Using sparse dose–response data for wildlife risk assessment. (10th December 2013) - Hill, Ryan A; Pyper, Brian J; Lawrence, Gary S; Mann, Gary S; Allard, Patrick; Mackintosh, Cheryl E; Healey, Norm; Dwyer, James; Trowell, Jennifer
ABSTRACT Hazard quotients based on a point‐estimate comparison of exposure to a toxicity reference value (TRV) are commonly used to characterize risks for wildlife. Quotients may be appropriate for screening‐level assessments but should be avoided in detailed assessments, because they provide little insight regarding the likely magnitude of effects and associated uncertainty. To better characterize risks to wildlife and support more informed decision making, practitioners should make full use of available dose–response data. First, relevant studies should be compiled and data extracted. Data extractions are not trivial—practitioners must evaluate the potential use of each study or its components, extract numerous variables, and in some cases, calculate variables of interest. Second, plots should be used to thoroughly explore the data, especially in the range of doses relevant to a given risk assessment. Plots should be used to understand variation in dose–response among studies, species, and other factors. Finally, quantitative dose–response models should be considered if they are likely to provide an improved basis for decision making. The most common dose–response models are simple models for data from a particular study for a particular species, using generalized linear models or other models appropriate for a given endpoint. Although simple models work well in some instances, they generally do not reflect the full breadth of information in a dose–response data set, because they apply only for particular studies, species, and endpoints. More advanced models are available that explicitly account for variation among studies and species, or that standardize multiple endpoints to a common response variable. Application of these models may be useful in some cases when data are abundant, but there are challenges to implementing and interpreting such models when data are sparse. Integr Environ Assess Manag 2014;10:3–11. © 2013 SETAC