A hybrid double-observer sightability model for aerial surveys

Journal of Wildlife Management
By: , and 



Raw counts from aerial surveys make no correction for undetected animals and provide no estimate of precision with which to judge the utility of the counts. Sightability modeling and double-observer (DO) modeling are 2 commonly used approaches to account for detection bias and to estimate precision in aerial surveys. We developed a hybrid DO sightability model (model MH) that uses the strength of each approach to overcome the weakness in the other, for aerial surveys of elk (Cervus elaphus). The hybrid approach uses detection patterns of 2 independent observer pairs in a helicopter and telemetry-based detections of collared elk groups. Candidate MH models reflected hypotheses about effects of recorded covariates and unmodeled heterogeneity on the separate front-seat observer pair and back-seat observer pair detection probabilities. Group size and concealing vegetation cover strongly influenced detection probabilities. The pilot's previous experience participating in aerial surveys influenced detection by the front pair of observers if the elk group was on the pilot's side of the helicopter flight path. In 9 surveys in Mount Rainier National Park, the raw number of elk counted was approximately 80–93% of the abundance estimated by model MH. Uncorrected ratios of bulls per 100 cows generally were low compared to estimates adjusted for detection bias, but ratios of calves per 100 cows were comparable whether based on raw survey counts or adjusted estimates. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to DO modeling.

Study Area

Publication type Article
Publication Subtype Journal Article
Title A hybrid double-observer sightability model for aerial surveys
Series title Journal of Wildlife Management
DOI 10.1002/jwmg.612
Volume 77
Issue 8
Year Published 2013
Language English
Publisher Wiley
Contributing office(s) Forest and Rangeland Ecosystem Science Center
Description 13 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Wildlife Management
First page 1532
Last page 1544
Country United States
State Washington
Other Geospatial Mount Rainier National Park
Online Only (Y/N) Y
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