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dc.contributor.advisor Reckhow, Kenneth H en_US
dc.contributor.author Kashuba, Roxolana Oresta en_US
dc.date.accessioned 2010-05-10T19:55:43Z
dc.date.available 2010-05-10T19:55:43Z
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/10161/2366
dc.description Dissertation en_US
dc.description.abstract <p>Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals). Impervious surfaces and artificial drainage systems speed the delivery of contaminants to streams, while bypassing soil filtration and local riparian processes that can mitigate the impacts of these contaminants, and disrupting the timing and volume of hydrologic patterns. Aquatic habitats where biota live are degraded by sedimentation, channel incision, floodplain disconnection, substrate alteration and elimination of reach diversity. These compounding changes ultimately lead to alteration of invertebrate community structure and function. Because the effects of urbanization on stream ecosystems are complex, multilayered, and interacting, modeling these effects presents many unique challenges, including: addressing and quantifying processes at multiple scales, representing major interrelated simultaneously acting dynamics at the system level, incorporating uncertainty resulting from imperfect knowledge, imperfect data, and environmental variability, and integrating multiple sources of available information about the system into the modeling construct. These challenges can be addressed by using a Bayesian modeling approach. Specifically, the use of multilevel hierarchical models and Bayesian network models allows the modeler to harness the hierarchical nature of the U.S. Geological Survey (USGS) Effect of Urbanization on Stream Ecosystems (EUSE) dataset to predict invertebrate response at both basin and regional levels, concisely represent and parameterize this system of complicated cause and effect relationships and uncertainties, calculate the full probabilistic function of all variables efficiently as the product of more manageable conditional probabilities, and includes both expert knowledge and data. Utilizing this Bayesian framework, this dissertation develops a series of statistically rigorous and ecologically interpretable models predicting the effect of urbanization on invertebrates, as well as a unique, systematic methodology that creates an informed expert prior and then updates this prior with available data using conjugate Dirichlet-multinomial distribution forms. The resulting models elucidate differences between regional responses to urbanization (particularly due to background agriculture and precipitation) and address the influences of multiple urban induced stressors acting simultaneously from a new system-level perspective. These Bayesian modeling approaches quantify previously unexplained regional differences in biotic response to urbanization, capture multiple interacting environmental and ecological processes affected by urbanization, and ultimately link urbanization effects on stream biota to a management context such that these models describe and quantify how changes in drivers lead to changes in regulatory endpoint (the Biological Condition Gradient; BCG).</p> en_US
dc.format.extent 4713977 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Environmental Sciences en_US
dc.subject Statistics en_US
dc.subject Bayesian methods en_US
dc.subject expert elicitation en_US
dc.subject invertebrate metrics en_US
dc.subject uncertainty characterization en_US
dc.subject urbanization en_US
dc.subject water quality modeling en_US
dc.title Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems en_US
dc.type Dissertation en_US
dc.department Environment en_US

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