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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.
Received: 12 November 2014
Accepted: 7 May 2015
Subject Areas:
behaviour, evolution, genetics
behavioural syndrome, personality,
development, genetic correlation, pleiotropy,
genotypeage interaction
Author for correspondence:
Barbara Class
Electronic supplementary material is available
at or
A strong genetic correlation underlying a
behavioural syndrome disappears during
development because of genotypeage
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.
on August 5, 2015 from
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
young adult
trait 1
trait 2
−1 0 1
trait 1
trait 2
−0.5 0 0.5
trait 2
young adult
trait 1
trait 2
0 0.5 1.0
trait 1
trait 2
−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.) Proc. R. Soc. B 282: 20142777
on August 5, 2015 from
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
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]):
where yis a vector of all the information on all the individuals,
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
the vector of additive genetic (random) effects and Z
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
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
) and its elements (additive
genetic variances and covariances) was estimated using the coef-
ficient of co-ancestry
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 Proc. R. Soc. B 282: 20142777
on August 5, 2015 from
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
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
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
,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
) and in adults (HA
). 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
) was fixed at 0 using an LRT. The genetic
correlations describing the HABR behavioural syndrome in the different ontogenetic stages are printed in bold.
0.26 +0.04 –0.49 +0.09
0.38 +0.10
0.08 +0.11
0.28 +0.04 – 0.13 +0.11 0.50 +0.11
0.29 +0.06 0.07 +0.16
0.27 +0.06
1¼22:12, p,0.001.
1¼12:39, p,0.001.
1¼19:05, p,0.001. Proc. R. Soc. B 282: 20142777
on August 5, 2015 from
– 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:
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:
1¼9:49, p¼0.002
for BR;
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
)) 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
) 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
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
additive genetic correlation
Figure 2. Correlation between HA and BR in adults (r
)) and the
cross-trait-cross-ontogeny correlations (r
) and r
)), 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. Proc. R. Soc. B 282: 20142777
on August 5, 2015 from
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:
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-
¨. 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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... Studies of personality development conducted up to this point have broadly examined how consistent within-individual behaviour develops over the course of an organism's lifetime (Bell & Stamps, 2004;Carere, Drent, Privitera, Koolhaas, & Groothuis, 2005;Wilson & Krause, 2012), how experiential or environmental circumstances at one point in life affect behaviour later in life (Edenbrow & Croft, 2013;Johnson & Sih, 2005;Liedtke, Redekop, Schneider, & Schuett, 2015;White & Wilson, 2019), or have considered the effects of genetics on personality development (Class & Brommer, 2015;White & Wilson, 2019). Other studies have examined how the relationships between personality traits (i.e. ...
... We know of three studies reviewed here that assessed the ontogeny of personality in wild free-living populations directly in the field. Kelley et al. (2015) measured changes in activity and aggression in American red squirrels from juvenile to yearling stages, while Class and Brommer (2015) scored aggression and stress in blue tits, Cyanistes caeruleus, at 16 days of age and once they reached adulthood. Both studies found that behaviour changed throughout development. ...
Animal personality is defined as consistent individual behaviour over time and across contexts, yet most studies to date only measure behaviour within a single life stage. Up to this point, developmental perspectives in the field of animal personality have received little attention. Here, we review those empirical studies that have assessed personality across major developmental periods, including metamorphosis and/or sexual maturation. We specifically focus on life history differences between direct and indirect development to enhance our understanding of the mechanisms underlying personality development. We also discuss conceptual and methodological inconsistencies across the studies reviewed here that may impede the advancement of animal personality theory. We found that personality traits tend to be stable within life stages but typically are not consistent across critical developmental events (metamorphosis and/or sexual maturation). We conclude that assessing personality within single life stages only provides a snapshot of an individual's behavioural repertoire, while long-term consideration may offer a more complete understanding on the evolution and maintenance of animal personality.
... Of course, this conservation of behavioral (co)variance structure across contexts in the face of plasticity variation will not always be the case. In a study of blue tits (Cyanistes caerulues), Class and Brommer's (2015) found a behavioral syndrome of docility and handling-induced stress (measured as breathing rate) was present in nestlings but not adults, demonstrating clearly the need for correlations between labile traits to be assessed across different contexts. ...
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The study of behavioral syndromes aims to understand among-individual correlations of behavior, yielding insights into the ecological factors and proximate constraints that shape behavior. In parallel, interest has been growing in behavioral plasticity, with results commonly showing that animals vary in their behavioral response to environmental change. These two phenomena are inextricably linked-behavioral syndromes describe cross-trait or cross-context correlations, while variation in behavioral plasticity describes variation in response to changing context. However, they are often discussed separately, with plasticity analyses typically considering a single trait (univariate) across environments, while behavioral trait correlations are studied as multiple traits (multivariate) under one environmental context. Here, we argue that such separation represents a missed opportunity to integrate these concepts. Through observations of multiple traits while manipulating environmental conditions, we can quantify how the environment shapes behavioral correlations , thus quantifying how phenotypes are differentially constrained or integrated under different environmental conditions. Two analytical options exist which enable us to evaluate the context dependence of behavioral syndromes-multivariate reaction norms and character state models. These models are largely two sides of the same coin, but through careful interpretation we can use either to shift our focus to test how the contextual environment shapes trait covariances.
... Therefore selection for a higher mass should result in selection for a higher expression of this behavioral syndrome. Importantly, the behavior of an individual at fledging is genetically correlated to its behavior as a reproductive adult (Class and Brommer 2015), which was shown to influence adult survival and reproductive success (Class et al. 2014). As a result, some or several of the behaviors as well as the genetically correlated mass measured at fledging are likely under selection. ...
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Repeatable behaviors (i.e., animal personality) are pervasive in the animal kingdom and various mechanisms have been proposed to explain their existence. Genetic and nongenetic mechanisms, which can be equally important, predict correlations between behavior and body mass on different levels (e.g., genetic and environmental) of variation. We investigated multilevel relationships between body mass measured on weeks 1, 2, and 3 and three behavioral responses to handling, measured on week 3, which form a behavioral syndrome in wild blue tit nestlings. Using 7 years of data and quantitative genetic models, we find that all behaviors and body mass on week 3 are heritable (h2 = 0.18–0.23) and genetically correlated, whereas earlier body masses are not heritable. We also find evidence for environmental correlations between body masses and behaviors. Interestingly, these environmental correlations have different signs for early and late body masses. Altogether, these findings indicate genetic integration between body mass and behavior and illustrate the impacts of early environmental factors and environmentally mediated growth trajectory on behaviors expressed later in life. This study, therefore, suggests that the relationship between personality and body mass in developing individuals is due to various underlying mechanisms, which can have opposing effects. Future research on the link between behavior and body mass would benefit from considering these multiple mechanisms simultaneously.
... Strikingly, the animal model producing the most inflated heritability estimates (i.e., the one ignoring both dominance and maternal effects) is commonly fitted in evolutionary quantitative genetic studies. Indeed, maternal variance is commonly not included in animal models estimating the heritability of juvenile traits in pedigreed populations (e.g., Frentiu et al. 2007, Dingemanse et al. 2009, Weiß and Foerster 2013, Pavitt et al. 2014, Class and Brommer 2015, Stedman et al. 2017. Ignoring maternal variance is often justified by a limited number of breeding attempts per female or by testing beforehand that the inclusion of maternal identity returns nonsignificant or nonestimable maternal variance. ...
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Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained in situ . Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of non‐genetic variance in 8 traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2‐36%) of genetic variance. Simulations show that 1) inferences of all variance components for an average trait are unbiased; 2) the power to detect dominance variance is low; 3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential. This article is protected by copyright. All rights reserved
... Given the differences in among-individual correlations between traits at distinct age classes, a further avenue of research could therefore be to investigate whether any genetic covariation among parenting traits also depends upon age, and indeed how single traits are correlated across age classes at the genetic level (i.e. the existence of genotype)age interactions). The structure of the genetic varianceecovariance matrix (G) is not necessarily static as organisms age (Class & Brommer, 2015), so any evolutionary response to selection on traits may be determined by how G is shaped as a result of both internal and external processes. ...
Phenotypic plasticity is an important mechanism facilitating adaptation to environmental change that often varies among individuals. One reason for this individual variation is that plasticity may depend on state variables, such as size, condition or age, which affect the costs and benefits of plasticity. Recent theoretical work predicts that plasticity will decrease as an organism ages because costs of plasticity mean that flexible phenotypic adjustments by individuals to environmental change will be less beneficial as age-related survival prospects decrease. Here we used Nicrophorus vespilloides burying beetles to test this prediction in the context of parental care. Burying beetles use the carcasses of small vertebrates as resources for breeding and have complex, extended, flexible parental care. Our experiment manipulated female age and (the order of presentation of) carcass size in a repeated-measures design to test the prediction that older beetles are less plastic than younger beetles in parental care. We found evidence in support of our central prediction: young females showed greater mean levels of plasticity than older beetles for all traits (parental care, number of offspring, brood mass) except mean larval mass (i.e. size of offspring), with the response to changes in carcass size dependent on the order of carcass presentation for young females but not for older females. Between-trait correlation analysis revealed age-related trade-offs between the size and number of offspring for older, but not young, mothers. The three age-dependent traits, which were intercorrelated, were also repeatable, indicating potential for coevolutionary responses to selection.
... By contrast, the duration and distance of natal dispersal depends more on intrinsic factors associated with origin, and with sex being a predictor of dispersal distance. A lack of association between timing of emigration, and duration and distance of transfer indicates a breakdown of initial behavioural correlations as the process advances [61]. Pre-emigration and post-emigration dispersal behaviours related each to a different suite of pre-emigration correlates [62]. ...
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Equivocal knowledge of the phase-specific drivers of natal dispersal remains a major deficit in understanding causes and consequences of dispersal and thus, spatial dynamics within and between populations. We performed a field experiment combining partial cross-fostering of nestlings and nestling food supplementation in little owls (Athene noctua). This approach disentangled the effect of nestling origin from the effect of the rearing environment on dispersal behaviour, while simultaneously investigating the effect of food availability in the rearing environment. We radio-tracked fledglings to quantify the timing of pre-emigration forays and emigration, foray and transfer duration, and the dispersal distances. Dispersal characteristics of the pre-emigration phase were affected by the rearing environment rather than by the origin of nestlings. In food-poor habitats, supplemented individuals emigrated later than unsupplemented individuals. By contrast, transfer duration and distance were influenced by the birds' origin rather than by their rearing environment. We found no correlation between timing of emigration and transfer duration or distance. We conclude that food supply to the nestlings and other characteristics of the rearing environment modulate the timing of emigration, while innate traits associated with the nestling origin affect the transfer phases after emigration. The dispersal behaviours of juveniles prior and after emigration, therefore, were related to different determinants, and are suggested to form different life-history traits.
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Current debate in the field of animal personality revolves around whether personality is reflecting individual differences in resource allocation or acquisition. Despite the large body of literature, the proximate relationships between personality, resource allocation, and acquisition are still unclear, especially during early stages of development. Here we studied how among-individual differences in behaviour develop over the first 6 months of life, and their potential association with resource acquisition in a free-ranging population of fallow deer (Dama dama). We related proxies of neonate personality, i.e. neonate physiological (heart rate) and behavioural (latency to leave at release) anti-predator responses to human handling, to the proportion of time fawns allocated to scanning during their first summer and autumn of life. We then investigated whether there was a trade-off between scanning time and foraging time in these juveniles, and how it developed over their first 6 months of life. We found that neonates with longer latencies at capture (i.e. risk-takers) allocated less time scanning their environment, but that this relationship was only present when fawns were 3-6 months old during autumn, but not when fawns were only 1-2 months old during summer. We also found that time spent scanning was negatively related to time spent foraging, a relationship rarely tested in juveniles of large mammals, and that this relationship becomes stronger over time, as fawns gradually switch from a nutrition rich (milk) to a nutrition poor (grass) diet. Our results highlight a potential mechanistic pathway in which neonate personality may drive differences in early-life resource acquisition, through allocation, of a large social mammal.
The notion that men are more variable than women has become embedded into scientific thinking. For mental traits like personality, greater male variability has been partly attributed to biology, underpinned by claims that there is generally greater variation among males than females in non-human animals due to stronger sexual selection on males. However, evidence for greater male variability is limited to morphological traits, and there is little information regarding sex differences in personality-like behaviours for non-human animals. Here, we meta-analysed sex differences in means and variances for over 2100 effects (204 studies) from 220 species (covering five broad taxonomic groups) across five personality traits: boldness, aggression, activity, sociality and exploration. We also tested if sexual size dimorphism, a proxy for sex-specific sexual selection, explains variation in the magnitude of sex differences in personality. We found no significant differences in personality between the sexes. In addition, sexual size dimorphism did not explain variation in the magnitude of the observed sex differences in the mean or variance in personality for any taxonomic group. In sum, we find no evidence for widespread sex differences in variability in non-human animal personality.
Multiple behaviors can correlate with each other at the individual level (behavioral syndrome), and behavioral syndromes can vary in their direction between populations within a species. Within-species variation in behavioral syndromes is predicted to be associated with alternative reproductive tactics (ARTs), which evolve under different selection regimes. Here, we tested this using a water strider species, Gerris gracilicornis, in which males employ 2 ARTs that are fixed for life: signaling males (producing courtship ripples) versus nonsignaling males (producing no courtship ripples). We measured multiple behaviors in males with both of these ARTs and compared behavioral syndromes between them. Our results showed that signaling males were more active and attempted to mate more frequently than nonsignaling males. This shaped an overall behavioral syndrome between activities in mating and nonmating contexts when we pooled both ARTs. In addition, the behavioral syndromes between cautiousness and mating activity differed significantly between ARTs. In signaling males, the syndrome was significantly negative: signaling males more eager to mate tended to leave their refuges more rapidly. However, mating activity and cautiousness were not correlated in nonsignaling males. This might be because active males, in the context of predation risk and mating, were favored during the evolution and maintenance of the unique intimidating courtship tactic of G. gracilicornis males. Thus, our findings suggest that ARTs facilitate behavioral divergence and also contribute to the evolution of tactic-specific behavioral syndromes. We also show that research on ARTs and behavioral syndromes can be harmonized to study behavioral variation.
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All their life, individuals have to make decisions that may strongly affect their fitness. To optimize their decisions, they can use personally acquired information but also information obtained from observing other individuals (“social information”). The propensity to gather and use social information and the information meaning might depend on both individual and environmental factors. Studying what drives within- and between-individual differences in social information use should help us understand the evolutionary potential of this supposedly adaptive behaviour. The aim of my PhD was to empirically investigate sources of variability in heterospecific social information use for breeding habitat selection. I worked on a natural population of collared flycatchers (Ficedula albicollis, Gotland Island, Sweden), a passerine species shown to cue on the presence, density, reproductive investment and nest site preference of dominant titmice for settlement decisions. Using both long term and experimental data, I showed that the use of heterospecific social information, measured as the probability to copy tit nest preference, is not heritable but depends on male age and aggressiveness and on tit apparent breeding investment at the time of flycatcher settlement. Using a playback experiment, I also showed that female flycatchers can fine-tune nest site choice according to (i) song features supposedly reflecting great tit (Parus major) quality and (ii) their own aggressiveness level. This thesis highlights the importance of personality in the use of heterospecific social information for breeding site selection in this population, and broadens the traditionally known sources of heterospecific information to fine song characteristics reflecting heterospecifics’ quality. To fully understand the evolutionary mechanisms and consequences of heterospecific social information use, genetically based plasticity and fitness consequences remains to be explored
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Behavioral differences between individuals that are consistent over time characterize animal personality. The existence of such consistency contrasts to the expectation based on classical behavioral theory that facultative behavior maximizes individual fitness. Here, we study two personality traits (aggression and breath rate during handling) in a wild population of blue tits during 2007-2012. Handling aggression and breath rate were moderately heritable (h (2) = 0.35 and 0.20, respectively) and not genetically correlated (r A = 0.06) in adult blue tits, which permits them to evolve independently. Reciprocal cross-fostering (2007-2010) showed that offspring reared by more aggressive males have a higher probability to recruit. In addition, offspring reared by pairs mated assortatively for handling aggression had a higher recruitment probability, which is the first evidence that both parents' personalities influence their reproductive success in the wild in a manner independent of their genetic effects. Handling aggression was not subjected to survival selection in either sex, but slow-breathing females had a higher annual probability of survival as revealed by capture-mark-recapture analysis. We find no evidence for temporal fluctuations in selection, and thus conclude that directional selection (via different fitness components) acts on these two heritable personality traits. Our findings show that blue tit personality has predictable fitness consequences, but that facultative adjustment of an individual's personality to match the fitness maximum is likely constrained by the genetic architecture of personality. In the face of directional selection, the presence of heritable variation in personality suggests the existence of a trade-off that we have not identified yet.
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The role of ontogenetic processes for the emergence of personality has received only little attention in the past. One reason for the lack of experimental studies on personality development may be that trait consistency over time is one of the cornerstones of the definition of animal personality, whereas, also by definition, ontogeny deals with change over time. Single traits or correlations between traits may be consistent or inconsistent throughout ontogeny; however, the proximate and ultimate causes are not well understood. Environmental factors acting upon individuals during early ontogeny potentially influence personality development substantially. Early environments may severely constrain but also adaptively shape individuals. We examined the personality development of cavies, Cavia aperea, when exposed to increasing and decreasing photoperiod before and after birth. We determined how these predictive environmental cues influenced the development of three behavioural and two physiological traits, their temporal consistency and the correlations between them. We found remarkable plasticity in the development of personality in the cavy, despite a relatively high degree of temporal consistency in most traits. There were stable correlations, some of which became tighter over time, between basal cortisol levels, resting metabolic rate and fearlessness across two different ontogenetic stages. However, we also found that some correlations emerged only after maturation or disappeared over time. Whereas exploration behaviour was tightly correlated with basal cortisol and boldness was correlated with resting metabolic rate, both correlations disappeared in mature animals. Instead, a correlation between exploration and boldness was evident in mature animals. These results call for a broader incorporation of developmental aspects into personality research.
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In evolutionary and behavioural ecology, increasing attention is being paid to the fact that functionally distinct behaviours are often not independent from each other. Such phenomenon is labelled as behavioural syndrome and is usually demonstrated by phenotypic correlations between behaviours like activity, exploration, aggression and risk-taking across individuals in a population. However, published studies disagree on the strength, and even on the existence of such relationships. To make general inferences from this mixed evidence, we quantitatively reviewed the literature using modern meta-analytic approaches. Based on a large dataset, we investigated the overall relationship between behaviours that are expected to form a syndrome and tested which factors can mediate heterogeneities in study outcomes. The average strength of the phenotypic correlation between behaviours was weak; we found no effect of the phylogeny of species but did observe significant publication bias. However, even accounting for this bias, the mean effect size was positive and statistically different from zero (r = 0.198). Effect sizes showed considerable heterogeneity within species, implying a role for population-specific adaptation to environmental factors and/or between-study differences in research design. There was a significant positive association between absolute effect size and repeatability of behaviours, suggesting that within-individual variation of behavioural traits can set up an upper limit for the strength of the detected phenotypic correlations. Moreover, spatial overlap between the contexts in which different behaviours were assayed increased the magnitude of the association. The small effect size for the focal relationship implies that a huge sample size would be required to demonstrate a correlation between behaviours with sufficient statistical power, which is fulfilled only in very few studies. This suggests that behavioural syndromes often remain undetected and unpublished. Collectively, our meta-analysis revealed a number of points that might be worth to consider in the future study of behavioural syndromes.
Individual differences in animal behaviour could elucidate the differences in stress coping style, which have consequences for production, health and welfare. Therefore, individual behavioural differences in pigs and consistency of responses in different test situations were studied. If differences in behaviour reflect coping characteristics, then behaviour in one situation should predict behavioural reactions in other situations and at other times. In this study, a backtest was performed on 315 Great Yorkshire* Dutch Landrace piglets at 3, 10 and 17 days of age. On day 3, groups of approximately 10 piglets per sow were formed, based on escape behaviour in the first backtest (backtest score): high resisting (HR, all scores >3), low resisting (LR, all scores <3), miscellaneous (MISC, various scores between 0 and 10) or original (OR) litters to determine if group composition would influence coping behaviour. In weeks 5-7 and/or 10-12, a human approach Lest (HAT), a novel object test (NOT), and an open door Lest (ODT) were performed with all pigs simultaneously, in the home pen. Pearson correlation coefficients were calculated between the test results and a factor analysis was performed. Furthermore, data were analysed on pen level, and within MISC- and OR-pens on animal level, using multivariate linear models. Significant correlations were found between the backtests and between HAT, NOT and ODT. Backtest results on three ages loaded on the same factor, and HAT, NOT and ODT at one age also loaded on one factor. No differences were found in HAT, NOT and ODT for die different pens (HR, LR, MSC and OR). On animal level, animals with higher backtest scores also had higher HAT scores at 5-7 weeks (P<0.05) within the MISC-pens. At 10-12 weeks, no differences were found. This study suggests that there are consistencies in behaviour of pigs over time and across situations, so coping can be regarded as a trait variable, However, since correlations are well below one, we suggest that other factors such as time (development) and (test) situation may also play an important role in determining an individual's behavioural reaction. The absence of correlations between backtest and the group tests is explained by the theory that these different tests measure different aspects of the coping style.
Individual personality is an important source of variation in animal behavior. However, few studies have examined the reliability of individual behaviors across both time and context for even common temperament traits such as boldness, especially in mammals. We tested a laboratory colony of Siberian dwarf hamsters (Phodopus sungorus) in two similar assays, a tunnel maze and an open field, both provisioned with a home nestbox for shelter. Animals were tested in each assay at three ages, beginning at weaning. Principal components analysis on each assay identified an axis of activity level in both tests, boldness and reactivity in the tunnel maze, and nestbox orientation in the open field. All traits were moderately (7–18%) heritable. Individual activity level was the most reliably consistent trait, both within and between tests. Tunnel maze boldness, tunnel maze reactivity, and open field nestbox orientation did not correlate at any age. Correlation between boldness and activity changes from positive to negative as animals age, while reactivity was consistently negatively associated with activity. A negative correlation emerged in adults between open field activity and nestbox orientation. These results suggest that either development or habituation results in different personality trait associations in an individual over time. Individual temperament traits such as general activity level may be quite stable, but caution should be used in generalizing single assays to represent boldness across time and across contexts.
Behaviors are commonly correlated between individuals in so-called behavioral syndromes. Between-individual correlations of phenotypic traits can change the trajectories of evolutionary responses available to populations and even prevent evolutionary change if underpinned by genetic correlations. Whether behavioral syndromes also influence the course of evolution in this manner remains unknown. Here, we provide the first test of the degree to which evolutionary responses might be affected by behavioral syndrome structure. This test, based on a meta-analysis of additive genetic variancecovariance matrices, shows that behavioral syndromes constrain potential evolutionary responses by an average of 33%. For comparison, correlations between life-history or between morphological traits suggest constraints of 1318%. This finding demonstrates that behavioral syndromes might substantially constrain the evolutionary trajectories available to populations, prompts novel future directions for the study of behavioral syndromes, emphasizes the importance of viewing syndrome research from an evolutionary perspective, and provides a bridge between syndrome research and theoretical quantitative genetics.
Psychosocial stressors activate two distinct stress-response systems, a central, behavioral response, and a peripheral, endocrine response. Both behavioral and endocrine responses to stressors are subject to individual and developmental variables, but it is not known whether stressor induced behaviors are stable across development, and how they correspond with changes in the endocrine component of the stress response. We characterized the development and stability of behavioral responses to a mild psychosocial stressor in marmosets (Callithrix geoffroyi), and assessed the degree to which the behavioral and endocrine stress-response systems were co-activated. The behavioral response to stressors was stable within individuals, but only some stressor-induced behaviors changed as the monkeys developed. Overall, there was more variability in the development of behavioral responses compared to stress-induced endocrine profiles found previously [French et al., 2012. Horm Behav 61:196-203]. In young marmosets, only increased alarm calling was correlated with increased cortisol reactivity, and in older marmosets increased cage manipulations and motor activity were associated with poorer post-stressor cortisol regulation. Because these relationships were so few, we conclude that while the behavioral and endocrine systems follow a similar developmental trajectory, each system maintains a level of independence. Furthermore, the relationship between stressor-induced behaviors and HPA activity changes across development. Am. J. Primatol. © 2013 Wiley Periodicals, Inc.