title: Predictive risk thresholds for survival protection of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc.
abstract: 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.
authors.affiliation: Ecotoxicological Modeling Center, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan 10617, Republic of China. cmliao@ntu.edu.tw
authors.forename: Chung-Min
authors.initials: CM
authors.lastname: Liao
authors.forename: Berry Yun-Hua
authors.initials: BY
authors.lastname: Chou
mesh_terms: D000818:Animals; D017756:Aquaculture; D003657:Decision Making; D005544:Forecasting; D015233:Models, Statistical; D008974:Mollusca; D009010:Monte Carlo Method; D018570:Risk Assessment; D014873:Water Pollutants / Q000633:toxicity*; D015032:Zinc / Q000633:toxicity*
chemical_list: D014873:Water Pollutants; D015032:Zinc