1. Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island
- Author
-
Davidovitch, Lior, Stoklosa, Richard, Majer, Jonathan, Nietrzeba, Alex, Whittle, Peter, Mengersen, Kerrie, and Ben-Haim, Yakov
- Subjects
Mechanical engineering -- Case studies ,Mechanical engineering -- Analysis ,Surveillance equipment -- Case studies ,Surveillance equipment -- Analysis ,Universities and colleges -- Case studies ,Universities and colleges -- Analysis ,Ecology -- Case studies ,Ecology -- Analysis ,Invasive species -- Case studies ,Invasive species -- Analysis ,Quarantine -- Case studies ,Quarantine -- Analysis ,Environmental issues - Abstract
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2009.03.011 Byline: Lior Davidovitch (a), Richard Stoklosa (b), Jonathan Majer (c), Alex Nietrzeba (c), Peter Whittle (d)(e), Kerrie Mengersen (d)(e), Yakov Ben-Haim (a) Abstract: Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy. Author Affiliation: (a) Faculty of Mechanical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel (b) Chevron, Perth WA 6000, Australia (c) Department of Environmental Biology, Curtin University of Technology, Perth WA 6845, Australia (d) Faculty of Science, Queensland University of Technology, Brisbane QLD 4001, Australia (e) Cooperative Research Centre for National Plant Biosecurity, Bruce ACT 2617, Australia Article History: Received 10 July 2008; Revised 21 February 2009; Accepted 19 March 2009
- Published
- 2009