Zusammenfassung: The focus of this dissertation was the estimation of theoretical reference levels such as K, the environmental carrying capacity, and MNPL, the maximum net productivity level, for three fishes off Southern California and for the northern fur seal (Callorhinus ursinus) population of St. Paul Island, Alaska. The estimation techniques were based on computer modeling of historical population trends. The assessment of these theoretical reference levels for nature populations is fundamentally important to the development and testing of concepts in theoretical ecology and population dynamics. In the first study, maximum-likelihood estimates of pre-exploitation biomass were made for the white seabass (Atractoscion nobilis) and the yellowtail (Seriola lalandei) by assuming that development of corresponding commercial fisheries led to a shift in size distributions of these fishes, as indicated by records of the Avalon Tuna Club, Santa Catalina Island, California. Pre-exploitation biomass for Southern California populations of both these fishes was estimated to be about 20,000 tons. The pre-exploitation biomass of the giant sea bass (Stereolepis gigas) was estimated to be 1300 tons. Due to insufficient natural history information, this estimate was based on commercial catch records only. In the second study, estimates of theoretical reference levels for the northern fur seal population of St. Paul Island were based on computer simulations of pup production from 1912 to 1970, repetitive simulations were used to construct frequency distributions of estimates for MNPL, K, the number of pups born at MNPL and K, and the ratio MNPL/K. These distributions serve as a measure of the confidence that can be placed in single estimates of reference levels, and thereby provide a general context within which these reference levels can be evaluated. The final study of this dissertation investigated the pelagic migration of northern fur seal pups. Results indicated that initial migration paths for these pups are more widely dispersed than previously assumed. This information contributes to the understanding of northern fur seal life histories. A fuller understanding of life history information will eventually facilitate more accurate modeling of northern fur seal population dynamics. (DBO)

Zusammenfassung: Increasing numbers of students now study natural resource and environmental economics at undergraduate level, and this interest in the subject has been matched by a corresponding expansion in the number of textbooks and other learning material available for use. Unfortunately one very important aspect of natural resource economics which is often not adequately dealt with in the undergraduate literature concerns the question of dynamics: how rapidly will a resource become exploited, and what time path will it follow? These issues are particularly relevant to the management of renewable resources such as fish stocks whose survival depends on factors such as the rate of entry and exit by the exploiting firms, and the ability of the resource to recover from the effects of intense harvesting. The fault of most textbooks stems not from a failure to cover the question of dynamics, but rather that the treatment is often too advanced or mathematical for the majority of students. Computers can make a useful contribution to our understanding of the dynamics of natural resource exploitation, and in this example the case of marine commercial fishery exploited under 'open-access' conditions is considered. The quantitative assessment of environmental contaminants is a complex process. It involves nonlinear models and the characterization of variables, factors, and parameters that are distributed and dependent each other. Assessments based on point estimates are easy to perform, but since they are unreliable, Monte Carlo simulations have become a standard procedure. Simulations pose two challenges: They require the numerical characterization of parameter distributions and they do not account for dependencies between parameters. This paper offers strategies for dealing with both challenges. The first part discusses the characterization of data with the S-distribution. This distribution offers several advantages, which include simplicity of numerical analysis, flexibility in shape, and easy computation of quantiles. The second part outlines how the S-distribution can be used for hierarchical Monte Carlo simulations. In these simulations the selection of parameter values occurs sequentially, and each choice depends on the parameter values selected before. The method is illustrated with preliminary simulation analyses that are concerned with mercury contamination in king mackerel (Scomberomorus cavalla). It is demonstrated that the results of such hierarchical simulations are generally different from those of traditional Monte Carlo simulations.