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Scientific Investigations Report 2012–5030

Prepared in cooperation with Duke University

Linking Urbanization to the Biological Condition Gradient (BCG) for Stream Ecosystems in the Northeastern United States Using a Bayesian Network Approach

By Roxolana Kashuba, Gerard McMahon, Thomas F. Cuffney, Song Qian, Kenneth Reckhow, Jeroen Gerritsen and Susan Davies

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Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable.

In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

First posted April 2, 2012

For additional information contact:
Roxolana Kashuba
U.S. Geological Survey
3916 Sunset Ridge Road
Raleigh, NC 27607
Telephone: 919–571–4088
FAX 919–571–4041

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Suggested citation:

Kashuba, Roxolana, McMahon, Gerard, Cuffney, T.F., Qian, Song, Reckhow, Kenneth, Gerritsen, Jeroen, and Davies, Susan, 2012, Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach: U.S. Geological Survey Scientific Investigations Report 2012–5030, 48 p. (Available online at




Purpose and Scope


USGS Urban Stream Data

Biological Condition Gradient

Assigning BCG Tier Membership in the Northeastern United States

Bayesian Network Model

Creating a Bayesian Network Model

Developing the Northeastern U.S. Bayesian Network Prior Model Using Expert Elicitation

Model Structure

Variable Selection and Discretization

Conditional Probability Tables

Prior Weights

Updating the Prior Model with Data

Predicting Effects of Urbanization on Biota

Prior Bayesian Network Model

Data-Only Bayesian Network Model

Posterior Bayesian Network Model

Assessing the Value of a Bayesian Network Approach

Benefits of Using a Bayesian Network for Urban Development Modeling

Unresolved Issues


References Cited

Appendix 1. Distribution Forms for Bayesian Updating

Appendix 2. Supplemental Prior and Posterior Conditional Probability Tables, Data Tables, and Bayesian Network Diagrams

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