Using nested models and laboratory data for predicting population effects of contaminants on fish: A step toward a bottom-up approach for establishing causality in field studies

Predicting the effects of contaminants on fish populations is difficult due to their complex life history and high interannual variation in their population abundances. We present an approach that extrapolates laboratory data on contaminant effects, including behavioral effects, to the population level by using a series of nested statistical and simulation models. The approach is illustrated using PCB effects on Atlantic croaker. Laboratory experiments were performed that estimated PCB effects on fecundity, egg mortality, and the swimming speed and predator evasion behavior of larvae. A statistical model converted impaired predator evasion to reduced probability of escaping a predatory fish. An individual-based model then converted the output of the statistical model into changes in larval stage duration and survival, which were used to change elements of the matrix model. A matrix projection model simulated population dynamics for 100 years for baseline conditions and for two hypothetical PCB exposure scenarios. PCB effects were imposed in the model by reducing the fecundity of exposed adults, increasing egg mortality, and increasing the larval stage duration and mortality rate. Predicted population effects of PCBs were small relative to the interannual variation. Our analysis is a step toward understanding population responses to stressors and for ultimately establishing causality in field situations.