J. Avian Biol. 40: 481 490, 2009 doi: 10.1111/j.1600-048X.2008.04682.x # 2009 The Authors. J. Compilation # 2009 J. Avian Biol. Received 7 August 2008, accepted 7 November 2008
Individual distinctiveness in the mobbing call of a cooperative bird, the noisy miner Manorina melanocephala
Robert A. W. Kennedy, Christopher S. Evans and Paul G. McDonald
R. A. W. Kennedy, C. S. Evans and P. G. McDonald (correspondence), Centre for the Integr. Study of Anim. Behav., Macquarie Univ., Sydney, Australia 2109. Email: [email protected]
Individual differentiation is usually advantageous in maximising the fitness benefits of interactions with conspecifics. In social species, where intraspecific interactions are frequent, this is likely to be particularly important. Indeed, some form of differentiation underpins most hypotheses proposed to account for cooperative behaviour in birds. The auditory modality is a likely candidate for this function, particularly for species where individuals are widely spaced and in dense vegetation. In this study, we examined the acoustic structure of a distinctive mobbing signal, the ‘chur’ call, of the cooperatively breeding noisy miner Manorina melanocephala. Using 250 calls from 25 individuals, a combination of spectrographic-based measurement of call parameters, cross-correlation and multi-dimensional scaling was used to test for systematic individual differences in call structure. Strong differences between individuals were observed in all measures, indicating that this call encodes sufficient information to facilitate individual differentiation. We then conducted a series of field playbacks to test the effect of the behaviour on conspecifics. Results demonstrated that the call, in isolation, has a clear attractant effect. Given that chur calls are synonymous with the characteristic cooperative mobbing behaviour of this species, these findings suggest they are likely to have an important function in coordinating complex social behaviour.
Individual differences in the vocalisations of territorial, The benefits of signalling identity must outweigh any socially monogamous, birds have been examined for many costs associated with doing so for individuality to be years (Stoddard 1996 for review). These studies have lead to retained and/or developed in a population. The auditory the development of such rich areas of research as the ‘dear modality has the potential to be a relatively low cost method enemy effect’ (e.g. Falls 1982) and the development of of signalling individual identity, particularly in habitats communication network theory (McGregor 2004). How- where visibility may be interrupted (due to dense vegeta- ever, the vocalisations of social species have received tion, for example). Individually distinctive vocal ‘signatures’ comparatively little attention, despite similar putative have characterised many call types across a wide range of benefits of individual recognition. For example, individuals avian taxa (gannets: White et al. 1970, penguins: Jouventin may vary the level of resources and/or aggression expended et al. 1999, skuas: Charrier et al. 2001, gulls: Mathevon during interactions with different individuals. Fully 9% of et al. 2003, passerines: Mammen and Nowicki 1981, Sharp avian taxa breed cooperatively (Cockburn 2003, 2006), that and Hatchwell 2005, McDonald et al. 2007). Differences in is, in systems where individuals apart from the breeding pair acoustic properties have even been used in the field to assist in rearing offspring. Cooperation may also extend to census individuals from species that are difficult to observe other modalities, such as the mobbing of predators. directly (e.g. Puglisi and Adamo 2004). Individual differentiation in these groups is of particular Whether individuals of such species actually attend to interest, because evolutionary models have suggested a range and utilise the individual vocal differences measurable in the of pathways through which direct and indirect benefits may acoustic structure of their calls remains largely unknown. accrue to such helpers. These include kin selection For example, song sparrows Melospiza melodia appear not to (Hamilton 1964), ‘pay to stay’ (Gaston 1978), group use vocal individuality to identify a single singer as such, but augmentation (Woolfenden and Fitzpatrick 1978, Kokko rather to discriminate on the basis of song type (Beecher et al. 2001), and social ‘prestige’ (Zahavi 1977, Wright et al. 1994). Similarly, many studies point to the ability of 1999). In all of these cases, individualised signals, and the songbirds to distinguish among the songs and/or song discrimination of such signals by receivers, are typically repertoires of individuals according to categories, such as either assumed to be present, or at the very least would be mate versus non-mate (Lind et al. 1996), or neighbour/ highly advantageous. Nevertheless, there have been few group member versus stranger (Payne et al. 1988). Other experimental studies of this to date. species have been shown to use calls to distinguish between
481 flock members or social partners, or offspring versus other playback trials aimed at determining the function of this young (Beecher 1981, Beecher et al. 1981, Nowicki 1982, vocalisation. Wanker et al. 1998). Experimental evidence of individual discrimination has been documented in great tits Parus major (Weary and Krebs 1992), however the results obtained Methods varied according to the playback equipment used and were based on a low number of exemplars, leading some Focal populations to question its broad applicability (e.g. Beecher et al. 1994). Better evidence for individual differentiation has been Calls were recorded from 2 different cohorts of noisy miners. The first cohort of birds (n 11) were housed obtained through the use of operant conditioning, however individually in a compound of 3 3 m cages at Macquarie this has been carried out for only a few species (e.g. Brown Univ., New South Wales, 338 46?S, 1518 06?E. Birds were et al. 1988, Sturdy et al. 1999, Phillmore et al. 2002). local residents trapped within 25 m of the cages between 13 Noisy miners Manorina melanocephala are honeyeaters May and 21 June 2007. They were recorded from 20 June (Meliphagidae) endemic to wooded country in southern through 15 August, and released between 14 July and 21 and eastern Australia. Multi-brooded, obligate cooperative Aug. The second cohort were free-living birds located breeders, they form large colonies with complex internal opportunistically at 14 different sites in the surrounding social structures that occupy areas up to 200 ha (Dow 1977, region, visited during the breeding season between 20 July Higgins et al. 2001). Extra-pair fertilizations are typically through 8 Aug 2007. Recording sites were separated by rare (3.5% of nestlings: Po˜ldmaa et al. 1995), with broods 320 2800 m from the next (mean: 1132 m9572 SD), attended by up to 22 helpers in addition to the breeding maximising the probability that each recording sampled a pair (Dow 1979a). These helpers are invariably from within different individual. Noisy miners are usually found within the colony, usually male, and of variable degrees of individual activity spaces within their colony, for which relatedness to the brood (Po˜ldmaa et al. 1995). mean diameters of 114 m for males and 74 m for females In addition to provisioning nestlings, noisy miners show have been reported (Dow 1979b), that is a third of the another kind of ‘helping’ behaviour. Adult members of a distance between recording sites used herein. As calls were colony work in groups to expel potential food competitors only recorded from birds present from the beginning of a and predators from their territory (Arnold et al. 2005). stimulus presentation, or which arrived within 1 min, it is While doing this they often give a repeated, loud, highly unlikely that birds were recorded at multiple sites. monosyllabic vocalization, henceforth referred to as the Vocalizations were recorded using a Sennheiser MKH ‘chur’ call (Date 1982, Higgins et al. 2001). This call is 816T microphone and a Sony TCD-D10 Pro II DAT generally given by an individual as soon as it perceives a recorder. A second microphone (Yoga FX 108) was used to terrestrial or avian predator of little imminent threat to record dictation by the observer (RK) onto the second adults (e.g. an egg thief). Other noisy miners may then join recording channel. Chur calls were elicited by presentation the initial caller in a vociferous ‘chorus’ of chur-calling. of taxidermic models (European fox Vulpes vulpes and While the chur call is predominately used in these mobbing domestic cat Felis cattus), typical contemporary ground contexts, it is also sometimes given in the apparent absence predators in the Sydney region. No apparent differences in of any threat, perhaps as a form of a contact call. Indeed, it behaviours elicited were detected according to model has been suggested that this vocalization leads to the species, nor were the calls of birds exposed to both models gathering of colony members, thus facilitating communal specific to each model type (n 4, multivariate test: Wilks’ mobbing (Dow 1977). Such gatherings may also subse- Lambda 0.882, F2,31 2.073, P 0.143). Until record- quently become intense social interactions, involving a ing began, the models were kept covered in a dark blue range of vocalizations, displays and chases, which are cloth, with no alarm calls given to the model in this state. thought to reflect local dominance hierarchies (Dow The model was placed on the ground 3 6 m in front of the 1975, Arnold 2000). focal bird (caged or free-living) and vocalizations recorded The apparent function of the chur vocalisation, and the from a distance of 3 7.5 m. The focal bird was kept group behaviour associated with it, suggests that it may be constantly in view and dictation into one channel used to pivotal in the noisy miner’s complex sociality, and therefore denote calls of the focal bird for later isolation. After a prime candidate for individual distinctiveness. However, a 10 min, if no calls were elicited or the focal bird moved out system of individual recognition based on vocal character- of sight, sessions were terminated. istics requires that the acoustic features of a vocalisation show repeatable structural variation that is greater between, Call sampling rather than within, individuals (Borror 1959, Marler 1960, Falls and McNicholl 1982, Charrier et al. 2001). The total DAT recordings of each session were acquired into AIFF information content of such a signal must also be high format at 48 kHz with 16-bit sampling, using Peak Pro enough to support a sufficiently large number of distinctive 5.2 (Bias Inc., Petaluma). We used Raven 1.3 (Cornell signatures for effective discrimination between individuals Lab. of Ornithol.) to process and sample raw recordings (Beecher 1989). In the present study we analyzed the noisy for analysis. The chur call consists of a single unit or miner ‘chur’ call to test for consistent individual differences syllable given repeatedly in rapid succession. However, that could encode caller identity. We also examined the the stream of call units is not simply continuous, but impact of familiar versus unfamiliar calls when eliciting rather subdivided by irregular pauses into groups of units, approaches from free-living birds in a series of field or bouts (Fig. 1a). Bouts were defined as groups of call
482 Figure 1. Examples of recordings of the noisy miner chur call used as stimuli in playback experiments, showing: (a) a sequence of 16 call units over 4 bouts, (b) temporal and amplitude matched white noise analogue of calls in (a), (c) waveform of 4 call units, and (d) the same units after conversion to white noise pulses demonstrating the matched amplitude profiles. Spectrograms constructed with a sample rate of 512 points, smoothing enabled, 3 dB bandwidth filter at 122 Hz, Hamming window, 75% overlap, grid spacing at 93.8 Hz, grey scale represents a 44 dB range. units separated by pauses of at least 0.42 s (twice the mean as this was the mean number elicited from birds challen- call unit length). ged with models (9.4 9 7.4 SD, range 1 31), with the All birds sampled for the acoustic analysis gave at least number of calls per bout kept to within 1 SD of the mean 10 bouts of calling when challenged with a model. For each (7.4 9 5.9 SD, range 1 84). The final playback sequences bout we extracted the first call unit free from interference were 30.1s 9 6.1 SD in duration, and were filtered (e.g. other bird calls) for subsequent analysis. In total (highpass 500 Hz) to remove unwanted background noise. we sampled 10 call units, each from a different bout, from Control stimuli were constructed in Peak Pro 5.2. 25 individual birds, yielding 250 vocalizations in total. Each was temporally matched to a specific playback Spectrograms were made using Raven 1.3 (512 points, sequence, but rather than consisting of call notes, these Hamming window, overlap 75%, grid spacing 93.8 Hz). Six were temporally matched pulses of white-noise generated parameters were measured from the fundamental frequency in Soundtrack Pro for Macintosh (Fig. 1b). Each white contour of each call: delta time (duration of call unit), noise pulse was edited in Peak Pro to match the amplitude minimum frequency (lowest frequency recorded during the profile of the corresponding original call unit (Fig. 1c d). call unit), maximum frequency (highest frequency recorded during call unit), delta frequency (the range of frequency recorded during the call unit), frequency at maximum Playback procedure amplitude (frequency of the call unit when it was loudest), and frequency at the temporal centre of call unit. Playbacks were undertaken at 10 locations at which free- living birds had been recorded during the data collection phase (13 14 September 2007). Each location was used for Playback stimuli 1 trial of each of 4 conditions, with 2 trials conducted per d, separated by an average of 202 min 9 26 SD: (1) playback Each playback stimulus consisted of 9 calling bouts with of a familiar recording (chur calls recorded at same 2-13 call units/bout. These were sampled from recordings location), (2) playback of an unfamiliar recording (chur of 41 different free-living birds. Nine bouts were used, calls recorded at a location greater than 800 m distant),
483 (3) playback of the white-noise analogue of the familiar the dimensions as dependent variables and individual recording, and (4) playback of the white-noise analogue identity as the grouping factor, to determine whether calls of the unfamiliar recording. Trial order was fully counter- varied significantly according to individual bird. balanced across locations. A Macintosh Powerbook G4 and Finally, the number of birds responding to each speaker (Advent AV570, USA) were used to play stimuli at playback was analyzed using planned pairwise Wilcoxon an amplitude matched to that of the original recording. signed-ranks tests. Cohen’s d was calculated to determine During playbacks, RK and PM scanned the left and effect size (Cohen 1992). Power analyses were made using right sides, respectively, of the speaker setup. Immediately PASS: number cruncher statistical software (J. Hintze, before a stimulus was broadcast, the number of noisy Kayesville). All other analyses were conducted using SPSS miners present within a 10 m radius of the speaker was v13 for Mac OSX. recorded; point counts were then made at 1 and 3 min after playback commenced. Baselines were subtracted from point-counts to obtain 2 difference scores representing Results the number of birds attracted by each playback. The maximum numbers of birds present during each trial Calls produced in response to models was also recorded. During playback, we used a 702 high resolution digital audio recorder (Sound Devices, Call unit length ranged from 0.146 to 0.312 s (mean Reedsburg), and a Sony (San Diego, California) electret 0.215 s 9 0.03 SD), with a rapid onset/end to maximum condenser microphone (ECM 77B) to record vocalisa- amplitude combined with simultaneous frequency mod- tions produced by focal birds. Spectrograms of these ulation (FM), with the middle section of calls characterised recordings were subsequently examined to quantify vocal by relatively slow FM (Fig. 2). Calls were harmonically responses to playbacks. rich, with most energy concentrated in the first 2 or 3 harmonics, rather than the fundamental. Initial perusal of spectrograms revealed a possible categorisation of the calls into 2 subtypes, which were also subjectively distinguish- Statistical analyses able by ear. Statistically, this dichotomy was reflected in the distinctly bimodal distribution of the parameter We used Kruskal-Wallis tests to determine the significance measurements high frequency and frequency at maximum of individual differences in call parameters measured. The amplitude. This likely corresponds to the distinction made repeatability (r, intra-class correlation coefficient) of each in the literature between ‘low-’ and ‘high-intensity’ variants parameter was determined using the formula presented by of this call (Dow 1975, Higgins et al. 2001). Calls in the Lessells and Boag (1987). This ratio provides an indicator high-intensity group (22.8%, n 250) tended to be of how variable measurements are within individuals, shorter, increase in frequency through the temporal relative to variation between individuals. We report midpoint, and reach higher frequencies overall (Fig. 2b). 1 r, with greater values indicating greater within- They also showed considerable irregularity in the steepness individual repeatability. The potential of individuality of their upward inflection and in their FM pattern coding (PIC) for each variable was also calculated using generally. ‘Low intensity’ calls were less variable, and the formula based on coefficients of variation (Charrier were characterised by decreasing frequency after the et al. 2001, Mathevon et al. 2003). A PICB1 indicates temporal centre (Fig. 2a, c). The majority of birds gave greater intra- in comparison to inter-individual variation, only low-intensity calls, some gave both, and some mainly, and therefore a much reduced possibility of that parameter but never exclusively, produced high-intensity calls. encoding individuality, while PIC 1 indicates increasing Although high-intensity calls have been linked to ‘extreme potential to encode individuality. As several parameters alarm’ (Jurisevic and Sanderson 1994, Higgins et al. were highly correlated, the extent to which the acoustic 2001), no systematic correlations were found in this study structure of this call (as defined by the parameter between social or environmental circumstances and call measurements) could support multiple signatures encoding subtypes. The change between types was often gradual individuality was assessed using the information capacity within individuals, with a range of structurally intermedi- measure developed by Beecher (1989). Essentially this ate calls (e.g. Fig. 2d and 3). All 250 sampled calls were procedure assesses the number of discrete ‘signatures’ or therefore included in the analyses that follow. vocalisations that may exist in a population given the level of variance in calls measured, using an information theory approach (Beecher 1989). Quantification of vocal individuality parameter Multi-dimensional scaling (MDS) was used to convert measurement data representing calls into standardized measures of relative similarity in n dimensions (Kruskal and Wish 1978). All Significant individual differences were found between 250 vocalizations were first processed using the batch measurements of 6 temporal and frequency parameters spectrographic cross-correlation function in Raven 1.3 to across the entire sample (Table 1). Repeatability and the produce a 250 250 matrix identifying levels of peak PIC within individual measurements were high for all correlation between a given spectrogram and all others. This variables (Table 1), particularly for measures of call unit matrix was then analyzed using MDS with a proxscal frequency. Principal components analysis (PCA) reduced algorithm, to generate a list of proximity coordinates in the data to 2 factors with eigenvalues over 1, explaining three dimensions for each of the units. A Type III GLM 89.6% of variance in call parameters. The first extracted manova was then conducted on this set of coordinates, with component explained 70.6% of the variation in the dataset,
484 Figure 2. Example spectrograms of four exemplar call units from each of four different individual birds: (a), (b), (c), and (d). Spectrograms constructed with specifications as for Fig. 1. loading mostly on high frequency (factor loading score: significant using all 3 dimensions (multivariate: Wilks’ 0.987), frequency at maximum amplitude (0.982), and Lambda 0.108, F72, 667.294 10.241) or each of the 2 centre frequency (0.982). The second component explained dimensions alone (dimension 1: F24, 225 7.618, R 2 19.0% of the variation in the dataset, loading most heavily 0.448, dimension 2: F24, 225 12.707, R 0.575, dimen- 2 on low frequency ( 0.643) and delta frequency (0.749). sion 3: F24, 225 10.046, R 0.517, all PB0.001). The total Hs based on the principal components, which can This pattern reveals significant systematic individual dis- be interpreted as the information capacity of the call unit as tinctiveness in call structure. defined by the variables measured, was found to be 8.70 bits, indicating a possible 416 potential individual signatures in the system under investigation. This figure Responses to familiar and unfamiliar call sequences compares well to the largest colony sizes reported of c.400 individuals (Higgins et al. 2001). Playback experiments contrasted the response of noisy miners to playbacks of both familiar and unfamiliar calls, and to white-noise control sequences synthesised to match Quantification of vocal individuality spectrographic the temporal structure of natural calls. Responses to cross correlation controls were minimal, whether in the form of approaches to the speaker or vocalisations (Table 2, Fig. 5). We Multi-dimensional scaling (MDS) represented the spec- observed numerous vocal and other behavioural responses trogram cross-correlation matrix well in 3 dimensions to chur call playbacks that were never observed following (normalized raw stress 0.108, DAF 0.892). Plotting all white-noise playback (Table 2). Miners responded to chur 250 calls as points in the MDS common space revealed a call playbacks with chur calls in 50% of both familiar and noticeable clustering of calls from individuals. However, the unfamiliar trials. Other calls recorded during playbacks considerable degree of spread and overlap in 3 dimensions were either a) fledgling begging chips heard in the makes the resulting graph difficult to interpret. Figure 4 background, or b) ‘social’ calls that are all typically given presents 7 of the 25 individuals, chosen at random. in any context where multiple birds are in the same vicinity, To quantify these individual differences, a Manova e.g. ‘tiu’, ‘woo’, ‘yammer’ and ‘Q4’ (Higgins et al. 2001). was performed of the common-space coordinates with None of the small differences in responses between individual identity as a fixed factor. Differences were playbacks to familiar or unfamiliar calls approached
485 Figure 3. Four spectrograms of call units recorded from a single bird during a single session, depicting (a), (b) the ‘low intensity’, and (c), (d) various transitions of the ‘high intensity’ subtype. Spectrograms constructed with specifications as for Fig. 1. significance (Table 2). There was also no significant 3 min post-playback (Z 2.823, P 0.005, Fig. 5). difference between responses to white-noise stimuli The difference between the number of birds attracted based on familiar or unfamiliar call recordings (at 1 min: by playbacks of familiar calls and unfamiliar calls did Z 0.447, P 0.655, at 3 min: Z 0.000, P 1). For not differ at 1 minute (Wilcoxon test: Z 0.447, simplicity, the mean of these two measures were used in P 0.655), however, there was a trend for birds to subsequent analyses, although the outcome of statistical remain closer to the speaker in unfamiliar trials at 3 min tests did not alter if they were included separately. (Z 1.913, P 0.056, Cohen’s d 0.699). In contrast, in the majority of cases noisy miner groups responded strongly to both familiar and unfamiliar chur- call playbacks by approaching the speaker (Fig. 5). The Discussion difference between the mean of the white-noise controls and the 2 playback conditions was significant both at 1 Analyses of structure revealed significant and repeatable (Z 2.823, P 0.005), and at 3 min (Z 2.209, individual differences in the spectral features of the noisy P 0.027). Time was also a significant factor, as the miner chur call, suggesting that sufficient information is number of birds present declined significantly from 1 to encoded in these calls for a system of individual recognition
486 Table 1. Parameter measurements of chur calls. Measurements taken from recordings of 10 units each from 25 noisy miners, including means, standard deviations, ranges and maximum difference between individuals expressed as a percentage. Measures of inter- versus intra- individual variation in parameters are also presented: repeatability or intra-class correlation coefficient (r), Potential of individuality coding (PIC) and the ratio of coefficient of variation between individuals to coefficient of variation within individuals. Significance of individual differences assessed with Kruskal- Wallis tests. Two principal components with eigenvalues 1 extracted from parameter measurements have also been analysed for systematic individual differences. Asterisks indicate P-valuesB0.001.
2 Parameter Mean SD Range Max. diff. r PIC Individual x 24
Delta time (s) 0.21 0.03 0.166 57.4 0.281 4.000 182.572* Min. freq. (Hz) 725.29 209.75 1003.6 101.2 0.351 3.155 161.737* Max. freq. (Hz) 1716 278.99 1134.3 50.9 0.266 4.693 180.721* Delta freq. (Hz) 990.76 203.29 1353.4 64.2 0.427 2.850 155.316* Freq. max. amp. (Hz) 1500.37 278.73 1031.2 62.4 0.247 3.042 175.908* Centre freq. (Hz) 1487.24 256.60 937.5 56.1 0.259 4.545 181.163* Component 1 185.561* Component 2 133.853*
to operate. The playback trials also demonstrated that the of 3.71. The measure of information capacity, Hs, was also frequency structure of the chur call, as opposed to its high for this call, at 8.7 bits. A similar value has been amplitude profile or temporal pattern, was responsible for calculated for the single-syllable call given immediately recruiting conspecifics to the area. Coupled with this before feeding nestlings in the congeneric bell miner species’ highly social and aggressive behaviour, these Manorina melanophrys (9.01 bits, McDonald et al. 2007). findings indicate there is potential for acoustic signals of At least part of the scatter and overlap evident in the caller identity to influence helping and other decisions by MDS plot is likely due to the presence of two subtypes of receivers throughout a noisy miner colony. chur call, as birds that gave both types of call were Noisy miner chur calls are individually distinctive. represented in the plot by ‘stretched’ or bimodal clusters. Multi-dimensional plotting clearly shows the clustering Additionally, other factors, such as level of motivation, may of each noisy miner’s calls (Fig. 4). The repeatability and also account for some of the variance not accounted for by PIC of the measured parameters were moderate to high. individual identity. While we have identified clear statistical PIC values as low as 2.80 have elsewhere been taken to differences in signal structure between individuals, whether indicate a substantial potential for individuality (e.g. or not noisy miners themselves utilise these differences will Charrier et al. 2001). Here we observed an average PIC need to be investigated utilising techniques such as the
Figure 4. A three-dimensional common space plot generated by MDS analysis of cross-correlations of 250 noisy miner chur-call units, 10 each from 25 individuals. 10 call units from 7 randomly chosen individuals only are shown here as representative. Calls belonging to a given bird are identified by a single shared symbol.
487 Table 2. Behavioural responses to playbacks of familiar or unfamiliar chur calls and temporally matched white noise sequences. Responses are presented either in terms of a percentage of 40 trials, mean time spent performing a given behaviour in seconds, or the number of individuals engaged in a particular behaviour.
Condition Calls Chur ‘Social’ ‘Hostile’ Mean time Mean Mean time Mean No. of No. of given calls given calls given calls given calling time chur- ‘social’ birds chur- swoops approaches (trials) (trials) (trials) (trials) calling calling calling toward to speaker speaker
Familiar 80% 50% 40% 10% 11.5s 13.5s 0.75s 1.6 1 1 Unfamiliar 80% 50% 30% 20% 8.5s 5.75s 2s 1.2 2 2 White noise 0 0 0 0 0 0 0 0 0 0 habituation-dishabituation paradigm (e.g. Rendall et al. Signal design 1996, Hauser 1998). The acoustic structure of noisy miner chur calls also fits long-recognised criteria for high locatability and detect- The nature of individual signatures ability (Marler 1955, Knudsen 1980). Experimental studies of the distress calls of black-headed gulls Larus ridibundus Several species of birds, particularly passerines, have been have shown that slow frequency modulation and investment shown to be sensitive to very slight differences of note of energy in the first two or three harmonics both features frequency as well as frequency modulation, and to use shared with noisy miner chur calls are resistant to acoustic frequency traits, as opposed to amplitude modulation or degradation over medium to long distances (Bre´mond and temporal structure, to discriminate between acoustic stimuli Aubin 1990). Because bouts of chur calling are composed of (Brooks and Falls 1975, Dooling 1982, Jouventin et al. numerous repeated units, there are, moreover, multiple 1999). Great tits rely on frequency to categorize natural sequential onsets of sound, and this is known to facilitate song notes, and a recent study of vocal individuality in location of the source of sounds (Falls 1982). The frequency another cooperatively breeding passerine, the long-tailed tit structure of an individual noisy miner’s chur call therefore Aegithalos caudatus, also found frequency parameters to be not only potentially encodes its individuality, but also the best discriminators between individuals in two calls appears to broadcast this information favourably over (Sharp and Hatchwell 2005). Only frequency variables considerable distances. The call is thus eminently well loaded onto the first component of the PCA designed to designed to serve as the building block of a multi- analyse variation in all measured noisy miner call para- directional acoustic ‘communication network’ (McGregor meters, suggesting that a mechanism involving frequency 2004) in this highly social species in which individuals may well be operating in the noisy miner system. Certainly are, nevertheless, often quite widely dispersed throughout a differences in frequency parameters were more marked and large colony. In this species, mobbing plays a very consistent between individuals than amplitude or temporal important role and is evidently allocated substantial time differences. and energy indeed, noisy miners are able to exclude nearly
Figure 5. Changes in the number of birds within 10 metres of the speaker at 1 and 3 minutes after playback of white noise (solid bars), familiar (open bars) or unfamiliar chur calls (shaded bars). Error bars indicate one standard error, playbacks undertaken at 10 different sites (see Methods for details).
488 all other avian species from their colonies (Higgins et al Beecher, M. D., Beecher, I. M. and Lumpkin, S. 1981. Parent- 2001). Thus it is clear that having a widely-broadcast call to offspring recognition in bank swallows (Riparia riparia). encourage recruitment of conspecifics to an area would be I. Natural history. Anim. Behav. 29: 86 94. advantageous in terms of minimising the costs of a given Beecher, M. D., Campbell, S. E. and Burt, J. M. 1994. Song individual’s response to incursion, and maximising the perception in the song sparrow: birds classify by song type but not by singer. Anim. Behav. 47: 1343 1351. benefits of making such a response known. Borror, D. J. 1959. Variation in the songs of the rufous-sided towhee. Wils. Bull. 71: 54 72. Bre´mond, J.-C. and Aubin, T. 1990. Responses to distress calls by Theoretical significance of individually distinctive black-headed gulls, Larus ridibundus: the role of non-degraded vocalisations features. Anim. Behav. 39: 503 511. Brooks, R. J. and Falls, J. B. 1975. Individual recognition by song Long-established theories of social evolution invoked to in white-throated sparrows. I. Discrimination of neighbours account for the apparent altruism of cooperatively breeding and strangers. Can. J. Zool. 53: 879 888. species (see introduction), typically require, either explicitly Brown, S. D., Dooling, R. J. and O’Grady, K. 1988. Perceptual or implicitly, that individual recognition be present. For organization of acoustic stimuli by budgerigars (Melopsittacus example, systems potentially based on image scoring or undulatus): III. Contact calls. J. Comp. Psych. 102: 236 ‘competitive’ altruism such as social prestige require that 247. individuals be identifiable, at least in certain contexts, to Charrier, I., Jouventin, P., Mathevon, N. and Aubin, T. 2001. Individual identity coding depends on call type in the South make their quality known or to accrue prestige (Zahavi Polar skua Catharacta maccormicki. Pol. Biol. 24: 378 382. 1977, Wright 1999). Noisy miners do not cooperate solely Cockburn, A. 2003. Cooperative breeding in oscine passerines: with kin (Higgins et al. 2001), and thus any model does sociality inhibit speciation? Proc. R. Soc. B 270: 2207 attempting to describe cooperation in this species needs to 2214. incorporate both indirect and direct benefits. Given this, it Cockburn, A. 2006. Prevalence of different modes of parental would seem the development of individual differentiation care in birds. Proc. R. Soc. B 273: 1375 1383. within this system would be particularly favoured, as this Cohen, J. 1992. A power primer. Psychol. Bull. 112: 155 159. would allow helping effort with given individuals to be Date, E. M. 1982. A comparison of the locatability of the adjusted according to the direct benefits associated vocalisations of noisy miners (Manorina melanocephala) and with each interaction. Indeed, while the social behaviour bell miners (M. melanophrys) in relation to differences in of many species of birds and mammals strongly implies the habitat. B. Sci. thesis, Univ. Queensland, Brisbane, Australia. Dooling, R. J. 1982. Auditory perception in birds. In: operation of true individual recognition (Stoddard 1996), Kroodsma, D. E. and Miller, E. H. (eds). Acoustic commu- the mechanisms and proximate causes of such processes nication in birds. Volume 1: Production, perception have rarely been comprehensively elucidated (Rendall and design features of sounds. Academic Press, New York, 2004). Given this, future research into the perception of pp. 237 78. individual differences in call structure by noisy miners is Dow, D. 1975. Displays of the honeyeater Manorina melanoce- warranted, potentially shedding light on how complex phala. Z. Tierpsychol. 38: 70 96. sociality evolves and operates (McGregor 2004). Dow, D. 1977. Indiscriminate interspecific aggression leading to almost sole occupancy of space by a single species of bird. Emu 77: 115 121. Dow, D. 1979a. The influence of nests on the social behaviour of Acknowledgements Thanks go to Darren Burke, Anne Mouland, males in Manorina melanocephala, a communally breeding Danielle Sulikowski, Alan Taylor, Mark Wiese and the graduate honeyeater. Emu 79: 71 83. students at CISAB for assistance with this project. Ximena Nelson Dow, D. 1979b. Agonistic and spacing behaviour of the noisy provided helpful comments on an earlier draft of the manuscript. miner Manorina melanocephala, a communcally breeding Funding was provided for this study by a Macquarie Univ. honeyeater. Ibis 121: 423 436. Res. Fellowship to PM. Approval for this research was pro- Falls, J. B. 1982. Individual recognition by sounds in birds. In: vided by the Macquarie University Animal Ethics Committee Kroodsma, D. E., Miller, E. H. and Ouellet, H. (eds). (ARA 2007/010). Acoustic communication in birds. Academic Press, New York, pp. 237 278. Falls, J. B. and McNicholl, M. K. 1982. Neighbour-stranger discrimination by song in male blue grouse. Can. J. Zool. References 57: 457 462. Gaston, A. J. 1978. The evolution of group territorial behaviour Arnold, K. E. 2000. Group mobbing behaviour and nest defence and cooperative breeding. Am. Nat. 112: 1091 1110. in a cooperatively breeding Australian bird. Ethology 106: Hamilton, W. D. 1964. The genetical evolution of social 385 393. behaviour. J. Theor. Biol. 7: 17 52. Arnold, K. E., Owens, I. P. F. and Goldizen, A. W. 2005. Hauser, M. D. 1998. Fucntional referents and acoustic similarity: Division of labour within cooperatively breeding groups. field playback experiments with rhesus monkeys. Anim. Behaviour 142: 1577 1590. Behav. 55: 1647 1658. Beecher, M. D. 1981. Parent-offspring recognition in bank Higgins, P. J., Peter, J. M. and Steele, W. K. (eds). 2001. swallows (Riparia riparia): II. Development and acoustic basis. Handbook of Australian, New Zealand and Antarctic birds, Anim. Behav. 29: 95 101. Oxford Univ. Press, Melbourne. Beecher, M. D. 1989. Signalling systems for individual recogni- Jouventin, P., Aubin, T. and Lengagne, T. 1999. Finding a parent tion: an information theory approach. Anim. Behav. 38: in a king penguin colony: the acoustic system of individual 248 261. recognition. Anim. Behav. 57: 1175 1183.
489 Jurisevic, M. A. and Sanderson, K. J. 1994. Alarm vocalisations in Po˜ldmaa, T., Montgomerie, R. and Boag, P. 1995. Mating Australian birds: convergent characteristics and phylogenetic system of the cooperatively breeding noisy miner Manorina differences. Emu 94: 69 77. melanocephala, as revealed by DNA profiling. Behav. Ecol. Knudsen, E. I. 1980. Sound localization in birds. In: Popper Sociobiol. 37: 137 143. A. N. and Fray, R. R. (eds). Comparative studies of hearing in Puglisi, L. and Adamo, C. 2004. Discrimination of individual vertebrates. Springer, Berlin, pp. 289 322. voices in male great bitterns (Botaurus stellaris) in Italy. Auk Kokko, H., Johnstone, R. A. and Clutton-Brock, T. H. 2001. The 121: 541 547. evolution of cooperative breeding through group augmenta- Rendall, D. 2004. ‘Recognizing’ kin: Mechanisms, media, minds, tion. Proc. R. Soc. B 268: 187 196. modules and muddles. In: Chapais, B. and Berman, C. (eds). Kruskal, J. B. and Wish, M. 1978. Mutlidimensional scaling. Kinship and behaviour in primates. Oxford Univ. Press, Beverly Hills, Sage Publ. pp. 295 316. Lessells, C. M. and Boag, P. T. 1987. Unrepeatable repeatabilities: Rendall, D., Rodman, P. S. and Emond, R. E. 1996. Vocal a common mistake. Auk 104: 116 121. recognition of individuals and kin in free-ranging rhesus Lind, H., Dabelsteen, T. and McGregor, P. K. 1996. Female great monkeys. Anim. Behav. 51: 1007 1015. tits can identify mates by song. Anim. Behav. 52: 667 671. Sharp, S. P. and Hatchwell, B. J. 2005. Individuality in the contact Mammen, D. L. and Nowicki, S. 1981. Individual differences and calls of cooperatively breeding long-tailed tits (Aegithalos within-flock convergence in chickadee calls. Behav. Ecol. caudatus). Behaviour 142: 1559 1575. Sociobiol. 9: 179 186. Stoddard, P. K. 1996. Vocal recognition of neighbors by territorial Marler, P. 1955. Characteristics of some animal calls. Nature passerines. In: Kroodsma, D. E. and Miller, E. H. (eds). 176: 6 8. Ecology and evolution of acoustic communication in birds. Marler, P. 1960. Bird song and mate selection. In: Lanyon Cornell Univ. Press, pp. 356 374. W. F. and Tavolga, W. (eds). Animal sounds and commu- Sturdy, C.B., Phillmore, L.S., Price, J. L. and Weisman, R. G. nication. A.I.B.S., Washington, D.C., pp. 348 357. 1999. Song-note discriminations in zebra finches (Taeniopygia Mathevon, N., Charrier, I. and Jouventin, P. 2003. Potential for individual recognition in acoustic signals: a comparative study guttata): categories and pseudocategories. J. Comp. Psych. of two gulls with different nesting patterns. Ethology 326: 113: 204 212. 329 337. Wanker, R., Apcin, J., Jennerjahn, B. and Waibel, B. 1998. McDonald, P., Heathcote, C. F., Clarke, M. F., Wright, J. and Discrimination of different social companions in spectacled Kazem, A. J. N. 2007. Provisioning calls of the cooperatively parrotlets (Forpus conspicillatus): evidence for individual vocal breeding bell miner Manorina melanophrys encode sufficient recognition. Behav. Ecol. Sociobiol. 43: 197 202. information for individual discrimination. J. Avian Biol. 38: Weary, D. M. and Krebs, J. R. 1992. Great tits classify songs by 113 121. individual voice characteristics. Anim. Behav. 43: 283 287. McGregor, P. 2004. Animal communication networks. Cam- White, S. J., White, R. E. C. and Thorpe, W. H. 1970. Acoustic bridge Univ Press. basis for individual recognition by voice in the gannet. Nowicki, S. 1982. Flock-specific recognition of chickadee calls. Nature 225: 1156 1158. Behav. Ecol. Sociobiol. 12: 317 320. Woolfenden, G. E. and Fitzpatrick, J. W. 1978. Inheritance of Payne, R. B., Payne, L. L. and Rowley, I. 1988. Kin and social territory in group-breeding birds. Bioscience 28: 104 108. relationships in splendid fairy-wrens: recognition by song in a Wright, J. 1999. Altruism as a signal Zahavi’s alternative to kin cooperative bird, Anim. Behav. 36: 1341 1351. selection and reciprocity. J. Avian Biol. 30: 108 115. Phillmore, L. S., Sturdy, C. B., Turyk, M., -R., M. and Weisman, Zahavi, A. 1977. Reliability in communication systems and the R. G. 2002. Discrimination of individual vocalizations by evolution of altruism. In: Stonehouse, B. and Perrins, C. black-capped chickadees (Poecile atricapilla). Anim. Learn. (eds). Evolutionary ecology. Univ. Park Press, Baltimore, pp. Behav. 30: 43 52. 253 259.
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