2010 journal article
Detection capacity, information gaps and the design of surveillance programs for invasive forest pests
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 91(12), 2535–2546.
author keywords: Info-gap; Sirex noctilio; Robustness; Opportuneness; Invasion model; Spatial simulation; Pareto frontier; Multi-criteria ranking
MeSH headings : Animals; Introduced Species; Models, Statistical; Pinus / parasitology; Population Dynamics; Uncertainty; United States; Wasps
TL;DR:
An alternative approach in which these outputs are combined via the Pareto ranking technique into a single priority map that outlines the survey regions with the best trade-offs between both surveillance strategies.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
Integrated pest risk maps and their underlying assessments provide broad guidance for establishing surveillance programs for invasive species, but they rarely account for knowledge gaps regarding the pest of interest or how these can be reduced. In this study we demonstrate how the somewhat competing notions of robustness to uncertainty and potential knowledge gains could be used in prioritizing large-scale surveillance activities. We illustrate this approach with the example of an invasive pest recently detected in North America, Sirex noctilio Fabricius. First, we formulate existing knowledge about the pest into a stochastic model and use the model to estimate the expected utility of surveillance efforts across the landscape. The expected utility accounts for the distribution, abundance and susceptibility of the host resource as well as the value of timely S. noctilio detections. Next, we make use of the info-gap decision theory framework to explore two alternative pest surveillance strategies. The first strategy aims for timely, certain detections and attempts to maximize the robustness to uncertainty about S. noctilio behavior; the second strategy aims to maximize the potential knowledge gain about the pest via unanticipated (i.e., opportune) detections. The results include a set of spatial outputs for each strategy that can be used independently to prioritize surveillance efforts. However, we demonstrate an alternative approach in which these outputs are combined via the Pareto ranking technique into a single priority map that outlines the survey regions with the best trade-offs between both surveillance strategies.