## 2 Materials and Methods  
We conducted this study in September 2016 at Foret Montmorecy (47.4 N, 71.1 W), a teaching and research forest located north of Quebec City, Quebec, Canada. The study area is a dense balsam fir/white birch (_Betula papyrifera_) boreal forest landscape with altitudes ranging from 750 to 1000 m, covered by a dense network of forestry roads (for details see [11]). We conducted seven flights, within two sectors each covering \(\sim\)0.2 km\({}^{2}\), each composed of a matrix of old, mid-successful, and early-successful balsam fir stands resulting from clearcutting (Figure 1). Tree height in the study site varied from \(\sim\)4 m to 12 m, with \(\sim\)2500-10,000 stems/ha. Each flight was performed by a custom-built heavy-lift quadrotor drone based on a Gryphon Dynamics airframe (Daegu, South Korea) and a Pixhawk flight controller (3D Robotics, Berkeley, CA, USA), with a payload capacity of about 4 kg including the battery. The drone was programmed to fly at a fixed altitude of 50 m above the ground at the location where it was launched and a forward speed of 5 m/s. We automated flights from takeoff to landing, and monitored them from the ground using a tablet computer. We mounted a radio receiver system on the ground-facing side of the drone. The radio receiver system was composed of a hanging omnidirectional dipole antenna attached to a weight at the bottom end, and coupled to a Funcube Pro+ dongle. The dongle was connected to a BeagleBone computer programmed to monitor and record signals simultaneously from multiple transmitters (for details see [12]). Before each flight, we deployed five radio-transmitters at distances ranging from 18 m to 507 m from one another (Figure 1). We placed each transmitter in a tree at ca. 1.3 m above ground, with the antenna pointing up, down or horizontal. Transmitters were avian nanotags model NTQB-4-2, Lotek Wireless Inc., Newmarket, ON, Canada. Each nanotag had a unique set of pulses delivered each 5 s at a frequency of 166.38 MHz (VHF; very high frequency), a standard used by the Motus Wildlife Tracking System [12, 13]. After the completion of each flight, we downloaded data from two sources: the telemetry receiver and the drone navigation log. Timestamps from the two sources were synchronized, allowing us to match each signal reception from test transmitters to the exact location of the drone and in turn, the distance between the transmitters and the receiver. As pointed out earlier, several factors may influence signal reception and strength. However, on average, signal strength should provide an unbiased estimate of the distance between the transmitter and the receiver. Thus, a two-dimensional array, i.e., a map of signal strengths, should inform us about the true location of the transmitter. We estimated the location errors with two methods. First, we retained the drone location at which the signal was strongest, and calculated the Euclidean distance between the drone XY coordinates, and the XY coordinates of the source. Second, we modelled signal strength as a function of the drone's XY coordinates in meters from a Modified Transverse Mercator map projection (Easting and Northing) using two quadratic functions:  
\[\text{Easting: Dbm}\sim\beta_{0}+\beta_{1}(\text{Easting})+\beta_{2}(\text{ Easting}^{2})+\varepsilon \tag{1}\]  
\[\text{Northing: Dbm}\sim\beta_{0}+\beta_{1}(\text{Northing})+\beta_{2}(\text{ Northing}^{2})+\varepsilon \tag{2}\]  
where Dbm is the signal's strength, \(\beta_{i}\) regression estimates, and \(\varepsilon\) a vector of model residuals. The formulas yielded a peak signal strength when the regression estimate for the quadratic term was negative. Note that in the presence of a peak signal strength both on X and Y coordinates, only _relative_ signal strength will be required to estimate transmitter locations. Differences in signal strength among  
Figure 1: Flight transects and test transmitter locations, in yellow. Takeoff times are indicated.