Vocal mechanics in Darwin's finches: correlation of beak gape and song frequency
1 Department of Biology, University of Massachusetts, Amherst, MA 01003,
USA
2 Projeto Baleia Jubarte, Rua 7 de Setembro 178, Caravelas, Bahia 45900-000,
Brazil
* Author for correspondence (e-mail: jpodos{at}bio.umass.edu)
Accepted 27 October 2003
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Summary |
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Key words: vocal mechanics, song production, beak gape, vocal frequency, song evolution, Darwin's finch, songbird
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Introduction |
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Songbird song often includes rapid changes in source frequencies. According
to the resonance model of vocal tract function, birds should track changes in
source frequencies with adjustments in the physical configuration of their
vocal tracts if songs are to retain their pure-tonal quality
(Nowicki and Marler, 1988;
Fletcher and Tarnopolsky, 1999
;
Hoese et al., 2000
). This is
because a vocal tract of given volume is effective as a resonance filter for
only a narrow range of source frequencies. One way that birds can adjust vocal
tract volume is through changes in beak gape. The beak extends the volume of
the vocal tract, contributing to vocal tract resonances
(Fletcher and Tarnopolsky,
1999
), and adjustments in beak gape can be used to fine-tune the
strength of this contribution (Nowicki,
1987
; Westneat et al.,
1993
). Recent studies of beak gape during song production are
consistent with the vocal tract resonance model: birds have been shown to gape
widely during the production of high source frequencies and to reduce gape
during the production of low source frequencies
(Westneat et al., 1993
;
Podos et al., 1995
;
Moriyama and Okanoya, 1996
;
Gaunt and Nowicki, 1998
;
Fletcher and Tarnopolsky, 1999
;
Williams, 2001
). The
importance of beak movements in song production is supported by a study in
which the experimental obstruction of normal beak movements caused significant
degradation in the tonal quality of songs at predicted frequencies
(Hoese et al., 2000
).
Evidence that the beak contributes to songbird song production suggests the
possibility of correlated evolution among beaks and song. Beaks are a primary
axis of avian diversification, as exemplified within adaptive radiations such
as the Hawaiian honeycreepers and Galápagos finches
(Freed et al., 1987;
Grant, 1999
). Might the
evolutionary diversification of beaks influence, as an incidental consequence,
the evolution of song structure? Perhaps the most straightforward prediction
here is that species with long bills, and thus larger-volume vocal tracts,
should evolve songs with low vocal frequencies. This is because large-volume
vocal tracts are appropriate resonance filters for comparatively low source
frequencies. Palacios and Tubaro
(2000
) tested this hypothesis
in the dendrocolaptid woodcreepers, a group of birds with unusually pronounced
variation in bill length. After controlling for phylogeny and body size, these
authors identified a negative correlation between beak length and emphasized
vocal frequencies, as predicted. Laiolo and Rolando's study of corvids
(Laiolo and Rolando, 2003
)
identified the opposite relationship higher emphasized frequencies for
larger-billed birds although the `rattle calls' of this study were
non-tonal and thus may not be expected to fit the vocal tract resonance
model.
Patterns of song evolution might also be shaped by variation in beak
function. Nowicki et al.
(1992) hypothesized that
diversification of beak function could bias the evolution of song parameters
that make use of vocal tract reconfigurations (see also
Podos and Nowicki, in press
).
Two such song parameters are trill rate and frequency bandwidth, both of which
require increasingly rapid and/or pronounced beak movements at higher values
(Podos, 1997
). In a test of
this hypothesis, Podos (2001
)
examined the songs and morphology of individually marked birds from eight
species of Darwin's finches. Beak morphology and song features were found to
evolve in correlated fashion, both across species and within the medium ground
finch Geospiza fortis. Birds with large beaks were found to produce
songs with comparatively low trill rates and narrow frequency bandwidths,
whereas birds with small beaks were shown to produce songs with fast trill
rates and wide frequency bandwidths. These patterns fit the expectation that
beak size should covary negatively with vocal performance capacities, because
of forcespeed tradeoffs in jaw mechanics
(Podos, 2001
).
In the present study, we describe and analyze patterns of beak gape during
song production in seven species of Darwin's finch. Our first goal is to
determine whether these species modify beak gape in correspondence with
changing vocal frequencies, as has been demonstrated in other songbirds in
laboratory settings. We predict a positive relationship between beak gape and
song frequencies for two reasons. First, patterns of beaksong
co-evolution in Darwin's finches are consistent with the vocal tract resonance
model of song production, which includes as a baseline prediction the matching
of vocal tract and sound source activity
(Podos, 2001). Second, all
songbird species formally examined to date show evidence of a positive
relationship between beak gape and vocal frequency, which supports the
provisional hypothesis that this matching is a basal, conserved trait across
the songbirds (Podos, 1997
).
We also document relationships between beak gape and song amplitude, as in
Westneat et al. (1993
). The
influence of gape on song amplitude might provide additional insights into
vocal tract function.
Our second goal is to compare patterns of beak use during song production
among and within species. Wide variation in beak form and function, such as
that expressed in Darwin's finches, is likely to influence the potential
contributions of the beak to song production. However, we predict that beak
variation in Darwin's finches will exert only minimal influences on
gapesong relationships. This is because Darwin's finches appear to have
accommodated the evolutionary divergence of beaks, and resulting divergence in
vocal proficiency, with adjustments to song structure that preserve vocal
tract function (Podos, 2001).
Darwin's finches also vary widely in body size
(Grant et al., 1985
). Species
with larger bodies have correspondingly large syringes
(Cutler, 1970
) and, as a
general rule, produce lower-pitched vocalizations
(Bowman, 1983
). We therefore
hypothesize that y-intercepts of gape by frequency regressions will
correlate negatively with body size. Slopes of gape by frequency regressions
might also be influenced by species differences in body size, although we find
it difficult to make specific predictions. On the one hand, we might predict
steep regressions for small birds, because gape changes of a given magnitude
should impose relatively pronounced changes in vocal tract volume and
resonance properties (i.e. the volume and impedance of smaller vocal tracts
should be disproportionately affected by any given gape change). However,
small birds generally produce higher source frequencies, and the effects of
gape changes on vocal tract resonances are disproportionately strong at higher
frequencies (fig. 17 in Fletcher and
Tarnopolsky, 1999
). This suggests that small birds could track
given frequency changes via smaller gape changes. Because of these
conflicting predictions, we view our analysis of diversity in regression
slopes as exploratory.
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Materials and methods |
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In the field, we were able to approach and videotape singing birds at close
range, often within 10 m and on occasion as close as 2 m. This was made
possible by the unusual tameness of these birds in the field (e.g.
Grant, 1999). Our sampling
efforts were opportunistic our aim was to secure as many high-quality
recordings as possible although we focused in particular on two
species, Geospiza fortis and Camarhynchus parvulus, for
which additional behavioral studies were being conducted. We obtained
approximately 8 h of raw video footage.
Upon return to the laboratory, we selected a subset of song sequences for analysis following four criteria. First, the singing bird's head and beak had to be visible, in focus and discernible from the visual background during the entire sequence. Many sequences failed to meet this criterion because of a lack of contrast between the dark heads and beaks of these birds and the shadows cast by foliage, especially on bright, sunny days. Second, the bird's head and beak needed to appear in approximate profile, such that dynamic changes in both upper and lower beak tip position could be tracked during song production. Third, we discounted sequences that included head roll within the song sequence. Head pitch and yaw movements were acceptable for our analysis because such movements do not significantly affect calculations of beak gape. Our fourth criterion was that the audio signal from vocal sequences needed to be clearly discernible from background noise, to ensure meaningful acoustic frequency analyses. 164 vocal sequences from 56 individuals met these criteria (Table 1).
Beak gape: calculation and calibration
Sequences of video frames were transferred from the video camera to an
Apple iMac computer using iMovie 2.0 (Figs
1i,
2i). iMovie files (.mpg) were
converted to Quicktime files (.mov) and then imported into NIH Image 1.62
(.tiff) in order to measure beak gape. Beak gape was calculated as the
distance between the tips of the upper and lower mandibles as it varied during
song sequences. Beak gape was measured in pixels and then converted into cm
distances using a calibration macro in NIH Image. For video frames in which
the bird was not directly lateral to the camera, for example if the camera was
pointing up towards the bird, we estimated the angle of filming
(±15°) by comparing video frames to taxidermic mounts and adjusted
our gape calculations accordingly.
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Calibration of image data requires inclusion of standards of known
dimensions within video fields. We were unable to include external standards
in our film sequences because of difficulty in identifying and accessing the
perches from which birds sang. We instead relied on either beak length (the
distance between the anterior end of the nares and the tip of the upper
mandible) or, for sequences in which the nares were not discernible, `exposed
culmen' (the linear distance, in profile, from the tip of the upper mandible
to the dorsal juncture of the culmen and the forehead;
Pyle, 1997).
Actual beak length measurements were taken either from caliper data for
banded individuals or as mean values from our own measurements in these
populations. The latter approach is compromised by variation among individuals
in beak size, which is generally low in most species examined here
(Grant et al., 1985) but
unusually pronounced in one of the species, Geospiza fortis.
Calibration errors due to the imprecise assignment of beak lengths should have
little effect on the calculation of beak gape by acoustic frequency
regressions within individual birds but more substantial effects on
regressions pooled by species.
Direct field measurements of exposed culmen were unavailable. We thus calculated values of exposed culmen for each population using NIH Image 1.62 and a series of closeup photographs (Nikon Coolpix 995 digital camera) we had made of birds of known measurements. For each photograph, we calculated exposed culmen length using three known measurements: actual beak length, the pixel length of beak length and the pixel length of exposed culmen. After calibration, we plotted beak gape at 33.33 ms intervals over the course of each vocal sequence (e.g. Figs 1, 2, 3, 4).
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Calculation of song frequencies and amplitudes
We used the sound analysis program SIGNAL 3.1
(Beeman, 1999) to measure
acoustic frequency and amplitude values from audio clips of song sequences.
Audio tracks were imported directly into SIGNAL (25 kPt sample rate) from the
video camera to maximize audio quality. Audio clips were synchronized to their
respective iMovie video sequences (Figs
1,
2,
3,
4).
Peak acoustic frequencies and amplitudes were calculated for each 33.33 ms interval using customized macros in SIGNAL. The first and last 16.67 ms intervals of each audio track were omitted from this analysis in order to `center' audio intervals around their corresponding video frames. This optimized the correspondence of audio data to video frames. We did not attempt to correct for any offset between the reception of audio and video signals by our camera, because most of our samples were taken within 10 m of the singing bird (a distance at which video and audio signals recorded at 30 frames s1 are expected to be synchronized within the error of a single video frame). Frequency values were obtained using 256 point fourier frequency transforms, which yielded a frequency resolution of 97.7 Hz. Amplitude (root mean square) values were obtained from oscillograms using an automated function in SIGNAL. The maximum amplitude in each song sequence was standardized to 0 dB, and remaining amplitude values were calculated with reference to this standard.
A second customized SIGNAL macro generated sound spectrograms with vertical lines transposed over them at 33.33 ms intervals. This allowed us to determine which video frames in each sequence contained vocal output and which frames corresponded to pauses between notes. Only video frames and audio data in which birds were vocalizing were used in regression analyses.
Gape by frequency regressions
Statistical analyses were conducted using JMP (Version 4.04; SAS Institute
Inc., Cary, NC, 2002). Our first goal was to test the hypothesis that birds
modify beak gape in correspondence with changing song frequencies in the
positive direction predicted by the vocal tract resonance model. As a
preliminary step, we plotted acoustic frequency as a function of beak gape for
all song sequences (N=164). In approximately 40 song sequences, we
detected one or two conspicuous outliers in the upper-left quadrants of
bivariate plots (i.e. low gape and high frequency). Further inspection
indicated that these `low gapehigh frequency' outliers were almost
always introductory frames within song sequences and were thus likely to be a
result of autocorrelation error (Sokal and
Rohlf, 1995). That is, birds' beaks were generally closed before
they started to sing and apparently had not achieved normal gape by the first
frame of some song sequences. To minimize this error we applied the `Density
Ellipse' algorithm of JMP (99% confidence interval) to identify outliers for
removal from subsequent analysis. The outliers identified by this algorithm
(59 of 3665 total data points) corresponded closely to our own impressions of
outlying points on bivariate plots.
We next calculated least-squares regressions of acoustic frequency as a
function of beak gape for data pooled by individuals. Our pooling of data
within individuals is justified by the observation that different song
sequences produced by any individual were always of the same song type. We
then calculated regressions at the species level, after excluding
non-significant individual regressions (as in
Westneat et al., 1993). For
individual and species-level analyses, we applied sequential Bonferroni
adjustments to regression P-values to account for the high number of
calculations performed.
Our second goal was to characterize species differences in
gapefrequency correlations among the Darwin's finches. A preliminary
analysis of the interaction between gape and species, in which frequency was
the dependent variable, revealed significant heterogeneity in regression
slopes (F6,3035=13.85, P<0.001). We thus could
not apply analysis of covariance (ANCOVA) in our species comparisons
(Sokal and Rohlf, 1995). As an
alternative, we conducted one-way tests on slope and intercept values as they
varied across our sample. We detected unequal variances among species for both
slope and intercept data and thus used non-parametric tests. We ran two sets
of one-way KruskalWallis main effect tests (JMP 4.04) and pairwise
post-hoc tests (Conover,
1999
). Our first set of tests examined only our Darwin's finch
sample. We used individual slope and intercept values as replicates within
species. Our second set of tests also included data from swamp sparrows
(Melospiza georgiana) and white-throated sparrows (Zonotrichia
albicollis). Original regression data on sparrow species were provided by
Mark Westneat and colleagues (see Westneat
et al., 1993
). To facilitate this analysis, we pooled the sparrow
data by bird (N=5 for each species) across all note types analyzed
for each bird.
Next, we calculated correlations between body mass (compiled from our own
data and from Dunning, 1993)
and regression y-intercepts and slopes across the Darwin's finches
only and then including the two sparrow species.
Finally, we tested the contribution of between-individual variation to species regression values. For this analysis, we examined variation in slope and intercept values among the nine Geospiza fortis individuals with the largest sample sizes. We again used KruskalWallis tests on regression slopes and y-intercepts, with different song sequences as replicates.
Gape by amplitude regressions
We calculated least-squares regressions of song amplitude as a function of
beak gape for data pooled by individuals. The goal of this analysis was to
explore the possible influence of gape modulations on song amplitude. The
predictions of the vocal tract model concerning the effects of gape on
amplitude are specific to given frequencies, stating that deviations from
particular gapes should cause reductions in amplitude for acoustically matched
frequencies. By contrast, the vocal tract resonance model makes no predictions
about the relationship between gape and amplitude across a broad song sample
with wide variation in source frequencies, such as ours here. Our analyses are
intended to provide a point of comparison with those of Westneat et al.
(1993), who identified a
consistent influence of beak gape on song amplitude in sparrows for one of
five note types examined. Outlier data points were identified as above, using
the Density Ellipse algorithm of JMP at 99% confidence intervals, and removed
from regression analysis.
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Results |
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The y-intercepts of gape by frequency regressions were
significantly variable among the seven Darwin's finch species
(KruskalWallis test, 2=15.07, d.f.=6,
P=0.0197). Pairwise comparisons at the level of
=0.05 revealed
that intercepts of two species, Camarhynchus psittacula and
Geospiza fortis, were significantly lower than intercepts in two
other species, Camarhynchus parvulus and Certhidea olivacea.
Intercepts also differed between C. psittacula and Platyspiza
crassirostris. When sparrow data were included, intercepts again showed
significant variation among species (KruskalWallis test,
2=22.51, d.f.=8, P=0.0041). Previously identified
contrasts among Darwin's finch intercepts were preserved, with the addition of
a significant contrast between G. fortis and P.
crassirostris (Table 4).
Zonotrichia albicollis y-intercepts were significantly greater than
those of C. psittacula and G. fortis, and Melospiza
georgiana intercepts were significantly lower than those of C.
parvulus and C. olivacea
(Table 4).
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Analysis of slopes failed to reveal significant differences among Darwin's
finch species alone (KruskalWallis test, 2=5.24,
d.f.=6, P=0.5128, power=0.844). However, inclusion of sparrow data
led to an overall effect of slope that approached significance
(KruskalWallis test,
2=15.41, d.f.=8,
P=0.0517). We applied post-hoc tests to this data set, not
as an explicit test of species contrasts (given the borderline significance of
the overall effect) but as a preliminary analysis of trends
(Table 4). Slopes of the
Zonotrichia albicollis regressions were significantly lower than
slopes of four other species (Geospiza fuliginosa, C. parvulus, C.
psittacula and M. georgiana), and the M. georgiana
slopes exceeded those of G. fortis and Z. albicollis. There
were no significant contrasts between the regression slopes of any of the
Darwin's finch species.
Within the Darwin's finches, both intercepts and slopes of gapefrequency regressions correlated negatively with body mass (Pearson product moment correlation, R=0.427 and 0.209, respectively), although not at statistically significant levels (Spearman's Rho, P>0.30 in both tests). With the addition of the two sparrow species, the correlation of body mass by intercepts weakened (R=0.270), and the correlation of body mass by slope strengthened (R=0.325), although again neither achieved statistical significance (Spearman's Rho, P>0.55 in both instances).
Within our Geospiza fortis sample, we found significant variation
among birds in y-intercepts (KruskalWallis test,
2=19.24, d.f.=8, P=0.014;
Fig. 4) and especially
regression slopes (KruskalWallis test,
2=37.50, d.f.=8,
P<0.0001; Fig. 4).
To examine possible causes of between-individual variation in the G.
fortis sample, we ran two analyses. First, we calculated correlations
between regression parameters (slope, y-intercept and
r2 values) and song parameters (minimum frequency, peak
frequency, maximum frequency and frequency bandwidth). Second, we calculated
correlations between regression parameters and plumage scores. For each bird
in this analysis, we scored plumage on a scale of 15, as described in
Grant (1999
), using video
images and field annotations. The extent of coverage of dark plumage provides
a general indicator of age, with older birds possessing more dark plumage.
None of these correlations were statistically significant (Spearman's Rho,
P>0.05).
Gape by amplitude regressions
Regressions of amplitude as a function of beak gape were positive and
statistically significant for 17 of 56 song sequences
(Table 5). Again it appeared
that the probability that regressions would be significant depended upon
sample size; regressions from seven of the 20 individuals with the largest
sample sizes achieved statistical significance, compared with only two of the
20 individuals with the smallest sample sizes. r2 values
of significant gape by amplitude regressions averaged 0.35±0.23
(Table 5).
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Discussion |
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Prior studies of beak gape kinematics in songbirds have been conducted in
laboratory settings. Our study demonstrates the feasibility of conducting such
a study in the field, at least for tame birds such as Darwin's finches. Close
approaches to singers allowed us to maximize sound and video quality and to
minimize the timing offset between audio and video signals. Perhaps the most
severe limitation of our field method was the difficulty we had in identifying
exact calibration standards within video frames for some of our clips.
Fortunately, the availability of measurement data for these populations
allowed us to use images of the birds themselves for calibration. Most of our
birds studied here were not banded, so we estimated measurements of
individuals using population means. Resulting errors are expected to exert
only minimal effects on the species-level regressions, with the exception of
Geospiza fortis, which shows extensive variation in morphology
(Grant, 1999). We have
recently discovered that G. fortis at one of our field sites, El
Garrapatero, occurs in two morphs (`small') and (`large') that are highly
distinct in beak size, shape and bite-force capacities (J. Podos, A. Hendry,
A. Herrel and S. Huber, unpublished data). These morphs are identified readily
on video clips, and by taking average beak length measurements for each morph
we were able to reduce our calibration error for G. fortis at this
site. For species-level regressions, calibration errors were most likely
expressed as slight inaccuracies in regression intercept and slope values
(Table 3). In spite of this
error, the uniformly high F-ratios
(Table 3) suggest that improved
calibration accuracy would not change the overall finding that gape and
frequency are positively related in these birds.
Our study thus supports the generality of the resonance model of song
production. Furthermore, our study provides an opportunity to explore the
effect of morphological variation on patterns of beak use during song
production. Darwin's finches show particularly extensive variation in beak
morphology and body size (Grant et al.,
1985; Grant and Grant,
2002b
). How might variation in these attributes influence the
relationship between beak gape and song production? As a start, it is useful
to consider the relationship between beak function and the potential for beak
movements (Nowicki et al.,
1992
; Podos and Nowicki, in
press
). Because of a tradeoff between force and speed in jaw
mechanics, beak size and presumed force application potential are expected to
correspond negatively to the maximum potential speed of gape adjustments
(Podos, 2001
). Birds with
large beaks are expected to have difficulty performing rapid gape changes,
whereas small-beaked birds should be relatively free of this constraint. We
might therefore predict that birds with small beaks will undergo more rapid
and/or precise beak gape adjustments while singing. However, the frequency and
timing features of Darwin's finch songs have evolved in apparent compensation
with larger-beaked birds in effect evolving simpler songs
presumably in order to retain the filtering function of the vocal tract as
beaks have diverged (Podos,
2001
). As a result, we actually expected in this study that
variation in beak size and function would exert only minimal effects on the
expression of gape by frequency relationships.
This expectation is supported by our analysis of regression slopes, which failed to demonstrate any distinctions among the Darwin's finches (Fig. 5). Our confidence in the validity of this finding is supported by the high statistical power of the test employed. Uniformity among Darwin's finches is also suggested by their contrast as a group to the two sparrow species, as shown by the near-significant result of the test that included all species and by the post-hoc contrasts (Table 4). The conserved relationship in regression slopes within the Darwin's finches is consistent with their recent common ancestry and suggests that the gapefrequency relationship has not been influenced by the large-scale variations these birds express in beak morphology and body size.
Our expectation that body size variation should correspond to the y-intercepts of beak gape by frequency regressions received mixed support. We found a significant effect of species on regression intercepts, both including and excluding the sparrow species (Table 4). Furthermore, post-hoc tests revealed distinct categories of species based on intercept data. Several of these designations were consistent with the body size hypothesis. The two species with the smallest body masses, Certhidea olivacea (warbler finch) and Camarhynchus parvulus (small tree finch), for instance, were in the category of birds with the highest intercepts. However, other designations deviated from the body size hypothesis, particularly the placement of Platyspiza crassirostris (vegetarian finch), one of the two largest species, in this same category of high intercept species. The overall correlation between body mass and y-intercept was negative, as predicted, although not at a statistically significant level. One possible explanation for this result is the relatively low sample size for some of our species, four of which were represented by only a single bird (after we had excluded from analysis those individuals with non-significant regressions). There may be additional functional hypotheses, yet to be identified, to explain the diversification of regression intercepts.
Plots of beak gape by frequency, pooled by species, showed substantial
scatter around regression lines (Fig.
5). This variation might arise from within-individual and
inter-individual sources. In our analysis of Geospiza fortis, the
detection of significant differences in regression parameters among
individuals indicates that, at least in this species, inter-individual
variation exceeds within-individual variation. This is consistent with our
impressions that gape profiles from within the same bird, across different
renditions, tended to be very similar [for example, compare the gape profiles
in Figs 2 and
4 (Bird A), both of which are
from the same bird]. The acoustic stereotypy of Darwin's finch songs across
renditions (Bowman, 1983) thus
appears to be matched by stereotypy in patterns of beak movement.
Variations in regression parameters among conspecific birds might be caused by a number of factors. Errors in the calculation of gape, due to imprecise estimates of beak lengths in the calibration process, could lead to variation in regression intercepts but presumably not slopes. Ideally, we would have minimized calibration errors by obtaining direct beak length measurements from all subjects, although this was not possible given the opportunistic nature of our sampling procedure. More significantly, within-species variations in gape by frequency relationships may be caused by variation in biological factors such as age, condition and the features of songs produced. For example, perhaps birds with more experience are better able to match song patterns with precise adjustments in beak gape. However, we failed to detect significant correlations between regression values and plumage score (as an indicator of age) or between regression values and song frequency features. In one suggestive trend, birds that sang songs with higher frequencies and wider bandwidths tended to have higher regression slopes (e.g. Fig. 4, compare Birds A and C). This makes sense insofar as wider gapes should be required to help filter higher frequency notes.
Beak gape changes might conceivably play a functional role in amplitude as
well as frequency modulation, although the vocal tract resonance model makes
no such prediction. Support for a relationship between gape and amplitude was
relatively weak; 17 of 56 song sequences showed significant relationships.
Nevertheless, all of these significant regressions were positive. A likely
explanation for positive relationships between amplitude and gape was
identified by Westneat et al.
(1993), who noted for their
sparrow data that frequency and amplitude were often positively correlated and
that gape might thus correlate to amplitude values as a secondary consequence.
Indeed, from among the 17 song sequences in our sample for which amplitude and
gape were correlated, 15 showed positive correlations between frequency and
amplitude, nine to statistically significant degrees (Pearson product moment
correlations, P<0.05).
In summary, we find that seven Darwin's finch species match beak gape to
peak frequencies during song production (e.g. Figs
1,
2) and, furthermore, that this
matching is done in a similar way across species
(Fig. 5). This finding provides
potential insights into the evolution of song structure in these birds.
Different species of Darwin's finches are expected to vary in the versatility
of their beak movements because of a tradeoff in jaw mechanics between force
and speed. Tradeoffs between force and speed are common in vertebrate motor
systems and arise from biomechanical parameters (e.g. lever arm position) as
well as muscle architecture (e.g. degrees of muscle pennation; see
Herrel et al., 2002 for an
overview of forcespeed tradeoffs). Darwin's finches that have evolved
the ability to apply strong crushing forces are thus expected to have evolved,
as a necessary consequence, lower maximal speeds of gape change. Thus, in
order to conduct accurate vocal tract matching, as characterized in the
present study, birds that have evolved strong force potential should have
evolved, as a secondary response, songs that are comparatively `easy' to
produce in terms of vocal performance
(Podos and Nowicki, in press
).
Low-performance songs, according to our restricted definition, would require
only low-speed vocal tract reconfigurations. By contrast, birds with beaks not
adapted for strong crushing abilities should be free to evolve songs that
require greater vocal versatility. This hypothesis is supported by the
demonstration that beak morphology and song features have evolved in
correlated fashion in Darwin's finches, with smaller-beaked birds evolving
songs with more rapid trill rates and wider frequency bandwidths
(Podos, 2001
).
This is not to say that the evolution of beak form and function will
necessarily cause evolutionary changes in song structure
(Slabbekoorn and Smith, 2000;
Grant and Grant,
2002a
,b
).
The probability of beaksong co-evolution depends on many factors,
including patterns of diversification in jaw mechanics and the extent to which
birds' performance abilities are challenged during song production
(Podos and Nowicki, in press
).
Moreover, songs evolve through other mechanisms, including adaptation to
distinct acoustic habitats (Bowman,
1979
) and through the accumulation of copy errors
(Grant and Grant, 1996
). The
mechanism we have proposed song parameters evolving as an incidental
consequence of adaptation for feeding should apply most directly to
those song parameters such as trill rate that are influenced by vocal
performance and the ability to conduct rapid vocal tract reconfigurations. The
evolution of other song parameters such as note phonology may be influenced
more directly by other mechanisms such as copy error
(Grant and Grant, 1996
). It is
still an open question as to which song parameters are most relevant in song
function in Darwin's finches. If song function turns out to rely upon
performance-related parameters, particularly in the context of species
recognition, then the linked evolution of beaks and songs could facilitate
reproductive isolation and speciation in these birds
(Podos, 2001
).
In the long term, an in-depth understanding of beak use in Darwin's finch
song production will require a greater sampling effort that includes more
individuals and populations from additional islands. The strength of our
regression data suggests that the vocal tract resonance model will receive
further support with larger samples. Additionally, a greater sampling effort
should provide improved resolution on patterns of diversity in
y-intercepts. Video analysis of singing Darwin's finches can also be
expanded to address additional questions. It would be useful to evaluate beak
movements for a wider diversity of vocal forms than the songs studied here,
especially the high-frequency `whistles' (in the range of 1215 kHz)
produced by Darwin's finches (Bowman,
1983). We predict that whistles would be accompanied by gapes
exceeding levels shown here, although there is some evidence that the
precision of the relationships between gape and vocal tract resonances drops
off at higher frequencies (Fletcher and
Tarnopolsky, 1999
; Hoese et
al., 2000
; R. A. Suthers, F. Goller, R. Bermejo, J. M. Wild and H.
P. Zeigler, unpublished). It would also be useful to obtain video clips of
gape patterns using the higher sampling rates (e.g. 500 frames
s1) now available in portable field cameras. High-speed
video data would allow us to evaluate the relationship between gape and
frequency with greater precision and would also enable us to characterize the
velocity and acceleration of beak movements during song production as a way to
test the hypothesis that beak force capacities co-vary negatively with the
speed and precision of beak gape changes
(Podos, 2001
).
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Acknowledgments |
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Footnotes |
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References |
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