| Popis: |
'The objective of this project is to provide DOE with improved methods to assess risks from contaminants to wildlife populations. The current approach for wildlife risk assessment consists of comparison of contaminant exposure estimates for individual animals to literature-derived toxicity test endpoints. These test endpoints are assumed to estimate thresholds for population-level effects. For several reasons, uncertainties associated with this approach are considerable. First, because toxicity data are not available for most potential wildlife endpoint species, extrapolation of toxicity data from test species to the species of interest is required. There is no consensus on the most appropriate extrapolation method. Second, toxicity data are represented as statistical measures (e.g., NOAELs or LOAELs) that provide no information on the nature or magnitude of effects. The level of effect is an artifact of the replication and dosing regime employed, and does not indicate how effects might increase with increasing exposure. Consequently, slight exceedance of a LOAEL is not distinguished from greatly exceeding it. Third, the relationship of toxic effects on individuals to effects on populations is poorly estimated by existing methods. It is assumed that if the exposure of individuals exceeds levels associated with impaired reproduction, then population level effects are likely. Uncertainty associated with this assumption is large because depending on the reproductive strategy of a given species, comparable levels of reproductive impairment may result in dramatically different population-level responses. The authors are working on several tasks to address these problems: (1) investigation of the validity of the current allometric scaling approach for interspecies extrapolation and development of new scaling models; (2) development of dose-response models for toxicity data presented in the literature; and (3) development of matrix-based population models that, coupled with dose-response models, will allow for realistic estimation of population-level effects for individual responses. Uncertainties associated with the current approach to wildlife risk assessment may have direct impacts on DOE EM satisfactorily fulfilling it''s mission in two ways. First, risk estimates may be too conservative and therefore remediation may be recommended when it is not needed. Limited remediation funds may be spent for insignificant or non-existent risks and possibly cause a net increase in environmental damage due to unnecessary habitat destruction. Second, risk estimates may not be adequately protective and therefore remedial actions may not recommended when they are needed. The consequences of this uncertainty is environmental damage and potential NRDA liability. Either of these alternatives results in inefficient use of limited EM funds. This project will provide the tools to better estimate population-level effects and therefore reduce uncertainty associated with wildlife risk assessments.' |
