Echolocation Call Structure of Fourteen Bat Species in Korea

INTRODUCTION

Bats are the most unusual and specialized of all terrestrial mammals. They are the only extant vertebrates apart from birds that are capable of powered flight and, with the excep­

tion of Pteropodidae species, have mastered the night skies largely by using echolocation(biosonar) to perceive their surroundings(Griffin, 1958; Thomas et al., 2004). Echoloca­

tion calls show a great diversity in form, duration and ampli­

tude, and key characteristics are correlated with the environ­

ment in which the bat species generally flies(Jones and Teel­

ing, 2006). Typically, bat species which forage in an open habitat will emit a low­frequency ultrasonic call that will not rapidly attenuate, with a long duration and a narrow­

fre quency bandwidth. By contrast, bat species foraging in dense vegetation(i.e., a cluttered habitat) will emit short broadband calls, which are suited to localizing prey in clut­

ter(Schnitzler et al., 2003; Jones, 2005). Therefore, describ­

ing echolocation call structure can provide insights into the ecology of species that are otherwise difficult to study.

Recently the use of ultrasonic bat detectors to make acous­

tic surveys of bat activity has been rapidly increasing(e.g., Racey et al., 1998; Loeb and O’Keefe, 2006; Morris et al., 2010; Minderman et al., 2012; Stahlschmidt et al., 2012).

Acoustic survey can be especially useful in habitats where capture is generally difficult, such as large open fields and high in the forest canopy(Kunz et al., 2009). Also, acoustic surveys can be carried out at multiple sites simultaneously by using automated recording systems. To conduct such acous­

tic surveys, however, it is necessary to have a reference col­

lection of calls of known bat species and then reliable meth­

ods should be developed to identify the resident species.

The structure of echolocation calls varies, not only bet­

ween species, but also within species. A variety of factors have been shown to influence call structure, including geo­

graphical variation, foraging habitat and foraging mode(Al­

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*To whom correspondence should be addressed Tel: 81-167-42-2111, Fax: 81-167-42-2689 E-mail: fukuidai@uf.a.u-tokyo.ac.jp

Echolocation Call Structure of Fourteen Bat Species in Korea

Dai Fukui^1,2,*, David A. Hill^3,4, Sun-Sook Kim², Sang-Hoon Han²

1The University of Tokyo Hokkaido Forest, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Hokkaido 079-1563, Japan

2Division of Animal Resources, National Institute of Biological Resources, Incheon 404-708, Korea

3Wildlife Research Center, Kyoto University, Kyoto 606-8203, Japan

4Primate Research Institute, Kyoto University, Aichi 484-8506, Japan

ABSTRACT

The echolocation calls of bats can provide useful information about species that are generally difficult to observe in the field. In many cases characteristics of call structure can be used to identify species and also to obtain information about aspects of the bat’s ecology. We describe and compare the echolocation call structure of 14 of the 21 bat spe­

cies found in Korea, for most of which the ecology and behavior are poorly understood. In total, 1,129 pulses were analyzed from 93 echolocation call sequences of 14 species. Analyzed pulses could be classified into three types according to the pulse shape: FM/CF/FM type, FM type and FM/QCF type. Pulse structures of all species were con­

sistent with previous studies, although geographic variation may be indicated in some species. Overall classification rate provided by the canonical discriminant analysis was relatively low. Especially in the genera Myotis and Murina, there are large overlaps in spectral and temporal parameters between species. On the other hand, classification rates for the FM/QCF type species were relatively high. The results show that acoustic monitoring could be a powerful tool for assessing bat activity and distribution in Korea, at least for FM/QCF and FM/CF/FM species.

Keywords: bats, Chiroptera, Korean peninsula, echolocation call, interspecific variation

dridge and Rautenbach, 1987; Norberg and Rayner, 1987;

Thomas et al., 1987; Fenton, 1990; Barclay and Brigham, 1991; Obrist, 1995; Barclay et al., 1999). This means that reference calls recorded in a particular region, or in a parti­

cular habitat type, may not be applicable to other regions, or to other habitat types. Therefore, as far as possible, echo­

location call structure should be described and species iden­

tification methods developed for the particular region and habitat type where they are to be used. Numerous previous studies have analyzed and described echolocation call struc­

ture of a variety of bat species, although the majority has been done in Europe and North America(e.g., Russo and Jones, 2002; Rydell et al., 2002; Redgwell et al., 2009; Brit­

zke et al., 2011).

The Korean Peninsula is an important area to consider in biogeographical studies of East Asian mammals. At least 21 bat species are known to be distributed in Korea(Yoon, 2010). However, the ecology and behavior of Korean bats are almost unknown, except for some cave­ and house­dwel­

ling species(Chung et al., 2009, 2010b, 2011; Kim et al., 2009, 2012). Also, although Chung et al.(2010a, 2010c) have described the echolocation call structure of three Kor­

ean bat species, the echolocation call structure of most Kor­

ean bat species has not been analyzed or described yet. The aim of this study is to describe the echolocation call structure of 14 of the 21 Korean bat species. In this paper, we com­

pare characteristics of echolocation call structure between species. This is the first study to present an analysis of inter­

specific differences in echolocation calls of Korean bats.

MATERIALS AND METHODS

From 2011 to 2012, fourteen species of bats(Eptesicus sero­

tinus, Hypsugo alaschanicus, Miniopterus fuliginosus, Mur­

ina leucogaster, Mu. ussuriensis, Myotis aurascens, My. for­

mosus, My. ikonnikovi, My. macrodactylus, My. bombinus, My. petax, Pipistrellus abramus, Plecotus ognevi, and Rhi­

nolophus ferrumequinum) were caught from a range of loca­

tions throughout the country(Fig. 1), using mist nets and harp traps in forests, or using a hand net at day or night roosts (caves and houses). After capturing bats, we checked their sex, maturity and reproductive condition, and measured their forearm length using calipers and body weight using a balance. In this study scientific names follow Yoon(2010), except for My. aurascens, My. bombinus, My. petax, H. ala­

schanicus, Mi. fuliginosus, and P. ognevi. The taxonomic status of Korean small­sized Myotis bats is not well studied.

Although My. aurascens is not described in Yoon(2010), we follow Tsytsulina et al.(2012), who have suggested the occurrence of My. aurascens in Korea. Although My. petax

had been included in My. daubentonii in Asia, we follow Matveev et al.(2005), who proposed it as a valid species for the “eastern” group of My. daubentonii. Yoon(2010) has described My. nattereri from Korea, but the east Asian popu­

lation is now considered to be My. bombinus(Simmons, 2005). Although H.(Pipistrellus) alaschanicus had been treated as a subspecies of H.(Pipistrellus) savii, we follow Horácek et al.(2000), who argued that H. alaschanicus from Mongolia, China, the Russian Far East, Korea and Japan, should be recognized as a valid species of the genus Hyp­

sugo(Pipistrellus) and different from H. savii. Although Yoon(2010) treated the bent­winged bat from Korea as Mi.

schreibersii, we follow Tian et al.(2004), who proposed the Asian population as a separate species, Mi. fuliginosus. In addition, although P. auritus is described in Yoon(2010), recent genetic and morphological analyses indicate that the Far Eastern population is a distinct species P. ognevi(Spit­

zenberger et al., 2006).

Call recording

Calls were recorded using a Pettersson D240x bat detector

Fig. 1. Map of the locations where bats were captured or re- corded for this study.

(Pettersson Elektronik AB, Uppsala, Sweden) linked to a solid state recorder(R­05; Roland, Shizuoka, Japan). The D240x was set to time­expansion mode, in which it records 3.4s of ultrasound, slows the signal down ten times to bring it into the audible range, and plays it back. This technique preserves all characteristics of the original sound and allows accurate measurement of acoustic parameters. The record­

ings were made in the following four situations. (1) Hand­

release: Captured bats except all individuals of Rhinolophus ferrumequinum and some individuals of other species(see below) were released by hand on a forest road(width >5m) near the point of capture, and the calls produced from 1-2 seconds after release were recorded. Recordings were made with the detector held at the same height as, and about 2m away from the released bat. (2) Free­flight: If we were able to make positive species identification of flying bats(e.g., checking individuals at a night roost, or individuals that went into mist nets after observed flight), we recorded echoloca­

tion calls during their flight. (3) Flight room: Some bats were brought back to the laboratory to be prepared as specimens.

Before making specimens, these individuals were released in a flight room(4.7m length, 3.0m width, and 2.7m height) in National Institute of Biological Resources(NIBR), and echolocation calls were recorded during their flight inside the room. (4) Perch: For R. ferrum equinum, perch calls were recorded when the bats hung from the roof of a mesh bag(40 cm square by 40cm height).

Call analysis

The structure of recorded calls was analyzed using Bat Sound 3.1 software(Pettersson Elektronik AB) with a sampling rate of 44.1kHz and a Hanning window. Fast Fourier Transform size was 512 for sonograms and power spectra. All pulses in each 3.4s recorded call(call sequence) were analyzed. We

measured the following parameters from each pulse: dura­

tion(D), maximum frequency(FMAX, the highest frequency of the pulse), minimum frequency(FMIN, the lowest fre­

quency of the pulse), peak frequency(PF, frequency of max­

imum energy of the pulse), and interpulse interval(IPI, time from the beginning of measured pulse to the beginning of next pulse). D, FMAX, FMIN, and IPI were measured from spectrograms, and PF from power spectra.

Statistical procedures

If calls of the species were recorded in more than two situa­

tions, the differences in call parameters between recording situations(hand­release, flight room, free flight and perch) were analyzed using linear mixed models(LMMs). In the analyses, the recording situation was the fixed effect, and the call sequence was treated as a random effect to account for potential differences among individuals. A value of p^<0.05 was considered statistically significant. Next, we compared the pulse structures between each species by canonical dis­

criminant analysis(CDA) using all five parameters. In the CDA, the last pulse in each call sequence was excluded because it did not have a following call, and therefore there was no IPI(as defined above). All analyses were performed in the R environment for statistical computing(R Develop­

ment Core Team, 2012) and its extended nlme and MASS packages(Pinheiro et al., 2012).

RESULTS

In total, 93 echolocation call sequences were recorded from 93 individuals of 14 species(Table 1). From these 93 call sequences, 1,129 pulses were analyzed. Descriptive statis­

tics of calls from all species are shown in Appendix 1. Analy­

Table 1. Species recorded and number of individuals in each recording situation

Recording situations

Free flight Hand-released Flight room Perch

Rhinolophus ferrumequinum 12 0 0 25

Myotis aurascens 0 2 2 0

Myotis formosus 0 3 0 0

Myotis ikonnikovi 0 7 5 0

Myotis macrodactylus 0 3 0 0

Myotis bombinus 0 3 0 0

Myotis petax 0 2 0 0

Plecotus ognevi 0 0 3 0

Murina ussuriensis 0 2 3 0

Murina leucogaster 0 6 0 0

Eptesicus serotinus 0 3 0 0

Hypsugo alaschanicus 1 3 0 0

Miniopterus fuliginosus 1 4 0 0

Pipistrellus abramus 0 3 0 0

zed pulses could be classified into the following three types according to the pulse shape.

FM/CF/FM type species

Rhinolophus ferrumequinum produced typical FM/CF/FM echolocation pulses, i.e., pulses with a long, strictly cons­

tant­frequency(CF) component preceded and followed by a brief, frequency­modulated(FM) sweep(Fig. 2). Mean bandwidth was 4.7-19.5kHz. Calls of the individuals from Jeju island had significantly higher FMAX and PF than those from the peninsula(LMMs, FMAX: t = -9.29, p^<0.05;

PF: t= -12.22, p^<0.05)(Appendix 1). The only significant

Fig. 2. Sonograms of 3 species of bats in Korea. For Myotis aurascens, two types of calls recorded in different situations(flight room and hand-released) are shown.

difference between the free­flight and perch calls was in D (LMM, t= -2.51, p=0.02).

FM type species

Pulses of nine species of the genera Murina, Myotis, and Plecotus were classified as this call type. All six Myotis spe­

cies captured in this study had similar mean PF(about 40.0-­

60.0kHz(Figs. 2-4, Appendix 1). Pulses emitted by the genus Myotis had broad bandwidth(^>50kHz). Pulses emit­

ted by My. bombinus had particularly broad bandwith(71.4- 113.1kHz)(Fig. 3, Appendix 1) similar to those of genus Mu­

rina. My. formosus and My. ikonnikovi also emitted pulses with relatively broader bandwidth(71.6-87.7kHz and 56.8-­

92.9kHz, respectively)(Figs. 2, 3, Appendix 1). D for these

Fig. 3. Sonograms of 4 species of bats in Korea.

species was short(1.7-5.9ms) but slightly longer than for the genus Murina or Plecotus. For pulses of My. aurascens and My. ikonnikovi, which were recorded in two situations (flight room and hand release), there were no significant differences between situations in any parameter except for bandwidth of My. ikonnikovi(LMM, t =2.27, p =0.047).

Plecotus ognevi had the lowest mean PF of the nine FM

type species(38.5-39.2kHz)(Appendix 1). Pulses emitted by this species had shorter bandwidth(22.4-27.0kHz) and a distinct harmonic(Fig. 4). The mean PF of Murina ussuri­

ensis was the highest among those of the nine FM type spe­

cies(70.0-82.9kHz)(Appendix 1). Although it was variable, the mean PF of Murina leucogaster was also relatively high­

er(45.7-67.9kHz) than other FM type species(Appendix

Fig. 4. Sonograms of 4 species of bats in Korea.

1). D for both species was relatively short(1.6-4.7ms in Mu. ussuriensis and 1.9-3.5ms in Mu. leucogaster), and bandwidth was broad(67.9-85.4kHz and 67.3-102.4kHz, respectively)(Fig. 3, Appendix 1). There were no significant differences in call parameters of Mu. ussuriensis between different recording situations.

FM/QCF type species

Calls of Eptesicus serotinus, Hypsugo alaschanicus, Miniop­

terus fuliginosus, and Pipistrellus abramus were all defined as FM/QCF type(Figs. 4, 5). These pulses had two compo­

nents: they began with steep FM, and moved to shallow FM (quasi­constant frequency, QCF) in the latter part of the

Fig. 5. Sonograms of 4 species of bats in Korea. For Miniopterus fuliginosus, two types of calls recorded in different situations(free flight and hand-released) are shown.

pulse. Pulses of these species had relatively longer D and shorter bandwidth than the FM type species. Among these species, pulses of E. serotinus had the lowest mean PFs(29.7 -31.6kHz). D and bandwidth of E. serotinus were 8.0-8.9 ms and 32.7-42.3kHz, respectively(Appendix 1). H. ala­

schanicus also had lower mean PFs(34.0-36.2kHz) than all other species except for E. serotinus. D and bandwidth of H.

alaschanicus were 2.4-7.8ms and 15.6-34.2kHz, respecti­

vely(Appendix 1). Hand­released calls of H. alaschanicus had significantly higher PF than free­flight calls(LMM, t=

4.40, p=0.048), whereas there were no differences in other parameters. The mean PFs, Ds, and bandwidths of the pulses from Mi. fuliginosus were 50.3-54.2kHz, 2.9-7.2ms, and 9.4-37.6kHz, respectively(Appendix 1). Free­flight calls of M. fuliginosus had significantly longer D and IPI than hand­released calls(LMMs, D: t= -3.53, p=0.038; IPI:

t= -6.74, p=0.007). The mean PFs for Pi. abramus were slightly lower than those for Mi. fuliginosus(47.0-48.1kHz), whereas there were no distinct differences in Ds(5.2-6.0 ms) or bandwidths(19.9-30.0kHz)(Appendix 1).

CDA

CDA using 5 parameters resulted in 76.4% of 1,036 records of echolocation pulses being correctly classified to one of the 16 species(Table 2). The first 3 discriminant functions explained 97.5% of total variation(Table 3). Pulses from three species(Eptesicus serotinus, Plecotus ognevi, and Rhi­

nolophus ferrumequinum) were all classified correctly, and pulses from Miniopterus fuliginosus and Myotis bombinus were classified with more than 90% accuracy. On the other hand, pulses from My. petax were the least accurately classi­

fied(22.6%), and were mainly misclassified as being from My. ikonnikovi and My. macrodactylus. Pulses from Murina leucogaster, My. aurascens and My. formosus were also poorly classified(45.1%, 32.6%, and 38.3%, respectively).

In the canonical score plot, the cluster of R. ferrumequinum was distinct from clusters of other species(Fig. 6). There was considerable overlap among the clusters of the Myotis

Table 2. Summary of classification of 16 species by canonical discriminant analysis Classified asTrue species R. ferrume­ quinumMy. aura­ scensMy. for­ mosusMy. ikon­ nikoviMy. macro­ dactylusMy. bom­ binusMy. petaxPl. ogneviMu. ussu­ riensisMu. leu co­ gasterE. sero­ tinusH. alascha­ nicusMi. fuligi­ nosusPi. abra­ mus R. ferrumequinum My. aurascens My. formosus My. ikonnikovi My. macrodactylus My. bombinus My. petax Pl. ognevi Mu. ussuriensis Mu. leucogaster E. serotinus H. alaschanicus Mi. fuliginosus Pi. abramus n n correct % correct

230 0 0 0 0 0 0 0 0 0 0 0 0 0 230 230 100.0

0 15 1 9 11 0 1 0 0 5 4 0 0 0 46 15 32.6

0 0 23 26 0 0 0 0 0 11 0 0 0 0 60 23 38.3

0 20 0 154 1 0 4 0 1 7 5 0 0 0 192 154 79.7

0 3 0 2 40 0 4 0 0 0 0 0 1 1 51 40 76.5

0 1 0 0 0 46 0 0 0 1 3 0 0 0 51 46 90.2

0 1 0 11 10 0 7 0 0 0 0 1 1 0 31 7 22.6

0 0 0 0 0 0 0 49 0 0 0 0 0 0 49 49 100.0

0 0 0 19 0 0 0 0 48 0 0 0 6 0 73 48 65.8

0 0 5 24 6 7 0 1 3 36 0 0 0 0 82 36 45.1

0 0 0 0 0 0 0 0 0 0 36 0 0 0 36 36 100.0

0 0 0 0 0 0 0 4 0 0 1 23 0 0 28 23 82.1

0 0 0 0 0 0 0 0 0 0 0 0 67 3 70 67 95.7

0 0 0 0 0 0 1 0 0 0 0 0 17 19 37 19 51.4 Model relied on 5 parameters (FMAX, FMIN, PF, D, and IPI). Overall correct classification rate was 76.4%. FMAX, maximum frequency; FMIN, minimum frequency; PF, frequency of maximum energy; D, duration; IPI, inter-pulse interval.

Table 3. Coefficients of first 3 canonical discriminants of classi- fication of 14 species by linear discriminant analysis

CD1 CD2 CD3

FMAX FMIN PF D IPI Bandwidth

Proportion of trace(%)

-0.0210 0.1459 0.0366 0.0647 -0.0079 -0.0425 79.1

0.0293 0.0492 0.0597 0.0182 0.0014 0.0281 13.2

-0.0052 -0.1648 0.0291 0.1115 -0.0035 0.0143 5.2 Model relied on 5 parameters(FMAX, FMIN, PF, D, and IPI).

FMAX, maxi mum frequency; FMIN, minimum frequency; PF, frequency of maximum energy; D, duration; IPI, inter-pulse interval.

species, although that of My. bombinus was relatively dis­

tinct from other species. The clusters of FM/QCF type spe­

cies had relatively little overlap with each other.

DISCUSSION

Of the 14 bat species treated in this study, call structures of 11 species were described from Korea for the first time. The pulse parameters measured for each species were generally consistent with those of other studies in surrounding regions, such as China and Japan, whereas some species seem to show geographical variation in pulse parameters.

Rhinolophus ferrumequinum was the only species in this study with FM/CF/FM type calls. The pulse structures of this species have been described previously for other regions (Vaughan et al., 1997; Parsons and Jones, 2000; Russo and Jones, 2002; Huihua et al., 2003; Fukui et al., 2004; Sun et al., 2008), and our results agree with those of a previous study of this species in Korea(Chung et al., 2010c). It is known that PF of R. ferrumequinum shows a geographic cline along which frequency decreases continuously from west to east in its distributional range(Heller and von Hel­

versen, 1989). In East Asia, the PF of this species also decre­

ases from west to east(Beijing­China: 75.1kHz, Jilin Pro­

vince­China: 69.6kHz, Kyushu­Japan: 67-69kHz, western Honshu­Japan: 67-68kHz, eastern Honshu­Japan: 65-67 kHz, Hokkaido­Japan: 63-66kHz)(Huihua et al., 2003;

Fukui et al., 2004; Matsumura, 2005; Sun et al., 2008), so shows the same trend at a smaller spatial scale. The PF of this species from the Korean peninsula in this study(67.8-­

69.5kHz)(Appendix 1) is consistent with this trend. How­

ever, we found that the Jeju population of R. ferrumequinum has distinctively higher PF(72kHz)(Appendix 1) than pop­

ulations in surrounding regions such as Kyushu and the Kor­

ean Peninsula. This phenomenon may be due to the isola­

tion effect. In fact, the PF of R. ferrumequinum(69-70kHz) (Matsumura, 2005) from Tsushima island, which is also iso­

lated from the continent and Japanese main islands, is higher than surrounding populations. Yoshino et al.(2008) revealed that restricted dispersal of females causes echolocation call frequency variation within rhinolophid bats between regions because the CF component is determined by mother­off­

spring transmission. Further phylogenetic analysis in east Asia is needed to confirm the isolation effect.

Pulse characteristics of all six Myotis species were typical

Fig. 6. Individual scores on canonical discriminant functions 1 and 2.

Eptesicus serotinus Hypsugo alaschanicus Miniopterus fuliginosus Murina leucogaster Murina ussuriensis Myotis aurascens Myotis formosus Myotis ikonnikovi Myotis macrodactylus Myotis bombinus Myotis petax Pipistellus abramus Plecotus ognevi Rhinolophus ferrumequinum

CD2

CD1 6

4

2

0

-2

-4

- 5 0 5 10

FM calls that have been described for Myotis species by pre­

vious studies(e.g., Russo and Jones, 2002). Our results sug­

gest that all Myotis species in this study seem to be “narrow space gleaning foragers” or “edge space aerial/trawling fora­

gers”, following the call categorisation described by Schnitz­

ler et al.(2003). In fact, all individuals of Myotis were caught at the forest edge or interior, and we had frequently observed

“Myotis­like” bats flying above water surfaces(D. Fukui, personal observation). Of the six species, pulse structures of five(My. formosus, My. ikonnikovi, My. macrodactylus, My.

bombinus, and My. petax) have been described in previous studies(e.g., Fukui et al., 2004, 2007; Sun et al., 2008), and our results are all consistent with these studies. However, we cannot assess geographical variation for Myotis species because measured parameters of these species showed large intra­specific vari ation(Appendix 1), as has been noted by other studies(Fukui et al., 2004; Obrist et al., 2004).

Pulse characteristics of Plecotus ognevi were similar to those of Pl. auritus from Europe, and of other Plecotus spe­

cies. The PFs for Pl. ognevi in this study were 38.5-39.6 kHz(Appendix 1), whereas those of Pl. auritus from British, Swiss and Italian populations are 29.9, 37.7, 33.1kHz, res­

pectively(Parsons and Jones, 2000; Russo and Jones, 2002;

Obrist et al., 2004). In Japan, the mean PF of Pl. sacrimon­

tis, which was treated as Pl. auritus before Spitzenberger et al.(2006), is 36.7kHz(Fukui et al., 2007). Recent genetic and morphological analyses indicate that the east Asian pop­

ulations should be divided into several species, and the Kor­

ean population is considered to be Pl. ognevi(Spitzenberger et al., 2006). Thus, this variation in mean PF may represent inter­specific variation, although recording situation may also have influenced the results.

Pulse characteristics of both Murina species in this study were similar to those described for three Murina species in Malaysia(Kingston et al., 1999) and two in Japan(Fukui et al., 2004). Short, broad­band and low intensity FM calls are thought to be used for the detection of arthropod prey in clutter(Simmons et al., 1979; Neuweiler, 1989; Schnitzler et al., 2003). Echolocation calls of Murina in this study area would be suitable for highly cluttered forest understory. Al­

most all individuals in this study were caught inside forests (D. Fukui, personal observation). As with the genus Myotis, it is difficult to compare between populations because of large intra­specific variation in the measured parameters.

Pulse structures of four FM/QCF type species in this study were also consistent with previous studies, which have described pulse structures of these species in other regions.

Although pulse frequencies of Hypsugo alaschanicus and Pipistrellus abramus were similar to those of Japanese populations(PF of H. alaschanicus: 33.9-36.4kHz, mean PF of Pi. abramus: 46.9kHz)(Fukui et al., 2002, 2013), the

other two species seem to show variation between studies.

For example, PF of Eptesicus serotinus varies with regions (U.K.: 33.6kHz, Switzerland: 26.8kHz, Italy: 29.9kHz, Greece: 31.7kHz)(Parsons and Jones, 2000; Russo and Jones, 2002; Obrist et al., 2004; Papadatou et al., 2008).

Also, the PF of Miniopterus fuliginosus was different from that of Japanese Mi. fuliginosus(47.2-49.8kHz)(Funakoshi, 2010). It is possible that there are geographic variations in the pulse frequency. We cannot determine the extent to which these differences represent geographic variation, how­

ever, because recording conditions may have varied between studies. It is known that echolocation pulse parameters cha­

nge with flying situation(Schnitzler and Kalko, 2001), and our results showed differences in some pulse parameters of H. alaschanicus and Mi. fuliginosus between recording situations. To reveal geographic variations in the calls of FM/QCF and FM type species, we would need to compare calls of each population that were recorded under the same conditions.

In this study, the overall classification rate provided by the CDA was similar to or lower than previous studies where discriminant analysis was applied(e.g., Parsons and Jones, 2000; Russo and Jones, 2002; Fukui et al., 2004; Papadatou et al., 2008). This result may have been caused by low clas­

sification rates of FM type species, apart from Myotis bom­

binus, whose calls are identifiable because of their broad bandwidth. Generally, echolocation calls from most Myotis and Murina species show similar pulse structures and large overlap in spectral and temporal parameters(Vaughan et al., 1997; Russo and Jones, 2002; Hughes et al., 2011). Eight of the 14 species in this study were Myotis or Murina spe­

cies. On the other hand, the CDA showed relatively high classification rates for the FM/QCF type species, except for Pipistrellus abramus. Previous studies have also shown high classification rates on the FM/QCF type species(Parsons and Jones, 2000; Russo and Jones, 2002; Papadatou et al., 2008; Hughes et al., 2011). This may be related to the QCF component in the pulse. In general, the QCF component seems to be less variable than the FM component in spectral parameters. Coefficients of first canonical discriminants in­

dicate that the most important variable in the model was FMIN, which is measured from the QCF component of the FM/QCF type pulse. Our results indicate that acoustic moni­

toring in Korea may be a powerful tool to assess bat activity, including identification to species, at least for the four FM/

QCF and one FM/CF/FM species.

Conclusion

This study reveals echolocation call structures of the 14 Kor­

ean bat species and those of 11 species are described from Korea for the first time. Patterns of habitat use and foraging

behavior of bats have been interpreted in terms of the struc­

ture of echolocation calls. In theory, it should be possible to predict the kinds of habitat that each species in a bat com­

munity is likely to be associated with on the basis of these characteristics. Such predictions could then be used to devel­

op plans for conservation management that would enhance habitats for bats. In this study calls of seven additional spe­

cies that have been recorded in Korea could not be investi­

gated. To understand feeding habitats of Korean bats and make a more accurate and comprehensive acoustic identifi­

cation model, larger sample sizes are needed.

ACKNOWLEDGMENTS

We are sincerely grateful to Dr. Satoko Yoshikura and Mr.

Dae­Shik Oh for support of our fieldwork.

REFERENCES

Aldridge HDJN, Rautenbach IL, 1987. Morphology, echoloca­

tion and resource partitioning in insectivorous bats. Journal of Animal Ecology, 56:763­778. http://dx.doi.org/10.2307/

Barclay RMR, Brigham RM, 1991. Prey detection, dietary niche 4947 breadth, and body size in bats: why are aerial insectivorous bats so small? American Naturalist, 137:693­703. http://

dx.doi.org/10.1086/285188

Barclay RMR, Fullard JH, Jacobs DS, 1999. Variation in the echolocation calls of the hoary bat(Lasiurus cinereus): in­

fluence of body size, habitat structure, and geographic loca­

tion. Canadian Journal of Zoology, 77:530­534. http://dx.

doi.org/10.1139/cjz­77­4­530

Britzke ER, Duchamp JE, Murray KL, Swihart RK, Robbins LW, 2011. Acoustic identification of bats in the eastern United States: A comparison of parametric and nonpara­

metric methods. Journal of Wildlife Management, 75:660­

667. http://dx.doi.org/10.1002/jwmg.68

Chung CU, Han SH, Kim SD, Lim CW, Kim SC, Kim CY, Lee HJ, Kwon YH, Kim YC, Lee CI, 2011. Home­ranges of female Pipistrellus abramus(Chiroptera: Vespertilionidae) in different reproductive stages revealed by radio­telemetry.

Korean Journal of Environment and Ecology, 25:1­9(in Korean with English abstract).

Chung CU, Han SH, Lee CI, 2009. Use of bridges as roosting site by bats(Chiroptera). Korean Journal of Environment and Ecology, 23:294­301(in Korean with English abstract).

Chung CU, Han SH, Lee CI, 2010a. Development of vocal sig­

nals in the Pipistrellus abramus(Chiroptera: Vespertilioni­

dae). Korean Journal of Environment and Ecology, 24:202­

208(in Korean with English abstract).

Chung CU, Han SH, Lee CI, 2010b. Home­range analysis of

pipistrelle bat(Pipistrellus abramus) in non­reproductive season by using radio­tracking. Korean Journal of Envi­

ronment and Ecology, 24:487­492(in Korean with English abstract).

Chung CU, Han SH, Lim CW, Kim SC, Lee HJ, Kwon YH, Kim CY, Lee CI, 2010c. General patterns in echolocation call of greater horseshoe bat Rhinolophus ferrumequi­

num, Japanese pipistrelle bat Pipistrellus abramus and large­footed bat Myotis macrodactylus in Korea. Journal of Environmental Sciences, 19:61­68(in Korean with English abstract). http://dx.doi.org/10.5322/JES.2010.19.1.061 Fenton MB, 1990. The foraging behavior and ecology of ani­

mal­eating bats. Canadian Journal of Zoology, 68:411­422.

http://dx.doi.org/10.1139/z90­061

Fukui D, Agetsuma N, Hill DA, 2004. Acoustic identification of eight species of bat(Mammalia: Chiroptera) inhabiting forests of southern Hokkaido, Japan: potential for conser­

vation monitoring. Zoological Science, 21:947­955. http://

dx.doi.org/10.2108/zsj.21.947

Fukui D, Agetsuma N, Hill DA, 2007. Bat fauna in the Nakaga­

wa Experimental Forest, Hokkaido University. Research Bulletin of the Hokkaido University Forests, 64:29­36(in Japanese with English abstract).

Fukui D, Maeda K, Satô M, Kawai K, 2003. New record of the Japanese house­dwelling bat, Pipistrellus abramus from Hokkaido. Mammalian Science, 43:39­43(in Japanese with English abstract).

Fukui D, Mochida M, Yamamoto A, Kawai K, 2013. Roost and echolocation call structure of the Alashanian pipistrelle Hyp­

sugo alaschanicus: First confirmation as a resident species in Japan. Mammal Study, 38:61­66. http://dx.doi.org/10.

3106/041.038.0108

Funakoshi K, 2010. Acoustic identification of thirteen insectiv­

orous bat species from the Kyushu District, Japan. Mamma­

lian Science(Honyurui Kagaku), 50:165­175(in Japanese with English abstract).

Griffin DR, 1958. Listening in the dark. Yale University Press, New Haven, CT, pp. 1­413.

Heller KG, von Helversen O, 1989. Resource partitioning of sonar frequency bands in rhinolophoid bats. Oecologia, 80:

178­186. http://dx.doi.org/10.1007/BF00380148

Horácek I, Hanák V, Gaisler J, 2000. Bats of the palearctic re­

gion: a taxonomic and biogeographic review. In: Proceed­

ings of the VIIIth European Bat Research Symposium, Vol.

1, Approaches to biogeography and ecology of bats. Publi­

cation of the Chiropterological Information Center, Institute of Systematics and Evolution of Animal PAS, Krakow, pp.

11­157.

Hughes AC, Satasook C, Bates PJJ, Soisook P, Sritongchuay T, Jones G, Bumrungsri S, 2011. Using echolocation calls to identify Thai bat species: Vespertilionidae, Emballonuridae, Nycteridae and Megadermatidae. Acta Chiropterologica, 13:447­455. http://dx.doi.org/10.3161/150811011X624938 Huihua Z, Shuyi Z, Mingxue Z, Jiang Z, 2003. Correlations

between call frequency and ear length in bats belonging

to the families Rhinolophidae and Hipposideridae. Jour­

nal of Zoology, 259:189­195. http://dx.doi.org/10.1017/

S0952836902003199

Jones G, 2005. Echolocation. Current Biology, 15:R484­R488.

http://dx.doi.org/10.1016/j.cub.2005.06.051

Jones G, Teeling EC, 2006. The evolution of echolocation in bats. Trends in Ecology and Evolution, 21:149­156. http://

dx.doi.org/10.1016/j.tree.2006.01.001

Kim SS, Choi YS, Kim BH, Yoo JC, 2009. The current distribu­

tion and habitat preferences of hibernating Myotis formosus in Korea. Journal of Ecology and Field Biology, 32:191­

Kim SS, Choi YS, Yoo JC, 2012. Thermal preference and hi­195.

bernation period of Hodgson’s bats(Myotis formosus) in the temperate zone: how does the phylogenetic origin of a species affect its hibernation strategy? Canadian Journal of Zoology, 91:47­55. http://dx.doi.org/10.1139/cjz­2012­

Kingston T, Jones G, Akbar Z, Kunz TH, 1999. Echolocation 0145 signal design in Kerivoulinae and Murininae(Chiroptera:

Vespertilionidae) from Malaysia. Journal of Zoology, Lon­

don, 249:359­374. http://dx.doi.org/10.1111/j.1469­7998.

1999.tb00771.x

Kunz TH, Hodgkinson R, Weise C, 2009. Methods of capturing and handling bats. In: Ecological and behavioral methods for the study of bats. 2nd ed(Eds., Kunz TH, Parsons S).

Johns Hopkins University Press, Baltimore, MD, pp. 3­35.

Loeb SC, O’Keefe JM, 2006. Habitat use by forest bats in South Carolina in relation to local, stand, and landscape charac­

teristics. Journal of Wildlife Management, 70:1210­1218.

http://dx.doi.org/10.2193/0022­541x(2006)70[1210:hubfbi]

2.0.co;2

Matsumura S, 2005. Geographical variation of echolocation call in the microchiroptera. In: Natural history of zoogeo­

graphy: evolutionary biology of distribution and diversity of animals(Eds., Masuda R, Abe H). Hokkaido University Press, Sapporo, pp. 225­241(in Japanese).

Matveev VA, Kruskop SV, Kramerov DA, 2005. Revalidation of Myotis petax Hollister, 1912 and its new status in con­

nection with M. daubentonii(Kuhl, 1818)(Vespertilionidae, Chiroptera). Acta Chiropterologica, 7:23­37. http://dx.doi.

org/10.3161/1733­5329(2005)7[23:rompha]2.0.co;2 Minderman J, Pendlebury CJ, Pearce­Higgins JW, Park KJ,

2012. Experimental evidence for the effect of small wind turbine proximity and operation on bird and bat activity.

PLoS ONE, 7:e41177. http://dx.doi.org/10.1371/journal.

pone.0041177

Morris AD, Miller DA, Kalcounis­Rueppell MC, 2010. Use of forest edges by bats in a managed pine forest landscape.

Journal of Wildlife Management, 74:26­34. http://dx.doi.

org/10.2193/2008­471

Neuweiler G, 1989. Foraging ecology and audition in echolo­

cating bats. Trends in Ecology and Evolution, 4:160­166.

http://dx.doi.org/10.1016/0169­5347(89)90120­1

Norberg UM, Rayner JMV, 1987. Ecological morphology and

flight in bats(Mammalia; Chiroptera): wing adaptations, flight performance, foraging strategy and echolocation.

Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 316:335­427. http://dx.doi.

org/10.1098/rstb.1987.0030

Obrist MK, 1995. Flexible bat echolocation: the influence of individual, habitat and conspecifics on sonar signal design.

Behavioral Ecology and Sociobiology, 36:207­219. http://

dx.doi.org/10.1007/BF00177798

Obrist MK, Boesch R, Flückiger PF, 2004. Variability in echo­

location call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach. Mammalia, 68:307­322. http://dx.doi.org/10.1515/mamm.2004.030 Papadatou E, Butlin RK, Altringham JD, 2008. Identification

of bat species in Greece from their echolocation calls. Acta Chiropterologica, 10:127­143. http://dx.doi.org/10.3161/

150811008X331153

Parsons S, Jones G, 2000. Acoustic identification of twelve spe­

cies of echolocating bat by discriminant function analysis and artificial neural networks. Journal of Experimental Bio­

logy, 203:2641­2656.

Pinheiro J, Bates D, DebRoy S, Sarkar D, and the R Core team, 2012. nlme: linear and nonlinear mixed­effects models. R package version 3.1­103[Internet]. Accessed 21 April 2015,

<http://cran.r­project.org/web/packages/nlme/index.html>.

R Development Core Team, 2012. R: A language and environ­

ment for statistical computing. R Foundation for Statistical Computing, Vienna.

Racey PR, Swift SM, Rydell J, Brodie L, 1998. Bats and insects over two Scottish rivers with contrasting nitrate status. Ani­

mal Conservation, 1:195­202. http://dx.doi.org/10.1111/j.

1469­1795.1998.tb00029.x

Redgwell RD, Szewczak JM, Jones G, Parsons S, 2009. Clas­

sification of echolocation calls from 14 species of bat by support vector machines and ensembles of neural networks.

Algorithms, 2:907­924. http://dx.doi.org/10.3390/a2030907 Russo D, Jones G, 2002. Identification of twenty­two bat spe­

cies(Mammalia: Chiroptera) from Italy by analysis of time­

expanded recordings of echolocation calls. Journal of Zoo­

logy, 258:91­103. http://dx.doi.org/10.1017/s09528369020 01231

Rydell J, Arita HT, Santos M, Granados J, 2002. Acoustic iden­

tification of insectivorous bats(order Chiroptera) of Yucat­

an, Mexico. Journal of Zoology, 257:27­36. http://dx.doi.

org/10.1017/s0952836902000626

Schnitzler HU, Kalko EKV, 2001. Echolocation by insect­eat­

ing bats. Bioscience, 51:557­569. http://dx.doi.org/10.1641/

0006­3568(2001)051[0557:ebieb]2.0.co;2

Schnitzler HU, Moss CF, Denzinger A, 2003. From spatial ori­

entation to food acquisition in echolocating bats. Trends in Ecology and Evolution, 18:386­394. http://dx.doi.org/10.

1016/s0169­5347(03)00185­x

Simmons NB, 2005. Order Chiroptera. In: Mammal species of the world: a taxonomic and geographic reference, 3rd ed.

(Eds., Wilson DE, Reeder DM). Johns Hopkins University Press, Baltimore, MD, pp. 312­529.

Simmons JA, Fenton MB, O’Farrell MJ, 1979. Echolocation and pursuit of prey by bats. Science, 203:16­21. http://dx.

doi.org/10.1126/science.758674

Spitzenberger F, Strelkov PP, Winkler H, Haring E, 2006. A preliminary revision of the genus Plecotus(Chiroptera, Ves­

pertilionidae) based on genetic and morphological results.

Zoologica Scripta, 35:187­230. http://dx.doi.org/10.1111/

j.1463­6409.2006.00224.x

Stahlschmidt P, Pätzold A, Ressl L, Schulz R, Brühl CA, 2012.

Constructed wetlands support bats in agricultural landscapes.

Basic and Applied Ecology, 13:196­203. http://dx.doi.org/

10.1016/j.baae.2012.02.001

Sun K, Feng J, Jin L, Liu Y, Jiang Y, 2008. Identification of sympatric bat species by the echolocation calls. Frontiers in Biology in China, 3:227­231. http://dx.doi.org/10.1007/

s11515­008­0017­y

Thomas DW, Bell GP, Fenton MB, 1987. Variation in echolo­

cation call frequencies recorded from North American ves­

pertilionid bats: a cautionary note. Journal of Mammalogy, 68:842­847. http://dx.doi.org/10.2307/1381562

Thomas JA, Moss CF, Vater M, 2004. Echolocation in bats and dolphins. University of Chicago Press, Chicago, IL, pp. 1­

604.

Tian L, Liang B, Maeda K, Metzner W, Zhang S, 2004. Molec­

ular studies on the classification of Miniopterus schreibersii (Chiroptera: Vespertilionidae) inferred from mitochondrial cytochrome b sequences. Folia Zoologica, 53:303­311.

Tsytsulina K, Dick MH, Maeda K, Masuda R, 2012. Systemat­

ics and phylogeography of the steppe whiskered bat Myotis aurascens Kuzyakin, 1935(Chiroptera, Vespertilionidae).

Russian Journal of Theriology, 11:1­20.

Vaughan N, Jones G, Harris S, 1997. Identification of British bat species by multivariate analysis of echolocation call parameters. Bioacoustics, 7:189­207. http://dx.doi.org/10.

1080/09524622.1997.9753331

Yoon MH, 2010. Vertebrate Fauna of Korea. Vol. 5, No. 1. Chor­

data: Vertebrata: Mammalia: Theria: Chiroptera, Bats. Nat­

ional Institute of Biological Resources, Incheon, pp. 1­123.

Yoshino H, Armstrong KN, Izawa M, Yokoyama J, Kawata M, 2008. Genetic and acoustic population structuring in the Okinawa least horseshoe bat: are intercolony acoustic dif­

ferences maintained by vertical maternal transmission? Mol­

ecular Ecology, 17:4978­4991. http://dx.doi.org/10.1111/j.

1365­294x.2008.03975.x

Received April 24, 2015 Revised July 6, 2015 Accepted July 13, 2015

Appendix 1. Descriptive statistics for each echolocation call sequence of each species Call IDNo.FMAX(kHz)FMIN(kHz)PF(kHz)D(ms)IPI(ms)Band width(kHz)SituationLocality Rhinolophus ferrumequinum 1 769.5±0.1452.3±0.7568.9±0.0022.6±1.67 46.3±26.7817.3±0.76PerchMitan-myeon, Gangwon-do 21070.4±0.2852.0±1.9869.4±0.4417.5±1.61 31.0±10.5718.4±2.09Free flightMitan-myeon, Gangwon-do 31172.2±1.9459.4±1.3869.3±0.2663.6±10.81120.2±36.9212.9±1.21Free flightMitan-myeon, Gangwon-do 41070.2±0.2061.3±1.8169.3±0.1963.9±13.51115.3±40.80 9.0±1.92Free flightMitan-myeon, Gangwon-do 51069.1±0.2856.9±1.6368.3±0.0064.5±4.87 96.4±8.0412.3±1.80Free flightMitan-myeon, Gangwon-do 6 868.6±0.3855.3±1.2267.8±0.2062.2±11.11100.3±11.9513.2±1.46Free flightMitan-myeon, Gangwon-do 7 972.3±0.3066.2±3.0772.0±0.0055.7±12.52189.5±109.77 6.1±2.84PerchDaejeong-eup, Jeju-do 8 872.2±0.2460.9±1.1871.0±0.0045.7±13.05 87.8±11.7911.3±1.19PerchDaejeong-eup, Jeju-do 9 772.3±0.1461.7±1.2771.0±0.0057.1±4.36198.7±65.6410.6±1.40PerchDaejeong-eup, Jeju-do 10 572.4±0.1253.0±2.0172.0±0.0024.7±6.93 40.8±13.1719.5±1.98PerchDaejeong-eup, Jeju-do 11 972.4±0.1458.4±1.3972.0±0.0023.8±3.42 44.3±17.1914.0±1.35PerchDaejeong-eup, Jeju-do 12 672.3±0.0058.6±0.3771.0±0.0050.6±6.28101.3±20.1113.7±0.37PerchDaejeong-eup, Jeju-do 13 672.2±0.0057.9±1.0072.0±0.0052.2±2.38115.9±29.1114.4±1.00PerchPyoseon-myeon, Jeju-do 14 372.4±0.0057.2±0.9272.0±0.0029.7±4.05 38.5±4.2015.2±0.92PerchPyoseon-myeon, Jeju-do 15 872.2±0.0061.8±3.0672.0±0.0040.0±8.29134.6±56.1810.4±3.06PerchPyoseon-myeon, Jeju-do 16 872.6±0.0061.8±1.5872.0±0.0047.8±13.98 94.9±7.5710.8±1.58PerchPyoseon-myeon, Jeju-do 17 772.7±0.0061.5±2.0171.9±0.0035.4±7.32 68.2±20.8911.2±2.01PerchPyoseon-myeon, Jeju-do 18 872.3±0.0053.8±0.9072.0±0.0031.1±3.89 56.3±21.2018.5±0.90PerchHallim-eup, Jeju-do 19 971.4±0.0058.6±2.0871.0±0.0052.2±13.79101.9±26.1112.8±2.08PerchHallim-eup, Jeju-do 20 870.3±0.0060.3±2.4371.0±0.0053.3±7.57168.8±100.7810.0±2.43PerchHallim-eup, Jeju-do 21 572.2±0.2063.7±2.5471.4±0.4954.6±9.06139.8±54.31 8.4±2.62PerchHallim-eup, Jeju-do 22 871.4±0.0060.8±0.8670.7±0.0042.3±9.18 97.9±24.0910.7±0.86PerchHallim-eup, Jeju-do 23 469.5±0.1757.8±1.2869.1±0.1542.7±10.14 74.7±7.7011.7±1.42PerchJido-eup, Jeollanam-do 24 569.4±0.0052.1±0.9968.5±0.2444.1±8.54 84.7±16.6317.3±0.99PerchJido-eup, Jeollanam-do 25 468.3±0.4055.7±0.8568.0±0.3030.5±3.63131.6±3.6812.6±1.11PerchHaeje-myeon, Jeollanam-do 26 570.0±0.1565.3±3.3569.5±0.5028.0±5.59100.3±81.17 4.7±3.44PerchHaeje-myeon, Jeollanam-do 27 969.7±0.1756.4±1.0969.1±0.1252.2±6.54103.5±9.0613.4±1.17Free flightSonbul-myeon, Jeollanam-do 28 567.3±0.1653.3±0.8166.8±0.0033.0±4.95 56.6±11.3614.1±0.91Free flightJincheon-eup, Chungcheongbuk-do 29 769.0±0.0055.5±0.9167.7±0.0061.5±2.95140.4±26.1513.5±0.91Free flightJincheon-eup, Chungcheongbuk-do 30 572.8±0.1563.5±2.8472.0±0.0035.0±4.76160.3±95.56 9.3±2.83PerchJochon-eup, Jeju-do 31 770.0±0.0060.5±1.3768.9±0.3751.8±7.32169.9±34.16 9.5±1.37PerchYeongweol, Gangwon-do 32 870.0±0.2352.5±0.9869.1±0.1529.6±4.48 60.9±21.4117.5±1.07PerchYeongweol, Gangwon-do 33 768.7±0.0055.4±0.7168.3±0.0046.3±5.47 80.0±11.4013.3±0.71Free flightSeolcheon-myeon, Jeollabuk-do 34 669.3±0.0055.4±0.9068.3±0.0065.7±14.81105.9±27.5913.9±0.90Free flightSeolcheon-myeon, Jeollabuk-do 351166.8±0.3154.2±1.4766.0±0.2660.2±14.09 94.0±23.6112.7±1.35Free flightYeongweol, Gangwon-do 36 869.0±0.2354.4±0.4468.3±0.0050.3±18.98 77.5±28.8114.6±0.56Free flightYeongweol, Gangwon-do 37 669.8±0.1456.3±0.8968.3±0.1125.5±5.98 76.4±31.0013.6±0.99PerchBuseok-myeon, Gyeongsangbuk-do