Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Denys Yemshanov; Frank H. Koch; Bo Lu; Ronald Fournier; Gericke Cook; Jean J. Turgeon
    Date: 2017
    Source: Journal of Environmental Management
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: View PDF  (1.0 MB)

    Description

    Assessing risks of uncertain but potentially damaging events, such as environmental disturbances, disease outbreaks and pest invasions, is a key analytical step that informs subsequent decisions about how to respond to these events. We present a continuous risk measure that can be used to assess and prioritize environmental risks from uncertain data in a geographical domain. The metric is influenced by both the expected magnitude of risk and its uncertainty.We demonstrate the approach by assessing risks of human-mediated spread of Asian longhorned beetle (ALB, Anoplophora glabripennis) in Greater Toronto (Ontario, Canada). Information about the human-mediated spread of ALB through this urban environment to individual geographical locations is uncertain, so each locationwas characterized by a set of probabilistic rates of spread, derived in this case using a network model. We represented the sets of spread rates for the locations by their cumulative distribution functions (CDFs) and then, using the firstorder stochastic dominance rule, found ordered non-dominant subsets of these CDFs, which we then used to define different classes of risk across the geographical domain, from high to low. Because each non-dominant subset was estimated with respect to all elements of the distribution, the uncertainty in the underlying data was factored into the delineation of the risk classes; essentially, fewer non-dominant subsets can be defined in portions of the full set where information is sparse. We then depicted each non-dominant subset as a point cloud, where points represented the CDF values of each subset element at specific sampling intervals. For each subset, we then defined a hypervolume bounded by the outermost convex frontier of that point cloud. This resulted in a collection of hypervolumes for every nondominant subset that together serve as a continuous measure of risk, which may be more practically useful than averaging metrics or ordinal rank measures. Overall, the approach offers a rigorous depiction of risk in a geographical domain when the underlying estimates of risk for individual locations are represented by sets or distributions of uncertain estimates. Our hypervolume-based approach can be used to compare assessments made with different datasets and assumptions.

    Publication Notes

    • You may send email to pubrequest@fs.fed.us to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Yemshanov, Denys; Koch, Frank H.; Lu, Bo; Fournier, Ronald; Cook, Gericke; Turgeon, Jean J. 2017. A new hypervolume approach for assessing environmental risks. Journal of Environmental Management, Vol. 193: 13 pages.: 188-200. DOI:10.1016/j.jenvman.2017.02.021

    Cited

    Google Scholar

    Keywords

    Environmental risks, Non-dominant set, Hypervolume, Uncertainty, Asian longhorned beetle, Invasive species, Stochastic dominance

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/53813