Zusammenfassung: The characterization of an heterogeneous geological media can be approached by means of an statistical treatment of the field data. This lets us know its spatial variability behaviour, in probability terms. The algorithms presented in this work belongs to the scope of simulation of geological heterogeneous random fields. The aim of that characterization is related with the reseach studies of nuclear waste repositories in deep geological structures. Several stochastic generation algorithms are presented, based in the geostatistical regionalized variable, fractal geometry, and spectral Fourier theories. Some of them are deeply exposed here to their understanding. As a result of the paper, some comparative schemes are showed with numerical, geostatistical and spectral analysis of the implicit autorregresive generation methods.

Zusammenfassung: Standard 0-group indices and distribution maps are now produced based on hand-drawn maps using AutoCad with some additional procedures. This paper briefly describes the method. The paper further describes ways of importing coastlines and survey data directly into standard computer programs such as AutoCad and SAS. Standard methods are used for gridding data, producing isolines and further calculation of abundance indices and presentation of distributions. Interactive editing of distribution maps are made, which makes corresponding automatic updating of indices possible.

Zusammenfassung: For several decades, aquatic ecologists have debated differing viewpoints on the factors that control ecosystem productivity. Much of the controversy centers on the interpretation of empirical relationships between factors that limit the amount of energy transferred among trophic levels in aquatic ecosystems. The primary statistical tool used has been regression analysis, but such models often fail to explain a substantial portion of the variability observed. The suggestion has been made in the literature of aquatic ecology that regression models may be inappropriate, and that limiting factors may be better related to the maximum rather than mean response. Statistical models that account for the large variability in relationships between limiting factors and response variables at higher trophic levels and shift the focus from description of expected values to description of upper boundaries have not been developed. The goal of this project was to develop statistical models for the relationships between limiting factors and the maximum output of biological processes. A number of models may be formulated, one of the primary being Y sub(i) – gamma x sub(i)U sub(i) + sigma epsilon sub(i), where gamma > 0, U sub(i) similar to iid Beta ( alpha , beta ), the x's are considered fixed values on the real line, and the error terms, epsilon sub(i), are centered iid random variables. This model describes a triangular array of points lying between zero and a straight line with positive slope, a data pattern seen numerous times in the ecological literature. The response variable Y is an identifiable mixture of the random variable U, with the model representing a situation in which Y is observed but U is unobserved. Maximum likelihood estimates for the parameters ( alpha , beta , gamma , sigma ) may be computed using the EM algorithm. Estimation is based on maximization of the expected full-data likelihood where the expectation is taken with respect to the conditional density of U given Y. A portion of the dissertation is devoted to the confirmation of asymptotic properties as the response variable, Y sub(i)'s, are not identically distributed. A set of conditions depending on the identically distributed error terms is given for consistency and asymptotic normality of MLE's. The model specific conditions are met by a number of error densities. (DBO)

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.