Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats
RESEARCH ARTICLE

Coping with variation in aerial survey protocol for line-transect sampling

Jeff Laake A D , Richard J. Guenzel B , John L. Bengtson A , Peter Boveng A , Michael Cameron A and M. Bradley Hanson C
+ Author Affiliations
- Author Affiliations

A National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, WA 98115, USA.

B Wyoming Game and Fish Department, Laramie, WY 82070, USA.

C Northwest Fisheries Science Center, National Marine Fisheries Service, Seattle, WA 98112, USA.

D Corresponding author. Email: jeff.laake@noaa.gov

Wildlife Research 35(4) 289-299 https://doi.org/10.1071/WR07065
Submitted: 8 June 2007  Accepted: 12 December 2007   Published: 27 June 2008

Abstract

Altitude and flight speed affect detection probability and they typically vary during the course of most aerial surveys. We demonstrate how these sources of variation can be accommodated with covariates in a line-transect analysis using data from a pronghorn (Antilocapra americana) survey in Wyoming and a survey of Antarctic ice seals (Lobodon carcinophaga, Leptonychotes weddellii, Hydrurga leptonyx, Ommatophoca rossii). We also show how the likelihood for binned distance data can be modified to allow for variation in altitude. As an alternative, we develop an estimator for aerial line-transect sampling based on vertical angles rather than distance. With a small simulation study, we show that our estimators are unbiased and are preferable to using biased estimators based on fixed-distance intervals derived from average altitude.


Acknowledgements

The seal survey was supported by National Science Foundation grant OPP-9815961 to Bengtson, Boveng and Laake. The Wyoming Game and Fish Department has been instrumental in the development and implementation of distance sampling in pronghorn management by providing continued support for the development of improved methodology. We thank Devin Johnson, Rod Hobbs, Gary Duker, Jim Lee, Richard Barker and an anonymous reviewer for suggesting improvements to the manuscript.


References

Ackley, S. F. , Bengtson, J. L. , Boveng, P. , Castellini, M. , and Daly, K. L. , et al. (2003). Ecological importance of summer pack ice in the eastern Ross Sea, Antarctica, to seals, penguins, and their prey. The Polar Record 39, 219–230.
Bengtson J. , Blix A. S. , Boyd I. L. , Cameron M. F. , Hanson M. B. , and Laake J. L. (1996). Antarctic pack-ice seal research, February and March 1995. Antarctic Journal – Review 1995, 191–193.

Bester M. N. , and Stewart B. S. (eds.) (2006). The International Antarctic Pack Ice Seals (APIS) Program: Multi-disciplinary research into the ecology and behaviour of Antarctic pack ice seals, summary update. Scientific Committee on Antarctic Research. Scott Polar Research Institute, Cambridge. http://www.seals.scar.org/pdf/IntAPISSummUpdateRevis.pdf

Borchers D. L. , and Burnham K. P. (2004). General formulation for distance sampling. In ‘Advanced Distance Sampling’. (Eds S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers and L. Thomas.) pp. 108–189. (Oxford University Press: Oxford.)

Borchers, D. L. , Laake, J. L. , Southwell, C. , and Paxton, C. G. M. (2006). Accommodating unmodelled heterogeneity in double-observer distance sampling surveys. Biometrics 62, 372–378.
Crossref | GoogleScholarGoogle Scholar | PubMed | Buckland S. T. , Anderson D. R. , Burnham K. P. , Laake J. L. , Borchers D. L. , and Thomas L. (2001). ‘Introduction to Distance Sampling: Estimating Abundance of Biological Populations.’ (Oxford University Press: London.)

Burnham K. P. , and Anderson D. R. (2002). ‘Model Selection and Multimodel Inference: a Practical Information–Theoretic Approach.’ 2nd edn. (Springer-Verlag: New York.)

Caughley, G. (1974). Bias in aerial survey. Journal of Wildlife Management 38, 921–933.
Crossref | GoogleScholarGoogle Scholar | Guenzel R. J. (1997). Estimating pronghorn abundance using aerial line transect sampling. Wyoming Game and Fish Department, Cheyenne, WY.

Johnson, B. K. , Lindzey, F. G. , and Guenzel, R. J. (1991). Use of aerial line transect surveys to estimate pronghorn populations in Wyoming. Wildlife Society Bulletin 19, 315–321.
Marques F. F. C. , and Buckland S. T. (2004). Covariate models for the detection function. In ‘Advanced Distance Sampling’. (Eds S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers and L. Thomas.) pp. 108–189. (Oxford University Press: Oxford.)

Quang, P. X. , and Lanctot, R. B. (1991). A line transect model for aerial surveys. Biometrics 47, 1089–1102.
Crossref | GoogleScholarGoogle Scholar | R Development Core Team (2006). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org.

Samuel, M. D. , Garton, E. O. , Schlegel, M. W. , and Carson, R. G. (1987). Visibility bias during aerial surveys of elk in north central Idaho. Journal of Wildlife Management 51, 622–630.
Crossref | GoogleScholarGoogle Scholar | Smyser T. J. (2005). Response of pronghorn (Antilocapra americana) populations to habitat conditions with modification to survey methods. M.S. Thesis, University of Idaho, Moscow, ID.

Southwell, C. , de la Mare, B. , Underwood, M. , Quartararo, F. , and Cope, K. (2002). An automated system to log and process distance sight–resight aerial survey data. Wildlife Society Bulletin 30, 394–404.
Thomas L. , Laake J. L. , Strindberg S. , Marques F. F. C. , Buckland S. T. , et al. (2006). Distance 5.0. Release 2. Research Unit for Wildlife Population Assessment, University of St. Andrews, United Kingdom. http://www.ruwpa.st-and.ac.uk/distance/





Appendix 1. 
Click to zoom


Appendix 1 Contd. 
Click to zoom