A framework for the assessment of health risks for bottlenose dolphins (Tursiops truncatus) associated with exposure to PCBs
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A framework for the assessment of health risks for bottlenose dolphins (Tursiops truncatus) associated with exposure to PCBs

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    This research integrates methods and concepts from the fields of both ecological and human health risk assessment, as well as survival analysis and mathematical modeling, to develop the necessary paradigm for the assessment of health risks in wild marine mammal populations. The methodology produces probabilistic estimates of potential reductions in marine mammal populations due to a specified level of contaminant exposure. A simulation framework is implemented which couples individual-based modeling with two-dimensional Monte Carlo simulation to provide for the inclusion of knowledge uncertainty, as well as natural variability, inherent in marine mammal populations. The framework is demonstrated for the specific assessment of reproductive health risks for bottlenose dolphins (Tursiops truncatus) associated with exposure to polychlorinated biphenyls. Data from multiple sources are integrated into the simulation framework in order to carry out exploratory simulations for the determination of essential risk assessment parameters and parameter relationships. Specifically, measured contaminant burdens from two populations of bottlenose dolphins, age distribution information from a long-running study of a separate dolphin population, and a proposed dose-response model for the quantification of the relationship between PCBs and detrimental health effects are incorporated. Detailed analyses of associated uncertainty and sensitivities for the developed model are presented which indicate that the model is robust to moderate alterations in parameter inputs. Results indicate that uncertainty with regard to measured exposures is relatively inconsequential as compared to uncertainty in estimated dose-response model parameters, and that accurate representation of juvenile exposures is particularly important for the definitive determination of population-level risk.
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