Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems
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Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems by Roxolana Oresta Kashuba Department of Environment Duke University Date:_______________________ Approved: ___________________________ Kenneth H. Reckhow, Supervisor ___________________________ Song S. Qian ___________________________ Thomas F. Cuffney ___________________________ Gerard McMahon ___________________________ Emily S. Bernhardt Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Environment in the Graduate School of Duke University 2010 ABSTRACT Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems by Roxolana Oresta Kashuba Department of Environment Duke University Date:_______________________ Approved: ___________________________ Kenneth H. Reckhow, Supervisor ___________________________ Song S. Qian ___________________________ Thomas F. Cuffney ___________________________ Gerard McMahon ___________________________ Emily S. Bernhardt An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Environment in the Graduate School of Duke University 2010 Copyright by Roxolana Oresta Kashuba 2010 Abstract 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. iv 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). v Contents Abstract .........................................................................................................................................iv List of Tables.................................................................................................................................xi List of Figures ..............................................................................................................................xx Acknowledgements ................................................................................................................xxxii 1. Introduction ...............................................................................................................................1 1.1. Urbanization processes and their impacts...................................................................1 1.2. Invertebrate community metrics...................................................................................2 1.3. Biological Condition Gradient.......................................................................................7 1.4. Effects of Urbanization on Stream Ecosystems (EUSE) Dataset...............................8 1.5. Dissertation Objectives .................................................................................................10 2. Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States .....15 2.1. Introduction....................................................................................................................15 2.1.1. Purpose and scope....................................................................................................20 2.2. Methods ..........................................................................................................................23 2.2.1. Data collection...........................................................................................................25 2.2.1.1. Benthic Macroinvertebrate Data .....................................................................25 2.2.1.2. Land-Cover Data...............................................................................................26 2.2.1.3. Climate Data ......................................................................................................27 2.2.2. Data summary...........................................................................................................27 2.2.2.1. Urbanization Measures ....................................................................................28 vi 2.2.2.2. Macroinvertebrate Response Variables .........................................................32 2.2.2.3. Land-Cover Types ............................................................................................39 2.2.2.4. Scale-Dependent Variables..............................................................................43 2.2.3. Technique: Multilevel Hierarchical Models .........................................................46 2.2.3.1. Model Structure.................................................................................................55 2.2.3.2. Model Fitting .....................................................................................................67 2.2.3.3. Model Limitations.............................................................................................71 2.3. Predicting and Understanding Effects of Urbanization...........................................75 2.3.1. Preliminary Land Cover Analysis..........................................................................75 2.3.2. Model Summary (Model Results) ..........................................................................82 2.3.2.1. Model 1: No Region-Level Predictor..............................................................84 2.3.2.2. Model 2: PRECIP Region-Level Predictor .....................................................97 2.3.2.3. Model 3: TEMP Region-Level Predictor ......................................................107 2.3.2.4. Model 4: PRECIP and TEMP Region-Level Predictors..............................112 2.3.2.5. Model 5: AG (Continuous) Region-Level Predictor...................................118 2.3.2.6. Model 6: AG (Categorical) and PRECIP Region-Level Predictors...........123 2.3.2.7. Model 7: AG (Categorical) and TEMP Region-Level Predictors..............132 2.3.2.8. Model 8: AG (Categorical), PRECIP, TEMP Region-Level Predictors.....139 2.3.3. Model Interpretation..............................................................................................144 2.3.3.1. Effect of Precipitation .....................................................................................144 2.3.3.2. Effect of Temperature.....................................................................................147 2.3.3.3. Effect of Antecedent Agriculture..................................................................149 vii 2.4. Conclusions ..................................................................................................................152 2.4.1. General Ecological