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opencc-zeroMar 2021View details →

The SONOZOTZ project: assembling an echolocation calls library for bats in a megadiverse country

Bat acoustic libraries are important tools that assemble echolocation calls to allow the comparison and discrimination to confirm species identifications. The Sonozotz project represents the first nation-wide library of bat echolocation calls for a megadiverse country. It was assembled following a standardized recording protocol that aimed to cover different recording habitats, recording techniques, and call variation inherent to individuals. The Sonozotz project included 69 species of echolocating bats, a high species richness that represents 50% of bat species found in the country. We include recommendations on how the database can be used and how the sampling methods can be potentially replicated in countries with similar environmental and geographic conditions. To our knowledge, this represents the most exhaustive effort to date to document and compile the diversity of bat echolocation calls for a megadiverse country. This database will be useful to address a range of ecological questions including the effects of anthropogenic activities on bat communities through the analysis of bat sound.

opencc-zeroDec 2017View details →

Data from: Inconspicuous echolocation in hoary bats (Lasiurus cinereus)

Echolocation allows bats to occupy diverse nocturnal niches. Bats almost always use echolocation, even when other sensory stimuli are available to guide navigation. Here, using arrays of calibrated infrared cameras and ultrasonic microphones, we demonstrate that hoary bats (Lasiurus cinereus) use previously unknown echolocation behaviors that challenge our current understanding of echolocation. We describe a novel call type ("micro" calls) that has three orders of magnitude less sound energy than other bat calls used in open habitats. We also document bats flying close to microphones (< 3 m) without producing detectable echolocation calls. Acoustic modeling indicates that bats are not producing calls that exceed 70-75 dB at 0.1 m, a level that would have little or no known use for a bat flying in the open at speeds exceeding 7 m s-1. This indicates that hoary bats sometimes fly without echolocation. We speculate that bats reduce echolocation output to avoid eavesdropping by conspecifics during the mating season. These findings might partly explain why tens of thousands of hoary bats are killed at wind turbines each year. They also challenge the long-standing assumption that bats—model organisms for sensory specialization—are reliant on sonar for nocturnal navigation.

opencc-zeroNov 2022View details →

Phenotypic traits evolution and morphological traits associated with echolocation calls in cryptic horseshoe bats (Rhinolophidae)

Bats provide an excellent case study for studying evolution due to their remarkable flight and echolocation capabilities. In this study, we sought to understand the phenotypic evolution of key traits in Rhinolophidae (horseshoe bats) using phylogenetic comparative methods. We aim to test the phylogenetic signals of traits and evaluated the best-fit evolutionary models given the data for each trait considering different traits may evolve under different models (i.e., Brownian Motion (BM), Ornstein-Uhlenbeck (OU) and Early Burst (EB)) and reconstruct ancestral character states. We examined how phenotypic characters are associated with echolocation calls and minimum detectable prey size. We measured 34 traits of 10 Asian rhinolophids species (187 individuals). We found that the majority of traits showed a high phylogenetic signal based on Blomberg's K and Pagel's λ, but each trait may evolve under different evolutionary models. Sella traits were shown to evolve under stabilizing selection based on OU models, indicating sella traits have the tendency to move forward along the branches toward some medial value in equilibrium. Our findings highlight the importance of sella characters in association with echolocation call emissions in Rhinolophidae, as calls are important for spatial cognition and also influence dietary preferences. Minimum detectable prey size in Rhinolophidae was associated with call frequency, bandwidth, call duration, wingspan and wing surface area. Ultimately, understanding trait evolution requires sensitivity due to the differential selective pressures which may apply to different characteristics.

publicNov 2022View details →

Phenotypic traits evolution and morphological traits associated with echolocation calls in cryptic horseshoe bats (Rhinolophidae)

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publicMay 2020View details →

The SONOZOTZ project: assembling an echolocation calls library for bats in a megadiverse country

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publicApr 2018View details →

Data from: Inconspicuous echolocation in hoary bats (Lasiurus cinereus)

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opennotspecifiedNov 2017View details →

FIG. 2 in Recent surveys of bats from the Andaman Islands, India: diversity, distribution, and echolocation characteristics

FIG. 2. Representative spectrograms of calls of echolocating bats of Andaman Islands, India. Calls analysed in BatSound. Acronyms: R. a. — Rhinolophus andamanensis, R. c. — R. cognatus, H. p. — Hipposideros pomona, T. m. — Taphozous melanopogon, M. s. — Megaderma spasma, P. j. c. — Pipistrellus javanicus camortae, P. c. — P. coromandra, M. h. d. — Myotis horsfieldii dryas

opencc-zeroDec 2020View details →

Hearing sensitivity and amplitude coding in bats are differentially shaped by echolocation calls and social calls

Differences in auditory perception between species are influenced by phylogenetic origin and the perceptual challenges imposed by the natural environment, e.g. detecting prey- or predator-generated sounds and communication signals. Bats are well suited for comparative studies on auditory perception since they predominantly rely on echolocation to perceive the world, while their social calls and most environmental sounds have low frequencies. We tested if hearing sensitivity and stimulus level coding in bats differ between high and low frequency ranges by measuring auditory brainstem responses (ABRs) of 86 bats belonging to 11 species. In most species, auditory sensitivity was equally good at both high and low frequency ranges, while amplitude was more finely coded for higher frequency ranges. Additionally, we conducted a phylogenetic comparative analysis by combining our ABR data with published data on 27 species. Species-specific peaks in hearing sensitivity correlated with peak frequencies of echolocation calls and pup isolation calls, suggesting that changes in hearing sensitivity evolved in response to frequency changes of echolocation and social calls. Overall, our study provides the most comprehensive comparative assessment of bat hearing capacities to date and highlights the evolutionary pressures acting on their sensory perception.

opencc-zeroJul 2019View details →

Data from: Echolocation and roosting ecology determine sensitivity of forest-dependent bats to coffee agriculture

Species differ in vulnerability to anthropogenic landuse changes. Knowledge of the mechanisms driving differential sensitivity can inform conservation strategies but is generally lacking for species-rich taxa in the tropics. The diverse bat fauna of Southeast Asia is threatened by rapid loss of forest and expanding agricultural activities, but the associations between species, traits, vulnerability to agriculture and the underlying drivers have yet to be elucidated. We studied the responses of speciose insectivorous bat assemblages to robusta coffee cultivation in Sumatra, Indonesia. We compared abundance, species richness and assemblage structures of bats between forests and coffee farms based on trapping data and evaluated the influence of vegetation complexity on assemblage composition and species-level reactions. Bat abundance and species richness were significantly lower in coffee farms than in forests. Bat assemblage structure differed between landuses, and the overall variation can be largely explained by vegetation simplification. Species sensitive to coffee agriculture were associated with more complex vegetation structure, whereas tolerant species were associated with simpler vegetation structure. Sensitive and tolerant species differed in the type, frequency, and bandwidth of echolocation calls and roost use. Species sensitive to coffee use broadband and high-pitched frequency-modulated calls, which are efficient at detecting insects in complex vegetation, and roost in plant structures that may be lost as vegetation is simplified. In contrast, tolerant species used lower-pitched constant frequency calls and roost in caves. We advocate for greater use of trait analyses in studies seeking to clarify the influence of agriculture on diverse tropical bat faunas.

opencc-zeroDec 2017View details →

Data from: A 2.6‐g sound and movement tag for studying the acoustic scene and kinematics of echolocating bats

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.

opencc-zeroDec 2018View details →

Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies

Interspecific differences in traits can alter the relative niche use of species within the same environment. Bats provide an excellent model to study niche use because they have a wide variety of behavioural, acoustic and morphological traits that may lead to multi-species, functional groups. Predatory bats have been classified by their foraging location (edge, clutter, open space), ability to aerial hawk and/or substrate glean prey and echolocation call design and flexibility, all of which may dictate their diet. For example, high frequency, broadband calls do not travel far but offer high object resolution while high intensity, low frequency calls travel further but provide lower resolution. Because these behaviours can be flexible four behavioural categories have been proposed: (1) gleaning, (2) behaviourally flexible (gleaning and hawking), (3) clutter tolerant hawking, and (4) open space hawking. Recent studies of diet in bats use molecular tools to identify prey but mainly focus on one or two species in isolation and few studies provide evidence for substantial differences in prey use despite the many behavioural, acoustic and morphological differences. Here we analyse the diet of 17 sympatric species in the Chihuahuan desert and test the hypothesis that peak echolocation frequency and behavioural categories are linked to differences in diet. We find no significant correlation between dietary richness and echolocation frequency (though it spanned close to 100kHz across species). However, our data suggest that behaviourally flexible bats that use gleaning and aerial hawking have the broadest diets and are the most differentiated from clutter-tolerant aerial hawking species.

opencc-zeroDec 2017View details →

Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation

Echolocating bats are regularly studied to investigate auditory-guided behaviours and as important bioindicators. Bioacoustic monitoring methods based on echolocation calls are increasingly used for risk assessment and to ultimately inform conservation strategies for bats. As echolocation calls transmit through the air at the speed of sound, they undergo changes due to atmospheric and geometric attenuation. Both the speed of sound and atmospheric attenuation, however, are variable and determined by weather conditions, particularly temperature and relative humidity. Changing weather conditions thus cause variation in analysed call parameters, limiting our ability to detect and correctly analyse bat calls. Here, I use real-world weather data to exemplify the effect of varying weather conditions on the acoustic properties of air. I then present atmospheric attenuation and speed of sound for the global range of weather conditions and bat call frequencies to show their relative effects. Atmospheric attenuation is a non-linear function of call frequency, temperature, relative humidity and atmospheric pressure. While atmospheric attenuation is strongly positively correlated with call frequency, it is also significantly influenced by temperature and relative humidity in a complex non-linear fashion. Variable weather conditions thus result in variable and unknown effects on the recorded call, affecting estimates of call frequency and intensity, particularly for high frequencies. Weather-induced variation in speed of sound reaches up to about ±3%, but is generally much smaller and only relevant for acoustic localisation methods of bats. The frequency- and weather-dependent variation in atmospheric attenuation has a three-fold effect on bioacoustic monitoring of bats: it limits our capability (1) to monitor bats equally across time, space, and species, (2) to correctly measure frequency parameters of bat echolocation calls, particularly for high-frequencies, and (3) to correctly identify bat species in species-rich assemblies or for sympatric species with similar call designs.

opencc-zeroDec 2014View details →

Data from 'Sensorimotor model of obstacle avoidance in echolocating bats'

The entry contains all data reported in the paper. Data are provided as MATLAB mat files. These can be read using MATLAB and other (free) software, including: <ul> <li>R: https://www.r-project.org/</li> <li>SciPy: http://wiki.scipy.org/Cookbook/Reading_mat_files</li> </ul> Each .mat file contains the data for a separate simulation: <ul> <li>Experiment1.mat: randomly distributed reflectors in horizontal plane</li> <li>Experiment2.mat: randomly distributed reflectors in vertical plane</li> <li>Experiment3.mat: randomly distributed reflectors, 3D</li> <li>Experiment4.mat: torus setting</li> <li>Experiment7.mat: patch of fir forest</li> <li>Experiment8.mat: forest road</li> <li>Experiment9.mat: vertical wires</li> <li>Experiment10.mat: horizontal wires</li> <li>Experiment11.mat: randomly distributed reflectors in horizontal plane, for FM bat</li> <li>Experiment12.mat: randomly distributed reflectors in vertical plane, for FM bat</li> </ul> The files contain the same entries. The relevant entries are the following: condition labels: the various conditions (i.e. controller variants) are encoded using the values in the arrays RD, EF, OA and CS. For example, for experiment1.mat these have following values below. <ul> <li>RD = [0 0 1 2 0];</li> <li>EF = [0 1 0 0 0];</li> <li>OA = [0 0 0 0 0];</li> <li>RF = [1 1 1 1 1];</li> <li>CS = [0 0 0 0 1];</li> </ul> RD: a value of 1 in RD indicates controller random A, 2 indicates controller random B. EF: a value of 1 indicates the the fixed ear controller. OA: indicates the controller with the ears fixed of axis RF: indicates the phases of the reflections were randomized CS: indicates the controller with constrained elevation Hence, the 5 conditions/controllers simulated for the randomly distributed reflectors in horizontal plane are the Default controller (no random, no fixed ears, no constraints), Fixed Ear, Random A, Random B and Constrained controller, respectively. The controller types in the other .mat files can be decoded similarly. <ul> </ul> Batpositions is 4D matrix [simulation steps x dimension (x, y, z) x condition x replication]. This matrix contains the 3D positions for the simulated bat for all replications and controllers. Similarly, velocities, distances, reflectors contain the speed of the bat, the distance to the nearest reflector, the number of reflectors returning an echo > 0 dB, respectively. Worlds contains the 3D positions of the reflectors for each of the replications. Other variables are support variables used while running the simulations.

opencc-zeroJun 2022View details →

Tables of microRNA and gene expression of an echolocating bat

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play key roles in many biological processes, such as development and response to multiple stresses. However, little is known about their roles in generating novel phenotypes and phenotypic variation during the course of animal evolution. Here, we, for the first time, characterized the miRNAs of the cochlea in an echolocating bat (Rhinolophus affinis). We sampled eight individuals from two R. affinis subspecies with significant echolocation call frequency differences. We identified 365 miRNAs and 121 of them were novel. By searching sequences of these miRNAs precursors in multiple high-quality mammal genomes, no specific miRNAs were found to be shared by all echolocating mammals. Together with the matched mRNA-seq data, we identified 1,766 differentially expressed genes (DEGs) between the two subspecies and 555 of them were negatively regulated by differentially expressed miRNAs (DEMs). We found that almost half of known hearing genes in the list of all DEGs were regulated negatively by DEMs, suggesting an important role of miRNAs in call frequency variation of the two subspecies. These targeted DEGs included several important hearing genes (e.g. Piezo1, Piezo2 and CDH23) that have been shown to be important in ultrasonic hearing of echolocating mammals.

opencc-zeroJul 2024View details →

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.

opencc-by-4.0Nov 2020View details →

Hearing, echolocation, and beam steering from Day 0 in tongue-clicking bats

Dataset and MATLAB script for extraction of click parameters for the manuscript "Hearing, echolocation, and beam steering from Day 0 in tongue-clicking bats."

opencc-by-4.0Dec 2022View details →

Data accompanying "Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat"

Introduction to the dataset:  In this manuscript, we analyse recordings of brown long-eared bats in the wild collected with a 4-microphone array, which we use to measure the distance between the bat and the microphone. The distance is then used to correct the recordings for distance-dependent frequency and amplitude attenuation. To ensure that our measurements and corrections are correct, we fully calibrated the microphone array and analysis set-up.  The calibration includes a comparison between real distances and calculated distances between a loudspeaker and the microphone array as well as comparisons between the real amplitude and the calculated amplitude of the loudspeaker. To obtain the original amplitude of the calibration recording, we calibrated the loudspeaker as well In this folder, we provide the original bat recordings as well as the datasheet with all analysed call measurements, and the original data from the microphone calibration. DATA & FILE OVERVIEW Folder list:      - Bat data: folder containing the original Plecotus recordings and raw call parameters     - Calibration data : folder containing the array calibration raw data, as well as codes and recordings necessary to calibrate the loudspeaker.