A hierarchical dependent double-observer method for estimating waterfowl breeding pairs abundance from helicopters

We applied a dependent double-observer method for helicopter surveys and developed a hierarchical Bayesian model as a means to adjust counts of waterfowl for incomplete detection. We conducted our study using 52 plots in Labrador, Canada. A designated pair of primary observers reported counts and location of all waterfowl flocks that they detected to a pair of secondary observers, including details regarding the species, age and sex of observed birds. Secondary observers then reported any additional flocks observed by them but missed by the primary observers. The pairs of observers alternated between primary and secondary roles during the course of the survey, as well as position (front or back) within the helicopter. We used hierarchical Bayesian models to estimate detection probabilities of waterfowl flocks, as well as derive species-specific detection-corrected abundance and sex composition estimates of flocks. The hierarchical model output allowed us to derive estimates of indicated breeding pairs for each species in the survey area corrected for incomplete detection. Observers seated in the back of the helicopter had higher detection probabilities (0.89; 90% Bayesian Credible Intervals [BCI] = 0.82 – 0.95) than those in the front (0.74; 90% BCI = 0.66 – 0.83), and observer experience had a limited effect on detection. Total crew detection probabilities ranged between 0.99 (90% BCI = 0.97 – 1.00) and 0.97(90% BCI = 0.94 – 0.99), depending on the individual observers' position and role in the helicopter. Detection probabilities were higher for sea ducks and diving ducks and lower for dabbling ducks. Observers generally missed less than 5% of the total indicated pairs for all species. We recommend that detection in helicopter surveys be measured to control for observer turnover, observer experience, and aircraft-related differences in visibility.