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Using adaptive processes and adverse outcome pathways to develop meaningful, robust, and actionable environmental monitoring programs

T. Arciszewski,K. Munkittrick,G. Scrimgeour,M. Dubé,F. Wrona,R. Hazewinkel

Published 2017 in Integrated Environmental Assessment and Management

ABSTRACT

The primary goals of environmental monitoring are to indicate whether unexpected changes related to development are occurring in the physical, chemical, and biological attributes of ecosystems and to inform meaningful management intervention. Although achieving these objectives is conceptually simple, varying scientific and social challenges often result in their breakdown. Conceptualizing, designing, and operating programs that better delineate monitoring, management, and risk assessment processes supported by hypothesis‐driven approaches, strong inference, and adverse outcome pathways can overcome many of the challenges. Generally, a robust monitoring program is characterized by hypothesis‐driven questions associated with potential adverse outcomes and feedback loops informed by data. Specifically, key and basic features are predictions of future observations (triggers) and mechanisms to respond to success or failure of those predictions (tiers). The adaptive processes accelerate or decelerate the effort to highlight and overcome ignorance while preventing the potentially unnecessary escalation of unguided monitoring and management. The deployment of the mutually reinforcing components can allow for more meaningful and actionable monitoring programs that better associate activities with consequences. Integr Environ Assess Manag 2017;13:877–891. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Integrated Environmental Assessment and Management

  • Publication date

    2017-09-01

  • Fields of study

    Medicine, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1002/ieam.1938PMID 28383771
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar, PubMed

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