buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings

Quantification of bat communities and habitat heavily rely on non-invasive
acoustic bat surveys the scope of which has greatly amplified with
advances in remote monitoring technologies. Despite the unprecedented
amount of acoustic data being collected, analysis of these data is often
limited to simple species classification which provides little information
on habitat function. Feeding buzzes, the rapid sequences of echolocation
pulses emitted by bats during the terminal phase of prey capture, have
historically been used to evaluate foraging habitat quality. Automated
identification of feeding buzzes in recordings could benefit conservation
by helping identify critical foraging habitat. I tested if detection of
feeding buzzes in recordings could be automated with bat recordings from
Ontario, Canada. Data were obtained using three different recording
devices. The signal detection method involved sequentially scanning narrow
frequency bands with the “Bioacoustics” R package signal detection
algorithm, and extracting temporal and signal strength parameters from
detections. Buzzes were best characterized by the standard deviation of
the time between consecutive pulses, the average pulse duration, and the
average pulse signal-to-noise ratio. Classification accuracy was highest
with artificial neural networks and random forest algorithms. I compared
each model’s receiver operating characteristic curves and random forest
provided better control over the false-positive rate so it was retained as
the final model. When tested on a new dataset, buzzfindr’s overall
accuracy was 93.4% (95% CI: 91.5% - 94.9%). Overall accuracy was not
affected by recording device type or species frequency group. Automated
detection of feeding buzzes will facilitate their integration in the
analytical workflow of acoustic bat studies to improve inferences on
habitat use and quality.