Recounting bias can affect abundance estimates from intensive helicopter surveys of feral goats
John P. Tracey A B and Peter J. S. Fleming
A C D E *
+ Author Affiliations
- Author Affiliations
A Institute of Applied Ecology, University of Canberra, 11 Kirinari Street, Bruce, ACT 2617, Australia.
B Biosecurity and Food Safety, New South Wales Department of Primary Industries, Prince Street, Orange, NSW 2800, Australia.
C Vertebrate Pest Research Unit, New South Wales Department of Primary Industries, Orange Agricultural Institute, 1447 Forest Road, Orange, NSW 2800, Australia.
D School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
E Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, Qld 4350, Australia.
Wildlife Research 50(5) 389-397 https://doi.org/10.1071/WR22097
Submitted: 8 June 2022 Accepted: 12 October 2022 Published: 11 November 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: Aerial surveys are widely used for estimating the abundance of wildlife over large areas. The failure to count all animals within survey transects is commonly acknowledged and there are many techniques to measure and correct for underestimation. However, the possibility of animals being counted more than once in intensive surveys, which leads to overestimation, is rarely examined. Animals can move in response to observers or vehicles, and bias can occur when animals move before or after detection. Movement of animals immediately prior to and associated with observation processes is methodologically accommodated in distance sampling but bias attributable to responsive movement after observation platforms have passed requires investigation.
Aims: We sought to investigate potential biases caused by animal movement during intensive helicopter surveys of feral goats, and to quantify the probability that animals are available for recounting because of their responsive movements.
Methods: Using ground-based behavioural studies simultaneous with intensive helicopter strip surveys of feral goats, we measured the extent of responsive movement, distances and directions moved, and sampling design parameters, and contrasted those with random movements.
Key results: Feral goats did not move randomly in response to helicopters. Animals within the transect strips, and therefore potentially visible from the aircraft, were more likely to move than those outside the transect. Considerable responsive movement (flushing) occurred between transects and more animals (64%, n = 448) moved towards unsampled transects than towards transects already sampled. Because of the spatial separation of transects, 21% of goats were available for recounting in adjacent transects, leading to potential overestimation.
Conclusions: Although most extensive surveys of macropods and other wildlife in Australia account for overestimation in their design, surveys that sample intensively and apply valid corrections for undercounting are likely to produce positively biased estimates of abundance where flushing occurs. Likewise, intensive thermal surveys could be subject to positive bias for animals prone to flushing. This is routinely ignored in wildlife management and research where close transects are used to estimate abundance.
Implications: Responsive movement requires consideration when designing intensive aerial surveys of wildlife. Randomised transects without replacement or larger distances between transects will counteract recounting bias.
Keywords: aerial survey, antipredator response, Capra hircus, density, feral goat, responsive movement, ungulate, wildlife management.
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