Geographical variation in the high-duty cycle echolocation of the cryptic common mustached bat Pteronotus cf. rubiginosus (Mormoopidae)

The use of bioacoustics as a tool for bat research is rapidly increasing worldwide. There is substantial evidence that environmental factors such as weather conditions or habitat structure can affect echolocation call structure in bats and thus compromise proper species identification. However, intraspecific differences in echolocation due to geographical variation are poorly understood, which poses a number of issues in terms of method standardization. We examined acoustic data for Pteronotus cf. rubiginosus from the Central Amazon and the Guiana Shield. We provide the first evidence of intraspecific geographic variation in bat echolocation in the Neotropics, with calls significantly differing in almost all standard acoustic parameters for the two lineages of this clade. We complement our bioacoustic data with molecular and morphological data for both species. Considerable overlap in trait values prevents reliable discrimination between the two sympatric Pteronotus based on morphological characters. On the other hand, significant divergence in the frequency of maximum energy suggests that bioacoustics can be used to readily separate both taxa despite extensive intraspecific variability in their echolocation across the Amazon. Given the relative lack of barriers preventing contact between bat populations from the Central Amazon and French Guiana, the documented acoustic variation needs to be further studied in geographically intermediate locations to understand the potential isolation processes that could be causing the described divergence in echolocation and to determine whether this variation is either discrete or continuous.     

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.     

Social transmission of novel foraging behavior in bats: frog calls and their referents

The fringe-lipped bat, Trachops cirrhosus, uses prey-emitted acoustic cues (frog calls) to assess prey palatability . Previous experiments show that wild T. cirrhosus brought into the laboratory are flexible in their ability to reverse the associations they form between prey cues and prey quality . Here we asked how this flexibility can be achieved in nature. We quantified the rate at which bats learned to associate the calls of a poisonous toad species with palatable prey by placing bats in three groups: (a) social learning, in which a bat inexperienced with the novel association was allowed to observe an experienced bat; (b) social facilitation, in which two inexperienced bats were presented with the experimental task together; and (c) trial-and-error, in which a single inexperienced bat was presented with the experimental task alone. In the social-learning group, bats rapidly acquired the novel association in an average of 5.3 trials. In the social-facilitation and trial-and-error groups, most bats did not approach the call of the poisonous species after 100 trials. Thus, once acquired, novel associations between prey cue and prey quality could spread rapidly through the bat population by cultural transmission. This is the first case to document predator social learning of an acoustic prey cue.     

The use of automated identification of bat echolocation calls in acoustic monitoring: A cautionary note for a sound analysis

Bats are a species-rich order of mammals providing key ecosystem services. Because bats are threatened by human action and also serve as important bioindicators, monitoring their populations is of utmost importance. However, surveying bats is difficult because of their nocturnal habits, elusiveness and sensitivity to disturbance. Bat detectors allow echolocating bats to be surveyed non-invasively and record species that would otherwise be difficult to observe by capture or roost inspection. Unfortunately, several bat species cannot be identified confidently from their calls so acoustic classification remains ambiguous or impossible in some cases.The popularity of automated classifiers of bat echolocation calls has escalated rapidly, including that of several packages available on purchase. Such products have filled a vacant niche on the market mostly in relation to the expanding monitoring efforts related to the development of wind energy production worldwide.We highlight that no classifier has yet proven capable of providing correct classifications in 100% of cases or getting close enough to this ideal performance. Besides, from the literature available and our own experience we argue that such tools have not yet been tested sufficiently in the field. Visual inspection of calls whose automated classification is judged suspicious is often recommended, but human intervention a posteriori represents a circular argument and requires noticeable experience.We are concerned that neophytes – including consultants with little experience with bats but specialized into other taxonomical groups – will accept passively automated responses of tools still awaiting sufficient validation. We remark that bat call identification is a serious practical issue because biases in the assessment of bat distribution or habitat preferences may lead to wrong management decisions with serious conservation consequences. Automated classifiers may crucially aid bat research and certainly merit further investigations but the boost in commercially available software may have come too early. Thorough field tests need to be carried out to assess limitations and strengths of these tools.     

Weather conditions determine attenuation and speed of sound: Environmental limitations for monitoring and analyzing bat echolocation

Echolocating bats are regularly studied to investigate auditory‐guided behaviors 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 analyzed call parameters, limiting our ability to detect, and correctly analyze 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 nonlinear 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 nonlinear 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 localization methods of bats. The frequency‐ and weather‐dependent variation in atmospheric attenuation has a threefold 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.     

澳门翼手目物种多样性补充调查及其保护现状

<正>翼手目为哺乳动物中的第二大目,中国的翼手目多样性也在不断更新,Smith和解焱(2009)统计中国翼手目118种,蒋志刚等(2015)则认为中国翼手目有7科34属134种。广东省翼手目有6科24属55种(邹发生和叶冠锋,2016),此后也仍陆续有新纪录,如长指鼠耳蝠(Myotis longipes)(张琴等,2017)、卡氏伏翼(Hypsugo cadornae)(Xie et al., 2021);我们前期对澳门翼手目的调查,发现5科8属10种(黄继展等,2013)。     

Echolocating bats cry out loud to detect their prey

Echolocating bats have successfully exploited a broad range of habitats and prey. Much research has demonstrated how time-frequency structure of echolocation calls of different species is adapted to acoustic constraints of habitats and foraging behaviors. However, the intensity of bat calls has been largely neglected although intensity is a key factor determining echolocation range and interactions with other bats and prey. Differences in detection range, in turn, are thought to constitute a mechanism promoting resource partitioning among bats, which might be particularly important for the species-rich bat assemblages in the tropics. Here we present data on emitted intensities for 11 species from 5 families of insectivorous bats from Panamá hunting in open or background cluttered space or over water. We recorded all bats in their natural habitat in the field using a multi-microphone array coupled with photographic methods to assess the bats' position in space to estimate emitted call intensities. All species emitted intense search signals. Output intensity was reduced when closing in on background by 4-7 dB per halving of distance. Source levels of open space and edge space foragers (Emballonuridae, Mormoopidae, Molossidae, and Vespertilionidae) ranged between 122-134 dB SPL. The two Noctilionidae species hunting over water emitted the loudest signals recorded so far for any bat with average source levels of ca. 137 dB SPL and maximum levels above 140 dB SPL. In spite of this ten-fold variation in emitted intensity, estimates indicated, surprisingly, that detection distances for prey varied far less; bats emitting the highest intensities also emitted the highest frequencies, which are severely attenuated in air. Thus, our results suggest that bats within a local assemblage compensate for frequency dependent attenuation by adjusting the emitted intensity to achieve comparable detection distances for prey across species. We conclude that for bats with similar hunting habits, prey detection range represents a unifying constraint on the emitted intensity largely independent of call shape, body size, and close phylogenetic relationships.     

蝙蝠回声定位声波的可塑性及其生态适应

蝙蝠通过调节回声定位声波特征来满足自身的感官需求,表现出回声定位声波的可塑性及其对生态环境与需求的适应。声波频率、强度、脉冲持续时间和间隔时间等特征与蝙蝠所处的生态位密切相关,声波可塑性在蝙蝠进化过程中起着至关重要的作用。本文结合马铁菊头蝠(Rhinolophus ferrumequinum)和大趾鼠耳蝠(Myotis macrodactylus)回声定位声波可塑性的研究,从回声定位声波的方向性、目标距离、环境复杂度和应对干扰4个方面总结了蝙蝠如何通过改变回声定位声波特征来满足自身在导航和捕捉猎物过程中的感官需求与生态适应,并阐述了回声定位声波可塑性的研究现状,为开展蝙蝠声学和行为学研究提供参考。     

Heard but not seen: Comparing bat assemblages and study methods in a mosaic landscape in the Western Ghats of India

We used capture (mist‐netting) and acoustic methods to compare the species richness, abundance, and composition of a bat assemblage in different habitats in the Western Ghats of India. In the tropics, catching bats has been more commonly used as a survey method than acoustic recordings. In our study, acoustic methods based on recording echolocation calls detected greater bat activity and more species than mist‐netting. However, some species were detected more frequently or exclusively by capture. Ideally, the two methods should be used together to compensate for the biases in each. Using combined capture and acoustic data, we found that protected forests, forest fragments, and shade coffee plantations hosted similar and diverse species assemblages, although some species were recorded more frequently in protected forests. Tea plantations contained very few species from the overall bat assemblage. In riparian habitats, a strip of forested habitat on the river bank improved the habitat for bats compared to rivers with tea planted up to each bank. Our results show that shade coffee plantations are better bat habitat than tea plantations in biodiversity hotspots. However, if tea is to be the dominant land use, forest fragments and riparian corridors can improve the landscape considerably for bats. We encourage coffee growers to retain traditional plantations with mature native trees, rather than reverting to sun grown coffee or coffee shaded by a few species of timber trees.     

‘Compromise’ in Echolocation Calls between Different Colonies of the Intermediate Leaf-Nosed Bat (Hipposideros larvatus)

Each animal population has its own acoustic signature which facilitates identification, communication and reproduction. The sonar signals of bats can convey social information, such as species identity and contextual information. The goal of this study was to determine whether bats adjust their echolocation call structures to mutually recognize and communicate when they encounter the bats from different colonies. We used the intermediate leaf-nosed bats (Hipposideros larvatus) as a case study to investigate the variations of echolocation calls when bats from one colony were introduced singly into the home cage of a new colony or two bats from different colonies were cohabitated together for one month. Our experiments showed that the single bat individual altered its peak frequency of echolocation calls to approach the call of new colony members and two bats from different colonies adjusted their call frequencies toward each other to a similar frequency after being chronically cohabitated. These results indicate that the ‘compromise’ in echolocation calls might be used to ensure effective mutual communication among bats.     

Effectiveness of acoustic lures for increasing tropical forest understory bat captures

Bats are the most diverse mammalian order second to rodents, with 1400+ species globally. In the tropics, it is possible to find more than 60 bat species at a single site. However, monitoring bats is challenging due to their small size, ability to fly, cryptic nature, and nocturnal activity. Recently, bioacoustic techniques have been incorporated into survey methods, either through passive acoustic monitoring or acoustic bat lures. Lures have been developed on the premise that broadcasting acoustic stimuli increases the number of captures in harp traps or mist nets. However, this is a relatively new, niche method. This study tested the efficacy of two commonly used acoustic bat lure devices, broadcasting two different acoustic stimuli, to increase forest understory bat captures in the tropics. This is the first time an acoustic bat lure has been systematically tested in a tropical rainforest, and the first study to compare two lure devices (Sussex AutoBat and Apodemus BatLure). Using a paired experimental design, two synthesized acoustic stimuli were broadcasted, a feeding call and a social call, to understand the importance of the call type used on capture rates and genus‐specific responses. Using an acoustic lure significantly increased capture rates, while the type of device did not impact capture rates. The two acoustic stimuli had an almost even distribution of captures, suggesting that the type of call may be less important than previously thought. Results indicate a possible deterrent effect on Rhinolophous sp., while being particularly effective for attracting bats in the genera Murina and Kerivoula. This study highlights the effectiveness of lures, however, also indicates that lure effects can vary across genera. Therefore, lures may bias survey results by altering the species composition of bats caught. Future research should focus on a single species or genus, using synthesized calls of conspecifics, to fully understand the effect of lures.     

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.     

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.     

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.     

Effectiveness of an acoustic lure for surveying bats in British woodlands

1. A field experiment was used to test the effectiveness of a synthesized bat call as an acoustic lure to attract bats into mist nets in woodlands in southeast England. The stimulus was modelled on a social call of the rare Bechstein's bat Myotis bechsteinii. 2. In the Test condition, when the synthesized call was played, 23 bats of four species were captured, including six Bechstein's bats. In the Control condition, when no calls were played, only one bat was caught. 3. The bat call synthesizer is an effective tool for increasing capture rates for bats. Used as part of a systematic survey programme, it has the potential to provide the first baseline data on the distribution of bats in British woodlands.     

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.     

菲菊头蝠回声定位声波频率的性二态有利于性别识别

许多动物的叫声频率呈现性二态现象。蝙蝠夜间活动,主要利用声音信号导航空间、追踪猎物、传递交流信息。本研究选择成体菲菊头蝠作为研究对象,检验回声定位声波频率性二态是否有利于性别识别。研究发现,菲菊头蝠回声定位声波频率参数具有显著性别差异。播放白噪音、雄性回声定位声波及雌性回声定位声波期间,实验个体的反应叫声数量依次递减。播放白噪音、雌性回声定位声波及雄性回声定位声波后,实验个体的反应叫声数量依次递增。白噪音诱导反应叫声强度高于回声定位声波诱导反应叫声强度。研究结果表明,菲菊头蝠回声定位声波的频率参数编码发声者性别信息,有利于种群内部的性别识别。本研究暗示,回声定位声波可能在蝙蝠配偶选择中扮演一定作用。     

Bat Avoidance in Non-Aerial Insects: The Silence Response of Signaling Males in an Acoustic Moth

While the evasive responses of many flying acoustic insects to aerial‐hawking bats are duly recognized and studied, the responses of non‐aerial insects to gleaning bats are generally overlooked. It has been assumed that acoustic insects are deaf to these predators because gleaning bat echolocation calls are typically low in amplitude, brief (1–3 ms) and very high in frequency (>60 kHz). We tested this assumption in a series of playback experiments with a moth (Achroia grisella) that uses hearing in both predator evasion and mating. We report that ultrasound pulses ≥78 dB peSPL (peak equivalent sound pressure level) and ≥1 ms in duration inhibit stationary males from broadcasting their own ultrasonic advertisement calls, provided that the pulsed stimuli are delivered at a repetition rate ≤30/s. Further analyses suggest that inhibition by pulsed ultrasound comprises two processes performed serially. First, a startle response with a latency <50 ms is elicited by a single pulse ≥1 ms duration. Here, a male misses broadcasting several calls over a 50–100 ms interval. Secondly, the startle may be extended as a silence response lasting several to many seconds if subsequent pulses occur at a rate ≤30/s. Call inhibition cannot represent a simple response to acoustic power because of the inverse interaction between pulse duration and rate. On the other hand, the temporal and energy characteristics of inhibitory stimuli match those of gleaning bat echolocation calls, and we infer that inhibition is a specialized defensive behavior by which calling males may avoid detection by eavesdropping bats.