Accuracy of some aerial survey estimators: contrasts with known numbers
John P. Tracey A C , Peter J. S. Fleming A and Gavin J. Melville BA Vertebrate Pest Research Unit, NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange, NSW 2800, Australia.
B Biometrics Program, NSW Department of Primary Industries, Trangie Agricultural Research Centre, PMB 19, Trangie, NSW 2823, Australia.
C Corresponding author. Email: john.tracey@dpi.nsw.gov.au
Wildlife Research 35(4) 377-384 https://doi.org/10.1071/WR07105
Submitted: 27 July 2007 Accepted: 15 April 2008 Published: 27 June 2008
Abstract
Density estimates are seldom examined against actual population size, hence the ability of estimators to correct for bias is unknown. Studies that compare techniques are difficult to interpret because of the uncertainty of adherence to their respective assumptions. Factors influencing detection probability, estimators that correct for bias, the validity of their assumptions and how these relate to true density are important considerations for selecting suitable methods. Here we contrasted five estimates of feral goat (Capra hircus) densities obtained from aerial surveys (strip counts, Petersen, stratified Petersen, Chao, Alho) against known densities derived from total counts. After correcting for recounting, the Alho and stratified Petersen estimators applied to helicopter surveys were the most accurate (bias = 0.08 and –0.09 respectively), which suggests that estimates were improved by correcting individual observations according to the characteristics of each observation. An approach using modified Horvitz–Thompson equations for unequal-sized units is described and is recommended to allow for this. Both the Chao (bias = 0.35) and Petersen (bias = 0.22) estimators were positively biased, which is likely to be a consequence of averaging detection probability across all observations. Helicopter survey using capture–recapture with multiple observers is recommended for estimating the density of wildlife populations. However, adjustment for the factors that influence detection probability is required.
Acknowledgements
Thank you to Glen Saunders for his ongoing support. Funding provided by the Wildlife and Exotic Diseases Preparedness Program and the National Feral Animal Control Program through the Bureau of Rural Sciences and the Invasive Animals Cooperative Research Centre is appreciated. Particular thanks to Commercial Helicopters Australia P/L, Matt Hollingdale and Ken England for aerial survey and mustering assistance. We also thank Mike Martin, Doug and Richard Arnott and their staff, and Greg Jones, Matt Gentle, Ryan Breen, Glen Walker, Craig Faulkner, and Peter West for assistance on the ground, and Remy Van de Ven for assistance with data analysis and interpretation.
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