| Summary: | Efficient wildlife management requires precise monitoring methods, e.g., to estimate population density, reproductive success, and survival. Here, we compared the efficiency of the drone (equipped with an RGB camera) and ground approaches to detect and observe GPS-collared female moose ( Alces alces ) and their calves. Moreover, we quantified how drone (n = 42) and ground (n = 41) approaches affected moose behavior and space use (n = 24 individuals). The average time used for drone approaches was 17 minutes compared to 97 minutes for ground approaches, with drone detection probability being higher (95% of adult female moose and 88% of moose calves) compared to ground approaches (78% of adult females and 82% of calves). Drone detection success increased at lower drone altitudes (50-70 m). Adult female moose left the site in 35% of drone approaches (with > 40% of those moose becoming disturbed once the drone hovered < 50 m above ground) compared to 56% of ground approaches. We failed to find short-term effects (3-h after approaches) of drone approaches on moose space use, but moose moved > 4-fold greater distances and used larger areas after ground approaches (compared to before the approaches had started). Similarly, longer-term (24-h before and after approaches) space use did not differ between drone approaches compared to days without known disturbance, but moose moved comparatively greater distances during days of ground approaches. In conclusion, we could show that drone approaches were highly efficient in detecting adult moose and their calves in the boreal forest, being faster and less disturbing than ground approaches, making them a useful tool to monitor and study wildlife. Funding provided by: Norwegian Environment Agency Crossref Funder Registry ID: https://ror.org/023jta124 Award Number: 22SCE00F There are two data sets, one containing moose GPS data at a 10-minute fix rate three hours before and after drone or ground approaches had started, and one at a 2-hourly fix rate one day before ...
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