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.