Field evaluation of distance-estimation error during wetland-dependent bird surveys
Christopher P. Nadeau A C D and Courtney J. Conway BA Arizona Cooperative Fish and Wildlife Research Unit, 325 Biological Sciences East, University of Arizona, Tucson, AZ 85721, USA.
B US Geological Survey, Idaho Cooperative Fish and Wildlife Research Unit, Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844, USA.
C Present address: New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, Ithaca, NY 14853, USA.
D Corresponding author. Email: cpn28@cornell.edu
Wildlife Research 39(4) 311-320 https://doi.org/10.1071/WR11161
Submitted: 11 September 2011 Accepted: 26 February 2012 Published: 11 May 2012
Abstract
Context: The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys.
Aims: We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem.
Methods: We used two approaches to estimate the error associated with five surveyor’s distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor’s ability to estimate distance.
Key results: We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (error = –9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling.
Conclusions: Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error.
Implications: The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.
Additional keywords: detection probability, distance error, distance sampling, marsh birds.
References
Alldredge, M. W., Pollock, K. H., Simons, T. R., Collazo, J. A., and Shriner, S. A. (2007a). Time-of-detection method for estimating abundance during point-count surveys. The Auk 124, 653–664.| Time-of-detection method for estimating abundance during point-count surveys.Crossref | GoogleScholarGoogle Scholar |
Alldredge, M. W., Simons, T. R., and Pollock, K. H. (2007b). A field evaluation of distance measurement error in auditory avian point-count surveys. The Journal of Wildlife Management 71, 2759–2766.
| A field evaluation of distance measurement error in auditory avian point-count surveys.Crossref | GoogleScholarGoogle Scholar |
Alldredge, M. W., Pacifici, K., Simons, T. R., and Pollock, K. H. (2008). A novel field evaluation of the effectiveness of distance and independent observer sampling to estimate aural avian detection probabilities. Journal of Applied Ecology 45, 1349–1356.
| A novel field evaluation of the effectiveness of distance and independent observer sampling to estimate aural avian detection probabilities.Crossref | GoogleScholarGoogle Scholar |
Anderson, D. R. (2001). The need to get the basics right in wildlife field studies. Wildlife Society Bulletin 29, 1294–1297.
Anderson, D. R. (2003). Response to Engeman: index values rarely constitute reliable information. Wildlife Society Bulletin 31, 288–291.
Anderson, D. R. (2008). ‘Model based inference in the life sciences: a primer on evidence.’ (Springer: New York.)
Aylor, D. E. (1972a). Sound transmission through vegetation in relation to leaf area density, leaf width, and breadth of canopy. The Journal of the Acoustical Society of America 51, 411–414.
| Sound transmission through vegetation in relation to leaf area density, leaf width, and breadth of canopy.Crossref | GoogleScholarGoogle Scholar |
Aylor, D. E. (1972b). Noise reduction by vegetation and ground. The Journal of the Acoustical Society of America 51, 197–205.
| Noise reduction by vegetation and ground.Crossref | GoogleScholarGoogle Scholar |
Barton, K. (2010). ‘MuMIn: Multi-model Inference. R Package Version 0.13.17.’ Available at http://cran.r-project.org/package=MuMIn [Verified 15 March 2011.]
Borchers, D. L., Marques, T. A., Gunnlaugsson, T., and Jupp, P. E. (2010). Estimating distance sampling detection functions when distances are measured with errors. Journal of Agricultural Biological & Environmental Statistics 15, 346–361.
| Estimating distance sampling detection functions when distances are measured with errors.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: Estimating Abundance of Biological Populations.’ (Oxford Press: New York.)
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., and Thomas, L. (Eds) (2004). ‘Advanced Distance Sampling: Estimating Abundance of Biological Populations.’ (Oxford Press: New York.)
Burnham, K. P., and Anderson, D. R. (2001). ‘Model Selection and Multimodel Inference: A Practical Iinformation-theoretic Approach.’ (Springer: New York.)
Camp, R. J. (2007). Measurement error in Hawaiian forest bird surveys and their effect on density estimation. Hawai’i Cooperative Studies Unit Technical Report HCSU-005. University of Hawai’i at Hilo, Kīlauea Field Station, HI.
Conway, C. J. (2009). Standardized North American Marsh Bird Monitoring Protocols, US Geological Survey, Arizona Cooperative Fish and Wildlife Research Unit Wildlife Research Report #2009–01. US Geological Survey, Tucson, AZ.
Conway, C. J. (2011). Standardized North American marsh bird monitoring protocols. Waterbirds 34, 319–346.
| Standardized North American marsh bird monitoring protocols.Crossref | GoogleScholarGoogle Scholar |
Conway, C. J., and Droege, S. (2006). A unified strategy for monitoring changes in abundance of birds associated with North American tidal marshes. Studies in Avian Biology 32, 382–397.
Conway, C. J., and Timmermans, S. T. A. (2005). Progress toward developing field protocols for a North American marsh bird monitoring program. Bird Conservation Implementation and Integration in the Americas. In ‘Proceedings of the Third International Partners in Flight Conference’. pp. 997–1005. US Department of Agriculture General Technical Report PSW-GTR-191. (US Department of Agriculture: Albany, NY.)
Conway, C. J., Sulzman, C., and Raulston, B. A. (2004). Factors affecting detection probability of California black rails. The Journal of Wildlife Management 68, 360–370.
| Factors affecting detection probability of California black rails.Crossref | GoogleScholarGoogle Scholar |
DeSante, D. F. (1981). A field test of the variable circular-plot censusing technique in a California coastal scrub breeding bird community. Studies in Avian Biology 6, 177–185.
DeSante, D. F. (1986). A field test of the variable circular-plot censusing method in a Sierran subalpine forest habitat. The Condor 88, 129–142.
| A field test of the variable circular-plot censusing method in a Sierran subalpine forest habitat.Crossref | GoogleScholarGoogle Scholar |
Diefenbach, D. R., Brauning, D. W., and Mattice, J. A. (2003). Variability in grassland bird counts related to observer differences and species detection rates. The Auk 120, 1168–1179.
| Variability in grassland bird counts related to observer differences and species detection rates.Crossref | GoogleScholarGoogle Scholar |
Ellingson, A. R., and Lukacs, P. M. (2003). Improving methods for regional landbird monitoring: a reply to Hutto and Young. Wildlife Society Bulletin 31, 896–902.
Engeman, R. M. (2002). More on the need to get the basics right: population indices. Wildlife Society Bulletin 31, 286–287.
Farnsworth, G. L., Pollock, K. H., Nichols, J. D., Simons, T. R., Hines, J. E., and Sauer, J. R. (2002). A removal model for estimating detection probabilities from point-count surveys. The Auk 119, 414–425.
| A removal model for estimating detection probabilities from point-count surveys.Crossref | GoogleScholarGoogle Scholar |
Hutto, R. L., and Young, J. S. (2002). Regional landbird monitoring: perspectives from the northern Rocky Mountains. Wildlife Society Bulletin 30, 738–750.
Hutto, R. L., and Young, J. S. (2003). On the design of monitoring programs and the use of indices: a reply to Ellingson and Lukacs. Wildlife Society Bulletin 31, 903–910.
Jobin, B., Bazin, R., Maynard, L., McConnell, A., and Stewart, J. (2011). Least bittern (Ixobrychus exilis) survey protocol. Waterbirds 34, 225–233.
Johnson, D. H. (2008). In defense of indices: the case of bird surveys. The Journal of Wildlife Management 72, 857–868.
Kissling, M. L., and Garton, E. O. (2006). Estimating detection probability and density from point-count surveys: a combination of distance and double observer sampling. The Auk 123, 735–752.
| Estimating detection probability and density from point-count surveys: a combination of distance and double observer sampling.Crossref | GoogleScholarGoogle Scholar |
Marques, T. A. (2004). Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models. Biometrics 60, 757–763.
| Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models.Crossref | GoogleScholarGoogle Scholar |
Marten, K., and Marler, P. (1977). Sound transmission and its significance for animal vocalization. Behavioral Ecology and Sociobiology 2, 271–290.
| Sound transmission and its significance for animal vocalization.Crossref | GoogleScholarGoogle Scholar |
Nadeau, C. P., Conway, C. J., Smith, B. S., and Lewis, T. E. (2008). Maximizing detection probability of wetland-dependent birds during point-count surveys in northwestern Florida. Wilson Journal of Ornithology 120, 513–518.
| Maximizing detection probability of wetland-dependent birds during point-count surveys in northwestern Florida.Crossref | GoogleScholarGoogle Scholar |
Nichols, J. D., Hines, J. E., Sauer, J. R., Fallon, F. W., Fallon, J. E., and Heglund, P. J. (2000). A double-surveyor approach for estimating detection probability and abundance from point-counts. The Auk 117, 393–408.
| A double-surveyor approach for estimating detection probability and abundance from point-counts.Crossref | GoogleScholarGoogle Scholar |
Nichols, J. D., Thomas, L., and Conn, P. B. (2008). Inferences about landbird abundance from count data: recent advances and future directions. In ‘Modeling demographic processes in marked populations’. (Eds D. L. Thomson, E. G. Cooch and M. J. Conroy.) pp. 201–235. (Springer: New York.)
Norvell, R. E., Howe, F. P., and Parrish, J. R. (2003). A seven-year comparison of relative-abundance and distance-sampling methods. The Auk 120, 1013–1028.
| A seven-year comparison of relative-abundance and distance-sampling methods.Crossref | GoogleScholarGoogle Scholar |
R Development Core Team (2010). ‘R: a Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna.)
Ransom, D., and Pinchak, W. E. (2003). Assessing accuracy of a laser rangefinder in estimating grassland bird density. Wildlife Society Bulletin 31, 460–463.
Reynolds, R. T., Scott, J. M., and Nussbaum, R. A. (1980). A variable circular-plot method for estimating bird numbers. The Condor 82, 309–313.
| A variable circular-plot method for estimating bird numbers.Crossref | GoogleScholarGoogle Scholar |
Rosenstock, S. S., Anderson, D. R., Giesen, K. M., Leukering, T., and Carter, M. F. (2002). Landbird counting techniques: current practices and an alternative. The Auk 119, 46–53.
| Landbird counting techniques: current practices and an alternative.Crossref | GoogleScholarGoogle Scholar |
Scott, J. M., Ramsey, F. L., and Kepler, C. B. (1981). Distance estimation as a variable in estimating bird numbers from vocalizations. Studies in Avian Biology 6, 334–340.
Simons, T. D., Alldredge, M. W., Pollock, K. H., and Wettroth, J. M. (2007). Experimental analysis of the auditory detection process of avian point-counts. The Auk 124, 986–999.
| Experimental analysis of the auditory detection process of avian point-counts.Crossref | GoogleScholarGoogle Scholar |
Tacha, T. C., and Braun, C. E. (Eds.) (1994). ‘Management of Migratory Shore and Upland Game Birds in North America.’ (International Association of Fish and Wildlife Agencies: Washington, DC.)
Tarvin, K. A., Garvin, M. C., Jawor, J. M., and Dayer, K. A. (1998). A field evaluation of techniques used to estimate density of blue jays. Journal of Field Ornithology 69, 209–222.
Thompson, W. L. (2002). Towards reliable bird surveys: accounting for individuals present but not detected. The Auk 119, 18–25.
| Towards reliable bird surveys: accounting for individuals present but not detected.Crossref | GoogleScholarGoogle Scholar |