Detection of the spatiotemporal trends of mercury in Lake Erie fish communities: a Bayesian approach

Environ Sci Technol. 2011 Mar 15;45(6):2217-26. doi: 10.1021/es103054q. Epub 2011 Feb 17.

Abstract

The temporal trends of total mercury (THg) in four fish species in Lake Erie were evaluated based on 35 years of fish contaminant data. Our Bayesian statistical approach consists of three steps aiming to address different questions. First, we used the exponential and mixed-order decay models to assess the declining rates in four intensively sampled fish species, i.e., walleye (Stizostedion vitreum), yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieui), and white bass (Morone chrysops). Because the two models postulate monotonic decrease of the THg levels, we included first- and second-order random walk terms in our statistical formulations to accommodate nonmonotonic patterns in the data time series. Our analysis identified a recent increase in the THg concentrations, particularly after the mid-1990s. In the second step, we used double exponential models to quantify the relative magnitude of the THg trends depending on the type of data used (skinless-boneless fillet versus whole fish data) and the fish species examined. The observed THg concentrations were significantly higher in skinless boneless fillet than in whole fish portions, while the whole fish portions of walleye exhibited faster decline rates and slower rates of increase relative to the skinless boneless fillet data. Our analysis also shows lower decline rates and higher rates of increase in walleye relative to the other three fish species examined. The food web structural shifts induced by the invasive species (dreissenid mussels and round goby) may be associated with the recent THg trends in Lake Erie fish.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Environmental Monitoring / methods*
  • Fishes / metabolism*
  • Fresh Water / chemistry
  • Half-Life
  • Mercury / metabolism*
  • Models, Chemical
  • Ontario
  • Water Pollutants, Chemical / metabolism*
  • Water Pollution, Chemical / statistics & numerical data*

Substances

  • Water Pollutants, Chemical
  • Mercury