| Summary: | Dissertation (Ph.D.) University of Alaska Fairbanks, 2001 This is a comprehensive study on the fishery and management system (including the inseason stock abundance dynamics, the purse seine fleet dynamics and the inseason management) of pink salmon (Oncorhynchus gorbuscha) in the northern Southeast Alaska inside waters (NSE). Firstly, we presented a hierarchical Bayesian modelling approach (HBM) for estimating salmon escapement abundance and timing from stream count data, which improves estimates in years when data are sparse by "borrowing strength" from counts in other years. We presented a model of escapement and of count data, a hierarchical Bayesian statistical framework, a Gibbs sampling estimation approach for posterior distributions, and model determination techniques. We then applied the HBM to estimating historical escapement parameters for pink salmon returns to Kadashan Creek in Southeast Alaska. Secondly, a simulation study was conducted to compare the performance of the HBM to that of separate maximum likelihood estimation of each year's escapement. We found that the HBM was much better able to estimate escapement parameters in years where few or no counts are made after the peak of escapement. Separate estimates for such years could be wildly inaccurate. However, even a single postpeak count could dramatically improve the estimability of escapement parameters. Third, we defined major stocks and their migratory pathways for the NSE pink salmon. We estimated the escapement timing parameters of these stocks by the HBM. A boxcar migration model was then used to reconstruct the catch and abundance histories for these stocks from 1977 to 1998. Finally, we developed a stochastic simulation model that simulates this fishery and management system. Uncertainties in annual stock size and run timing, fleet dynamics and both preseason and inseason forecasts were accounted for explicitly in this simulation. The simulation model was applied to evaluating four kinds of management strategies with different ...
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