doi: 10.1111/j.1420-9101.2004.00734.x
Testing for microevolution in body size in three blue tit
populations
A. CHARMANTIER,* L. E. B. KRUUK, J. BLONDEL* & M. M. LAMBRECHTS*
*Centre d’Ecologie Fonctionnelle Evolutive, CNRS, Montpellier Cedex, France
Institute of Cell, Animal and Population Biology, University of Edinburgh, Edinburgh, UK
Keywords:
Abstract
additive genetic variance;
body mass;
habitat quality;
natural selection;
Parus caeruleus;
tarsus length.
Quantifying the genetic variation and selection acting on phenotypes is a
prerequisite for understanding microevolutionary processes. Surprisingly,
long-term comparisons across conspecific populations exposed to different
environments are still lacking, hampering evolutionary studies of population
differentiation in natural conditions. Here, we present analyses of additive
genetic variation and selection using two body-size traits in three blue tit
(Parus caeruleus) populations from distinct habitats. Chick tarsus length and
body mass at fledging showed substantial levels of genetic variation in the
three populations. Estimated heritabilities of body mass increased with habitat
quality. The poorer habitats showed weak positive selection on tarsus length,
and strong positive selection on body mass, but there was no significant
selection on either trait in the good habitat. However, there was no evidence
of any microevolutionary response to selection in any population during the
study periods. Potential explanations for this absence of a response to selection
are discussed, including the effects of spatial heterogeneity associated with
gene flow between habitats.
Introduction
Understanding the evolution of life-history and morphological traits in natural populations requires analysing
the genetic basis of these traits as well as the selection
acting on them. Studies combining a quantitative genetics
approach with selection analyses on long-term monitored
populations have recently grown in number (e.g. Merilä
& Fry, 1998; Kruuk et al., 2002; Sheldon et al., 2003).
However, generalizing results from single populations
may be misleading, and in particular, very little is known
of the variation in the genetic basis of and selection on
quantitative traits across space, between different populations of the same species occupying different habitats
(but see e.g. Ebert et al., 1991; Bennington & McGraw,
1996).
There has been evidence that the variance components
of phenotypic traits can vary with environmental condiCorrespondence: Anne Charmantier, Department of Zoology, Edward Grey
Institute, University of Oxford, Oxford OX1 3PS, UK.
Tel.: +44 1865 281995; fax: +44 1865 271168;
e-mail: [email protected]
732
tions (review in Hoffmann & Merilä, 1999). However,
tests of environmental effects on genetic variation in
laboratory experiments or natural populations gave
equivocal results (review in Hoffmann & Parsons, 1997).
Most studies on Drosophila or other invertebrates suggest
that stressful conditions tend to increase the heritability of
morphological traits such as thorax length (e.g. Ebert
et al., 1991; Imasheva et al., 1998; Bubliy et al., 2003).
However, investigations on size-related traits in birds
(Gebhardt-Henrich & van Noordwijk, 1991, 1994; Merilä,
1996; Larsson et al., 1997; Charmantier et al., 2004), fish
(Garant et al., 2003) or mammals (Réale et al., 1999) have
shown a decrease of heritability in unfavourable conditions. Data collected on birds usually concern variation of
conditions as a response to year-to-year variation
(van Noordwijk et al., 1988; Larsson, 1993; Larsson et al.,
1997) or due to brood size manipulation (Merilä, 1997;
Merilä & Fry, 1998). In these studies, lower heritability in
unfavourable conditions results from either increasing
environmental variance (VE) or from decreasing additive
genetic variance (VA). A decrease in VA in the harsh
environments is explained by genotype · environment
interactions (GEI) (Hoffmann & Merilä, 1999), i.e. when
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
the expression of genetic variance depends on the
environmental conditions. For example, unfavourable
conditions can constrain the expression of growthpromoting loci during food-shortage periods (Hoffmann
& Merilä, 1999), especially when there is a limited time
window in which growth occurs (van Noordwijk, 1988;
van Noordwijk & Marks, 1998). Overall, investigations of
variance components in different natural habitats remain
scarce (but see Garant et al., 2003, 2004).
When comparing different environmental conditions,
we also expect variation in selection pressures. We refer
here to directional selection because stabilizing selection
will not generate an evolutionary response of a change in
the mean; it is also less commonly reported (Kingsolver
et al., 2001). Directional selection on size-related traits
should be more intensive under poor conditions where
constraints on growth and development are higher than
in favourable environments (e.g. Endler, 1986; Nager
et al., 1998).
In this paper, we compare components of phenotypic
variation and selection pressures in three populations of a
passerine bird species occupying environments of different habitat quality. Having quantified the heritability and
directional selection on two body size traits, we can then
compare observed temporal trends with theoretical predictions of the magnitude of microevolutionary response.
However, in doing so, two limitations of selection
analyses need to be considered. First, measurements of
selection can be heavily biased in situations in which the
focal trait and fitness are both positively affected by
the same environmental factors (Rausher, 1992;
Stinchcombe et al., 2002). The appearance of positive
selection on the trait is then generated by the confounding factors of environmental conditions, but such
environmental covariance will not result in any evolutionary response (Kruuk et al., 2003). A recent method
avoids this bias by calculating selection gradients using
estimates of individual breeding values (i.e. the sum of
the additive effects of an individual’s genes on a given
trait) rather than phenotypic values (Rausher, 1992;
Mauricio & Mojonnier, 1997), thus eliminating bias
resulting from environmental covariances.
A second consideration is that evolutionary change in a
phenotypic trait depends not only on its heritability but
also on its genetic correlations with other traits (Falconer
& Mackay, 1996; Lynch & Walsh, 1998). When a
phenotypic character is correlated with other characters
through pleiotropy or linkage disequilibrium, selection on
this character occurs both because of direct selection and
also because of indirect selection on correlated traits
(Lande & Arnold, 1983). Avian morphometric traits such
as tarsus length and body mass are well known to be
correlated (e.g. Boag & van Noordwijk, 1987; Green,
2001), hence much of the variation they display will stem
from the same set of pleiotropic ‘body size genes’ (Boag &
van Noordwijk, 1987). However, multivariate statistics
have rarely been used in studies of wild populations (but
733
see Kruuk et al., 2001, 2002; Potti et al., 2002) because
they require substantially larger sample sizes than univariate analyses (Falconer & Mackay, 1996).
In an attempt to investigate evolutionary responses in
organisms occupying habitats which differ in quality, we
compare here the components of phenotypic variance
and the selection on fledgling morphometric traits in
three populations of blue tits (Parus caeruleus), and test
for corresponding patterns of microevolution. Previous
studies have shown that the three study habitats
constitute a gradient of food abundance and parasite
loads, and thus a gradient in resource availability for the
raising of chicks (Blondel et al., 1993, 1999; HurtrezBoussès et al., 1998; Tremblay et al., 2003). Through
long-term monitoring of these populations, data are
available on both morphological traits and pedigree
information, thus enabling the partitioning of phenotypic
variance.
The aim of this study was two-fold. First, we investigated the extent to which differences in environmental
conditions affect chick body mass and tarsus length, their
quantitative genetics and the selection on these traits. We
predicted (i) increasing heritability with increasing habitat quality due to increasing VA and possibly decreasing
VE (Hoffmann & Merilä, 1999), and (ii) weaker selection
on size-related traits in the higher-quality habitat.
Secondly, we tested whether predictions of evolution
derived from selection analyses and quantitative genetics
match observed changes over time.
Materials and methods
Study sites and data collection
The study was carried out in three populations of blue tits
P. caeruleus (see e.g. Blondel et al., 2001 for a description
of the study sites). Two of the populations were located
on the French island of Corsica; one is an evergreen
habitat dominated by the Holm oak Quercus ilex (Pirio,
hereafter called P) where blue tits have been monitored
using nest boxes since 1976, and the other is a deciduous
forest dominated by the Downy oak Q. humilis (Muro,
hereafter called M) monitored since 1994. These two
study sites are separated by only 25 km, yet gene flow
between the two populations is partly limited by a
mountain barrier culminating at 1700 m (with the
lowest mountain pass at 800 m) and by adaptive differences to local habitats in life-history traits such as laying
date (Lambrechts et al., 1997; Blondel et al., 1999) or
behavioural traits such as social dominance (Braillet
et al., 2002). Corsican blue tits belong to the subspecies
P. c. ogliastrae, which is c. 15% smaller than the mainland
counterpart (Dias & Blondel, 1996). The mainland study
site is a deciduous forest of downy oaks in the south of
France (La Rouvière, hereafter called R) where nest
boxes were erected in 1990. In the three study sites, nest
box density was c. 2.5 nest boxes ha)1.
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
734
A. CHARMANTIER ET AL.
The evergreen holm oak Q. ilex renews only 30% of its
foliage yearly; a critical point is that foliage development
occurs 1 month later in this species than in the deciduous
downy oak Q. humilis which renews at once the totality
of its foliage (Blondel et al., 1993). As a consequence, the
leaf-eating caterpillars representing the main prey for tits
occur 1 month later and in consistently fewer number in
evergreen than in deciduous habitats (Zandt et al., 1990).
The low food supply in evergreen habitats has repeatedly
been shown to be critical for blue tits so that study site P
is considered as a low quality habitat where blue tits are
significantly smaller and in worse condition than those
living in deciduous habitats (Blondel et al., 1993, 1999).
The routine collection of caterpillar frass for more than
10 years (details in Zandt et al., 1990) provided evidence
for (i) a low food abundance in site P, (ii) a c. 10-fold
higher abundance of food at site M, which is a 150-yearold forest, and (iii) an intermediate abundance of
caterpillars in the 70-year-old coppice in the mainland
site R. Moreover, studies of the dipteran blowflies of the
genus Protocalliphora have shown a very high ectoparasite
load in site P, which negatively affects chick growth and
breeding success (Hurtrez-Boussès et al., 1997a,b), and a
much lower ectoparasite abundance in M and R. Thus,
habitat quality in terms of food resources and parasite
load can be ranked as P < R < M.
Blue tit monitoring consisted of routine inspection of
nest boxes to collect breeding and morphometric parameters from all birds. Chicks were ringed during the
nestling period with individually numbered rings provided by the CRBPO, France. At an age of 14 or 15 days,
chick body mass was measured to the nearest 1/10 g with
1 a Pesola spring balance (PESOLA, Baar, Switzerland)
and their tarsus length to the 1/100 mm with a calliper.
Parents were captured in nest boxes when chicks were
8–15 days old and were identified or ringed. Standard
measures on body mass and tarsus length have been
recorded continuously for chicks since 1989, 1991 and
1994 for P, R and M, respectively. Data from broods used
in various cross-fostering or other experiments performed at the three study sites were excluded. The last
breeding season included in the analyses was 2002.
Offspring recruitment to the breeding population, i.e.
survival from chick to adulthood, was estimated by the
capture of breeding adults in nest boxes in succeeding
years. This is a classical ecological approach to estimate
contributions to following generations in long-term
individually based vertebrate studies (Clutton-Brock,
1988; Newton, 1989).
Quantitative genetic analysis and estimation of
heritability
Sample sizes of the quantitative genetic analyses are
shown in Table 1. For each population, variance components of chick tarsus length and body mass were
estimated using a restricted maximum likelihood (REML)
procedure of estimation using an ‘animal model’ (Lynch
& Walsh, 1998; Kruuk, 2004). This model was fitted
using the software package VCE versions 3 and 4
(Variance Component Estimation, available at
2 http://w3.tzv.fal.de/eg/vce4/vce4.html, Neumaier &
Groeneveld, 1998), as described and discussed for
collared flycatchers (Ficedula albicollis) and red deer
(Cervus elaphus) in Merilä et al. (2001b) and Kruuk et al.
(2002). VCE uses the pedigree information to fit an
individual animal model, a form of mixed model which
partitions the phenotypic value of individual traits into a
sum of fixed and random effects, including a random
effect of individual genetic merit, for which the variance–
covariance structure is determined by the additive
genetic relatedness matrix (Lynch & Walsh, 1998).
Table 1 Population means and standard errors (SE), components of phenotypic variances (VA: additive genetic variance; VB: variance
attributable to the common brood environment; VR: residual variance), heritability (h2), coefficient of additive genetic variation (CVA) and
correlations between chick tarsus length (mm) and body mass (g).
Tarsus length
Trait mean (SE)
VA
VB
VR
h2 (SE)
CVA
Number of chicks
Number of broods
Phenotypic correlations between both traits
Genetic correlations between both traits
Body mass
P
R
M
P
R
M
15.90 (0.01)
0.145***
0.076***
0.088***
0.469 (0.055)
2.39
2637
541
0.465***
0.195***
16.61 (0.01)
0.159***
0.083***
0.087***
0.483 (0.053)
2.402
3828
517
0.527***
0.243***
16.32 (0.01)
0.106***
0.028***
0.084***
0.485 (0.079)
1.995
1602
221
0.323***
0.091**
9.54 (0.02)
0.227***
0.401***
0.221***
0.267 (0.050)
4.948
3265
661
10.65 (0.02)
0.508***
0.815***
0.132**
0.349 (0.051)
6.718
3899
528
10.28 (0.02)
0.292***
0.065**
0.101**
0.638 (0.113)
5.264
1597
220
Estimated from animal models with year, measurer and sex as fixed effects and brood identity as a random effect, in three populations of blue
tits (P < R < M for habitat quality).
**P < 0.01; ***P < 0.001, after sequential Bonferroni corrections.
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
Initially developed by animal breeders, the animal model
approach is more flexible and yet more powerful than
the conventional models used in estimating quantitative
genetic parameters (e.g. parent–offspring regression, full
and half-sib analysis) because it can accommodate
unbalanced data sets and because it exploits the relatedness between all pairs of individuals in the pedigree (see
recent review in Kruuk, 2004). We firstly used standard
linear models (fitted in SAS, proc GLM) to determine the
fixed effects that should be included in our final analysis.
Year of measure, identity of measurer and sex had a
significant effect on tarsus length and body mass (all
P < 0.0001) and were thus fitted as fixed effects in the
variance components analysis. For 19% of chicks, sex
was known from molecular data. We combined this
information with the visual sexing in the field by the
three best measurers, whose mean visual accuracy was
88.0% (estimated by comparison with the genetic
sexing), amounting to 44% of chicks with attributed
sexes. Genetic sexing represented 42.6% of these 44%,
and the combined accuracy was 94.3%. Hence, sex was
included in the quantitative genetic analyses as a threelevel (male/female/unknown) fixed effect.
Our models included an individual’s additive genetic
value and brood identification number as random effects.
The total phenotypic variance of each morphometric trait
(VP) was therefore partitioned into the following components:
VP ¼ VA þ VB þ VR ;
where VA was the additive genetic variance, VB the
environmental variance attributable to the common
brood environment (and any dominance variance
between full siblings, Kruuk et al., 2001) and VR the
residual variance (including environmental effects other
than those specific to the brood, any further nonadditive
genetic effects and error variance, Falconer & Mackay,
1996).
The narrow-sense heritability defined as h2 ¼ VA/VP
describes the resemblance between parents and offspring.
Statistical significance of variance components and of
differences between heritabilities was assessed by t-tests.
To control for the high number of tests, we performed
sequential Bonferroni corrections for each type of analysis (Rice, 1989). We also provide the coefficient of
additive genetic variation defined as CVA ¼ 100(VA)/
trait mean (Houle, 1992), as a measure of additive
genetic variance that is unaffected by other components
of variance.
Finally, we predicted individual breeding values, i.e.
the sum of the additive effects for a given trait for each
individual (Falconer & Mackay, 1996). The breeding
value is a measure of individual genetic merit, defined as
the expected phenotypic value of its offspring. Using the
pedigree information and the estimated variance components, we calculated best linear unbiased predictors
(BLUPs) of the breeding values for each individual with
735
known phenotypic value (Lynch & Walsh, 1998)
running animal models with the software PEST
(Neumaier & Groeneveld, 1998).
Phenotypic and genetic correlations
We estimated pairwise Pearson’s correlation coefficients
between tarsus length and body mass. To control for the
fixed effects of year, measurer and sex, the Pearson’s
coefficients were estimated on the residuals of the traits’
regression over these three factors.
For each population, pairwise genetic correlations
were estimated using multivariate animal models with
year, measurer and sex as fixed effects generating genetic
covariances between the pairs of traits. The REML
procedure of estimation partitions the phenotypic
variance into the same sources of variation as those
used in the univariate model and was run with VCE
(Groeneveld, 1995; Neumaier & Groeneveld, 1998).
Selection analysis
Selection on each trait in each population was estimated
from its association with survival, specifically recruitment
to the breeding population. Observed local recruitment
rates in our populations were 4.9, 7.6 and 4.6% for P, R
and M, respectively, ranging from 1 to 13% depending
on year. Annual local recruitment rates were not significantly correlated among the three populations (pairwise
correlation coefficients: rP/R ¼ )0.19, n ¼ 11 years; rR/
M ¼ )0.41, n ¼ 8 years; rP/M ¼ )0.46, n ¼ 8; all n.s.). In
1997, a very strong rainstorm (see Blondel et al., 1999 for
details) occurred on June 5 and 6 in Corsica resulting in
high mortality of chicks and low population mean body
mass (8.7 g) of surviving fledglings at site P. Only one of
the 1997 P fledglings was recruited in later years, so data
from P in 1997 were excluded from the selection analyses
because they would give excessive statistical weight to
this unique recruited chick.
For the three study populations, we conducted yearspecific analyses using yearly standardized phenotypic
values (0 mean and unit variance) and yearly relative
fitness measures, that is 0 (not recruited) or 1 (recruited)
values divided by the yearly mean local recruitment. For
comparison, we also conducted overall analyses using
phenotypic and fitness values standardized using the
overall mean and variance and overall relative fitness,
thus averaging over temporal variation.
Selection differentials
We measured total directional and stabilizing (or disruptive) phenotypic natural selection on tarsus length and
body mass using univariate analyses with local recruitment as a measure of fitness (Arnold & Wade, 1984a,b).
We used least squares regression of relative recruitment
on the standardized trait values (proc GLM, SAS
Institute, Inc., 1992). The correlation between fledgling
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
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A. CHARMANTIER ET AL.
recruitment and the phenotypic measure of a trait is the
selection differential, with linear models giving an
estimation of the directional selection differential (S)
and quadratic models estimating the stabilizing (if
negative) or disruptive (if positive) selection differential
(c). Statistical significance of the selection differentials
was tested with a generalized linear mixed model (proc
MIXED, SAS Institute, Inc., 1992) with brood identification as a random effect to account for the nonindependence of chicks measured in the same brood (Merilä
et al., 1997; Kruuk et al., 2001).
To investigate the influence of parental origin (native
or immigrant to each study site) on the evolution of chick
morphology in response to selection, we repeated the
above analyses splitting broods in two categories: chicks
whose father or mother was a resident of the study site,
i.e. was born on the same site as its chicks, vs. chicks
for which neither parent was ringed as a chick on the
same site.
Results
Phenotypic variation between populations
Selection gradients
The previous univariate analyses were followed by
comparable multivariate regressions to disentangle the
direct and indirect effects of selection following Lande &
Arnold’s (1983) regression technique for correlated
characters. Direct selection was thus expressed in directional standardized selection gradients (b) estimated
from linear models and stabilizing (or disruptive) standardized selection gradients (c) estimated from quadratic
models.
Selection on breeding values
We repeated selection analyses using estimates of individual genetic merit instead of phenotype, to avoid the
potential problem of biases in selection estimates resulting from environmental covariances (Rausher, 1992;
Mauricio & Mojonnier, 1997; Stinchcombe et al., 2002).
We used the BLUP breeding values estimated in the
quantitative genetic analysis to test for directional or
stabilizing/disruptive selection on genotypes, using yearspecific and overall analyses equivalent to those described above for phenotypic values.
The overall estimations of selection on phenotypic
values as well as breeding values were run again
standardizing the data within cohorts instead of within
populations. The results for selection differentials, selection gradients and response to selection were similar for
the two types of standardization (data not shown).
Response to selection
We tested for changes over time in phenotypic values of
tarsus length and body mass as well as in estimated
breeding values. When a trait was under significant
selection, we computed the predicted response to selection using heritability estimates and selection differentials (R ¼ h2S, where h2 is the heritability of the trait
based on the univariate analyses and S is the nonstandardized directional selection differential, Falconer &
Mackay, 1996). Generation times estimated following
Charlesworth (1994) were 2.64 for P, 2.00 for R and 1.91
for M. We then tested whether the slope of the linear
regressions of annual means over time (proc REG, SAS
Institute, Inc., 1992) differed significantly from the
predicted value using t-tests.
We found significant phenotypic differences in chick
tarsus length and body mass among the three study sites
(means in Table 1, for tarsus length: F2,6510 ¼ 732,
P < 0.0001, for body mass: F2,6508 ¼ 573, P < 0.0001;
all pairwise comparisons also significant). Birds were
smaller in the island populations (P and M) than on the
mainland (R), and smaller in the poor evergreen habitat
P than in the richer M habitat (Table 1).
Quantitative genetic analysis and estimation of
heritability
Heritability of tarsus length and body mass was significantly greater than zero in all three populations (all
P < 0.0001; Table 1). For body mass, there was a trend of
increased heritability with increasing habitat quality for
body mass with heritability ranging from 0.267 ± 0.05 in
P to 0.638 ± 0.113 in M (Table 1, t-tests, P/R: t1187 ¼ 1.1,
n.s.; R/M: t746 ¼ 2.3, P < 0.05; P/M: t879 ¼ 3.0, P < 0.01).
The increase in heritability was attributable to an increase
in VA for R (P/R: t1187 ¼ 3.3, P < 0.001) and a decrease of
VB for M (R/M: t746 ¼ 17.65, P < 0.0001). However,
when standardizing the VA with the trait mean, CVA did
not show the same trend of increase with increasing
habitat quality (Table 1). On the contrary, for tarsus
length the levels of variance components VA, VB and VR
were of similar magnitude in the three populations, and
heritability of the trait did not differ with habitat quality
(Table 1, pairwise t-tests, all P n.s.).
Phenotypic and genetic correlations
There were positive phenotypic and genetic correlations
between tarsus length and body mass in all three
populations (0.32–0.53; Table 1). Phenotypic correlations
were consistently higher than genetic correlations, indicating that environmental correlations were also positive
for these traits.
Patterns of selection
Selection on phenotypes
Univariate analyses over the whole study period showed
significant positive directional selection on standardized
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
tarsus length and body mass in the evergreen Corsican
site P and the mainland site R (Table 2). In the year-byyear analyses in P, 10 of 12 annual selection differentials
on tarsus length were positive but only two significantly
differed from zero (1990, 1995), and 11 of 13 were
positive for body mass and only one significant (1989,
presumably reflecting reductions in statistical power in
the annual estimates). In R, all 11 selection differentials
were positive for both traits, but only one was significant
(1997) for tarsus length and five (1994, 1996, 1997,
2000, 2001) for body mass. There was no evidence for
stabilizing selection on the morphometric traits in any
population. There was also no significant directional
selection in the rich habitat (M), either for the overall
analysis or for individual years.
When including both traits simultaneously in the
analyses, the selection gradients did not confirm the
positive selection on tarsus length either in P or in R, or
the selection on body mass in P (all tests n.s.). The
positive selection on body mass in R was confirmed in
the multivariate analysis (P < 0.0001; Table 2). Ten of
11 year-specific selection gradients were positive of
which four were significantly greater than zero (1994,
1996, 1997, 2000). The multivariate analyses on the
deciduous Corsican site M showed no selection at all on
either trait.
Annual selection gradients were not significantly
correlated among the three populations, except for a
surprising negative correlation of body mass selection
between R and M (pairwise correlation coefficient:
rR/M ¼ )0.90; n ¼ 8; P < 0.01).
Within populations, directional selection gradients on
tarsus length and on body mass were not significantly
related to the recruitment rate (all regressions n.s.;
n ¼ 12, 11 and 8 years for P, R and M, respectively).
Table 2 Selection on tarsus length and body mass of blue tit chicks
for recruitment: overall standardized selection differentials (linear
models for directional selection: S and quadratic models for stabilizing/disruptive selection: c) and selection gradients (linear: b and
quadratic: c) from univariate and multiple regression models,
respectively.
Selection on breeding values
The univariate and multivariate analyses on breeding
values of tarsus length and body mass showed a positive
directional selection on breeding values of tarsus length
in P, which was not significant after the sequential
Bonferroni correction (overall selection differential:
P ¼ 0.035; overall selection gradient: P ¼ 0.026;
Pcritic ¼ 0.008, Table 3). The directional selection on
body mass in R was confirmed on breeding values in
both univariate and bivariate analyses. In M, there was
weak stabilizing selection on tarsus length which was not
significant after the Bonferroni correction (overall selection differential: P ¼ 0.040; overall selection gradient:
P ¼ 0.047; Pcritic ¼ 0.01, Table 3).
Annual selection gradients on breeding values were
not significantly correlated among the three populations
(all pairwise correlation coefficients n.s.).
Phenotypic and genetic response to selection
The linear regression of annual mean phenotypic values
over time showed no evidence of a statistically significant
change over time in any population (Fig. 1a,b; all tests
n.s.). Using the equation R ¼ h2S, the expected response
to selection on body mass in R is 0.176 g per generation
or 0.088 g year)1. Despite the positive selection on body
mass in R, chick body mass did not increase significantly
over the course of the study period (b ¼ 0.023 ±
0.035 g year)1, t11 ¼ 0.67, n.s.) and the observed rate
of change was significantly less than the predicted rate of
change (t11 ¼ 1.85, P < 0.05). Similarly, the observed
rate of change of chick tarsus length in R
(b ¼ )0.021 ± 0.011 mm year)1) was significantly less
than the predicted rates of change (R ¼ 0.031 mm
year)1; t11 ¼ 4.69, P < 0.001). Finally in P, the observed
rates of change of chick tarsus length and body mass
(b ¼ 0.008 ± 0.011 mm year)1 and )0.045 g year)1) differed significantly from the predicted rates of change
(R ¼ 0.028 mm year)1 and 0.030 g year)1; t12 ¼ 1.76
and 2.77, P < 0.05 and <0.01).
Table 3 Selection on breeding values of chick tarsus length and
body mass for recruitment as first-year breeders (see Table 2 for
legend).
n
n
S (SE)
c (SE)
b (SE)
Tarsus length
P 1598 0.274 (0.111)**
0.037 (0.069) 0.186 (0.124)
R 3046 0.212 (0.067)** )0.046 (0.040) )0.001 (0.077)
M 1277 )0.000 (0.134)
0.000 (0.092) )0.022 (0.147)
Body mass
P 2020
R 3118
M 1272
0.314 (0.099)*** )0.052 (0.063)
0.421 (0.065)*** 0.002 (0.044)
0.044 (0.133)
)0.019 (0.026)
n is the number of chicks measured.
**P < 0.01; ***P < 0.001.
c (SE)
0.035 (0.069)
)0.031 (0.042)
0.008 (0.093)
0.189 (0.132)
)0.032 (0.035)
0.419 (0.077)*** 0.013 (0.048)
0.049 (0.146)
)0.021 (0.026)
737
S (SE)
c (SE)
b (SE)
c (SE)
Tarsus length
P 1594 0.950 (0.433)* )0.152 (1.047) 1.179 (0.484)* )0.146 (1.042)
R 3043 0.379 (0.246) )0.958 (0.554) 0.588 (0.277) )0.767 (0.579)
M 1269 )0.326 (0.690) )4.807 (2.406)* )0.584 (0.741) )4.656 (2.437)*
Body mass
P 2020 0.159 (0.381) )0.197 (0.357) )0.532 (0.478) )0.078 (0.371)
R 3118 0.401 (0.134)** )0.212 (0.158) 0.385 (0.155)** )0.140 (0.168)
M 1272 0.252 (0.347) )0.181 (0.165) 0.343 (0.370) )0.196 (0.168)
*P < 0.05; **P < 0.01.
After sequential Bonferroni corrections, all P < 0.05 were not
significant.
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
738
A. CHARMANTIER ET AL.
(b)
17
11.5
16.8
11
Mean body mass (g)
Mean tarsus length (mm)
(a)
16.6
16.4
16.2
16
10.5
10
9.5
9
15.8
15.6
1988
1992
1996
8.5
1988
2000
1996
1992
1996
2000
(d)
Mean breeding value of body mass (g)
Mean breeding value of tarsus length
(mm)
(c)
1992
0.15
0.1
0.05
0
–0.05
1988
1992
1996
2000
0.15
0.1
0.05
0
–0.05
1988
2000
Year
Fig. 1 Variation over time and between populations P, M and R for (a) mean fledging tarsus length, (b) mean fledging body mass, (c)
mean estimated breeding values of tarsus length and (d) mean estimated breeding values of body mass. P: squares, M: diamonds and R:
triangles.
At the genotypic level linear regressions of annual
means did not show any changes in breeding values over
time (Fig. 1c,d; all P > 0.1) and the observed rate of
change of body mass breeding values in R
(b ¼ 0.009 ± 0.006 g year)1) was significantly less than
the predicted rate of change (R ¼ 0.070 g year)1;
t11 ¼ 10.53, P < 0.0001).
Finally, we compared patterns of change in chicks of
immigrant vs. resident birds. In the mainland site R,
58.2% of breeding males and 34.7% of breeding females
were ringed as chicks in the same study site. The
observed rate of change of body mass for offspring of
resident males (b ¼ 0.038 ± 0.049 g year)1) did not differ significantly from the predicted rate of change of
0.088 g year)1 (t11 ¼ 1.03, n.s.). However, the rate of
change was significantly less for chicks of nonresident
males
(b ¼ )0.014 ± 0.049 g year)1,
t11 ¼ 2.07,
P < 0.05). The results showed a similar pattern when
considering chicks of resident females (Fig. 2;
b ¼ 0.141 ± 0.068 g year)1; test of difference from predicted change: t11 ¼ 0.776, n.s.) and chicks of nonresident females (b ¼ 0.012 ± 0.040 g year)1, t11 ¼ 1.89,
P < 0.05). Nevertheless, changes in body mass for chicks
of resident or nonresident parents were still never
significantly different from zero (all t-tests, n.s.). Body
mass in the mainland site R was the only trait with
significant selection gradients; however, the other traits
under weak selection (body mass in P and tarsus length
in P and R) showed the same trend for a higher response
to selection for chicks of residents than for chicks of other
parents.
Discussion
Variance components and habitat quality
This study confirms earlier conclusions from other
vertebrate species (e.g. Larsson et al., 1998; Réale et al.,
1999; Kruuk et al., 2001, 2002) of substantial levels of
additive genetic variation underlying morphometric
traits, in spite of directional selection acting on these
traits. Bird tarsus length and body mass showed significant heritabilities in all three populations (Table 1), of
similar order of magnitude as those found before in this
or closely related species (Dhondt, 1982; van Noordwijk
et al., 1988; Merilä & Fry, 1998; Merilä & Sheldon, 2001).
We also showed that the variance components of
morphometric traits can vary between populations of the
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
not follow the predicted pattern of M > R > P (Table 1).
This emphasizes the necessity to consider genetic variance through different measures (Houle, 1992).
11.5
11
Body mass (g)
739
10.5
Response to selection
10
9.5
9
8.5
1990
1992
1994
1996
Year
1998
2000
2002
Fig. 2 Evolution over time of phenotypic body mass in R for chicks
of nonresident females (white triangles) and resident females (black
triangles). In both cases, regressions did not show any significant
change over time (see text for equations and tests).
same species occupying different habitats. The results for
body mass agree with the trend found in earlier studies of
vertebrate morphological traits that heritability decreases
as environmental conditions become more difficult (see
review of 19 studies of birds in Merilä & Sheldon, 2001).
In this case, the decrease in heritability in the harsh
quality environment was due to a decrease in the
additive genetic variance along with an increase in the
common brood-environment effect. The increase in
variance between broods can be interpreted as relatively
higher impact of parents and territory quality in
unfavourable habitats. When food is scarce, parental
care and availability of local resources will be an
important determinant of brood fitness, increasing the
between-brood variance in morphometric traits when
compared with the within-brood variance. We have
previously shown that in the evergreen Corsican site P,
infestation by the parasite Protocalliphora (HurtrezBoussès et al., 1997b) increases the between-brood
variance in chick morphology (Charmantier et al., 2004).
However, these results should be treated with caution
for two reasons. First, increasing heritability with habitat
quality is not fully supported by our study because the
heritability of tarsus length (in contrast to body mass) did
not differ between the populations, and because no
replication is available for habitat quality. Secondly,
higher heritability does not necessarily mean higher
evolvability. Houle (1992) has shown that in some cases,
CVA gives a more direct estimate of the evolvability of the
trait, or its ability to respond to selection, as it is
standardized by the trait mean and thus does not depend
on the magnitude of the total trait variance (Houle, 1992;
Roff, 1997). In our case, when comparing body mass
evolution in the three blue tit populations, higher
heritability in M than in P is due to high VA but also to
low variation between broods. Standardizing by the trait
mean instead of VP shows that relative evolvabilities do
The results in this study failed to reveal any evolutionary
response to selection. Using theoretical expectations of
evolutionary responses to selection, our analyses of chick
body mass in R predict an increase of 0.09 g each year.
Yet there was no evidence for a significant change in
chick body mass in the course of the study period. In the
same line of reasoning, the slight selection on body mass
in P and tarsus length in P and R did not result in any
increase in those traits over the study period (Fig. 1).
Such discrepancies have been frequently reported in
natural populations (e.g. Larsson et al., 1998; Kruuk
et al., 2001, 2002; Merilä et al., 2001a; Sheldon et al.,
2003). Merilä et al. (2001c) have reviewed the possible
explanations for this paradox and we will now discuss
seven potential explanations for the lack of correspondence between predicted and observed responses to
selection in our study: (i) overestimation of heritability
due to maternal/common environment effects; (ii) biased
estimation of heritability due to genotype · environment
interactions (GEI); (iii) biased estimation of heritability
due to misassigned paternities; (iv) overestimates of the
magnitude of selection due to environmental covariance;
(v) selection constrained by genetic correlations; (vi)
selection fluctuating in time or space; (vii) response to
selection affected by indirect genetic effects.
The first risk of bias in heritability estimates comes
from maternal and common environment effects of
offspring belonging to the same brood (Merilä et al.,
2001c), which will upwardly bias estimates of additive
genetic variance if not taken account of. One advantage
of our analyses was that we could include brood identity
as an additional random effect in the animal model and
thereby overcome the problem of inflation of heritability
due to common nest environment effect that occurs in
classical full sib analyses (Boag & van Noordwijk, 1987).
The common brood environment effect explained as
much as 47.3% of the variation in chick body mass in
P and 56.0% in R, and would have resulted in considerable overestimation of the heritability of body mass if
not included in the model (see example in Kruuk, 2004).
Genotype · environment interactions can also lead to
a biased estimation of heritability. GEI can occur when
parents and offspring are reared in different environments. In their review on avian quantitative genetics,
Merilä & Sheldon (2001, pp. 227–228) discuss a potential
bias arising when two environments are unbalanced in
size or frequency across the landscape, leading to higher
environmental correlation between parents and offspring
in central than in marginal habitats. In Corsica, the most
common habitat type is the evergreen one. If there was
higher environmental correlation between parents and
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
740
A. CHARMANTIER ET AL.
offspring in these habitats because most of the breeding
parents were recruits from evergreen habitats, then
heritability of traits would be artificially increased.
However, as heritabilities were lower in the evergreen
habitat in Corsica, our results are robust concerning this
potential bias.
A third potential source of error in heritability estimation comes from the occurrence of extra-pair paternity.
Levels of extra-pair paternity have been found to be
relatively high in these study sites, i.e. between 14 and
25% of chicks studied depending on the population
(Charmantier & Blondel, 2003). The incorrect paternal
links in the pedigree can result in an underestimation of
the additive genetic variance of the studied traits. We did
not have enough known genotypes in our pedigrees to
test how much the quantitative parameters would
change after removal of the extra-pair young. However,
analyses on morphometric traits in collared flycatchers
have shown that the bias due to similar levels of extrapair paternities may be negligible (Merilä et al., 1998);
note that these levels of error are also similar to those in
mammalian studies in which paternities were assigned
using genetic data (Kruuk et al., 2002). In any case,
misassigned paternity would result in a downward in the
predicted response to selection, and hence could not
explain the discrepancies in this study between predicted
and observed changes. Thus, none of the three situations
relating to heritability estimation provide an adequate
explanation for the lack of correspondence between
observed and predicted trends.
A fourth possible source of error is the incorrect
estimation of selection. One explanation discussed by
Merilä et al. (2001c), based on the reasoning of Price et al.
(1988), implies a stronger association of fitness with
phenotypic values than with breeding values, due to an
environmental covariance between the trait and the
fitness measure (see also Kruuk et al., 2002; Stinchcombe
et al., 2002). Selection gradients on the focal trait are
then overestimated because of confounding environmental factors influencing both the trait and the fitness
measure. One of the great advantages of the animal
model approach is that we can estimate individual
breeding values and therefore test the selection acting
on them (Rausher, 1992; Mauricio & Mojonnier, 1997;
Kruuk, 2004). Surprisingly, in our study, selection
gradients estimated on breeding values were either of
similar magnitude or even higher than those estimated
on phenotypes (Tables 2 and 3). Therefore the absence of
response to selection cannot be due to selection being
associated predominantly with the nonheritable component of the phenotype. Another potential source of error
comes from the use of local recruitment, which is used in
many bird studies to reflect the total recruitment of
breeding offspring. However, a review of over 22 longterm studies has shown that on average two-thirds of
the offspring emigrate to breed outside study plots
(Lambrechts et al., 1999). Investigations as to whether
this dispersal is random with regards to the morphometric traits in this study system would be necessary to fully
validate our estimations of selection differentials, but
studies of collared flycatchers found no relationship
between dispersal and size (Pärt, 1990).
A response to selection may also be constrained by
genetic correlations with other unmeasured traits under
opposing selection. Although we have used bivariate
statistics including tarsus length and body mass to control
for the correlation between these traits, there is still a
possibility that the absence of evolutionary response to
selection on body size is due to genetic correlations with
other targets of selection (Lande & Arnold, 1983). Ideally,
a multivariate analysis should include all other traits with
which body mass is genetically correlated, yet such
analysis is necessarily constrained to traits that have been
measured (Lande & Arnold, 1983; Merilä et al., 2001c).
Another genetic correlation which might constrain an
evolutionary response is that which occurs between sexes
(Roff, 1997), if it is associated with opposing selection
pressures in the two sexes. However, we ran sex-specific
analyses and did not find any evidence for sexually
antagonistic selection (data not shown); hence, this does
not explain the absence of evolutionary response.
As a sixth possible explanation for an absence of
response, theoretical models have shown that temporal
and spatial heterogeneity can maintain genetic variation
(Ewing, 1979). Selection can fluctuate in direction
and/or intensity between years, for example, when
contrasting climatic conditions induce changes in food
supply, as has been shown for a population of medium
ground finch Geospiza fortis on the island of Daphne
Major in the Galápagos (Gibbs & Grant, 1987, 1995). In
our study, the analyses showed some evidence for
spatial heterogeneity in selection in the three populations of blue tit occupying different habitats. Two of
these populations (P and M) are only separated by
25 km, yet the strength of annual selection on tarsus
length and body mass as well as on their estimated
breeding values did not show any correlation. For
example, directional selection on tarsus length was null
or negative (not significantly, see Tables 2 and 3) in the
rich habitat (M) but positive in the harsh environment
(P). Although there are probably some geographical and
ethological barriers to migration between populations P
and M (Lambrechts et al., 1997; Braillet et al., 2002),
they are presumably connected by gene flow (Dias et al.,
1996; Blondel et al., 1999, 2001): in M, only 22.8%
males and 15.6% females were ringed as chicks in the
same study site in which they subsequently bred, and in
P natal philopatry has similar levels: 37.0% of males and
16.1% of females. It is hence possible that because of
gene flow between the two populations, immigrants
from M which have not been under positive directional
selection are recruited in P, constraining the evolution
of body size traits in response to the positive selection
occurring in P. A mosaic of habitat patches dominated
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
by either deciduous or evergreen oaks also characterizes
the mainland landscape occupied by blue tits. However,
a preliminary analysis comparing chicks from resident
parents to chicks from immigrants shows that neither
the magnitude nor the temporal trend in immigrant
phenotypes are consistent with the hypothesis that gene
flow alone is responsible for the observed stasis (Fig. 2).
Finally, we found that all significant directional selection
gradients were positive. Interestingly, a previous selection analysis on reproductive success of blue tit adults
has shown that small adult males have a higher
breeding success than large males in P whereas the
opposite is true in R (Blondel et al., 2002). This results in
stronger sexual dimorphism in adults in R than P. It also
suggests that in P, natural selection acts in different
directions on males depending on life stages, with higher
recruitment for larger male chicks but higher reproductive success for smaller adult males. Such a trade-off
between survival and reproduction has already been
reported in the study of female body size in the song
sparrow Melospiza melodia (Schluter & Smith, 1986). This
type of variation of selection depending on life stages
can be an important source of maintenance of additive
genetic variance as well as a potential explanation for
the absence of response to selection. Hence, there is
some evidence for fluctuating selection pressures
through different life-history components.
A final potential source of bias in the estimation of
response to selection is the presence of indirect genetic
effects. Our analyses show large common environment
effects, which may be due in part to maternal effects.
These maternal effects may in part be due to the mother’s
genotype, i.e. indirect genetic effects. Such effects can
alter the response to selection, and may constrain or even
reverse an evolutionary response, depending on the sign
of the covariance between the direct and the indirect
genetic effects and the respective selection pressures
(Kirkpatrick & Lande, 1989; Lynch & Walsh, 1998).
Further complex quantitative genetics models, which
we will explore in future work, will be necessary
to quantify the relevant components of variance and
covariance.
In conclusion, our analyses illustrate high levels of
additive genetic variation underlying morphometric
traits, as well as substantial common brood environment
effects. Directional selection on heritable morphometric
traits in blue tits differed between environments occupied by different populations. The spatial heterogeneity
associated with gene flow between habitats as well as
potential indirect genetic effects are two of the possible
explanations for the absence of response to selection, and
call for further investigations. Finally, our comparison of
two traits in three different populations produced different impressions of the factors determining evolutionary
processes in each, suggesting the need for a degree of
caution when generalizing from results on a single
population or trait.
741
Acknowledgments
We warmly thank all the people who have participated in
data collection over the years. Thanks also to Erik Postma
for valuable discussions and to Philippe Jarne, Edmund
D. Brodie III, Arie van Noordwijk and two anonymous
referees whose comments greatly improved the manuscript. A. C. was supported by a grant from the ‘Ministère
Français de l’Education Nationale, de l’Enseignement et
de la Recherche’ and a Marie Curie Host Fellowship
(QLK5-1999-50768). LK is supported by the Royal
Society, London.
References
Arnold, S.J. & Wade, M.J. 1984a. On the measurement of
natural and sexual selection: applications. Evolution 38: 720–
734.
Arnold, S.J. & Wade, M.J. 1984b. On the measurement of
natural and sexual selection: theory. Evolution 38: 709–719.
Bennington, C.C. & McGraw, J.B. 1996. Environment-dependence of quantitative genetic parameters in Impatiens pallida.
Evolution 50: 1083–1097.
Blondel, J., Dias, P.C., Maistre, M. & Perret, P. 1993. Habitat
heterogeneity and life history variation of Mediterranean blue
tits. Auk 110: 511–520.
Blondel, J., Dias, P.C., Perret, P., Maistre, M. & Lambrechts,
M.M. 1999. Selection-based biodiversity at a small spatial
scale in a low-dispersing insular bird. Science 285: 1399–
1402.
Blondel, J., Perret, P., Dias, P.C. & Lambrechts, M.M. 2001. Is
phenotypic variation of blue tits (Parus caeruleus L.) in
mediterranean mainland and insular landscapes adaptive?
Genet. Sel. Evol. 33: S121-S139.
Blondel, J., Perret, P., Anstett, M.-C. & Thébaud, C. 2002.
Evolution of sexual size dimorphism in birds: test of
hypotheses using blue tits in constrated Mediterranean
habitats. J. Evol. Biol. 15: 440–450.
Boag, P.T. & van Noordwijk, A.J. 1987. Quantitative genetics. In:
Avian Genetics (F. Cooke & P. A. Buckley, eds), pp. 45–78.
Academic Press, New York.
Braillet, C., Charmantier, A., Archaux, F., Dos Santos, A., Perret,
P. & Lambrechts, M.M. 2002. Two blue tit Parus caeruleus
populations from Corsica differ in social dominance. J. Avian
Biol. 33: 446–450.
Bubliy, O., Loeschcke, V. & Imasheva, A. 2003. Genetic variation
of morphological traits in Drosophila melanogaster under poor
nutrition: isofemale lines and offspring-parent regression.
Heredity 86: 363–369.
Charlesworth, B. 1994. Evolution in Age-structured Populations,
2nd edn. Cambridge University Press, Cambridge.
Charmantier, A. & Blondel, J. 2003. A contrast in extra-pair
paternity levels on mainland and island populations of
Mediterranean blue tits. Ethology 109: 351–364.
Charmantier, A., Kruuk, L.E.B. & Lambrechts, M.M. 2004.
Parasitism reduces the potential for evolution in a wild bird
population. Evolution 58: 203–206.
Clutton-Brock, T. 1988. Reproductive Success. University of Cichago press, Chicago.
Dhondt, A.A. 1982. Heritability of blue tit tarsus length from
normal and cross-fostered broods. Evolution 36: 418–419.
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
742
A. CHARMANTIER ET AL.
Dias, P.C. & Blondel, J. 1996. Local specialization and
maladaptation in the Mediterranean blue tit (Parus caeruleus).
Oecologia 107: 79–86.
Dias, P.C., Verheyen, G.R. & Raymond, M. 1996. Source-sink
populations in Mediterranean blue tits: evidence using singlelocus minisatellite probes. J. Evol. Biol. 9: 965–978.
Ebert, D., Yampolsky, L. & van Noordwijk, A.J. 1991. Genetics of
life history in Daphnia magna. II. Phenotypic plasticity. Heredity
70: 344–352.
Endler, J.A. 1986. Natural Selection in the Wild. Princeton
University Press, Princeton, NJ.
Ewing, E.P. 1979. Genetic-variation in a heterogeneous environment. 7. Temporal and spatial heterogeneity in infinite
populations. Am. Nat. 114: 197–212.
Falconer, D.S. & Mackay, T.F.C. 1996. Introduction to Quantitative
Genetics. Longman, Essex, UK.
Garant, D., Dodson, J.J. & Bernatchez, L. 2003. Differential
reproductive success and heritability of alternative reproductive tactics in wild Atlantic salmon (Salmo salar L.). Evolution
57: 1133–1141.
Garant, D., Sheldon, B.C. & Gustafsson, L. 2004. Climatic and
temporal effects on the expression of secondary sexual
characters: genetic and environmental components. Evolution,
3 in press.
Gebhardt-Henrich, S.G. & van Noordwijk, A.J. 1991. Nestling
growth in the great tit I. Heritability estimates under different
environmental conditions. J. Evol. Biol. 3: 341–362.
Gebhardt-Henrich, S.G. & van Noordwijk, A.J. 1994. The
genetical ecology of nestling growth in the great tit. Environmental influences on the expression of genetic variances
during growth. Funct. Ecol. 8: 469–476.
Gibbs, H.L. & Grant, P.R. 1987. Oscillating selection on Darwin’s
finches. Nature 327: 511–513.
Grant, P.R. & Grant, R.B. 1995. Predicting microevolutionary
responses to directional selection on heritable variation.
Evolution 49: 241–251.
Green, A.J. 2001. Mass/length residuals: measures of body
condition or generators of spurious results? Ecology 82: 1473–
1483.
Groeneveld, E. 1995. REML VCE, a Multivariate Multi-Model
Restricted Maximum Likelihood (Co) Variance Component Estimation. Package, Version 4.2.5 User’s Guide. Institute of Animal
Husbrandry and Animal Behaviour, Federal Research Center
of Agriculture (FAL), Mariensee, Germany.
Hoffmann, A.A. & Merilä, J. 1999. Heritable variation and
evolution under favourable and unfavourable conditions.
Trends Ecol. Evol. 14: 96–101.
Hoffmann, A.A. & Parsons, P.A. 1997. Consistent heritability
changes under poor growth condition. Trends Ecol. Evol. 12:
460–461.
Houle, D. 1992. Comparing evolvability and variability of
quantitative traits. Genetics 130: 195–204.
Hurtrez-Boussès, S., Blondel, J., Perret, P. & Renaud, F. 1997a.
Relationship between intensity of blowfly infestation and
reproductive success in a Corsican population of blue tits.
J. Avian Biol. 28: 267–270.
Hurtrez-Boussès, S., Perret, P., Renaud, F. & Blondel, J. 1997b.
High blowfly parasitic loads affect breeding success in
a Mediterranean population of blue tits. Oecologia 112:
514–517.
Hurtrez-Boussès, S., Blondel, J., Perret, P., Fabreguettes, J. &
Renaud, F. 1998. Chick parasitism by blowflies affect feeding
rates in a Mediterranean population of blue tits. Ecol. Lett. 1:
17–20.
Imasheva, A.G., Loeschcke, V., Zhivotovsky, L.A. & Lazebny,
O.E. 1998. Stress temperatures and quantitative variation in
Drosophila melanogaster. Heredity 81: 246–253.
Kingsolver, J.G., Hoekstra, H.E., Hoekstra, J.M., Berrigan, D.,
Vignieri, S.N., Hill, C.E., Hoang, A., Gibert, P. & Beerli, P.
2001. The strength of phenotypic selection in natural popu4 lations. Am. Nat. 157: 245–261.
Kirkpatrick, M. & Lande, R. 1989. The evolution of maternal
characters. Evolution 43: 485–503.
Kruuk, L.E.B. 2004. Estimating genetic parameters in wild
populations using the ‘animal model’. Phil. Trans. R. Soc Lond.
5 B, in press.
Kruuk, L.E.B., Merilä, J. & Sheldon, B.C. 2001. Phenotypic
selection on a heritable size trait revisited. Am. Nat. 158: 557–
571.
Kruuk, L.E.B., Slate, J., Pemberton, J.M., Brotherstone, S.,
Guinness, F. & Clutton-Brock, T. 2002. Antler size in red deer:
heritability and selection but no evolution. Evolution 56: 1683–
1695.
Kruuk, L.E.B., Merila, J. & Sheldon, B.C. 2003. When environmental variation short-circuits natural selection. Trends Ecol.
Evol. 18: 207–209.
Lambrechts, M.M., Blondel, J., Hurtrez-Bousses, S., Maistre, M.
& Perret, P. 1997. Adaptive inter-population differences in
blue tit life-history traits on Corsica. Evol. Ecol. 11: 599–612.
Lambrechts, M.M., Blondel, J., Caizergues, A., Dias, P.C., Pradel,
R. & Thomas, D.W. 1999. Will estimates of lifetime recruitment of breeding offspring on small-scale study plots help us
to quantify processes underlying adaptation? Oikos 86: 147–
151.
Lande, R. & Arnold, S.J. 1983. The measurement of selection on
correlated characters. Evolution 37: 1210–1226.
Larsson, K. 1993. Inheritance of body size in the barnacle goose
under different environmental conditions. J. Evol. Biol. 6: 195–
208.
Larsson, K., Rattiste, K. & Lilleleht, V. 1997. Heritability of head
size in the common gull Larus canus in relation to environmental conditions during offspring growth. Heredity 79: 201–
207.
Larsson, K., van der Jeugd, H.P., van der Veen, I.T. & Forslund,
P. 1998. Body size declines despite positive directional
selection on heritable size traits in a barnacle goose population. Evolution 52: 1169–1184.
Lynch, M. & Walsh, B. 1998. Genetics and Analysis of Quantitative
Traits. Sinauer Associates Inc., Sunderland, Mass.
Mauricio, R. & Mojonnier, L.E. 1997. Reducing bias in the
measurement of selection. Trends Ecol. Evol. 12: 433–436.
Merilä, J. 1996. Genetic variation in offspring condition: an
experiment. Funct. Ecol. 10: 465–474.
Merilä, J. 1997. Expression of genetic variation in body size of
the collared flycatcher under different environmental conditions. Evolution 51: 526–536.
Merilä, J. & Fry, J.D. 1998. Genetic variation and causes of
genotype-environment interaction in the body size of blue tit
(Parus caeruleus). Genetics 148: 1233–1244.
Merilä, J. & Sheldon, B.C. 2001. Avian quantitative genetics.
Curr. Ornithol. 16: 179–255.
Merilä, J., Sheldon, B.C. & Ellegren, H. 1997. Antagonistic
natural selection revealed by molecular sex identification of
nestling collared flycatchers. Mol. Ecol. 6: 1167–1175.
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD
Body size microevolution
Merilä, J., Sheldon, B.C. & Ellegren, H. 1998. Quantitative
genetics of sexual size dimorphism in the collared flycatcher,
Ficedula albicollis. Evolution 52: 870–876.
Merilä, J., Kruuk, L.E.B. & Sheldon, B.C. 2001a. Cryptic
evolution in a wild bird population. Nature 412: 76–79.
Merilä, J., Kruuk, L.E.B. & Sheldon, B.C. 2001b. Natural
selection on the genetical component of variance in body
condition in a wild bird population. J. Evol. Biol. 14: 918–929.
Merilä, J., Sheldon, B.C. & Kruuk, L.E.B. 2001c. Explaining
stasis: microevolutionary studies in natural populations.
Genetica 112/113: 199–222.
Nager, R.G., Keller, L.F. & Van Noordwijk, A.J. 1998. Understanding natural selection on traits that are influenced by
environmental conditions. In: Adaptive Genetic Variation in the
Wild (T. A. Mousseau, B. Sinervo & J. A. Endler, eds), pp. 95–
115. Oxford University Press, New York.
Neumaier, A. & Groeneveld, E. 1998. Restricted maximum
likelihood estimation of covariances in sparse linear models.
Genet. Sel. Evol. 30: 3–26.
Newton, I. 1989. Lifetime Reproduction in Birds. Academic Press,
London.
van Noordwijk, A.J. 1988. Two stage selection for body size in
the great tit. Proc. Int. Orn. Congr. Ottawa 1986: 1408–1415.
van Noordwijk, A.J. & Marks, H.L. 1998. Genetic aspects of
growth. In: Avian Growth and Development (J. M. Starck &
R. E. Ricklefs, eds), pp. 305–323. Oxford University Press,
Oxford.
van Noordwijk, A.J., van Balen, J.H. & Scharloo, W. 1988.
Heritability of body size in a natural population of the great tit
(Parus major) and its relation to age and environmental
conditions during growth. Genet. Res. 51: 149–162.
Pärt, T. 1990. Natal dispersal in the collared flycatcher: possible causes and reproductive consequences. Orn. Scand. 21: 83–
88.
Potti, J., Davila, J.A., Tella, J.L., Frias, O. & Villar, S. 2002.
Gender and viability selection on morphology in fledgling pied
flycatchers. Mol. Ecol. 11: 1317–1326.
743
Price, T.D., Kirkpatrick, M. & Arnold, S.J. 1988. Directional
selection and the evolution of breeding date in birds. Science
240: 798–799.
Rausher, M.D. 1992. The measurement of selection on quantitative traits: biases due to environmental covariances between
traits and fitness. Evolution 46: 616–626.
Réale, D., Festa-Bianchet, M. & Jorgenson, J.T. 1999. Heritability of body mass varies with age and season in wild
6 bighorn sheep. Heredity 83: 526–532.
Rice, W.R. 1989. Analysing tables for statistical tests. Evolution
43: 223–225.
Roff, D.A. 1997. Evolutionary Quantitative Genetics. Chapman &
Hall, New York.
SAS Institute, Inc. 1992. SAS User’s Guide, Statistics. SAS Institute,
Inc., Cary, NC.
Schluter, D. & Smith, J.N.M. 1986. Natural selection on beak
and body size in the song sparrow. Evolution 40: 221–231.
Sheldon, B.C., Kruuk, L.E.B. & Merilä, J. 2003. Natural selection
and inheritance of breeding time and clutch size in the
collared flycatcher. Evolution 57: 406–420.
Stinchcombe, J.R., Rutter, M.T., Burdick, D.S., Tiffin, P.,
Rausher, M.D. & Mauricio, R. 2002. Testing for environmentally induced bias in phenotypic estimates of natural selection:
theory and practice. Am. Nat. 160: 511–523.
Tremblay, I., Thomas, D.W., Lambrechts, M.M., Blondel, J. &
Perret, P. 2003. Variation in blue tit breeding performance
across gradients in habitat richness. Ecology 84: 3033–3043.
Zandt, H., Strijkstra, A., Blondel, J. & van Balen, H. 1990. Food
in two mediterranean blue tit populations: do differences in
caterpillar availability explain differences in timing of the
breeding season. In: Population Biology of Passerine Birds. An
Integrated Approach (J. Blondel, A. Gosler, J. D. Lebreton &
R. McCleery, eds), pp. 145–155. Springer-Verlag, Berlin,
Heidelberg.
Received 27 November 2003; revised 18 February 2004; accepted 20
February 2004
J. EVOL. BIOL. 17 (2004) 732–743 ª 2004 BLACKWELL PUBLISHING LTD