@article { NPB1807,
	title = {Mobile traps are better than stationary traps for surveillance of airborne fungal spores},
	journal = {Crop Protection},
	year = {2012},
	abstract = {<p>Eradication of invasive fungal pathogens that undergo wind-assisted dispersal can be extremely difficult. Many fungal pathogens can survive on multiple hosts and wind-assisted dispersal can rapidly spread inoculum over large areas, leading to wide-spread, multiple loci of infection. When eradication attempts are made, a surveillance system is required that can provide an early warning if the attempt has been unsuccessful. Therefore, there is a need for large-scale surveillance systems that can detect the movement of airborne inoculum, and which can be deployed over a large area. Traditional methods for trapping airborne fungal spores make use of stationary traps, however, traps can also be mounted on remote piloted vehicles, allowing the use of mobile traps, which can provide a far more flexible approach to the sampling of airborne spores. In this paper we compared a range of surveillance strategies based on stationary or mobile traps, and evaluated the ability of these traps to detect airborne spores. Using a computational model, we simulated a number of dispersal events, and the use of various surveillance strategies to detect these events. Results of our simulations showed that strategies based on mobile traps have a much greater probability of detecting airborne spores than strategies based on stationary traps, and that mobile trap strategies required a far lower number of traps to achieve a reasonable probability of detection. Surveillance strategies based on mobile traps can be effectively employed to define the extent of a pathogen outbreak, and also to monitor the reduction in airborne inoculum once eradication attempts have commenced. If deployed over suitably long time periods, continual no-detection results by surveillance strategies based on mobile traps can also provide a high level of confidence that eradication has been successful.</p>},
	author = {David Savage and  Martin Barbetti and  William MacLeod and  Moin Salamc and  Michael Renton}
}