Using Sparse Dose–Response Data for Wildlife Risk Assessment Ryan A Hill,y Brian J Pyper,y Gary S Lawrence,z Gary S Mann,*z Patrick Allard,y Cheryl E Mackintosh,y Norm Healey,y James Dwyer,z and Jennifer Trowelly yAzimuth Consulting Group Partnership, Vancouver, British Columbia, Canada zGolder Associates Ltd., Burnaby, British Columbia, Canada (Submitted 1 February 2013; Returned for Revision 5 April 2013; Accepted 17 July 2013) 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 Keywords: Dose–response Ecological risk Exposure–response Toxicity reference values Wildlife INTRODUCTION Hazard quotients (HQs) are commonly used to identify risks of contaminants to wildlife. A typical HQ compares a point‐ estimate total oral dose of a chemical (the numerator) to a point estimate toxicity reference value (TRV; the denominator), where the TRV is assumed to represent a safe dose. In some instances, the concentration of the chemical in biological media (e.g., egg concentration, dietary concentration, tissue body burden) can be substituted for the ingested dose, but the concept remains the same. In most cases the TRVs are conservatively derived (e.g., using uncertainty factors) and are based on organism‐level responses, and therefore whenever HQ < 1 it is generally assumed that unacceptable adverse effects to local populations are highly unlikely. Conversely, if HQ > 1, the magnitude of potential effects is unknown and further evaluation may be warranted by examining toxicity information in a more realistic manner, or by using other lines of evidence relevant to wildlife at the organism or population level (Barnthouse et al. 2008). Accordingly, guidance in Canada (CCME 1996) envisioned that HQs would be generated only as part of a “screening” risk assessment, and that additional and detailed “quantitative” risk assessments would provide more meaningful estimates of risk in cases where HQ > 1. Similarly, guidance in the United States (USEPA 2005) states that ecological soil screening levels (that are dose‐based for wildlife) are intended for screening‐level risk calculations and should not be adopted or modified to drive remediation. Unfortunately, in practice, HQs are often the only line of evidence used for evaluating risks for wildlife and have been relied on directly to make decisions in cases where HQ > 1. Many practitioners have applied more sophisticated ap- proaches based on the HQ, such as using simulation models to generate probabilistic exposure estimates for comparison to TRVs (Tannenbaum et al. 2003). The reliance on HQ > 1 to rationalize risk management is fundamentally flawed in most cases—HQs estimated in Ecological Risk Assessment (ERA) are often overly conservative and can exceed 1 even for background exposures (Tannenbaum et al. 2003), due to the sparseness of available effects data, lack of consideration of bioavailability, conservatism often applied in TRV derivation (McDonald and Wilcockson 2003; Allard et al. 2010), as well as complexities in exposure estimation (Tannenbaum et al. 2003). The greatest limitation of a result of HQ > 1 is that it usually provides limited insight regarding the actual probability and magnitude of potential effects. TRVs used in the HQ denominator are often poorly linked to an effect magnitude (Kapustka 2008; Allard et al. 2010). Even if the effect associated with a TRV is known, the magnitude of effects at a particular site can only be understood for the rare case where A podcast discussing the contents of this article can be found at www.wiley.com/go/IEAMpod All Supplemental Data may be found in the online version of this article. * To whom correspondence may be addressed: gmann@azimuthgroup.ca Published online 3 August 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ieam.1477 Integrated Environmental Assessment and Management — Volume 10, Number 1—pp. 3–11 © 2013 SETAC 3 Critical Review