Network theory as a method for developing biosecurity preparedness: Netweork topology of Paratrechina longicornis in the Pacific and within New Zealand

One of the greatest challenges in eradicating pest species is determining when no further individuals remain: terminating the control programme too early means failure to eradicate, whereas continuing for too long can add considerable expense. Since monitoring tools are usually only qualitative and invariably imperfect, there may be considerable uncertainty about when and if eradication has been achieved. However, it is possible to quantify the efficacy of monitoring tools and to use this together with knowledge of the basic ecology of the target pest to robustly quantify the probability of successful eradication over time. Here, I describe one such approach and demonstrate its use in the large-scale eradication of painted apple moth (Teia anartoides) from Auckland, New Zealand. A population model for the production of male moths was used in conjunction with spatially-explicit pheromone trap locations and attraction radii to determine the daily probability of detecting a hypothetical wild population at a particular location. Over time, these probabilities compounded to decrease the likelihood of painted apple moth presence given an ongoing lack of detection. In this way, spatio-temporal risk maps were produced to inform managers and to suggest when eradication had been achieved to a predetermined level of certainty. The model suggested that eradication was likely to have been successful in the main infestation areas by mid 2005, with subsequent catches likely to represent further small incursions, as corroborated by evidence from mitochondrial DNA and stable isotope markers. While it was plausible that a wild population was present in the Otahuhu area in 2005, it was very unlikely that it remained by the end of 2006. Population probability models have potential for much wider use in border biosecurity and establishment of area freedom, particularly in combination with future automated trapping systems.