title: A general risk-based adaptive management scheme incorporating the Bayesian Network Relative Risk Model with the South River, Virginia, as case study.
abstract: Adaptive management has been presented as a method for the remediation, restoration, and protection of ecological systems. Recent reviews have found that the implementation of adaptive management has been unsuccessful in many instances. We present a modification of the model first formulated by Wyant and colleagues that puts ecological risk assessment into a central role in the adaptive management process. This construction has 3 overarching segments. Public engagement and governance determine the goals of society by identifying endpoints and specifying constraints such as costs. The research, engineering, risk assessment, and management section contains the decision loop estimating risk, evaluating options, specifying the monitoring program, and incorporating the data to re-evaluate risk. The 3rd component is the recognition that risk and public engagement can be altered by various externalities such as climate change, economics, technological developments, and population growth. We use the South River, Virginia, USA, study area and our previous research to illustrate each of these components. In our example, we use the Bayesian Network Relative Risk Model to estimate risks, evaluate remediation options, and provide lists of monitoring priorities. The research, engineering, risk assessment, and management loop also provides a structure in which data and the records of what worked and what did not, the learning process, can be stored. The learning process is a central part of adaptive management. We conclude that risk assessment can and should become an integral part of the adaptive management process. Integr Environ Assess Manag 2017;13:115-126. © 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: April J
authors.initials: AJ
authors.lastname: Markiewicz
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Kim 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: 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: Jonah M
authors.initials: JM
authors.lastname: Stinson
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
mesh_terms: D001499:Bayes Theorem; D017753:Ecosystem; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods; D012308:Risk Management / Q000379:methods*; D045483:Rivers / Q000737:chemistry; D014768:Virginia; D014874:Water Pollutants, Chemical / Q000032:analysis; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical
keywords: Adaptive management; Bayesian Network Relative Risk Model; Ecological risk assessment; South River, VA