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: As the CoastWatch regional node for the Great Lakes, the Great Lakes Environmental Research Laboratory (GLERL) is working to obtain and/or develop and deliver environmental data and products for near real-time monitoring of the Great Lakes to support environmental science and decision making. In support of this goal, GLERL is providing access to near real-time and retrospective satellite observations and in-situ data for the Great Lakes to Federal, state, and local agencies and academic institutions. There are currently nearly 40 regional CoastWatch users. The goals and objectives of the Great Lakes CoastWatch Program directly support agency statutory responsibilities in estuarine and marine science, living marine resource protection, and ecosystem monitoring and management. GLERL is currently receiving an enhanced digital image product suite of 26 images including satellite derived surface-temperature, visible and near-infrared reflectance, brightness temperatures, satellite and solar zenith angle data, and cloud masks from the NOAA-12 satellite. These products are downloaded from the NOAA Ocean Products Center (OPC) via INTERNET on a daily basis and archived at GLERL. Over 32,000 image products have been received and archived since becoming the Great Lakes CoastWatch Node in 1990. GOES-8 data is also currently being received.