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Similarity of songs within songbird repertoires: Analytic problems
806
Animal Behaviour, 39, 4
juvenile intruders when they are detected. Red kite adults, however, should be just as aggressive towards intruders as their fledglings are also able to change nests. We suggest that the relatively low nearestneighbour distance between red kite nests in our study area ( 2 = 893.1 m, range = 690-2250, N = 21; elsewhere in Europe 4-5 km; Davies & Davis 1973; Valet 1975) is the proximate cause for the high frequency of adoptions observed. Red kite fledglings fly up to 400 m from the nest during the first 2 weeks after fledging (J. Bustamante & F. Hiraldo, unpublished data). With a nearest neighbour at 4-5 km a still dependent fledgling is unlikely to enter another territory. Although in Dofiana black kites nest even closer to each other than red kites (2=205.7 m, range= 30-630, N=47) there are fewer black kite adoptions. Colonial species may be better at recognizing offspring than closely related solitary ones as has been demonstrated for hirundines (Stoddard & Beecher 1983). Black kites, which habitually nest in loose groups, behave aggressively towards intruding fledglings and never towards their offspring (Bustamante & Hiraldo, in press a) while the more solitary red kites never attack any fledglings coming near their nests (Bustamante & Hiraldo, in press c). This aggressive behaviour of black kites probably prevents frequent permanent adoptions, as fledglings of both species develop flying skills at a similar rate (Bustamante & Hiraldo in press b, unpublished data) and frequently visit close neighbouring kite nests. We therefore suggest that the higher frequency of adoption by red kites is due to a maladaptation to breed at high density. Funding was provided by CSIC-CAICYT and a Predoctoral Fellowship of the PFPI to J.B. We thank M. Hiraldo, G. Doval, F. Dominguez, F. Martinez, J. Jafiez, M. Aragoneses, J. Vifiuela, J. Chan, G. Vilchez and P. Ferreras for assistance in the field and E. Aguilera, J. Aguilar-Amat, A. J. F. Holley, and R. Pierotti for helpful suggestions on a first version of this paper. J. BUSTAMANTE F. HIRALDO Estacidn Bioldgiea de Do~ana ( CSIC), Pabell6n del Perk, Avda. de Maria Luisa s/n, 41013-Sevilla, Spain.
References Bitterbaum, E. J. & Brown, C. R. 1981. A martin house is not a home. Nat. Hist., 90, 64457. Bustamante, J. & Hiraldo, F. In press a. Factors influencing family rupture and parent-offspring conflict in the black kite (Milvus migrans). Ibis.
Bustamante, J. & Hiraldo, F. In press b. Post-fledging dependence period and maturation of flightskills in the black kite Milvus migrans. Bird Study. Bustamante, J. & Hiraldo, F. In press c. Parental investment in nest-defence by kites Milvus milvus and M. migrans, during the post-fledging dependence period. Behav. Ecol. Sociobiol. Davies, P. W. & Davis, P. E. 1973. The ecology and conservation of the red kite in Wales. Br. Birds, 66, 183 224. Holley, A. J. F. 1981. Naturally arising adoption in herring gulls. Anim. Behav., 29, 302-303. Holley, A. J. F. 1988. Intergenerational conflict in gulls. Anim. Behav., 36, 619 620. Pierotti, R. 1980. Spite and altruism in gulls. Am. Nat., 115, 290-300. Pierotti, R. 1982. Spite, altruism and semantics:a reply to Waltz. Am. Nat., 119, 116-130. Pierotti, R., Brunton, D. & Murphy, E. C. 1988. Parentoffspringand sibling siblingrecognitionin gulls. Anim. Behav., 36, 620~621. Pierotti, R. & Murphy, E. C. 1987. Intergenerational conflicts in gulls. Anim. Behav,, 35, 435-444. Poole, A. 1982. Breeding ospreys feed fledglingsthat are not their own. Auk, 99, 781 785. Stoddard, P. K. & Beecher, M. D. 1983. Parental recognition of offspring in the cliff swallow. Auk, 100, 795-799. Valet, G. 1975. La sedentarisation du milan royal Milvus milvus en Auxois. Alauda, 43, 263-269. Waltz, E. C. 1981. Reciprocalaltruism and spite in gulls:a comment. Am. Nat., 118, 588-592. (Received 24 April 1989; initial acceptance 27 June 1989; final acceptance 14 August 1989; MS. number: sc-514)
Similarity of Songs Within Songbird Repertoires: Analytic Problems According to several hypotheses, song repertoires are an adaptation that reduces habituation of listening birds (see references in Whitney 1981). These hypotheses predict that repertoires should be composed of dissimilar songs: listening birds will generalize little between these songs and therefore will habituate more slowly than to repertoires of more similar songs. Results consistent with this prediction have been obtained for two species, great tits, Parus major, and varied thrushes, Zoothera naevia. The phrase length of great tit songs is more variable within repertoires than among repertoires (Krebs 1976). The dominant frequency (Hz) and modulation rate of varied thrush songs are each more variable within repertoires than would be expected if the songs were drawn at random from all the songs in the population (Whitney 1981).
Short Communications According to Kroodsma (1982), these results should not be taken to imply that individual great tits and varied thrushes learn a non-random subset of contrasting songs. The greater variability within repertoires, he wrote, 'may merely be an epiphenomenon of song learning' (page 137). Kroodsma defended this view using simulations of song sharing among males. My objectives are (1) to rebut Kroodsma's interpretation of his simulations, and (2) to discuss other problems that may arise in the analysis of similarity of songs within repertoires. Kroodsma simulated song sharing in a population of four birds, where each bird had a repertoire of four songs. Songs varied along a single dimension (phrase length) and took randomly assigned values ranging from 1 to 99. Variability within a repertoire was measured using a difference index (DI) that summed the differences in phrase length of all pair-wise combinations of songs in the repertoire 3
DI=Z i=1
4
Z
Ix,-xj[
j=i+l
where xx, x2, x 3 and x 4 are the phrase lengths of the four songs in the repertoire. In the more realistic of Kroodsma's two simulations, the four birds shared one song type (a phrase length chosen at random), and the rest of the songs (three in each bird's repertoire) were chosen independently from the random numbers table. The variability within these repertoires was compared with the variability within four-song repertoires chosen at random from the 16 songs in the original repertoires. The difference indexes of 10 random repertoires were compared with the median difference index of the four original repertoires. This entire procedure was repeated 20 times, for a total of 200 random repertoires. Overall, the variability within the original repertoires was greater than that within the random ones. Such a difference will arise, Kroodsma argued, whenever birds learn their songs by imitation . . . vocal learning and the sharing of songs among the members of (a) population w i l l . . . create a convergence in the song parameters of population members and thus a relative divergence of parameters within the repertoire of a single male (page t39). I acknowledge that song learning leads to sharing of songs and a convergence in song parameters among individuals. But I do not agree with Kroodsma's view that this convergence necessarily
807
acts to confound the results o f studies such as Krebs (1976) and Whitney (1981). In the simulation described above, Kroodsma arranged that the birds would all share a single song type. This 'forced sharing' ensured that the convergence among the original repertoires would be greater than that among random repertoires, and thus that the variability within the original repertoires would be greater than that within random repertoires. Something similar to forced sharing is known to occur in some songbirds. For example, Bewick's wrens, Thryomanes bewickii, are predisposed to learn a single version of each song type present in the population (Kroodsma 1974). Great tits and varied thrushes, however, are among the many species that do not appear to have such a predisposition. Even the most common song type in a population is found in only a small proportion of repertoires (see McGregor & Krebs 1982, Fig. 4; Whitney 1981, Fig. 1). Extreme convergence among repertoires may also arise in situations where each male learns its songs primarily from a single tutor (e.g. Clayton 1987). According to a quantitative analysis, however, this one-on-one learning does not occur in great tits (McGregor & Krebs 1982). The varied thrush data do not lend themselves to a similar analysis, but none of the birds in my sample shared more than a small proportion of the songs in its repertoire (Whitney, unpublished data), But what about the inevitable convergence among repertoires that occurs when birds learn by imitation and thus share songs? Consider further my analysis of varied thrush songs. I compared the variability within observed repertoires with that within repertoires selected at random from the sample of songs (Whitney 1981). In other words, I established a null hypothesis of random learning. Given that varied thrushes do not have a predisposition to learn a single version of each song type in the population and that individuals do not learn primarily from a single tutor, and given that certain other conditions were met (see below), one would expect no less convergence among random repertoires than among observed repertoires. Thus, the procedure eliminated convergence as a confounding factor. A similar argument can be made in defence of the variance ratio procedure used by Krebs (1976). Other problems, however, may arise in the analysis of similarity of songs within repertoires. Consider first the problem of sample size. Recall that the null hypothesis in my study of varied thrushes was that each bird learned its songs completely at random from all the songs in the population. I assumed that in this situation the variability within observed repertories would be
Animal Behaviour, 39, 4
808
Table L Results of simulations; DIo and DI~ are difference indexes for original and random repertoires~ respectively
Line l 2 3 4 5 6 7
Number of birds
Repertoire size
Number of replicates Dlo > DI r
DIo< DI~
DIo = DI r
X2.
P
4 6 8 10 20 8 4
4 4 4 4 4 4 8
614 593 562 551 518 575 579
378 401 434 443 475 420 416
8 6 4 6 8 5 5
56.15
<0.001
0.03
>0.75
*Tied results (DIo=DI~) were not used in chi-squared analyses. One-sample chi-squared test for line 1 compares observed results with expectation of 496 ([614+ 378]/2) replicates for both DIo > DIr and D1o < DIr. A two-samplechi-squared test was used to compare the results in lines 6-7.
equal to that within repertoires chosen at random from all the songs sampled. This assumption turns out to be false for small sample sizes, as I discovered by doing simulations. Consider a simulation constructed like Kroodsma's, except that all four songs in each of the four original (i.e. 'observed') repertoires were chosen at random. (Another slight difference from Kroodsma's simulation was that the median difference index of the original repertoires was not compared with the separate difference indexes of the 10 random repertoires but with the median difference index of the random repertoires. One thousand such comparisons were generated.) The results showed that the variability within the original repertoires was greater than that within the random repertoires (Table I, line 1). Thus, the procedure is biased toward rejection of the null hypothesis. The magnitude of the bias decreases as the number of birds increases (Table I, lines I-5). Further simulations revealed that this trend is not an effect of the number of birds per se but of the number of songs sampled to construct the original repertoires. Thus, for example, the results were similar for a simulation having eight four-song repertoires and one having four eight-song repertoires (Table I, lines 6-7). In my simulations, bias appears to be negligible for samples greater than about 80 songs, but the relationship of bias to sample size may be different for other distributions of values along the song dimensions measured. Thus, it remains to be determined whether the sample sizes of Krebs (1976) and Whitney (1981) were adequate. (Note: in simulations designed to correct for the bias due to small sample size, I confirmed that the forced sharing in
Kroodsma's simulation did indeed result in greater variability within the original repertories than in the random ones.) There is a danger of a different sort of bias in large field samples. In many songbirds, the structure of song varies microgeographically (reviewed by Krebs & Kroodsma 1980). Suppose a sample of repertoires is collected fi-om an area in which such variation occurs. Individual birds in this area may have learned their repertoires from subsets of songs that are structurally more homogenous than the set of songs found in the entire area. A test to determine whether the birds learned contrasting songs would be biased toward acceptance of the null hypothesis if one compared observed repertoires with repertoires chosen at random from all songs in the area (Whitney 1981). Temporal variation in song is also a potential problem. One must assume that the distribution of values along the song dimensions used for analysis was the same when the birds learned their repertoires as when the repertoires were recorded. In light of the rapid changes in song structure that have been reported for several species (including great tits) this assumption may not always be valid (Thompson 1970; Jenkins 1978; Ince et al. 1980; McGregor & Krebs 1982). Kroodsma (1982) suggested that the best test of whether birds are predisposed to learn repertoires of contrasting songs is to study song development in captive birds. I agree with this suggestion, yet I know from experience that an enormous effort is required to achieve an adequate sample in such a study (Whitney & Miller 1987). To my mind, field studies are also worth pursuing; they simply must be done more carefully.
Short Communications
I thank P. Gowaty, D. Kroodsma, J. Miller, K. Yasukawa and an anonymous referee for their comments on earlier versions of this manuscript. CARL L. WHITNEY Department o f Biology, College o f Charleston, Charleston, S C 29424, U.S.A.
References Clayton, N. S. 1987. Song tutor choice in zebra finches. Anita. Behav., 35, 714-721. Ince, S. A. Slater, P. J. B. & Weismann, C. 1980. Changes with time in the songs of a population of chaffinches. Condor, 82, 285-290. Jenkins, P. F. 1978. Cultural transmission of song patterns and dialect development in a free-livingbird population. Anita. Behav., 25, 50-78. Krebs, J. R. 1976.Habituation and song repertoires in the great tit. Behav. Ecol. Sociobiol., 1,215-227. Krebs, J. R. & Kroodsma, D. E. 1980. Repertoires and geographical variation in bird song. In: Advances in the Study of Behavior. Vol. 11 (Ed. by J. S. Rosenblatt, R. A. Hinde, C. Beer & M.-C. Busnel),pp. 143-177. New York: AcademicPress. Kroodsma, D. E. 1974. Song learning, dialects, and dispersal in the Bewick's wren. Z. Tierpsychol., 35, 352-380. Kroodsma, D. E. 1982. Song repertoires: problems in their definition and use. In: Acoustic Communication in Birds. Vol. 2. (Ed. by D. E. Kroodsma & E. H. Miller), pp. 125-146. New York: AcademicPress. 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, 76, 126-152. Thompson, W. L. 1970. Song variation in a population of indigo buntings. Auk, 87, 58-71. Whitney, C. L. 1981. Patterns of singing in the varied thrush: I. the similarity of songs within individual repertoires. Z. Tierpsychol., 57, 131-140. Whitney, C. L. & Miller, J. 1987. Song learning in the wood thrush. Can. J. Zool., 65, 1038-1042. (Received 3 January 1989; initial acceptance 7 February 1989;final acceptance 17 July 1989; MS. number." ,4s-600)
Breeding-season Aggression of Female Yellow Warblers to Models of Male and Female Conspecific Intruders Kleptogamy (May & Robertson 1980) occurs when an individual unwittingly cares for offspring that are not its own, and specifically refers to cases in which no obvious selective benefits accrue to the care-taker. Males may avoid kleptogamy by guarding their fertile females (reviewed by Birkhead et al. 1987). However, female responses to threats of
809
kleptogamy remain poorly understood, and few studies have tested predictions of sex-specific threats to parentage (references in Weatherhead & Robertson 1980; Gowaty & Wagner 1988). Thus, we investigated the responses of female yellow warblers, Dendroica petechia, to models of conspecific male and female intruders placed near their nests at successive stages of the nesting cycle. The genetic parentage of female yellow warblers on our study site is threatened through conspecific brood parasitism (Sealy et al. 1989) and brood parasitism by the brown-headed cowbird, Molothrus ater (Goossen & Sealy 1982). We tested the hypotheses (1) that females should respond more aggressively to female intruders and (2) that this aggression should be most intense early in the breeding season when females may be susceptible to conspecific brood parasitism. From 25 May to 8 July 1986 and 19 May to 8 July 1987, we studied responses of yellow warblers toward models of conspecifics in the dune-ridge forest that separates Delta Marsh and Lake Manitoba, Manitoba (MacKenzie 1982). Ninety yellow warbler pairs with at least one individual uniquely colour banded were chosen randomly for testing with models. We chose only first nests whose stages were known, and tested each nest only once. Male and female yellow warbler models were freeze-dried specimens mounted in perching positions and attached to branches or foliage with clips at nest height facing the nest bowl. We placed male and female models in random order at each nest. We recorded responses for 5 min beginning when one of the parents returned or until the focal bird left the nest area during the trial. Timing started after the observer returned to the blind, and at least 20min (maximum of 70min) elapsed between successive model presentations. We performed the trials between 1000 and 1930 hours. We scored the proximity and behaviour of yellow warblers to the model for each 10-s period within a trial as follows: (1) the distance of focal birds from the model in one of three distance classes: less than 2 m, 2-5 m, greater than 5 m; (2) alarm calling, 'chip' or 'seet' calls (Hobson & Sealy 1989a); (3) perch changes; (4) in view and perched; (5) close pass or hover over model; (6) contacts model by swooping; (7) distraction display (Hobson & Sealy 1989a); (8) sitting in nest; (9) preening; (10) ruffling feathers or head-scratching; (11) bill wiping; (12) foraging; and (13) out of sight or leaves area (see Hobson &Sealy 1989a for more information on methods). Females responded first in 85 and 90 of the trials involving male and female models, respectively (Table I). Aggression and distraction displays in response to the male model were observed rarely.
Animal Behaviour, 39, 4
juvenile intruders when they are detected. Red kite adults, however, should be just as aggressive towards intruders as their fledglings are also able to change nests. We suggest that the relatively low nearestneighbour distance between red kite nests in our study area ( 2 = 893.1 m, range = 690-2250, N = 21; elsewhere in Europe 4-5 km; Davies & Davis 1973; Valet 1975) is the proximate cause for the high frequency of adoptions observed. Red kite fledglings fly up to 400 m from the nest during the first 2 weeks after fledging (J. Bustamante & F. Hiraldo, unpublished data). With a nearest neighbour at 4-5 km a still dependent fledgling is unlikely to enter another territory. Although in Dofiana black kites nest even closer to each other than red kites (2=205.7 m, range= 30-630, N=47) there are fewer black kite adoptions. Colonial species may be better at recognizing offspring than closely related solitary ones as has been demonstrated for hirundines (Stoddard & Beecher 1983). Black kites, which habitually nest in loose groups, behave aggressively towards intruding fledglings and never towards their offspring (Bustamante & Hiraldo, in press a) while the more solitary red kites never attack any fledglings coming near their nests (Bustamante & Hiraldo, in press c). This aggressive behaviour of black kites probably prevents frequent permanent adoptions, as fledglings of both species develop flying skills at a similar rate (Bustamante & Hiraldo in press b, unpublished data) and frequently visit close neighbouring kite nests. We therefore suggest that the higher frequency of adoption by red kites is due to a maladaptation to breed at high density. Funding was provided by CSIC-CAICYT and a Predoctoral Fellowship of the PFPI to J.B. We thank M. Hiraldo, G. Doval, F. Dominguez, F. Martinez, J. Jafiez, M. Aragoneses, J. Vifiuela, J. Chan, G. Vilchez and P. Ferreras for assistance in the field and E. Aguilera, J. Aguilar-Amat, A. J. F. Holley, and R. Pierotti for helpful suggestions on a first version of this paper. J. BUSTAMANTE F. HIRALDO Estacidn Bioldgiea de Do~ana ( CSIC), Pabell6n del Perk, Avda. de Maria Luisa s/n, 41013-Sevilla, Spain.
References Bitterbaum, E. J. & Brown, C. R. 1981. A martin house is not a home. Nat. Hist., 90, 64457. Bustamante, J. & Hiraldo, F. In press a. Factors influencing family rupture and parent-offspring conflict in the black kite (Milvus migrans). Ibis.
Bustamante, J. & Hiraldo, F. In press b. Post-fledging dependence period and maturation of flightskills in the black kite Milvus migrans. Bird Study. Bustamante, J. & Hiraldo, F. In press c. Parental investment in nest-defence by kites Milvus milvus and M. migrans, during the post-fledging dependence period. Behav. Ecol. Sociobiol. Davies, P. W. & Davis, P. E. 1973. The ecology and conservation of the red kite in Wales. Br. Birds, 66, 183 224. Holley, A. J. F. 1981. Naturally arising adoption in herring gulls. Anim. Behav., 29, 302-303. Holley, A. J. F. 1988. Intergenerational conflict in gulls. Anim. Behav., 36, 619 620. Pierotti, R. 1980. Spite and altruism in gulls. Am. Nat., 115, 290-300. Pierotti, R. 1982. Spite, altruism and semantics:a reply to Waltz. Am. Nat., 119, 116-130. Pierotti, R., Brunton, D. & Murphy, E. C. 1988. Parentoffspringand sibling siblingrecognitionin gulls. Anim. Behav., 36, 620~621. Pierotti, R. & Murphy, E. C. 1987. Intergenerational conflicts in gulls. Anim. Behav,, 35, 435-444. Poole, A. 1982. Breeding ospreys feed fledglingsthat are not their own. Auk, 99, 781 785. Stoddard, P. K. & Beecher, M. D. 1983. Parental recognition of offspring in the cliff swallow. Auk, 100, 795-799. Valet, G. 1975. La sedentarisation du milan royal Milvus milvus en Auxois. Alauda, 43, 263-269. Waltz, E. C. 1981. Reciprocalaltruism and spite in gulls:a comment. Am. Nat., 118, 588-592. (Received 24 April 1989; initial acceptance 27 June 1989; final acceptance 14 August 1989; MS. number: sc-514)
Similarity of Songs Within Songbird Repertoires: Analytic Problems According to several hypotheses, song repertoires are an adaptation that reduces habituation of listening birds (see references in Whitney 1981). These hypotheses predict that repertoires should be composed of dissimilar songs: listening birds will generalize little between these songs and therefore will habituate more slowly than to repertoires of more similar songs. Results consistent with this prediction have been obtained for two species, great tits, Parus major, and varied thrushes, Zoothera naevia. The phrase length of great tit songs is more variable within repertoires than among repertoires (Krebs 1976). The dominant frequency (Hz) and modulation rate of varied thrush songs are each more variable within repertoires than would be expected if the songs were drawn at random from all the songs in the population (Whitney 1981).
Short Communications According to Kroodsma (1982), these results should not be taken to imply that individual great tits and varied thrushes learn a non-random subset of contrasting songs. The greater variability within repertoires, he wrote, 'may merely be an epiphenomenon of song learning' (page 137). Kroodsma defended this view using simulations of song sharing among males. My objectives are (1) to rebut Kroodsma's interpretation of his simulations, and (2) to discuss other problems that may arise in the analysis of similarity of songs within repertoires. Kroodsma simulated song sharing in a population of four birds, where each bird had a repertoire of four songs. Songs varied along a single dimension (phrase length) and took randomly assigned values ranging from 1 to 99. Variability within a repertoire was measured using a difference index (DI) that summed the differences in phrase length of all pair-wise combinations of songs in the repertoire 3
DI=Z i=1
4
Z
Ix,-xj[
j=i+l
where xx, x2, x 3 and x 4 are the phrase lengths of the four songs in the repertoire. In the more realistic of Kroodsma's two simulations, the four birds shared one song type (a phrase length chosen at random), and the rest of the songs (three in each bird's repertoire) were chosen independently from the random numbers table. The variability within these repertoires was compared with the variability within four-song repertoires chosen at random from the 16 songs in the original repertoires. The difference indexes of 10 random repertoires were compared with the median difference index of the four original repertoires. This entire procedure was repeated 20 times, for a total of 200 random repertoires. Overall, the variability within the original repertoires was greater than that within the random ones. Such a difference will arise, Kroodsma argued, whenever birds learn their songs by imitation . . . vocal learning and the sharing of songs among the members of (a) population w i l l . . . create a convergence in the song parameters of population members and thus a relative divergence of parameters within the repertoire of a single male (page t39). I acknowledge that song learning leads to sharing of songs and a convergence in song parameters among individuals. But I do not agree with Kroodsma's view that this convergence necessarily
807
acts to confound the results o f studies such as Krebs (1976) and Whitney (1981). In the simulation described above, Kroodsma arranged that the birds would all share a single song type. This 'forced sharing' ensured that the convergence among the original repertoires would be greater than that among random repertoires, and thus that the variability within the original repertoires would be greater than that within random repertoires. Something similar to forced sharing is known to occur in some songbirds. For example, Bewick's wrens, Thryomanes bewickii, are predisposed to learn a single version of each song type present in the population (Kroodsma 1974). Great tits and varied thrushes, however, are among the many species that do not appear to have such a predisposition. Even the most common song type in a population is found in only a small proportion of repertoires (see McGregor & Krebs 1982, Fig. 4; Whitney 1981, Fig. 1). Extreme convergence among repertoires may also arise in situations where each male learns its songs primarily from a single tutor (e.g. Clayton 1987). According to a quantitative analysis, however, this one-on-one learning does not occur in great tits (McGregor & Krebs 1982). The varied thrush data do not lend themselves to a similar analysis, but none of the birds in my sample shared more than a small proportion of the songs in its repertoire (Whitney, unpublished data), But what about the inevitable convergence among repertoires that occurs when birds learn by imitation and thus share songs? Consider further my analysis of varied thrush songs. I compared the variability within observed repertoires with that within repertoires selected at random from the sample of songs (Whitney 1981). In other words, I established a null hypothesis of random learning. Given that varied thrushes do not have a predisposition to learn a single version of each song type in the population and that individuals do not learn primarily from a single tutor, and given that certain other conditions were met (see below), one would expect no less convergence among random repertoires than among observed repertoires. Thus, the procedure eliminated convergence as a confounding factor. A similar argument can be made in defence of the variance ratio procedure used by Krebs (1976). Other problems, however, may arise in the analysis of similarity of songs within repertoires. Consider first the problem of sample size. Recall that the null hypothesis in my study of varied thrushes was that each bird learned its songs completely at random from all the songs in the population. I assumed that in this situation the variability within observed repertories would be
Animal Behaviour, 39, 4
808
Table L Results of simulations; DIo and DI~ are difference indexes for original and random repertoires~ respectively
Line l 2 3 4 5 6 7
Number of birds
Repertoire size
Number of replicates Dlo > DI r
DIo< DI~
DIo = DI r
X2.
P
4 6 8 10 20 8 4
4 4 4 4 4 4 8
614 593 562 551 518 575 579
378 401 434 443 475 420 416
8 6 4 6 8 5 5
56.15
<0.001
0.03
>0.75
*Tied results (DIo=DI~) were not used in chi-squared analyses. One-sample chi-squared test for line 1 compares observed results with expectation of 496 ([614+ 378]/2) replicates for both DIo > DIr and D1o < DIr. A two-samplechi-squared test was used to compare the results in lines 6-7.
equal to that within repertoires chosen at random from all the songs sampled. This assumption turns out to be false for small sample sizes, as I discovered by doing simulations. Consider a simulation constructed like Kroodsma's, except that all four songs in each of the four original (i.e. 'observed') repertoires were chosen at random. (Another slight difference from Kroodsma's simulation was that the median difference index of the original repertoires was not compared with the separate difference indexes of the 10 random repertoires but with the median difference index of the random repertoires. One thousand such comparisons were generated.) The results showed that the variability within the original repertoires was greater than that within the random repertoires (Table I, line 1). Thus, the procedure is biased toward rejection of the null hypothesis. The magnitude of the bias decreases as the number of birds increases (Table I, lines I-5). Further simulations revealed that this trend is not an effect of the number of birds per se but of the number of songs sampled to construct the original repertoires. Thus, for example, the results were similar for a simulation having eight four-song repertoires and one having four eight-song repertoires (Table I, lines 6-7). In my simulations, bias appears to be negligible for samples greater than about 80 songs, but the relationship of bias to sample size may be different for other distributions of values along the song dimensions measured. Thus, it remains to be determined whether the sample sizes of Krebs (1976) and Whitney (1981) were adequate. (Note: in simulations designed to correct for the bias due to small sample size, I confirmed that the forced sharing in
Kroodsma's simulation did indeed result in greater variability within the original repertories than in the random ones.) There is a danger of a different sort of bias in large field samples. In many songbirds, the structure of song varies microgeographically (reviewed by Krebs & Kroodsma 1980). Suppose a sample of repertoires is collected fi-om an area in which such variation occurs. Individual birds in this area may have learned their repertoires from subsets of songs that are structurally more homogenous than the set of songs found in the entire area. A test to determine whether the birds learned contrasting songs would be biased toward acceptance of the null hypothesis if one compared observed repertoires with repertoires chosen at random from all songs in the area (Whitney 1981). Temporal variation in song is also a potential problem. One must assume that the distribution of values along the song dimensions used for analysis was the same when the birds learned their repertoires as when the repertoires were recorded. In light of the rapid changes in song structure that have been reported for several species (including great tits) this assumption may not always be valid (Thompson 1970; Jenkins 1978; Ince et al. 1980; McGregor & Krebs 1982). Kroodsma (1982) suggested that the best test of whether birds are predisposed to learn repertoires of contrasting songs is to study song development in captive birds. I agree with this suggestion, yet I know from experience that an enormous effort is required to achieve an adequate sample in such a study (Whitney & Miller 1987). To my mind, field studies are also worth pursuing; they simply must be done more carefully.
Short Communications
I thank P. Gowaty, D. Kroodsma, J. Miller, K. Yasukawa and an anonymous referee for their comments on earlier versions of this manuscript. CARL L. WHITNEY Department o f Biology, College o f Charleston, Charleston, S C 29424, U.S.A.
References Clayton, N. S. 1987. Song tutor choice in zebra finches. Anita. Behav., 35, 714-721. Ince, S. A. Slater, P. J. B. & Weismann, C. 1980. Changes with time in the songs of a population of chaffinches. Condor, 82, 285-290. Jenkins, P. F. 1978. Cultural transmission of song patterns and dialect development in a free-livingbird population. Anita. Behav., 25, 50-78. Krebs, J. R. 1976.Habituation and song repertoires in the great tit. Behav. Ecol. Sociobiol., 1,215-227. Krebs, J. R. & Kroodsma, D. E. 1980. Repertoires and geographical variation in bird song. In: Advances in the Study of Behavior. Vol. 11 (Ed. by J. S. Rosenblatt, R. A. Hinde, C. Beer & M.-C. Busnel),pp. 143-177. New York: AcademicPress. Kroodsma, D. E. 1974. Song learning, dialects, and dispersal in the Bewick's wren. Z. Tierpsychol., 35, 352-380. Kroodsma, D. E. 1982. Song repertoires: problems in their definition and use. In: Acoustic Communication in Birds. Vol. 2. (Ed. by D. E. Kroodsma & E. H. Miller), pp. 125-146. New York: AcademicPress. 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, 76, 126-152. Thompson, W. L. 1970. Song variation in a population of indigo buntings. Auk, 87, 58-71. Whitney, C. L. 1981. Patterns of singing in the varied thrush: I. the similarity of songs within individual repertoires. Z. Tierpsychol., 57, 131-140. Whitney, C. L. & Miller, J. 1987. Song learning in the wood thrush. Can. J. Zool., 65, 1038-1042. (Received 3 January 1989; initial acceptance 7 February 1989;final acceptance 17 July 1989; MS. number." ,4s-600)
Breeding-season Aggression of Female Yellow Warblers to Models of Male and Female Conspecific Intruders Kleptogamy (May & Robertson 1980) occurs when an individual unwittingly cares for offspring that are not its own, and specifically refers to cases in which no obvious selective benefits accrue to the care-taker. Males may avoid kleptogamy by guarding their fertile females (reviewed by Birkhead et al. 1987). However, female responses to threats of
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kleptogamy remain poorly understood, and few studies have tested predictions of sex-specific threats to parentage (references in Weatherhead & Robertson 1980; Gowaty & Wagner 1988). Thus, we investigated the responses of female yellow warblers, Dendroica petechia, to models of conspecific male and female intruders placed near their nests at successive stages of the nesting cycle. The genetic parentage of female yellow warblers on our study site is threatened through conspecific brood parasitism (Sealy et al. 1989) and brood parasitism by the brown-headed cowbird, Molothrus ater (Goossen & Sealy 1982). We tested the hypotheses (1) that females should respond more aggressively to female intruders and (2) that this aggression should be most intense early in the breeding season when females may be susceptible to conspecific brood parasitism. From 25 May to 8 July 1986 and 19 May to 8 July 1987, we studied responses of yellow warblers toward models of conspecifics in the dune-ridge forest that separates Delta Marsh and Lake Manitoba, Manitoba (MacKenzie 1982). Ninety yellow warbler pairs with at least one individual uniquely colour banded were chosen randomly for testing with models. We chose only first nests whose stages were known, and tested each nest only once. Male and female yellow warbler models were freeze-dried specimens mounted in perching positions and attached to branches or foliage with clips at nest height facing the nest bowl. We placed male and female models in random order at each nest. We recorded responses for 5 min beginning when one of the parents returned or until the focal bird left the nest area during the trial. Timing started after the observer returned to the blind, and at least 20min (maximum of 70min) elapsed between successive model presentations. We performed the trials between 1000 and 1930 hours. We scored the proximity and behaviour of yellow warblers to the model for each 10-s period within a trial as follows: (1) the distance of focal birds from the model in one of three distance classes: less than 2 m, 2-5 m, greater than 5 m; (2) alarm calling, 'chip' or 'seet' calls (Hobson & Sealy 1989a); (3) perch changes; (4) in view and perched; (5) close pass or hover over model; (6) contacts model by swooping; (7) distraction display (Hobson & Sealy 1989a); (8) sitting in nest; (9) preening; (10) ruffling feathers or head-scratching; (11) bill wiping; (12) foraging; and (13) out of sight or leaves area (see Hobson &Sealy 1989a for more information on methods). Females responded first in 85 and 90 of the trials involving male and female models, respectively (Table I). Aggression and distraction displays in response to the male model were observed rarely.