Echolocation while drinking: Pulse-timing strategies by high- and low-frequency FM bats

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.

Task-dependent vocal adjustments to optimize biosonar-based information acquisition

Animals need to acquire adequate and sufficient information to guide movements, yet information acquisition and processing are costly. Animals thus face a trade-off between gathering too little and too much information and, accordingly, actively adapt sensory input through motor control. Echolocating animals provide a unique opportunity to study the dynamics of adaptive sensing in naturally behaving animals, as every change in the outgoing echolocation signal directly affects information acquisition and the perception of the dynamic acoustic scene. Here, we investigated the flexibility with which bats dynamically adapt information acquisition depending on a task. We recorded the echolocation signals of wild-caught Western barbastelle bats (Barbastella barbastellus) while they were flying through an opening, drinking on the wing, landing on a wall and capturing prey. We show that the echolocation signal sequences during target approach differed in a task-dependent manner; bats started the target approach earlier and increased the information update rate more when the task became increasingly difficult, and bats also adjusted the dynamics of call duration shortening and peak frequency shifts accordingly. These task-specific differences existed from the onset of object approach, implying that bats plan their sensory-motor programme for object approach exclusively based on information received from search call echoes. We provide insight into how echolocating animals deal with the constraints they face when sequentially sampling the world through sound by adjusting acoustic information flow from slow to extremely fast in a highly dynamic manner. Our results further highlight the paramount importance of high behavioural flexibility for acquiring information.