Zero-tolerance biosecurity protects high-conservation-value island nature reserve.

Barrow Island, north-west coast of Australia, is one of the world's significant conservation areas, harboring marsupials that have become extinct or threatened on mainland Australia as well as a rich diversity of plants and animals, some endemic. Access to construct a Liquefied Natural Gas (LNG) plant, Australia's largest infrastructure development, on the island was conditional on no non-indigenous species (NIS) becoming established. We developed a comprehensive biosecurity system to protect the island's biodiversity. From 2009 to 2015 more than 0.5 million passengers and 12.2 million tonnes of freight were transported to the island under the biosecurity system, requiring 1.5 million hrs of inspections. No establishments of NIS were detected. We made four observations that will assist development of biosecurity systems. Firstly, the frequency of detections of organisms corresponded best to a mixture log-normal distribution including the high number of zero inspections and extreme values involving rare incursions. Secondly, comprehensive knowledge of the island's biota allowed estimation of false positive detections (62% native species). Thirdly, detections at the border did not predict incursions on the island. Fourthly, the workforce detected more than half post-border incursions (59%). Similar approaches can and should be implemented for all areas of significant conservation value.

Info-gap theory and robust design of surveillance for invasive species: the case study of Barrow Island.

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.