Application of Uncertainty Analysis to Ecological Risks of Pesticides
Edited by William J. WarrenHicks, Andy Hart
CRC Press
April 2010
Hardcover 228 pp ISBN 9781439807347
£77.00






While current methods used in ecological risk assessments for pesticides are largely deterministic, probabilistic
methods that aim to quantify variability and uncertainty in exposure and effects are attracting growing interest from
industries and governments. Probabilistic methods offer more realistic and meaningful estimates of risk and hence,
potentially, a better basis for decisionmaking. Application of Uncertainty Analysis to Ecological Risks of Pesticides
examines the applicability of probabilistic methods for ecological risk assessment for pesticides and explores
their appropriateness for general use.
The book presents specific methods leading to probabilistic decisions concerning the registration and
application of pesticides and includes case studies illustrating the application of statistical methods. The
authors discuss Bayesian inference, firstorder error analysis, firstorder (nonhierarchical) Monte Carlo
methods, secondorder Bayesian and Monte Carlo methods, interval analysis, and probability bounds
analysis. They then examine how these methods can be used in assessments for other environmental stressors
and contaminants.
There are many methods of analyzing variability and uncertainty and many ways of presenting the results.
Inappropriate use of these methods leads to misleading results, and experts differ on what is appropriate.
Disagreement about which methods are appropriate will result in wasted resources, conflict over findings,
and reduced credibility with decision makers and the public. There is, therefore, a need to reach a consensus
on how to choose and use appropriate methods, and to present this in the form of guidance for prospective users.
Written in a clear and concise style, the book examines how to use probabilistic methods within a riskbased decision
paradigm.
Contents
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 FirstOrder Error Analysis, W. J. WarrenHicks, 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
FirstOrder 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. WarrenHicks,
S. Qian, A Fairbrother, T. Aldenberg, T. Barry, R. Luttik, and H.T. Ratte
Introduction
Variability and Uncertainty
Simple 2ndOrder 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
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