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In animal populations, as in humans, behavioural differences between individuals that are consistent over time and across contexts are considered to reflect personality, and suites of correlated behaviours expressed by individuals are known as behavioural syndromes. Lifelong stability of behavioural syndromes is often assumed, either implicitly or explicitly. Here, we use a quantitative genetic approach to study the developmental stability of a behavioural syndrome in a wild population of blue tits. We find that a behavioural syndrome formed by a strong genetic correlation of two personality traits in nestlings disappears in adults, and we demonstrate that genotype-age interaction is the likely mechanism underlying this change during development. A behavioural syndrome may hence change during organismal development, even when personality traits seem to be strongly physiologically or functionally linked in one age group. We outline how such developmental plasticity has important ramifications for understanding the mechanistic basis as well as the evolutionary consequences of behavioural syndromes. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
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Cite this article: Class B, Brommer JE. 2015
A strong genetic correlation underlying a
behavioural syndrome disappears during
development because of genotypeage
interactions. Proc. R. Soc. B 282: 20142777.
http://dx.doi.org/10.1098/rspb.2014.2777
Received: 12 November 2014
Accepted: 7 May 2015
Subject Areas:
behaviour, evolution, genetics
Keywords:
behavioural syndrome, personality,
development, genetic correlation, pleiotropy,
genotypeage interaction
Author for correspondence:
Barbara Class
e-mail: barbara.class@utu.fi
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2014.2777 or
via http://rspb.royalsocietypublishing.org.
A strong genetic correlation underlying a
behavioural syndrome disappears during
development because of genotypeage
interactions
Barbara Class and Jon E. Brommer
Department of Biology, University of Turku, University Hill, Turku 20014, Finland
In animal populations, as in humans, behavioural differences between individ-
uals that are consistent over time and across contexts are considered to reflect
personality, and suites of correlated behaviours expressed by individuals are
known as behavioural syndromes. Lifelong stability of behavioural syndromes
is often assumed, either implicitly or explicitly. Here, we use a quantitative
genetic approach to study the developmental stability of a behavioural
syndrome in a wild population of blue tits. We find that a behavioural syn-
drome formed by a strong genetic correlation of two personality traits in
nestlings disappears in adults, and we demonstrate that genotype– age inter-
action is the likely mechanism underlying this change during development.
A behavioural syndrome may hence change during organismal develop-
ment, even when personality traits seem to be strongly physiologically or
functionally linked in one age group. We outline how such developmental
plasticity has important ramifications for understanding the mechanistic
basis as well as the evolutionary consequences of behavioural syndromes.
1. Introduction
Personality refers to a measure of behaviour that shows repeatable differences
between individuals [1]. A remarkable number of studies in a wide variety of
animal taxa indeed find individual consistency in behaviour, and personality
hence is a widespread phenomenon in nature [2]. One further striking feature
in personality research is that different behaviours tend to be correlated [3], form-
ing what are termed behavioural syndromes [4]. However, despite personality
showing plasticity across ages (e.g. [5,6]), individuals are typically assumed,
implicitly or explicitly, to maintain their relative ranking in one or more aspects
of personality over age, producing consistent behavioural differences and consist-
ency in the magnitude and sign of the behavioural syndrome correlation across
development [1] (figure 1a). Surprisingly, this assumption is not based on a
solid empirical ground. On the one hand, psychology studies agree that person-
ality is relatively stable over the ontogeny in humans [7– 10]. On the other hand,
the few studies conducted in other species typically find contrasting results
for single traits [1117]. In addition, behavioural syndromes may appear or
disappear as individuals age [18– 25].
A major shortcoming is that most of the human and animal studies have con-
sidered personality only on the phenotypic level. As a consequence, observed
phenotypic changes in personality and behavioural syndromes may largely
reflect age-related changes in non-heritable factors. Therefore, phenotypic pat-
terns do not necessarily inform us of underlying, intrinsic causes of observed
age-relatedchanges in personality, which are needed to gain a proper mechanistic
or evolutionary understanding of age-related changes in personality. In particu-
lar, increased understanding of ontogenetic changes in the additive genetic
(co)variances of personality traits is needed for properly understanding the
potential of evolutionary forces in shaping these traits [26]. In this paper, we
use, for the first time to our knowledge, a quantitative genetic approach to
&2015 The Author(s) Published by the Royal Society. All rights reserved.
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bring new insight into the genetic basis of personality and
behavioural syndromes during development.
Variation in behaviour is likely to be caused by the joint
effects of many genes (polygenicity), and a behavioural syn-
drome is likely to arise from the genetic correlation of two
behaviours [27]. A genetic correlation between behaviours
arises when (part of the) genes underlying both behaviours
are the same (pleiotropy) or the genes are (largely) different
but are associated through linkage disequilibrium (physical
or not). Genetic correlations between behaviours during devel-
opment reflect the joint effects of many genes expressed by the
same individuals (i.e. genomes) at different ages. A behaviour-
al syndrome will show consistency over development when
the effects of the genes underlying both behaviours are corre-
lated across development, either because the causal genetic
architecture is strictly maintained over ontogeny, or due to a
strong functional link between them, referred to as structured
pleiotropy [28]. A genetic correlation underlying a behaviou-
ral syndrome can change over the ontogeny as a result of two
possible mechanisms. First, the expression of genes determin-
ing one or both behaviours might change over time as a
result of genotype– age interaction (GAI; figure 1b). For
instance, some genes underlying the focal behaviours can be
turned on or off during development, or the effect sizes of
genes differ when expressed at different ages, determining
behaviour in ‘young’ versus ‘old’ individuals. Whenever GAI
occurs in one or more traits, the genetic correlation between
two traits is likely to change over the developmental trajectory
(figure 1c,d), unless the two traits are determined through
structured pleiotropy, causing the relative ranking of geno-
types to be maintained for both traits across age classes [29].
Lastly, selection may lead to not all individuals expressing
behaviours during the entire developmental trajectory; many
juveniles do not survive to adulthood, especially in wild popu-
lations [30]. Selection, by favouring a particularcombination of
breeding values, therefore has great potential to alter a genetic
correlation between behaviours during development, indepen-
dently of the presence of GAI [31] (figure 1e). For example,
linkage disequilibrium between genes underlying two beha-
viours, and hence a genetic correlation, can arise during
development through correlational selection, or linkage dis-
equilibrium between two traits in juveniles may be broken
down by selection, and hence disappear in adults (figure 1e).
In this study, we quantify the genetic correlations under-
lying a behavioural syndrome across the ontogeny from
juvenileto adult. We hence test thestability of a behavioural syn-
drome during development. We study a wild pedigreed
population of blue tits (Cyanistes caeruleus) and use a sophisti-
cated quantitative genetic approach to estimate all relevant
additive genetic (co)variances in addition to considering the
putative role of selection in shaping the genetic correlations
across ontogeny. The behavioural syndrome we study is
trait 1
(a)
−1.0
−0.5
0
0.5
1.0
1.5
young adult
trait 1
trait 2
(c)
−1
0
1
2
−1 0 1
trait 1
trait 2
(e)
−2
−1
0
1
−0.5 0 0.5
trait 2
(b)
−1
0
1
2
young adult
trait 1
trait 2
(d)
0
0.5
1.0
1.5
2.0
0 0.5 1.0
trait 1
trait 2
(f)
−2
−1
0
1
−0.5 0 0.5
Figure 1. Theoretical plot illustrating the notions of consistency over the ontogeny, GAIs and selection on the genetic correlation between trait 1 and trait 2.In(a,b),
each line represents one individual, which is reported as a point in (c,d). In (a), the rank order of the individuals’ breeding values for trait 1 remains stable across
ontogeny. In (b), the rank order of the breeding values for trait 2 is different in young and adults because of GAI. As a consequence, the positive genetic correlation
between trait 1 and trait 2 in young individuals (c) disappears in adults (d). Figures (e) and (f) represent the breeding values of nestlings that recruited (red) or not
(grey), assuming a 5% recruitment probability. In (e), the breeding values of the two traits are not negatively correlated in recruits, which is why the genetic
correlation is 0 when these individuals are measured as adults. In (f), the individuals are selected randomly and thus the correlation stays negative when
they are measured as adults. (Online version in colour.)
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composed of two personality traits measured during handling
in nestlings and adults, which are heritable and correlated on
the phenotypic and genetic level in nestlings [32]. We find that
this genetic correlation disappears in adults and demonstrate
that GAIs are underlying this developmental change.
2. Material and methods
(a) Study species and measures of behaviour
The study was conducted on a population of blue tits (C. caeruleus)
breeding in nest-boxes in southwestern Finland near the city of
Tammisaari (608010N, 238310E). The nest-boxes were made avail-
able for breeding starting from 2003 in an area of approximately
10 km
2
of mixed boreal forest. Each year, the birds were monitored
during the breeding season (April–July). All individuals were
identified by a metal ring placed on them when they were 9 days
old, or during first capture as an unringed adult. During handling,
the behaviour of adults and 16-day-old nestlings was scored.
Handling aggression (HA) is a score (ranging from 1 to 5) reflecting
whether the individual is passive and docile when held by the
observer (low HA score) or whether it fights back (high HA
score) [32]. Breath rate (BR) is quantified by timing with a stop-
watch how long it takes an individual to breathe 30 times,
carried out two consecutive times. BR (calculated as the average
breaths per second over the two measures) is considered an indi-
cation of stress in birds, where a higher BR is associated with a
higher stress response [33]. The details of the method of handling
nestlings prior to assaying their behaviours changed in 2011 (all
placed in one large paper bag) relative to before 2011 (all placed
in individual paper bags), but the genetic correlation in nestling
behaviour before and after 2011 did not differ from unity (elec-
tronic supplementary material S1) and we hence consider the
two approaches equivalent. In our population, HA and BR
scores are in adults associated with fecundity (in males) and survi-
val (in females), and are hence ecologically important behaviours
[34]. In addition, work on the stress response to handling in
the closely related great tits Parus major selected for extreme
exploration scores suggests that BR is genetically correlated
to exploration score [35]. The latter is an important aspect of
personality in wild birds [36– 38].
(b) Quantitative genetics
The focus of our analysis was to contrast the genetic correlation
underlying the HABR behavioural syndrome in 16-day-old
nestlings with the genetic correlation in adults (more than or
equal to 1-year-old birds). We estimated the additive genetic
variance–covariance Gmatrix of the four traits (HA and BR in
adults and in nestlings) using the following linear mixed
model (animal model [36,39]):
y¼X
b
þZAuAþXZkukþe,(2:1)
where yis a vector of all the information on all the individuals,
b
is a vector of one or more fixed effects, Xis a design matrix (of
zeros and ones) relating the appropriate fixed effects to each indi-
vidual, u
A
the vector of additive genetic (random) effects and Z
A
the design matrix relating the appropriate additive genetic effect
to each individual. We included the sex of the individual, year
and identity of the observer as fixed effects. The summation
PZ
k
u
k
allows for more random effects such as permanent
environment and common environment effects. Permanent
environment effects were included for adult behaviours in
order to capture the (co)variances between individuals that are
not due to additive genetic effects, but are caused by other
environmental or non-additive genetic (e.g. dominance) effects
that are conserved across repeated records [26]. Common
environmental effects on nestlings’ behaviours due to the
environment (biotic and abiotic conditions) shared by nestlings
were modelled by including the ID of their nest. A reciprocal
cross-fostering procedure was carried out where part of the nest-
lings were swapped between two nests at 2 days post-hatching
between 2006 and 2010 (see [40] for a detailed description),
and we used the ID for the nest of rearing as the common
environment for cross-fostered nestlings. Finally, eis a vector
for residual errors, which represents the difference between the
trait values observed and the values expected on the basis of
the fixed and random effects. This mixed model was
implemented in ASREML (VSN International, Hemel Hempstead,
UK) and solved using restricted maximum likelihood.
The (co)variances for nestling and adult traits were estimated
on the additive genetic and residual levels. In addition, (co)vari-
ances for nest of rearing and permanent environment were
estimated for nestling and adult behaviours, respectively. The
additive genetic and other random effects for the four traits
were assumed to be normally distributed with a mean of zero
(i.e. defined relative to the trait-specific fixed-effect mean), and
with multivariate normal trait-specific variances and covari-
ances. The Gmatrix (for vector u
A
) and its elements (additive
genetic variances and covariances) was estimated using the coef-
ficient of co-ancestry
u
ij
between individuals iand j, which was
derived from the population pedigree. This Gmatrix contains
the trait-specific additive genetic variances and all pairwise gen-
etic covariances. Statistical tests of elements in this Gmatrix were
conducted by comparing the likelihood,using likelihood-ratio tests
(LRTs) between a model constraining the elements and the uncon-
strained model with the degrees of freedom calculated as the
difference in variance components between the constrained and
unconstrained models. Phenotypic (co)variances were calculated
as the sum of all estimated (co)variance components.
The data used in these analyses consisted of 8079 observations
made on 7191 individuals between 2006 and 2014, including
744 individuals measured as adults only, 414 recruits (measured
as nestling and adult) and 6033 nestlings which have not recruited
(detailed in electronic supplementary material, table S1).
The pruned pedigree, which included only individuals for
which we have at least one measure for one of the four traits,
holds records for 7203 individuals (including 781 founders),
6036 maternities, 6392 paternities, 31 457 pairs of full sibs and
32 422 pairs of half sibs. The mean family size is 12.6 and lineage
of multiple generations is recorded with a maximal lineage depth
of seven generations. This is a social pedigree, where offspring
hatched in one nest were assumed to be full siblings. Half-sibs
in a social pedigree arise when a parent produces a recruit
with a different partner (e.g. in a different year). Because some
social fathers have not sired the offspring for which they provide
care, there are likely to be errors in the paternal links in this ped-
igree. We do not know the proportion of extra-pair paternity in
this population. We evaluated the sensitivity of our findings to
the inclusion of the uncertainty in paternity using a simulation
approach. We assumed the distribution of extra-pair young
(EPY) was described by the hierarchical model developed
by Brommer et al. [41], parametrized using empirical data on
EPY in nine blue tit populations [42] (model values used were
m¼0.875, s¼0.156). This parametrized model provided a
description of the expected distribution of EPY and was applied
to our social pedigree to generate 1000 pedigrees where the
paternal links of all randomly drawn ‘EP nestlings’ were
assumed to be unknown. These random pedigrees were sub-
sequently used to obtain 1000 estimates of all (co)variances
based on the animal model described above, and their mode
and 95% credible intervals were calculated using a density
kernel (see electronic supplementary material S2 for the R
code). In addition, the LRT statistic of the model where the gen-
etic correlation between HA and BR was constrained to be the
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same versus unconstrained was calculated for each simulated
pedigree. We assumed that our findings were robust to the
inclusion of uncertainty of unknown extra-pair paternity in
case the LRT statistic exceeded the
x
2
threshold value for 1 d.f.
in more than 95% of model comparisons using the pedigree
with simulated extra-pair paternities, and when quantitative
genetic estimates based on our social pedigree were within the
95% credible interval of estimates based on the pedigree with
simulated extra-pair paternity.
Our analysis contrasts nestlings versus adults. Clearly, an indi-
vidual’s breeding value of HA and BR may undergo changes
during adulthood (because of GAI), such that the grouping of
differently aged adult individuals in one age class may not fully
represent ontogenetic changes in additive genetic (co)variances.
There was nevertheless no evidence that the genetic correlations
in HA or BR between 1, 2 and 3þyears old was lower than
unity (electronic supplementary material, table S2), and the pool-
ing of adults of different ages was hence representative with
respect to adults’ breeding values for these two behaviours.
(c) Selection
Selection has the potential to make or break a genetic correlation
across the developmental trajectory (figure 1e). In our case, we
contrast the genetic correlations of 16-day-old nestlings and
adults. The putative selection then implies that only nestlings
with certain combinations of breeding values for HA and BV
would recruit into the breeding population. We tested this
hypothesis by extending the above-described multivariate
animal model to six character states, estimating the genetic corre-
lations between HA and BR in nestlings that recruited as
breeding adults in our population, in nestlings that did not
recruit, and in adults. The hypothesis that selection alters the
magnitude and/or sign of the genetic correlation in adults com-
pared with its magnitude and/or sign in juveniles predicts that
the genetic correlation between HA and BR in recruited nestlings
must be similar to the one in adults, but different from the one in
non-recruited nestlings. Alternatively, when selection does not
‘pick out’ certain combinations of breeding values, the genetic
correlation in recruited offspring is similar to that in non-
recruited offspring, but different from the one in adults. Because
these hypotheses specify non-nested models, their fit to the data
was compared using the Akaike information criterion (AIC) [43].
AIC was calculated as –2 log(L)þ2K, where log(L) is the
log-likelihood of the model and Kthe number of different corre-
lations between HA and BR, which ranged from 1 (all the same)
to 3 (all independent). Support for each model was calculated as
exp(– 1
2DAIC
i
)/
S
exp(– 1
2DAIC), where a higher value indicates
a great support for a particular model relative to the other
candidates [44].
(d) Simulation of expected change in genetic
correlation across ontogeny
A second mechanism that can change the magnitude or sign of a
genetic correlation between two traits across ontogeny is GAI
leading to age-related changes in the relative ranking of the
breeding values in one or both traits. As a result, a positive,
but not perfect (i.e. r
A
,1), cross-ontogeny correlation in each
of the two traits forming a behavioural syndrome will cause a
decrease in the magnitude of the genetic correlation between
these two traits when comparing their correlation in the juvenile
stage with older ontogenetic stages. This is an unavoidable con-
sequence, because imperfect genetic correlations across ontogeny
of each trait imply that a certain amount of ‘noise’ is added to the
covariance between the two traits across ontogeny. The magni-
tude of the correlation between two traits then decreases across
ontogeny. We wanted to investigate the extent to which this
phenomenon was responsible for changes in the genetic corre-
lation between our two behaviours expressed at the juvenile
versus the adult stages. To this end, we generated an expectation
of the genetic correlation between HA and BR in adults, based on
the estimated genetic correlations between nestlings and adults
for HA and BR alone. We first generated breeding values for
1000 individuals according to the genetic (co)variance matrix
for these traits in nestlings, and then applied the estimated gen-
etic correlations for HA and BR across ontogeny to generate
expected breeding values in adults (see electronic supplementary
material S3 for the R script). Genetic correlations were calculated
as the correlations between the simulated breeding values,
between traits across ontogeny and within the adult age class.
This procedure was repeated 1000 times, and the expected corre-
lation and 95% credible intervals of the genetic correlations were
calculated as the mode and the 95% interval using a density
kernel (see electronic supplementary material S3).
3. Results and discussion
The Gmatrix obtained from the animal model showed that all
four traits are moderately heritable (table 1; electronic sup-
plementary material, table S3). There was a negative genetic
correlation ( –0.49 +0.09) between HA and BR in nestlings
and a low, positive genetic correlation (0.07 +0.16) in adults
(table 1). On the phenotypic level, these correlations were
Table 1. Genetic correlations and heritabilities of handling aggression and breathing rate in nestlings (HA
n
,BR
n
) and in adults (HA
a
,BR
a
). Genetic correlations
(upper triangle) and heritabilities (diagonal) are represented as estimate +s.e. and are derived from the matrix of additive genetic effects estimated by a
multivariate animal model. Information on variances and correlations for other components than the additive genetic component are provided in the electronic
supplementary material, tables S3 and S4. Fixed effects are reported in the electronic supplementary material, table S5. The significance of a particular genetic
correlation was tested by comparing the unconstrained model with models where that genetic correlation (r
A
) was fixed at 0 using an LRT. The genetic
correlations describing the HABR behavioural syndrome in the different ontogenetic stages are printed in bold.
HA
n
BR
n
HA
a
BR
a
HA
n
0.26 +0.04 –0.49 +0.09
a
0.38 +0.10
b
0.08 +0.11
BR
n
0.28 +0.04 – 0.13 +0.11 0.50 +0.11
c
HA
a
0.29 +0.06 0.07 +0.16
BR
a
0.27 +0.06
a
LRT:
x
2
1¼22:12, p,0.001.
b
LRT:
x
2
1¼12:39, p,0.001.
c
LRT:
x
2
1¼19:05, p,0.001.
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– 0.36+0.03 and 0.07 +0.03, respectively (see electronic sup-
plementary material, table S4, for correlations on all levels).
The model corrected for the fixed-effect differences between
observer, years and sexes (electronic supplementary material,
table S5). The genetic correlations underlying the behavioural
syndrome between HA and BR differed significantly between
nestlings and adults (LRT:
x
2
1¼9:78, p,0.01). Thus, we
find clear evidence that the genetic underpinning in a behav-
ioural syndrome changes during development. These
findings were robust to inclusion of uncertainty in pedigree
links due to extra-pair paternity (electronic supplementary
material, figures S1 and S2).
We constructed a multivariate animal model for six traits—
HA and BR in nestlings which recruited as breeding adults
in our population, in nestlings which did not recruit and in
adults—in order to test whether the genetic correlation of
recruited offspring differs from that of the non-recruited nest-
lings (cf. figure 1e). The top-ranked model was one where the
genetic correlations in recruited and non-recruited nestlings
are the same, but differ from that in adults (model ‘S/S/I’in
table 2). Indeed, the estimated genetic correlations (of the fully
unconstrained model ‘I/I/I’ in table 2) underlined that the esti-
mates of the genetic correlation between HA and BR in recruited
and non-recruited nestlings were highly similar ( – 0.59 +0.27
and – 0.46 +0.09, respectively; electronic supplementary
material, table S6).Given that selection on the geneticcorrelation
in nestlings was not responsible for the absence of genetic corre-
lation in adults, GAIs are likely to be the main factor behind the
change in genetic correlation over development.
Genetic correlations of BR and HA across ontogeny (nest-
ling to adult) are both positive (0.50 +0.11 and 0.38 +0.10,
respectively; table 1), but these correlations are not perfect
as they fall significantly below 1 (LRT:
x
2
1¼9:49, p¼0.002
for BR;
x
2
1¼20:07, p,0.001 for HA), indicating some chan-
ging in the ranking of an individual’s breeding values for
these traits expressed across ontogeny. We therefore calcu-
lated the genetic correlation between HA and BR we would
expect in adults (r
A
(HA
a
,BR
a
)) given these low genetic corre-
lations in HA and BR across ontogeny. Our simulations
suggest the observed low genetic correlations in HA and
BR across ontogeny act to reduce the genetic correlation for
the HABR behavioural syndrome in adults we would
expect (expected r
A
(HA
a
,BR
a
) is 0.09, 95% credible interval
0.16, 0.04). The 95% CI of the observed genetic correlation
between HA and BR in adults indeed encompasses the
expected correlation (figure 2). Thus, the change in ranking of
the breeding values across ontogeny in both HA and BR
(i.e. the GAI) is sufficient to explain the breakdown in the gen-
etic correlation of the syndrome during development. A second
striking feature is that the estimated genetic correlation between
HA in nestlings and BR in adults r
A
(HA
n
,BR
a
)hasamuch
lower magnitude (i.e. absolute value) than the genetic corre-
lation one would expect (figure 2), which further underlines
that GAI uncouples these two traits across ontogeny.
HA and BR are traits measured in response to the stress of
handling, and one would hence intuit these traits to be
caused by common mechanisms underlying stress response.
In addition, BR is known to be directly linked to the physio-
logical response to stress through the activation of the
parasympathetic system and is considered itself as a physio-
logical parameter by some authors [33,45]. Hence, it can be
argued that the genetic correlation between HA and
BR may reflect a physiological– behavioural correlation.
Indeed, major hypotheses explaining syndrome covariance
postulate that behaviours, physiological and possibly life-
history traits reflect variation along a common axis (e.g. the
hypothalamuspituitary– adrenal axis; coping styles, see
[46]) or pace-of-life-syndrome [47].
Here, we document a breakdown of the strong genetic
correlation between HA and BR in nestlings as they mature
into adults. This finding hence implies that the mechanistic
underpinning (in terms of genes and/or physiological
Table 2. Model ranking for hypotheses testing whether selection shapes the genetic correlation between handling aggression and breathing rate across
development. Each hypothesis specifies a certain combination of constraints (or not) on the genetic correlation in nestlings which have recruited, in nestlings
which have not recruited and in adults. The genetic correlations were constrained to the same value (S) or were independent (I). Models are sorted by
ascending order of AIC, and DAIC is the difference between the AIC of each model and AIC of the top model.
hypothesis recruited/not recruited/adults log(L)KAIC DAIC support
no selection S/S/I2411.850 2 827.7 0 0.46
selection S/I/S2412.704 2 829.4 1.71 0.20
all different I/I/I2411.758 3 829.5 1.82 0.19
all the same S/S/S2413.953 1 829.9 2.21 0.15
−0.4
−0.2
0
0.2
0.4
HAaBRaHAnBRaBRnHAa
additive genetic correlation
Figure 2. Correlation between HA and BR in adults (r
A
(HA
a,
BR
a
)) and the
cross-trait-cross-ontogeny correlations (r
A
(BR
n,
HA
a
) and r
A
(HA
n,
BR
a
)), esti-
mated by the animal model (filled symbols) and derived from the
simulation (open symbols). For the simulation, the 95% credible interval
were estimated using the highest posterior density distribution of the corre-
lations, and for the estimates derived from the animal model, approximate
confidence intervals were obtained by multiplying their s.e. by 1.96.
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mechanisms) of the expression of these behaviours changes
across development. Technically, these behaviours are linked
through non-structured pleiotropy (cf. [28]). Arguably,
research into the mechanistic underpinning of a behavioural
syndrome would be particularly interesting for syndromes
where the underlying genetic correlation remains consistent
in sign and magnitude over development, because such a pat-
tern would signal a mechanism where it is likely that the same
genes (expression) or hormonal pathways are underlying the
association of personality traits at different ages. Thus, the
quantitative genetic approach taken here is a potentially fruit-
ful first step for identifying syndromes where detailed research
into the mechanistic underpinning of the behavioural or
behavioural–physiological syndrome is attractive.
From an ultimate perspective, we note that genetic corre-
lations act as evolutionary constraints because genetically
correlated traits cannot evolve independently in response
to selection (e.g. [48]). Meta-analysis underlines that the gen-
etic correlations underlying behavioural syndromes exert
strong evolutionary constraints, possible stronger than those
acting on life-history traits [49]. Our findings hence suggest
that, when the genetic underpinning of behavioural syn-
dromes is explored from a lifetime perspective, behavioural
syndromes may be far less evolutionarily constrained than
originally perceived on the basis of genetic correlations
measured in one particular stage of development. Assuming
that genetic correlations are consistent across the ontogeny
can hence lead to inaccurate predictions of the evolutionary
trajectory of the behaviours or the evolutionary constraints
acting upon them.
Our findings underline the importance of studying beha-
viours during multiple periods in the development of
organisms, because a functional, physiological or developmental
link between two behaviours expressed at one particular age is
not sufficient to demonstrate their validity over the entire devel-
opment of the organism. This phenomenon, which is likely to
occur in other organisms, should be taken into account in
future studies of personality. Currently, we know very little
about how personality develops and several calls have been
made to stimulate research in this direction [4– 6,22,50]. After
all, studying the development of personality is a necessary step
to reach a complete understanding of its evolution and causation.
Ethics. All experiments on blue tits described in this paper complied
with the Finnish law on animal experiments, and were approved
by the relevant authorities: Helsinki University Animal Experiment
Committee (2003– 2008), Animal Experiment Committee of Southern
Finland (2007 onwards).
Data accessibility. Population pedigree and phenotypic data are
deposited in Dryad: http://dx.doi.org/10.5061/dryad.443g2.
Authors’ contributions. B.C. carried out the statistical analysis, participated
in data collection and authored the manuscript. J.E.B. conceived and
designed the study, collected data and authored the manuscript.
Competing interests. We declare we have no competing interests.
Funding. B.C. was funded through a CIMOfellowship and Turun Yliopis-
tosa
¨a
¨tio
¨. The Academy of Finland (J.E.B.), Oskar O
¨flunds Stiftelse and
Societas pro Fauna et Flora Fennica funded part of the data collection.
Acknowledgements. We thank the land owners for permission to work
on their land. We thank Edward Kluen, Lasse Kurvinen, Jaana
Kekkonen, Maaike de Heij and Laura Harjula for their many hours
of fieldwork. Two anonymous reviewers are thanked for their
constructive comments.
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