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.