Detection capacity,information gaps and the design of surveillance programs for invasive forest pests |
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Authors: | Denys Yemshanov Frank H Koch Yakov Ben-Haim William D Smith |
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Institution: | 1. Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, ON, Canada P6A 2E5;2. Department of Forestry and Environmental Resources, North Carolina State University, USDA Forest Service, Forest Health Monitoring Program, 3041 Cornwallis Road, Research Triangle Park, NC 27709, USA;3. Technion, Israel Institute of Technology, Faculty of Mechanical Engineering, Haifa 32000, Israel;4. USDA Forest Service, Southern Research Station, 3041 Cornwallis Road, Research Triangle Park, NC 27709, USA |
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Abstract: | 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. |
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