We appraise a first-order two-compartment model describing zinc (Zn) bioaccumulation in abalone Haliotis diversicolor supertexta and their food source, red alga Gracilaria tenuistipitata var. liui by probabilistic analysis of the biokinetic parameter variabilities. The model was parameterized using field and laboratory data, and predictions were quantitatively compared with field-measured tissue Zn concentrations obtained from selected abalone farms. Based on the reliable information from the published literature, we assigned the lognormal distribution model to characterize model inputs. Input variables included bioconcentration factor (BCF) of abalone, biomagnification factor of abalone, BCF of algae and depuration rate constants of abalone (k 2 ) for Zn from water and food. Compared with the field data, most of the measurements fall within the predicted 25th and 75th percentile range, indicating applying Monte Carlo technique to the first-order two-compartment model generated probabilistic estimates of Zn concentrations in abalone and algae that were consistent with field observations. Sensitivity analysis reveals that the input critical parameters that most influence the model output are BCF and k 2 of abalone. Our results suggest that the probabilistic approach allows a range of possible outcomes and their likelihood; it better informs both aquacultural risk assessors and risk managers. The degree of conservatism in the deterministic bioaccumulation models can also be evaluated against this distribution.