Aerial surveys of multiple species: critical assumptions and sources of bias in distance and mark–recapture estimators
Gavin J. Melville A C , John P. Tracey B , Peter J. S. Fleming B and Brian S. Lukins BA Biometrics Program, NSW Department of Primary Industries, Trangie Agricultural Research Centre, PMB 19, Trangie, NSW 2823, Australia.
B Vertebrate Pest Research Unit, NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange, NSW 2800, Australia.
C Corresponding author. Email: gavin.melville@dpi.nsw.gov.au
Wildlife Research 35(4) 310-348 https://doi.org/10.1071/WR07080
Submitted: 2 July 2007 Accepted: 7 May 2008 Published: 27 June 2008
Abstract
Recent developments in the application of line-transect models to aerial surveys have used double-observer sampling to account for undercounting on the transect line, a crucial step in obtaining correct population estimates. This method is commonly called the mark–recapture line-transect sampling method and estimates the detection probability at zero distance to correct line-transect estimates of abundance. An alternative approach, which uses the same methodology during data collection, is to use a range of covariates, including distance from the transect, in a mark–recapture model. This approach overcomes the implicit assumption of uniform distribution of distances in line-transect estimators. In this paper, we use three alternative approaches (a multiple-covariates distance method, a distance method incorporating adjustment for incomplete detection on the transect line using mark–recapture sampling, and a mark–recapture method with distance as a covariate) to estimate the abundance of several medium-sized mammals in semiarid ecosystems. Densities determined with the three estimators varied considerably within species and sites. In some cases distance estimates were larger than mark–recapture estimates and vice versa. Despite large numbers of observations, distance uniformity was not observed for any species at any site, nor for any species where sites were combined. Possible reasons, which include sampling variability, movement in response to the aircraft and failure of the mark–recapture independence assumption, are discussed in detail.
Acknowledgements
Thanks to Rob Hurst, Rob Bartell, and Jim Balnaves who assisted with enumeration and discussions on the practicalities of aerial surveys. Funding was from the Invasive Animals Cooperative Research Centre, NSW Department of Environment and Conservation and NSW Department of Primary Industries. We acknowledge the comments of an anonymous referee who provided a useful discussion on various issues raised in the paper.
Alho, J. M. (1990). Logistic regression in capture–recapture models. Biometrics 46, 623–635.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Borchers, D. L. , Zucchini, W. , and Fewster, R. M. (1998). Mark–recapture models for line transect surveys. Biometrics 54, 1207–1220.
| Crossref | GoogleScholarGoogle Scholar |
Borchers, D. L. , Laake, J. L. , Southwell, C. , and Paxton, C. G. M. (2006). Accommodating unmodeled heterogeneity in double-observer distance sampling surveys Biometrics 62, 372–378.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Burnham, K. P. , Anderson, D. R. , and Laake, J. L. (1980). Estimation of density from line transect sampling of biological populations. Wildlife Monographs 72, 1–202.
Dawson, T. J. , Read, D. , Russell, E. M. , and Herd, R. M. (1984). Seasonal variation in daily activity patterns, water relations and diet of emus. Emu 84, 93–102.
Fewster, R. M. , Laake, J. L. , and Buckland, S. T. (2005). Line transect sampling in small and large regions. Biometrics 61, 856–859.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Graham, A. , and Bell, R. (1989). Investigating observer bias in aerial survey by simultaneous double-counts. Journal of Wildlife Management 53, 1009–1016.
| Crossref | GoogleScholarGoogle Scholar |
Huggins, R. M. (1989). On the statistical analysis of capture experiments. Biometrika 76, 133–140.
| Crossref | GoogleScholarGoogle Scholar |
Laake, J. L. , Calambokidis, J. C. , Osmek, S. D. , and Rugh, D. J. (1997). Probability of detecting harbor porpoise from aerial surveys; estimating g(0). Journal of Wildlife Management 61, 63–75.
| Crossref | GoogleScholarGoogle Scholar |
Marsh, H. , and Sinclair, D. F. (1989). Correcting for visibility bias in strip transect aerial surveys of aquatic fauna. Journal of Wildlife Management 53, 1017–1024.
| Crossref | GoogleScholarGoogle Scholar |
Schweder, T. (1990). Independent observer experiments to estimate the detection function in line transect surveys of whales. Report of the International Whaling Commission 40, 349–355.
Tracey, J. P. , and Fleming, P. J. S. (2007). Behavioural responses of feral goats (Capra hircus) to helicopters. Applied Animal Behaviour Science 108, 114–128.
| Crossref | GoogleScholarGoogle Scholar |
Tracey, J. P. , Fleming, P. J. S. , and Melville, G. J. (2005). Does variable probability of detection compromise the use of indices in aerial surveys of medium-sized mammals? Wildlife Research 32, 245–252.
| Crossref | GoogleScholarGoogle Scholar |
Tracey, J. P. , Fleming, P. J. S. , and Melville, G. J. (2008). Accuracy of some aerial survey estimators: contrasts with known numbers. Wildlife Research 35, 377–384.
| Crossref | GoogleScholarGoogle Scholar |
Trick, L. M. , and Pylyshyn, Z. W. (1994). Why are small and large numbers enumerated differently? A limited-capacity preattentive stage in vision. Psychological Review 101, 80–102.
| Crossref | GoogleScholarGoogle Scholar | PubMed |