Abstract:
|
The dynamical processes driving fish stock abundance and productivity are often hidden by observation and process noise that is embedded within complex population structure (e.g., age, size, spatial location). State-space models are useful in fisheries science for testing specific hypotheses about both the underlying dynamical process parameters as well as the structure of stochastic processes. Insights drawn from state-space modeling of population processes are increasingly used to test robustness of fisheries harvest strategies. In this talk, I present state-space modeling applications to a series of increasingly complex fisheries problems and data sets for large-scale Canadian fisheries
|