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Assessing the predictive performance of risk-based water quality criteria using decision error estimates from

Douglas B McLaughlin¹

¹National Council for Air and Stream Improvement, A-114 Parkview Campus, Mail Stop 5436, Western Michigan University, Kalamazoo, Michigan, USA. douglas.mclaughlin@wmich.edu

Integrated Environmental Assessment and Management

|February 23, 2012

Summary

Receiver operating characteristics (ROC) analysis offers a robust method for evaluating ecological models. This approach provides a comprehensive assessment of prediction errors, aiding water quality management decisions.

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Area of Science:

Environmental Science
Ecology
Water Quality Management

Background:

Increasing use of field data to link aquatic ecosystem responses with water quality stressors in the US.
Need for transparent quantitative methods to assess ecological prediction models for water quality criteria development.

Purpose of the Study:

To evaluate the performance of models predicting ecological conditions using stressor-response relationships.
To assess the utility of Receiver Operating Characteristics (ROC) analysis for water quality management decisions.

Main Methods:

Application of ROC analysis to three water quality stressor-response datasets (simulated and field data).
Comparison of ROC analysis with linear regression and conditional probability analysis.
Emphasis on characterizing prediction error rates (false positives, false negatives, etc.).

Main Results:

ROC analysis provides a more comprehensive characterization of prediction error rates than other methods studied.
Identified specific error types including false positive, false negative, positive predictive, and negative predictive errors.
Demonstrated the utility of ROC analysis for evaluating risk-based environmental criteria.

Conclusions:

ROC analysis is a valuable tool for assessing the performance of ecological prediction models in water quality management.
The comprehensive error rate information from ROC analysis can inform the setting of quality objectives and predictive performance assessments.
This method supports evidence-based decision-making for protecting aquatic life through improved water quality criteria.