Predicting the effects of endocrine disrupting chemicals on fish populations

This study evaluates the applicability and sensitivity of fish population dynamics modeling in assessing the potential effects of individual chemicals on population sustainability and recovery. Fish reproductive health is all increasingly important issue for ecological risk assessment following international concern over endocrine disruption. Life-history data from natural brook trout and fathead minnow populations were combined with effects data from laboratory-based studies, mainly concerning species other than brook trout and fathead minnows, to assess the likely impact of nonylphenol (NP) and methoxychlor (MXC) on brook trout (Salvelinus fontinalis) and fathead minnow (Pimephales promelas) population size. A delay differential equation (DDE) model with a 1-day timestep was used to predict the population dynamics of the brook trout and fathead minnows. The model predicts that NP, could enhance populations by tip to 17% at a concentration of 30 mug l(-1) based oil the results of reduction in survival and increased fecundity from life-cycle toxicity tests, however attempting to allow for growth reduction and its effect on fecundity results in a prediction of a 28% reduction in population numbers. For fathead minnows the DDE model predicts that the same concentration of NP could cause a population reduction of 21%. The differences in these predictions are related to these two species having different life history strategies, which are considered in the parameterization of the model. Post-application concentrations of MXC may peak around 300 mug l(-1) and then decline rapidly with time. Predictions show that such applications could cause a reduction of up to 30% in brook trout populations if the application occurs at the peak of the spawning season on successive years but that the effect would be less than 1% if the spawning season is avoided. Effects on the fathead minnow population size are predicted to be smaller (<4%) even if application occurs during the spawning period. Risk based statistics generated by the population dynamics models, Such as interval decline risk or quasiextinction risk and predicted time to recovery complement traditional effects parameters such as LC50 and LOEC and may ultimately prove to be more useful in risk assessment.