Intensity and directionality of bat echolocation signals

The paper reviews current knowledge of intensity and directionality of bat echolocation signals. Recent studies have revealed that echolocating bats can be much louder than previously believed. Bats previously dubbed “whispering” can emit calls with source levels up to 110 dB SPL at 10 cm and the louder open space hunting bats have been recorded at above 135 dB SPL. This implies that maximum emitted intensities are generally 30 dB or more above initial estimates. Bats' dynamic control of acoustic features also includes the intensity and directionality of their sonar calls. Aerial hawking bats will increase signal directionality in the field along with intensity thus increasing sonar range. During the last phase of prey pursuit, vespertilionid bats broaden their echolocation beam considerably, probably to counter evasive maneuvers of eared prey. We highlight how multiple call parameters (frequency, duration, intensity, and directionality of echolocation signals) in unison define the search volume probed by bats and in turn how bats perceive their surroundings. Small changes to individual parameters can, in combination, drastically change the bat's perception, facilitating successful navigation and food acquisition across a vast range of ecological niches. To better understand the function of echolocation in the natural habitat it is critical to determine multiple acoustic features of the echolocation calls. The combined (interactive) effects, not only of frequency and time parameters, but also of intensity and directionality, define the bat's view of its acoustic scene

Open-source workflow approaches to passive acoustic monitoring of bats

The work was funded by grants to PTM from Carlsberg Semper Ardens Research Projects and the Independent Research Fund Denmark.The affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats. We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst. Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.Publisher PDFPeer reviewe

A 2.6-gram sound and movement tag for studying the acoustic scene and kinematics of echolocating bats

This study was supported by the Carlsberg Foundation via a Semper Ardens grant, ONR, N00014-17-1- 2736; AFOSR FA9550-14-1-0398, and NSF NCS-FO:1734744 and a Human Frontiers Science Program Long-Term Fellowship to AS. These experiments were approved by The Danish Council for Experiments on Animals under permit number: 2016-15-0201-00989 and by the Johns Hopkins University Animal Care and Use Committee under protocol number BA17A107. We thank Uwe Firzlaff and Lutz Wiegrebe for their help.1. To study sensorimotor behaviour in wild animals, it is necessary to synchronously record the sensory inputs available to the animal, and its movements. To do this, we have developed a biologging device that can record the primary sensory information and the associated movements during foraging and navigating in echolocating bats. 2. This 2.6 -gram tag records the sonar calls and echoes from an ultrasonic microphone, while simultaneously sampling fine-scale movement in three dimensions from wideband accelerometers and magnetometers. In this study, we tested the tag on an European noctula (Nyctalus noctula) during target approaches and on four big brown bats (Eptesicus fuscus) during prey interception in a flight room. 3. We show that the tag records both the outgoing calls and echoes returning from objects at biologically relevant distances. Inertial sensor data enables the detection of behavioural events such as flying, turning, and resting. In addition, individual wing-beats can be tracked and synchronized to the bat's sound emissions to study the coordination of different motor events. 4. By recording the primary acoustic flow of bats concomitant with associated behaviours on a very fine time-scale, this type of biologging method will foster a deeper understanding of how sensory inputs guide feeding behaviours in the wild.PostprintPeer reviewe

Oilbirds

A Quick guide to oilbirds: nocturnal birds found only in Neotropical rainforests that, rather like many bat species, live in caves where they use echolocation for orientation

Raw data deposit Oilbird paper

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Data from: Oilbirds produce echolocation signals beyond their best hearing range and adjust signal design to natural light conditions

Oilbirds are active at night, foraging for fruits using keen olfaction and extremely light-sensitive eyes, and echolocate as they leave and return to their cavernous roosts. We recorded the echolocation behaviour of wild oilbirds using a multi-microphone array as they entered and exited their roosts under different natural light conditions. During echolocation, the birds produced click bursts (CBs) lasting less than 10 ms and consisting of a variable number (2–8) of clicks at 2–3 ms intervals. The CBs have a bandwidth of 7–23 kHz at −6 dB from signal peak frequency. We report on two unique characteristics of this avian echolocation system. First, oilbirds reduce both the energy and number of clicks in their CBs under conditions of clear, moonlit skies, compared with dark, moonless nights. Second, we document a frequency mismatch between the reported best frequency of oilbird hearing (approx. 2 kHz) and the bandwidth of their echolocation CBs. This unusual signal-to-sensory system mismatch probably reflects avian constraints on high-frequency hearing but may still allow oilbirds fine-scale, close-range detail resolution at the upper extreme (approx. 10 kHz) of their presumed hearing range. Alternatively, oilbirds, by an as-yet unknown mechanism, are able to hear frequencies higher than currently appreciated

Dunstans Cave, single bird, exit in darkness

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Dunstans Cave, single bird, exit in moonlight

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Dunstans Cave, two birds, exit in darkness

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