Recommendations for the development and application of wildlife toxicity reference values
Toxicity reference values (TRVs) are essential in models used in the prediction of the potential for adverse impacts of environmental contaminants to avian and mammalian wildlife; however, issues in their derivation and application continue to result in inconsistent hazard and risk assessments that present a challenge to site managers and regulatory agencies. Currently, the available science does not support several common practices in TRV derivation and application. Key issues include inappropriate use of hazard quotients and the inability to define the probability of adverse outcomes. Other common problems include the continued use of no-observed- and lowest-observed-adverse-effect levels (NOAELs and LOAELs), the use of allometric scaling for interspecific extrapolation of chronic TRVs, inappropriate extrapolation across classes when data are limited, and extrapolation of chronic TRVs from acute data without scientific basis. Recommendations for future TRV derivation focus on using all available qualified toxicity data to include measures of variation associated with those data. This can be achieved by deriving effective dose (EDx)-based TRVs where x refers to an acceptable (as defined in a problem formulation) reduction in endpoint performance relative to the negative control instead of relying on NOAELs and LOAELs. Recommendations for moving past the use of hazard quotients and dealing with the uncertainty in the TRVs are also provided.
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Available from: Colin Austin Chapman
- "A number of recent studies investigating wild or recently captured primates have provided similar data on lead concentration in hair. This allows us to evaluate the potential biologic significance of the lead levels we documented for the howler monkeys in Balancán, but the real need is for reference values associated with the adverse health or behavioral impacts of such high environment lead levels (Allard et al. 2010). Engel et al. (2010) quantified the average lead content in hair from free-ranging rhesus monkeys (Macaca mulatta) only 3 km from the densely populated city of Kathmandu, Nepal to be 4.5 ppm of hair, and the maximum concentration in their sample was 10.2 ppm. "
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ABSTRACT: To construct informed conservation plans, researchers must go beyond understanding readily apparent threats such as habitat loss and bush-meat hunting. They must predict subtle and cascading effects of anthropogenic environmental modifications. This study considered a potential cascading effect of deforestation on the howler monkeys (Alouatta pigra) of Balancán, Mexico. Deforestation intensifies flooding. Thus, we predicted that increased flooding of the Usumacinta River, which creates large bodies of water that slowly evaporate, would produce increased lead content in the soils and plants, resulting in lead exposure in the howler monkeys. The average lead levels were 18.18 ± 6.76 ppm in the soils and 5.85 ± 4.37 ppm in the plants. However, the average lead content of the hair of 13 captured howler monkeys was 24.12 ± 5.84 ppm. The lead levels in the animals were correlated with 2 of 15 blood traits (lactate dehydrogenase and total bilirubin) previously documented to be associated with exposure to lead. Our research illustrates the urgent need to set reference values indicating when adverse impacts of high environmental lead levels occur, whether anthropogenic or natural, and the need to evaluate possible cascading effects of deforestation on primates.
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- "Taxonomicgroup:Inmostcases,thereceptorsofconcern inwildliferiskassessmentarenotthesamespeciesfor whichdose–responsedataexist.Consequently,and consistentwithcurrentpracticeinwildliferiskassessment (Allardetal.2010),werecommendthatformost applications,datashouldbegatheredforallbirds(for applicationtoavianreceptors)orallmammals(for applicationtomammalianreceptors).Thesameprinciples wouldapplyforreptilesandamphibians.Oncedataare gathered,itmaybepossibletorationalizenarrower taxonomicrangesforparticularreceptors.Forexample, anassessmentfordeermaybebasedondose–responsedata forruminantsonly.However,itismorecommonthat screeningofdataproceedsonlytoacoarselevelof ecologicalortaxonomicresolution.Forexample,common aviansubgroupingsforwhichtoxicitydataareavailableare raptors(e.g.,falcon,kestrel),passerines(e.g.,swallow, robin),galliformes(e.g.,quail,pheasant,chicken),and waterfowl(e.g.,mallardduck,blackduck). "
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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 utility 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. While 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 © 2013 SETAC.
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