New Zealand Journal of Ecology (2011) 35(2): 182- 188

Evaluation of feral pig control in Hawaiian protected areas using Bayesian catch-effort models

Research Article
Mandy C. Barron 1*
Dean P. Anderson 1
John P. Parkes 1
Samuel M. 'Ohukani'ohi'a Gon III 2
  1. Landcare Research, PO Box 40, Lincoln 7640, New Zealand
  2. The Nature Conservancy of Hawai'i, 923 Nu'uanu Avenue, Honolulu, HI 96817
*  Corresponding author

In 2007 The Nature Conservancy (TNC) undertook an intensive ungulate control programme throughout three of its preserves on the Hawaiian islands of Maui and Moloka'i, with one aim being to reduce feral pig numbers to zero or near zero. The preserves were divided into manageable zones and over a 2 to 5 month period hunted from the ground with dogs in a series of up to four sweeps across the zones. More focussed hunting followed at sites with evidence of survivors. We used the data collected by the hunters to evaluate the efficacy of the control programme. The data comprised the number of pigs shot per zone per sweep and the hunters’effort and were used to fit a Weibull catch-effort model within a Bayesian framework. The fitted model provided posterior parameter estimates of the initial number of pigs resident in each zone and the relationship between hunting effort and the probability of detecting (and dispatching) a pig. The large shape parameter estimate indicated that the probability of detecting a pig increased substantially with cumulative hunting effort or
experience in that zone. The control programme was successful in six out of eight of the control zones reducing pig numbers to zero or one per zone (equating to <1 pig per km2) but was less successful in two zones where an estimated 9–14 pigs remained. However there were large credible intervals around some of the parameter estimates, suggesting an additional source of variation that was not captured by the current model. We suggest this was due to immigration of pigs back into the preserves. The quantified relationship between search effort and the probability of detecting a pig was used to make predictions on how much effort is required to detect all pigs, and can be used by TNC to interpret future monitoring data.