Predictive risk thresholds for survival protection of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc

Using a probabilistic risk-based framework, we have developed a simple predictive risk threshold model for protecting the survival of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc (Zn). Probabilistic techniques using Monte Carlo analysis propagate parameter uncertainty/variability throughout the model, providing decision makers with a credible range of information and increased flexibility in establishing a specific Zn level in aquacultural ecosystems. We coupled a first-order two-compartment bioaccumulation model with a reconstructed dose-response profile based on a three-parameter Hill equation model to form a probabilistic risk model in order to determine the risk quotient associated with a 10% probability of exceeding the abalone 5% effect concentration (EC(5)) at site-specific abalone farms. Sensitivity analysis revealed that waterborne Zn concentration (C(w)) and algae bioconcentration factor (BCF(a)) have a significant effect on Zn levels in abalone. Using multiple nonlinear regression analysis with C(w) and BCF(a) as the parameters, a predictive risk threshold equation that can be used in a variety of site-specific conditions was developed for protecting the survival of farmed abalone. We believe this probabilistic framework provides an effective method for conceptualizing a public policy decision vis-a-vis the establishment of a specific acceptable risk threshold for aquacultural water quality management.