Stridulation is used by male katydids to produce sound via the rubbing together of their specialised forewings, either by sustained or interrupted sweeps of the file producing different tones and call structures. There are many species of Orthoptera that remain undescribed and their acoustic signals are unknown. This study aims to measure and quantify the mechanics of wing vibration, sound production and acoustic properties of the hearing system in a new genus of Pseudophyllinae with taxonomic descriptions of two new species. The calling behaviour and wing mechanics of males were measured using micro-scanning laser Doppler vibrometry, microscopy, and ultrasound sensitive equipment. The resonant properties of the acoustic pinnae of the ears were obtained via μ-CT scanning and 3D printed experimentation, and numerical modelling was used to validate the results. Analysis of sound recordings and wing vibrations revealed that the stridulatory areas of the right tegmen exhibit relatively narrow frequency responses and produce narrowband calls between 12 and 20 kHz. As in most Pseudophyllinae, only the right mirror is activated for sound production. The acoustic pinnae of all species were found to provide a broadband increased acoustic gain from ~40-120 kHz by up to 25 dB, peaking at almost 90 kHz which coincides with the echolocation frequency of sympatric bats. The new genus, named Satizabalus n. gen., is here derived as a new polytypic genus from the existing genus Gnathoclita, based on morphological and acoustic evidence from one described (S. sodalis n. comb.) and two new species (S. jorgevargasi n. sp. and S. hauca n. sp.). Unlike most Tettigoniidae, Satizabalus exhibits a particular form of sexual dimorphism whereby the heads and mandibles of the males are greatly enlarged compared to the females. We suggest that Satizabalus is related to the genus Trichotettix, also found in cloud forests in Colombia, and not to Gnathoclita.

Echolocating bats are known to vary their waveforms at the phases of searching, approaching, and capturing the prey. It is meaningful to estimate the parameters of the calls for bat species identification and the technological improvements of the synthetic systems, such as radar and sonar. The type of bat calls is species-related, and many calls can be modeled as hyperbolic frequency- modulated (HFM) signals. To obtain the parameters of the HFM-modeled bat calls, a reversible integral transform, i.e., hyperbolic scale transform (HST), is proposed to transform a call into two-dimensional peaks in the \"delay-scale\" domain, based on which harmonic separation and parameter estimation are realized. Compared with the methods based on time-frequency analysis, the HST-based method does not need to extract the instantaneous frequency of the bat calls, only searching for peaks. The verification results show that the HST is suitable for analyzing the HFM-modeled bat calls containing multiple harmonics with a large energy difference, and the estimated parameters imply that the use of the waveforms from the searching phase to the capturing phase is beneficial to reduce the ranging bias, and the trends in parameters may be useful for bat species identification.

Echolocating bats are among the most social and vocal of all mammals. These animals are ideal subjects for functional MRI (fMRI) studies of auditory social communication given their relatively hypertrophic limbic and auditory neural structures and their reduced ability to hear MRI gradient noise. Yet, no resting-state networks relevant to social cognition (e.g., default mode-like networks or DMLNs) have been identified in bats since there are few, if any, fMRI studies in the chiropteran order. Here, we acquired fMRI data at 7 Tesla from nine lightly anesthetized pale spear-nosed bats (Phyllostomus discolor). We applied independent components analysis (ICA) to reveal resting-state networks and measured neural activity elicited by noise ripples (on: 10 ms; off: 10 ms) that span this species\' ultrasonic hearing range (20 to 130 kHz). Resting-state networks pervaded auditory, parietal, and occipital cortices, along with the hippocampus, cerebellum, basal ganglia, and auditory brainstem. Two midline networks formed an apparent DMLN. Additionally, we found four predominantly auditory/parietal cortical networks, of which two were left-lateralized and two right-lateralized. Regions within four auditory/parietal cortical networks are known to respond to social calls. Along with the auditory brainstem, regions within these four cortical networks responded to ultrasonic noise ripples. Iterative analyses revealed consistent, significant functional connectivity between the left, but not right, auditory/parietal cortical networks and DMLN nodes, especially the anterior-most cingulate cortex. Thus, a resting-state network implicated in social cognition displays more distributed functional connectivity across left, relative to right, hemispheric cortical substrates of audition and communication in this highly social and vocal species.

Recent work suggests that the adult human brain is very adaptable when it comes to sensory processing. In this context, it has also been suggested that structural \"blueprints\" may fundamentally constrain neuroplastic change, e.g. in response to sensory deprivation. Here, we trained 12 blind participants and 14 sighted participants in echolocation over a 10-week period, and used MRI in a pre-post design to measure functional and structural brain changes. We found that blind participants and sighted participants together showed a training-induced increase in activation in left and right V1 in response to echoes, a finding difficult to reconcile with the view that sensory cortex is strictly organized by modality. Further, blind participants and sighted participants showed a training induced increase in activation in right A1 in response to sounds per se (i.e. not echo-specific), and this was accompanied by an increase in gray matter density in right A1 in blind participants and in adjacent acoustic areas in sighted participants. The similarity in functional results between sighted participants and blind participants is consistent with the idea that reorganization may be governed by similar principles in the two groups, yet our structural analyses also showed differences between the groups suggesting that a more nuanced view may be required.

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine\'s nacelle. The radar\'s detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife.

Bats use echolocation to navigate and hunt in darkness, and must in that process segregate target echoes from unwanted clutter echoes. Bats may do this by approaching a target at steep angles relative to the plane of the background, utilizing their directional transmission and receiving systems to minimize clutter from background objects, but it remains unknown how bats negotiate clutter that cannot be spatially avoided. Here, we tested the hypothesis that when movement no longer offers spatial release, echolocating bats mitigate clutter by calling at lower source levels and longer call intervals to ease auditory streaming. We trained five greater mouse-eared bats (Myotis myotis) to land on a spherical loudspeaker with two microphones attached. We used a phantom-echo setup, where the loudspeaker/target transmitted phantom clutter echoes by playing back the bats\' own calls at time delays of 1, 3 and 5 ms with a virtual target strength 7 dB higher than the physical target. We show that the bats successfully landed on the target, irrespective of the clutter echo delays. Rather than decreasing their source levels, the bats used similar source level distributions in clutter and control trials. Similarly, the bats did not increase their call intervals, but instead used the same distribution of call intervals across control and clutter trials. These observations reject our hypothesis, leading us to conclude that bats display great resilience to clutter via short auditory integration times and acute auditory stream segregation rather than via biosonar adjustments.

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.

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.

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.

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.