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they are at risk of being significantly disturbed by drone flight .
Using bats as our study subject , we flew three different drones in bat habitats . We conducted acoustic detection before , during , and after drone flights to measure how bat activity differed between periods of drone disturbance compared to periods of no drone disturbance . Acoustic profiles were conducted on the three drones to define their characteristics . The significant characteristics compared in this study were the drones ’ dimensions , weight , noise intensity and frequency range . Based on how bat activity responded to flight with each drone , we can see which drone characteristics are most disruptive .
Fewer bat passes are detected during flights with larger drones
Figure 2 . A wide variety of commercial drones are available . Three rotary quadcopters were used in this study , two of which are featured here . On the left , the DJI Phantom 4 was the largest and loudest drone used for this study . On the right , the DJI Mavic Mini was the smallest and quietest drone used for this study . Photo credit : Kayla Kuhlmann .
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acoustic recorders that pick-up ultrasound frequencies . Traditionally , bat biologists detect bats on foot and carry the acoustic recorder by hand . Even though acoustic recorders make bat detection significantly easier , bats are still difficult to survey accurately since they occupy aerial habitats . Essentially , bat species that forage at certain heights above the forest canopy or occupy certain frequencies with their echolocation calls are underrepresented in survey counts because of the limited detection range of acoustic recorders . Since many bat species lack reliable population counts , the conservation status of several species is unknown .
Drones would be useful tools to help survey bats , since elevating the acoustic recorder would bring it into the range of detection for bats that are frequently missed . Additionally , some acoustic recorders are small enough to be added as a payload to many drones . Researchers would be more optimistic about conducting bat surveys with drones if not for the large disruption drones may cause bats . Since bats are nocturnal and tend to avoid noisy environments ,
Our acoustic profiles demonstrated that drone size and noise intensity are correlated , indicating that the largest drone ( in size and dimension ) was the loudest and the smallest drone was the quietest . However , when it comes to frequency range , there is no correlation between drone size and frequencies emitted . These profiles were key to deciphering which drone characteristics disturbed bats the most .
The least bat passes were detected when the loudest and largest drone flew , and bat activity was unaffected by the quietest and smallest drone . Indeed , bat activity was directly correlated with the size and noise intensity of the drones . The differing frequencies emitted by the drones did not have any detectable impact on bat activity , as the
Figure 3 . The properties of each of the drones used for this study . The largest and loudest drone is on the left , and the smallest and quietest on the right . Noise correlated with drone size , but frequency range did not . Drone images sourced from dji . com , infographic produced by Kayla Kuhlmann .
45 Grassroots Vol 22 No 1 March 2022