Testing the performances of automated identification of bat echolocation calls: A request for prudence

Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100%) distinguish rain from bat calls, usually (68–100%) identify bats to group (Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella) and usually (83–99%) recognize typical calls of some species whose echolocation pulses are structurally distinct (Pipistrellus pygmaeus, Barbastella barbastellus). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16–26%), Myotis spp. (4–93%) and Plecotus auritus (0–89%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.     

Acoustic characterization of bats from Malta: setting a baseline for monitoring and conservation of bat populations

Bioacoustic research has made several advancements in developing systems to record extensive acoustic data and classify bat echolocation calls to species level using automated classifiers. These systems are useful as echolocation calls give valuable information on bat behaviour and ecology and hence are widely used for research and conservation of bat populations. Despite the challenges associated with automated classifiers, due to the interspecific differences in call characteristics of bat species found in the Maltese Islands, the use of a quantitative and automated approach is investigated. The sound analysis pipeline involved the use of an algorithm to clean sound files from background noise and measure temporal and spectral parameters of bat echolocation calls. These parameters were then fed to a trained and validated artificial neural network using a bat call library built from reference bat calls from Malta. The automatic classifier achieved an overall correct classification rate of 98%. This high correct classification rate for reliable species identification may have benefitted from the absence of typically problematic species, such as species in the genus Myotis, in the analyses. This study’s results pave the way for efficient and reliable bat acoustic surveys in Malta in aid of necessary monitoring and conservation by providing an updated bat species list and their echolocation characteristics.     

A comparison of two bat detectors: which is most likely to detect New Zealand’s Chalinolobus tuberculatus?

ABSTRACT

The presence of bat species is commonly determined by placing acoustic bat detectors that record bat echolocation calls in the habitat they are likely to use. Detection rates are affected by variables including type of detection unit used. We compared detection rates of long-tailed bat (Chalinolobus tuberculatus) echolocation calls between two types of automated bat detectors: Wildlife Acoustics SMZC Zero Crossing Bat Recorders (ZC), and Frequency Compression Automated Bat Monitoring units (FC) produced by New Zealand’s Department of Conservation. Units were placed in locations where bats were known to be present, but not all detected bats. The median number of bat passes recorded by FC units over 10 nights was 20 compared with a median of 3 bat passes for ZC units. ZC units also detected bats over significantly fewer nights than FC units. These results suggest FC units are more sensitive and therefore better to use where long-tailed bats are expected to be at low abundance or only present infrequently. Because of inconsistencies in detection rates, we recommend the use of only one model of the detector within a monitoring project. Our data also suggests that surveys should take place over long periods to maximise likelihood of detecting bats, if present.     

Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of >650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting a desired accuracy probability threshold of 95% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.     

A comparison of supervised learning techniques in the classification of bat echolocation calls

Today's acoustic monitoring devices are capable of recording and storing tremendous amounts of data. Until recently, the classification of animal vocalizations from field recordings has been relegated to qualitative approaches. For large-scale acoustic monitoring studies, qualitative approaches are very time-consuming and suffer from the bias of subjectivity. Recent developments in supervised learning techniques can provide rapid, accurate, species-level classification of bioacoustics data. We compared the classification performances of four supervised learning techniques (random forests, support vector machines, artificial neural networks, and discriminant function analysis) for five different classification tasks using bat echolocation calls recorded by a popular frequency-division bat detector. We found that all classifiers performed similarly in terms of overall accuracy with the exception of discriminant function analysis, which had the lowest average performance metrics. Random forests had the advantage of high sensitivities, specificities, and predictive powers across the majority of classification tasks, and also provided metrics for determining the relative importance of call features in distinguishing between groups. Overall classification accuracy for each task was slightly lower than reported accuracies using calls recorded by time-expansion detectors. Myotis spp. were particularly difficult to separate; classifiers performed best when members of this genus were combined in genus-level classification and analyzed separately at the level of species. Additionally, we identified and ranked the relative contributions of all predictor features to classifier accuracy and found measurements of frequency, total call duration, and characteristic slope to be the most important contributors to classification success. We provide recommendations to maximize accuracy and efficiency when analyzing acoustic data, and suggest an application of automated bioacoustics monitoring to contribute to wildlife monitoring efforts.     

Little Brown Bats (Myotis lucifugus) Recognize Individual Identity of Conspecifics Using Sonar Calls

Bats use sonar calls to locate prey and orient in their environment but they may also be used by conspecifics to obtain information about a caller. Statistical analysis of sonar calls provides evidence that variation carries social information about a caller, including individual identity. We hypothesized that little brown bats (Myotis lucifugus) would be able to recognize individuals given the potential fitness benefits of doing so. We performed playback trials using a habituation‐discrimination design to determine whether little brown bats are able to recognize the individual identity of a caller based on variation in their sonar calls. Each subject bat was played the calls of bat A until they habituated (defined as a 50% decrease from the beginning call rate), then the calls of bat B or a new call sequence of bat A (a control, referred to as bat A’) were played. Each subject received a unique pair of playback recordings (bat A and B) from adult female bats from the same colony (but a different colony from the subject) and the order of trials was randomized. The response measures were habituation time (s) and call rate (calls/s). Within a trial, subjects habituated to calls of bat A and transferred this habituation to the bat A’ sequence. In addition, they increased their call rates when played calls of bat B. Comparing between trials, subjects increased their call rate to the calls of bat B to a greater relative extent than to the calls of bat A’. These results provide the first evidence that bats recognize individual identity of conspecifics (as opposed to discrimination of groups), which has implications for the social interactions of bats.     

Social Learning of a Novel Foraging Task by Big Brown Bats (Eptesicus fuscus)

Acquiring information via observation of others can be an efficient way to respond to changing situations or learn skills, particularly for inexperienced individuals. Many bat species are gregarious, yet few studies have investigated their capacity for learning from conspecifics. We tested whether big brown bats (Eptesicus fuscus) can learn a novel foraging task by interacting with knowledgeable conspecifics. In experimental trials 11 na?ve bats (7 juveniles, 4 adults) interacted freely with trained bats that were capturing tethered mealworms, while in control trials 11 na?ve bats (7 juveniles, 4 adults) flew with untrained bats. Na?ve bats were then assessed for their ability to capture tethered mealworms. While no bat in the control group learned the task, a significant number of experimental bats, including juveniles with little or no experience foraging, showed evidence of learning. Eighty-two per cent of experimental bats and 27% of control bats directed feeding buzzes (echolocation calls associated with prey capture) at the mealworm. Furthermore, seven experimental bats (64%) showed evidence of learning by attacking and/or capturing the mealworm, while no bat in the control group attacked or captured the prey. Analyses of high-speed stereo video recordings revealed increased interaction with demonstrators among bats attacking or capturing the mealworm. At the time they displayed evidence of learning, bats flew closer together during feeding buzzes than during other portions of trials. Our results demonstrate that social interaction with experienced bats, and listening to feeding buzzes in particular, may play an integral role in development of foraging skills in bats.     

Distress Calls of a Fast-Flying Bat (Molossus molossus) Provoke Inspection Flights but Not Cooperative Mobbing

Many birds and mammals produce distress calls when captured. Bats often approach speakers playing conspecific distress calls, which has led to the hypothesis that bat distress calls promote cooperative mobbing. An alternative explanation is that approaching bats are selfishly assessing predation risk. Previous playback studies on bat distress calls involved species with highly maneuverable flight, capable of making close passes and tight circles around speakers, which can look like mobbing. We broadcast distress calls recorded from the velvety free-tailed bat, Molossus molossus, a fast-flying aerial-hawker with relatively poor maneuverability. Based on their flight behavior, we predicted that, in response to distress call playbacks, M. molossus would make individual passing inspection flights but would not approach in groups or approach within a meter of the distress call source. By recording responses via ultrasonic recording and infrared video, we found that M. molossus, and to a lesser extent Saccopteryx bilineata, made more flight passes during distress call playbacks compared to noise. However, only the more maneuverable S. bilineata made close approaches to the speaker, and we found no evidence of mobbing in groups. Instead, our findings are consistent with the hypothesis that single bats approached distress calls simply to investigate the situation. These results suggest that approaches by bats to distress calls should not suffice as clear evidence for mobbing.     

IDENTIFICATION OF BRITISH BAT SPECIES BY MULTIVARIATE ANALYSIS OF ECHOLOCATION CALL PARAMETERS

ABSTRACT

Previous studies have found variability and individual distinctiveness in the echolocation calls of bats. We consider two implications of individually distinct echolocation calls: 1) whether bats may be able to use such variation to recognise familiar conspecifics, and 2) whether investigators could use such variation to identify known individuals or to census populations. We compared the discriminability of the echolocation calls of big brown bats (Eptesicus fuscus) recorded in three situations: (a) while held in the hand, (b) while perched on a platform, and (c) while flying in an anechoic chamber. Using variables describing each sonar call, we employed discriminant function analysis (DFA) to assign calls to recording situation or to bat. Discrimination of calls by recording situation was largely unsuccessful, although flying calls could be distinguished from platform calls. Assignment of calls to individual bat across recording situations yielded 72% success, and, within a given recording situation, yielded 87% success. Stepwise DFA reduced the number of variables needed to discriminate between individuals with only a slight decrease in correct classification. These results suggest that bats (or researchers) may be able to use the information contained in the echolocation calls for individual recognition. Individual distinctiveness raises the possibility of censusing bats by sound. We used cluster analysis in an attempt to determine whether, given a sample of calls from an unknown number of bats, a reasonable estimate of the number of bats could be obtained. Results were unsatisfactory, suggesting that cluster analysis probably will not permit acoustic censusing of bats in the field.     

Female big brown bats, Eptesicus fuscus, recognize sex from a caller's echolocation signals

The echolocation calls of bats function in prey capture and navigation but are not commonly regarded as communicative signals. However, because bats' echolocation calls show patterns of variability, they may transmit information about a bat, such as its age, individual identity or sex. For echolocation calls to function in this manner, variation in calls must be reliably linked to the characteristics of the bat, as has been shown in a number of studies. However, few studies have asked whether bats respond to this variation. We tested whether female big brown bats can identify the sex of an unfamiliar bat from playbacks of its echolocation calls. Playback consisted of a 30-s preplayback period, a 60-s playback period of either male or female echolocation calls, and a 30-s postplayback period. In the playback and postplayback periods the vocalization rates of female bats changed significantly relative to the preplayback period depending on the sex of the playback stimulus, indicating that they could determine sex from the echolocation calls. These findings support the possibility that echolocation calls play a role in communication in big brown bats.     

Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation

Expanding digital data sources, including social media, online news articles and blogs, provide an opportunity to understand better the context and intensity of human-nature interactions, such as wildlife exploitation. However, online searches encompassing large taxonomic groups can generate vast datasets, which can be overwhelming to filter for relevant content without the use of automated tools. The variety of machine learning models available to researchers, and the need for manually labelled training data with an even balance of labels, can make applying these tools challenging. Here, we implement and evaluate a hierarchical text classification pipeline which brings together three binary classification tasks with increasingly specific relevancy criteria. Crucially, the hierarchical approach facilitates the filtering and structuring of a large dataset, of which relevant sources make up a small proportion. Using this pipeline, we also investigate how the accuracy with which text classifiers identify relevant and irrelevant texts is influenced by the use of different models, training datasets, and the classification task. To evaluate our methods, we collected data from Facebook, Twitter, Google and Bing search engines, with the aim of identifying sources documenting the hunting and persecution of bats (Chiroptera). Overall, the ‘state-of-the-art’ transformer-based models were able to identify relevant texts with an average accuracy of 90%, with some classifiers achieving accuracy of >95%. Whilst this demonstrates that application of more advanced models can lead to improved accuracy, comparable performance was achieved by simpler models when applied to longer documents and less ambiguous classification tasks. Hence, the benefits from using more computationally expensive models are dependent on the classification context. We also found that stratification of training data, according to the presence of key search terms, improved classification accuracy for less frequent topics within datasets, and therefore improves the applicability of classifiers to future data collection. Overall, whilst our findings reinforce the usefulness of automated tools for facilitating online analyses in conservation and ecology, they also highlight that the effectiveness and appropriateness of such tools is determined by the nature and volume of data collected, the complexity of the classification task, and the computational resources available to researchers.     

Operator bias in software‐aided bat call identification

Software-aided identification facilitates the handling of large sets of bat call recordings, which is particularly useful in extensive acoustic surveys with several collaborators. Species lists are generated by “objective” automated classification. Subsequent validation consists of removing any species not believed to be present. So far, very little is known about the identification bias introduced by individual validation of operators with varying degrees of experience. Effects on the quality of the resulting data may be considerable, especially for bat species that are difficult to identify acoustically. Using the batcorder system as an example, we compared validation results from 21 volunteer operators with 1–26 years of experience of working on bats. All of them validated identical recordings of bats from eastern Austria. The final outcomes were individual validated lists of plausible species. A questionnaire was used to enquire about individual experience and validation procedures. In the course of species validation, the operators reduced the software''s estimate of species richness. The most experienced operators accepted the smallest percentage of species from the software''s output and validated conservatively with low interoperator variability. Operators with intermediate experience accepted the largest percentage, with larger variability. Sixty-six percent of the operators, mainly with intermediate and low levels of experience, reintroduced species to their validated lists which had been identified by the automated classification, but were finally excluded from the unvalidated lists. These were, in many cases, rare and infrequently recorded species. The average dissimilarity of the validated species lists dropped with increasing numbers of recordings, tending toward a level of ˜20%. Our results suggest that the operators succeeded in removing false positives and that they detected species that had been wrongly excluded during automated classification. Thus, manual validation of the software''s unvalidated output is indispensable for reasonable results. However, although application seems easy, software-aided bat call identification requires an advanced level of operator experience. Identification bias during validation is a major issue, particularly in studies with more than one participant. Measures should be taken to standardize the validation process and harmonize the results of different operators.     

Low-cost acoustic design of a bat test room

Experiments with captive bats need a flight room that is acoustically neutral, especially when recording and analysing bat calls or the response of bats to certain sound stimuli. Our aim was to identify an isolation material with the best quality–price relationship to acoustically coat such a flight room. For this, we built a flight room divided into two compartments that were to be acoustically isolated from one another. Audible and infrasonic waves are difficult to attenuate with low-cost materials but the attenuation of ultrasounds is rather straightforward. We evaluated the absorbing capacities of different low-cost materials – felt fabric, polystyrene, egg boxes, egg boxes coated with felt fabric, absorbing pyramidal foams, polyurethane foams and cork. The material that showed the best quality–price relationship was the polyurethane foam of open cells (5 cm thickness), which was able to attenuate approximately 20 dB at ultrasonic frequencies.     

Uncertainty and ignored information in the analysis of bat ultrasound: Bayesian approximation to the rescue

Bat ultrasound analysis has been around for several decades and it is one of the most important tools in studies of bat ecology. Discrimination between species is based on intra-specific features of echolocation calls. Identification of species and genera in audio files can be attempted either manually or through software which performs a fully automated discrimination between species. However, significant overlap in various features (e.g. frequencies of calls) exists between species and even genera. Species ID is therefore often not an absolute conclusion, but rather an opinion or best guess, as opposed to DNA tests or measurements on external characters of captured bats. To make things even worse, the probability of actually observing a bat of a given species in space and time is ignored when performing bat ultrasound analysis. This study introduces Bayesian approximation through a new method we have named Alternative Bayesian Bat Analysis (ABBA). We show, through a simple proof-of-concept example, the importance of adding information about the local composition of the bat community, hence making informed decisions regarding which species is most likely present in audio files. The superior performance of ABBA is also shown through an example using R code. Here, we use simulated data for three Pipistrellus spp., a genus with significant overlap in frequencies, but the code can easily be adapted to other bat species and genera worldwide. ABBA outperformed the non-Bayesian approach for all three species. The rare species in the simulated data set was super-inflated when using the non-Bayesian method. Further the results show, contrarily to common belief, that the frequency dominated by a given species in a data set, depends on the composition of the bat fauna and not just means and SDs reported in the literature. ABBA allows researchers to include all observations in statistical modeling, rather than excluding observations, an approach which can affect the reliability of studies. This study also, to a great extent, explains the poor performance of software attempting automated bat ID. Implementing Bayesian algorithms, and thereby allowing users to interact with the software, should significantly improve their performance.     

Brevity is prevalent in bat short-range communication

Animal communication follows many coding schemes. Less is known about the coding strategy for signal length and rates of use in animal vocal communication. A generalized brevity (negative relation between signal length and frequency of use) is innovatively explored but remains controversial in animal vocal communication. We tested brevity for short-range social and distress sounds from four echolocating bats: adult black-bearded tomb bat Taphozous melanopogon, Mexican free-tailed bat Tadarida brasiliensis, adult greater horseshoe bat Rhinolophus ferrumequinum, and adult least horseshoe bat Rhinolophus pusillus. There was a negative association between duration and number of social but not distress calls emitted. The most frequently emitted social calls were brief, while most distress calls were long. Brevity or lengthiness was consistently selected in vocal communications for each species. Echolocating bats seem to have convergent coding strategy for communication calls. The results provide the evidence of efficient coding in bat social vocalizations, and lay the basis of future researches on the convergence for neural control on bats’ communication calls.     

Acoustic identification of eight species of bat (mammalia: chiroptera) inhabiting forests of southern hokkaido, Japan: potential for conservation monitoring

Assessing the impact of forest management on bat communities requires a reliable method for measuring patterns of habitat use by individual species. A measure of activity can be obtained by monitoring echolocation calls, but identification of species is not always straightforward. We assess the feasibility of using analysis of time-expanded echolocation calls to identify free-flying bats in the Tomakomai Experimental Forest of Hokkaido University, Hokkaido, northern Japan. Echolocation calls of eight bat species were recorded in one or more of three conditions: from hand-released individuals, from bats flying in a confined space and from bats emerging from their roost. Sonograms of 171 calls from 8 bat species were analyzed. These calls could be categorized into 3 types according to their structure: FM/CF/FM type (Rhinolophus ferrumequinum), FM types (Murina leucogaster, Murina ussuriensis, Myotis macrodactylus and Myotis ikonnikovi) and FM/QCF types (Eptesicus nilssonii, Vespertilio superans and Nyctalus aviator). Sonograms of the calls of R. ferrumequinum could easily be distinguished from those of all other species by eye. For the remaining calls, seven parameters (measures of frequency, duration and inter-call interval) were examined using discriminant function analysis, and 92% of calls were correctly classified to species. For each species, at least 80% of calls were correctly classified. We conclude that analysis of echolocation calls is a viable method for distinguishing between species of bats in the Tomakomai Experimental Forest, and that this approach could be applied to examine species differences in patterns of habitat-use within the forest.     

Female greater wax moths reduce sexual display behavior in relation to the potential risk of predation by echolocating bats

Female greater wax moths Galleria mellonella display by wing fanning in response to bursts of ultrasonic calls produced bymales. The temporal and spectral characteristics of these callsshow some similarities with the echolocation calls of batsthat emit frequency-modulated (FM) signals. Female G. mellonellatherefore need to distinguish between the attractive signalsof male conspecifics, which may lead to mating opportunities,and similar sounds made by predatory bats. We therefore predictedthat (1) females would display in response to playbacks of male calls; (2) females would not display in response to playbacksof the calls of echolocating bats (we used the calls of Daubenton'sbat Myotis daubentonii as representative of a typical FM echolocatingbat); and (3) when presented with male calls and bat callsduring the same time block, females would display more whenperceived predation risk was lower. We manipulated predationrisk in two ways. First, we varied the intensity of bat callsto represent a nearby (high risk) or distant (low risk) bat.Second, we played back calls of bats searching for prey (lowrisk) and attacking prey (high risk). All predictions weresupported, suggesting that female G. mellonella are able todistinguish conspecific male mating calls from bat calls, andthat they modify display rate in relation to predation risk.The mechanism (s) by which the moths separate the calls ofbat and moth must involve temporal cues. Bat and moth signalsdiffer considerably in duration, and differences in durationcould be encoded by the moth's nervous system and used in discrimination.     

Automated classification of bat echolocation call recordings with artificial intelligence

Acoustic recorders are commonly used to remotely monitor and collect data on bats (Order Chiroptera). These efforts result in many acoustic recordings that must be classified by a bat biologist with expertise in call classification in order to obtain useful information. The rarity of this expertise and time constraints have prompted efforts to automatically classify bat species in acoustic recordings using a variety of learning methods. There are several software programs available for this purpose, but they are imperfect and the United States Fish and Wildlife Service often recommends that a qualified acoustic analyst review bat call identifications even if using these software programs. We sought to build a model to classify bat species using modern computer vision techniques. We used images of bat echolocation calls (i.e., plots of the pulses) to train deep learning computer vision models that automatically classify bat calls to species. Our model classifies 10 species, five of which are protected under the Endangered Species Act. We evaluated our models using standard model validation procedures, and performed two external tests. For these tests, an entire dataset was withheld from the procedure before splitting the data into training and validation sets. We found that our validation accuracy (92%) and testing accuracy (90%) were higher than when we used Kaleidoscope Pro and BCID software (65% and 61% accuracy, respectively) to evaluate the same calls. Our results suggest that our approach is effective at classifying bat species from acoustic recordings, and our trained model will be incorporated into new bat call identification software: WEST-EchoVision.     

Behavioural Response of Bats to Perceived Predation Risk While Foraging

The ability to detect and respond to predation risk while foraging may have important fitness consequences for prey organisms. Anti‐predator behaviours may reduce the probability of mortality because of predation, but they may also be associated with reduced foraging efficiency. Several behaviours of bats have been suggested to serve as anti‐predator responses, and there is evidence that predation, particularly by avian predators such as owls, may be an important cause of bat mortality. Previous studies have attempted to determine whether predator presence affects bat behaviour when emerging from roost sites, but few have examined effects of predator presence on bat behaviour while foraging. In this study, we investigated whether foraging bats respond to predator cues by presenting bats with an acoustic cue simulating the presence of an owl. Within matched trials, which were conducted at different locations each of 18 nights, significantly fewer bat detections were recorded at owl playback stations than at control stations (no auditory cue), suggesting an avoidance response by bats. An acoustic control (i.e. station playing woodpecker calls), however, did not have significantly more detections than the stations playing the owl calls, suggesting that bats may simply be avoiding noise and more detailed investigation is warranted. Although evidence for owl predation on bats is minimal in North America, results of this study may indicate that the perceived presence of owls may represent a factor influencing the behaviour of bats while foraging.     

Social calls used by a leaf-roosting bat to signal location

Social calls in bats have many functions, including mate attraction and maintaining contact during flight. Research suggests that social calls may also be used to transfer information about roosts, but no studies have yet demonstrated that calls are used to actively attract conspecifics to roosting locations. We document the social calls used by Spix''s disc-winged bat (Thyroptera tricolor) to actively recruit group members to roosts. In acoustic trials, we recorded two sets of calls; one from flying individuals termed ‘inquiry calls’, and another from roosting bats termed ‘response calls’. Inquiry calls were emitted by flying bats immediately upon release, and quickly (i.e. 178 ms) elicited production of response calls from roosting individuals. Most flying bats entered the roost when roosting individuals responded, while few bats entered the roost in the absence of a response. We argue that information transfer concerning roost location may facilitate sociality in T. tricolor, given the ephemeral nature of roosting structures used by this species.