Miniaturization eliminates detectable impacts of drones on bat activity

We performed trials starting 30 min after dusk from 21h00 to 0h00, following the procedure described by Ednie et al. (2021). Trials consisted of three phases where we acoustically recorded bat activity before, during, and after drone treatment. At each site, we placed a control detector 250-400m away from the treatment, enough distance to attenuate drone noise but remain in similar habitat. Upon arriving to one of our eight detection sites, we began the first phase of the trial by attaching the Echo Meter Touch 2 plugin acoustic bat detector (Wildlife Acoustics, Maynard, MA, USA) to a tablet and initiating recording with the corresponding Wildlife Acoustics app (Wildlife Acoustics, Maynard, MA, USA). Phase 1 consisted of audio recording from the ground with the Echo Meter Touch 2 for five minutes with no drone activity. After five minutes, we began Phase 2 by setting up one of three drones for the randomly chosen treatment. We either launched the drone to 15 m above the site of detection where the drone remained hovering; or we carried the drone to a launch site 50 m away, ascended it to 15 m above the launch site, and then flew the drone above the detection site. The drone hovered in place for five min, and afterwards was returned to its launch site. Once we powered off the drone, we continued to Phase 3, i.e. another five min of audio bat detection with no drone disturbance. This completed the trial, then we repeated the procedure at a new site, conducting one trial at each site every night of the experiment. Bat calls detected were automatically saved and identified to species using Kaleidoscope Pro Analysis Software (Wildlife Acoustics, Maynard, MA, USA) integrated in the Wildlife Acoustics app. Due to the difficulty differentiating between frequency spectrograms accurately, some species were combined: Myotis species: the eastern small-footed bat (Myotis leibii), the little brown bat (M. lucifugus), and the northern long-eared bat (M. septentrionalis) and Eptesicus complex: the big brown bat (Eptesicus fuscus) and the silver-haired bat (Lasionycteris noctivagans). Automatic identifications were then followed with blind manual classification to determine if all bat recordings were valid. We completed the manual classification using the spectrogram viewer in Kaleidoscope 5.4.1a (Wildlife Acoustics, Maynard, MA, USA), and the guide to acoustics for Québec bats (Fabianek, 2015); bats were identified by the shape and frequency range of their calls. False detections were excluded from and incorrect detections corrected for the new set of manually identified data.