New developments in catch-effort estimation of important demographic population parameters

This thesis specifically involves new model developments and study designs in which maximum likelihood methods are used to estimate important demographic parameters in catch-effort analyses. We evaluated the performance of least squares regression techniques for catch-effort estimation of closed populations in comparison to maximum likelihood estimation using real datasets and with simulations. Simulations showed that maximum likelihood tended to produce less biased and more precise estimates than the regression methods, although the differences were slight for Leslie's regression method. DeLury's method proved to be negatively biased for estimating population size and is not recommended for general use. Maximum likelihood also allows for greater model flexibility as illustrated with several examples.; A new catch-effort study design, based on the robust design in the capture-recapture literature, is presented. By combining open and closed population models, the robust design allows for greater model flexibility than open population or closed population models alone. The robust design allows for variable mortality and catchability across primary periods in each of several model scenarios and has other model-specific advantages as well. Simulations illustrated the advantages of the robust design over a previously defined open population regression model for several scenarios of catchability and mortality.; The effects of measurement error in catch and effort on catch-effort estimates of population size and catchability were determined using two traditional regression approaches and a maximum likelihood approach to estimation. Our simulations indicate that measurement errors in catch and effort positively bias the naive estimators of population size and catchability, the magnitude of the bias depending on the size of the measurement error variance and warrant the need for bias correction.; As a means of correcting for bias in catch-effort population size estimates caused be measurement errors in catch and effort, we investigated the utility of a recently developed simulation-based procedure (SIMEX). The technique, which assumes knowledge or a good estimate of the measurement error variance, involves adding additional measurement error in known increments to the data and extrapolating back to the parameter estimate with no measurement error. Our results indicate that the SIMEX estimator reduces bias, but can lead to a larger variance in some cases.