Dynamics of an endangered New Zealand skink: accounting for incomplete detectability in estimating patch occupancy
- Department of Zoology, University of Otago, PO Box 56, Dunedin 9054, New Zealand
- Department of Conservation, PO Box 743, Invercargill 9840, New Zealand
- Proteus Wildlife Research Consultants, PO Box 5193, Moray Place, Dunedin 9058, New Zealand
- Present address: 32B Huria Lane, Woodend 7610, North Canterbury, New Zealand
The endangered grand skink (Oligosoma grande) is a New Zealand endemic lizard that persists as metapopulations occupying rock patches within matrices of mixed native vegetation and modified agricultural pasture. Parameterisation of metapopulation models applied in conservation biology assumes complete detectability of target species. Incomplete detectability may result in underestimates of occupancy and biased estimates of extinction and colonisation rates. Recent techniques use multiple surveys of sampling sites to model detectability and derive robust estimates of occupancy, and extinction and colonisation rates. Five years (1998–2002) of presence/absence survey data were analysed to determine grand skink site occupancy and estimate colonisation and extinction rates. Mean site occupancy was 0.38 (SE 0.07), compared with a naïve estimate of 0.29. Occupancy, extinction and colonisation probabilities were habitat specific, varying according to tussock or a modified pasture matrix. Colonisation probability was higher in tussock than in pasture, whereas extinction probability was higher in pasture. Derived model-averaged estimates showed that occupancy was higher in tussock (range 0.515(0.02) – 0.532 (0.02)) than in pasture (range 0.226 (0.03) – 0.234 (0.01)), with a slight trend of decline in pasture areas and increase in tussock areas over time, with the result that overall occupancy has been reasonably static over the 5 years. Detectability varied interannually, ranging from 0.63 to 0.83. The difference between the naïve occupancy estimate and the model-averaged estimate highlights the importance of deriving robust estimates of metapopulation parameters that take incomplete detectability into account. Unbiased estimates allow managers to predict and track responses to management interventions.