS, UW
January 19, 2016 9:00 (PST): FSH 213
Exploring Bayesian state-space age-structured fisheries population dynamics models
Fisheries stock assessment models use incomplete observational data and knowledge of the system we are modeling to provide estimates of key management parameters, and require that we assume that the resulting uncertainty is properly reflected in the outputs. Current models typically follow a ‘deterministic’ approach. They are deterministic in the sense that the population follows sets of equations that deterministically define the population state from a previous state, without stochastic error.
State-space models provide an alternative approach to implementing fisheries assessment models. They can incorporate both observation uncertainty and the uncertainty that arises from modelling a simplification of reality — usually referred to as process error. Specifically, state-space models relate observations to unobserved states (e.g. numbers of fish in this context) through stochastic observation equations. Stochastic transition equations define how the unobserved states are assumed to evolve over time. Because state-space models incorporate both observation and process error explicitly, they may be able to help us better quantify the uncertainty of parameters of interest for management.
Until recently, state-space models have not been more frequently applied because they have been technically difficult to implement. However as computational methods in general and Bayesian methods in particular, become more sophisticated, the approach is becoming increasingly tractable.