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.

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 and after drone or ground approaches had started.

For drone approaches, the drone (“DJI Mavic 2 Enterprise Dual” using a GPS+ GLONASS system with a ± 1.5 m horizontal and ± 0.5 m vertical accuracy range) was programmed to fly to the last known GPS position of the moose (flight speed was 6 m/s) at 100 m altitude while the operator stayed ≥ 500 m away (but within the visual line of sight). When the drone arrived at the last known position, the operator manually searched for the moose and – if it was detected – flew the drone over the exact location of the moose, where it hovered for two minutes while recording video, using a built-in RGB camera (1920x1080 resolution). If the moose did not flee from the site, the drone was progressively lowered to 70, 50, 30, and 20 m altitude with a 1-min hovering time for each altitude interval. At each altitude, we noted the presence of offspring and moose cow behavior. Behavior was classified into 4 categories: (1) lying, (2) standing still, (3) walking, which often included foraging, and (4) running. If the moose started running at any time during the approach, or when the drone had hovered for one minute at 20 m altitude, the approach was completed, i.e., the drone was flown back to 100 m altitude and returned to the original position. The speed for lowering or elevating the drone was set to 2 m/s. Drone operations were conducted by a licensed operator (open category A1/A3).

Ground approaches were conducted by a single person on foot to detect if female moose had calves. We approached the last known moose GPS position while using a VHF receiver (RX98, Followit AB, Sweden) in case the moose had moved. All approaches were done with headwind and the track was recorded with a handheld GPS unit. We approached each moose close enough to determine the presence or absence of a calf/calves. We sneaked back downwind to minimize the risk of the female moose detecting us. We recorded the duration of approaches from the start (≥500 m from the last moose position) until the moose was detected or the approach stopped, using handheld GPS tracklogs for ground approaches and timestamps from drone videos.