Bat Echolocation Researc h - Bat Conservation International

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hythm. Taken together, these cues will allow reliable identification to the species level, or a group of 2-3 similar species in most cases (Ahlén 1990; Ahlén and Baagøe 1999; Limpens and Roschen 1995; Limpens et al. 1997). The observer can compare aural and visual parameters with his or her own sound and image memory. Knowing and visualizing the basic principles of pulse design and of flight in relation to wing and body morphology enhances the ability to observe and interpret parameters (Fig. 1). When interpreting this mixture of cues, most species can be ruled out immediately. The parameters that are most important for discriminating between the two or three species left to be compared depends on the species involved. Examples I deliberately position myself on an open, uncluttered part of a road between a small village and a wooded hilltop. Here I expect to have the best view of passing bats and hear the most distinctive sounds. At dusk, I observe a large bat flying towards the hilltop. I can see that it is larger than E. serotinus and has broader wings. The heterodyne detector tuned to 40 to 45 kHz does not produce either the metallic tonal output associated with the harmonic of the best listening frequency for a serotine, nor its typical galloping rhythm. Rather the output is a slow, rather regular, and relatively loud rhythm like a dry ‘tick tick tick tick ...’. I record the bat pass in the memory of the time-expansion detector. The next bat flying by gives me the chance to tune down to between 30 and 35 kHz. At that frequency, the bat’s call produces a short, smacking tonal quality, like a regular ‘tack tack tack tack ....’. In the beam of my light, I see a white belly and, in the silhouette against the sky, a V-shaped tail membrane, lacking the free last vertebrate to be expected in E. serotinus. These aural and visual cues together tell me that these must be mouse-eared bats (Myotis myotis or Myotis blythi; a Figure 13 a and b: The length of the pulses and the interpulse intervals together represent the repetition rate, which differs between species. c-e: Irregularities in the pulse emission and use of alternating pulse types result in characteristic rhythms. species pair not able to be separated in flight). Later analysis of my time-expansion recordings confirms this identification. The calls are relatively long (up to 10 ms), FM pulses with a more narrowband portion between 30 and 35 kHz (e.g., Limpens and Roschen 1995; Limpens et al. 1997). Following the bats’ flight path back to the village in the morning, or checking larger attics, might allow me to find the roost and refine my identification as to whether they are M. myotis or M. blythii (Limpens 1993; Limpens and Roschen 1996, 2002). In another situation, I choose a spot on a wooded riverbank where I can silhouette bats against the dusk sky. Among others, M. daubentoni start hunting in their typical flight style, taking insects from the water with their hind feet. Their flight behavior, white bellies, and longer and broader wings distinguish them from pipistrelles. They produce a fast rhythm of dry ‘ticks,’ with a best listening frequency of 40-45 kHz. The pulse repetition rate is variable, but increases and decreases in a regular way. Later in the evening, other Myotis start to hunt higher up and parallel to the vegetation on the bank. Is this a different Myotis species, or are these still Daubenton’s bats? These bats have a white belly, too, but seem a little smaller; their wings are shorter and their bodies more compact and round. Again a dry rhythm with a best listening frequency of 40-45 kHz, but the rhythm is slower and quite regular, and lacking the twostroke rhythm Daubenton’s bats can have when flying in long loops. My working hypothesis is that these are Myotis mystacinus/brandtii. Again these bats are sibling species that are difficult to identify to the species level, based on sound and flight behavior. Analysis of time-expansion recordings show similar pulse designs for both recorded Myotis species: moderately steep, somewhat sinusoidal FM pulses, from about 70 to 25 kHz, with a little shallow FM at 45 kHz. Larger and more regular pulse intervals can be observed in the latter species group. The Daubenton’s pulses show a typical knotted form, on the spectrogram as well the oscillogram, due to the interference between signal and echo from the water surface (Kalko and Schnitzler 1989). Especially when the recordings of whiskered/ Brandt’s bats are from individuals flying farther away from the vegetation, this distortion is not evident. This is not diagnostic, however, since a whiskered bat flying down to the water to drink would give the same artifact. At dawn, I observe a concentration of these ‘whiskered/Brandt’s bats’ again on a bank near the village. I follow them to their roost and find them swarming at a wood-covered wall, making turns of up to 4-5 m. Some other white-bellied, somewhat larger bats with broad wings and larger ears are also swarming at the exact same spot. They are flying slower and much closer (< 1 m) to the wall. The overall call rhythm of the swarming bats is a clutter of fast, dry ticking, from which it is impossible to make out individuals or species. The belly of the new species is too white and their ears are too small to be long-eared bats (Plecotus sp.). They must be Myotis nat- 54 Bat Echolocation Research: tools, techniques & analysis
tereri. Hand netting on the following evening reveals pregnant M. mystacinus and pregnant M. nattereri. We have identified the bats to species, found their roost, flight path, and hunting sites and determined their reproductive status and sex. ACKNOWLEDGEMENTS I thank the organizers of the symposium for providing the opportunity for such an intensive exchange and discussion, as well as those who read and commented on earlier versions of this manuscript, including: Ingemar Ahlén, Lothar Bach, Michel Barataud, Brock Fenton, and Gary McCracken. I thank the reviewers and editors for their help with the manuscript, and Peter Twisk for his beautiful illustration of the ‘puzzle of identification’ in the field, as well as his comments on the text. Section 2: Acoustic Inventories LITERATURE CITED AHLÉN, I. 1980a. Field identification of bats and survey methods based on sounds. Myotis 18/19:128- 136. AHLÉN, I. 1980b. Problems on bat identification on sounds. Biophon 7:12-14. AHLÉN, I. 1981. Identification of Scandinavian bats by their sounds. Department of Wildlife Ecology, Swedish University of Agricultural Sciences, Uppsala, Report 6:1-56. AHLÉN, I.1990. Identification of bats in flight. Swedish Society for Conservation of Nature and the Swedish Youth Association for Environmental Studies and Conservation. AHLÉN, I. 1993. Species identification of bats in flight. Pp. 3-10 in Proceedings of the first European bat detector workshop (K. Kapteyn, ed.). The Netherlands Bat Research Foundation, Amsterdam, The Netherlands. AHLÉN, I., and H. J. BAAGØE. 1999. Use of ultrasound detectors for bat studies in Europe - experiences from field identification, surveys and monitoring. Acta Chiropterologica 1:137-150. ALDRIDGE, H. D. J. N., and I. L. RAUTENBACH. 1987. Morphology, echolocation, and resource partitioning in insectivorous bats. Journal of Animal Ecology 56:773-778 BAAGØE, H. J. 1987. The Scandinavian bat fauna: adaptive wing morphology and free flight in the field. Pp. 57-74 in Recent advances in the study of bats (M. B. Fenton,P. A. Racey and J. M. V. Rayner, eds.). Cambridge University Press, Cambridge, United Kingdom. BARATAUD, M. 1992. Reconnaissance des espèces de Chiroptères français, à l’aide d’un détecteur d’ultrasons: le point sur les possibilités actuelles. Pp. 58- 68 in Acts of the XVIth French colloquium on mammalogy. Société Française pour l’Etude et la Protection des Mammifères, Muséum d’Histoire Figure 14: Echolocation calls and the social sounds produced by male Pipistrellus kuhlii in display flight. Naturelle de Grenoble. BARATAUD, M. 1996. The world of bats: acoustic identification of French bats. Sittelle, France. BARCLAY, R. M. R. 1999. Bats are not birds – a cautionary note on using echolocation calls to identify bats: a comment. Journal of Mammalogy 80:290-296. BIBBY, C. J., N. D. BURGESS and D. A. HILL. 1992. Bird census techniques. Academic Press. Published for the BTO and the RSPB. BOONMAN, A. M. 2001. Signal design and dynamics in FM bats – implications for echo processing. Ph.D. dissertation, University of Bristol, Bristol, United Kingdom. BRITZKE, E. R., D. W. BOSSI, B. M. HADLEY and L. W. ROB- BINS. 1999. The acoustic identification of bats in Missouri. Bat Research News 39:158. BURNETT, S. C. and W. M. MASTERS. 1999. The use of neural networks to classify echolocation calls of bats. Journal of the Acoustical Society of America 106:2198. FENTON, M. B. 1986. Design of bat echolocation calls: implications for foraging ecology and communication. Mammalia. 50:193-203. FENTON, M. B. 1988. Variation in foraging strategies in five species of insectivorous bats – implications for echolocation call design. Pp. 607-611 in Animal sonar: processes and performance (P. E. Nachtigall and P. W. B. Moore, eds.). Plenum Press, New York. FENTON, M. B. 1999. Describing the echolocation calls and behaviour of bats. Acta Chiropterologica 1:127-136. FENTON, M. B. and G. P. BELL. 1981. Recognition of species of insectivorous bats by their echolocation calls. Journal of Mammalogy 62:233-243. GRIFFIN, D. R. 1958. Listening in the dark. Yale University Press, New Haven, Connecticut. GRIFFIN, D. R. 1971. The importance of atmospheric attenuation for the echolocation of bats (Chiroptera). Animal Behaviour 19:55-61. HABERSETZER, J. 1986. Vergleichende flügelmorphologische Untersuchungen an einer Fledermausge- 55
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Bat Echolocation R esearc h tools,
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TABLE OF CONTENTS CO N T R I B U T
Page 5 and 6:
CONTRIBUTING AUTHORS Ingemar Ahlén
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I N T R O D U C T I O N Bat Echoloc
Page 9 and 10: BAT NATURAL HISTORY AND ECHOLOCATIO
Page 11 and 12: species (Thies et al. 1998). Before
Page 13 and 14: y the African bat Cardioderma cor (
Page 15 and 16: participants in this symposium. Sin
Page 17 and 18: INTRODUCTION Bat detectors convert
Page 19 and 20: less sensitive than a heterodyne de
Page 21 and 22: 14 advanced computer software made
Page 23 and 24: COMMUTING AND FORAGING BEHAVIOR Fig
Page 25 and 26: NIGHTLY ACTIVITY BASED ON CAPTURE W
Page 27 and 28: 20 Recordings of echolocation calls
Page 29 and 30: 22 and analyzing echolocation calls
Page 31 and 32: 24 Resource partitioning in rhinolo
Page 33 and 34: Section 2 ACOUSTIC INVENTORIES ULTR
Page 35 and 36: f tuned = f bat . As an example, wi
Page 37 and 38: The chance of detection also depend
Page 39 and 40: ent species. But in the field, it i
Page 41 and 42: Section 2: Acoustic Inventories Fig
Page 43 and 44: Suchflug und Richtungskarakteristik
Page 45 and 46: Journal N Percentage of total (%) A
Page 47 and 48: ential power of data. Hayes (1997)
Page 49 and 50: For instance, we recorded a single
Page 51 and 52: KALCOUNIS, M. C., K. A. HOBSON, R.
Page 53 and 54: and McCracken (this volume) discuss
Page 55 and 56: using other methods (Fig. 4). Ident
Page 57 and 58: Figure 6: Schematic diagram of diff
Page 59: AMPLITUDE-RELATED PARAMETERS Loudne
Page 63 and 64: flight in bats. Pp. 93-108 in Bat b
Page 65 and 66: difficult to understand why the aut
Page 67 and 68: Figure 5: Call sequence showing the
Page 69 and 70: function analysis and artificial ne
Page 71 and 72: species identifications. Echolocati
Page 73 and 74: tially from calls emitted in the op
Page 75 and 76: LITERATURE CITED BARCLAY, R. M. R.,
Page 77 and 78: HETERODYNE AND TIME-EXPANSION METHO
Page 79 and 80: 74 QUANTIFYING SOUND FEATURES To qu
Page 81 and 82: Figure 14: Pulse rhythm diagrams fo
Page 83 and 84: A FINAL WORD Figure 21: Heterodyne
Page 85 and 86: 80 INTRODUCTION Ultrasonic detector
Page 87 and 88: 82 munity was uncovered when ultras
Page 89 and 90: 84 INTRODUCTION Being nocturnal, ba
Page 91 and 92: A Figure 2: Surveys of bat activity
Page 93 and 94: LITERATURE CITED Figure 5A: Pipistr
Page 95 and 96: 90 In order to set a foundation for
Page 97 and 98: 92 diagram, e.g., a spectrogram, fo
Page 99 and 100: ecording time of the cassette. When
Page 101 and 102: 96 detail is not helpful for specie
Page 103 and 104: tification, providing the immediate
Page 105 and 106: spectrum, which is a graph of the e
Page 107 and 108: since there is no evidence that the
Page 109 and 110: 104 Figure 5: Different modes in se
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currently the case. Even at the sim
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108 INTRODUCTION Much of our knowle
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Figure 1: On-axis search call recor
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112 SUMMARY To summarize, flight-ca
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114 SIGNAL PROCESSING TECHNIQUES FO
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al a situation as possible. Ideally
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For all networks, correct identific
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ety of London 69:111-128. JONES, G.
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eveal species-specific vocal signat
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Figure 6: Comparison of Myotis cili
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ity of the two Myotis spp. calls (F
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Section 4 RESOURCES, RESEARCH, AND
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A The data extracted from the diffe
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used. This theoretically makes it p
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ple, will not show individual calls
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Figure 10: Fast Fourier transform p
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ACKNOWLEDGEMENTS I thank the organi
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munity (now called the European Uni
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staff to collect all of the require
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and van Parijs 1993). Early studies
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Figure 5: Variation in the echoloca
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47:60-69. JONES, G., and R. D. RANS
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RECORDING TECHNIQUES Section 4: Res
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monly observed with such broadband
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ple sonograms and power spectra can
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ences between Myotis californicus a
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intensity). Our ability to detect m
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• The detector is kept stationary
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the number of bat passes in real ti
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SHERWIN, R. E., W. L. GANNON, and S
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tion trends, while they also raise
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