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.

Data was collected during a waterfowl helicopter survey.  The flock composition file contains all the individuals obervations while the total flock file contain the totla number of flock of each species observer in each plot that was surveyed.

Also included are a R file that contains all the previous data manipulations to put the data in a structure that is ready for analysis with JAGS, a R-script to run the analysis in JAGS, and the JAGS model to carry out the analysis.

There are no obervations in plots: "P61","S09", "S24", "S32", "S58",  and "S60" so these plots are not included in the flock composition file. The code to manipulate teh data and run the analysis in R format are also provided in the article supplementary material.