Abstract
Surveys of cliff-nesting seabirds from sea level or ground level often miss individuals hidden by topography, leading to underestimates of abundance. Thick-billed Murres Uria lomvia are especially challenging to survey accurately due to their dense nesting on vertical cliff ledges in remote northern regions. In Canada, updated colony surveys are needed, as currently used population estimates, done from boats, are outdated. Drones offer better vantage points and may reduce availability bias (i.e., the proportion of birds present but not visible), but direct comparisons with boat-based counts are essential. We compared murre counts from drone- and boat-based photographs of six vertical cliff sections at East Digges Island, Nunavut. Each section spanned from sea level to ~100 m, and over 50,000 murres were manually counted. On average, drone images detected 14% more murres than boat images, with differences ranging from 4% to 21%. The disparity was greater in upper cliff areas (10%-28%) than lower ones (2%-17%), where visibility from sea level is better. In one extreme case, the drone-based count was 101% higher than the boat count for the same area. We conclude that drone imagery improves detectability, especially at higher cliff elevations, and provides more reliable colony size estimates. These findings have direct relevance for assessing historical estimates and planning future surveys. Drone-based monitoring should be prioritized where feasible, and where boat-based imagery is used, correction factors based on cliff structure may improve colony estimates. More accurate estimates will enhance the management and conservation of Thick-billed Murres in Canada, particularly when informing harvest regulations.
DOI
http://doi.org/10.5038/2074-1235.54.1.1683
Recommended Citation
Macdonnell, Ewan; Gutowsky, Sarah E.; and Elliott, Kyle H.
(2026)
"Comparing counts made from drone-based and boat-based photography for surveying Thick-billed Murre Uria lomvia cliff colonies.,"
Marine Ornithology: Vol. 54
:
Iss.
1
, Article 20.
http://doi.org/10.5038/2074-1235.54.1.1683
Available at:
https://digitalcommons.usf.edu/marine_ornithology/vol54/iss1/20