Cover Image

PAPERBACK
$49.00



View/Hide Left Panel

be reduced, but often they can be quantified using empirical regression techniques (Suter et al., 1983), time series analysis (Jassby and Powell, 1990), or formal model uncertainty analysis (Bartell et al., 1992). Di Toro and Fogarty et al. provided examples of model uncertainty analyses in their case study papers (Appendix E). Uncertainties related to inadequacies of models (or scientific ignorance in general) are much more difficult to quantify.

Choices between risk assessment methodologies often involve tradeoffs between different types of uncertainty. For example, decisions about the need for pesticide testing are now based on qualitative evaluation of toxicity and exposure data (Urban and Cook, 1986). Explicit models of the effects of toxicant exposure on the abundance and persistence of bird populations have been developed (Grier, 1980; Tipton et al., 1980; Samuels and Ladino, 1983) and could be used to quantify uncertainties related to variability in exposures or extrapolation from field plots to natural landscapes. Relying on expert judgment avoids the need to postulate particular mechanisms of exposure or complex population dynamics but prevents risk assessors from providing information on the value of collecting additional information to reduce uncertainties or providing information on the ecological costs and benefits of regulatory decisions. Using a model to quantify uncertainties would in principle permit more useful risk assessments, but if the model itself is a poor representation of reality, the results might be totally meaningless.

The committee believes that improvements are needed in techniques for qualitative and quantitative analysis of uncertainty for ecological risk assessment. Techniques for model uncertainty analyses developed by systems engineers have been used by ecologists for more than a decade (Gardner et al., 1981; Bartell et al., 1992; Di Toro, Appendix E). The large and growing technical literature on decision analysis (Raiffa, 1970; Lindley, 1985; Von Winterfeldt and Edwards, 1986) has been much less thoroughly exploited (see Walters (1986) and Reckhow (1990) for examples of ecological applications of Bayesian decision theory) and should be surveyed for potentially useful approaches.

VALIDATION OF PREDICTIVE TOOLS

Improvements in the mathematical models, qualitative and quantitative



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 262
KEY SCIENTIFIC PROBLEMS LIMITING APPLICATION OF ECOLOGICAL RISK 262 ASSESSMENT original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution. be reduced, but often they can be quantified using empirical regression techniques (Suter et al., 1983), time series analysis (Jassby and Powell, 1990), or formal model uncertainty analysis (Bartell et al., 1992). Di Toro and Fogarty et al. provided examples of model uncertainty analyses in their case study papers (Appendix E). Uncertainties related to inadequacies of models (or scientific ignorance in general) are much more difficult to quantify. Choices between risk assessment methodologies often involve tradeoffs between different types of uncertainty. For example, decisions about the need for pesticide testing are now based on qualitative evaluation of toxicity and exposure data (Urban and Cook, 1986). Explicit models of the effects of toxicant exposure on the abundance and persistence of bird populations have been developed (Grier, 1980; Tipton et al., 1980; Samuels and Ladino, 1983) and could be used to quantify uncertainties related to variability in exposures or extrapolation from field plots to natural landscapes. Relying on expert judgment avoids the need to postulate particular mechanisms of exposure or complex population dynamics but prevents risk assessors from providing information on the value of collecting additional information to reduce uncertainties or providing information on the ecological costs and benefits of regulatory decisions. Using a model to quantify uncertainties would in principle permit more useful risk assessments, but if the model itself is a poor representation of reality, the results might be totally meaningless. The committee believes that improvements are needed in techniques for qualitative and quantitative analysis of uncertainty for ecological risk assessment. Techniques for model uncertainty analyses developed by systems engineers have been used by ecologists for more than a decade (Gardner et al., 1981; Bartell et al., 1992; Di Toro, Appendix E). The large and growing technical literature on decision analysis (Raiffa, 1970; Lindley, 1985; Von Winterfeldt and Edwards, 1986) has been much less thoroughly exploited (see Walters (1986) and Reckhow (1990) for examples of ecological applications of Bayesian decision theory) and should be surveyed for potentially useful approaches. VALIDATION OF PREDICTIVE TOOLS Improvements in the mathematical models, qualitative and quantitative