Automated echolocation classifiers vary in accuracy for northeastern U.S. bat species.

Acoustic surveys of bat echolocation calls are an important management tool for determining presence and probable absence of threatened and endangered bat species. In the northeastern United States, software programs such as Bat Call Identification (BCID), Kaleidoscope Pro (KPro), and Sonobat can automatically classify ultrasonic detector sound files, yet the programs' accuracy in correctly classifying calls to species has not been independently assessed. We used 1,500 full-spectrum reference calls with known identities for nine northeastern United States bat species to test the accuracy of these programs using calculations of Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity (SN), Specificity (SP), Overall Accuracy, and No Information Rate. We found that BCID performed less accurately than other programs, likely because it only operates on zero-crossing data and may be less accurate for recordings converted from full-spectrum to zero-crossing. NPV and SP values were high across all species categories for SonoBat and KPro, indicating these programs' success at avoiding false positives. However, PPV and SN values were relatively low, particularly for individual Myotis species, indicating these programs are prone to false negatives. SonoBat and KPro performed better when distinguishing Myotis species from non-Myotis species. We expect less accuracy from these programs for acoustic recordings collected under normal working conditions, and caution that a bat acoustic expert should verify automatically classified files when making species-specific regulatory or conservation decisions.

Tiger beetles produce anti-bat ultrasound and are probable Batesian moth mimics.

Echolocating bats and their eared insect prey are in an acoustic evolutionary war. Moths produce anti-bat sounds that startle bat predators, signal noxiousness, mimic unpalatable models and jam bat sonar. Tiger beetles (Cicindelidae) also purportedly produce ultrasound in response to bat attacks. Here we tested 19 tiger beetle species from seven genera and showed that they produce anti-bat signals to playback of authentic bat echolocation. The dominant frequency of beetle sounds substantially overlaps the sonar calls of sympatric bats. As tiger beetles are known to produce defensive chemicals such as benzaldehyde and hydrogen cyanide, we hypothesized that tiger beetle sounds are acoustically advertising their unpalatability. We presented captive big brown bats (Eptesicus fuscus) with seven different tiger beetle species and found that 90 out of 94 beetles were completely consumed, indicating that these tiger beetle species are not aposematically signalling. Instead, we show that the primary temporal and spectral characteristics of beetle warning sounds overlap with sympatric unpalatable tiger moth (Arctinae) sounds and that tiger beetles are probably Batesian mimics of noxious moth models. We predict that many insect taxa produce anti-bat sounds and that the acoustic mimicry rings of the night sky are hyperdiverse.

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data.

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

Superfast Lombard response in free-flying, echolocating bats.

Acoustic cues are crucial to communication, navigation, and foraging in many animals, which hence face the problem of detecting and discriminating these cues in fluctuating noise levels from natural or anthropogenic sources. Such auditory dynamics are perhaps most extreme for echolocating bats that navigate and hunt prey on the wing in darkness by listening for weak echo returns from their powerful calls in complex, self-generated umwelts.1,2 Due to high absorption of ultrasound in air and fast flight speeds, bats operate with short prey detection ranges and dynamic sensory volumes,3 leading us to hypothesize that bats employ superfast vocal-motor adjustments to rapidly changing sensory scenes. To test this hypothesis, we investigated the onset and offset times and magnitude of the Lombard response in free-flying echolocating greater mouse-eared bats exposed to onsets of intense constant or duty-cycled masking noise during a landing task. We found that the bats invoked a bandwidth-dependent Lombard response of 0.1-0.2 dB per dB increase in noise, with very short delay and relapse times of 20 ms in response to onsets and termination of duty-cycled noise. In concert with the absence call time-locking to noise-free periods, these results show that free-flying bats exhibit a superfast, but hard-wired, vocal-motor response to increased noise levels. We posit that this reflex is mediated by simple closed-loop audio-motor feedback circuits that operate independently of wingbeat and respiration cycles to allow for rapid adjustments to the highly dynamic auditory scenes encountered by these small predators.

Accurate species classification of Arctic toothed whale echolocation clicks using one-third octave ratios.

Passive acoustic monitoring has been an effective tool to study cetaceans in remote regions of the Arctic. Here, we advance methods to acoustically identify the only two Arctic toothed whales, the beluga (Delphinapterus leucas) and narwhal (Monodon monoceros), using echolocation clicks. Long-term acoustic recordings collected from moorings in Northwest Greenland were analyzed. Beluga and narwhal echolocation signals were distinguishable using spectrograms where beluga clicks had most energy >30 kHz and narwhal clicks had a sharp lower frequency limit near 20 kHz. Changes in one-third octave levels (TOL) between two pairs of one-third octave bands were compared from over one million click spectra. Narwhal clicks had a steep increase between the 16 and 25 kHz TOL bands that was absent in beluga click spectra. Conversely, beluga clicks had a steep increase between the 25 and 40 kHz TOL bands that was absent in narwhal click spectra. Random Forest classification models built using the 16 to 25 kHz and 25 to 40 kHz TOL ratios accurately predicted the species identity of 100% of acoustic events. Our findings support the use of echolocation TOL ratios in future automated click classifiers for acoustic monitoring of Arctic toothed whales and potentially for other odontocete species.

Correlated evolution between body size and echolocation in bats (order Chiroptera).

BACKGROUND: Body size and echolocation call frequencies are related in bats. However, it is unclear if this allometry applies to the entire clade. Differences have been suggested between nasal and oral emitting bats, as well as between some taxonomic families. Additionally, the scaling of other echolocation parameters, such as bandwidth and call duration, needs further testing. Moreover, it would be also interesting to test whether changes in body size have been coupled with changes in these echolocation parameters throughout bat evolution. Here, we test the scaling of peak frequency, bandwidth, and call duration with body mass using phylogenetically informed analyses for 314 bat species. We specifically tested whether all these scaling patterns differ between nasal and oral emitting bats. Then, we applied recently developed Bayesian statistical techniques based on large-scale simulations to test for the existence of correlated evolution between body mass and echolocation. RESULTS: Our results showed that echolocation peak frequencies, bandwidth, and duration follow significant allometric patterns in both nasal and oral emitting bats. Changes in these traits seem to have been coupled across the laryngeal echolocation bats diversification. Scaling and correlated evolution analyses revealed that body mass is more related to peak frequency and call duration than to bandwidth. We exposed two non-exclusive kinds of mechanisms to explain the link between size and each of the echolocation parameters. CONCLUSIONS: The incorporation of Bayesian statistics based on large-scale simulations could be helpful for answering macroevolutionary patterns related to the coevolution of traits in bats and other taxonomic groups.

Click detection rate variability of central North Pacific sperm whales from passive acoustic towed arrays.

Passive acoustic monitoring (PAM) is an optimal method for detecting and monitoring cetaceans as they frequently produce sound while underwater. Cue counting, counting acoustic cues of deep-diving cetaceans instead of animals, is an alternative method for density estimation, but requires an average cue production rate to convert cue density to animal density. Limited information about click rates exists for sperm whales in the central North Pacific Ocean. In the absence of acoustic tag data, we used towed hydrophone array data to calculate the first sperm whale click rates from this region and examined their variability based on click type, location, distance of whales from the array, and group size estimated by visual observers. Our findings show click type to be the most important variable, with groups that include codas yielding the highest click rates. We also found a positive relationship between group size and click detection rates that may be useful for acoustic predictions of group size in future studies. Echolocation clicks detected using PAM methods are often the only indicator of deep-diving cetacean presence. Understanding the factors affecting their click rates provides important information for acoustic density estimation.

Echolocating Bats Have Evolved Decreased Susceptibility to Noise-Induced Temporary Hearing Losses.

Glenis Long championed the application of quantitative psychophysical methods to understand comparative hearing abilities across species. She contributed the first psychophysical studies of absolute and masked hearing sensitivities in an auditory specialist, the echolocating horseshoe bat. Her data demonstrated that this bat has hyperacute frequency discrimination in the 83-kHz range of its echolocation broadcast. This specialization facilitates the bat's use of Doppler shift compensation to separate echoes of fluttering insects from concurrent echoes of non-moving objects. In this review, we discuss another specialization for hearing in a species of echolocating bat that contributes to perception of echoes within a complex auditory scene. Psychophysical and behavioral studies with big brown bats show that exposures to long duration, intense wideband or narrowband ultrasonic noise do not induce significant increases in their thresholds to echoes and do not impair their ability to orient through a naturalistic sonar scene containing multiple distracting echoes. Thresholds of auditory brainstem responses also remain low after intense noise exposures. These data indicate that big brown bats are not susceptible to temporary threshold shifts as measured in comparable paradigms used with other mammals, at least within the range of stimulus parameters that have been tested so far. We hypothesize that echolocating bats have evolved a decreased susceptibility to noise-induced hearing losses as a specialization for echolocation in noisy environments.

Bioinspiration from bats and new paradigms for autonomy in natural environments.

Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats' skill can be replicated in man-made autonomous systems.

Echo detection thresholds in big brown bats (Eptesicus fuscus) vary with echo spectral content.

Echolocating big brown bats (Eptesicus fuscus) broadcast downward frequency-modulated sweeps covering the ultrasonic range from 100-23 kHz in two harmonics. They perceive target range from the time delay between each broadcast and its returning echo. Previous experiments indicated that the bat's discrimination acuity for broadcast-echo delay declines when the lowest frequencies (23-35 kHz) in the first harmonic of an echo are removed. This experiment examined whether echo detection is similarly impaired. Results show that detection thresholds for echoes missing these lowest frequencies are raised. Increased thresholds for echoes differing in spectra facilitates the bat's ability to discriminate against clutter.

Molecular adaptations underlying high-frequency hearing in the brain of CF bats species.

BACKGROUND: The majority of bat species have developed remarkable echolocation ability, especially for the laryngeally echolocating bats along with high-frequency hearing. Adaptive evolution has been widely detected for the cochleae in the laryngeally echolocating bats, however, limited understanding for the brain which is the central to echolocation signal processing in the auditory perception system, the laryngeally echolocating bats brain may also undergo adaptive changes. RESULT: In order to uncover the molecular adaptations related with high-frequency hearing in the brain of laryngeally echolocating bats, the genes expressed in the brain of Rhinolophus ferrumequinum (CF bat) and Myotis pilosus (FM bat) were both detected and also compared. A total of 346,891 genes were detected and the signal transduction mechanisms were annotated by the most abundant genes, followed by the transcription. In hence, there were 3,088 DEGs were found between the two bat brains, with 1,426 highly expressed in the brain of R. ferrumequinum, which were significantly enriched in the neuron and neurodevelopmental processes. Moreover, we found a key candidate hearing gene, ADCY1, playing an important role in the R. ferrumequinum brain and undergoing adaptive evolution in CF bats. CONCLUSIONS: Our study provides a new insight to the molecular bases of high-frequency hearing in two laryngeally echolocating bats brain and revealed different nervous system activities during auditory perception in the brain of CF bats.

Effects of insect pursuit on the Doppler shift compensation in a hipposiderid bat.

Doppler shift compensation (DSC) is a unique feature observed in certain species of echolocating bats and is hypothesized to be an adaptation to detecting fluttering insects. However, current research on DSC has primarily focused on bats that are not engaged in foraging activities. In this study, we investigated the DSC performance of Pratt's roundleaf bat, Hipposideros pratti, which was trained to pursue insects in various motion states within a laboratory setting. Our study yielded three main results. First, H. pratti demonstrated highly precise DSC during insect pursuit, aligning with previous findings of other flutter-detecting foragers during orientation or landing tasks. Second, we found that the motion state of the insect prey had little effect on the DSC performance of H. pratti. Third, we observed variations in the DSC performance of H. pratti throughout the course of insect pursuit. The bats exhibited the highest DSC performance during the phase of maximum flight speed but decreased performance during the phase of insect capture. These findings of high precision overall and the time-dependent performance of DSC during insect pursuit support the hypothesis that DSC is an adaptation to detecting fluttering insects.

Narwhal (Monodon monoceros) echolocation click rates to support cue counting passive acoustic density estimation.

Estimating animal abundance is fundamental for effective management and conservation. It is increasingly done by combining passive acoustics with knowledge about rates at which animals produce cues (cue rates). Narwhals (Monodon monoceros) are elusive marine mammals for which passive acoustic density estimation might be plausible, but for which cue rates are lacking. Clicking rates in narwhals were investigated using a dataset from sound and movement tag records collected in August 2013-2016 and 2019 in East Greenland. Clicking rates were quantified for ∼1200 one-second-long systematic random samples from 8 different whales. Generalized additive models were used to model (1) the probability of being in a clicking state versus depth and (2) the clicking rate while in a clicking state, versus time and depth. The probability of being in a clicking state increased with depth, reaching ∼1.0 at ∼500 m, while the number of clicks per second (while in a clicking state) increased with depth. The mean cue production rate, weighted by tag duration, was 1.28 clicks per second (se = 0.13, CV = 0.10). This first cue rate for narwhals may be used for cue counting density estimation, but care should be taken if applying it to other geographical areas or seasons, given sample size, geographical, and temporal limitations.

Hector's dolphins (Cephalorhynchus hectori) produce both narrowband high-frequency and broadband acoustic signals.

Odontocetes produce clicks for echolocation and communication. Most odontocetes are thought to produce either broadband (BB) or narrowband high-frequency (NBHF) clicks. Here, we show that the click repertoire of Hector's dolphin (Cephalorhynchus hectori) comprises highly stereotypical NBHF clicks and far more variable broadband clicks, with some that are intermediate between these two categories. Both NBHF and broadband clicks were made in trains, buzzes, and burst-pulses. Most clicks within click trains were typical NBHF clicks, which had a median centroid frequency of 130.3 kHz (median -10 dB bandwidth = 29.8 kHz). Some, however, while having only marginally lower centroid frequency (median = 123.8 kHz), had significant energy below 100 kHz and approximately double the bandwidth (median -10 dB bandwidth = 69.8 kHz); we refer to these as broadband. Broadband clicks in buzzes and burst-pulses had lower median centroid frequencies (120.7 and 121.8 kHz, respectively) compared to NBHF buzzes and burst-pulses (129.5 and 130.3 kHz, respectively). Source levels of NBHF clicks, estimated by using a drone to measure ranges from a single hydrophone and by computing time-of-arrival differences at a vertical hydrophone array, ranged from 116 to 171 dB re 1 µPa at 1 m, whereas source levels of broadband clicks, obtained from array data only, ranged from 138 to 184 dB re 1 µPa at 1 m. Our findings challenge the grouping of toothed whales as either NBHF or broadband species.

A bat biomimetic model for scenario recognition using echo Doppler information.

The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a three-dimensional clutter model of the flying state of bat bio-sonar for bats emitting constant frequency signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a convolutional neural network (CNN). The short-time Fourier transform of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN's powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB signal noise ratio. Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.

Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform.

Many man-made marine structures (MMS) will have to be decommissioned in the coming decades. While studies on the impacts of construction of MMS on marine mammals exist, no research has been done on the effects of their decommissioning. The complete removal of an oil and gas platform in Scotland in 2021 provided an opportunity to investigate the response of harbour porpoises to decommissioning. Arrays of broadband noise recorders and echolocation detectors were used to describe noise characteristics produced by decommissioning activities and assess porpoise behaviour. During decommissioning, sound pressure spectral density levels in the frequency range 100 Hz to 48 kHz were 30-40 dB higher than baseline, with vessel presence being the main source of noise. The study detected small-scale (< 2 km) and short-term porpoise displacement during decommissioning, with porpoise occurrence increasing immediately after this. These findings can inform the consenting process for future decommissioning projects.

The effects of range and echo-phase on range resolution in bottlenose dolphins (Tursiops truncatus) performing a successive comparison taska).

Echolocating bats and dolphins use biosonar to determine target range, but differences in range discrimination thresholds have been reported for the two species. Whether these differences represent a true difference in their sensory system capability is unknown. Here, the dolphin's range discrimination threshold as a function of absolute range and echo-phase was investigated. Using phantom echoes, the dolphins were trained to echo-inspect two simulated targets and indicate the closer target by pressing a paddle. One target was presented at a time, requiring the dolphin to hold the initial range in memory as they compared it to the second target. Range was simulated by manipulating echo-delay while the received echo levels, relative to the dolphins' clicks, were held constant. Range discrimination thresholds were determined at seven different ranges from 1.75 to 20 m. In contrast to bats, range discrimination thresholds increased from 4 to 75 cm, across the entire ranges tested. To investigate the acoustic features used more directly, discrimination thresholds were determined when the echo was given a random phase shift (±180°). Results for the constant-phase versus the random-phase echo were quantitatively similar, suggesting that dolphins used the envelope of the echo waveform to determine the difference in range.

A tradeoff evolution between acoustic fat bodies and skull muscles in toothed whales.

Toothed whales have developed specialized echolocation abilities that are crucial for underwater activities. Acoustic fat bodies, including the melon, extramandibular fat body, and intramandibular fat body, are vital for echolocation. This study explores the transcriptome of acoustic fat bodies in toothed whales, revealing some insight into their evolutionary origins and ecological significance. Comparative transcriptome analysis of acoustic fat bodies and related tissues in a harbor porpoise and a Pacific white-sided dolphin reveals that acoustic fat bodies possess characteristics of both muscle and adipose tissue, occupying an intermediate position. The melon and extramandibular fat body exhibit specific muscle-related functions, implying an evolutionary connection between acoustic fat bodies and muscle tissue. Furthermore, we suggested that the melon and extramandibular fat body originate from intramuscular adipose tissue, a component of white adipose tissue. The extramandibular fat body has been identified as an evolutionary homolog of the masseter muscle, supported by the specific expression of MYH16, a pivotal protein in masticatory muscles. The intramandibular fat body, located within the mandibular foramen, shows possibilities of the presence of several immune-related functions, likely due to its proximity to bone marrow. Furthermore, this study sheds light on leucine modification in the catabolic pathway, which leads to the accumulation of isovaleric acid in acoustic fat bodies. Swallowing without chewing, a major toothed whale feeding ecology adaptation, makes the masticatory muscle redundant and leads to the formation of the extramandibular fat body. We propose that the intramuscular fat enlargement in facial muscles, which influences acoustic fat body development, is potentially related to the substantial reorganization of head morphology in toothed whales during aquatic adaptation.

Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar.

This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.

Musculoskeletal morphogenesis supports the convergent evolution of bat laryngeal echolocation.

The order Chiroptera (bats) is the second largest group of mammals. One of the essential adaptations that have allowed bats to dominate the night skies is laryngeal echolocation, where bats emit ultrasonic pulses and listen to the returned echo to produce high-resolution 'images' of their surroundings. There are two possible scenarios for the evolutionary origin of laryngeal echolocation in bats: (1) a single origin in a common ancestor followed by the secondary loss in Pteropodidae, or (2) two convergent origins in Rhinolophoidea and Yangochiroptera. Although data from palaeontological, anatomical, developmental and genomic studies of auditory apparatuses exist, they remain inconclusive concerning the evolutionary origin of bat laryngeal echolocation. Here we compared musculoskeletal morphogenesis of the larynx in several chiropteran lineages and found distinct laryngeal modifications in two echolocating lineages, rhinolophoids and yangochiropterans. Our findings support the second scenario that rhinolophoids and yangochiropterans convergently evolved advanced laryngeal echolocation through anatomical modifications of the larynx for ultrasonic sound generation and refinement of the auditory apparatuses for more detailed sound perception.