Improving statistical catch-at-age stock assessments

My dissertation addresses three objectives: (1) to estimate fishing mortality rates and abundance of yellow perch in southwestern Lake Michigan during 1986--2002 to determine the contribution of fishing to the collapse of yellow perch in southwestern Lake Michigan, (2) to determine robust methods of dealing with time-varying fishery catchability within a statistical catch-at-age analysis (SCA) framework, and (3) to determine whether using Bayesian model selection, specifically Deviance Information Criterion (DIC) and an approximation of Bayes factors, results in using accurate models for prediction of important fisheries management quantities.; In chapter 1, I conducted an age-, size-, and sex-structured stock assessment of yellow perch to estimate population size and examine historical trends in fishing mortality in Illinois and Wisconsin waters of southwestern Lake Michigan. Model estimates indicated that yellow perch abundance in 2002 was less than 10% of 1986 abundance in Wisconsin and about 20% in Illinois. Annual mortality rates for females age 4 and older averaged 69% during 1986--1996 in Wisconsin and 60% in Illinois during 1986--1997, which are quite high for a species like yellow perch that can live longer than 10 years. Estimated fishing mortality rates on adult females during 1986--1996 exceeded widely used reference points, suggesting that overfishing may have occurred. I believe unsustainably high fishing mortality rates were a substantial contributing cause of the rapid decline of mature females in the mid-1990s.; The relationship between fishing mortality and fishery effort (catchability) may change over time through either density dependent or density independent processes. I used Monte Carlo simulations in chapter 2 to evaluate how different methods of estimating fishery catchability within an SCA model performed when models were confronted with different data generating scenarios. I evaluated performance of the estimation models by their accuracy and precision in determining quantities of interest such as biomass in the last year. In many cases, including fishery effort data in the estimation model and allowing catchability to follow a random walk performed as well or better than other methods. Exceptions were cases where fishing mortality was low and catchability trended over time. The estimation model that ignored fishery effort data performed well in cases with a good survey, but performance degraded as survey precision decreased. White noise and density dependent estimation models performed poorly in situations where catchability trended over time. No estimation model was best for all underlying models of catchability, hence I recommend fitting multiple SCA models with alternative assumptions.; Structural flaws in SCA models may cause considerable bias in model estimates of mortality rates, abundance, and recruitment. I used simulations to evaluate whether using Deviance Information Criterion (DIC) or approximate Bayes factors to select the best SCA model provided more accurate estimates of quantities important for management than using a single model in all cases. Using the model selected by DIC or approximate Bayes factors resulted in estimates with lower mean square errors than using any single model.