Journal of Threatened Taxa | www.threatenedtaxa.org | 26 November 2025 | 17(11): 27897–27931

 

ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print) 

https://doi.org/10.11609/jott.9550.17.11.27897-27931

#9550 | Received 11 December 2024 | Final received 20 October 2025 | Finally accepted 22 October 2025

 

 

Bat echolocation in South Asia

 

Aditya Srinivasulu 1 , Chelmala Srinivasulu 2 , Bhargavi Srinivasulu 3 , Deepa Senapathi 4 

& Manuela González-Suárez 5

 

1,5 Ecology and Evolutionary Biology, School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6UR, United Kingdom.

2,3 Wildlife Biology & Taxonomy Lab, Department of Zoology, University College of Science, Osmania University, Hyderabad, Telangana 500007, India.

2 Centre for Biodiversity and Conservation Studies, Osmania University, Hyderabad, Telangana 500007, India.

3 Systematics, Ecology and Conservation Laboratory, Zoo Outreach Organisation, 3A2 Varadarajulu Nagar, FCI Road, Coimbatore, Tamil Nadu 641006, India.

4 Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AH, United Kingdom.

1 a.chelmala1@gmail.com, 2 chelmala.srinivasulu@osmania.ac.in (corresponding author), 3 bharisrini@gmail.com, 4 g.d.senapathi@reading.ac.uk, 5 manuela.gonzalez@reading.ac.uk

 

 

 

Editor: H. Raghuram, Sri S. Ramasamy Naidu Memorial College, Sattur, India.       Date of publication: 26 November 2025 (online & print)

 

Citation: Srinivasulu, A., C. Srinivasulu, B. Srinivasulu, D. Senapathi & M. González-Suárez (2025). Bat echolocation in South Asia. Journal of Threatened Taxa 17(11): 27897–27931. https://doi.org/10.11609/jott.9550.17.11.27897-27931

  

Copyright: © Srinivasulu et al. 2025. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and distribution of this article in any medium by providing adequate credit to the author(s) and the source of publication.

 

Funding: University of Reading International PhD Studentship (GS21-026).

 

Competing interests: The authors declare no competing interests.

 

Author details: Aditya Srinivasulu is a research affiliate at the Deccan Regional Station of Zoo Outreach Organisation, India. He is interested in understanding factors driving habitat suitability, extinction risk, and biodiversity loss in the Global South. His work explores conservation knowledge shortfalls, bat and herpetofauna macroecology, extinction risk, and the climate and land-use futures of South Asian vertebrates. Chelmala Srinivasulu is a professor of Zoology at Osmania University, Hyderabad, where he heads the Wildlife Biology and Taxonomy Lab and directs the Centre for Biodiversity and Conservation Studies. He researches biodiversity conservation, systematics, and taxonomy of mammals, reptiles, and birds, as well as climate-change modelling. Bhargavi Srinivasulu is a senior scientist at the Deccan Regional Station of Zoo Outreach Organisation, India. Her research focuses on bat systematics, phylogenetics, and conservation, and herpetofauna macroecology. Deepa Senapathi is a professor of Research Ecology and the University of Reading. Her research focuses on the impacts on environmental change on biodiversity and ecosystems particularly the impacts of climate change and land-use change on avian populations and insect pollinator communities. She works with policy makers, non-government organisations, delivery agencies and land managers to prioritise biodiversity and ecosystem services. Manuela González-Suárez is an associate professor in Ecological Modelling at the University of Reading. She is interested in characterising the main drivers of biodiversity loss, develop methods to anticipate change, and propose effective strategies to halt it. Most of her work covers large (continental and global) geographical scales, focusing on vertebrate species, particularly mammals and birds.

 

Author contributions: AS led the conceptualisation, methodology, investigation, formal analysis, data curation, visualisation, writing, and funding acquisition for the study. CS and BS contributed to conceptualisation, investigation, data curation, and writing. DS contributed to methodology, validation, writing, and supervision. MGS contributed to conceptualisation, methodology, formal analysis, visualisation, writing, and funding acquisition.

 

Acknowledgements: AS was funded by a University of Reading (UK) International Research PhD Studentship [GS21-026]. AS, MGS and DS thank the University of Reading (UK) for their research facilities and support. CS and BS thank Osmania University, Hyderabad (India) for the necessary facilities. BS acknowledges research funding from UGC, New Delhi. CS acknowledges research support for field studies from DST-SERB, UGC-UKIERI, DST-UKIERI, ANRF and MoE-RUSA, Govt. of India. We also thank our various collaborators, colleagues, and friends for sharing their information, resources, and data, and we are grateful for their support in this work.

 

 

Abstract: The study of echolocation traits can assist in developing robust tools for the detection and monitoring of bats. The advent of non-invasive and passive acoustic monitoring techniques has increased the availability of echolocation data including in highly diverse regions, such as South Asia, where 145 of the 155 extant bat species are known to use laryngeal, nasal, or lingual echolocation. However, information remains disperse with no existing review of the state of echolocation knowledge in this region. Here we present a review that collates and catalogues echolocation data to facilitate access and reveal general patterns and knowledge gaps. We conducted a systematic review that returned 35 peer-reviewed publications containing echolocation data to which we added ~6,000 unpublished recordings from various collections (including the open-source ChiroVox database). We created a foundational database reporting on six standard echolocation functional traits to be used in identification. The dataset provides data for ~60% (n = 86) of the echolocating bat species in South Asia, with 299 distinct observations (unique combinations of recording techniques, equipment, and conditions for a given species). Mapping data locations we describe spatial biases and propose priority regions for future work in areas where species richness is high, but echolocation knowledge is limited or completely absent. These priority regions largely fell within the Western Ghats and Eastern Ghats of India, northeastern India, and Sri Lanka, with smaller clusters in peninsular, western, and eastern India. Our review offers a first assessment and a ready-to-use echolocation dataset for bats in South Asia. We hope this motivates an appraisal of functional trait data collection in diverse and data-poor regions and facilitates future research.

 

Keywords: Acoustic monitoring, biodiversity hotspots, functional traits, knowledge gaps, research priorities, species identification.

 

 

Introduction

 

South Asia is a large subcontinent comprising Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka, sometimes referred to as the Indian subcontinent. Spanning approximately five million square kilometres, it is bounded by the Himalaya, Hindukush, and Dhaula Dhar mountains to the north, and the Arabian Sea, Lakshadweep Sea, Bay of Bengal, and Indian Ocean to the south. It supports a wide diversity of bats, with 155 species currently described (Srinivasulu et al. 2025). Bats in this region can be found across a wide range of habitats and locations with some species distributed across the entire subcontinent, while others are restricted to single localities (Bates & Harrison 1997; Srinivasulu & Srinivasulu 2012). Owing to their status as ecological indicators and contributors of essential ecosystem services (Jones et al. 2009; Stathopoulos et al. 2018), it is important to understand the diversity, distribution, and traits of bats, and harness suitable tools and methods to effectively monitor changes that can inform management and conservation action.

A widely used approach to detect and monitor bats is based on listening to and analysing echolocation calls (Kunz & Parsons 2009; Fraser et al. 2020; Russ 2021). Echolocation is used for communication and navigation by many taxa including bats, and bat echolocation is unique in its highly complex and diverse design which allows it to not only be used to recognise and identify taxa, but also to understand their ecological function and diversity (Kunz & Parsons 2009; Stathopoulos et al. 2018). Using complex nasal, laryngeal, or facial structures in combination with highly specialised ear neuroanatomy, echolocating bats are able to perceive their environment in a high level of detail, including the range, direction, size, texture, and in many cases the type of objects in their surroundings, especially at night when vision is less effective (Jones 2005; Sulser et al. 2022). Echolocating behaviour has allowed some species to evolve more complex flight patterns, varied diets, and highly specialised ecological interactions, and varies between species to a high degree. Different types of echolocation calls have very different impacts on the flight behaviour and dietary choices of species, which also in turn have impacts on the evolution of their echolocation (Jones 2005). For instance, narrowband echolocation tends to favour longer calls and the detection of targets, while broadband echolocation favours shorter calls, and the localisation of targets in space. Traits describing echolocation (in terms of frequency, call shape, inter-pulse interval, number of passes, duration) have been used in the past to classify taxa and understand their habitat use, diet, and niche breadth, as each taxon has a unique combination of these traits, which often also vary over the taxon’s distribution.

In South Asia, 145 bat species are known to use either mouth-emitted or nose-emitted laryngeal echolocation, or click-based lingual echolocation (Bates & Harrison 1997; Srinivasulu & Srinivasulu 2012). Several studies have separately collected, described, and classified echolocation calls from various species in South Asia (most recently including Chakravarty et al. 2020; Raman et al. 2020; Raman & Hughes 2020; Shah & Srinivasulu 2020; Saikia et al. 2020, 2021, 2025; Sharma et al. 2021; Devender & Srinivasulu 2022; Kusuminda et al. 2022; Singh & Sharma 2023; Srinivasulu & Srinivasulu 2023; Saikia & Chakravarty 2024; Sail & Borkar 2024). With the advent of non-invasive passive acoustic monitoring alongside the development of automated call extraction and classification methodologies, echolocation data has become more readily available, and data collection methods are becoming more accessible (López-Baucells et al. 2019; Roemer et al. 2021; Froidevaux et al. 2023), expanding the potential for using echolocation calls in biodiversity monitoring, and research in comparison to morphological characters, which require invasive sampling, and physical handling of animals.

The exploration of functional trait data variations across species, geography, and time has been used effectively to answer ecological questions in many contexts, sometimes offering greater explanatory power than comparable indices of diversity (Kearney et al. 2021; Stewart et al. 2023). Functional trait data (including echolocation for bats) is often collected at local and community levels, and only recently have these data collection & analysis techniques been adapted to continental, and global scales (Etard et al. 2020; Migliavacca et al. 2021; Görföl et al. 2022). Adapting trait-based methods to larger scales and wider species groups comes with the problem of data completeness – trait analyses often rely on incomplete data, which can lead to biases, and uncertainty in inference (Toussaint et al. 2021; Stewart et al. 2023) – this paucity in trait knowledge is referred to as the Raunkiæran shortfall (Malaterre et al. 2019; Gonçalves-Souza et al. 2023). Some estimates of functional diversity are robust enough to withstand data incompleteness in a majority of species (up to 70% in the case of richness and divergence; Stewart et al. 2023), and new methods of imputation are being developed, and advanced to account for missing data (Johnson et al. 2021). Still, it is vital to collect, and catalogue functional trait data in widely accessible dynamic databases, with the aim of quantifying intraspecific variation, and capturing the depth of functional diversity in a group (Stewart et al. 2023). Certain morphological traits in bats are well-reported and relatively consistent - for instance, most species descriptions report the forearm length, and the lengths of the first & second phalanges of the second & third metacarpals (in South Asia often following Bates & Harrison 1997 and Srinivasulu et al. 2010). Various craniodental measurements including condyle-canine length and the lengths of the upper & lower toothrows are also widely used morphometrics to identify species. The translation of such characters to function becomes clear when the diet, behaviour, and life-history of the species is known (Norberg & Rayner 1987; Santana et al. 2010, 2012; Arbour et al. 2019; Luo et al. 2019; Zou et al. 2022). There have been some studies on the wing morphology of bats in comparison to their echolocation (Zou et al. 2022), distribution (Luo et al. 2019), and ecological interactions (Wood & Cousins 2023), but an overview of the state of knowledge for functional traits in bats is lacking in South Asia, especially, for echolocation trait data.

In this study, we assess the current knowledge on South Asian bat echolocation to assess the degree of Raunkiæran shortfall and further our understanding of bat species, and trait diversity in this region, by compiling published & unpublished call data from South Asian echolocating bat species. We assess the taxonomic, functional, and geographic variations in the data, comparing across studies, regions, equipment, and recording conditions, and bring it together into a foundational large-scale dataset, which can be expanded with new data in the future. Using this dataset, we describe the current knowledge gaps, and potential biases in the available echolocation information, and identify knowledge priority regions (i.e., areas with relatively large diversity of extant echolocating bat species but from which little or no call data has been reported) in order to aid future research, and conservation of bats in South Asia.

 

 

Methods

 

Collation of peer-reviewed literature

To assess the current state of knowledge on bat echolocation in South Asia, we first reviewed the existing literature. We conducted an initial naïve search by querying the Semantic Scholar, Google Scholar, and SCOPUS databases using their respective query syntax to recover any publications including all of: the terms “echolocation”, “call”, or “acoustic”, the name of each echolocating bat genus (based on Srinivasulu et al. 2025), the names of all the countries in South Asia, and the term “kHz*” to filter publications where frequency information is given (Table 1). In the case of the Great Evening Bat Ia io and the Particoloured Bat Vespertilio murinus, we used the entire species name as the relevant search term on all databases, as their respective generic names recovered many irrelevant results. In the case of the genus Cnephaeus, we also queried for Eptesicus, as before Cláudio et al. (2023) all species currently assigned to Cnephaeus in South Asia were assigned to Eptesicus. The search was conducted using Publish or Perish v8.17 (Harzing 2007) to allow for repeatable and consistent querying. The studies recovered through the naïve search were then imported into the systematic review software, Rayyan (Ouzzani et al. 2016) for further evaluation, and screening. We initially excluded any irrelevant texts, then excluded any texts with no relevant data, and those which were not peer-reviewed (including preprints), then assessed the full texts of each included study to exclude studies with unclear or absent data and also recover any additional sources from cited references. The process of the literature search and screening was recorded using a PRISMA flow diagram (Supplementary Material 1). The family Pteropodidae was excluded from the naïve search; although some bats in the genus Rousettus are known to use tongue-click echolocation (Waters & Vollrath 2003; Holland et al. 2004; Yovel et al. 2011; Smarsh et al. 2021), these calls tend to fall within the audible frequencies, are fundamentally different to echolocation calls seen in other echolocating bats, and are difficult to distinguish from noise, and identify accurately in passive acoustic monitoring, requiring much more detailed analysis.

For our final screening, we used three filters: first, we only selected studies focused on exploration- and orientation-based calls in the species’ typical habitat – these are most useful for species identification (Kunz & Parsons 2009) compared to social, and interaction calls, which can differ significantly, and are considered less useful (Pfalzer & Kusch 2003; López-Bosch et al. 2021). Second, studies were filtered based on appropriate recording conditions (contexts in which recordings were made), depending on the call types. We selected studies reporting calls recorded in free flight, after release, or hand-held conditions for species which use constant-frequency (CF) echolocation (Rhinolophids and hipposiderids). As calls are known to vary greatly between recording conditions in non-CF species (Fraser et al. 2020), for these species we considered only studies reporting calls recorded after release or in free flight (once identity was confirmed), and excluded hand-held recordings unless no other information was available (as happened for one species, see Results). Third, we selected studies that provided numeric information for all of the following four call characters: frequency of maximum energy (FMAXE, defined as the frequency containing the highest energy in the call, in kHz), highest frequency (HF, the highest frequency value of the call, in kHz), lowest frequency (LF, the lowest frequency value of the call, in kHz), and duration (D, the duration of a single call, in milliseconds). From D, HF, and LF we then calculated bandwidth (B, the difference between the highest and lowest frequencies of a call, in kHz), and sweep rate (SR, the ratio between the bandwidth and the duration, with higher values representing steeper calls).

 

Collation of unpublished data

Additional data were obtained by searching for echolocating bat species found in South Asia on ChiroVox, a large open-access database of original bat call recordings (Görföl et al. 2022) with highly detailed metadata on detectors used, and recording conditions. We also compiled unpublished calls from various surveys conducted between 2000 and 2023 by the authors, and collaborators. For these unpublished calls, we gathered metadata on recording condition, detectors used, and the geolocation of the recording. All unpublished calls were then analysed in Batsound Pro v4.0 (FFT size 512, Hanning window; Pettersson Elektronik AB) for full-spectrum or time-expansion recordings, and AnalookW (default parameters; Titley Scientific) for zero-crossing recordings. We selected calls with high signal-to-noise ratio (assessed using the visual clarity of the call signal in the spectrogram), choosing 3–5 ‘passes’ (where a pass is defined as a single sequence of 3 or more signals signifying a single crossing of the bat through the zone of detection; following Fraser et al. 2020), and selecting 5–10 ‘pulses’ (defining a pulse as a single call signal with a clearly identifiable start and end, and at least one clearly visible harmonic) from each set depending on the signal-to-noise ratio. We followed well-defined pre-existing methods (e.g., Jones et al. 2000; Holland et al. 2004; Papadatou et al. 2008; Hackett et al. 2017; Srinivasulu et al. 2017; Chakravarty et al. 2020; Fraser et al. 2020; López-Bosch et al. 2021; Rai et al. 2021; Győrössy et al. 2024; Saikia et al. 2025) to extract the call characters FMAXE, HF, LF, and D from unpublished recordings, then deriving the character B from HF & LF, and SR from B & D as described above. Where recordings were available for peer-reviewed published data, we prioritised the published data.

 

Dataset of call parameters

For call description and cataloguing, we organised the collected published and unpublished data into ‘observations’, where each observation was defined as a unique combination of call parameters, location, detector used, and recording condition for any given species. This allows us to not only compare the call parameters of various species, but also assess intra-specific differences caused by using different detectors in different recording conditions, and in different regions. As such, a published study used as a source may contain multiple unique observations depending on the diversity of species, locations, recording conditions, and detectors used.

Based on visual assessment of call shape and grouping similar call characters, we identified major sonotypes. All assessed calls from both published and unpublished data could be classified based on a visual assessment into the sonotypes, but many species showed overlapping call characters that do not permit unambiguous species-level classification. To further support species identification, a comprehensive dataset was generated describing seven main variables for each identified observation: HF (in kHz), LF (in kHz), B (in kHz), FMAXE (in kHz), D (in milliseconds), SR (in kHz/milliseconds), number of pulses recorded, and sonotype (Figure 1). From published sources, we used the average values, and standard deviations for each parameter as published; from unpublished data, we summarised all recordings made as part of the single observation into mean, and standard deviation values for each parameter. We also collected eight metadata variables for each observation, describing: the detector used, the country & region where the recording was made, the identified taxonomic family and species name, the species’ IUCN status as of January 2025, and the full citation or source information for the data. We also classified recording conditions for published data based on the available information written in the source publication’s methodology; conditions were classified into one of five categories: hand-held (where the recording was made while the bat was held in hand), flight-clutter (recordings made in free flight in a cluttered environment), flight-open (recordings made in free flight in an open environment), release-clutter (recordings made shortly after the bat was released in a cluttered environment), release-open (recordings made shortly after the bat was released in an open environment).

 

Traits, distribution, and knowledge gaps

The comprehensive dataset of echolocation observations allowed us to explore the availability of call data across taxonomic families (Srinivasulu et al. 2025), IUCN status as of January 2025 (IUCN 2025), call families, methodologies (detector and conditions as described by the data collectors), and data sources (published, unpublished, ChiroVox), and assess representation, potential biases, and knowledge gaps in trait data.

We explored spatial coverage in available call data, and proposed priority areas for future bat call data collection. We used QGIS v3.40.6 and the terra package (Hijmans 2024) in R 4.4.1 (R Core Team 2024) to match the locations of all collected observations onto a 0.5° x 0.5° grid covering South Asia. From this map, we estimated the per-cell metric ‘species with call data’ (SCD) as the number of distinct species with at least one observation reported in each grid cell. We then matched occurrence point localities from a dataset of compiled published and unpublished distribution data (expanded from Srinivasulu et al. 2024) to the same 0.5° x 0.5° grid to calculate the per-cell metric ‘species richness’ as the number of distinct bat species reported as occurring in each cell. Finally, we characterised ‘echolocation knowledge ratio’ (EKR) as the proportion of species in a cell for which at least one observation was available. EKR values could range from 0 representing no echolocation knowledge for any extant echolocating species, to 1 representing at least one observation reported for each extant echolocating species, and were calculated per-cell using the formula:

                                                        Species with call data

Echolocation knowledge ratio = ––––––––––––––––––

                                                            Species richness

Finally, regional priorities for future data collection were identified by classifying grid cells into three species richness categories: none (no echolocating bats present), low (< 10 species present), or high (≥ 10 species); and three EKR categories: none (EKR = 0), low (0 < EKR < 0.25), and high (EKR ≥ 0.25, representing more than ¼ of extant echolocating bat species in that cell with available call data). Based on combinations of these categories we defined six cell types that represent potential research priorities and opportunities. In particular, we classified all areas with species richness = none as no species recorded/unknown species richness, where the priority would be basic biodiversity surveys in these areas to ascertain true species diversity. We then separated areas with low species richness into three categories depending on EKR values: Low survey priority areas are those with EKR = 0, where future studies are needed but not a top priority, both to assess the true species richness in the region, and to collect echolocation data for known species; low knowledge priority areas are those with low EKR where, future studies could be valuable to supplement echolocation data, and potentially understand the true species richness in the region; and good knowledge, areas with high EKR where future work could expand from existing knowledge to study behaviour, diet, or implement passive acoustic monitoring (Darras et al. 2025). Finally, we also separated areas of high species richness into three categories depending on EKR values: High survey priority are those areas where despite the occurrence of many species we found no echolocation data (EKR = 0) and thus, areas we see as key locations for targeted studies to prioritise collecting echolocation data; High knowledge priority areas are those with low EKR that present good opportunities to collect echolocation data for more species; and good knowledge areas, as above, reflect those with high EKR where future work could focus on more detailed studies. Each of these priority categories represent regions that are best suited for various types of research questions and can be associated with separate potential research actions (Table 2). We show the locations of areas within these categories using a bivariate choropleth map generated in QGIS v3.40.6.

 

 

Results

 

Collation of existing knowledge

The initial searches of the Semantic Scholar, Google Scholar, and SCOPUS databases resulted in an initial set of 76 publications (Supplementary Material 1), including duplicates, and irrelevant studies. After the screening process, we selected a final set of 35 peer-reviewed publications for further assessment. From these publications, we recovered a total of 185 unique observations of 86 species from India, Pakistan, Nepal, and Sri Lanka (Supplementary Material 2). From the ChiroVox database, we recovered seven unique observations of five species across Bangladesh. Finally, from our analysis of a total of 6,190 unpublished calls, we recovered a total of 107 unique observations of 36 species from India. This resulted in a combined database of 299 observations of 86 species across Bangladesh, India, Nepal, Pakistan, Sri Lanka, sourced from published, and unpublished data (Supplementary Material 3).

From our assessment of the call shape and characters of all collected calls, we grouped South Asian bat echolocation calls into eight sonotypes (Figure 1; Supplementary Material 3). These sonotypes are defined within the context of South Asian bat echolocation:

Short Constant Frequency (SCF; genus Hipposideros; 68 observations of 13 species): pulses comprising a short (< 15 ms) constant frequency (CF) component followed by a steep frequency-modulated (FM) downward sweep.

Long Constant Frequency (LCF; genus Rhinolophus; 56 observations of 11 species): pulses comprising a CF component preceded and followed by a FM downward sweep.

Frequency Modulation (FM; genera Harpiocephalus, Hesperoptenus, Kerivoula, Miniopterus, Murina, Myotis, Phoniscus, and Submyotodon; 58 observations of 28 species): pulses comprising a short and steep, broadband FM component (in cluttered free flight) or a short and relatively steep FM component (in open flight).

Frequency Modulation with Quasi-CF (FM-QCF; genera Arielulus, Cnephaeus, Eudiscopus, Hypsugo, Mirostrellus, Nyctalus, Pipistrellus, Scotophilus, and Tylonycteris; 50 observations of 20 species): pulses comprising a short and relatively steep FM component (in cluttered flight), or a short and relatively shallow FM component (in open flight), both followed by a distinct short and shallow quasi-CF component; call shape sometimes resembles a hockey stick.

5. Long Multiharmonic (LMH; genera Mops, Otomops, Rhinopoma, Tadarida, Taphozous; 44 observations of 10 species): calls of long duration (> 5 ms) with one or occasionally more harmonics seen; number of harmonics seen depends on distance of the bat from the detector. These calls are hard to distinguish from each other based on call shape and characters alone; species range and habitat must be considered when inferring species presence based on these calls. The degree of clutter also impacts the sweep rate (slope) and the general shape of the call: for instance, free-flying Mops plicatus from Sigiriya (Sri Lanka; Kusuminda & Yapa 2017) called using characteristically-shaped long yet steep multiharmonic pulses (Figure 1).

6. Megadermatid (ME; genera Lyroderma and Megaderma; 16 observations of 2 species): characteristic short (duration often < 2 ms) and broadband (BW often > 50 kHz) pulses of three or more harmonics of similar amplitude seen close together.

7. Plecotine (PL; genus Plecotus; 2 observations of 2 species): relatively short (duration < 5 ms) multiharmonic calls comprising downward sweeps of one or two harmonics of almost equal amplitude (Chakravarty et al. 2020). Call shape & characters tend to overlap both within the genus, and with other FM, and FM-QCF species.

8. Barbastelle (BA; genus Barbastella; 2 observations of 1 species, B. darjelingensis): calls vary highly based on environmental clutter, flight behaviour, and vegetation structure (see Denzinger et al. 2001), ranging from short steep narrowband multiharmonic FM pulses as recorded by Chakravarty et al. (2020) and Wordley (2014), to characteristic alternating FM pulses of two distinct shapes, and amplitudes (Denzinger et al. 2001; Seibert et al. 2015). Barbastelle pulses are of relatively low amplitude (< 110 dB; Lewanzik et al. 2023), but this is not fully explored in South Asia.

HF may vary greatly, especially in broadband FM & FM-QCF calls, due to atmospheric attenuation and the distance between the bat and the detector. Additionally, both HF & LF, and also B & D, and thus SR greatly vary based on the degree of clutter in the location where the bat is flying, ranging from shallow and low-SR calls in open areas to steep and high-SR calls in cluttered areas.

 

Variations in call characters

The echolocation data for several species were highly varied based on geography, in some cases including distinct phonic types of the same species, possibly indicating cryptic diversity – more detailed call data is required to establish more robust diagnostic boundaries for species identity. Thabah et al. (2006) reported two distinct phonic types of Hipposideros larvatus in Meghalaya, India, each using an FMAXE of around 85 kHz and 98 kHz, respectively. They did not report the durations of these distinct calling types and thus it is hard to infer whether this may be an artifact of environmental clutter or a distinct group of individuals. Similarly, Chattopadhyay et al. (2010) reported a distinct phonic type of Rhinolophus rouxii from across Tamil Nadu, India, calling at an FMAXE of around 94 kHz. This is higher than seen elsewhere in southern India – e.g., 82 kHz reported from Kerala by Raman & Hughes (2020) – and Sri Lanka – e.g., 74 kHz reported across the country by Kusuminda et al. (2018). A similarly high frequency (92 kHz) was reported from the Valparai Plateau in the southern Western Ghats (Wordley 2014), we also report similarly high frequencies (91–94 kHz) from the southern Western Ghats in Kerala (Supplementary Material 3). This distinct phonic type was assigned the name Rhinolophus indorouxii by Chattopadhyay et al. (2012), however this species is a nomen nudum and therefore synonymised under R. rouxii.

There is also considerable variation and overlap in the call characters of many species, especially FM and FM-QCF bats. In our experience (and corroborated by published data), we have found that the calls of Pipistrellus ceylonicus tend to vary widely across its distribution, with mean FMAXE values ranging around 35–45 kHz. Saikia et al. (2025) have reported Pipistrellus babu from Himachal Pradesh, India, calling at an average FMAXE of 40 kHz, which falls within the range for P. ceylonicus. Hence, in cases such as these, care must be taken to either confirm species-level identity through other means or to refer to call identities as pertaining to species-groups. Additionally, Raghuram et al. (2014) report calls of Pipistrellus tenuis from Kudremukh National Park (Karnataka, India) at an FMAXE of 38 kHz. These calls were recorded only in flight and could be misidentified, instead representing P. ceylonicus, as they are very different from the expected FMAXE around 50 kHz for P. tenuis (Supplementary Material 3). Finally, there is a high degree of inconsistency in megadermatid call characters between regions (Supplementary Material 3). This is likely due to the characteristic short multiharmonic nature of the calls, and that the FMAXE tends to fall within one or more harmonics. More investigation is needed to ascertain the various situations in which specific harmonics are produced with more energy, and thus we recommend treating echolocation calls of megadermatids (including those presented in this study) with care.

Intraspecific variability in call characters differed between species – enough data was available to assess intraspecific variations in characters recorded in the same recording condition for 31 species; It is important to note that our collected data does not account for variations between detectors and other such impacting factors, and much more detailed data is needed to analyse such variations. The most data-rich species were Hipposideros speoris (15 observations) and Rhinolophus rouxii (12 observations; variation detailed above). Duration in all calls varied between recording conditions – as different environmental structures and degrees of clutter impact pulse duration and inter-pulse interval (Fraser et al. 2020) – but remained relatively consistent between locations within species, with shorter calls sometimes associated with higher mean FMAXE; however, this relationship was not consistently observed. Variation of mean FMAXE in most CF species was under 5 kHz between locations, with some notable exceptions. For instance, in cluttered flight recordings, the mean FMAXE of the Havelock Island population of Hipposideros gentilis is approximately 10 kHz higher than its sister Andaman Islands populations (Srinivasulu et al. 2017); in cluttered flight recordings recorded on the Pettersson D500X, the mean FMAXE of Indian Hipposideros speoris varied between 128 kHz in Andhra Pradesh and 138 kHz in Telangana (present study); and mean FMAXE in cluttered flight recordings of Indian Rhinolophus rouxii in Kerala was 10–12 kHz higher than those recorded in Karnataka and Maharashtra on the same detectors (Pettersson D500X and Wildlife Acoustics SM3BAT respectively; present study). In FM bats, FMAXE variation was under 10 kHz (except in the case of Miniopterus phillipsi, for which the mean FMAXE in Maharashtra, India, recorded on a Wildlife Acoustics SM3 was 18 kHz lower than calls recorded on a Pettersson D500X in a different location in Maharashtra and calls recorded on a Pettersson M500 in Uva, Sri Lanka; Kusuminda et al. 2022; present study). HF, LF, and D (and consequently B) all varied widely between locations in some species, in the same recording conditions. This may be due to differing attenuation of calls based on various conditions present in the recording location including foliage and habitat structure, flight elevation, and individual variations, but could also reflect difficulties in establishing species identity based on calls alone, especially in regions of overlapping distribution of species with similar calls.

 

Patterns and gaps in metadata

The published data comprised 185 observations of 86 species, of which the calls of Kelaart’s pipistrelle Pipistrellus ceylonicus were reported by the most studies – seven in total, of which six were from India and one from Sri Lanka. Of the 86 species, 43 were reported only in one study each (50%; Supplementary Material 3). The data were mostly distributed in India (26 out of 35 studies; 74%), and the greatest number of studies per region was six studies from the south Indian state of Karnataka (Chattopadhyay et al. 2012; Raghuram et al. 2014; Srinivasulu et al. 2015, 2016; Deshpande & Kelkar 2015; Srinivasulu & Srinivasulu 2023). The greatest number of total unique observations reported from any region was from Uttarakhand (n = 34; Chakravarty 2017; Chakravarty et al. 2020; Singh & Sharma 2023).

Unfortunately, detailed information was lacking in some published studies. For instance, Raman & Hughes (2020) compiled the calls of 48 species from the Western Ghats, but recording locations were not provided. Kusuminda et al. (2022) described the new species Phillips’ Bent-winged Bat Miniopterus phillipsi but provided only the FMAXE and no other characters from Sri Lanka, similarly to those of Hipposideros larvatus from Meghalaya, reported by Thabah et al. (2006). Unpublished data was found for 36 species from the authors’ field recordings across India, which were analysed according to consistent standardised methods (see Methods). All of these species were previously reported in published data, but our unpublished data covers some spatial gaps in the distribution of knowledge, especially in Peninsular India (Figure 2). Additional unpublished records were found on the ChiroVox database and were distributed in India, and Bangladesh. The calls from India corresponded to those published in Chakravarty et al. (2020), and thus, we prioritised published information, and the calls from Bangladesh included in this study represented six species recorded in various conditions (Supplementary Material 3; Figure 2).

Nearly three-quarters of the species for which echolocation data was found (63 out of 86 species) are assessed as Least Concern (LC) in the IUCN Red List (IUCN 2025), with eight others listed in more at-risk categories (Hodgson’s Myotis Myotis formosus and Painted Bat Kerivoula picta as ‘Near Threatened’; Durga Das’ Roundleaf Bat Hipposideros durgadasi, Rickett’s Big-footed Myotis Myotis pilosus, and Mandelli’s Myotis Myotis sicarius as ‘Vulnerable’; Pomona Roundleaf Bat Hipposideros pomona and the Andaman Horseshoe Bat Rhinolophus cognatus as ‘Endangered’; and the Kolar Roundleaf Bat Hipposideros hypophyllus as ‘Critically Endangered’. Of the 19 remaining species with echolocation data, eight are ‘Data Deficient’, and 11 have not been assessed yet (NA; Supplementary Material 3; Figure 3). There were more species with echolocation data than without in all Red List assessment categories except NT (two species with data and four species without data) and DD (eight species with data and 10 species without data; Supplementary Material 3; Figure 3). Approximately two-thirds of all extant LC, NA, and EN species, and all extant VU and CR species, have echolocation data.

It is vital to understand the variations in echolocation data that arise due to differences in the recording location, as both the degree of clutter in the environment and the specific type of recording (hand-held, in-flight, or at-release) greatly influence the shape and parameters of echolocation calls for certain species (Hiryu et al. 2006; Fraser et al. 2020). Of the 299 unique observations, 185 observations corresponding to 64 species (around 61% of the total data) were recorded in flight in either cluttered or open environments – usually in-situ near the bats’ roosts or foraging sites, or in clearings, and open fields (Figure 3; Table 3). Many of these species (31 species) were urban-resilient vespertilionids recorded in urban/semi-urban ecotone areas (e.g., Kelaart’s Pipistrelle Pipistrellus ceylonicus), forest-associated vespertilionids recorded in clearings (e.g., Horsfield’s Myotis Myotis horsfieldii), scrubland-associated hipposiderids (e.g., Schneider’s Roundleaf Bat Hipposideros speoris), or high-flying molossids (e.g., Egyptian Free-tailed Bat Tadarida aegyptiaca). Release calls made up 83 observations corresponding to 61 species – the process of recording these involved capturing the bat, confirming its identity, and then releasing it either in a cluttered (40 observations of 33 species) or open environment (43 observations of 37 species). The remaining 31 observations of 19 species were recorded while the bat was held in hand – these species were all CF bats, excepting the Kachin Woolly Bat Kerivoula kachinensis from Meghalaya, India (Uttam Saikia et al. 2020), a FM species for which no other recording was available (Table 3; Table 4; Supplementary Material 3). For 16 species (Eudiscopus denticulus, Harpiocephalus harpia, Hippsosideros ater, Hipposideros lankadiva, Kerivoula crypta, K. picta, Miniopterus magnater, Mops plicatus, Myotis pilosus, Myotis sicarius, Otomops wroughtoni, Pipistrellus babu, Rhinolophus macrotis, Tadarida aegyptiaca, Tylonycteris fulvida, Tylonycteris malayana), the only observations available were recorded in-flight, in all cases after the species identity was confirmed (Supplementary Material 3). Despite flight data being the most accurate representation of the species’ actual echolocation calls, the data for these 16 species must be used with caution as misidentification is possible in areas with multiple species.

 

Distribution and knowledge gaps

Most of the published data was distributed across mainland India, with additional locations in the Andaman Islands and the Lakshadweep Islands, as well as in Nepal, Pakistan, and Sri Lanka (Figure 2). Most localities were in northern India (Himachal Pradesh and Uttarakhand states), western India (Gujarat), and peninsular India (Andhra Pradesh, Karnataka, Kerala, Maharashtra, Tamil Nadu, and Telangana states; Figure 2). Some data was also distributed in eastern India (Bihar, Meghalaya, and Mizoram states). New data reported as part of this study was mostly distributed in peninsular India, with some records from central India (Madhya Pradesh; Figure 2). Especially in the Western Ghats and the Deccan Plateau, unpublished data covered gaps in the existing published data. Unpublished ChiroVox data was only distributed in Bangladesh and was also the only data we found from the country (Figure 2).

Species with Call Data (SCD; the number of distinct species with at least one observation reported in each grid cell; see Methods) varied across South Asia, with hotspots of call data richness in Uttarakhand (India; 25 species near Dehradun and 15 species near Kedarnath Wildlife Sanctuary), the southern Western Ghats (India; 14 species in the Valparai Plateau), and the central Western Ghats (India; 12 species in and around Kudremukh National Park). It must be noted that, as the resolution of the spatial analyses is relatively coarse (0.5° approximately corresponding to 50 km on average in South Asia), each hotspot represents a very wide region of approximately 2,500 km2. Echolocation Knowledge Ratio (EKR; the proportion of extant echolocating species in each cell for which echolocation data was found) also ranged across the region, with much of South Asia having at least one reported echolocating species but no echolocation data (Figure 3). Similarly to SCD, hotspots where EKR was 1 – i.e. all the reported echolocating species had echolocation data available – were seen in India: in Gujarat, Himachal Pradesh, central coastal Karnataka, the Western Ghats in Kerala, northern & southeastern Maharashtra, the Khasi & Garo Hills in Meghalaya, the Eastern Ghats & Nilgiris in Tamil Nadu, northern & eastern Telangana, and Uttarakhand; and in the eastern Chittagong Division of Bangladesh (Figure 2).

More than 90% of the study area was either classified as having ‘No Species Recorded/Unknown Species Richness’ (i.e., there is no knowledge of either species richness or echolocation data from the region; 65%; approximately 3.4 million km2; Table 2), or as low survey priority regions (i.e., regions with low species richness and no EKR; 27%; approximately 1.4 million km2; Table 2). These regions are widespread across South Asia, comprising almost all of Afghanistan and Bangladesh, all of Bhutan, large areas of northern & central India, western Nepal, central & southern Pakistan, and northern Sri Lanka (Figure 4). Regions of ‘good knowledge’ (i.e., regardless of high or low species richness, more than ¼ of the extant echolocating bats have echolocation data reported; Table 2) only comprised around 3% of the study area (approximately 165,000 km2). These regions were seen in large contiguous clusters south of the Himalaya (Himachal Pradesh and Uttarakhand, India) and in the central & southern Western Ghats (Karnataka and Kerala, India). Smaller fragmented clusters were seen across the region, including in the Indus Valley and Hindukush Range (Punjab, Pakistan), western & central India (Gujarat and Madhya Pradesh), peninsular India (Maharashtra, Odisha, Tamil Nadu, and Telangana), northeastern India (Meghalaya), eastern Nepal (Bagmati), western Bangladesh (Rajshahi), and southeastern Bangladesh (Chittagong; Figure 4). Regions of ‘low knowledge priority’ (i.e., where species richness and EKR are low) comprised 1.6% of South Asia (approximately 85,000 km2), and were seen in small, fragmented clusters across the entire study region, with a higher density in peninsular India (Figure 4).

Regions of ‘high knowledge priority’ (i.e., where the per-cell echolocating species richness is more than 10 species, but EKR is less than ¼; Table 2) comprised 1% of the study area (around 54,000 km2). These regions were mostly seen in contiguous clusters with regions of ‘high survey priority’ (where the per-cell echolocating species richness is more than 10 species, but no echolocation knowledge exists for any of them from that cell; Table 2), which comprised 2.6% of the study area (around 135,000 km2; Figure 4). Combined clusters of ‘high knowledge priority’ and ‘high survey priority’ were seen in northeastern India, the Western Ghats, the Eastern Ghats, the Brahmani-Mahanadi doab (Odisha, India), and in the Central, Sabaragamuwa, Southern, Uva, and Western provinces of Sri Lanka (Figure 4). Regions of ‘moderate knowledge priority’ alone were seen in southern India (Tamil Nadu and Kerala), northern India (Uttarakhand), and western India (Gujarat; Figure 4). Finally, regions of ‘high survey priority’ alone were seen in various regions of Afghanistan (Faryab, Kabul, Kandahar, and Nangarhar provinces), India (Assam, Gujarat, Himachal Pradesh, Jammu & Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Meghalaya, Mizoram, Nagaland, Rajasthan, Sikkim, Tamil Nadu, Telangana, Uttarakhand, and West Bengal states), Nepal (Bagmati, Gandaki, and Koshi provinces), Pakistan (Khyber Pakhtunkhwa and Punjab provinces), and the disputed territories of Gilgit-Baltistan and Azad Kashmir (Figure 4).

 

 

Discussion

 

This study reviews bat echolocation research in South Asia, collating 299 unique observations of echolocation characters for 86 species by integrating published literature, unpublished recordings, and data from the ChiroVox database. It is the first compilation of its kind for South Asia, exploring data gaps, geographic variations (species characters differing from location to location), and situational variations (species characters being collected using various combinations of techniques, equipment, and conditions) in the echolocation characters for bats, supporting the development of non-invasive acoustic monitoring techniques in the region. It also identifies geographic regions of high and low echolocation knowledge density, importantly identifying research priority regions – where species diversity is relatively high and echolocation knowledge is low – for the prioritisation of future research efforts to increase our knowledge of bat echolocation in South Asia.

Data represented nearly all families, without strong taxonomic biases, except in the case of Hipposideridae (which only lack data for four out of 18 extant species) and Emballonuridae (which only lack data for one out of six extant species). No echolocation data has been reported from South Asia for the Trident Bat Triaenops persicus, the only species representing the family Rhinonycteridae in the region. In some small families (Molossidae and Megadermatidae) data were found for all species. Most observations represented species in Vespertilionidae, the most diverse family in the region with 84 extant species, of which we have data for 48. Vespertilionid bats are highly diverse, and some species are widespread across the region; species like Kelaart’s Pipistrelle Pipistrellus ceylonicus and Least Pipistrelle Pipistrellus tenuis are commonly found in or near human settlements (Bates & Harrison 1997), increasing the likelihood of recording their echolocation during surveys that are not targeted or species-specific. However, just over half of the species in this family are represented in our echolocation dataset, representing a large knowledge gap of 36 species from one family alone. Despite their widespread distribution and high diversity (Bates & Harrison 1997; Srinivasulu & Srinivasulu 2012), much is still unknown about vespertilionid species, and future species-specific research must be directed towards this family.

Data were also available for species in various categories of extinction risk (IUCN Red List status), although we note that data was unavailable for four of the six species listed as Near Threatened, 10 out of the 18 Data Deficient species, and six out of the 17 species Not Evaluated. Additionally, 34 out of the 97 Least Concern species have no data; surveys targeted towards filling these gaps are vital. It is also important to note that over 60% of the observations in our dataset (n = 185) were collected in flight after species identity was confirmed using physical identification and (in most cases) release calls were recorded (Supplementary Material 3). For 16 species however, flight calls were the only call type available, either in published or unpublished data (see Results; Supplementary Material 3; Table 3). Care must be taken to ensure the species identity of an individual is established firmly before recording free-flying calls, as while call data from free-flying bats is more representative of actual calls recorded during acoustic monitoring, reference calls cannot be published based on echolocation-derived species identity alone due to variations in call characters. Ideally, an individual must be identified to species level, and both release and flight calls (and in the case of CF bats hand-held calls) must be recorded from the individual in as quiet a location as possible. If possible, multiple detectors (calibrated appropriately according to each detector’s specific settings and the recording conditions, as different detectors and different calibrations can introduce variation; Adams et al. 2012) and multiple recording conditions may also be employed to capture a breadth of data. In regions where reference calls are available however, calls based on release and free-flight (which were recorded on the same detector in the same condition; Table 4) can be used as references to identify bats at least to sonotype- and family-level, though the authors advise caution with species-level identification using ambiguous and overlapping species characters.

The availability of echolocation data was highly varied across South Asia. In many regions, limited or no data was available, and most of the study area has low species richness but no echolocation data. However, in some parts of South Asia, especially in northeastern, northern, western, and peninsular India, all occurring species had echolocation data reported (Figure 4). Many more records were obtained from India than other countries, but this may be due in part to the size of the country itself, and due to all our unpublished data being from central, western, and peninsular India. We did not find published echolocation data for many extant species in large regions of Afghanistan, Bangladesh, Nepal, Pakistan, and Sri Lanka, even those which have relatively high species richness, but ChiroVox data covered several species across Bangladesh. Within India as well, the Gangetic Plains, the northern Deccan and central India, the Nilgiri Hills, the central Western Ghats, and northeastern India have relatively sparse echolocation data despite being relatively species-rich (Bates & Harrison 1997; Srinivasulu & Srinivasulu 2012). Data availability may be affected in some cases by the accessibility of study sites to researchers, a bias which is not uncommon in empirical data (González-Suárez et al. 2012; Hughes et al. 2021), but there are variations across the region. In Tamil Nadu, Uttarakhand, and Telangana, there are clusters of high data density near Madurai, Dehradun, and Hyderabad, all which contain major academic institutions – however, there are also regions like Valparai, the Garo and Khasi Hills, and most of the Gujarat peninsula, where this bias of availability is not seen (Figure 4). There are also sites of special interest, like Kolar in southern India, where the presence of the Critically Endangered Kolar Roundleaf Bat Hipposideros hypophyllus, has promoted site- and species-specific surveys since 2014 (Srinivasulu et al. 2014, 2016). To support future surveys and data collection, we identified ‘high survey priority’ and ‘high knowledge priority regions’ (with low or high species richness but low or no call data availability) where field surveys could lead to new data for several species. Two main priority areas fall in the Western Ghats and Sri Lanka and the Himalaya hotspots of biodiversity (Myers et al. 2000). Surveys in these areas of high ecological importance could contribute to expand our understanding of bat echolocation and diversity. Itis important to keep in mind that regions of high echolocation knowledge ratio (classified as good knowledge) still do not necessarily represent areas where we have enough data to be able to identify species to high certainty from echolocation calls alone – more work is needed to understand trait variations, and the specific identification boundaries between species based on echolocation must be analysed in further detail before we can truly confirm that the knowledge in regions identified as ‘good knowledge’ is enough for species-level identification. Future efforts to gather echolocation data should combine site- and species-targeted surveys with clear knowledge priorities, and our study identifies groups and areas where those efforts may be directed based on the priority of the study.

Research using functional traits to examine species interactions in ecosystems has been consistently advancing, starting with studies by early ecologists including Elton, Hutchinson, and Raunkiær (Malaterre et al. 2019). Recent work has developed newer protocols for the standardisation of functional traits in invertebrates (Moretti et al. 2017) and birds (Tobias et al. 2022), and the evaluation of the impact of anthropogenic activities on functional diversity (Carmona et al. 2021). Despite this breadth of research, the use of functional traits in animal studies has been criticised for its arbitrariness (Kearney et al. 2021), and the need for structured approaches to the collection, collation, and selection of trait data has often been recommended (Gonçalves-Souza et al. 2023). Echolocation has been long known as a vital trait in bat biology (Griffin 1953). Variation in echolocation traits has been explained using several non-exclusive hypotheses including relationships with body size, nasal chamber and laryngeal size, and evolutionary arms-races between hearing-moths and bats (Castro et al. 2024). However, this variation has not been truly quantified or explored across large groups of species, and we only know a small fraction of the echolocation characters of echolocating bat species across the world, especially in regions of high diversity such as South Asia. While our study attempts to reduce this gap by collecting and assessing the state of knowledge for bat echolocation in South Asia, it is deeply limited by the lack of depth in the data itself. The collected data comprises recordings from a limited set of recording conditions, and with the amount of data we have collected it is not possible to fully explore the breadth of all variations in species calls across geography, recording scenarios, and detectors used, amongst other factors. Thus, we only recommend using the collated data as a guideline, to follow standard methodology to collect new data, and to prioritise surveys towards regions of high knowledge and survey priorities in order to collect as much new information as possible and improve the state of echolocation knowledge in this region.

Key to effective conservation planning is a deep understanding of a study region’s species diversity, including their distribution and traits (Margules & Pressey 2000). The dataset of echolocation observations and sonotypes in this study offer a foundational knowledge base for bats in South Asia which we hope will form a base for future research. Species-level trait data for South Asian bats is sorely lacking, yet trait data is key to understand the functional dimension of biodiversity (Cernansky 2017; Stewart et al. 2023), which is being eroded (Carmona et al. 2021), and is linked to important ecosystems services and functions (Cadotte et al. 2011). This study aids in the compilation of echolocation call characteristics for South Asian bats contributing understanding to an important dimension of bat functional traits (see Denzinger & Schnitzler 2013). We hope that our research promotes further interest in trait research and data compilation. Moving forward, our bat echolocation database and additional analysis of research priority regions in South Asia can support more targeted research and species- and site-specific survey planning, leading to positive long-term impacts on data collection and collation, conservation prioritisation, and policymaking.

 

 

Table 1. List of search terms and strings used for each database in the literature search.

Database

Search string format

Google Scholar

"Genus" AND (“echolocation" OR "call" OR "acoustic") AND "kHZ" AND intitle: ("Afghanistan" OR "Bangladesh" OR "Bhutan" OR "India" OR "Nepal" OR "Pakistan" OR "Sri Lanka")

SCOPUS

Keywords: "Genus" AND (“echolocation" OR "call" OR "acoustic") AND "kHZ"

Title words: ("Afghanistan" OR "Bangladesh" OR "Bhutan" OR "India" OR "Nepal" OR "Pakistan" OR "Sri Lanka")

SemanticScholar

"Genus" AND "echolocation" OR "call" OR "acoustic" AND "kHz" AND ("afghanistan" OR "bangladesh" OR "bhutan" OR "india" OR "nepal" OR "pakistan" OR "sri lanka")

 

Table 2. Priority categories for regions across South Asia, based on their Species Richness and Echolocation Knowledge Ratio (EKR).

*For all levels of species richness knowledge, true species diversity may be underestimated especially in unstudied areas. Gathering more data on extant species diversity is thus a universal priority in all categories.

Priority category

Research opportunity

Species richness

EKR

Knowledge gaps

Data collection priority

Biodiversity

Echolocation

No species recorded/Unknown species richness

Discovery

None

None

True diversity may be underestimated in unstudied areas*.

High priority in unstudied areas

If species are detected

Low Survey Priority

Biodiversity and echolocation knowledge

Low

(< 10 spp.)

None

Lack of echolocation data.

Medium priority in unstudied areas

Medium priority

High Survey Priority

Priority echolocation research

High

(≥ 10 spp.)

None

Lack of echolocation data.

Low priority

High priority

Low Knowledge Priority

Biodiversity knowledge

Low

Low

(0 – 0.25)

Limited echolocation data.

Medium priority in unstudied areas

Low priority

High Knowledge Priority

Echolocation research

High

Low

Limited echolocation data.

Low priority

Medium priority

Good Knowledge

Deepen knowledge

Low or High (> 0 species)

High (≥ 0.25)

True diversity may be underestimated in understudied areas*.

 

Echolocation knowledge strong, but incomplete.

Medium priority in understudied areas

Low priority

 

Potential for other studies using echolocation (e.g., behaviour, diet, interactions)

 

 

Table 3. Number of echolocation observations recorded in each recording condition (rows), described by sonotype (columns).

Recording Condition vs Sonotype

Sonotype

SCF

LCF

FM

FM-QCF

LMH

ME

PL

BA

Recording Condition

Flight – Clutter

28

27

19

13

18

10

0

0

Flight – Open

18

6

10

19

15

2

0

0

Release – Clutter

5

6

15

9

1

3

0

1

Release – Open

2

3

3

11

10

1

2

1

Hand-held

15

15

1

0

0

0

0

0

Total

68

57

48

52

44

16

2

2

 

 

Table 4. Number of echolocation observations recorded in each recording condition (rows), described by the detector used (columns).

Recording Condition vs Detector

Detector

Anabat SD1

Anabat Walkabout

Pettersson D240X

Pettersson D500X

Pettersson D980

Pettersson D1000X

Pettersson M500-384

Pettersson M500

Wildlife Acoustics EchoMeter Touch 2

Wildlife Acoustics EchoMeter Touch 2 Pro

Wildlife Acoustics SM3BAT

Wildlife Acoustics SM4BAT

Ultrasound-Advice S25

Ultrasound-Advice SM2

Ultrasound-Advice U30

Recording Condition

Flight – Clutter

14

16

4

27

11

5

1

11

2

2

19

0

0

0

3

Flight – Open

10

5

7

13

1

1

16

2

1

0

12

0

0

1

1

Release – Clutter

1

7

0

22

0

2

1

0

0

0

2

5

0

0

0

Release – Open

2

22

0

10

0

0

0

0

0

0

9

0

0

0

0

Hand-held

9

8

0

3

0

4

0

2

0

0

1

0

4

0

0

Total

36

58

11

75

12

12

18

15

3

2

43

5

4

1

4

 

 

For figures & Supplementary Materials - - click here for the full PDF

 

References

 

Adams, A.M., M.K. Jantzen, R.M. Hamilton & M.B. Fenton (2012). Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats. Methods in Ecology and Evolution 3(6): 992–998. https://doi.org/10.1111/j.2041-210X.2012.00244.x

Arbour, J.H., A.A. Curtis & S.E. Santana (2019). Signatures of echolocation and dietary ecology in the adaptive evolution of skull shape in bats. Nature Communications 10(1): 2036. https://doi.org/10.1038/s41467-019-09951-y

Bates, P.J.J. & D.L. Harrison (1997). Bats of the Indian Subcontinent. Harrison Zoological Museum, Sevenoaks, UK, 258 pp.

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