NC State Develops Model to Predict Hot Spots for Mercury Levels in Fish

Mercury levels in fish are prompting widespread consumption advisories and uncertainty among consumers over which species are safe to eat. Now researchers at North Carolina State University have developed a model that will help scientists and regulators around the country predict which areas are likely to have fish with high mercury levels – a breakthrough that should help officials address public uncertainty by developing health advisories for specific water bodies and fish species.

The NC State researchers have created a statistical model that can incorporate data on the variety of factors that influence mercury levels in fish tissue – such as the pH of the water and the size and species of the fish – to identify those aquatic ecosystems that are likely to have fish with high mercury levels. “We want to be predictive,” says NC State’s Dr. Derek Aday, “rather than reacting to events after they’ve happened.” Aday, an assistant professor of biology at NC State who is part of the research team, says the model can be used “to develop specific health advisories for water bodies and species rather than sweeping advisories.” Current advisories tend to restrict consumption of certain species for an entire state or region out of concern that mercury levels in the fish could adversely affect human health.

While the NC State effort has so far focused on North Carolina, Aday says, “The goal is to create a template that could be used in systems throughout the country. Specific variables may change, but the approach would be the same.” In fact, Aday says, “we’ve identified a suite of variables that we believe will be consistent in driving mercury dynamics across many aquatic systems.” The new model is a synthesis of a number of smaller statistical models that allows researchers to examine the combination of factors that can drive contaminants in aquatic systems.

In order to collect data for use in the new model, the researchers synthesized water quality, fish tissue mercury and environmental data that had been collected by North Carolina agencies since 1990. That database was then used to construct the statistical model.

The researchers recently presented their findings in a mercury symposium at the University of North Carolina System’s Water Resources Research Institute Annual Conference, which was held in October at NC State. Dana K. Sackett, a Ph.D. student in biology at NC State, was the lead author of the presentation. The co-authors were Aday; Dr. James A. Rice, professor of biology at NC State; and Dr. W. Gregory Cope, associate professor of toxicology at NC State.

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Note to editors: The presentation abstract follows.

“A statewide assessment of fish tissue mercury dynamics”

Authors: Dana K. Sackett, D. Derek Aday, James A. Rice, W. Gregory Cope, North Carolina State University

Presented: Oct. 8, 2008, at the Water Resources Research Institute of the University of North Carolina’s Mercury Symposium in Raleigh, N.C.

Abstract: Mercury contamination of aquatic systems has received much recent attention because of potential health concerns for wildlife and humans. Although factors affecting mercury deposition, conversion to biologically-active methylmercury (MeHg), and bioaccumulation in aquatic systems have been identified, equivocal results specific to particular species and systems have hampered policy making. Our study addresses this problem through a comprehensive, statewide synthesis of current data on mercury contamination and the environmental factors associated with MeHg formation and transport through aquatic food webs. Using data collected by the North Carolina Department of Environment and Natural Resources, the Environmental Protection Agency and others, we examined the relationship between a suite of biotic and abiotic factors and tissue mercury concentrations from fish in North Carolina waterbodies. Multivariate tests were conducted to create predictive models relating environmental variables to mercury in fish, and Akaike’s Information Criterion (AIC) was used to examine the relative strength of candidate models. The best models in our analyses included fish species (ranked by mean total length), fish trophic status, ecoregion, and pH. Thus, we expect high concentrations of MeHg in large species that are of high trophic level (piscivores) from systems that exhibit low pH in the coastal plain. Also important were site type (e.g., swamps, lakes, rivers, bays) and land-use patterns (percent of the sub-basin that is agricultural). Although previous investigations have indicated similar trends, our study is unique in that we examined the relative importance of a large number of biotic and abiotic variables across a range of environments and ecosystems. The results of these analyses should help policy makers in risk assessment decisions, and should serve as a template for future contaminant investigations.