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Enumerating white-tailed deer using unmanned aerial vehicles

March 18, 2021

The white‐tailed deer (Odocoileus virginianus) is an ecologically important species in forests of North America. Effective management of forests requires accurate, precise estimates of deer population abundance to plan and justify management actions. Spotlight surveys in combination with distance sampling are a common method of estimating deer population abundance; however, spotlight surveys are known to have serious drawbacks such as high costs and sampling biases. Therefore, we tested the effectiveness of enumerating deer from unmanned aerial vehicle (UAV) flights, conducted 1 and 6 March 2018, to develop population and density estimates in 2 United States National Parks: Harpers Ferry National Historic Park (HAFE) and Monocacy National Battlefield (MONO). Concurrent spotlight surveys at MONO enabled us to compare estimates obtained by the 2 methods. Deer density estimates by 4 observers of UAV‐obtained thermal imagery from HAFE were 94.5 ± 3.9 deer/km2. Concurrent UAV and spotlight surveys at MONO found 19.7 ± 0.5 deer/km2 and 6.4 ± 4.9 deer/km2, respectively; suggesting that spotlight surveys may significantly underestimate deer densities. Despite the logistical challenges to UAV operation, our findings demonstrate that UAVs will become an invaluable tool for wildlife management as technology improves. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Publication Year 2021
Title Enumerating white-tailed deer using unmanned aerial vehicles
DOI 10.1002/wsb.1149
Authors Todd M. Preston, Mark L. Wildhaber, Nicholas S. Green, Janice L. Albers, Geoffrey P. Debenedetto
Publication Type Article
Publication Subtype Journal Article
Series Title Wildlife Society Bulletin
Index ID 70220293
Record Source USGS Publications Warehouse
USGS Organization Arizona Water Science Center; Columbia Environmental Research Center; Northern Rocky Mountain Science Center