A new hypervolume approach for assessing environmental risks. (15th May 2017)

• Record Type: Journal Article
• Authors: Yemshanov, Denys; Koch, Frank H.; Lu, Bo; Fournier, Ronald; Cook, Gericke; Turgeon, Jean J.
• Is Part Of: Journal of environmental management. Volume 193(2017)
• Page Start: 188
• Page End: 200
• Publication Date: 2017-05-15
• Subjects: Environmental risks -- Non-dominant set -- Hypervolume -- Uncertainty -- Asian longhorned beetle -- Invasive species -- Stochastic dominance; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2017.02.021
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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 location was 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 first-order 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 non-dominant 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. Highlights: We present a new continuous risk measure to prioritize environmental risks from uncertain data. The metric is based on a hypervolume concept and influenced by both the magnitude of risk and its uncertainty. We demonstrate the concept by assessing risks of Asian longhorned beetle invasion in Greater Toronto, Canada. The new measure is continuous and is more useful than averaging risk estimates based on uncertain data.