title: The multiple stressor ecological risk assessment for the mercury-contaminated South River and upper Shenandoah River using the Bayesian network-relative risk model.
abstract: We have conducted a regional scale risk assessment using the Bayesian Network Relative Risk Model (BN-RRM) to calculate the ecological risks to the South River and upper Shenandoah River study area. Four biological endpoints (smallmouth bass, white sucker, Belted Kingfisher, and Carolina Wren) and 4 abiotic endpoints (Fishing River Use, Swimming River Use, Boating River Use, and Water Quality Standards) were included in this risk assessment, based on stakeholder input. Although mercury (Hg) contamination was the original impetus for the site being remediated, other chemical and physical stressors were evaluated. There were 3 primary conclusions from the BN-RRM results. First, risk varies according to location, type and quality of habitat, and exposure to stressors within the landscape. The patterns of risk can be evaluated with reasonable certitude. Second, overall risk to abiotic endpoints was greater than overall risk to biotic endpoints. By including both biotic and abiotic endpoints, we are able to compare risk to endpoints that represent a wide range of stakeholder values. Third, whereas Hg reduction is the regulatory priority for the South River, Hg is not the only stressor driving risk to the endpoints. Ecological and habitat stressors contribute risk to the endpoints and should be considered when managing this site. This research provides the foundation for evaluating the risks of multiple stressors of the South River to a variety of endpoints. From this foundation, tools for the evaluation of management options and an adaptive management tools have been forged. Integr Environ Assess Manag 2017;13:85-99. © 2016 SETAC.
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Wayne G
authors.initials: WG
authors.lastname: Landis
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Kimberley K
authors.initials: KK
authors.lastname: Ayre
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Annie F
authors.initials: AF
authors.lastname: Johns
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Heather M
authors.initials: HM
authors.lastname: Summers
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Jonah
authors.initials: J
authors.lastname: Stinson
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Meagan J
authors.initials: MJ
authors.lastname: Harris
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Carlie E
authors.initials: CE
authors.lastname: Herring
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: April J
authors.initials: AJ
authors.lastname: Markiewicz
mesh_terms: D001499:Bayes Theorem; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D008396:Maryland; D008628:Mercury / Q000032:analysis*; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods; D045483:Rivers / Q000737:chemistry; D013312:Stress, Physiological; D014768:Virginia; D014874:Water Pollutants, Chemical / Q000032:analysis*; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical; D008628:Mercury
keywords: Bayesian network relative risk model; Ecological risk assessment; Mercury; South River; Virginia