Behavioral Ecology
doi:10.1093/beheco/arl041
Advance Access publication 31 August 2006
The blue tit’s song is an inconsistent signal of
male condition
Timothy H. Parker,a,b Iain R. Barr,c,d and Simon C. Griffitha,e
Edward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, South
Parks Road, Oxford OX1 3PS, UK, bDivision of Biology, Ackert Hall, Kansas State University,
Manhattan, KS 66506, USA, cDepartment of Animal and Plant Sciences, University of Sheffield,
Sheffield S10 2TN, UK, dSchool of Biological Sciences, University of East Anglia, Norwich NR4 7TJ,
UK, and eSchool of Biological, Earth, and Environmental Sciences, University of New South Wales,
Sydney, New South Wales 2052, Australia
a
Sexually selected traits are often hypothesized to signal male condition or quality, though empirical evidence is mixed, and
a number of alternative models of sexual selection do not require condition dependence. We examined the relationship between
various measures of condition and dawn songs in male blue tits (Cyanistes caeruleus). We detected 6 largely independent measures of
variation (i.e., variables) in these songs. None of these variables were related to blue tits’ ultraviolet–blue plumage, a demonstrated
sexual signal, thus failing to support the redundant signal hypothesis. We found some evidence that the song variables we measured
signaled male quality. There were correlations between body size and certain song traits, though neither male age nor male
recapture in the subsequent breeding season (apparent local survival) predicted any song variation. We combined our results
with published effect sizes comparing blue tit song with male quality variables using meta-analysis and found that a few song
measures are correlates of male quality, though as in our field data, neither male age nor survival appeared related to song. Our
relatively large sample sizes (.60), combined with our meta-analytical integration of 89 effect sizes, make the results regarding the
signaling value of our measured components of blue tit song robust. These results demonstrate that 1) only certain aspects of signal
variation may be condition dependent and 2) even when components of a sexual signal appear correlated with condition in some
studies, these signal components may be unrelated or inconsistently related to a variety of condition indices. Key words: condition
dependence, dawn song, meta-analysis, Parus caeruleus, survival, ultraviolet. [Behav Ecol 17:1029–1040 (2006)]
umerous studies have demonstrated that variation in the
expression of sexual signals amongst individuals within
a population can be related to differences in the condition
of those individuals (Andersson 1994; Ligon 1999). Condition-dependent ornamental traits convey information about
male quality (Andersson 1986), and thus might facilitate female choice of high quality mates (Griffith and Pryke 2006),
or male choices concerning competitive interactions (e.g.,
Parker and Ligon 2002). However, not all hypotheses regarding the evolution and maintenance of sexual signals predict
strong condition dependence of ornamental traits. For instance, some sexual signals may induce a response in a receiver
because of a sensory bias on the part of the receiver (Ryan
1998). Sexual signals may convey information about heritable
fitness entirely due to sexual attractiveness, and sometimes
males with reduced condition or survival may be most attractive (Kokko et al. 2002). Other sexual signals, especially those
with multimodal expression, may help receivers identify individual signalers (Dale 2000). Alternatively, certain signal measurements may not vary with male condition or quality but the
context in which the signal is used may (Mennill et al. 2002).
Empirical generalizations about whether sexual signals are
typically expressed with a high level of condition dependence
remain difficult.
In many species, individuals display multiple sexual signals
(e.g., Ligon et al. 1998; Pryke et al. 2001), and a number of
N
Address correspondence to T.H. Parker, who is now at the Department of Biology, Whitman College, 345 Boyer Ave, Walla Walla, WA
99362, USA. E-mail: [email protected].
Received 1 February 2006; revised 8 May 2006; accepted 3 August
2006.
The Author 2006. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved.
For permissions, please e-mail: [email protected]
hypotheses have been proposed to explain this phenomenon.
The multiple message hypothesis proposes that different signals each convey different information about the signaler,
although it could be that different signals convey redundant
information instead (Møller and Pomiankowski 1993). It may
also be that if one signal is condition dependent, selection will
not favor condition dependence in other signals (Iwasa and
Pomiankowski 1994) or multiple signals might evolve through
a process of sensory drive, where new, more detectable signals
spread rapidly because females notice them (Schluter and
Price 1993).
Although many aspects of bird song have been extensively
studied, the possibility that it can be a condition-dependent
signal has only recently begun to receive extensive scrutiny,
for instance with experimental manipulation (e.g., Spencer
et al. 2003). Song is a sexual signal in birds, typically produced
by males in the context of both territory defense and mate
attraction (Catchpole and Slater 1995). At least 3 different
mechanisms for condition dependence of bird song have
been investigated. A component of the avian brain important
to song learning, the high vocal center, has been shown to
be sensitive to manipulations of condition in captive studies
(Buchanan et al. 2004). This translates to an influence of condition, especially during brain development, on song learning
(Nowicki et al. 1998), and both correlative and manipulative
studies indicate that song complexity (e.g., repertoire size, the
number of distinct song types an individual learns and sings)
is condition dependent in some species (Nowicki et al. 2000;
Spencer et al. 2003, 2004, but see Forstmeier and Leisler
2004). It has long been supposed that song production might
be energetically demanding and that variables such as rate,
length, or consistency of song production might therefore
show condition dependence. There is support for this idea,
Behavioral Ecology
1030
but evidence varies among studies and species. For instance,
evidence from the field suggests that fat reserves may be
needed for long duration song output (e.g., Thomas 2002),
but respirometry chamber results are mixed, with some suggesting singing is metabolically demanding (Eberhardt 1994;
but see Gaunt et al. 1996) and others suggesting it is not (e.g.,
Ward et al. 2004). A third possibility is that constraints on song
production are enforced by social interactions such that poor
condition males of low status produce a substandard signal
because of the risk of punishment from higher status males.
For instance, dominant and subordinate individuals might
alter aspects of their song production, such as the timing of
the song phrase, in relation to other males singing in the
vicinity (e.g., Mennill et al. 2002).
The blue tit (Cyanistes caeruleus) has multiple sexual ornaments. Particularly striking are the ultraviolet (UV)–blue
plumage patches. The UV–blue crown plumage is sexually
dichromatic (Andersson et al. 1998) and has been found to
be related to both overwinter survival (Griffith et al. 2003) and
the sex ratio of offspring (Sheldon et al. 1999). One measurement of this plumage trait, UV chroma (the proportion of the
reflectance curve in the UV wavelengths), has consistently
appeared important in sexual signaling (Andersson et al.
1998; Sheldon et al. 1999; Griffith et al. 2003). It has also been
suggested that another plumage patch, the yellow breast, may
be a signal of parenting ability (Senar et al. 2002). In addition
to these ornamental colors, the male blue tit sings a moderately complex song (Bijnens and Dhondt 1984) that has been
the focus of several studies (Bijnens 1988; Kempenaers et al.
1997; Doutrelant et al. 2000; Poesel et al. 2001; Foerster et al.
2002; Dreiss et al. 2006). Much of this work has focused on
recordings made during the dawn chorus, the only extended
period of uninterrupted singing in this species. As blue tits
only participate in the dawn chorus after pairing and territory
formation, dawn singing is thought to serve functions such as
mate guarding (Mace 1989; Kempenaers et al. 1997), attraction of extrapair partners (Mace 1989; Kempenaers et al.
1997), or territory defense (Slagsvold et al. 1994). Components of dawn song may signal male quality or condition,
but significant relationships between song production and
variables such as body size, hormone level, and survival have
not been replicated among studies (Appendix 1). For instance, tarsus length showed significantly positive relationships with song variables in 2 studies, but in one study, the
song variable was the number of song types sung in a single
chorus (Doutrelant et al. 2000), and in another it was performance time, the proportion of the song’s duration that a male
was actually vocalizing (Poesel et al. 2001). In a third study,
performance time was significantly related to male mass and
testosterone level, but a relationship with tarsus length was not
reported (Foerster et al. 2002). Such results suggest a role for
song in sexual selection, but the lack of consistency between
studies is a concern, and clearly further work is necessary
before strong, general conclusions can be drawn.
When attempting to draw general conclusions about a study
system, meta-analytical synthesis of published data can be
fruitful (e.g., Sheldon and West 2004; Parker et al. 2005) by
combining published results into single statistical tests of
hypotheses (Rosenthal 1984; Gurevitch and Hedges 1999).
A meta-analytical approach is well suited to cases such as blue
tit song condition dependence, where results differ among
studies. This approach will allow identification of particular
song variables that tend to be most strongly related to condition or vice versa.
Our objectives with this study of blue tits were to 1) define
the major forms of variation (i.e., variables) in dawn song
production and thus determine the extent to which different
aspects of song were redundant signals, 2) test for redundancy
between a well established plumage color signal and dawn
song variables, 3) assess whether various potential condition
or quality indices were predictors of dawn song variables, and
4) decide, based on a combination of published data and our
own field data, whether current evidence supports the hypothesis that blue tit song production signals condition and, if so,
determine which components of song appear involved.
METHODS
Study site
We studied blue tit song in 3 years (2002–2004) in Wytham
Wood, a 380 ha woodland in Oxfordshire, UK (120#W,
5147#N). Between 330 and 450 blue tit pairs bred in artificial
nest boxes in each breeding season.
Song recording
During a female blue tit’s fertile period (from several days
before egg laying begins until the penultimate day of the
laying period [Mace 1989]), each day before dawn her mate
produces one bout of song typically lasting 15–60 min. This is
the most predictable singing bout produced by male blue tits,
and it is usually also their longest period of singing uninterrupted by other activities. Because of the link between this
male signal and the female’s fertile period and because earlier
research suggested that dawn song may be sexually selected in
blue tits (Doutrelant et al. 2000; e.g., Kempenaers et al. 1997;
Poesel et al. 2001), we chose to focus on this aspect of male
song.
As with some other studies of blue tit song (e.g., Doutrelant
et al. 2000), birds on our study site were not individually color
ringed. Therefore, we carefully observed behavior of individual birds in the weeks leading up to recording to determine
territory boundaries associated with particular nest boxes.
This involved confirming that males spent at least 5 min unchallenged in the vicinity of the nest box (e.g., Doutrelant
et al. 2000) and following males to map locations of boundary
conflicts and thus delineate territories. A male’s dawn song
was recorded from his first vocalizations of the day until the
termination of his dawn song bout. The end of the dawn song
is generally unambiguous and occurs when the pair copulates.
After this, the song rate drops dramatically, and the male can
be observed in other activities such as foraging. If we did not
observe copulation, then the termination was defined as .5
min of either 1) no singing or 2) a switch to other activities,
typically foraging, with long (.30 s) pauses between singing.
Not all dawn recording efforts were successful. In some cases,
we could not confirm unambiguous association with a nest
box during or after the dawn chorus and such recordings were
not used in our analyses. Because we were conservative in
applying these standards, we are confident that few, if any,
recordings were attributed to an incorrect male.
Although females are fertile before egg laying begins, we
limited our assessment of songs to those recorded after the
start of laying to increase standardization of the conditions
the pairs were experiencing at the time of data gathering.
Several males were recorded in more than one year. When
comparing song variables with color and condition variables,
we used only the first recording made for a given male to
avoid pseudoreplication. After excluding from our data set
the duplicate recordings, those that were recorded before or
after the laying period, recordings from males we never subsequently captured and measured (see below), and recordings
that could not be confidently attributed to particular males,
we were left with 63 usable recordings over the 3 years.
Parker et al.
•
Blue tit song
Approximately half the songs were recorded using Marantz
PMD680 digital recorders and Sennheiser short shotgun microphones (ME66). The other songs were recorded using
a Sennheiser long gun microphone (MKH816T) and a Sony
Pro-2 professional Walkman.
Song processing
Each recording was processed by one of 2 observers (IRB or
THP) using programs Raven 1.0–1.2 (Cornell Lab of Ornithology; THP) or Avisoft SASLab 3.2 (Avisoft Bioacoustics, Berlin;
IRB). Before beginning this process, we developed a standard
library of note and strophe (group of notes sung together in a
predictable pattern, typically lasting 0.5–2.0 s, Figure 1) types
to facilitate repeatable classification of all songs.
Broad frequency, harmonic scold-type vocalizations (types
B1-B7 in Bijnens and Dhondt 1984) constituted varying portions of dawn song. When these notes were produced (with or
without high frequency, pure tone introductory notes), they
were excluded from our analyses. These calls are often much
more variable in length and form than other dawn song strophe types (IR Barr and TH Parker, personal observation), are
produced year round and at any time of day in response to
threats (Bijnens and Dhondt 1984), and in blue tits and related species, the rate of scold note production is a function of
the type and proximity of a threat (Bijnens and Dhondt 1984;
Templeton et al. 2005). Thus, we decided not to consider
these notes as part of the blue tit sexual signal.
For each strophe in a male’s entire dawn song, we classified
its type, measured its length, and measured the length between adjacent strophes (Figure 1). When summing number
of strophe types produced, we did not include types produced
only once during the song. In the rare case when a strophe
type was sung just once, we could not be confident that it
was ever sung again and was actually a part of the individual’s
repertoire rather than an accidental combination of notes
produced, for instance, by being interrupted or switching strophe types in mid-strophe.
Because a male’s dawn song typically ceases when his mate
emerges from the nest box, the duration of his song is largely
dependent on his mate’s decision rather than his own. Therefore, we did not use duration of the song as a variable in the
analyses, and where relevant, all song variables were converted
to rates. The first song rate variable, number of strophes per
minute, may be likely to reflect condition if singing is metabolically demanding. The next 3 rate variables were designed
to capture information about song complexity. For the number of strophe types per minute and the number of switches
between strophe types per minute, the total number of strophe types or the total number of times the bird switched
between strophe types for the entire dawn song was divided
by the total number of minutes in the dawn song. For the
Figure 1
Sound spectrogram of an example of blue tit song. Depicted are
3 strophes, 2 strophe types (A and B), and one switch between
strophe types.
1031
third song rate variable designed to address song complexity,
number of strophe types per strophe, the number of different
strophe types produced over the entire dawn song was divided
by the total number of strophes sung during the entire dawn
song. One measure of song complexity we did not consider
was absolute repertoire size. This is because we recorded each
male on only one morning and so had insufficient data to
determine asymptotic repertoire size. However, the song complexity variables described above provide information regarding the repertoire per unit time or song and thus may be
influenced by the same factors hypothesizes to influence overall repertoire size.
We also calculated mean strophe length and mean pause
length. Both of these might be influenced by metabolic demands. For mean pause length, we excluded the pauses between strophe types, as these pauses were often longer than
pauses between strophes of the same type. We calculated performance time, another variable possibly influenced by metabolic demands, as the length of a strophe divided by the sum
of the length of the strophe and the length of the preceding
pause. These calculations excluded the initial strophe in a
series of given strophe type because, as stated above, pause
length between strophe types was often longer than pause
lengths within strophes. We used the mean performance time
value in subsequent analyses.
We calculated the coefficient of variation (CV) for strophe
length and pause length because we hypothesized that consistency of strophe length or timing among strophes might reflect aspects of condition such as current metabolic fatigue or
a legacy of developmental stress. These were calculated separately for all strophe types, though strophe types sung fewer
than 10 times (and their associated pauses) were excluded.
The strophe and pause CVs for each strophe type were then
averaged within a song to produce the average CV of strophe
length and the average CV of pause length.
It has been shown that while blue tits are singing a given
strophe type, the pause length between individual strophes
tends to increase between successive strophes, possibly because of fatigue (Poesel and Kempenaers 2000). Because strophe length does not tend to change, the increase in pause
length leads to a decrease in performance time (Poesel et al.
2001). This is termed drift. Although it occurs within a series
of strophes of the same type (strophe series), it is reset after
each change in strophe type (Poesel and Kempenaers 2000).
We estimated the strength of drift for individual birds as the
slope from a general linear model (PROC GLM, SAS 8.2) in
which performance time was the dependent variable, time was
the predictor, and strophe series was a covariate. By controlling for strophe series, a separate intercept was allowed for
each run of strophes of a given type, thus accounting for
the tendency for drift not to carry over when song type is
switched. The slope was negative for nearly all birds, demonstrating that drift was close to ubiquitous. Some authors have
classified songs into those showing drift and those not showing drift based on a significance test for negative slope (Poesel
and Kempenaers 2000); however, we considered drift to be
a continuous variable. First, unlike a previously reported pattern (Poesel et al. 2001), we observed a normal rather than
a bimodal distribution of slope. Second, sample size differed
dramatically based on the length of the song in our data set,
so a significantly negative slope would be more likely from
longer songs if the strength of the relationship between time
and performance time was held constant.
Other field methods
At each box containing a blue tit nest, we noted first egg date,
clutch size, and hatching date. We attempted to capture each
Behavioral Ecology
1032
adult between day 6 and day 14 of the chick-feeding period.
Any unringed adults were given a uniquely numbered BTO
ring at this point. For each captured adult, we quantified the
color of the crown and primary coverts. Spectral reflectance
was measured using a USB2000 spectrometer (Ocean Optics,
Dunedin, FL) with illumination from a xenon light source
(Ocean Optics PX-2). A sheath was fixed to the fiber end to
standardize measuring distance (7 mm) and exclude ambient
light. The fiber-optic reflectance probe was held (not pressed)
perpendicular to the plumage, and 3–5 scans were taken from
the center of each patch, removing the probe between each.
The reflectance was measured relative to a WS-2 white standard scanned prior to each individual. We chose to limit our
index of color to the one measurement that has consistently
appeared important to sexual signaling in this species, UV
chroma (R320-400/R320-700). This measurement addresses
the specific importance of UV (Andersson and Amundsen
1997; Andersson et al. 1998; Sheldon et al. 1999). Its relevance in signaling is indicated by previous observations of its
sexual dimorphism, correlation with mate choice, prediction
of offspring sex ratio, and prediction of over-winter survival
(Andersson et al. 1998; Sheldon et al. 1999; Griffith et al.
2003). We also measured 5 potential condition indices. For
each male, we measured tarsus and wing length (millimeters)
and mass (grams) and determined age (second year vs. after
second year) based on wing covert molt. We did not combine
the 3 body size measurements prior to analyses because they
were not strongly correlated with each other (r ¼ 0.17–0.33)
and because we wished to compare our results with those from
other studies that have considered these body size measurements separately. Our fifth measure of male quality or condition was recapture in the subsequent year. Because most
individuals bred in nest boxes, all boxes were monitored, and
a major effort was made to capture the breeding male at every
nest box; most individuals that survived to breed on the site in
a subsequent year were detected, and thus, the variable ‘‘recapture’’ can be considered a good approximation of local
survival. None of these variables alone are ideal condition or
quality indices, but they have been used in this context before
(e.g., Bijnens 1988; Doutrelant et al. 2000; Nowicki et al. 2000).
Age often predicts expression of condition-dependent signals,
presumably because yearling males are in poorer condition or
of lower dominance rank on average (e.g., Greene et al. 2000;
Parker et al. 2003; Griffith and Pryke 2006). Survival has been
shown to be influenced by aspects of condition (e.g., Gosler
1996; e.g., Lambrechts and Dhondt 1986). Tarsus length has
been used as an index of condition experienced during development (Nowicki et al. 2000) and mass as an index of current
condition (but see, Gosler and Harper 2000). In blue tits, older
males have longer wings than yearling males (TH Parker, IR
Barr, and SC Griffith, unpublished data) and so wing length
may be a condition indicator as well. Strong correlations between one of these condition indices and song production
would be consistent with a role of song in condition signaling.
Data analyses
All our continuously distributed variables that were not normally distributed were transformed to approximate normality.
Recapture and age were classified as binomial variables. Birds
were either recaptured or not in the next breeding season and
were either second year or after second year adults.
We next identified axes of independent variation among
our original 10 song variables. The first step was to generate
a correlation matrix to determine which, if any, song variables
were strongly correlated with each other. We then included
each group of correlated song variables in its own principal
component analysis (PCA) to generate a single principal com-
ponent that would capture the majority of the variation in this
group of variables. In all such cases, we substituted this principal component for the group of correlated variables in subsequent analyses.
Before conducting further data analyses, we identified potential covariates that we hypothesized might influence song
production or condition variable values. Any of our variables
of interest could have varied by year or geographically among
the 9 subareas of the study site. Song variables could have
been influenced by the timing of the recording, and so we
considered recording date and both the number of days after
clutch initiation and the number of days before egg laying
ended that the bird was recorded. We considered an effect
of low temperature on the dawn of recording because a cold
night might force birds to forage at the expense of singing.
We also considered the possibility of an effect of identity
of the scorer of the song recording (IRB vs. THP). For the
morphological measurements, we considered a role of age
(wing length and mass), identity of the measurer, and date of
measurement (mass).
We then determined whether our song variables were providing the same information as the best-studied signal in this
species, UV–blue plumage color. We compared each of our
song variables with the UV chroma of the wing coverts and cap
using general linear models, which allow inclusion of relevant
covariates.
We determined which hypothesized condition correlates,
if any, predicted our song production variables by conducting
general linear model analyses in a 2-step process. First, for
each song variable, we analyzed a global model containing
all 5 condition variables as predictors. If any condition variables had at least a marginally significant (P , 0.1) utility in
predicting the song variable in question, a second analysis
excluding all nonsignificant condition variables was conducted for that song variable. We obtained effect sizes and
parameter estimates for the nonsignificant predictor variables
from the global model and for the significant predictor variables from the reduced model. Appropriate covariates were
included in each analysis, as discussed below in Results.
Published data
We located each published study comparing blue tit song
with potential indices of individual quality or condition
(Appendix 1). We then extracted all statistical information
from each comparison of a song variable with a condition variable. Our intention was to compare results based on sign
and magnitude of correlation coefficients. If no correlation
coefficient was presented for a given relationship, we estimated one based on statistics available in the published paper
with the statistical calculator option in the meta-analysis program MetaWin 2.1 (Rosenberg et al. 1997). In the one paper
(Dreiss et al. 2006) where most data reporting was insufficient
for inclusion in meta-analysis, we obtained more detailed information from the authors.
We located 5 studies reporting 42 relationships between
blue tit song variables and condition-related variables in sufficient detail for meta-analysis. We received unpublished information for an additional 11 relationships from one of these
studies (Appendix 1). We plotted all these effect sizes (correlation coefficients, y axis) describing relationships between
song variables and aspects of condition in blue tits against
the sample size for the respective studies (x axis). This should
produce a funnel-shaped plot with a large vertical spread of
points to the left, where sample sizes are small and sampling
variance is thus large, and a narrower distribution of points to
the right, where sample sizes are larger and sampling variance
is thus reduced (Palmer 1999). The points should converge
Parker et al.
•
Blue tit song
1033
on the true effect size(s) as the sample size increases on the
right side of the plot. If, when plotted, the published effect
sizes do not follow a funnel-shaped distribution, publication
bias may be to blame (Palmer 1999). Typical publication bias
is against negative or nonsignificant results, especially in studies with small sample sizes. If the expected effect size is positive, then negative and modestly positive effects are likely to
be nonsignificant when samples are small, and these results
may often go unpublished (Rosenthal 1984). This can lead to
a linear, rather than a funnel-shaped, distribution, with published effects from small-sample studies mostly high and positive, but becoming lower with increasing sample sizes (Palmer
1999).
To supplement our visual inspection of the funnel plot, we
conducted a regression analysis to test for the negative relationship between sample size and effect size predicted in the
case of publication bias. Before making this comparison, we
converted all correlation coefficients (r) to Fisher’s z-transformation fz ¼ 1=2ln½ð11r Þ=ð1 r Þg and log-transformed sample sizes. We included all published effects in this analysis,
as well as the 11 effects for which we obtained necessary details
from the authors. Because not all the details of these data
were published, this is not strictly a test for publication bias,
but rather a test for bias in the data we obtained from published and unpublished sources. We did not include our field
data in the test for bias in previously available data.
Finally, we conducted meta-analyses to determine whether
certain song variables appeared more likely to be related to
condition or certain condition variables seemed more likely to
be related to song. We combined our field results with those
obtained from other published and nonpublished sources.
For these analyses, we used mixed models and bootstrapgenerated significance tests (based on 9999 iterations) in program MetaWin version 2.1. Mixed models in meta-analysis
account for expected heterogeneity in true effect sizes among
song and condition variables and bootstrapping accounts for
violations of distributional assumptions (Rosenberg et al.
2000). These analyses were based on Fisher’s z-transformation
of correlation coefficients (r) and variance inversely proportional to the sample size (vz ¼ 1/n3, where n ¼ sample size
for the given study [Rosenberg et al. 2000]). With program
MetaWin, it is only possible to model the effect of one class of
variables at a time. Thus, we first tested for differences among
effect sizes (z) related to song variable, and then we tested for
difference among effect sizes related to male quality or condition variables. For each song and condition variable, we asked
whether the bootstrap-generated 95% confidence interval (CI)
included zero.
RESULTS
Identifying independent variation in songs
Of the 10 song variables we compared with each other using
a correlation matrix (PROC PRINCOMP, SAS 8.2), 4 were not
strongly correlated with any others (all jr j , 0.42) and the
remaining 6 fell into 2 groups with high intragroup correlation (each variable with at least one jr j . 0.65, Table 1). Thus,
a minimum of 6 largely independent types of variation (i.e., 6
variables) were present in these blue tit songs. The 4 variables
not strongly correlated with any others, mean strophe length,
drift, CV of strophe length, and CV of pause length, were
considered separately in further song analyses. The variation
in the 2 groups of correlated variables was summarized in 2
PCAs (PROC PRINCOMP, SAS 8.2). In the first PCA, we included the variables strophes per minute, performance time,
and mean pause length. Principal component 1 (PC1) explained 88% of the variation in these 3 variables and had
strong loading from all 3 variables (negative for pause
length), so we used PC1 in further analyses, and we termed
this variable ‘‘singing intensity.’’ In the second PCA, we included the number of strophe types per minute, the number
of strophe types per strophe, and the number of strophe type
switches per minute. PC1 from this analysis explained 74% of
the variation in these 3 variables, and all 3 variables had strong
loadings. Thus, we used this new variable, which we termed
‘‘strophe turnover,’’ in all further analyses. We were thus left
with 6 song variables for further analyses.
Identifying covariates of song and condition variables
We identified covariates of song and condition variables and
included them in further analyses of these traits. Strophe
length had no covariates, but drift was related to subarea of
the study site; singing intensity was a function of the number
of days after clutch initiation that the recording was made;
and strophe turnover, CV strophe length, and CV pause
length were all related to the scorer of the song recording
(IRB vs. THP). The effect of identity of the song scorer may
have resulted from the nonrandom geographic and temporal
distributions of the sets of songs analyzed by the 2 scorers. We
examined the scorer effect for 38 recordings made in one
subarea of the study site (28 scored by IRB, 10 scored by
THP), and the scorer effect disappeared for strophe turnover
and CV pause length. We retained scorer as a covariate in
these cases with the understanding that factors correlated with
scorer identity were likely influencing the patterns we observed.
Among male quality or condition variables, age had no covariates
Table 1
Correlation coefficients describing relationships between pairs of blue tit song variables
Strophe
length
Pause length
0.32
Performance time
0.20
Strophes per minute 0.42
CV Strophe length
0.04
CV Pause length
0.15
Drift
0.09
Strophe types per
minute
0.08
Strophe types per
strophe
0.29
Switches per minute
0.07
Pause
length
Performance
time
Strophes
per minute
CV Strophe
length
CV Pause
length
Drift
0.83
0.93
0.34
0.05
0.01
0.69
0.41
0.04
0.11
0.25
0.04
0.00
0.19
0.14
0.07
0.00
0.02
0.16
0.15
0.30
0.28
0.56
0.27
0.46
0.23
0.46
0.32
0.30
0.01
0.18
0.29
0.25
0.34
Strophe types Strophe types
per minute
per strophe
0.79
0.65
0.36
The purpose of this table is to describe the magnitude and direction of relationships among song variables in our recordings to determine
how variables might be grouped into composite variables, not to test for statistical significance.
Behavioral Ecology
1034
Table 2
Correlation coefficients describing the proportion of variation in
blue tit color measurements described by blue tit song variables
Strophe Singing
length intensity Drift
Strophe CV strophe CV pause
turnover length
length
good predictors of the 2 color variables in the 61 individuals
for whom we had sufficient color data (Table 2). No relationships were significant even before correction for multiple
comparisons, and all correlations (jr j) were less than 0.24.
Condition indices as predictors of song output
Head
chroma 0.13
Wing
chroma 0.12
0.21
0.04
0.03
0.12
0.00
0.16
0.23
0.09
0.11
0.19
N ¼ 61 for all comparisons.
but wing length was a function of age, measurer, and subarea
of the study site; tarsus length was related to measurer and year;
and year also influenced both mass and recapture.
Assessing redundancy of song and color variables
According to our general linear model analyses (PROC GLM,
SAS 8.2), the 6 largely independent song variables were not
We found evidence that some measured aspects of condition
predicted song production in blue tits. Using general linear
models with normal error (PROC MIXED, SAS 8.2), we identified one or more body size traits that predicted variation in 4
of 6 song variables (jr j , 0.41, Table 3). Birds with longer tarsi
tended to sing longer strophes (Table 3). Tarsus length also
explained the distribution of CV of strophe length, but
counter to prediction, males with longer tarsi sang strophes
that were less consistent in length within a strophe type (Table
3). One of the 2 strongest pattern was for heavier males to
have a higher singing intensity (a greater proportion of time
singing) (Table 3), and this was the only song–condition relationship to remain significant (P , 0.05) if we conducted
a conservative Bonferroni adjustment for 30 comparisons
(6 song variables 3 5 condition variables). The relationship
Table 3
The strength of condition variables as predictors of song variables in male blue tits
Song (dependent)
variable
Condition (independent)
variable
n
F
df
P
Slope
Standard error
ra
Strophe length
Mass
Tarsus
Wing length
Age
Recapture
Mass
Tarsus
Wing length
Age
Recapture
Mass
Tarsus
Wing length
Age
Recapture
Mass
Tarsus
Wing length
Age
Recapture
Mass
Tarsus
Wing length
Age
Recapture
Mass
Tarsus
Wing length
Age
Recapture
61
63
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
63
61
61
61
61
63
61
61
61
0.34
6.27
0.86
1.23
1.39
10.96
1.69
0.14
1.83
1.33
9.72
6.60
1.22
0.78
0.03
1.82
0.88
0.97
0.01
0.01
0.03
5.30
0.56
0.53
1.39
0.20
1.94
0.16
0.00
2.11
1,52
1,58
1,52
1,52
1,52
1,56
1,51
1,51
1,51
1,51
1,52
1,52
1,49
1,49
1,49
1,51
1,51
1,51
1,51
1,51
1,51
1,57
1,51
1,51
1,51
1,51
1,57
1,51
1,51
1,51
0.56
0.02
0.35
0.27
0.24
,0.01b
0.20
0.71
0.18
0.25
,0.01c
0.01
0.27
0.38
0.86
0.18
0.35
0.33
0.92
0.93
0.87
0.02
0.46
0.47
0.24
0.66
0.17
0.69
0.99
0.15
0.009
0.032
0.005
0.016
0.018
1.27
0.56
0.06
0.54
0.51
0.0002
0.0001
0.00002
0.00004
0.00000
0.56
0.39
0.16
0.04
0.03
0.02
0.25
0.03
0.08
0.15
0.006
0.016
0.002
0.0002
0.021
0.015
0.013
0.006
0.014
0.015
0.38
0.43
1.67
0.40
0.44
0.00005
0.00005
0.00002
0.00005
0.00005
0.42
0.41
0.17
0.38
0.42
0.13
0.11
0.05
0.11
0.13
0.014
0.012
0.006
0.0132
0.014
0.08
0.31
0.13
0.15
0.16
0.40
0.18
0.05
0.19
0.16
0.40
0.34
0.16
0.13
0.02
0.19
0.13
0.14
0.01
0.01
0.02
0.29
0.10
0.10
0.16
0.06
0.18
0.06
0.00
0.20
Singing intensity
Drift
Strophe turnover
CV strophe
CV pause
a
b
c
Covariates are described in Results but not shown in this table. Reported statistics were derived from the global model (all condition variables and
covariates for a given song variable) except in cases where one or more condition variables were minimally effective predictors (P , 0.1) of a
given song variable, in which case statistics for those predictors were derived from the reduced model including only the effective condition
predictors and relevant covariates. This explains variation in degrees of freedom among condition variables for a given song variable model.
Any model containing the variable mass had an n ¼ 61 rather than 63 because we lacked mass data for 2 males.
Correlation coefficient (r) equivalents were derived from degrees of freedom and the F - values using the statistical calculator in program
MetaWin. These correlation coefficients were the standard effect sizes required for inclusion of these data in subsequent meta-analyses. In this
table and in our meta-analyses, positive correlation coefficients indicate higher or more consistent (lower CV of strophe or pause length and less
drift) song production from individuals in better condition (see Appendix 1).
Exact P value (0.0016) remains significant (experiment-wide alpha ¼ 0.05) after correcting for multiple (30) comparisons.
Exact P value (0.003) does not remain significant (experiment-wide alpha ¼ 0.05) after correcting for multiple (30) comparisons.
Parker et al.
•
Blue tit song
1035
between body size and drift was inconsistent, with a strong
trend for heavier males to show more drift, but a somewhat
weaker pattern of males with longer tarsi showing less drift.
Neither age nor recapture significantly predicted any song
variables (Table 3).
These results are consistent with certain song variables signaling
at least some aspects of condition in blue tits. However, these
results must be interpreted with caution because the evidence
for publication bias suggests that our rather low meta-analysis–
generated effect size estimates may be biased upward.
Summarizing published relationships between song
and condition
DISCUSSION
Published correlations between song variables and male condition variables ranged from r ¼ 0.29 to 0.86 (Appendix 1).
Both a visual assessment of the funnel plot (see especially
open and filled circles, Figure 2) and a quantitative assessment of the same data indicate that there may have been bias
against publishing of negative or nonsignificant relationships
between condition variables and song production in blue tits
(F1,51 ¼ 11.8, P ¼ 0.001, r ¼ 0.43, slope ¼ 0.49 6 0.14
standard error [SE]). Studies with small sample sizes tended
to report large positive effect sizes, and reported effect sizes
declined with increasing sample size. Several of the particularly strong effects at small sample sizes describe relationships
between testosterone level and song variables. Because effect
sizes may tend to be larger for studies of physiological effects
(Møller and Jennions 2002), we conducted our test for publication bias a second time, excluding the testosterone effects.
We still found a significant negative slope with this reduced
data set (F1,48 ¼ 4.0, P ¼ 0.050, r ¼ 0.28, slope ¼ 0.31 6 0.15
SE), again supporting the hypothesis of publication bias,
though the effect seemed less marked.
When we combined our results with previously published
results using meta-analysis, we found evidence that certain
song traits more consistently correlated with condition than
others (Table 4). Singing intensity and strophe length both
had significantly positive effect sizes. Within-strophe drift,
a variable assessed by only one study, also showed a significant
positive effect size. A similar pattern was observed when the
testosterone studies were excluded, but the strophe length
effect was no longer significant (Table 4). Only one possible
condition or quality indicator, testosterone level, had a significantly positive relationship with song variables, although the
95% CI for mass was only marginally nonsignificant (Table 4).
Figure 2
Funnel plot depicting the relationship between sample size and
effect size (correlation coefficient, r) for published tests of the relationship between aspects of blue tit song and measures of male
condition or quality (filled circles). Open circles designate the 3
relationships between testosterone level and song production. Filled
squares represent the 11 published effects for which additional details were needed and obtained from the authors. Open triangles
depict our field data published herein, and open squares represent
our unpublished data from a condition manipulation with a small
sample size (n ¼ 15) described in the appendix.
There are 5 major results of this study. We show that 1) a minimum of 6 largely independent variables exist in dawn blue tit
song, 2) none of these variables correlated strongly with variation in an established measurement of color signal expression, 3) of male quality or condition indicators, aspects of
body size, but not age or a survival index, predicted variation
in certain song measurements, especially singing intensity,
4) a bias toward publication of strong positive results may have
inflated the perceived importance of blue tit song as a
condition-dependent signal, and 5) a subset of song variables,
particularly strophe length and singing intensity, may be most
likely to signal condition in this species. Our large sample
sizes, combined with our thorough meta-analysis, make our
conclusions particularly robust.
In blue tits, the dawn song appears to be a multicomponent
signal, and we identified 6 largely independent forms of
variation. There are any number of song variables that we did
not quantify, such as amplitude (Forstmeier et al. 2002), withinstrophe drift (Bijnens 1988), and the ratio of trill rate to frequency breadth (Podos et al. 2004), and with a complex signal
such as song, the number of variables examined is limited only
by the researcher’s time and creativity. Thus, 6 independent
song variables is a minimum estimate. However, just because
one can measure a song component does not mean that it serves
as a signal. Without demonstrated signal utility through study of
conspecific responses to different song components, it will remain unknown what aspects of blue tit song serve as signals and
therefore how many meaningful forms of variation exist.
We demonstrated that our measured song variables are not
strongly correlated with UV chroma of cap and wing plumage
in blue tits, a well established sexual signal. This is consistent
with other findings (Dreiss et al. 2006), and thus, we can unambiguously reject the redundant signal hypothesis as an explanation of the evolution of the song and color signals we
measured in this system (Møller and Pomiankowski 1993). To
the extent that these different measures are signaling information about individual males, they are signaling different
information. Other alternative hypotheses explaining the presence of multiple ornaments (e.g., Møller and Pomiankowski
1993; Schluter and Price 1993; Iwasa and Pomiankowski 1994)
cannot be rejected or supported.
We found significant relationships between song variables
and 2 of the 5 condition variables. These 2 condition variables
were mass and tarsus length, and at least one of them was
a significant predictor (before correction for multiple comparisons) of each of the following: strophe length, drift, singing intensity, and CV strophe. Although the relationship
between strophe length and tarsus length in our field data
was not robust to correction for multiple comparisons, when
we combined our strophe length–male quality relationship
effects with the 17 published relationships involving strophe
length, we found a significant, albeit weaker, positive effect.
Evidence for condition dependence of drift was mixed. Drift
was positively related to one of our body size measurements
but negatively related to another. Further, the effect of drift
was very small and not significant in meta-analysis. Our
results suggested the strongest role for the composite variable
singing intensity in predicting male quality: heavier males
tended to spend a larger proportion of their time singing,
had shorter pauses, and sang more strophes per minute, and
Behavioral Ecology
1036
Table 4
Strength of the relationships between blue tit song variables and various hypothesized condition indices
derived from meta-analyses combining our findings with those from other studies
a
b
c
d
e
Variable
N
z
95% CI
Condition
Wing length
Tarsus length
Mass
Age
Survivala
Mass condition index
Condition manipulationb
Testosterone level
10
21
20
10
10
5
10
3
0.032
0.045
0.098
0.049
0.046
0.164
0.087
0.893c
0.041
0.053
0.004
0.075
0.071
0.060
0.240
0.448
to
to
to
to
to
to
to
to
0.096
0.157
0.200
0.182
0.183
0.417
0.134
1.293
Song variables (including testosterone data)
CV pause
6
CV strophe
6
Drift
9
Within-strophe drift
5
d
16
Singing intensity
Repertoire size
8
Strophe length
23
e
16
Strophe turnover
0.082
0.083
0.027
0.125c
0.182c
0.062
0.100c
0.019
0.210
0.205
0.165
0.055
0.059
0.124
0.003
0.042
to
to
to
to
to
to
to
to
0.016
0.047
0.170
0.206
0.320
0.323
0.199
0.111
Song variables (excluding testosterone data)
CV pause
6
CV strophe
6
Drift
9
Within-strophe drift
5
d
15
Singing intensity
Repertoire size
8
Strophe length
22
15
Strophe turnovere
0.082
0.083
0.028
0.125c
0.167c
0.060
0.093
0.015
0.213
0.206
0.167
0.055
0.046
0.118
0.002
0.050
to
to
to
to
to
to
to
to
0.013
0.044
0.172
0.206
0.297
0.323
0.194
0.103
Number of available estimates (N), meta-analysis derived average Z (positive values indicate a positive
relationship between song production or song consistency and condition), and associated bootstrap
generated 95% CIs.
Survival as indicated by recapture of marked birds in subsequent years.
Manipulation of chick or parent condition through brood size manipulation.
Indicates an effect size estimate significantly (P , 0.05) different from zero.
Includes variables ‘‘performance time’’ and ‘‘song rate’’ from published studies.
Includes variables ‘‘versatility,’’ ‘‘strophe diversity,’’ and ‘‘strophe bout length’’ from published studies
(see Appendix 1).
this result was highly significant. As with strophe length, metaanalysis results indicate that the proportion of time a male blue
tit spends singing provides a signal of some aspect of his quality.
Although the presence of publication bias means we must interpret the meta-analysis results with caution, it appears that
both strophe length and singing intensity may be condition
dependent. These 2 song variables could be linked to body size
in multiple ways. Although it is possible that larger males have
more energy reserves to devote to song production (Thomas
2002), another possibility is that song production is socially
mediated (Mennill et al. 2002) and that only larger males
can afford to risk aggression from other males (Parker and
Ligon 2002) by singing longer or more frequent strophes.
Several other lines of evidence indicate that the components of blue tit song that we measured may not be general
indicators of male condition. Although correlated with some
body size traits, our measurements of blue tit song appeared
completely unrelated to age or the probability of being recaptured after the subsequent winter. Further, in meta-analysis,
neither of these condition variables was related to song. Many
condition-dependent male sexual signals are age dependent
(e.g., Greene et al. 2000; Parker et al. 2003; Griffith and Pryke
2006), presumably because older males have higher dominance rank or are more efficient at performing the tasks that
determine the environmental effects on their signal expression. Thus, either age is unrelated to male condition in blue
tits or, more likely, the song variables we examined are not
particularly sensitive to male condition or the components of
male condition that relate to age. In tits, it is well demonstrated that aspects of male quality and condition, including
male dominance, influence overwinter survival (e.g., Gosler
1996; e.g., Lambrechts and Dhondt 1986), and so presumably
our negative results with regard to recapture in the subsequent year are not due to a lack of correlation between male
survival and condition but rather to song traits lacking dependence on the aspects of condition influencing survival.
Our effect sizes are within the wide range observed in other
bird species (0.6 , r , 0.6) where measures of condition
similar to ours have been compared with aspects of song production (e.g., Lampe and Espmark 1994; Galeotti et al. 1997;
Otter et al. 1997; Balsby 2000; Rinden et al. 2000; Gil et al.
2001; Forstmeier et al. 2006; Kipper et al. 2006). As with the
blue tit data, results vary among and within studies. Certain combinations of condition and song variables appear important
in some studies, and other combinations appear important
in other studies. One fairly consistent pattern is that of an age
effect on song complexity variables (Lampe and Espmark 1994;
Balsby 2000; Rinden et al. 2000; Gil et al. 2001; Forstmeier et al.
Parker et al.
•
Blue tit song
1037
2006), something we failed to detect in blue tits. Relationships
between other condition and song variables are more varied,
and a formal meta-analysis will be required to determine how
the condition variables we studied tend to be related to song
production across bird species.
Our meta-analysis demonstrates the importance of formal
synthesis of published results rather than casual review of notable published relationships. Without meta-analysis of the
blue tit song literature, any general conclusion we might have
drawn would have been dubious because of the heterogeneity
in results among studies. Meta-analysis does not eliminate uncertainty concerning interpretation of published results, but
it provides a formal framework for assessing these results and
thus minimizes biased interpretation. It also allows for identification of potential biases in the published literature and as
such promotes caution in interpretation when such bias is
identified. Because sexual signal content may vary among
populations of a species (Badyaev et al. 2001; Forstmeier
and Leisler 2004), an appropriate interpretation of our metaanalysis results is that they represent the distribution of, and
average patterns for, song–condition relationships in this
species. Thus, regardless of whether the distribution of data
in our meta-analyses represents geographically and temporally
divergent patterns or simply sampling error, we can still conclude that many aspects of blue tit song are either inconsistent
or weak predictor of male condition or both. Even the most
consistent blue tit song signal of condition, singing intensity,
has a weaker meta-analysis–generated effect size than that detected in our field data and may not be condition dependent
in all situations (e.g., Dreiss et al. 2006).
Although we found some evidence of blue tit song signal
utility, we cannot eliminate the possibility that unmeasured
components of blue tit song production are better signals of
quality. For instance, although we quantified aspects of song
complexity, we did not measure absolute repertoire size,
a song variable found to be important in some case (Nowicki
et al. 2000; Spencer et al. 2003, 2004, but see Forstmeier and
Leisler 2004). We also did not consider the social context of
song production. As with previous research on signal content
in blue tit song, our song measures did account for neither
the proximity of conspecifics such as competing males nor the
ongoing vocalizations of other chorusing males. Male songbirds can adjust their singing behavior in response to that of
their neighbors (Catchpole and Slater 1995), and patterns of
counter singing have been shown in a closely related species
to be an important source of information for females assessing male quality (Mennill et al. 2002). Future work with blue
tits will need to address these possibilities if the signal content
of male song is to be understood.
It could also be that we measured appropriate song variables
but did not measure the best component of male quality to
compare with these variables. One such unmeasured variable
is male dominance rank, which can relate to song production
and have meaningful fitness implications (Lambrechts and
Dhondt 1986; Mennill et al. 2002). In any study with negative
results, it always remains possible that important variable(s)
went unmeasured.
Despite some unmeasured variables, we are still in a position
to draw certain robust general conclusions. Many of the song
variables we studied have previously been hypothesized to
signal male condition (e.g., Eberhardt 1994; Nowicki et al.
1998; Poesel and Kempenaers 2000; Foerster et al. 2002),
but for most of these song components, we found at best
mixed evidence that they consistently signal male quality in
blue tits. Thus, researchers need to continue to test hypotheses concerning condition dependence on a case by case basis
and should remain hesitant to assume condition dependence
of sexual signals without strong empirical support.
We are not yet in position to identify the evolutionary mechanism maintaining variation in blue tit song. However, one
aspect of variation, the production of different song types,
may be maintained by the necessity of neighbor recognition
as seen in many other species (Catchpole and Slater 1995)
and more generally for other types of multimodal signals
(Dale 2000). Maintenance of variation in other traits is less
clear. Although a lack of correlation between signal expression and condition is likely in some models of signal evolution
that do not rely on a fitness payoff to female choice, such as
sensory bias (Ryan 1998), theory predicts this relationship can
be absent even in cases where genetic benefits to mate choice
are important, for instance, if males trade-off condition
against sexual attractiveness (Kokko et al. 2002). Our results
lend support to the hypothesis that certain aspects of sexual
traits signal condition but also lead us to reject the simple
scenario of a sexual signal providing consistent information
about male condition. Given the common perception of the
importance of this sort of condition signaling, this is an
important conclusion.
APPENDIX 1
Comparisons of blue tit song variables to potential condition indices in other studies
Study
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Bijnens
Site
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
(1988)
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Peerdsbos,
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Song variable
Condition index
Sample
size
Repertoire sizea
Repertoire size
Repertoire size
Repertoire size
Repertoire size
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Strophe length
Within-strophe driftb
Within-strophe drift
Age
Survival
Tarsus length
Mass
Wing length
Age
Survival
Tarsus length
Mass
Wing length
Age
Survival
Tarsus length
Mass
Wing length
Age
Survival
42
42
35
34
34
28
28
18
19
19
28
28
23
23
23
29
29
Reported
2-tailed
P value
Other
reported
statistics
r
0.06
0.2
.0.05
.0.05
.0.05
0.02
0.02
.0.05
.0.05
.0.05
0.03
0.04
.0.05
.0.05
.0.05
.0.1
.0.1
U ¼ 285
U ¼ 166
r ¼ 0.11
r ¼ 0.15
r ¼ 0.05
U ¼ 44
U ¼ 46
r ¼ 0.21
r ¼ 0.15
r ¼ 0.04
U ¼ 50.5
U ¼ 53.5
r ¼ 0.22
r ¼ 0.14
r ¼ 0.02
U ¼ 67
U ¼ 107
0.29
0.20
0.11
0.15
0.05
0.44
0.44
0.21
0.15
0.04
0.41
0.39
0.22
0.14
0.02
0.02
0.06
Behavioral Ecology
1038
Appendix1, Continued
Reported
2-tailed
P value
Other
reported
statistics
r
r ¼ 0.23
r ¼ 0.18
r ¼ 0.19
F1,10 ¼ 16.49
F1,18 ¼ 4.06
F1,12 ¼ 1.38
t ¼ 2.08
t ¼ 0.74
U ¼ 39.5
r ¼ 0.48
r ¼ 0.34
r ¼ 0.04
r ¼ 0.39
r ¼ 0.23
r ¼ 0.02
r ¼ 0.05
r ¼ 0.19
r ¼ 0.30
r ¼ 0.86
r ¼ 0.51
r ¼ 0.44
r ¼ 0.51
r ¼ 0.62
r ¼ 0.42
R2 ¼ 0.09
r ¼ 0.102
r ¼ 0.08
R2 ¼ 0.41
r ¼ 0.039
r ¼ 0.067
R2 ¼ 0.01
r ¼ 0.058
r ¼ 0.02
R2 ¼ 0.04
r ¼ 0.058
r ¼ 0.19
t ¼ 0.81
0.23
0.18
0.19
0.79
0.43
0.32
0.09
0.06
0.11
0.48
0.34
0.04
0.39
0.23
0.02
0.05
0.19
0.30
0.86
0.51
0.44
0.51
0.62
0.42
0.30
0.10
0.08
0.64
0.04
0.07
0.10
0.06
0.02
0.20
0.06
0.19
0.20
Study
Site
Song variable
Condition index
Sample
size
Bijnens (1988)
Bijnens (1988)
Bijnens (1988)
Doutrelant et al. (2000)
Doutrelant et al. (2000)
Doutrelant et al. (2000)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Poesel et al. (2001)
Foerster et al. (2002)
Foerster et al. (2002)
Foerster et al. (2002)
Foerster et al. (2002)
Foerster et al. (2002)
Foerster et al. (2002)
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
Dreiss et al. (2006)g
TH Parker et al.
(unpublished data)k
TH Parker et al.
(unpublished data)k
TH Parker et al.
(unpublished data)k
TH Parker et al.
(unpublished data)k
TH Parker et al.
(unpublished data)k
TH Parker et al.
(unpublished data)k
Peerdsbos, Belgium
Peerdsbos, Belgium
Peerdsbos, Belgium
Pirio, Corsica
Muro, Corsica
Rouviere, France
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Kolbeterberg, Austria
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Aube, France
Oxford, UK
Within-strophe drift
Within-strophe drift
Within-strophe drift
Repertoire sizec
Repertoire size
Repertoire size
Drift
Drift
Drift
Singing intensitye
Singing intensity
Singing intensity
Strophe length
Strophe length
Strophe length
Strophe turnoverf
Strophe turnover
Strophe turnover
Singing intensitye
Singing intensity
Singing intensity
Singing intensity
Strophe length
Strophe turnoverf
Singing intensityh
Singing intensityh
Singing intensityh
Strophe turnoveri
Strophe turnoveri
Strophe turnoveri
Strophe turnoverj
Strophe turnoverj
Strophe turnoverj
Strophe length
Strophe length
Strophe length
Strophe length
Tarsus length
Mass
Wing length
Tarsus length
Tarsus length
Tarsus length
Tarsus length
Mass
Mass condition indexd
Tarsus length
Mass
Mass condition index
Tarsus length
Mass
Mass condition index
Tarsus length
Mass
Mass condition index
Testosterone level
Mass
Mass
Mass condition index
Testosterone level
Testosterone level
Rearing manipulation
Tarsus length
Mass
Rearing manipulation
Tarsus length
Mass
Rearing manipulation
Tarsus length
Mass
Rearing manipulation
Tarsus length
Mass
Effort manipulationl
19
20
20
12
20
14
20
20
20
20
20
20
20
20
20
20
20
20
7
28
25
28
7
5
14
95
93
14
91
89
14
91
89
14
95
93
15
.0.05
.0.05
.0.05
0.002
0.059
0.26
0.69
0.79
0.63
0.03
0.17
0.89
0.09
0.35
0.94
0.83
0.46
0.22
0.03
0.007
0.032
0.007
.0.1
.0.4
0.28
0.32
0.45
0.024
0.71
0.53
0.81
0.59
0.85
0.51
0.58
0.07
0.43
Oxford, UK
Drift
Effort manipulationl
15
0.91
t ¼ 0.12
l
0.03
Oxford, UK
Singing intensity
Effort manipulation
15
0.61
t ¼ 0.53
0.10
Oxford, UK
Strophe turnover
Effort manipulationl
15
0.99
t ¼ 0.00
0.00
l
Oxford, UK
CV strophe length
Effort manipulation
15
0.38
t ¼ 0.92
0.23
Oxford, UK
CV pause length
Effort manipulationl
5
0.09
t ¼ 1.86
0.44
If no correlation coefficient (r) was presented, we derived one using the statistical calculator function in MetaWin (2.1). Correlation coefficients
reported here are positive if consistent with prediction of higher and more consistent song production (larger repertoire size, longer strophes,
greater proportion of time spent singing, less drift, greater turnover) from individuals in better condition (larger, older, more likely to survive,
higher testosterone).
a
In this study, the asymptotic number of strophe types sung by an individual based on multiple recordings.
b
The progressive lengthening of pauses between trill notes within a strophe.
c
In this study, the number of strophe types sung in a single dawn song.
d
Mass controlled for tarsus length.
e
In this study, reported as ‘‘performance time,’’ strophe length divided by the sum of strophe length and pause length.
f
In this study, reported as ‘‘versatility,’’ the product of number of strophe types sung and the number of switches between strophe types.
g
Details sufficient for inclusion in meta-analysis obtained from authors.
h
Represented by the measurement ‘‘song rate,’’ the number of strophes per minute.
i
Represented by the measurement ‘‘strophe bout length,’’ the average duration between strophe type switches.
j
Represented by the measurement ‘‘strophe diversity,’’ the number of strophe types divided by the number of strophe bouts.
k
TH Parker, IR Barr, and SC Griffith. Details of this experiment and these results available from the corresponding author upon request.
l
Song output of adult males that had reared either enlarged or reduced (by 30%) broods in the previous breeding season.
Parker et al. • Blue tit song
N. Aspey, J. Higham, K. Jones, J. McMahon, H. Tillin, and R. Whitfield
helped record dawn songs and L. Rowe and B. Sheldon contributed to
the joint data set of Wytham blue tit reproduction and survival. Oxford
University provided access to the study site. A. Dreiss kindly furnished
additional details of published results for inclusion in our metaanalysis. Comments from B. Sheldon, W. Forstmeier, and 2 anonymous
referees improved the manuscript. T.H.P. was hosted by B. Sheldon at
the Edward Grey Institute of Field Ornithology and was supported by
a US National Science Foundation International Research Fellowship
(INT-0202704), I.R.B. was supported by a Natural Environment
Research Council studentship, and S.C.G. by a Natural Environment
Research Council Fellowship (NER/I/S/1999/00138). Temperature
data were provided by the British Atmospheric Data Centre.
REFERENCES
Andersson M. 1986. Evolution of condition-dependent sex ornaments
and mating preferences: sexual selection based on viability differences. Evolution 40:804–16.
Andersson M. 1994. Sexual selection. New Jersey: Princeton University
Press.
Andersson S, Amundsen T. 1997. Ultraviolet colour vision and ornamentation in bluethroats. Proc R Soc Lond B Biol Sci 264:1587–91.
Andersson S, Ornborg J, Andersson M. 1998. Ultraviolet sexual
dimorphism and assortative mating in blue tits. Proc R Soc Lond
B Biol Sci 265:445–50.
Badyaev AV, Hill GE, Dunn PO, Glen JC. 2001. Plumage color as
a composite trait: developmental and functional integration of
sexual ornamentation. Am Nat 158:221–35.
Balsby TJS. 2000. Song activity and variability in relation to male quality and female choice in Whitethroats Sylvia communis. J Avian Biol
31:56–62.
Bijnens L. 1988. Blue tit Parus caeruleus song in relation to survival,
reproduction and biometry. Bird Study 35:61–7.
Bijnens L, Dhondt AA. 1984. Vocalizations in a Belgian blue tit, Parus c.
caeruleus, population. Le Gerfaut 74:243–69.
Buchanan KL, Leitner S, Spencer KA, Goldsmith AR, Catchpole CK.
2004. Developmental stress selectively affects the song control
nucleus HVC in the zebra finch. Proc R Soc Lond B Biol Sci
271:2381–6.
Catchpole CK, Slater PJB. 1995. Bird song: biological themes and
variations. Cambridge, UK: Cambridge University Press.
Dale J. 2000. Ornamental plumage does not signal male quality in
red-billed queleas. Proc R Soc Lond B Biol Sci 267:2143–9.
Doutrelant C, Blondel J, Perret P, Lambrechts MM. 2000. Blue tit song
repertoire size, male quality and interspecific competition. J Avian
Biol 31:360–6.
Dreiss A, Richard M, Moyen F, White J, Møller AP, Danchin E. 2006.
Sex ratio and male sexual characters in a population of blue tits,
Parus caeruleus. Behav Ecol 17:13–9.
Eberhardt LS. 1994. Oxygen-consumption during singing by male
Carolina wrens (Thryothorus ludovicianus). Auk 111:124–30.
Foerster K, Poesel A, Kunc H, Kempenaers B. 2002. The natural
plasma testosterone profile of male blue tits during the breeding
season and its relation to song output. J Avian Biol 33:269–75.
Forstmeier W, Hasselquist D, Bensch S, Leisler B. 2006. Does song
reflect age and viability? A comparison between two populations
of the great reed warbler Acrocephalus arundinaceus. Behav Ecol
Sociobiol 59:634–43.
Forstmeier W, Kempenaers B, Meyer A, Leisler B. 2002. A novel song
parameter correlates with extra-pair paternity and reflects male longevity. Proc R Soc Lond B Biol Sci 269:1479–85.
Forstmeier W, Leisler B. 2004. Repertoire size, sexual selection, and
offspring viability in the great reed warbler: changing patterns in
space and time. Behav Ecol 15:555–63.
Galeotti P, Saino N, Sacchi R, Moller AP. 1997. Song correlates with
social context, testosterone and body condition in male barn
swallows. Anim Behav 53:687–700.
Gaunt AS, Bucher TL, Gaunt SLL, Baptista LF. 1996. Is singing costly?
Auk 113:718–21.
Gil D, Cobb JLS, Slater PJB. 2001. Song characteristics are age dependent in the willow warbler, Phylloscopus trochilus. Anim Behav
62:689–94.
Gosler AG. 1996. Environmental and social determinants of winter fat
storage in the great tit Parus major. J Anim Ecol 65:1–17.
1039
Gosler AG, Harper D. 2000. Assessing the heritability of body condition in birds: a challenge exemplified by the great tit Parus major L.
(Aves). Biol J Linn Soc 71:103–17.
Greene E, Lyon BE, Muehter VR, Ratcliffe L, Oliver SJ, Boag PT. 2000.
Disruptive sexual selection for plumage coloration in a passerine
bird. Nature 407:1000–3.
Griffith SC, Ornborg J, Russell AF, Andersson S, Sheldon BC. 2003.
Correlations between ultraviolet coloration, overwinter survival and
offspring sex ratio in the blue tit. J Evol Biol 16:1045–54.
Griffith SC, Pryke SR. 2006. Benefits to female birds of assessing color
displays. In: Hill GE, McGraw KJ, editors. Bird coloration, volume 2:
function and evolution. Cambridge, MA: Harvard University Press.
p 233–79.
Gurevitch J, Hedges LV. 1999. Statistical issues in ecological metaanalyses. Ecology 80:1142–49.
Iwasa Y, Pomiankowski A. 1994. The evolution of mate preferences for
multiple sexual ornaments. Evolution 48:853–67.
Kempenaers B, Verheyen GR, Dhondt AA. 1997. Extrapair paternity in
the blue tit (Parus caeruleus): female choice, male characteristics,
and offspring quality. Behav Ecol 8:481–92.
Kipper S, Mundry R, Sommer C, Hultsch H, Todt D. 2006. Song repertoire size is correlated with body measures and arrival date in
common nightingales, Luscinia megarhynchos. Anim Behav 71:211–7.
Kokko H, Brooks R, McNamara JM, Houston AI. 2002. The sexual
selection continuum. Proc R Soc Lond Biol Sci Ser B 269:1331–40.
Lambrechts M, Dhondt AA. 1986. Male quality, reproduction, and survival in the great tit (Parus major). Behav Ecol Sociobiol 19:57–63.
Lampe HM, Espmark YO. 1994. Song structure reflects male quality in
pied flycatchers, Ficedula hypoleuca. Anim Behav 47:869–76.
Ligon JD. 1999. The evolution of avian breeding systems. Oxford:
Oxford University Press.
Ligon JD, Kimball R, Merola-Zwartjes M. 1998. Mate choice by female
red junglefowl: the issues of multiple ornaments and fluctuating
asymmetry. Anim Behav 55:41–50.
Mace R. 1989. The dawn chorus in the great tit Parus major is directly
related to female fertility. Nature 330:745–6.
Mennill DJ, Ratcliffe LM, Boag PT. 2002. Female eavesdropping on
male song contests in songbirds. Science 296:873.
Møller AP, Jennions MD. 2002. How much variance can be explained
by ecologists and evolutionary biologists? Oecologia 132:492–500.
Møller AP, Pomiankowski A. 1993. Why have birds got multiple sexual
ornaments. Behav Ecol Sociobiol 32:167–76.
Nowicki S, Hasselquist D, Bensch S, Peters S. 2000. Nestling growth
and song repertoire size in great reed warblers: evidence for song
learning as an indicator mechanism in mate choice. Proc R Soc
Lond B Biol Sci 267:2419–24.
Nowicki S, Peters S, Podos J. 1998. Song learning, early nutrition and
sexual selection in songbirds. Am Zool 38:179–90.
Otter K, Chruszcz B, Ratcliffe L. 1997. Honest advertisement and song
output during the dawn chorus of black-capped chickadees. Behav
Ecol 8:167–73.
Palmer AR. 1999. Detecting publication bias in meta-analyses: a case
study of fluctuating asymmetry and sexual selection. Am Nat
154:220–33.
Parker TH, Ligon JD. 2002. Dominant male red junglefowl test the
dominance status of other males. Behav Ecol Sociobiol 53:20–4.
Parker TH, Stansberry BM, Becker CD, Gipson PS. 2003. Do melaninor carotenoid-pigmented plumage ornaments signal condition and
predict pairing success in the Kentucky warbler? Condor 105:
663–71.
Parker TH, Stansberry BM, Becker CD, Gipson PS. 2005. Edge and
area effects on the occurrence of migrant forest songbirds. Conserv
Biol 19:1157–67.
Podos J, Huber SK, Taft B. 2004. Bird song: the interface of evolution
and mechanism. Annu Rev Ecol Evol Syst 35:55–87.
Poesel A, Foerster K, Kempenaers B. 2001. The dawn song of the blue tit
Parus caeruleus and its role in sexual selection. Ethology 107:521–31.
Poesel A, Kempenaers B. 2000. When a bird is tired from singing:
a study of drift during the dawn chorus. Etologı́a 8:1–7.
Pryke SR, Andersson S, Lawes MJ. 2001. Sexual selection of multiple
handicaps in the red-collared widowbird: female choice of tail
length but not carotenoid display. Evolution 55:1452–63.
Rinden H, Lampe HM, Slagsvold T, Espmark YO. 2000. Song quality
does not indicate male parental abilities in the pied flycatcher
Ficedula hypoleuca. Behaviour 137:809–23.
1040
Rosenberg KV, Adams DC, Gurevitch J. 2000. MetaWin 2.0 user’s
manual. Sunderland, MA: Sinauer Associates.
Rosenberg MS, Adams DC, Gurevitch J. 1997. MetaWin 2.1 ed.
Sunderland, MA: Sinauer Associates.
Rosenthal R. 1984. Meta-analytic procedures for social research. Beverly Hills, CA: Sage.
Ryan MJ. 1998. Sexual selection, receiver biases, and the evolution of
sex differences. Science 281:1999–2003.
Schluter D, Price T. 1993. Honesty, perception and population divergence in sexually selected traits. Proc R Soc Lond B Biol Sci
253:117–22.
Senar JC, Figuerola J, Pascual J. 2002. Brighter yellow blue tits make
better parents. Proc R Soc Lond B Biol Sci 269:257–61.
Sheldon BC, Andersson S, Griffith SC, Ornborg J, Sendecka J. 1999.
Ultraviolet colour variation influences blue tit sex ratios. Nature
402:874–7.
Sheldon BC, West SA. 2004. Maternal dominance, maternal condition,
and offspring sex ratio in ungulate mammals. Am Nat 163:40–54.
Behavioral Ecology
Slagsvold T, Dale S, Saetre GP. 1994. Dawn singing in the great tit
(Parus major): mate attraction, mate guarding, or territorial defense.
Behaviour 131:115–38.
Spencer KA, Buchanan KL, Goldsmith AR, Catchpole CK. 2003. Song
as an honest signal of developmental stress in the zebra finch
(Taeniopygia guttata). Horm Behav 44:132–9.
Spencer KA, Buchanan KL, Goldsmith AR, Catchpole CK. 2004.
Developmental stress, social rank and song complexity in the
European starling (Stumus vulgaris). Proc R Soc Lond B Biol Sci
271:S121–3.
Templeton CN, Greene E, Davis K. 2005. Allometry of alarm calls:
black-capped chickadees encode information about predator size.
Science 308:1934–7.
Thomas RJ. 2002. The costs of singing in nightingales. Anim Behav
63:959–66.
Ward S, Lampe HM, Slater PJB. 2004. Singing is not energetically
demanding for pied flycatchers, Ficedula hypoleuca. Behav Ecol
15:477–84.