Double-observer evaluation of pronghorn aerial line-transect surveys
Timothy J. Smyser A B F , Richard J. Guenzel C E , Christopher N. Jacques D and Edward O. Garton AA Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844, USA.
B Present address: National Wildlife Research Center, 4101 LaPorte Avenue, Fort Collins, CO 80521, USA.
C Wyoming Game and Fish Department, Laramie, WY 82070, USA.
D Department of Biological Sciences, Western Illinois University, Macomb, IL 61455, USA.
E Retired.
F Corresponding author. Email: Timothy.J.Smyser@aphis.usda.gov
Wildlife Research 43(6) 474-481 https://doi.org/10.1071/WR16006
Submitted: 28 March 2015 Accepted: 16 August 2016 Published: 3 October 2016
Abstract
Context: Distance sampling is used to estimate abundance for several taxa, including pronghorn (Antilocapra americana). Comparisons between population estimates derived from quadrat sampling and distance sampling suggest that distance sampling underestimates pronghorn density, likely owing to violations of the critical assumption of distance sampling that all pronghorn within the innermost distance band (A band; nearest to the aircraft) are detected.
Aims: We sought to rigorously test the assumption that all pronghorn clusters are detected within the innermost distance band by applying a double-observer approach to an established pronghorn aerial-survey protocol. Additionally, we evaluated potential effects of cluster size, landscape composition and seat position (front seat versus rear) on the probability of detection.
Methods: We conducted aerial line-transect distance-sampling surveys using independent, paired observers and modelled the probability of detection with mark–recapture distance-sampling (MRDS) analysis techniques that explicitly estimate the probability of detection for pronghorn clusters in the innermost distance band. We compared density estimates produced by the MRDS analysis with those produced by multiple-covariate distance sampling (MCDS), a method that assumes complete detection for clusters on the transect line.
Key results: We identified violations of the assumption that all clusters within the innermost distance band were detected, which would contribute to proportional biases in density estimates for analysis techniques that assume complete detection. The frequency of missed clusters was modest from the front-seat position, with 45 of the 47 (96%) clusters in the A band detected. In contrast, the frequency of missed clusters was more substantial for the rear position, from which 37 of 47 (79%) clusters in the A band were detected. Further, our analysis showed that cluster size and landscape composition were important factors for pronghorn sightability.
Conclusions: When implementing standard survey methodologies, pronghorn aerial-line transect surveys underestimated population densities. A double-observer survey configuration allowed us to quantify and correct for the bias caused by the failure of observers to detect all pronghorn clusters within the innermost distance band.
Implications: Population monitoring programs should incorporate double-observer validation trials to quantify the extent of bias owing to undetected clusters within the innermost distance band realised under typical survey conditions. Wildlife managers can improve the precision of pronghorn aerial line-transect surveys by incorporating cluster size and measures of landscape composition and complexity into detection models without incurring additional survey costs.
Additional keywords: Antilocapra americana, detection bias, distance sampling, mark–recapture, population estimation.
References
Borchers, D. L., Zucchini, W., and Fewster, R. M. (1998). Mark–recapture models for line transect surveys. Biometrics 54, 1207–1220.| Mark–recapture models for line transect surveys.Crossref | GoogleScholarGoogle Scholar |
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., and Thomas, L. (2001). ‘Introduction to Distance Sampling.’ (Oxford University Press: New York.)
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., and Thomas, L. (2004). ‘Advanced Distance Sampling.’ (Oxford University Press: New York.)
Buckland, S. T., Plumptre, A. J., Thomas, L., and Rexstad, E. A. (2010). Design and analysis of line transect surveys for primates. International Journal of Primatology 31, 833–847.
| Design and analysis of line transect surveys for primates.Crossref | GoogleScholarGoogle Scholar |
Burnham, K. P., and Anderson, D. R. (2002). ‘Model Selection and Multimodel Inference: a Practical Iinformation-theoretic Approach.’ 2nd edn. (Springer-Verlag: New York.)
Burt, M. L., Borchers, D. L., Jenkins, K. J., and Marques, T. A. (2014). Using mark–recapture distance sampling methods on line transect surveys. Methods in Ecology and Evolution 5, 1180–1191.
| Using mark–recapture distance sampling methods on line transect surveys.Crossref | GoogleScholarGoogle Scholar |
Caughley, G., Sinclair, R., and Scott-Kemmis, D. (1976). Experiments in aerial survey. The Journal of Wildlife Management 40, 290–300.
| Experiments in aerial survey.Crossref | GoogleScholarGoogle Scholar |
Graham, A., and Bell, R. (1989). Investigating observer bias in aerial survey by simultaneous double-counts. The Journal of Wildlife Management 53, 1009–1016.
| Investigating observer bias in aerial survey by simultaneous double-counts.Crossref | GoogleScholarGoogle Scholar |
Guenzel, R. J. (1986). Pronghorn ecology in southcentral Wyoming. M.Sc. Thesis, University of Wyoming, Laramie, WY.
Guenzel, R. J. (1997). ‘Estimating Pronghorn Abundance using Aerial Line Transect Sampling.’ (Wyoming Game and Fish Department: Cheyenne, WY.)
Guenzel, R. J. (2007). ‘Procedures for Estimating Pronghorn Abundance in Wyoming using Aerial Line Transect Sampling.’ (Wyoming Game and Fish Department: Cheyenne, WY.)
Jacques, C. N., Jenks, J. A., Grovenburg, T. W., Klaver, R. W., and DePerno, C. S. (2014). Incorporating detection probability into northern Great Plains pronghorn population estimates. The Journal of Wildlife Management 78, 164–174.
| Incorporating detection probability into northern Great Plains pronghorn population estimates.Crossref | GoogleScholarGoogle Scholar |
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.
Laake, J. L., and Borchers, D. L. (2004). Methods for incomplete detection at distance zero. In ‘Advanced Distance Sampling: Estimating Abundance of Biological Populations’. (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: New York.)
Laake, J., Borchers, D., Thomas, L., Miller, D., and Bishop, J. (2013) ‘mrds:Mark–Recapture Distance Sampling (mrds). R package version 2.1.4.’ Available at http://CRAN.R-project.org/package=mrds [Accessed 17 July 2015]
Marsh, H., and Sinclair, D. F. (1989). Correcting for visibility bias in strip transect aerial surveys of aquatic fauna. The Journal of Wildlife Management 53, 1017–1024.
| Correcting for visibility bias in strip transect aerial surveys of aquatic fauna.Crossref | GoogleScholarGoogle Scholar |
Pojar, T. M. (2004). Survey methods to estimate population. In ‘Pronghorn Ecology and Management’. (Eds B. W. O’Gara and J. D. Yoakum.) pp. 631–644. (University Press of Colorado: Boulder, CO.)
Pojar, T. M., and Guenzel, R. J. (1999). Comparison of fixed-wing line transect and helicopter quadrat pronghorn surveys. Proceedings of the Pronghorn Antelope Workshop 18, 64–68.
Pojar, T. M., Bowden, D. C., and Gill, R. B. (1995). Aerial counting experiments to estimate pronghorn density and herd structure. The Journal of Wildlife Management 59, 117–128.
| Aerial counting experiments to estimate pronghorn density and herd structure.Crossref | GoogleScholarGoogle Scholar |
Potvin, F., Breton, L., and Rivest, L. P. (2004). Aerial surveys for white-tailed deer with double-count technique in Quebec: two 5-year plans completed. Wildlife Society Bulletin 32, 1099–1107.
| Aerial surveys for white-tailed deer with double-count technique in Quebec: two 5-year plans completed.Crossref | GoogleScholarGoogle Scholar |
Quang, P. X., and Becker, E. F. (1996). Line transect sampling under varying conditions with application to aerial surveys. Ecology 77, 1297–1302.
| Line transect sampling under varying conditions with application to aerial surveys.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ [Accessed 7 July 2015]
Rabe, M. J., Rosenstock, S. S., and deVos, J. C. (2002). Review of big-game survey methods used by wildlife agencies of the western United States. Wildlife Society Bulletin 30, 46–52.
Thomas, L., Buckland, S. T., Rexstad, E. A., Laake, J. L., Stringberg, S., Hedley, S. L., Bishop, J. R. B., Marques, T. A., and Burnham, K. B. (2010). Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47, 5–14.
| Distance software: design and analysis of distance sampling surveys for estimating population size.Crossref | GoogleScholarGoogle Scholar | 20383262PubMed |
Wyoming Gap Analysis (1996). Land Cover for Wyoming: University of Wyoming, Spatial Data and Visualization Center, Laramie, WY.
