Yan-Hong Xiao,LeiWang,Joseph R.Hoyt,Ting-Lei Jiang,Ai-Qing Lin,Jiang Feng,2,＊

1 Jilin ProvincialKey Laboratory ofAnimalResource Conservation and Utilization,NortheastNormalUniversity,Changchun Jilin 130117,China

2 Schoo lofAnim a lScience&Techno logy,Jilin Agricultura lUniversity,Changchun Jilin 130118,China

3 Department ofEcology and Evolutionary Bio logy,University ofCalifornia,Santa Cruz CA 95064,USA

INTRODUCTION

Anim als have evolved a variety of communication system s across all sensory modalities,such as visual,chem ical,electrical,and acoustic signals. O f the communication modalities, acoustic signals have the advantage of broadcasting information for longer distances w ith less interference from physicalbarriers.This advantage was likely a driving force in the evolution of acoustic communication in a w ide range of taxa(Seyfarth et al.,2010;W ilkins et al.,2012).Acoustic communication fulfills an im portant function in social interactions,especially for bats,who are among the most diverse and gregarious of allmamm als and live in predom inately dark environments(Kerth,2008).The acoustic communication systems observed in echolocating bats are extremely com plex in the mammalian world because they can em it two kinds of calls:echolocation and social calls,which serve different functions(Kanwal,2009,2012).Socialcalls are mainly used to transm it information under special behavioral contexts.Many batspecies dem onstrate a diverse repertoire of socialcalls undera variety of behavioral conditions(Bohn etal.,2008;Clem ent et al.,2006;Gadziola etal.,2012;Knörnschild et al.,2010;Ma et al.,2006). These characteristics of bats provide a unique opportunity for research on acoustic communication of animal sociality.

The motivation-structure hypothesis states that social call types are decided by the behavioral context of birds and mammals(Kanwal,2009;Morton,1977).In bats,the behavioral settings of their vocalizations include aggressive or warning,courtship,communication,distress,mother-infant recognition, and clustering behaviors. Intra-specific communication under specific behavioral contexts usually carries significant inform ation about the signaler(Behr et al.,2009;Behr&von Helversen,2004;Knörnschild and von Helversen,2008).Under some behaviors,intra-specific differences and elaborate sequences of acoustic signals are of importance,and contribute to accurate mother-infant and individual recognition within a colony.For instance,songs of free-tail bats contain multiple elements(e.g.,syllables,notes and/or phrases)with highly structured,rather than random,arrangement. Males often modify types of calls through modulating the repeated syllables,which can differ among individuals(Bohn et al.,2009).Clustering behavior is another important context in which bats em it social calls.Mammals living in low-temperature ambient environments often huddle together to reduce energy consumption and promote member fitness(Gilbert et al.,2010).The central temperature of a cluster is higher,and the central site is safer than the border position(Hatch well et al.,2009).Thus,achieving the central position is often the objective of an individual within a cluster.Acoustic signals containing characteristic information may contribute to accurate recognition,which can help individuals save energy and time during contention for the central cluster site(Dugatkin and Earley,2004).Although several studies have examined the clustering behavior of bat species(Kerth,2008),there have been no studies on the diversity and structural characteristics of acoustic communication that occur during the clustering of gregarious bats.

Big-footed m yotis,Myotismacrodactylus,belongs to the subgenusLeuconoëof the genusMyotis(Yangochiroptera,Vespertilionidae). Sexual segregation in colonies is known to occur in m any bat species,with separate clustering of fem ales during the summ er pupping season(Bradbury,1977;McCracken and Wilkinson,2000).Females rear their young communally with the formation of maternity colonies,whereas males are solitary or form small bachelor groups(Senior et al.,2005).The clustering of pregnant bats is a cooperative behavior thatperm its individuals involved in social ther moregulation to minimize heat loss and thereby lower their energy expenditure,and may allow them to reallocate the saved energy to other functions such as grow th and reproduction(Gilbert et al.,2010).Thus,the cluster center is the optimal position for pregnant bats,and may assist better fetal development.According to our observations,the last individuals(LI)returning to a group will usually try to enter the central position of the cluster. Approaching behaviors are always accompanied by social calls produced by the LI until physical contact with the other bats.Thus,we assumed that the social calls emitted by the LIattempting to enter the cluster center are related to this special behavior.We therefore examined two questions:(1)Are these social calls associated with successful entrance into the center of the cluster? (2)Does the variety or structure of the social calls influence this behavior?We recorded and analyzed the social calls emitted byM.macrod actylusunder this behavioral context.

MATERIALS AND METHODS

Acquisition and main tenance of animals

The study was conducted from May to June 2010. Five pregnantM.macrodactylusindividuals were random ly chosen from 90 pregnantbats in a naturalcave in Jilin Province,China.They were banded and roosted together as a colony in a tem perature-and hum idity-controlled animal room(20 m×10 m×4 m)at Northeast Normal University in Changchun,China.The day-night cycle was controlled at12 h dark and 12 h light.They could fly freely w ithin the anim al facility and were given water and meal wormsadlibitum.Before the experim ent,all five subjects were habituated to handling and to the testing cage.

Acoustic recordings

We recorded and analyzed the social calls and associated behaviors of the captive bats.To optimize recording quality,the five animals were housed in a small testing cage(200 cm×80 cm×100 cm)in a room lined with anechoic foam,separate from the animal holding room.To reduce environment alinterference with the ultrasonic calls,environmental temperature and humidity of the room were keptconstant.

Vocalizations were recorded using two ultrasonic condenser m icrophones(CM 16,Avisoft Bioacoustics,Berlin,Germ any)connected to am p lifiers and A/D converters(UltraSoundGate 416 H).The m icrophone had a flat frequency response from 10–250 kHz. The gain of each microphone was independently ad justed to optimize the signal-to-noise ratio of the recording.Acoustic signals were digitized at 375 kHz with 16-bitdepth,and monitored in real time with RECORDER software.We used two infrared surveillance cameras(Pensee,PIS-322EG)to simultaneously record the behavior of all animals.We defined the last animal to join the group(LI)as the last individual moving toward the cluster and displaying“approaching”behavior(Kobayasiet al.,2012).In most cases,the LI was observed as roosting last within a cluster.Most recordings occurred during the clustering process after bats had eaten(Figure 1).The characteristics of their social calls and the time they took to join the cluster were analyzed using Avisoft-SasLab Pro software,Version 5.1.20(Avisoft Bioacoustics,Berlin,Germany).Time here was defined as consumed time(CT).The study was conducted for 38 d from 2100 h–0100 h each night.

Analysis of vocalizations

Figu re 1 Schem atic show ing the cage set-up fo r sim u ltaneous aud io and video reco rd ings o f cap tive M.m acrodac tylus

We exam ined the social call sequences of the LI using nomenclature similar to Kanwal et al.(1994)and Bohn et al.(2008),with a syllable considered the small estacoustic unit of a vocalization defined as one continuous emission surrounded by background noise,and a call considered the simp lest em ission pattern of a vocalization used for communication consisting of one ormore elements(syllables).Vocalizations were sem i-automatically analyzed using Avisoft-SasLab Pro software(Version 5.1.20 Avis oft Bioacoustics).Syllable start and end times were automatically detected using a standard threshold level(5%)and hold time(2 m s),and manually adjusted when necessary. Signals were high-pass filtered at 1 kHz and the amplitude of each pulse was normalized to 0.75 V(Bohn et al.,2008;Gadziola et al.,2012;Kanwal et al.,1994). As a result,social call sequences were of similar amplitude and unwanted echolocation signals were removed.For analyses,we random ly selected three calls per call type from all individuals that were of sufficient quality for measurements and syllable identification.The social calls were automatically analyzed with the Avisoft-SasLab Pro software(Version 5.1.20 Avisoft Bioacoustics).Spectral and temporal analyses were obtained from the spectrogram and oscillogram with a Fast Fourier Transform(FFT)length of 512 points(87.5%overlap),which allowed for a frequency resolution of 732 Hz and temporal resolution of 0.171 ms.We measured the following parameters for each social call(Figure 2):total duration of call(totdur),minimum(fmin)and m aximum(fmax)frequencies of call,and mean frequency of highest energy(freq).This was done by measuring the peak frequency of each component(and then calculating average);number of components to call(nocomp);and number of different syllables(Difptype)in each call(Russ et al.,2004;Russo&Jones,1999).Ifmore than one harmonic was present,values of the first(fundamental)harmonic were taken.Additionally,we used oscillograms and spectrogram s to identify syllables within calls.We also examined the com position of each call in term s of the total number of each syllable type and the proportion of calls with syllables.Finally,for each call recorded,we assigned a call variant and call type based on its sequence of syllables.

Statistic alanalyses

Call parameters were checked for normality using the Kolmogorov-Smirnov test. Since all call parameters were normally distributed,differences in call characteristics between individuals were com pared using one-way ANOVA,followed by Tukey multiple-comparison tests. Discriminant function analysis(DFA)was used to determine to which individual certain social calls should belong.We examined the general relationships between the variables across alltheLIs,including call variants and CTs,by performing Spearman correlations.We used a simple linear regression analysis to assess the statistical significance of call variation as a predictor of individual recognition of CT in the process of approaching clusters.All statistical analyses were perform ed using SPSS 15.0 statistical software(SPSS Inc.,Chicago,IL,USA).The alpha level was set to 0.05 and data were presented as means±SD.

RESULTS

Acoustic structure of calls

Figure 2 Spec trog ram s(cen ter)o f social calls o f Myo tis m acrodac tylus show ing the change o f frequency w ith tim e

Figu re 3 Echo location pu lse(FM)and sim p le sy llab les

A total of 885 call sequences were analyzed from five individuals when the LI approached the cluster. We distinguished a total of four syllable types,which could be combined to form 10 sim plemono syllabic calls and 27 com p lex multisyllabic calls. The four syllable types and their 37 combinations are outlined in Figure 3 and Figure 4:downward frequency-modulated syllable(DFM;Figure 3A),shortquasi-V frequency-modulated syllable (VFM;Figure 3B),arched frequency-modulated syllable(AFM;Figure 3C)and sinusoidal frequency-modulated syllable(SFM;Figure 3D).In the syllable sequence(Figure 5),social calls started with DFM syllables,which,in turn,usually gave way to VFM syllables.The VFM syllables were followed by SFM syllables,usually at the end of a call.The AFM syllables were only interjacentin the sequence and only made transition with DFM syllables.Typically,SFM syllables had a longer duration(10.4±2.8 m s)than that of the othersyllable types(DFM,1.6±0.7m s;VFM,4.2±0.8m s;AFM,3.5±0.8 m s).

Individualacoustical differences

To establish whether the social calls were individual-specific,we performed a DFA using four spectro temporal and two syllable parameters(Table 1).This analysis resulted in two canonical functions with eigenvalues greater than 1,which together explained 90.1%of the variation in the calls from the five bats(Figure 6).Subsequent univariate ANOVA showed that the first and second canonical functions could be used to differentiate between social calls of individual bats(PC1:F4,67=36.6,P＜0.001;PC2:F4,67=13.4,P＜0.001).Maximum frequency and duration were the most distinctive features of these calls,indicating different acoustic repertoires.These analyses suggested that social calls from different individuals should be distinguishable by conspecifics.

Figu re 4 Spectrog ram s and oscillog ram s o fm u lti-elem en t soc ial ca lls from fem ale Myo tis m acrodacty lus

Figu re 5 Mode lo f ca lls based on transition syllab les

Tab le 1 Soc ial ca llparam eters from Myo tis m acrodacty lus fem ales

Figure 6 Distribution o f social calls from five fem ales(m arked by d ifferent sym bo ls)on fi rst two canonicalaxes

Callvarian ts and c luster success

The clustering success of females was measured as the time taken to join a cluster(CT).We correlated the CT with the number of call variants em itted during one complete process of returning to the colony.Because call variants were most influenced by type and number of syllables,a negative correlation between the call variants of the LI and cluster success was derived(Figure 7).Clustering success tended to increase with the total number of call variants produced.The number of social calls for the five individuals varied from 4 to 29 during the entire research session.We found that call diversity was positively correlated with clustering efficiency.

DISCUSSION

Intra-species variability of LI calls was found in the number of syllables,syllable order and syllable repetition across call renditions. These data confirmed that differences in social calls existed betweenM.macrodactylusindividuals during the process of approaching the colony.Our results further confirmed the hypothesis that social call richness was significantly negatively correlated with time taken for the successful reunion ofM.macrodactylusfemales in a cluster.

Figu re 7 Relationship betw een ca llvarian ts and tim e taken to join a cluster in Myo tism acrodac tylus

The LI can em it 10 simple monosyllabic and 27 complex multisyllabic call types com posed of four syllable types.In previous studies,simple monosyllabic calls have been recorded inMyotisspecies at maternity roosts before bats leave and after they arrive(Pfalzer&Kusch,2003),and usually begin with downward frequency-modulated signals.In mammals,repeated calls in communication signals can indicate a generalcoding rule(Fischer etal.,2001;Schehka et al.,2007).However,com p lex multisyllabic calls inPipistrellus pipistrellusconstitute different syllables,which m ay attract conspecifics and thereby help to deter predators(Budenz et al.,2009).We found that comp lex multisyllabic calls were em itted byM.macrodactylusfemales returning to the colony;furthermore,the diversity of calls was affected by syllable type and unique syllable types led to specific calls.New evidence has suggested thatMegadermalyracan regularly modify the structure of their calls,including the number and repetition of syllables,according to competing strength(Bastian&Schmidt,2008),and signalers may convey their own properties in acoustic signals(Janßen and Schmidt,2009).

The sequences of pulses inM.macrodactylusfemale calls demonstrated a stable pattern. The social calls always began with a downward frequency-modulated component and were followed with one or two other sub-syllables;by adding syllables or types of syllables based on this rule,call variants were increased.Sim ilar descriptions of calls with high diversity have been found in studies onSaccopteryxbilineata(Behr&von Helversen,2004),Tadaridabrasiliensis(Bohn etal.,2009)andPipistrellusnathusii(Russ and Racey,2007;Jahelková et al.,2008)courtship songs. These studies noted large inter-individual differences in calls,w ith m ales able to em it diverse courtship calls,and differences in individual calls most evident in regard to m aximum frequency and duration.Higher diversity in the community may contribute to species identification in bats.In our study,individuals emitting low diversity calls required more time to join the group,and occasionally could not attain the central position.The border temperature of a group is lower than that of the central position,so bats need more energy consumption for therm ogenesis.Therefore,the LI must repeatedly communicate with others when returning to the colony to obtain maximum benefit.To reduce the time taken for the recognition process,social behaviormay drive the diversity of individual calls.

We also found that the same calls were emitted by different individuals;these calls may function as identification signals among members of a colony and have a universal meaning.However,variation in the number and type of syllablesmay be due to specific information conveyed by individuals.Huddling together in a roost is likely a thermoregulatory strategy used byM.macrodactylusto conserve energy and increase survivalor fitness.In this study,five individuals were random ly selected from 90 fem ales,with no significant differences in forearm length or body size im pacting the results.Each of the five bats could potentially be the LI,but the time spent alone by each bat was negatively correlated to their call diversity.Under natural circum stances,calls m ay be relevant to status identification.The clustering of numerous individuals would make diverse calls more identifiable by others.In this study,individuals that em itted multiple social calls m ay be more easily identified and accepted by others,such that individual calls may evolve towards comp lexity under natural selection.This article suggests a role for the sophisticated acoustic communication ability developed by bats.Further research is needed to elucidate the process of identification through social calls,which can be conducted through multiple methods and is affected by behavioral context.

Our results indicated that the social calls emitted by clusteringM.macrodactylusfemales always exhibited a stable structure and diversity.Furthermore,differences in social call variability betweenM.macrodactylusfemales was found,and individuals em itting more com p lex social calls took less time to rejoin the cluster. However,further work is needed to investigate the specific function of this diversity in socialcalls.

COMPETING INTERESTS

The authors declare that they have no com peting interests.

AUTHORS’CONTRIBUTIONS

Y.H.X.and L.W.completed the experiments in lab,and supervised the analyses,and drafted the manuscript.L.W.and J.F.conceived the study and designed the study.L.W.,T.L.J.and A.Q.L.participated in the field work.Y.H.X.and J.R.H.revised the manuscript.All authors read and approved the final manuscript.

Bastian A,Schm idt S.2008.Affect cues in voca lizations of the bat,Megadermalyra,during agonistic interactions.TheJournaloftheAcoustical SocietyofAmerica,124(1):598–608.

Behr O,von Helversen O.2004.Bat serenades-com p lex courtship songs of the Sac-w inged bat(Saccopteryxbilineata).BehavioralEcologyand Sociobiology,56(2):106–115.

BehrO,Knörnschild M,von Helversen O.2009.Territorial counter-singing in male sac-w inged bats(Saccopteryxbilineata):low-frequency songs trigger a stronger response.BehavioralEcologyandSociobiology,63(3):433–442.Bohn KM,Schmidt-French B,Ma ST,Pollak GD.2008.Syllable acoustics,temporal patterns,and call com position vary with behavioral context in Mexican free-tailed bats.TheJournaloftheAcousticalSocietyofAmerica,124(3):1838–1848.

Bohn KM,Schm idt-French B,Schwartz C,Smotherman M,Pollak GD.2009.Versatility and stereotypy of free-tailed bat songs.PLoSOne,4(8):e6746.

Bradbury JW.1977.Socialorganization and communication.In:W im sattWA.Biology of Bats.New York:Academ ic Press,2–72.

Budenz T,Heib S,Kusch J.2009.Functions of batsocialcalls:the influence of localabundance,interspecific interactions and season on the production of pipistrelle(Pipistrelluspipistrellus)type Dsocialcalls.ActaChiropterologica,11(1):173–182.

ClementMJ,Dietz N,Gupta P,Kanwal JS.2006.Audiovocal communication and socialbehavior inmustached bats.In:Kanwal JS,EhretE.Behaviorand Neurodynam ics for Auditory Communication.Cambridge,UK:Cambridge University Press.

Dugatkin LA,Earley RL.2004.Individualrecognition,dom inance hierarchies and w innerand loser effects.ProceedingsoftheRoyalSocietyB:Biological Sciences,271(1547):1537–1540.

Fischer J,Hammerschm idt K,Cheney DL,Seyfarth RM.2001.Acoustic features of female chacm a baboon barks.Ethology,107(1):33–54.

Gadziola MA,Grim sley JMS,Faure PA,Wenstrup JJ.2012.Social vocalizations o fbig brown bats vary with behavioralcontext.PLoSOne,7(9):e44550.

GilbertC,McCafferty D,Le Maho Y,Martrette JM,Giroud S,Blanc S,Ancel A.2010.One for all and all for one:the energetic benefits of hudd ling in endotherm s.BiologicalReviews,85(3):545–569.

HatchwellBJ,Sharp SP,SimeoniM,McGowan A.2009.Factors influencing overnight loss of body mass in the communal roosts of a social bird.FunctionalEcology,23(2):367–372.

JahelkováH,Horáˇcek I,Bartoniˇcka T.2008.The advertisem ent song ofPipistrellusnathusii(Chiroptera,Vespertilionidae):a com p lex message containing acoustic signatures of individuals.ActaChiropterologica,10(1):103–126.

Janßen S,Schm idt S.2009.Evidence for a percep tion of prosodic cues in bat communication:contactcallclassification byMegadermalyra.Journalof ComparativePhysiologyA,195(7):663–672.

Kanwal JS,Matsumura S,Oh lem iller K,Suga N.1994.Analysis of acoustic elements and syntax in communication sounds em itted by mustached bats.TheJournaloftheAcousticalSocietyofAmerica,96(3):1229–1254.

Kanwal JS.2009.Audiovoca l communication in bats.In:Squire L R.Encyclopedia of Neuroscience.Oxford,UK:Academ ic Press,681–690.

Kanwal JS.2012.Right–leftasymmetry in the corticalprocessing of sounds for social communication vs.navigation in mustached bats.European JournalofNeuroscience,35(2):257–270.

Kerth G.2008.Causes and consequences of socia lity in bats.BioScience,58(8):737–746.

Knörnschild M,von Helversen O.2008.Nonmutual vocal mother-pup recognition in the greater sac-w inged bat.AnimalBehaviour,76(3):1001–1009.

Knörnschild M,Glöckner V,von Helversen O.2010.The voca l repertoire of two sym patric species of nectar-feeding bats(GlossophagasoricinaandG.commissarisi).ActaChiropterologica,12(1):205–215.

Kobayasi KI,Hiryu S,Shimozawa R,Riquimaroux H.2012.Vocalization of echolocation-like pulses for interindividual interaction in horseshoe bats(Rhinolophusferrumequinum).TheJournaloftheAcousticalSocietyof America,132(5):EL417–EL422.

Ma J,Kobayasi K,Zhang SY,Metzner W.2006.Voca l communication in adult greater horseshoe bats,Rhinolophusferrumequinum.Journalof ComparativePhysiologyA,192(5):535–550.

McCracken GF,W ilkinson G S.2000.Batmating system s.In:Crichton EG,Krutzsch PH.Reproductive Biology of Bats.New York:Academ ic Press,321–362.

Morton ES. 1977. On the occurrence and significance of motivation-structura l rules in some bird and m ammalsounds.TheAmerican Naturalist,111(981):855–869.

Pfalzer G,Kusch J.2003.Structure and variability of bat social calls:im p lications for specificity and individual recognition.JournalofZoology,261(1):21–33.

Russ JM,Jones G,Mackie IJ,Racey PA.2004.Interspecific responses to distress calls in bats(Chiroptera: Vespertilionidae): a function for convergence in calldesign?AnimalBehaviour,67(6):1005–1014.

Russ JM,Racey PA.2007.Species-specificity and individual variation in the song of male Nathusius’pipistrelles(Pipistrellusnathusii).Behavioral EcologyandSociobiology,61(5):669–677.

Russo D,Jones G.1999.The social calls of Kuhl’s pipistrellesPipistrellus kuhlii(Kuhl,1819):structure and variation(Chiroptera:Vespertilionidae).JournalofZoology,249(4):476–481.

Schehka S,Esser KH,Zimmermann E.2007.Acoustical expression of arousal in conflict situations in tree shrews(Tupaiabelangeri).Journalof ComparativePhysiologyA,193(8):845–852.

Senior P,Butlin RK,Altringham JD.2005.Sex and segregation in tem perate bats.ProceedingsoftheRoyalSocietyB:BiologicalSciences,272(1580):2467–2473.

Seyfarth RM,Cheney DL,Bergm an T,Fischer J,Zuberbühler K,Hammerschm idt K.2010.The central importance of information in studies of animal communication.AnimalBehaviour,80(1):3–8.

Wilkins MR,Seddon N,Safran RJ.2012.Evo lutionary divergence in acoustic signals:causes and consequences.TrendsinEcology&Evolution,28(3):156–166.