Application of Uncertainty Analysis to Ecological Risks of Pesticides

Introduction and Objectives, A. Hart, D. Farrar, D. Urban, D. Fischer, T. La Point,
K. Romijn, and S. Ferson
Introduction
Variability and Uncertainty
Importance of Variability and Uncertainty in Risk Assessment
Current Methods for Dealing with Variability and Uncertainty Are Inadequate
Variability and Uncertainty Hinder the Regulatory Process
Understanding Uncertainty and Variability Is Critical When Developing a Credible Risk Assessment
Quantitative Analysis of Variability and Uncertainty Can Help
When Is Quantitative Analysis of Variability and Uncertainty Required?
What If the Bounds Are Very Wide?
Need for Consensus on Appropriate Methods
Workshop Objectives and Key Issues
References

Problem Formulation for Probabilistic Ecological Risk Assessments, A. Hart, S. Ferson, J. Shaw, G. W. Suter II, P. F. Chapman, P. L. de Fur, W. Heger, and P. D. Jones
Introduction
Main Steps in Problem Formulation
Integration of Available Information for Probabilistic Assessments
Definition of Assessment Endpoints for Probabilistic Assessments
Definition of Assessment Scenarios
Developing Conceptual Models for Probabilistic Assessments
Analysis Plans for Probabilistic Assessment
References

Issues Underlying the Selection of Distributions, D. Farrar, T. Barry, P. Hendley, M. Crane, P. Mineau, M. H. Russell, and E. W. Odenkirchen
Introduction
Technical Background
Some Practical Aspects of the Selection of Univariate Distributions
Using Scanty and Fragmentary Data
References

Monte Carlo, Bayesian Monte Carlo, and First-Order Error Analysis, W. J. Warren-Hicks, S. Qian, J. Toll, D. L. Fischer, E. Fite, W. G. Landis, M. Hamer, and E. P. Smith
Introduction
Practical Aspects of a Monte Carlo Analysis
Mathematical and Statistical Underpinnings of Monte Carlo Methods
Bayesian Monte Carlo Analysis
First-Order Error Analysis
A Monte Carlo Case Study: Derivation of Chronic Risk Curves for Atrazine in Tennessee Ponds Using Monte Carlo Analysis
Conclusions
References

The Bayesian Vantage for Dealing with Uncertainty, D. A. Evans, M. C. Newman, M. Lavine, J. S. Jaworska, J. Toll, B. Brooks, and T. C. M. Brock
Introduction
Conventional (Frequentist) Inference Methods
Experiments Change the State of Knowledge
Rules of Probability
Bayes’s Theorem
Examples Relevant to Uncertainty in Risk Assessment Quantifying Plausibility of a Cause–Effect Model
Conclusion
References

Bounding Uncertainty Analyses, S. Ferson, D. R. J. Moore, P. Van den Brink, T. L. Estes, K. Gallagher, R. O’Connor, and F. Verdonck
Introduction
Robust Bayes
Probability Bounds Analysis
Numerical Example
How to Use Bounding Results
Seven Challenges in Risk Analyses
What Bounding Cannot Do
Example: Insectivorous Birds’ Exposure to Pesticide
Conclusion
Appendix
References

Uncertainty Analysis Using Classical and Bayesian Hierarchical Models, D. R. J. Moore, W. J. Warren-Hicks, S. Qian, A Fairbrother, T. Aldenberg, T. Barry, R. Luttik, and H.-T. Ratte
Introduction
Variability and Uncertainty
Simple 2nd-Order Monte Carlo Analysis Case Study
Bayesian Hierarchical Modeling
References

Interpreting and Communicating Risk and Uncertainty for Decision Making, J. L. Shaw, K. R. Tucker, K. Aden, J. M. Giddings, D. M. Keehner, and C. Kriz
Introduction
Participants in Risk Communication
Communicating Uncertainty to Stakeholders and Participants
Process for Communication
Risk Assessor and Decision Maker Roles and Responsibilities
Communication of Uncertainty for Regulatory Decision Making
References

How to Detect and Avoid Pitfalls, Traps, and Swindles, G. Joermann, T. W. La Point, L. A. Burns, J. P. Carbone, P. D. Delorme, S. Ferson, D. R. J. Moore, and T. P. Traas
Introduction
Meaningful Problem Formulation
Suitability of Input Data
Parameterization of the Distribution of Input Variables
Correlations and Dependencies
Model Uncertainties
Software Tools and Computational Issues
Presentation and Interpretation of Results
Conclusions
References

Conclusions, A. Hart, T. Barry, D. L. Fischer, J. M. Giddings, P. Hendley, G. Joermann, R. Luttik, D. R. J. Moore, M. C. Newman, E. Odenkirchen, and J. L. Shaw
Introduction
Which Methods of Uncertainty Analysis Are Appropriate under What Circumstances?
What Are the Implications of Probabilistic Methods for Problem Formulation?
How Can Uncertainty Analysis Methods Be Used Efficiently and Effectively in Decision Making?
When and How Should We Separate Variability and Uncertainty?
How Can We Take Account for Uncertainty Concerning the Structure of the Risk Model for the Assessment?
How Should We Select and Parameterize Input Distributions When Data Are Limited?
How Should We Deal with Dependencies, Including Nonlinear Dependencies and Dependencies about Which Only Partial Information Is Available?
How Can We Take Account of Uncertainty When Combining Different Types of Information in an Assessment (e.g., Quantitative Data and Expert Judgment, Laboratory Data, and Field Data)?
How Can We Detect and Avoid Misleading Results?
How Can We Communicate Methods and Outputs Effectively to Decision Makers and Stakeholders?
What Are the Priorities for Further Development, Implementation, and Training?
References
Glossary