Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats
RESEARCH ARTICLE

Does variable probability of detection compromise the use of indices in aerial surveys of medium-sized mammals?

John P. Tracey A C D , Peter J. S. Fleming A C and Gavin J. Melville B
+ Author Affiliations
- Author Affiliations

A Applied Ecology Research Group, University of Canberra, Canberra, ACT 2601, Australia.

B Biometrics Program, NSW Agriculture, Trangie Agricultural Research Centre, PMB 19, NSW 2823, Australia.

C Current address: Vertebrate Pest Research Unit, NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange, NSW 2800, Australia.

D Corresponding author. Email: john.tracey@agric.nsw.gov.au

Wildlife Research 32(3) 245-252 https://doi.org/10.1071/WR03126
Submitted: 23 December 2003  Accepted: 19 October 2004   Published: 22 June 2005

Abstract

Although aerial surveys are an effective and commonly used method of monitoring wildlife populations, variable detection probability may result in unreliable indices or biased estimates of absolute abundance. Detection probability can vary between sites, sampling periods, species, group sizes, vegetation types and observers. These variables were examined in helicopter surveys of a suite of medium-sized mammals in a hilly environment in central eastern New South Wales. Maximum-likelihood methods were used to investigate the effects of these variables on detection probability, which was derived using the double-count technique. Significant differences were evident between species in the overall analysis, and group size, vegetation, observer pair and sampling period for various individual species when analysed separately. The implications for monitoring wildlife populations between sites and across time are discussed. This paper emphasises that aerial survey indices may be effective in detecting large differences in population size but can be improved by quantifying detection probabilities for a range of variables.


Acknowledgments

We thank Ken England, Greg Jones, Glen Walker and Richard Mason for their assistance in aerial surveys, and Matt Hollingdale and Mark Rogers for reliable and safe helicopter piloting. Mike, Ant and Chris Martin, Donald and Doug Arnott, and Kevin Cluff kindly allowed us to count feral goats on their land. Funding of NSW Department of Primary Industries, the National Feral Animal Control Program and the Wildlife and Exotic Disease Preparedness Program is gratefully acknowledged. Remy Van de Ven, Jim Hone and Steven McLeod provided constructive comments and discussion.


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Appendix. Variance estimate for average detection probability

Consider the double count method of Caughley and Grice (1982). In their paper they quote estimates for p1 and p2, which are the unconditional detection probabilities for two observers who independently count groups of emus in an aerial survey. They use the average of these, WR03126_E1.gif, as an estimate for mean detection probability which is then used in a correction factor for the purpose of estimating true density. Since the estimate of average detection probability can be regarded as a function of multinomial variates, we use the delta method to derive a variance estimate for WR03126_I1.gif.

Suppose n is the total number of animals (or groups) detected in the survey, partitioned into S1 = the number seen by observer 1, S2 = the number seen by observer 2, and B = the number seen by both observers. This leaves NS1S2B animals undetected, where N is the total number of animals in the survey area. The estimates for the unconditional detection probabilities are given by

E2

The average detection probability then becomes

E3

Using the delta method the approximate variance of WR03126_I1.gif (unconditional on n) will be

E4

where each of the terms in this expression appear below.

E5

The variance estimate follows by replacing N and the parameters p1 and p2 with their estimates. The following estimate for N is a suitable choice

E6


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