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Great tits classify songs by individual voice characteristics
Anita. Behav., 1992,43, 283-287
Great tits classify songs by individual voice characteristics D. M. W E A R Y * & J. R. K R E B S Edward Grey Institute o f Field Ornithology, South Parks Road, Oxford OX1 3PS, U.K.
(Received 21 May 1990; initial acceptance 13 August 1990; final acceptance 13 June 1991; MS. number: 3582)
Abstract. The first experimental evidence that birds are able to recognize the general voice characteristics of an individual's song repertoire is reported. Five captive male great tits, Parus major, were taught to discriminate between songs from the repertoires of two individuals. They were then tested with unfamiliar songs from the same two birds, which they were able to assign to the correct individual. These results provide a mechanism by which the well-known phenomenon of individual recognition by voice in birds can be achieved.
In several species of birds, individuals are able to discriminate between one another on the basis of song (Krebs 1971; Brooks & Falls 1975; Falls 1982), but little is known about how birds perform this discrimination. Poorer performance by species with repertoires (as opposed to those that sing only one song type) suggests that a repertoire may hinder individual recognition (Falls 1982). However, the fact that discrimination still occurs in these species indicates that there are distinctive features in an individual's songs upon which discrimination could be based. Three mechanisms may be involved: (1) repertoires may be individually distinctive in that they contain song types that are peculiar to an individual, a distinctive combination of song types, or a distinctive sequencing of their delivery; (2) each song type may show individually distinctive variation, as in species with a single song; or (3) all the songs in a repertoire may share a distinctive quality, as in the human voice. A recent study on great tits, Parus major, showed that individuals can be statistically discriminated on the basis of all three alternatives outlined above (Weary et al. 1990). However, knowing which features are available for discrimination between individuals does not tell us how birds are actually performing this task. Discrimination on the basis of signature songs or distinctive repertoires (mechanism 1 above) would be difficult, or at least time consuming for great tits to use as they sing many repetitions of the same song type before *Present address: Department of Biology, McGill University, 1205 Docteur Penfield Avenue, Montreal, Quebec H3A 1B1, Canada. 0003-3472/92/020283 + 05 $03.00/0
switching, and thus may require long periods to cycle through their repertoire. In several species, including great tits, discrimination can take place on the basis of a single song type (see Falls 1982). To do this, birds must be using the distinctive ways in which different individuals sing the same song type (2 above), or the distinctive qualities across all the songs in an individual's repertoire (i.e. voice characteristics, 3 above). To use the distinctive way an individual produces a song type requires that the song type be identified by the listener. Using this information, the variation in acoustic features due to the song type could be separated from that due to the singer. This mechanism requires learning all song types separately, which may be costly to birds. Individual recognition based on voice quality has the advantage that the listener can correctly identify an individual on the basis of songs never heard before. We tested whether great tits can use voice characteristics to identify individuals. Birds were trained to discriminate between two individuals on the basis of half their repertoires, and then tested for discrimination using the other, unfamiliar songs from these two repertoires. METHODS
Experimental Songs Male great tits have repertoires of about three or four clearly distinct but apparently functionally interchangeable song types (Krebs et al. 1978; McGregor & Krebs 1982). In this experiment, two males (A and B), each with a repertoire of four
9 1992The Association for the Study of Animal Behaviour 283
284
Animal Behaviour, 43, 2
song types (Fig. 1), were randomly selected from a population of wild birds in Wytham Woods, Oxford, U.K. We recorded each of the eight songs on separate occasions, always from within 10 m of the singing bird and with the same equipment (Uher 4000IC tape recorder with a Sennheiser MK815 gun microphone) and we filtered all recordings with a Kemo dual high pass filter set at 2 kHz. This approach was used to reduce the possibility of an artefact in the recordings, such as background noise, which might have been used by the birds to categorize songs. Two song types were chosen at random from each male and used as the training stimuli: Once the subjects learnt to discriminate between the repertoires using these songs, they were played the other two songs from each repertoire. These formed a test for the ability of the subjects to assign songs they had never heard before to the correct singer. Great tit songs consist of repetitions of identical phrases. Phrases consist of groups of notes, with notes being defined as temporally continuous sounds. The number of phrases in a song is highly variable within and between singers. In this experiment, all training and test songs were standardized to four phrases.
Subjects and Procedure Five male great tits were captured more than 20 km from where we recorded the songs. As great tits rarely disperse more than 5 km (Greenwood et al. 1979), the experimental songs were likely to be unfamiliar to our subjects. In January-June 1988, these subjects were trained in an operant cage to discriminate between the songs of two individuals. A 'Go, No go' training procedure was used as follows. The test cage contained a central perch on which the bird had to sit for 0.25 s to initiate a trial. A trial consisted of one of the four training songs being played once through a speaker-amplifier. This presentation was followed by a 5 s period during which the subject could respond by approaching the speaker and breaking the infra-red beam in front of a universal feeder. If the song was a Go stimulus, the feeder opened, rewarding the bird with a single pupa of Lucilia sencata. If the bird responded to a No go stimulus by approaching the feeder the house lights were extinguished for 20 s, after which the bird could initiate a new trial by sitting on the central perch. If the bird correctly stayed on the central perch following a No go stimulus, it could initiate a new trial immediately.
The experiment was controlled and recorded by a microcomputer. The stimuli, which were matched for sound pressure level at the central perch, were presented in a random order. During initial training 100% of Go stimuli were rewarded. After the birds had reached a criterion of at least 80% correct Go responses in three sessions (each of at least 1000 trials), the reward probability was reduced to 50%. Reward rate was reduced so as to slow the reduction in response to the non-rewarded test trials. When the birds reached the criterion at the 50% reward 'probability, test sessions were begun. During test sessions training was continued, but non-rewarded test songs were now played on a random 20% of trials. A test session would usually comprise at least 50 probe trials per test song. At the end of a session the proportion of Go responses was calculated (Go responses/total (i.e. G o + N o go) responses). More detailed descriptions of the materials are given in Weary (1989).
Analysis Three different tape recorders were used to play the test songs during each session, and the experiment was structured such that each of the four test songs was played once on each recorder. Once this test song by tape recorder matrix was complete, a second round of test sessions was performed in a similar manner so that each cell contained two observations. A mean value for the proportion of Go responses was then calculated for each of the 12 cells from each subject. These values were then subjected to an arcsine square root transformation, as is appropriate for proportional data (Sokal & Rohlf 1981). Results were analysed using repeated measures analysis of variance (ANOVAR) as described in Winer (1971). For two of the subjects the songs of male A were the Go stimuli, while for the other three the songs of B were the Go stimuli. This difference was included in the ANOVAR as a between-subject effect. Each subject was played two different test songs from both bird A and bird B, from each of the three test tape recorders. Thus, in addition to the between-subject training group effect (with two levels), we also had three withinsubject effects: repertoire (two levels), song (two levels, nested within repertoire) and tape recorder (three levels). Differences between subjects was considered a random effect, but as usual with ANOVAR was not tested (Winer 1971). The remaining effects were considered fixed. For
Weary & Krebs: Song classification by great tits Bird A
+f -..
0
AO
-..
Training songs
BirdB
II [/ _+.
...
t
!
=7 Test songs
~
t
e
m
0
,. n
0
285
r
#
r
] 1 1 I
01.5
0
ols
Time (s)
Figure 1. Sonagrams of the two training songs and two test songs from male A and male B. Sonagrams were made on a Kay 6061 B using the narrow band filter. analysis we used a generalized linear model (SAS Institute 1986). Owing to small within cell sample sizes, tests of the assumptions of normality and homogeneity of variance are not meaningful (Mardia 1980). As we had no a priori reason to suspect otherwise, we proceeded with these assumptions. Before examining the results of the ANOVAR we tested the assumption of compound symmetry of the covariance matrix. Since covariance asymmetry for our data was never significant (Mauchly's criterion >0"05), P-values from the ANOVAR did not require adjustment (Huynh & Feldt 1970; Potvin et al. 1990). Although we report
the unadjusted P-values, the adjusted P-values (Greenhouse-Geiser or Huynh-Feldt) did not differ in any important respect.
RESULTS Subjects were able to assign correctly the completely unfamiliar test songs according to the individual singer (Fig. 2). Subjects trained to Go to bird B's song responded positively to unfamiliar songs from bird B almost twice as frequently as to those from bird A. Similarly, subjects trained to Go to
Animal Behaviour, 43, 2
286 0.2
"6 =~0.1 o
o-o
Subjects trained to Go Subjects trained to Go to bird A to bird B
Figure 2. Responses (X+SE) to the test songs from bird A's repertoire (D) and from bird B's (11). (a) Responses by the two subjects trained to Go to the training songs from bird A; (b) responses by the three subjects trained to Go to the training songs of bird B. bird A's songs responded positively to test songs from male A more frequently than to those from male B. Thus both groups of subjects correctly assigned test songs. The discrimination was statistically significant. Subjects showed a higher proportion of Go responses to test songs when they formed part of the repertoire to which they had been trained to Go than to the No go ones (F1,3= 18.36, P<0"025). The nested effect of the actual test song played was not significant (F1, 3 =0-00, NS). However, subjects did respond somewhat differently to songs played on different tape recorders (F2,6 = 6' 33, P < 0'05). Birds trained to Go to the repertoire of bird A responded less, on average, than those who received the opposite training. However, this difference was not statistically significant (F1,3 = 1.48, NS), and subjects responded more to these test songs than to the negative training stimuli. Also, subjects trained to Go to bird A appeared less effective in discriminating between the repertoires, although again this interaction was not statistically significant (F1, 3 = 2-69, Ns). All other interaction effects were tested but none was significant.
classify the test songs. In principle they could also have learned to perform the discrimination in the training session by learning each song type individually without attending to common properties of each repertoire, but if this had been the case the correct assignment of the novel songs would not have occurred. To determine which voice characteristics are involved in individual recognition will require further experimentation, but two recent studies have identified some features that could in principle be used (Lambrechts & Dh0ndt 1987; Weary et al. 1990). The significant features were song duration (number of phrases in a song), drift (duration of the first phrase minus that of the last phrase) and maximum frequency. F o r two other song measures (phrase duration and minimum frequency), differences between the individuals were not significant. Thus the songs in an individual's repertoire tend to be consistent in terms of duration, drift and maximum frequency. Other experiments have shown that great tits are particularly sensitive to frequency differences in classifying songs (Weary 1990, 1991). Therefore pitch might be an important component in classifying individuals on voice characteristics. Only two other studies have examined how birds recognize an individual's songs, and both of these were based on variants of a single song type. Nelson (1989) and Brooks & Falls (1975) found that song frequency was an important component for field sparrows, Spizella pusilla, and white-throated sparrows, Zonotrichia albicollis, respectively. Individual recognition based on voice quality is advantageous in that the listener can correctly identify an individual on the basis of songs never heard before. In fact, it may even allow identification on the basis of calls, or parts of songs. Clearly this would be an ideal method for a listener to use. It neither requires learning the complexities of all the sounds produced by individuals the listener interacts with (as in mechanism 2), nor does it require the time to listen for a specific sound (mechanism 1).
DISCUSSION Our results show that after learning to discriminate between two songs from each of two repertoires, the subjects were able to assign correctly totally unfamiliar songs from the same two repertoires. In other words, the subjects must have learned individual voice characteristics and used these to
ACKNOWLEDGMENTS We thank M. Dawkins, B. Falls, G, Klump, A. Hurly, A. Inman, P. McGregor, J. Mongrain, K. Norris, C. Potvin and an anonymous statistical referee for their help.
Weary & Krebs: Song classification by great tits REFERENCES Brooks, R. J. & 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. Falls, J. B. 1982. Individual recognition by sound in birds. In: Acoustic Communication in Birds. Vol. 2 (Ed. by D. E. Kroodsma & E. H. Miller), pp. 237-273. New York: Academic Press. Greenwood, P. J., Harvey, P. H. & Perrins, C. M. 1979. Mate selection in the great tit (Parus major) in relation to age, status, and natal dispersal. Ornis fenn. 56, 75-86. Huynh, H. & Feldt, L. S. 1970. Conditions under which mean square ratios in repeated measurements designs have exact F-distributions. J. Am. stat. Assoc., 65, 1582-1589. Krebs, J. R. 1971. Territory and breeding density in the great tit, Parus major L. Ecology, 52, 2-22. Krebs, J. R., Ashcroft, R. & Webber, M. 1978. Song repertoires and territory defence in the great tit. Nature, Lond., 271,539-542. Lambrechts, M. & Dhondt, A. A. 1987. Differences in singing performance between male great tits. Ardea, 75, 43-52. McGregor, P. K. & Krebs, J. R. 1982. Song types in a population of great tits (Parus major): their distribution, abundance, and acquisition by individuals. Behaviour, 79, 126-152.
287
Mardia, K. V. i 980. Tests of univariate and multivariate normality. In: Handbook of Statistics. Vol. 1 (Ed. by P. R. Krishnaiah), pp. 279-320. Amsterdam: NorthHolland. Nelson, D. A. 1989. Song frequency as a cue for recognition of species and individuals in the field sparrow (Spizella pusilla). J. comp. Psychol., 103, 171-176. Potvin, C., Lechowicz, M. J. & Tardif, S. 1990. The statistical analysis of ecophysical response curves obtained from experiments involving repeated measures. Ecology, 71, 1389-1400. SAS Institute 1986. SAS User's Guide: Statistics. 1986 edn. Cary: SAS Institute. Sokal, R. R. & Rohlf, F. J. 1981. Biometry. New York: W. H. Freeman. Weary, D. M. 1989. Categorical perception of bird song: how do great tits (Parus major) perceive temporal variation in their own song? J. comp. Psychol., 103, 320~325. Weary, D. M. 1990. Categorization of song notes in great tits: which acoustic features are used and why? Anim. Behav., 39, 450-457. Weary, D. M. 1991. How great tits use song-note and whole-song features to categorize their songs. Auk, 108, 187-189. Weary, D. M., Norris, K. J. & Falls, J. B. 1990. Song features birds use to identify individuals. Auk, 107, 623-625. Winer, B. J. 1971. Statistical Principles in Experimental Design. New York: McGraw-Hill.
Great tits classify songs by individual voice characteristics D. M. W E A R Y * & J. R. K R E B S Edward Grey Institute o f Field Ornithology, South Parks Road, Oxford OX1 3PS, U.K.
(Received 21 May 1990; initial acceptance 13 August 1990; final acceptance 13 June 1991; MS. number: 3582)
Abstract. The first experimental evidence that birds are able to recognize the general voice characteristics of an individual's song repertoire is reported. Five captive male great tits, Parus major, were taught to discriminate between songs from the repertoires of two individuals. They were then tested with unfamiliar songs from the same two birds, which they were able to assign to the correct individual. These results provide a mechanism by which the well-known phenomenon of individual recognition by voice in birds can be achieved.
In several species of birds, individuals are able to discriminate between one another on the basis of song (Krebs 1971; Brooks & Falls 1975; Falls 1982), but little is known about how birds perform this discrimination. Poorer performance by species with repertoires (as opposed to those that sing only one song type) suggests that a repertoire may hinder individual recognition (Falls 1982). However, the fact that discrimination still occurs in these species indicates that there are distinctive features in an individual's songs upon which discrimination could be based. Three mechanisms may be involved: (1) repertoires may be individually distinctive in that they contain song types that are peculiar to an individual, a distinctive combination of song types, or a distinctive sequencing of their delivery; (2) each song type may show individually distinctive variation, as in species with a single song; or (3) all the songs in a repertoire may share a distinctive quality, as in the human voice. A recent study on great tits, Parus major, showed that individuals can be statistically discriminated on the basis of all three alternatives outlined above (Weary et al. 1990). However, knowing which features are available for discrimination between individuals does not tell us how birds are actually performing this task. Discrimination on the basis of signature songs or distinctive repertoires (mechanism 1 above) would be difficult, or at least time consuming for great tits to use as they sing many repetitions of the same song type before *Present address: Department of Biology, McGill University, 1205 Docteur Penfield Avenue, Montreal, Quebec H3A 1B1, Canada. 0003-3472/92/020283 + 05 $03.00/0
switching, and thus may require long periods to cycle through their repertoire. In several species, including great tits, discrimination can take place on the basis of a single song type (see Falls 1982). To do this, birds must be using the distinctive ways in which different individuals sing the same song type (2 above), or the distinctive qualities across all the songs in an individual's repertoire (i.e. voice characteristics, 3 above). To use the distinctive way an individual produces a song type requires that the song type be identified by the listener. Using this information, the variation in acoustic features due to the song type could be separated from that due to the singer. This mechanism requires learning all song types separately, which may be costly to birds. Individual recognition based on voice quality has the advantage that the listener can correctly identify an individual on the basis of songs never heard before. We tested whether great tits can use voice characteristics to identify individuals. Birds were trained to discriminate between two individuals on the basis of half their repertoires, and then tested for discrimination using the other, unfamiliar songs from these two repertoires. METHODS
Experimental Songs Male great tits have repertoires of about three or four clearly distinct but apparently functionally interchangeable song types (Krebs et al. 1978; McGregor & Krebs 1982). In this experiment, two males (A and B), each with a repertoire of four
9 1992The Association for the Study of Animal Behaviour 283
284
Animal Behaviour, 43, 2
song types (Fig. 1), were randomly selected from a population of wild birds in Wytham Woods, Oxford, U.K. We recorded each of the eight songs on separate occasions, always from within 10 m of the singing bird and with the same equipment (Uher 4000IC tape recorder with a Sennheiser MK815 gun microphone) and we filtered all recordings with a Kemo dual high pass filter set at 2 kHz. This approach was used to reduce the possibility of an artefact in the recordings, such as background noise, which might have been used by the birds to categorize songs. Two song types were chosen at random from each male and used as the training stimuli: Once the subjects learnt to discriminate between the repertoires using these songs, they were played the other two songs from each repertoire. These formed a test for the ability of the subjects to assign songs they had never heard before to the correct singer. Great tit songs consist of repetitions of identical phrases. Phrases consist of groups of notes, with notes being defined as temporally continuous sounds. The number of phrases in a song is highly variable within and between singers. In this experiment, all training and test songs were standardized to four phrases.
Subjects and Procedure Five male great tits were captured more than 20 km from where we recorded the songs. As great tits rarely disperse more than 5 km (Greenwood et al. 1979), the experimental songs were likely to be unfamiliar to our subjects. In January-June 1988, these subjects were trained in an operant cage to discriminate between the songs of two individuals. A 'Go, No go' training procedure was used as follows. The test cage contained a central perch on which the bird had to sit for 0.25 s to initiate a trial. A trial consisted of one of the four training songs being played once through a speaker-amplifier. This presentation was followed by a 5 s period during which the subject could respond by approaching the speaker and breaking the infra-red beam in front of a universal feeder. If the song was a Go stimulus, the feeder opened, rewarding the bird with a single pupa of Lucilia sencata. If the bird responded to a No go stimulus by approaching the feeder the house lights were extinguished for 20 s, after which the bird could initiate a new trial by sitting on the central perch. If the bird correctly stayed on the central perch following a No go stimulus, it could initiate a new trial immediately.
The experiment was controlled and recorded by a microcomputer. The stimuli, which were matched for sound pressure level at the central perch, were presented in a random order. During initial training 100% of Go stimuli were rewarded. After the birds had reached a criterion of at least 80% correct Go responses in three sessions (each of at least 1000 trials), the reward probability was reduced to 50%. Reward rate was reduced so as to slow the reduction in response to the non-rewarded test trials. When the birds reached the criterion at the 50% reward 'probability, test sessions were begun. During test sessions training was continued, but non-rewarded test songs were now played on a random 20% of trials. A test session would usually comprise at least 50 probe trials per test song. At the end of a session the proportion of Go responses was calculated (Go responses/total (i.e. G o + N o go) responses). More detailed descriptions of the materials are given in Weary (1989).
Analysis Three different tape recorders were used to play the test songs during each session, and the experiment was structured such that each of the four test songs was played once on each recorder. Once this test song by tape recorder matrix was complete, a second round of test sessions was performed in a similar manner so that each cell contained two observations. A mean value for the proportion of Go responses was then calculated for each of the 12 cells from each subject. These values were then subjected to an arcsine square root transformation, as is appropriate for proportional data (Sokal & Rohlf 1981). Results were analysed using repeated measures analysis of variance (ANOVAR) as described in Winer (1971). For two of the subjects the songs of male A were the Go stimuli, while for the other three the songs of B were the Go stimuli. This difference was included in the ANOVAR as a between-subject effect. Each subject was played two different test songs from both bird A and bird B, from each of the three test tape recorders. Thus, in addition to the between-subject training group effect (with two levels), we also had three withinsubject effects: repertoire (two levels), song (two levels, nested within repertoire) and tape recorder (three levels). Differences between subjects was considered a random effect, but as usual with ANOVAR was not tested (Winer 1971). The remaining effects were considered fixed. For
Weary & Krebs: Song classification by great tits Bird A
+f -..
0
AO
-..
Training songs
BirdB
II [/ _+.
...
t
!
=7 Test songs
~
t
e
m
0
,. n
0
285
r
#
r
] 1 1 I
01.5
0
ols
Time (s)
Figure 1. Sonagrams of the two training songs and two test songs from male A and male B. Sonagrams were made on a Kay 6061 B using the narrow band filter. analysis we used a generalized linear model (SAS Institute 1986). Owing to small within cell sample sizes, tests of the assumptions of normality and homogeneity of variance are not meaningful (Mardia 1980). As we had no a priori reason to suspect otherwise, we proceeded with these assumptions. Before examining the results of the ANOVAR we tested the assumption of compound symmetry of the covariance matrix. Since covariance asymmetry for our data was never significant (Mauchly's criterion >0"05), P-values from the ANOVAR did not require adjustment (Huynh & Feldt 1970; Potvin et al. 1990). Although we report
the unadjusted P-values, the adjusted P-values (Greenhouse-Geiser or Huynh-Feldt) did not differ in any important respect.
RESULTS Subjects were able to assign correctly the completely unfamiliar test songs according to the individual singer (Fig. 2). Subjects trained to Go to bird B's song responded positively to unfamiliar songs from bird B almost twice as frequently as to those from bird A. Similarly, subjects trained to Go to
Animal Behaviour, 43, 2
286 0.2
"6 =~0.1 o
o-o
Subjects trained to Go Subjects trained to Go to bird A to bird B
Figure 2. Responses (X+SE) to the test songs from bird A's repertoire (D) and from bird B's (11). (a) Responses by the two subjects trained to Go to the training songs from bird A; (b) responses by the three subjects trained to Go to the training songs of bird B. bird A's songs responded positively to test songs from male A more frequently than to those from male B. Thus both groups of subjects correctly assigned test songs. The discrimination was statistically significant. Subjects showed a higher proportion of Go responses to test songs when they formed part of the repertoire to which they had been trained to Go than to the No go ones (F1,3= 18.36, P<0"025). The nested effect of the actual test song played was not significant (F1, 3 =0-00, NS). However, subjects did respond somewhat differently to songs played on different tape recorders (F2,6 = 6' 33, P < 0'05). Birds trained to Go to the repertoire of bird A responded less, on average, than those who received the opposite training. However, this difference was not statistically significant (F1,3 = 1.48, NS), and subjects responded more to these test songs than to the negative training stimuli. Also, subjects trained to Go to bird A appeared less effective in discriminating between the repertoires, although again this interaction was not statistically significant (F1, 3 = 2-69, Ns). All other interaction effects were tested but none was significant.
classify the test songs. In principle they could also have learned to perform the discrimination in the training session by learning each song type individually without attending to common properties of each repertoire, but if this had been the case the correct assignment of the novel songs would not have occurred. To determine which voice characteristics are involved in individual recognition will require further experimentation, but two recent studies have identified some features that could in principle be used (Lambrechts & Dh0ndt 1987; Weary et al. 1990). The significant features were song duration (number of phrases in a song), drift (duration of the first phrase minus that of the last phrase) and maximum frequency. F o r two other song measures (phrase duration and minimum frequency), differences between the individuals were not significant. Thus the songs in an individual's repertoire tend to be consistent in terms of duration, drift and maximum frequency. Other experiments have shown that great tits are particularly sensitive to frequency differences in classifying songs (Weary 1990, 1991). Therefore pitch might be an important component in classifying individuals on voice characteristics. Only two other studies have examined how birds recognize an individual's songs, and both of these were based on variants of a single song type. Nelson (1989) and Brooks & Falls (1975) found that song frequency was an important component for field sparrows, Spizella pusilla, and white-throated sparrows, Zonotrichia albicollis, respectively. Individual recognition based on voice quality is advantageous in that the listener can correctly identify an individual on the basis of songs never heard before. In fact, it may even allow identification on the basis of calls, or parts of songs. Clearly this would be an ideal method for a listener to use. It neither requires learning the complexities of all the sounds produced by individuals the listener interacts with (as in mechanism 2), nor does it require the time to listen for a specific sound (mechanism 1).
DISCUSSION Our results show that after learning to discriminate between two songs from each of two repertoires, the subjects were able to assign correctly totally unfamiliar songs from the same two repertoires. In other words, the subjects must have learned individual voice characteristics and used these to
ACKNOWLEDGMENTS We thank M. Dawkins, B. Falls, G, Klump, A. Hurly, A. Inman, P. McGregor, J. Mongrain, K. Norris, C. Potvin and an anonymous statistical referee for their help.
Weary & Krebs: Song classification by great tits REFERENCES Brooks, R. J. & 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. Falls, J. B. 1982. Individual recognition by sound in birds. In: Acoustic Communication in Birds. Vol. 2 (Ed. by D. E. Kroodsma & E. H. Miller), pp. 237-273. New York: Academic Press. Greenwood, P. J., Harvey, P. H. & Perrins, C. M. 1979. Mate selection in the great tit (Parus major) in relation to age, status, and natal dispersal. Ornis fenn. 56, 75-86. Huynh, H. & Feldt, L. S. 1970. Conditions under which mean square ratios in repeated measurements designs have exact F-distributions. J. Am. stat. Assoc., 65, 1582-1589. Krebs, J. R. 1971. Territory and breeding density in the great tit, Parus major L. Ecology, 52, 2-22. Krebs, J. R., Ashcroft, R. & Webber, M. 1978. Song repertoires and territory defence in the great tit. Nature, Lond., 271,539-542. Lambrechts, M. & Dhondt, A. A. 1987. Differences in singing performance between male great tits. Ardea, 75, 43-52. McGregor, P. K. & Krebs, J. R. 1982. Song types in a population of great tits (Parus major): their distribution, abundance, and acquisition by individuals. Behaviour, 79, 126-152.
287
Mardia, K. V. i 980. Tests of univariate and multivariate normality. In: Handbook of Statistics. Vol. 1 (Ed. by P. R. Krishnaiah), pp. 279-320. Amsterdam: NorthHolland. Nelson, D. A. 1989. Song frequency as a cue for recognition of species and individuals in the field sparrow (Spizella pusilla). J. comp. Psychol., 103, 171-176. Potvin, C., Lechowicz, M. J. & Tardif, S. 1990. The statistical analysis of ecophysical response curves obtained from experiments involving repeated measures. Ecology, 71, 1389-1400. SAS Institute 1986. SAS User's Guide: Statistics. 1986 edn. Cary: SAS Institute. Sokal, R. R. & Rohlf, F. J. 1981. Biometry. New York: W. H. Freeman. Weary, D. M. 1989. Categorical perception of bird song: how do great tits (Parus major) perceive temporal variation in their own song? J. comp. Psychol., 103, 320~325. Weary, D. M. 1990. Categorization of song notes in great tits: which acoustic features are used and why? Anim. Behav., 39, 450-457. Weary, D. M. 1991. How great tits use song-note and whole-song features to categorize their songs. Auk, 108, 187-189. Weary, D. M., Norris, K. J. & Falls, J. B. 1990. Song features birds use to identify individuals. Auk, 107, 623-625. Winer, B. J. 1971. Statistical Principles in Experimental Design. New York: McGraw-Hill.