Skip to main content
NCBI home page
The Journal of the Acoustical Society of America logoLink to The Journal of the Acoustical Society of America
. 2010 Aug;128(2):919–923. doi: 10.1121/1.3455835

Communication calls of little brown bats display individual-specific characteristics

Karla V Melendez 1,a), Albert S Feng 2
PMCID: PMC2933263  PMID: 20707462

Abstract

Bats’ echolocation signals have been shown to be situation-, colony-, and individual-specific, but whether or not these findings apply to bats’ communication signals is not fully understood. The primary goal of this study was to test the hypothesis that the communication calls of adult little brown bats (Myotis lucifugus) are individual specific. Bats were paired to form focal pairs from June 2007 to August 2008. Each bat’s vocalizations were recorded on a PC-based digital recorder with a custom made ultrasonic microphone. The vocal signals were first classified using a previously established classification scheme. Three acoustic parameters (the minimum and maximum frequencies, and the call duration) of two of the dominant call-types, the steep-FM and broadband noise bursts, of individual bats were further analyzed. Discriminant function analysis, and multi- and univariate analyses of variance of these parameters revealed that these vocal signals were individually distinct and likely contain individual signatures to allow bats to identify individuals acoustically.

INTRODUCTION

The echolocation system of bats has been studied extensively, and research over the last several decades has shed light on the spectrotemporal characteristics of echolocation signals, how they are used to acquire the static and dynamic information of a target, and how they are processed in the brain. Studies of bats’ echolocation signals have revealed that these signals are situation-(Siemers and Schnitzler, 2004), colony-(Pearl and Fenton, 1996), and individual-specific (Masters et al., 1995; Kazial et al., 2001; Fenton et al., 1999; Yovel et al., 2009a, 2009b). At this time, it is unclear the extent to which these findings apply to bats’ communication signals.

Echolocating bats are highly social animals, and sound communication plays an important role in their social interactions (Fenton, 1984). Recent studies showed that communication calls produced by infants during mother-young interactions carry individual signatures (Bohn et al., 2007; Knornschild and von Helverson, 2008). Another recent study in Brazilian free-tailed bats (Bohn et al., 2009) showed that their songs (i.e., stereotypical combinations of acoustic elements) also differ individually; whether or not the acoustic elements themselves are individually distinct in the adults was not determined, however. Nonetheless, individual signatures have been found in communication signals of adult marine mammals that echolocate, such as dolphins (Watwood et al., 2005; Janik et al., 2006) and whales (Weiß et al., 2006). Therefore, it is plausible that communication calls of adult echolocating bats are also individual specific (i.e., have acoustic characteristics which differ among individuals).

To test the above hypothesis, we investigated the sound communication calls of adult male little brown bats (Myotis lucifugus). We analyzed in detail three acoustic parameters (the minimum and maximum frequencies, and the call duration) of two of the dominant call-types that these bats produce. Discriminant function analysis, and multi- and univariate ANOVA of these parameters revealed that these signals were individually distinct.

METHODS

Subjects

Adult male little brown bats (M. lucifugus) were collected in Spring 2007 and Spring 2008 from Starved Rock State Park in Utica, IL and maintained in an environmental room at 27 °C and 60% relative humidity. The bats were kept in two separate cages (46×61×46 cm), divided by collection year. Therefore, there was one home cage for the Spring 2007 collection and one home cage for the Spring 2008 collection. Typically, permit restrictions allow collection of male bats only; however a female bat was accidentally collected in 2008 and therefore included in the study. Food (meal worms) and water were changed daily and made available ad libitum. Following 2 weeks of acclimation to the laboratory environment, microchips were implanted into the neck of individual bats by the staff members of the Department of Animal Resources at the University of Illinois, as a means of individual bat identification.

Experimental procedures for analysis of communication calls

Between June 2007 and August 2008, pairs of identified bats (n=5 pairs) were placed in a plastic mesh cage (7×21×25 cm with Plexiglass frame) in a sound attenuated booth once a week for 30 min (Fig. 1). Recordings were conducted at peak times of daily activity (∼5 pm, midnight, and 2 a.m.; Melendez et al., 2006).

Figure 1.

Figure 1

Open in a new tab

Schematic diagram of the experimental setup showing the placements of the audiovisual recording devices and playback loudspeaker.

Under infrared lighting (Kodak safelight filter #10 affixed to a 60-Watt incandescent lamp), the behaviors of paired bats were recorded with a Sony camcorder (DCR-DVD-301 equipped with nightshot feature). Bats were recorded as pairs, to induce the exchange of communication calls. The video signal was synched to the bats’ vocal signals recorded on a PC-based digital recorder (PC-Tape), with a custom made ultrasonic microphone having a flat frequency response from 15–120 kHz, with a roll-off of 10 dB per octave and 6 dB per octave at15 kHz and120 kHz, respectively (Siemers and Schnitzler, 2004; Feng et al., 2006). The microphone was placed 15 cm from the cage.

Bats’ vocalizations were analyzed by two independent observers. At the beginning of each recording, an independent observer was instructed to recite the identification number of an individual bat and to visually indicate which bat corresponded to the particular identification number. Calls were analyzed for one bat per pair. Acoustic communication signals were digitized (16-bit A∕D conversion) at a sampling rate of 192 kHz, saved as WAV files, and displayed for analysis (fast Fourier transformed with 1024 points) using Selena, a custom-designed sound analysis software (Feng et al., 2006).

Individual vocalizations were classified using an established classification scheme (Melendez et al., 2006). We found that pairs consisting of bats from the same home cage displayed very low levels of interaction. For this reason, experimental pairs consisted of bats from different home cages. Additionally, each bat was only paired once. Three of the pairs recorded contained one male and one female (recorded prior to her death in April 2008). The female bat rarely called, and therefore data collection and analysis were limited to males’ calls.

Data analysis

Discriminant function analysis (DFA) and multivariate analysis of variance (MANOVA) were used to determine whether or not communication calls were individual-specific. DFA provides an assessment of whether or not bats’ sound communication calls can be used to discriminate between individuals. MANOVA assesses the likelihood that the means of two or more groups have the same sampling distribution; in this case, whether or not individual bat’s calls are the same. Posthoc univariate ANOVA was conducted subsequently to confirm results from the MANOVA (Dunteman, 1984; Naguib et al., 2001).

RESULTS

A total of 15,103 calls were recorded from 5 focal pairs. As reported previously (Melendez et al., 2006), there were four dominant call-types (Fig. 2A, 2B, 2C, 2D): broadband noise-burst (BNB), downward-FM (DFM), steep-FM (StFM) and broadband click trains (BCT). StFM was the most frequently emitted (94.4%; Table 1), and BNB was the second most common communication calls (4.24%; Table 1). DFM and BCT were the least common calls (1.3% and 0.03%, respectively; Table 1).

Figure 2.

Figure 2

Open in a new tab

Spectrograms of the two dominant communication call types (FFT size 1024, Hamming window): (A) Broadband noise burst. (B) Downward FM call. (C) Steep-FM call. (D) Broadband click train

Table 1.

Number of calls emitted for each call type.

Call type Number of calls
Steep-FM 14 262
Broadband noise burst 641
Downward FM 196
Broadband click train 4
Open in a new tab

To investigate whether or not bat’s communication signals were individual specific, we analyzed the StFM and BNB calls quantitatively. Specifically, we analyzed the minimum and maximum frequencies, and the signal duration of 30 StFM and 30 BNB calls per bat (Table 2)—these were features used for identifying different call-types in an earlier study of vocal communication in little brown bats (Barclay et al., 1979). A total of 30 calls were randomly chosen from different recording sessions for only one bat per bat pair. Discriminant function analysis revealed that 89% of the StFM calls and 66% of the BNB calls could be correctly assigned to individual bats.

Table 2.

Analysis of the minimum and maximum frequency and the duration of the steep-FM calls (n=30) and broadband noise bursts (n=30) of 5 individual bats. Shown are mean and standard error of these acoustic parameters.

Call type Bat N Min freq Max freq Duration
Steep-FM A2 30 36.04±0.36 96±0 8.2±0.40
A8 30 44.71±0.45 92.7±0.89 7.93±0.34
A9 30 36.84±0.52 96±0 10.17±0.31
A10 30 46.84±0.24 91.96±0.25 7.6±0.32
A12 30 39.38±0.59 96±0 10.37±0.26
 
Broadband noise burst A2 30 4.26±0.04 85.58±1.54 38.43±7.58
A8 30 4.2±0 95.82±0.13 38.6±1.39
A9 30 4.2±0 95.9±0.1 25.8±3.14
A10 30 4.23±0.01 95.8±0.14 20.9±1.65
A12 30 4.2±0 95.9±0.1 129.3±5.73
Open in a new tab

Multivariate analysis of variance (MANOVA) revealed that both the StFMs (Wilks’ λ=0.130; F=25.83; p<0.001) and BNBs (Wilks’ λ=0.113; F=40.40; p<0.001) were individually distinct. Subsequent univariate ANOVA showed that (i) all three acoustic parameters chosen could be used to differentiate between StFM calls of individual bats, and (ii) only the maximum frequency and duration could be used for differentiating BNB calls of individuals (Table 3).

Table 3.

Univariate analysis of variance (ANOVA) on individual acoustic features.

Call Acoustic parameter F P
Steep-FM Min frequency 140.1 <0.0001
Max frequency 23.82 <0.0001
Duration 15.9 <0.0001
 
Broadband noise burst Min frequency 1.85 0.122
Max frequency 44.46 <0.0001
Duration 95.26 <0.0001
Open in a new tab

DISCUSSION

Results of the present study showed that two communication calls (the StFM and BNB calls) of adult M. lucifugus were individual specific, displaying distinct spectral and temporal characteristics. These calls can potentially be differentiated by three acoustic parameters: the minimum frequency, maximum frequency, and duration of the call. However, it remains to be determined whether or not the individual-specificity represents individual signatures, allowing bats to identify individuals acoustically. Previous in-depth analysis of the vocal signature of bats has focused on their echolocation calls (Masters et al., 1995; Obrist, 1995; Kazial et al., 2001; Fenton et al., 1999; Siemers and Kerth, 2006; Kazial et al., 2008a, 2008b; Yovel et al., 2009a, 2009b). The echolocation calls of the big brown bat (Eptesicus fuscus) have been shown to differ among individuals within a family and between adults and juveniles (Masters et al., 1995; Kazial et al., 2001). Comparative studies in several other bat species, including Euderma maculatum, Eptesicus fuscus, Lasiurus borealis, and Lasiurus cinereus, showed intra- and inter-individual variability in echolocation calls. Unlike the communication calls for which individual differences occur in both the spectral and time domains, the differences in echolocation calls were predominantly in their call frequency and FM pattern (Obrist, 1995). The temporal characteristics of echolocation calls, such as the call duration, are actively controlled during hunting and optimized to avoid overlap between the emitted pulse and returning echo (Jones, 1999), and therefore the variability between individuals is difficult to assess.

Previously, Balcombe showed that mother bats can identify their pups based on the pups’ vocal signals (Balcombe, 1990). Therefore, it would not be too surprising if adult bats can also discriminate their own vocal communication signals, as it would be evolutionarily advantageous for bats to have the ability to recognize a non-threatening familiar individual over a potentially threatening stranger using acoustic cues (Osborne, 2005; Tripovich et al., 2008). This ability is particularly handy because bats live in dark caves, form large social colonies, and have poor vision (Fenton, 1984).

Results of our study are compatible with those of Carter and colleagues in adult white-winged vampire bats, Diaemus youngi (Carter et al., 2008). These investigators showed that the antiphonal call of adult white-winged vampire bats carried individual signatures. In addition to individual calls, Bohn and colleagues recently studied the songs of adult Brazilian free-tailed bats, Tadarida brasiliensis (Bohn et al., 2009); they defined bat “songs” using the classification scheme in birds (Marler and Slabbekoorn, 2004; Catchpole and Slater, 2008), i.e., songs are longer and contain multiple syllables (or notes and∕or phrases) that are often combined in a stereotypical manner. Bohn et al., (2009) reported significant variations between individuals; however, the researchers cautioned that these results could have been affected by the more pronounced level of variation found within individuals. That is, there was a high level (72%–92%) of variation from one individual’s song rendition to the next rendition, which they suggested could have had an effect on individual signature analysis. Therefore, analysis of the acoustic syllables themselves i.e., calls (versus songs) as in this study, is necessary for gaining further insight into individual signatures. Putting the caveat aside, the various studies in echolocating bats collectively show the presence of individual-specific characteristics in not only their echolocation signals but also their sound communication signals.

Results from echolocating bats are compatible with those from studies in echolocating marine mammals: dolphins (Watwood et al., 2005; Janik et al., 2006) and whales (Weiss et al., 2006). These studies in marine mammals show that features such as duration and frequency spectrum can be used to distinguish the sound communication signals of individuals. Furthermore, these individual call signatures appear to be dependent on the behavioral context. For example, Watwood et al. (2005) showed that signature whistles, defined as the most common whistle recorded from an individual, are more likely to be used when a dolphin is separated from their partner than when the dolphins are together. Although the present study did not explore the dependence of individual signatures on the nature of social interactions, it is possible that a similar dependence exists, since little brown bats are also highly social animals.

Individual signatures of communication calls have been well documented in frogs and birds. Studies in the American bullfrog revealed that these animals can discriminate individuals on the basis of their vocal signals; the critical difference being the call’s fundamental frequency (Davis, 1987; Bee and Gerhardt, 2001a, 2001b, 2001c). A recent study in the concave-eared torrent frogs (Odorrana tormata), a species producing variable and highly complex calls, revealed that these animals also have individual-specific vocal signatures, despite its unlimited call repertoire (Feng et al., 2009b). Similar to the little brown bats, the signatures are contained in the spectral and temporal domains of the vocal signals, and are behaviorally important for neighbor-stranger discrimination (Feng et al., 2009a). Numerous studies in birds revealed that they can discriminate among individuals based on song signatures (Krebs et al., 1981; Brooks and Falls, 1975; Falls, 1982); birdsongs can differ according to distinct song types, song sequences, or vocal signatures (Weary et al., 1990; Weary and Krebs, 1992; Pincemy and Guyomarc’h, 2005; Sharp et al., 2005; Ranjard and Ross, 2008).

In conclusion, the present study shows that the communication calls of adult little brown bats carry individual-specific differences. Thus, for bats that echolocate, it may be possible to discriminate individuals on the basis of the animals’ echolocation and∕or communication calls. Further studies are necessary to determine: (i) whether or not these individual-specific differences also serve as their individual call signatures which can be used to distinguish individuals acoustically (ii) whether or not these phenomena are common to all species of echolocating bats, (iii) whether the call differences are dependent on the animal’s behavioral context.

ACKNOWLEDGMENTS

This research was supported by a Grant No. R01DC04998 and a Fellowship No. F31DC008759 from the National Institute for Deafness and Communication Disorders. We thank the Illinois Department of Natural Resources for permitting collection of bats, and the UIUC Department of Animal Resources for assistance in care of the bats.

References

  1. Balcombe, J. P. (1990). “Vocal recognition of pups by mother Mexican free-tailed bats, Tadarida brasiliensis mexicana,” Anim. Behav. 39, 960–966. 10.1016/S0003-3472(05)80961-3 [DOI] [Google Scholar]
  2. Barclay, R. M. R., Fenton, M. B., and Thomas, D. W. (1979). “Social behavior of the little brown bat, Myotis lucifugus,” Behav. Ecol. Sociobiol. 6, 137–146. 10.1007/BF00292560 [DOI] [Google Scholar]
  3. Bee, M. A., and Gerhardt, H. C. (2001a). “Neighbour-stranger discrimination by territorial male bullfrogs (Rana catesbeiana): I. Acoustic basis,” Anim. Behav. 62, 1129–1140. 10.1006/anbe.2001.1851 [DOI] [Google Scholar]
  4. Bee, M. A., and Gerhardt, H. C. (2001b). “Neighbour-stranger discrimination by territorial male bullfrogs (Rana catesbeiana): II Perceptual basis,” Anim. Behav. 62, 1141–1150. 10.1006/anbe.2001.1852 [DOI] [Google Scholar]
  5. Bee, M. A., and Gerhardt, H. C. (2001c). “Habituation as a mechanism of reduced aggression between neighboring territorial male bullfrogs, Rana catesbiana,” J. Comp. Psychol. 115, 68–82. 10.1037/0735-7036.115.1.68 [DOI] [PubMed] [Google Scholar]
  6. Bohn, K. M., Schmidt-French, B., Schwartz, C., Smotherman, M., and Pollak, G. D. (2009). “Versatility and stereotypy of free-tailed bat songs,” PLoS ONE 4, e6746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bohn, K. M., Wilkinson, G. S., and Moss, C. F. (2007). “Discrimination of infant isolation calls by female greater spear-nosed bats, Phyllostomus hastatus,” Anim. Behav. 73, 423–432. 10.1016/j.anbehav.2006.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brooks, R. J., and Falls, J. B. (1975). “Individual recognition by song in white-throated sparrows. III Song features used in individual recognition,” Can. J. Zool. 53, 1749–1761. 10.1139/z75-210 [DOI] [Google Scholar]
  9. Carter, G., Swowronski, M., Faure, P., and Fenton, B. (2008). “Antiphonal calling allows individual discrimination in white-winged vampire bats,” Anim. Behav. 76, 1343–1355. 10.1016/j.anbehav.2008.04.023 [DOI] [Google Scholar]
  10. Catchpole, C. K., and Slater, P. J. B. (2008). Bird Song: Biological Themes and Variations (Cambridge University Press, Cambridge: ). [Google Scholar]
  11. Davis, M. S. (1987). “Acoustically mediated neighbor recognition in the North American bullfrog, Rana catesbeiana,” Behav. Ecol. Sociobiol. 21, 185–190. 10.1007/BF00303209 [DOI] [Google Scholar]
  12. Dunteman, G. H. (1984). Introduction to Multivariate Analysis (Sage, Thousand Oaks: ). [Google Scholar]
  13. Falls, J. B. (1982). Individual Recognition by Sound in Birds (Academic, New York: ). [Google Scholar]
  14. Feng, A. S., Arch, V. S., Yu, Z., Yu, X. -J., Yu, Z. -M., and Shen, J. -X. (2009a). “Neighbor-stranger discrimination in concave-eared torrent frogs, Odorrana tormota,” Ethology 115, 851–856. 10.1111/j.1439-0310.2009.01666.x [DOI] [Google Scholar]
  15. Feng, A. S., Narins, P. M., Xu, C. H., Lin, W. Y., Yu, Z. L., Qiu, Q., Xu, Z. M., and Shen, J. X. (2006). “Ultrasonic communication in frogs,” Nature (London) 440, 333–336. 10.1038/nature04416 [DOI] [PubMed] [Google Scholar]
  16. Feng, A. S., Riede, T., Arch, V. S., Yu, Z., Xu, Z. -M., Yu, X. -J., and Shen, J. -X. (2009b). “Diversity of the vocal signals of concave-eared torrent frogs (Odorrana tormota): Evidence for individual signatures,” Ethology 115, 1015–1028. 10.1111/j.1439-0310.2009.01692.x [DOI] [Google Scholar]
  17. Fenton, M. B. (1984). “Echolocation: Implications for ecology and evolution of bats,” Q. Rev. Biol. 59, 33–53. 10.1086/413674 [DOI] [Google Scholar]
  18. Fenton, M. B., Jacobs, D. S., Richardson, E. J., Taylor, P. J., and White, W. (1999). “Individual signatures in the frequency modulated sweep calls of large African large-eared free-tailed bats (Otornops martiensseni; Chiroptera: Molossidae,” J. Zool. 262, 11–19. 10.1017/S095283690300431X [DOI] [Google Scholar]
  19. Janik, V. M., Sayigh, L. S., and Wells, R. S. (2006). “Signature whistle shape conveys identity information to bottlenose dolphins,” Proc. Natl. Acad. Sci. U.S.A. 103, 8293–8297. 10.1073/pnas.0509918103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Jones, G. (1999). “Scaling of echolocation call parameters in bats,” J. Exp. Biol. 202, 3359–3367. [DOI] [PubMed] [Google Scholar]
  21. Kazial, K. A., Burnett, S. C., and Masters, W. M. (2001). “Individual and group variation in echolocation calls of big brown bats, Eptesicus fuscus (Chiroptera: Verpertilionidae),” J. Mammal. 82, 339–351. [DOI] [Google Scholar]
  22. Kazial, K. A., Kenny, T. L., and Burnett, S. C. (2008a). “Little brown bats (Myotis lucifugus) recognize individual identity of conspecifics using sonar calls,” Ethology 114, 469–478. 10.1111/j.1439-0310.2008.01483.x [DOI] [Google Scholar]
  23. Kazial, K. A., Pacheco, S., and Zielinski, K. N. (2008b). “Information content of sonar calls of little borwn bats, Myotis lucifugus, potential for communication,” J. Mammal. 89, 25–33. 10.1644/06-MAMM-A-409.1 [DOI] [Google Scholar]
  24. Knornschild, M., and von Helverson, O. (2008). “Nonmutual vocal mother-pup recognition in the greater sac-winged bat,” Anim. Behav. 76, 1001–1009. 10.1016/j.anbehav.2008.05.018 [DOI] [Google Scholar]
  25. Krebs, J. R., Ashcroft, R., and Van Orsdol, K. (1981). “Song-matching in the great tit Parsus major,” Anim. Behav. 29, 918–923. 10.1016/S0003-3472(81)80029-2 [DOI] [Google Scholar]
  26. Marler, P., and Slabbekoorn, H. (2004). Nature’s Music: The Science of Birdsong (Elsevier Academic, Amsterdam: ). [Google Scholar]
  27. Masters, W. M., Raver, K. A. S., and Kazail, K. A. (1995). “Sonar signals of big brown bats, Eptesicus fuscus, contain information about individual identity, age, and family affiliation,” Anim. Behav. 50, 1243–1260. 10.1016/0003-3472(95)80041-7 [DOI] [Google Scholar]
  28. Melendez, K. V., Jones, D. L., and Feng, A. S. (2006). “Classification of communication signals of the little brown bat,” J. Acoust. Soc. Am. 120, 1095–1102. 10.1121/1.2211488 [DOI] [PubMed] [Google Scholar]
  29. Naguib, M., Hammerschmidt, K., and Wirth, J. (2001). “Microgeographic variation, habitat effects and individual signature cues in calls of chiffchaffs Phylloscopus collybita canarensis,” Ethology 107, 341–355. 10.1046/j.1439-0310.2001.00669.x [DOI] [Google Scholar]
  30. Obrist, M. K. (1995). “Flexible bat echolocation—The influence of individual, habitat, and conspecifics on sonar signal design,” Behav. Ecol. Sociobiol. 36, 207–219. 10.1007/BF00177798 [DOI] [Google Scholar]
  31. Osborne, L. (2005). “Rival recognition in the territorial tawny dragon (Ctenophorus decresii),” Acta Ethologica. 8, 45–50. 10.1007/s10211-005-0108-6 [DOI] [Google Scholar]
  32. Pearl, D. L. and Fenton, M. B. (1996). “Echolocation calls provide information about group identity in the little brown bat, Myotis lucifugus,” Can. J. Zool. 74, 2184–2192. 10.1139/z96-247 [DOI] [Google Scholar]
  33. Pincemy, G., and Guyomarc’h, C. (2005). “Inter-individual variability during morning choruses in Japanese quail males (Coturnix coturnix japonica),” C. R. Biol. 328, 97–104. 10.1016/j.crvi.2004.10.005 [DOI] [PubMed] [Google Scholar]
  34. Ranjard, L., and Ross, H. A. (2008). “Unsupervised bird song syllable classification using evolving neural networks,” J. Acoust. Soc. Am. 123, 4358–4368. 10.1121/1.2903861 [DOI] [PubMed] [Google Scholar]
  35. Sharp, S. P., McGowan, A., Wood, M. J., and Hatchwell, B. J. (2005). “Learned kin recognition cues in a social bird,” Nature (London) 434, 1127–1130. 10.1038/nature03522 [DOI] [PubMed] [Google Scholar]
  36. Siemers, B. M., and Kerth, G. (2006). “Do echolocation calls of wild colony-living Bechstein’s bats (Myotis bechsteinii) provide individual-specific signatures?,” Behav. Ecol. Sociobiol. 59, 443–454. 10.1007/s00265-005-0068-x [DOI] [Google Scholar]
  37. Siemers, B. M., and Schnitzler, H. -U. (2004). “Echolocation signals reflect niche differentiation in five sympatric congeneric bat species,” Nature (London) 429, 657–661. 10.1038/nature02547 [DOI] [PubMed] [Google Scholar]
  38. Tripovich, J. S., Charrier, I., Rogers, T. L., Canfield, R., and Arnould, J. P. Y. (2008). “Acoustic features involved in the neighbour-stranger vocal recognition process in male Australian fur seals,” Behav. Processes 79, 74–80. 10.1016/j.beproc.2008.04.007 [DOI] [PubMed] [Google Scholar]
  39. Watwood, S. L., Owen, E. C. G., Tyack, P. L., and Wells, R. S. (2005). “Signature whistle use by temporarily restrained and free-swimming bottlenose dolphins, Tursiops truncatus,” Anim. Behav. 69, 1373–1386. 10.1016/j.anbehav.2004.08.019 [DOI] [Google Scholar]
  40. Weary, D., and Krebs, J. (1992). “Great tits classify song by individual voice characteristics,” Anim. Behav. 43, 283–287. 10.1016/S0003-3472(05)80223-4 [DOI] [Google Scholar]
  41. Weary, D. M., Norris, K. J., and Falls, J. B. (1990). “Song features birds use to identify individuals,” Auk 107, 623–625. [Google Scholar]
  42. Weiss, B. M., Ladich, F., Spong, P., and Symonds, H. (2006). “Vocal behavior of resident killer whale matrilines with newborn calves: The role of family signatures,” J. Acoust. Soc. Am. 119, 627–635. 10.1121/1.2130934 [DOI] [PubMed] [Google Scholar]
  43. Yovel, Y., Melcon, M. L., Franz, M. O., Denzinger, A., and Schnitzler, H. -U. (2009a). “The voice of bats: How greater mouse-eared bats recognize individuals based on their echolocation calls,” PLOS Comput. Biol. 5, e1000400. 10.1371/journal.pcbi.1000400 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Yovel, Y., Melcon, M. L., Franz, M. O., Denzinger, A., and Schniztler, H. U. (2009b). “The voice of bats: How greater mouse-eared bats recognize individuals based on their echolocation calls,” PLOS Comput. Biol. 5, e1000400. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from The Journal of the Acoustical Society of America are provided here courtesy of Acoustical Society of America

RESOURCES