title: Allocating surveillance resources to reduce ecological invasions: maximizing detections and information about the threat.
abstract: Allocating resources to detect invasive pests, diseases, and pathogens on exposure pathways requires a trade-off between the need to detect as many contaminated items as possible and the need to acquire knowledge about contamination rates. We develop a model and an algorithm that provide guidance for the allocation of inspection resources across multiple dynamic pathways in cases where not every item can be inspected. The model uses a null hypothesis that the contamination rate of a pathway is above a specified level: a risk cutoff. Pathways with a risk above the cutoff are fully inspected, and those with a risk below the cutoff level are monitored at a rate that would detect a change of the risk to being above the cutoff level with high probability. We base our decision on the 95% upper confidence limit for the contamination rate. We demonstrate via simulations and a data set that focusing inspection resources on specific pathways can result in substantially more effective intervention, and that the reduction in overall effectiveness of monitoring low-risk pathways need not be substantial. Use of the model demands the selection of the risk cutoff, and this limit can be set according to projected consequences.
authors.affiliation: Australian Centre of Excellence for Risk Analysis, Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3010, Australia. A.Robinson@ms.unimelb.edu.au
authors.forename: Andrew
authors.initials: A
authors.lastname: Robinson
authors.forename: Mark A
authors.initials: MA
authors.lastname: Burgman
authors.forename: Rob
authors.initials: R
authors.lastname: Cannon
mesh_terms: D000465:Algorithms; D003247:Conservation of Natural Resources / Q000379:methods*; D017753:Ecosystem; D058865:Introduced Species; D008954:Models, Biological