Improving the efficiency of wildlife monitoring by estimating detectability: a case study of foxes (Vulpes vulpes) on the Eyre Peninsula, South AustraliaS. A. Field A B , A. J. Tyre C , K. H. Thorn D , P. J. O’Connor B E and H. P. Possingham A
A The Ecology Centre, University of Queensland, St Lucia, Qld 4072, Australia.
B School of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005, Australia.
C School of Natural Resource Sciences, University of Nebraska, Lincoln, NE 68583-0819, USA.
D West Coast Integrated Pest Management Program, PO Box 60, Wudinna, SA 5652, Australia.
E Department of Water, Land and Biodiversity Conservation, GPO Box 2834, Adelaide, SA 5001, Australia.
Wildlife Research 32(3) 253-258 https://doi.org/10.1071/WR05010
Submitted: 21 January 2005 Accepted: 3 May 2005 Published: 22 June 2005
Demonstrating the existence of trends in monitoring data is of increasing practical importance to conservation managers wishing to preserve threatened species or reduce the impact of pest species. However, the ability to do so can be compromised if the species in question has low detectability and the true occupancy level or abundance of the species is thus obscured. Zero-inflated models that explicitly model detectability improve the ability to make sound ecological inference in such situations. In this paper we apply an occupancy model including detectability to data from the initial stages of a fox-monitoring program on the Eyre Peninsula, South Australia. We find that detectability is extremely low (<18%) and varies according to season and the presence or absence of roadside vegetation. We show that simple methods of using monitoring data to inform management, such as plotting the raw data or performing logistic regression, fail to accurately diagnose either the status of the fox population or its trajectory over time. We use the results of the detectability model to consider how future monitoring could be redesigned to achieve efficiency gains. A wide range of monitoring programs could benefit from similar analyses, as part of an active adaptive approach to improving monitoring and management.
We thank Darryl Mackenzie and an anonymous reviewer for helpful comments on the manuscript, and David Peacock and Kirstin Long for helpful discussions. This work was supported by funding from the Australian Research Council, the West Coast Integrated Pest Management Program and the South Australian Department of Environment and Heritage, in partnership with the Elliston–Le Hunte Animal & Plant Control Board, the Western Animal & Plant Control Board, the Eyre Peninsula Natural Resource Management Group and the Natural Heritage Trust. This paper is a contribution of the University of Nebraska Agricultural Research Division, Lincoln, NE 68583, Journal Series No. 14833.
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