New Zealand Journal of Ecology (2011) 35(2): 195- 195

Spatially-explicit wildlife surveillance to prove freedom from diseases or pests

Conference Abstract
Dave Ramsey 1
Graham Nugent  
John Parkes  
  1. Department of Agriculture and Food Western Australia, PO Box 1231, Bunbury WA 6231, Australia

Surveillance undertaken to prove freedom from a disease has traditionally focussed on sampling a specified proportion of the population to determine the presence of the infective agent. Based on a sample of individuals testing negative for disease, standard probability theory using the binomial or hypergeometric distribution can be used to make inference about the probability of disease being absent. However, if the disease agent involves a wildlife host, then determining what proportion of the population was sampled becomes problematic as it requires an estimate of population abundance, which may be difficult and expensive to obtain. Surveillance during the eradication of pests such as feral ungulates often make use of the Judas technique, whereby radio-collared individuals are released and followed in the hope that the sociality of the individuals will betray the locations of conspecifics, which can then be dispatched. Data collected during such surveillance is explicitly spatial, containing information about the ‘search area’ of the Judas animal. However, there has been no attempt to use such data to make inference about the probability of the area being free of the pest, given Judas animals fail to detect any conspecifics.
We present a framework for making specific use of the spatial nature of such wildlife surveillance data to make inference about the probability of freedom from the disease or pest. Underlying the sampling framework is a model of the detection process by sampled individuals. Estimates of individual detection probability are spatially smoothed using the extent of individual movements to produce a spatially- explicit detection surface. Bayes theorem is then used to combine this 2-dimensional surface with prior information on
the probability of pest or disease presence, prior to sampling, to estimate the probability that the area is free from the disease or pest, given surveillance fails to detect evidence of their presence. We illustrate the method with examples of the detection of bovine Tb in wildlife in New Zealand and the detection of pigs (Sus scrofa) using the Judas technique during the feral pig eradication programme on Santa Cruz Island, California.