A Bayesian assessment of the PCB temporal trends in Lake Erie fish communities

Document Type


Publication Date


Publication Title

Journal of Great Lakes Research





First Page


Last Page





The temporal trends of polychlorinated biphenyls (PCBs) in Lake Erie fish were evaluated using 30. years of fish contaminant data (1977-2007). The first step of our statistical analysis was based on simple exponential decay models parameterized with Bayesian inference techniques to assess the declining rates in four intensively sampled fish species, i.e., walleye (Stizostedion vitreum), coho salmon (Oncorhynchus kisutch), rainbow trout (Oncorhynchus mykiss) and white bass (Morone chrysops). Because the exponential model postulates monotonic decrease of the PCB levels, we included first- or second-order random error terms in our statistical formulations to accommodate non-monotonic patterns in the dataset studied. Generally, our results suggest that the PCBs have been decreasing over the last 30. years with relatively weak rates that vary among the different fish species examined. Yet, our analysis with the exponential decay model also identified an increasing trend in the PCB concentrations of walleye skinless-boneless filet data, which is manifested after the mid-90s. In the second step, we used dynamic linear modeling (DLM) analysis to account for the fact that the fish length covaries with the PCB concentrations and that different sized fish may have been sampled over time. Our DLM analysis suggests that the previously reported trend of the walleye filet data is actually an artifact associated with the bias of the fish sampling practices followed. The coho salmon and rainbow trout PCB concentrations have been decreasing steadily during the study period but the associated rates were relatively weak. Finally, the PCB trends in white bass appear to have been stabilized over that last decade, although the robustness of this result remains to be confirmed due to the temporal inconsistencies of the information used. We conclude by emphasizing the importance of explicitly accounting for the different covariates (e.g., length, age, lipid content) that can potentially hamper the detection of the actual temporal trends of fish contaminants. © 2011 International Association for Great Lakes Research.