Temporal variability and ignorance in Monte Carlo contaminant bioaccumulation models: A case study with selenium in Mytilus edulis

Although the parameters for contaminant bioaccumulation models most likely vary over time, lack of data makes it impossible to quantify this variabili ty. As a consequence, Monte Carlo models of contaminant bioaccumulation oft en treat all parameters as having fixed true values that are unknown. This can lead to biased distributions of predicted contaminant concentrations. T his article demonstrates this phenomenon with a case study of selenium accu mulation in the mussel Mytilus edulis in San Francisco Bay. "Ignorance-only " simulations (in which phytoplankton and bioavailable selenium concentrati ons are constant over time, but sampled from distributions of field measure ments taken at different times), which an analyst might be forced to use du e to lack of data, were compared with "variability and ignorance" simulatio ns (sampling phytoplankton and bioavailable selenium concentrations each mo nth). It was found that ignorance only simulations may underestimate or ove restimate the median predicted contaminant concentration at any time, relat ive to variability and ignorance simulations. However, over a long enough t ime period (such as the complete seasonal cycle in a seasonal model), treat ing temporal variability as if it were ignorance at least gave a range of p redicted concentrations that enclosed the range predicted by explicit treat ment of temporal variability. Comparing the temporal variability in field d ata with that predicted by simulations may indicate whether the right amoun t of temporal variability is being included in input variables. Sensitivity analysis combined with biological knowledge suggests which parameters migh t make important contributions to temporal variability. Temporal variabilit y is potentially more complicated to deal with than other types of stochast ic variability, because of the range of time scales over which parameters m ay vary.