Pyrmnesium parvum, commonly known as golden algae, is an algal species that under certain circumstances releases toxins which can lead to fish kills and the death of other economically and ecologically important organisms. One of the major objectives of the study was to investigate whether a relationship exists between land use and Prymnesium parvum abundance in littoral sites of Lake Texoma, USA. Another objective was to investigate whether a relationship exists between other physical variables and counts of P. parvum. Lastly, developing a valid model that predicts P. parvum abundance was an objective of the study. Through stepwise regression, a small but highly significant amount of the variation in P. parvum counts was found to be explained by wetlands, soil erodibility and lake elevation. The developed model provides insight for potential golden algae management plans, such as maintaining wetlands and teaching land owners the relationship between soil erosivity and golden algae blooms.

With advances in computer technology, organizations are able to store large amounts of data in data warehouses. There are two fundamental issues researchers must address: the dimensionality of data and the interpretation of multiple statistical tests. The first issue addressed by this research is the determination of the number of components to retain in principal components analysis. This research establishes regression, asymptotic theory, and neural network approaches for estimating mean and 95th percentile eigenvalues for implementing Horn's parallel analysis procedure for retaining components. Certain methods perform better for specific combinations of sample size and numbers of variables. The adjusted normal order statistic estimator (ANOSE), an asymptotic procedure, performs the best overall. Future research is warranted on combining methods to increase accuracy. The second issue involves interpreting multiple statistical tests. This study uses simulation to show that Parker and Rothenberg's technique using a density function with a mixture of betas to model p-values is viable for p-values from central and non-central t distributions. The simulation study shows that final estimates obtained in the proposed mixture approach reliably estimate the true proportion of the distributions associated with the null and nonnull hypotheses. Modeling the density of p-values allows for better control of the true experimentwise error rate and is used to provide insight into grouping hypothesis tests for clustering purposes. Future research will expand the simulation to include p-values generated from additional distributions. The techniques presented are applied to data from Lake Texoma where the size of the database and the number of hypotheses of interest call for nontraditional data mining techniques. The issue is to determine if information technology can be used to monitor the chlorophyll levels in the lake as chloride is removed upstream. A relationship established between chlorophyll and the energy reflectance, which can be measured by satellites, enables more comprehensive and frequent monitoring. The results have both economic and political ramifications.

Simultaneous size fractionation of plankton populations associated with NaH^14CO_3 and ^3H-glucose uptake was employed in eutrophic Lake Texoma (Texas and Oklahoma) and oligotrophic Flathead Lake (Montana). Autoradiography was utilized to determine the role of specific microorganisms in community metabolism. Ultraplankton (0.45-10 μm) dominated plankton numbers and metabolic activity in both aquatic systems. Many of the most abundant species were not the most productive, in terms of inorganic C fixation. Rates of heterotrophic uptake of ^3H-glucose were small in comparison to photolithotrophic uptake in both lakes, Photoheterotrophy was more extensive in Flathead Lake, Autoradiographs indicated that bacteria were responsible for observed photoheterotrophy. Oscillatoria sp. exhibited. mixotrophy in Lake Texoma,

Managing water quality aspects requires the use of integrative tools that allow a holistic approach to this problem. Water quality models coupled to hydrodynamic models are these tools. This study presents the application of the water quality model WASP coupled to the hydrodynamic model DYNHYD for two distinct reservoirs: Lake Texoma and Tocoma Reservoir. Modeling the former included simulations of water velocities, water level, and four chemical and physical compounds: chlorides, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total suspended solids (TSS); and validation of the results by comparing with observed values during March - May, 1997. The latter is still under project status and the simulation was performed in a prospective way. The analysis included simulations of water velocities under current and for expected conditions, DO and BOD. Both models, DYNHYD and WASP, fitted pretty well to observed conditions for Lake Texoma and for where Tocoma Reservoir has been planned. Considering management and decision support purposes, the role of boundary and loading conditions also was tested. For Lake Texoma, controlling boundary conditions for chlorides is a determinant factor for water quality of the system. However, DO and TSS in the reservoir are governed by additional process besides the condition of the boundary. Estimated loadings for this system did not provided significant effects, even though the allocation of a load for chlorides resulted in significant changes in the trend for expected chloride concentrations at the Washita River Arm of Lake Texoma. For Tocoma Reservoir, the expected concentration of DO all over the reservoir is going to driven by boundary conditions, as well as by the management of autochthonous BOD loadings provided by vegetation decomposition. These two factors will be determinant for the resulting water quality of the future reservoir.