Bat Calls Research Articles

Food-associated calls in disc-winged bats

Food-associated calls in disc-winged bats

Temporal Study of Environmental DNA and Acoustic Data Reveals Coexistence of Sympatric Bat Species in a North American Ecosystem

ABSTRACTBats are a species‐rich mammalian order that provide a host of ecosystem services, but presently face threats from habitat loss, disease, climate change, and insect declines. Bat species often co‐occur with other ecologically similar bats, making them a suitable group in which to study niche overlap and partitioning. This study aimed to compare different non‐invasive sources of data on wildlife populations, while examining dietary, temporal, and spatial partitioning patterns among sympatric bat species. We used two different methods to assess niche partitioning among insectivorous bats at a site in the San Francisco Bay Area, California: (1) eDNA sequencing of bat feces that were collected weekly from a bat roost, and (2) nightly acoustic recordings of ultrasonic bat calls from recorders at multiple sites. Both the eDNA and acoustic data were collected over the course of an entire roosting season in 2020. We hypothesized that the insectivorous bats at this site would rely on one or more niche partitioning mechanisms to promote interspecific coexistence and limit competition. We found evidence of fine‐scale spatial partitioning of the broad community of bat species in our study area based on acoustic data, as well as temporal differences in activity of different species. The two species using the roosting site, Tadarida brasiliensis and Eptesicus fuscus, displayed some differences in the identities and relative abundances of prey species consumed, but both ultimately exhibited a strong reliance on dipterans and aquatic‐dependent insects. We demonstrate differences between the acoustic data and eDNA data, which has implications for how such datasets may be interpreted in future research. The study finds evidence of some types of niche partitioning in this community and characterizes baseline interactions between species, providing a foundation for future efforts to non‐invasively monitor for unexpected biological change in local ecosystems.

The calls of Vietnamese bats: a major step toward the acoustic characterization of Asian bats

Southeast Asia includes several global biodiversity hotspots and bats account for nearly one-third of mammal species currently known in the region. While acoustic methods have become widespread in bat research, basic information is often lacking on the echolocation calls produced by Asian bat species. Since such information can aid a wide variety of research and conservation initiatives, descriptions of the calls emitted by Asian bats are fundamental. The aim of our study was to provide a standardized analysis and description of the Vietnamese bat echolocation calls. We analyzed call recordings of 87 species arranged in eight families. This constitutes 74% of the echolocating bats presently known in Vietnam and includes the first call descriptions for five taxa. Our use of an open-source software and the deposition of recordings in the ChiroVox repository will facilitate comparative studies in Asia and the information we provide represents one of the most comprehensive bioacoustic databases for Asian bats to date.

Importance of Manually Vetting Acoustic Bat Call Files: A Case Study for Northern Long-eared Bats

Importance of Manually Vetting Acoustic Bat Call Files: A Case Study for Northern Long-eared Bats

Parameter estimation of the hyperbolic frequency-modulated bat calls using hyperbolic scale transform

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.

Detection and validation of common noctule bats (Nyctalus noctula) with a pulse radar and acoustic monitoring in the proximity of an onshore wind turbine.

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 monitoring: a classification procedure of bats behaviours based on Hawkes processes

Abstract We are interested in the problem of classifying commuting and foraging behaviour of bats at delimited geographical areas, namely sites, throughout France. To predict the majority behaviour on these sites, we use echolocation call data recorded as part of Vigie-Chiro participatory project. As the temporal distribution of calls is a relevant indicator of behaviour, providing an adequate model of this distribution is a matter of great interest. Given the self-exciting dynamics observed in foraging behaviour, we propose to model bat calls by Hawkes processes. Specifically, we consider that the start time of each call emitted on a site is an event of a Hawkes process. Taking advantage of this modelling, we use a suitable procedure that relies on the empirical risk minimization principle to discriminate between the 2 classes. Then, the performance of the procedure is assessed on synthetic data through comprehensive numerical experiments. The overall methodology is evaluated with a goodness-of-fit test. Finally, we present the obtained results on the real data set. The classification results are convincing and show the relevance of our method, which could contribute to a better understanding of behavioural determinants and open up broad perspectives in spatial ecology.

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.

Using mobile device built-in microphones to monitor bats: a new opportunity for large-scale participatory science initiatives

Citizen science has become a crucial tool in biodiversity monitoring, often facilitated by the diffusion of mobile devices, such as smartphones and tablets. High costs of professional equipment often limit large-scale monitoring, particularly in bat monitoring programmes based on acoustic surveys. Here we present the potential of using mobile devices for bat monitoring, allowing for large-scale, volunteer-based monitoring programmes. We initially compared mobile devices’ performance with a professional bat detector for recording low-frequency bat calls. We then conducted a citizen science pilot study to test the method’s feasibility in a real-world setting, recording echolocation and social calls of nine European bat species. We found high similarity in spectrogram quality (0.74 ± 0.09 for Samsung, 0.90 ± 0.01 for Huawei, 0.86 ± 0.09 for Xiaomi, 0.69 ± 0.09 for Apple) and average peak frequency (differences of 0.2 ± 0.5 kHz for Samsung, 0.1 ± 0.7 kHz for Huawei, 0.5 ± 1.0 kHz for Xiaomi, 0.1 ± 0.8 kHz for Apple) between calls recorded by mobile devices and professional bat detectors. The number of recorded bat calls per sampling session was also similar. However, differences in sound quality and effectiveness among mobile device brands were found. iOS devices outperformed professional detectors at recording bat calls at increasing distances. The citizen science pilot study tested 35 mobile device models, all of which effectively recorded bats. This study suggests that mobile devices could be an accessible, no-cost tool for large-scale bat monitoring. Incorporating mobile devices into existing monitoring networks or creating new dedicated programmes could not only enhance data collection, but also boost public knowledge and awareness about bats, ultimately promoting informed decision-making and better conservation strategies.

Geographic variation and discrimination of social calls in male great himalayan leaf-nosed bats: do functionally similar calls have similar patterns?

Geographic variation and discrimination of social calls in male great himalayan leaf-nosed bats: do functionally similar calls have similar patterns?

Investigation of duty cycles for measuring activity in passive acoustic bat monitoring

Echolocating bats are important bioindicators that can be monitored effectively using passive acoustic monitoring (PAM) techniques. In PAM, duty-cycle-based temporal subsampling is often used to collect data at ON/OFF intervals to circumvent the limitations of recorder battery and storage capacity to enable long-term monitoring. However, potential bias introduced by temporal subsampling has not been systematically investigated for bat monitoring. Here, we use continuous audio recordings from the Union Bay Natural Area in Seattle in Summer 2022 to simulate the effects of temporal subsampling using different duty cycle parameters. We detected bat calls automatically using a deep learning model [Aodha et al., 2022, BioRxiv] and calculated three metrics as proxy for bat activity: number of calls, Activity Index (AI), and Bout Time Percentage (BTP). We found that although the number of calls and AI are more readily computable using the detected calls, BTP is likely a more accurate measure that relies less on the performance of automated call detection methods. In addition, reduced sampling for only a portion of the night (e.g., 4 hrs) was generally inadequate for capturing bat activity. Our results suggest that considering species-specific acoustic characteristics is crucial for reducing sampling bias for PAM of bats.

Absence of lunar phobia in European swarming vespertilionid bats

“Lunar phobia” in bats has been widely discussed since its description in tropical bats in 1978. The phenomenon has been frequently contested and supported and was first reported in European bats in 2020. Our study seeks to clarify the debate by describing the relationship between the activity of selected swarming vespertilionid bats (Family: Vespertilionidae) and moonlight levels. To verify a potential connection to the latter, a swarming dataset was analysed in respect of estimated moonlight illumination. Moonlight estimates were based on geographical location and several lunar parameters, to accurately characterise the non-linear relationship between moon phase and illumination (lux). The swarming data consisted of 32 netting and 14 echolocation recording sessions collected between August and October 2014 and 2015. Our data included 3,265 netted bats from 13 species and 15,919 bat calls from 10 confirmed species. Data was collected at the large Central European hibernation/swarming site – Natura 2000 PLH080003 “Nietoperek” in western Poland (N 52.394400, E 15.480600). Generalised linear mixed models (GLMMs) determined insignificant relationships between bats and moonlight illumination. Our analysis confirms an absence of impact of moonlight intensity on swarming bats and thereby rejects the lunar phobia phenomena in at least six insectivorous bat species (Myotis myotis, M. daubentonii, M. nattereri, M. bechsteinii, Barbastella barbastellus, Plecotus auritus) swarming in the autumn.

Remarks on the diversity and echolocation calls of hipposiderid bats (Chirroptera: Hipposideridae) in Cuc Phuong National Park, northeastern Vietnam

Cuc Phuong is the oldest and most well-known National Park in Vietnam. With its large primary forest area and karst cave systems, it is assumed to be an ideal home to a highly diverse bat fauna. However, species diversity and echolocation of bats in this park are poorly characterized. Between April 2021 and April 2022, three bat surveys were conducted in the park with special attention on the species of the family Hipposideridae. The bat specimens collected previously from the park were also examined. According to past records, hipposiderids in the park comprised at least seven species: Aselliscus stoliczkanus, Coelops frithii, Hipposideros alongensis, Hipposideros armiger, Hipposideros gentilis, Hipposideros lylei and Hipposideros poutensis. In this series of field surveys, six of these seven species were captured while Coelops frithii was only confirmed from specimen examination. Each species is clearly distinguishable by their morphology and echolocation calls. This study provides the first comparison of echolocation calls of all seven species which will be useful for potential acoustic identification and monitoring bats in the park and surrounding areas in future.

Global birdsong embeddings enable superior transfer learning for bioacoustic classification

Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on diverse datasets, including different bird calls and dialect types, bat calls, marine mammals calls, and amphibians calls. The embeddings extracted from the models trained on bird vocalization data consistently allowed higher quality classification than the embeddings trained on general audio datasets. The results of this study indicate that high-quality feature embeddings from large-scale acoustic bird classifiers can be harnessed for few-shot transfer learning, enabling the learning of new classes from a limited quantity of training data. Our findings reveal the potential for efficient analyses of novel bioacoustic tasks, even in scenarios where available training data is limited to a few samples.

Echolocation call characterization of insectivorous bats from caves and karst areas in southern Luzon Island, Philippines

Bats are excellent bioindicators and are increasingly used to assess ecosystem health and monitor changes in the environment. Due to increased awareness of the potential transmission of pathogens from bats to humans and recognizing the limitations of traditional bat sampling methods, the use of of non-invasive sampling techniques such as bat recorders were recommended for field-based monitoring studies. In the Philippines, however, bat bioacoustics is still a growing field, and the scarcity of acoustic data hinders the use of echolocation calls to conduct accurate inventories and population monitoring of echolocating bats. Here, we recorded and characterized echolocation calls of insectivorous bats from caves and karst areas located in southern Luzon Island, Philippines. In addition, we compared our results with other studies performed within and outside the country to identify possible regional and local variation in acoustic characters for some species. A total of 441 echolocation calls were recorded from six bat families: Hipposideridae (five species), Rhinolophidae (five species), Vespertilionidae (three species), Miniopteridae (two species), Megadermatidae (one species), and Emballonuridae (one species). Discriminant function analyses (DFA) with leave-one-out cross validation correctly classified bats emitting calls dominated with a constant frequency (CF) component (rhinolophids and hipposiderids) with >97% success and those producing frequency modulated (FM) calls (Miniopteridae and Vespertilionidae) with 88.9% success. We report echolocation calls for Philippine population of two species (Megaderma spasma and Hipposideros lekaguli) for the first time. Moreover, we present geographical variations in call frequencies for some species by comparing previously reported acoustic data elsewhere across the species’ range. This underscores the importance of establishing a readily accessible and comprehensive local reference library of echolocation calls which would serve as a valuable resource for examining taxonomic identities of echolocating bats, particularly those whose calls exhibit biogeographic variations.

Bat activity is related to habitat structure and time since prescribed fire in managed pine barrens in New England.

Several insectivorous bat species are found in New England, yet research on them is still scarce. Current research shows the ecological importance of bats due to their control of insect populations, but they are endangered by habitat loss and White Nose Syndrome, among other threats. Pine barrens are an uncommon ecosystem found in New England that supports other rare taxa and could be important for these bat species. With hand-held audio recorders, we surveyed for bats in Montague Plains Wildlife Management Area in Massachusetts and Concord Pine Barrens in New Hampshire in June 2022. Our study objectives were to (1) describe the most common bat species and (2) compare bat activity across different habitat types at two managed pine barrens in New England. In particular, we examined bat activity related to habitat type (scrub oak, mature pitch pine, treated pitch pine, hardwood forest), habitat structure (i.e., canopy closure), time since prescribed fire, and path width. We analyzed our data through generalized linear modeling and logistic regression. Overall, we were able to measure the presence of five out of the nine total species found in the area, including the endangered Myotis lucifugus (little brown bat). We recorded 293 bat calls, with the majority of calls from big brown bats (71%). We found significant differences (p<0.05) in bat activity in relation to time since prescribed fire and habitat structure. The index of bat activity was greatest in pitch pine and hardwood forests and lowest in scrub oak and treated pitch pine habitats. With preliminary data, we also found that silver-haired bat presence was influenced by habitat type, with more detections at survey points in hardwood forests. These findings demonstrate the importance of pine barrens as an ecosystem that supports bats in New England. According to the activity of bats in our study, closed canopy and mature pitch pine habitats may be prioritized in conservation efforts at managed barrens for bat species. Further research is recommended to better understand the relationship between prescribed fires, which are common in managed barrens, and bat activity.

Predator–prey interactions between gleaning bats and katydids

Bats are voracious predators of insects, and many insects have ears sensitive to the high-frequency echolocation calls of bats. Eared insects show a variety of defences when they detect bat echolocation calls. Professor Brock Fenton was an early contributor to the field of bat–insect interactions, inspiring many students to pursue investigations that have advanced our understanding of the relationship between predators and prey. Reflecting on the integrative nature of Dr. Fenton's research, this review highlights research on the evolutionary arms race between gleaning insectivorous bats and katydid prey. Studies on this system have enhanced the field of sensory ecology by illuminating how animal auditory systems can encode and distinguish between signals that overlap in their acoustic properties but have very different consequences for the listener (sex or death). These studies also inform us about the ecological and evolutionary selection pressures on signalers and receivers that can shape mate attraction and predator avoidance behaviour. In particular, many Neotropical katydids rely on preventative instead of reactive defences against gleaning bats, likely due to the regular presence of echolocation calls from non-gleaning bats that reduce the information content of predator cues. We conclude with suggestions for future research on these fascinating animals.

Combining acoustic tracking and LiDAR to study bat flight behaviour in three-dimensional space

BackgroundHabitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ.MethodsHere, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats’ positions and vegetation structures.ResultsOur results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light.ConclusionBy combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction.