DOI: 10.5061/dryad.cnp5hqccv
title: Monitoring GPS-collared moose by ground versus drone approaches: efficiency and disturbance effects

description: 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
 &gt; 40% of those moose becoming disturbed once the drone hovered
 &lt; 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 &gt; 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.
description: 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.
description: # Monitoring GPS-collared moose by ground versus drone approaches:
 efficiency and disturbance effects
 [https://doi.org/10.5061/dryad.cnp5hqccv](https://doi.org/10.5061/dryad.cnp5hqccv) ## Description of the data and file structure Both data sets (10-min and 2-hourly GPS locations of moose) contain information concerning the individual moose ID, date and time, latitude, longitude, period (before or after an approach had started), the type of approach (treatment), moose step length, the presence of a calf/calves, the distance to the closest road, whether a moose was flushed or not, and tree cover density. ### Variables and their descriptions * IndividId = ID of the GPS-collared moose * Rounded_hour = Date and rounded local time when the GPS position was taken * Latitude = Latitude of GPS position * Longitude = Longitude of GPS position * Period = Period of approach; i.e. before or after the approach had started * Treatment = Type of approach (drone, ground, or control) * Exp_ID = ID of the approach * Step_length = Straight-line distance to the next GPS positions in m * Calf_present = Whether or not a female moose had a calf/calves * Dist_to_road = Straight-line distance to the closest road in m * Flushed = Whether or not a female moose was flushed during the approach * Forest_density = Tree cover density