University of Groningen
Personality and basal metabolic rate in a wild bird population
Bouwhuis, Alexandra; Quinn, John L.; Sheldon, Ben C.; Verhulst, Simon
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10.1111/j.1600-0706.2013.00654.x
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Bouwhuis, S., Quinn, J. L., Sheldon, B. C., & Verhulst, S. (2014). Personality and basal metabolic rate in a
wild bird population. Oikos, 123(1), 56-62. DOI: 10.1111/j.1600-0706.2013.00654.x
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Oikos 123: 56–62, 2014
doi: 10.1111/j.1600-0706.2013.00654.x
© 2013 The Authors. Oikos © 2013 Nordic Society Oikos
Subject Editor: Wendt Muller. Accepted 25 April 2013
Personality and basal metabolic rate in a wild bird population
Sandra Bouwhuis, John L. Quinn, Ben C. Sheldon and Simon Verhulst
S. Bouwhuis ([email protected]), Inst. of Avian Research, An der Vogelwarte 21, DE-26386 Wilhelmshaven, Germany.
– J. L. Quinn, School of Biological Earth and Environmental Sciences, Univ. College Cork, Distillery Fields, Cork, Ireland. – B. C. Sheldon,
Edward Grey Inst., Dept of Zoology, Univ. of Oxford, South Parks Road, Oxford, OX1 3PS, UK. – S. Verhulst, Behavioural Biology Group,
Univ. of Groningen, PO Box 11103, NL-9700 CC Groningen, the Netherlands.
Personality and metabolic rate are predicted to show covariance on methodological and functional grounds, but empirical
studies at the individual level are rare, especially in natural populations. Here we assess the relationship between
exploration behaviour, an important axis of personality, and basal metabolic rate (BMR) for 680 free-living great tits Parus
major, studied over three years. We find that exploration behaviour is weakly negatively related to BMR among female,
but not male, birds. Moreover, we find exploration behaviour to be independent of methodological aspects of BMR
measurements (e.g. activity levels, time to acclimatize) which have been suggested to be indicative of personality-related
activity or stress levels during measurement. This suggests that the weak link between exploration behaviour and BMR
found here is functional rather than methodological. We therefore test the hypothesis that selection favours covariance
between exploration behaviour and metabolic rate, but find no evidence for correlational survival or fecundity selection.
Our data therefore provide at best only very weak evidence for a functional link between personality and metabolic rate,
and we suggest that studies of personality and metabolic strategies, or personality and daily energy expenditure, are
required to further resolve the link between personality and metabolic rate.
Inter-individual variation underpins evolution, if that
variation is heritable and under selection. Substantial
inter-individual variation is found in many traits, including metabolic rate (Nespolo and Franco 2007, Burton
et al. 2011) and behaviour (i.e. personality, Bell et al.
2009, Dall et al. 2012), and it has recently been proposed
that the two traits are expected to be linked based on two
distinct arguments. The first is that, methodologically,
personality-related activity or stress levels during metabolic measurement may affect metabolic rate (Careau et al.
2008). Second, personality and metabolism could both
be aspects of a general slow–fast life-history continuum
(Careau et al. 2008, Biro and Stamps 2010, Réale et al.
2010), such that while some individuals adopt a slow life
history and behave cautiously, have slow growth and low
metabolic rates, others adopt a fast life history and behave
boldly, have fast growth and high metabolic rates. At
the proximate level, such general strategies could be
maintained by selection removing individuals with different trait combinations, favouring the evolution of genetic
covariance between the two traits, or result from the
integration of traits through organisational or activational
effects of hormones. In the latter case, testosterone is a
potential candidate, as it is known to affect metabolic rate
(Buchanan et al. 2001) and behaviour (van Oers et al.
2011), as well as growth (Schwabl 1996) and the reproduction–survival tradeoff (Reed et al. 2006).
56
To date, relatively few empirical studies have investigated
links between personality traits and metabolic rate. In a
recent review, Careau and Garland (2012) found a positive
relationship between personality traits and measures of
metabolic rate in nine out of 21 case studies. Most of these
case studies (19/21) were performed, however, using captive
or domesticated animals, while such relationships, the processes that shape them and their fitness consequences are
preferably studied under ecologically relevant conditions.
Of the reviewed studies, only two were performed in
natural populations. The first, on meadow voles Microtus
pennsylvanicus, found no link between personality and
metabolic rate, but was based on a restricted sample size (35
individuals, Timonin et al. 2011). The second, on root
voles Microtus oeconomus, found the expected positive relationship between personality and metabolic rate, but only
in females, and only during the non-reproductive season
(Lantová et al. 2011). A third study, on common lizards
Zootoca vivipara, which was published after the review,
found no relationship between personality and metabolic
rate per se, but found evidence for correlational firstyear survival selection favouring individuals with either high
metabolic rate and low exploration scores, or with low
metabolic rate and high exploration scores (Le Galliard
et al. 2013). Such correlational selection should lead to a
negative relationship between personality and metabolic
rate in adults, which would contradict predictions from the
slow–fast life-history scenario (Careau et al. 2008, Biro and
Stamps 2010, Réale et al. 2010). The three existing studies
from natural populations therefore provide inconclusive
evidence for links between personality, metabolic rate, and
fitness, highlighting the need for further extensive study.
Here, we report on a three-year, large-scale study on
personality and metabolic rate in wild-caught great tits
Parus major. The great tit has become a model species for
animal personality research, in which a simple assay of
exploration behaviour characterises personality. Exploration
behaviour is correlated with a suite of other behaviours,
such as risk-taking (Verbeek et al. 1996), foraging (Quinn
et al. 2012, Verbeek et al. 1994), dispersal (Dingemanse
et al. 2003, Quinn et al. 2011), promiscuity (Patrick et al.
2011, van Oers et al. 2008) and social dominance (Cole and
Quinn 2011, Dingemanse et al. 2004) both in our, and in
other, populations. Moreover, exploration behaviour is
known to be repeatable, heritable and related to fitness
(Dingemanse et al. 2002, 2004; Quinn et al. 2009, van Oers
et al. 2004a, b). As a measure of metabolic rate, we use basal
metabolic rate (BMR), the energy expenditure of organisms
as measured when they are inactive, post-adsorptive, nongrowing, non-reproductive and under thermoneutrality
(McNab 1997). BMR is assumed to represent the minimum
cost of living, is often correlated with organ mass and
other measures of energy expenditure (Daan et al. 1990),
has often been related to lifespan to test the rate-of-living
theory (Pearl 1928, Speakman et al. 2003, Wiersma et al.
2007) and is therefore a good candidate trait for testing
links between personality and metabolic rate (Careau et al.
2008). In our study population, BMR was previously
found to be highly variable but repeatable (Bouwhuis
et al. 2011). Besides studying the link between exploration
behaviour and BMR per se, we also investigate links
between exploration behaviour and methodological aspects
of metabolic rate measurements, since this will help distinguish between methodological or functional reasons underlying a potential relation between personality and metabolic
rate (Careau et al. 2008), but has received even less empirical
attention (Careau et al. 2011).
Material and methods
BMR
Great tits were captured at temporary feeding stations
between November and March, in 2005–2008 inclusive,
and individually housed with ad libitum food and water in
Wytham field station near Oxford, UK. At sunset, up to
eight individuals were taken from their cages, weighed, and
randomly transferred to one of eight chambers. Metabolic
measurements are described in detail elsewhere (Bouwhuis
et al. 2011), but, in short, chambers were kept in a dark
climate cabinet at 26°C and attached to an open-circuit
respirometer. Air-flow through the chambers was 20 l h1.
In- and out-flowing air was dried and oxygen percentages
were recorded by a paramagnetic oxygen-analyser at 5 to
10-min intervals, depending on the number of chambers
occupied. At sunrise, birds were weighed and returned to
their cages, before being assayed for personality and released
at the site of capture. Oxygen consumption was calculated
using Eq. 6 of Hill (1972) assuming a respiratory quotient of
0.72. An energy equivalent of 19.7 kJ l1 oxygen was used to
calculate energy expenditure in watt (W). BMR was taken
to be the minimum value of a 30-min running average.
Whole-animal BMR showed a repeatability of 36%
(Bouwhuis et al. 2011), while repeatability of mass-specific
BMR was much lower at 14% (Bouwhuis et al. 2011).
Average ( SD) body mass was 17.6 0.8 g for females
and 18.9 0.8 g for males.
Methodological measures of BMR
Methodological measures of metabolic rate that we chose
to analyse are 1) the standard deviation of metabolic rate
within the 30-min interval over which BMR was measured
(SDBMR), 2) the time (in hours) it took birds to reach BMR
(TBMR) upon placement in the metabolic chamber, and 3)
the level of activity of birds within the 30-min interval
over which BMR was measured (ACTBMR), recorded using
passive infrared sensors and expressed as the number of
movements per minute.
SDBMR is thought to measure nocturnal arousal and sleep
fragmentation, and has previously been shown to respond to
hormonal manipulation in growing (Spencer and Verhulst
2008), as well as adult (Astheimer et al. 1992, Buttemer
et al. 1991), birds, although in opposite directions. If personality variation were to affect the stress response to
being taken into captivity, and therefore the release of corticosterone, we would hypothesise it to affect metabolic rate
via an effect on SDBMR.
As well as the magnitude, the duration of a stress
response may also be affected by personality in birds
(Carere and van Oers 2004). While excluding the first
measurement hour to prevent problems of incomplete mixture of air in the metabolic chamber and birds not yet
having reached a post-adsorptive state, we hypothesised
that the time it takes birds to settle into sleep after disturbance and to reach their BMR would inform us of such an
effect in studies of BMR.
Finally, while BMR is defined as the energy expenditure
of inactive, post-adsorptive, non-reproductive adults measured during the resting phase of the circadian cycle and
within the thermoneutral zone (McNab 1997), many avian
studies do not measure whether birds are indeed inactive. We
measured activity using passive infrared sensors and found
that 209 out of 680 individuals (31%) showed (possibly
small) movements while their level of metabolic rate was at
its lowest. We hypothesised that such movement would
reflect overall activity levels linked to our personality measure.
Descriptive statistics for BMR, SDBMR, TBMR and
ACTBMR are given in Table 1.
Exploration behaviour
Following at least an hour of undisturbed feeding in
individual cages after metabolic measurement, assays of
exploration behaviour were conducted between 08:00 and
13:00 h as described elsewhere (Quinn et al. 2009). In short,
assays were initiated by opening a trapdoor connecting a
birds’ individual cage to a novel environment consisting of
57
Table 1. Descriptive statistics for basal metabolic rate (BMR, in Watts), the standard deviation of metabolic rate within the 30-minute
interval over which BMR was measured (SDBMR, in Watts), the time it took birds to reach BMR (TBMR, in hours) upon placement in the
metabolic chamber, the level of activity of birds within the 30-minute interval over which BMR was measured (ACTBMR, in number of
movements per minute), and the within-individual variation in BMR within a winter season (ΔBMR, in Watts).
Males
Parameter
BMR
SDBMR
TBMR
ACTBMR
ΔBMR
Females
range
average SD
range
average SD
0.242–0.568
0.000–0.177
1.000–11.783
0.000–4.166
0.098–0.076
0.456 0.040
0.018 0.027
6.810 2.504
0.180 0.411
0.000 0.028
0.281–0.531
0.000–0.167
1.000–12.167
0.000–3.000
0.077–0.077
0.437 0.038
0.016 0.026
6.619 2.632
0.163 0.404
0.000 0.021
artificial trees and novel objects (after Verbeek et al. 1994),
and switching off the light in the room with the cage while
switching on the light in the novel environment room. From
20 s after entering the novel environment, and during
8 min, the frequency and location of all movements were
recorded. Twelve behavioural measures (1–5, total number
of visits to each of five zones; 6–10, total number of visits to
each of five objects; 11, total flight duration and 12, total
hop duration) were entered into a principal component
analysis. The first component, with a positive loading for
all measures (Quinn et al. 2009), reflects activity and the
propensity to explore novel objects and areas, and is referred
to as exploration behaviour (EB). EB represents an important axis of personality: it is heritable, linked to fitness
and correlated with a range of behavioural traits in our and
other populations. There is no significant effect of ambient
temperature, Ta (in °C, provided by the Radcliffe Observatory in Oxford, 5 km east of the study area), on EB
(Ta: 0.005 0.005, χ 21 0.903, p 0.342).
Data selection and statistical analyses
We collected 812 metabolic measurements on 694 individual birds. Previous analyses of between-individual variation
in BMR in our population (Bouwhuis et al. 2011) used a
dataset in which each individual was represented once, by
choosing the first measurement in the latest season a bird was
measured, to increase the proportion of older birds in the
sample and to avoid potential habituation effects within a
winter season. The analyses revealed that variation in BMR
was positively related to body mass (in grams), and was additionally explained by interactive effects of sex, age (in years)
and seasonal date (in days since 1 September, divided by
365) with the previous 5-day average minimum ambient
temperature, such that BMR decreases with increasing ambient temperature in males, first-year birds and in the first half
of the winter season. To this linear mixed model, which also
included random effects of year (i.e. winter season) and sector of the wood, we added EB, both as a main effect, and in
interaction with all terms. Since technical problems prevented us from assessing EB in 14 out of 694 birds, the
model was run on data from 680 birds. The model was simplified by backward stepwise removal of least-significant
terms, where significance (p 0.05, two-tailed) was assessed
using the Wald statistic
For analyses of between-individual variation in SDBMR,
TBMR and ACTBMR, our initial linear mixed models included
fixed effects of body mass, sex, seasonal date, age, ambient
temperature and EB, both as main effects and in all possible
58
two-way interactions, and random effects of year and sector
of the wood. For SDBMR, the initial model also included the
covariate Nbirds, which quantified the number of measurements included in the calculation of SDBMR. These models
were again run on data from 680 birds and simplified by
backward stepwise removal of least-significant terms.
For 72 of the 680 birds for which we had information on
EB, we had repeat measurements of BMR within winter seasons (previously used to calculate repeatability, Bouwhuis
et al. 2011). We used these 153 repeat measurements to test
whether repeatability of whole-animal or mass-corrected
BMR differed between the sexes, and to test whether factors
related to between-individual variation in BMR were also
related to within-individual variation in BMR. For each
bird we calculated its average BMR, body mass, date of
capture and ambient temperature prior to capture, and
used it to calculate the deviation from the average for
each variable for each measurement (van de Pol and Wright
2009). The deviation in BMR was then analysed in relation
to fixed effects of the deviation in body mass, seasonal date
and ambient temperature, as well as in relation to fixed
effects of sex and EB, and all two-way interactions. Random
effects included were year, sector of the wood and individual
identity, and models were simplified by backward stepwise
removal of least-significant terms.
Standardised linear, quadratic and correlational selection
gradients for BMR, mass-corrected BMR and EB were calculated following Lande and Arnold (1983). Selection gradients were calculated for each sex and winter season separately
using relative fitness (an individual’s fitness divided by the
average fitness) and trait values standardised to a mean of 0
and unit variance. The estimates for the quadratic selection
gradients were doubled (Stinchcombe et al. 2008). Significance of selection gradients was determined in generalized
linear models assuming a binomial or Poisson distribution.
As fitness measures we used whether birds were observed
breeding in the breeding season following their BMR measurement, the number of fledglings they produced in the
breeding season following their BMR measurement, and
whether they were observed in any breeding season or winter
(up to 2011) following their BMR measurement.
All models were run in MLwiN 2.02 (Rasbash et al. 2005).
Results
We found a relationship between EB and BMR that
differed between males and females (Table 2). Subsequent
sex-specific analyses showed this to be due to a negative
Table 2. Results from models testing the effect of exploration behaviour (EB) on basal metabolic rate in 680 great tits. Shown are
parameter estimates with standard errors and significance
(∗p 0.05, ∗∗p 0.01, ∗∗∗p 0.001). Values for non-significant
terms are presented as estimated when re-added to the minimal
adequate model. For sex, the estimate compares males to females;
Ta denotes ambient temperature.
Parameter
mass
sex
date
age
Ta
sex Ta
date Ta
age Ta
EB
EB mass
EB sex
EB date
EB age
EB Ta
EB sex Ta
EB date Ta
EB age Ta
Estimate SE
χ2
1
0.020 0.002
0.014 0.006
0.042 0.020
0.010 0.002
0.007 0.002
0.002 0.001
0.011 0.004
0.001 0.000
0.011 0.004
0.002 0.004
0.015 0.006
0.053 0.033
0.000 0.003
0.001 0.001
0.002 0.002
0.013 0.010
0.002 0.001
157.281***
5.774*
4.492*
24.288***
13.599***
3.776*
7.715**
5.657*
6.293*
0.264
6.424*
2.661
0.002
2.114
1.308
1.616
2.067
relationship in females (EB: 0.010 0.004, χ 21 5.259,
p 0.022, Fig. 1), while the relationship was slightly
positive, but not significantly different from zero, in males
(EB: 0.005 0.005, χ 21 1.267, p 0.260, Fig. 1).
Variation in SDBMR was explained by an interactive
effect of seasonal date and ambient temperature, showing
that it was reduced in birds captured after experiencing cold
conditions early in the season (Supplementary material
Appendix A1 Table A1, Fig. A1). Variation in SDBMR was
not explained by EB (EB: 0.001 0.002, χ 21 0.007,
Figure 1. Basal metabolic rate (corrected for other fixed effects) in
relationship to exploration behaviour (EB) in 345 female (white
circles, dashed line) and 335 male (black circles, solid line) great
tits. Shown are individual data points and average BMR ( SD) for
EB categories of equal sample size.
p 0.933), or any interactions including EB (data not
shown). SDBMR was negatively related to BMR (SDBMR:
0.416 0.049, χ 21 71.769, p 0.001), suggesting that
the least stable BMR measurements more often include a
low recording of metabolic rate. Variation in SDBMR
could not be explained by variation in the number of
measurements included in its calculation (Nbirds: 0.000 0.001, χ 21 0.601, p 0.438).
Variation in TBMR was explained by variation in body
mass, seasonal date, age and an interactive effect of sex and
ambient temperature (Supplementary material Appendix A1
Table A2). The effects of body mass, seasonal date and
age were positive, such that heavier birds, birds captured
later in the season and older birds took longer to reach their
basal metabolic rate (Supplementary material Appendix A1
Fig. A2a–c). The interactive effect of sex and ambient temperature showed that males captured after experiencing
warm conditions reached their BMR sooner than males
captured after experiencing cold conditions, while there
was no such effect in females (Supplementary material
Appendix A1 Fig. A2d). There was no effect of EB (EB:
0.263 0.221, χ 21 1.417, p 0.234) or any interactions including EB (data not shown) on TBMR, and
variation in TBMR did not explain variation in BMR
(TBMR: 0.000 0.001, χ 21 0.021, p 0.885).
Variation in ACTBMR could not be explained by any
of our candidate variables, including EB (EB: –0.010 0.035, χ 21 0.087, p 0.768) or any interactions including
EB (data not shown). Variation in ACTBMR did also not
explain variation in BMR (ACTBMR: 0.003 0.003,
χ 21 0.835, p 0.361), which suggests that the low activity levels that we did observe most likely represent small
movements made during sleep.
The sex-specific link between EB and BMR remained
when adding SDBMR (sex EB: 0.013 0.006, χ 21 4.802,
p 0.028), TBMR (sex EB: 0.015 0.006, χ 21 6.420,
p 0.011), or ACTBMR (sex EB: 0.016 0.006,
χ 21 6.585, p 0.010) as a covariate to the minimal
adequate model, or when removing all individuals with
ACTBMR 0 from our data (sex EB: 0.014 0.007, χ 21 3.875, p 0.049, n 471). This suggests that the link
was unlikely to have been driven by behavioural variation
during the metabolic measurement, or by birds not conforming to standard conditions for BMR measurement.
Male and female repeatability ( SE) estimates of
whole-animal BMR (0.266 0.139 and 0.392 0.143,
respectively) and mass-corrected BMR (0.098 0.145
and 0.207 0.160, respectively) overlapped, such that a
sex difference in consistency of BMR is unlikely to underlie
the sex-specific link between EB and BMR. Moreover,
within-individual change in BMR within winters was
explained by an interactive effect of sex and withinindividual change in body mass (Supplementary material
Appendix A1 Table A3, Fig. A3), but not by EB (EB:
0.001 0.005, χ 21 0.041, p 0.840) or any interactions
including EB (data not shown).
Standardised correlational selection gradients for BMR,
mass-corrected BMR and EB on fecundity and survival were
small and did not reach statistical significance (Tables 3,
Supplementary material Appendix A1 Tabel A4). Linear and
quadratic selection gradients for BMR, mass-corrected BMR
59
and EB separately were also small, but the quadratic gradients for BMR and EB reached statistical significance in
males when the number of fledglings produced in the
breeding season subsequent to BMR measurement was
used as a fitness measure (Table 3, Supplementary material
Appendix A1 Tabel A4). These gradients suggest stabilising
selection on male BMR, but disruptive selection on male
EB. These results did not depend on whether the capture
date of the birds was taken into account (data not shown).
Discussion
Using data collected over three winters on nearly 700 individual great tits, we tested a recently proposed hypothesis
that personality and metabolic rate are interlinked, and that
this link arises for methodological or functional reasons, or
both (Careau et al. 2008, Réale et al. 2010). In our study
population, we have previously found variation in basal
metabolic rate to be related to body mass and interactive
effects of sex, age, seasonal date and ambient temperature
(Bouwhuis et al. 2011). Against these background variables,
we found weak support for a relationship between exploratory behaviour – an important axis of personality – and
BMR. In males, there was no significant relationship
between EB and BMR at all, while in females there was a
negative relationship. This latter relationship showed that
over the full range of EB, the most active and exploratory
females had a 4.5% lower BMR than the least active and
exploratory females in our sample.
A negative relationship between personality and metabolic rate does not agree with the expectation that personality and metabolism could both be aspects of a general
slow-fast life-history continuum, and hence be positively
associated (Careau et al. 2008, Biro and Stamps 2010, Réale
et al. 2010). Instead, if genuine, such a relationship might
suggest that allocation is more important: if females are
limited by resource availability (i.e. their daily energy
expenditure, DEE, has a maximum upper limit), energy
spent on exploration and activity limits the energy that can
be allocated to BMR (reviewed by Wiersma and Verhulst
2005, Careau and Garland 2012). This explanation has previously been proposed in an interspecific study on 19
muroid species, which found that more explorative muroid
species had lower BMR (Careau et al. 2009). In that study
it was also suggested that this pattern should reflect adaptive
individual variation in an environment where resource
availability is patchy and unpredictable (Careau et al.
2009). Specifically, it was argued that fast explorers should
outcompete slow explorers during resource shortage because
1) they are better at finding food, and 2) their low BMR
would compensate for food shortage and promote survival
under harsh conditions (Careau et al. 2009). To maintain
variation, slow explorers should then outcompete fast
explorers under favourable conditions, for example because
costs of aggression (e.g. injury) and of low BMR (e.g. slow
growth and delayed onset of reproduction, Careau et al.
2009) come into play. Under this scenario, we would expect
correlational selection on EB and BMR, with selection
favouring individuals with fast exploration and low BMR
or with slow exploration and high BMR. Such correlational
selection was indeed found in a recent study on common
lizards (Le Galliard et al. 2013), but could not be replicated
in our current study with fitness measures of breeding
probability, fecundity or survival, since estimates for interactions between EB and BMR were small and did not
reach statistical significance (Table 3). Instead, we found
stabilising selection on BMR, but disruptive selection on
EB, when assessing fecundity in male great tits, but no
evidence for selection on either trait in females.
Table 3. Standardised linear, quadratic and correlational selection gradients for basal metabolic rate (BMR) and exploratory behaviour
(EB) in relation to three fitness measures in 335 male and 345 female great tits. Gradients for mass-corrected BMR and EB are shown in
Supplementary material Appendix A1 Table A4. Shown are parameter estimates obtained from three models for each sex and fitness trait
combination (one for linear gradients including BMR and EB terms, one for quadratic gradients including BMR, EB, BMR2 and EB2 terms, and
one for correlational gradients including BMR, EB, BMR2, EB2 and BMR EB terms) with standard errors and χ2-values and significance
calculated from logistic and Poisson regressions (∗p 0.05, ∗∗p 0.01, ∗∗∗p 0.001).
Males
Estimate SE
Reproduction
BMR
EB
BMR2
2
EB
BMR EB
No. of offspring
BMR
EB
BMR2
2
EB
BMR EB
Survival
BMR
EB
2
BMR
2
EB
BMR EB
60
0.039 0.064
0.009 0.064
0.064 0.037
0.128 0.061
0.071 0.059
0.035 0.070
0.020 0.070
0.076 0.040
0.200 0.067
0.048 0.065
0.046 0.051
0.023 0.051
0.008 0.030
0.028 0.049
0.027 0.048
Females
χ²1
0.559
0.018
2.589
1.877
1.034
2.495
1.563
15.968***
16.650***
1.254
0.380
0.024
0.134
0.360
0.031
Estimate SE
χ2
1
0.034 0.056
0.038 0.056
0.062 0.033
0.068 0.056
0.031 0.055
0.001
0.848
0.002
0.022
0.028
0.029 0.065
0.016 0.065
0.082 0.038
0.104 0.065
0.034 0.064
0.012
0.887
0.391
2.012
0.611
0.004 0.050
0.049 0.050
0.066 0.030
0.058 0.050
0.057 0.116
0.677
1.984
0.081
0.180
0.027
Our analyses provided some support for a functional
relationship between personality and BMR, at least in
females, but no evidence for a methodological relationship,
since EB did not explain between-individual variation in
variability of BMR measurements, the time after which
BMR was reached, or the level of activity of birds during
BMR measurement. Moreover, we found no effect of any
of these methodological aspects of metabolic rate
measurements on the sex-specific link between EB and
BMR. Importantly, we also did not find an effect of
SDBMR, TBMR and ACTBMR on BMR itself. This is reassuring, because it suggests that birds settled down and that any
remaining movements were most likely small and made
within sleep, such that birds conformed to standard conditions for BMR measurement. We would, however, like to
point out that measures like SDBMR, TBMR or ACTBMR
are infrequently reported in studies of metabolic rate
(Spencer and Verhulst 2008) but that they are informative
regarding the quality of the metabolic measurements
(this study) or for analysis of links between personality and
metabolic rate (Careau et al. 2008).
Our weak support for a functional link between
exploration behaviour and basal metabolic rate adds to the
inconclusive support provided by three other studies in natural populations, which found no link (Timonin et al.
2011), a positive link, but only in females and only during
the non-reproductive season (Lantová et al. 2011), or no
link, but correlational selection (Le Galliard et al. 2013). At
present, we can only speculate as to why between-individual
variation in personality measures shows no straightforward
correlation with variation in metabolic rate measures. One
aspect may be that both personality and metabolic rate only
have moderate repeatabilities, which would unavoidably
cause relationships between the two to be weak unless
they fluctuate in parallel within individuals. This may also
explain why we found a relationship in females only, since
repeatability of both whole-animal and mass-corrected
BMR was higher, although not significantly so, in females
than in males. Perhaps, especially in male great tits BMR is
too inconsistent to show a relationship with other moderately repeatable traits. Alternatively, while personality is
defined by consistent behavioural differences over time or
across situations (Réale et al. 2010), levels of metabolic
rate are known to vary between seasons (McKechnie 2008).
For example, we have shown previously that the withinindividual change from winter to summer is not necessarily
consistent between individuals (repeatability of 1.3%,
Bouwhuis et al. 2011). Perhaps individuals adopt different
metabolic strategies, and perhaps the strategy, instead of the
level of metabolic rate, is related to personality. In our study
we only have limited data to investigate this (n 72), but
within-season changes in BMR were not found to be related
to EB. Using data on 55 females captured in the breeding
season subsequent to their BMR measurement, we can also
show that breeding BMR is not related to EB when correcting for mass, age and ambient temperature effects (EB:
0.014 0.015, χ21 0.843, p 0.359), nor is the change
from winter to breeding BMR when correcting for the change
in mass and ambient temperature (EB: 0.009 0.019,
χ 21 0.222, p 0.638). More longitudinal data are, however, needed to better quantify metabolic strategies and
their potential link with personality (also see Biro and
Stamps 2010).
Finally, most studies to date have used measures of basal
or resting metabolic rate to study links with personality
(Careau and Garland 2012). Although these measures are
more easily obtained in natural populations than measures
of daily energy expenditure, for which individuals need to
be captured multiple times, predictions for the shape of a
potential relationship between personality and metabolic
rate depend on how measures of basal and resting metabolic rate are related to daily energy expenditure. If metabolic strategies do exist (also see Vézina et al. 2006),
then not only may individuals vary in their responses in
BMR to seasonal or other environmental changes in relation to their personality, but also in their relationship
between BMR and DEE. Holistic studies of links between
personality and metabolic rate therefore ideally adopt
multiple measures of metabolic rate.
In conclusion, our analyses provide no evidence for a
methodological link between personality and metabolic rate
in a wild bird population. Evidence for a functional link
between personality and metabolic rate was found only in
females. We suggest that future studies of links between
personality and metabolic rate should focus on complete
metabolic profiles over longer periods of time, but realise
that such data are not easily collected.
Acknowledgements – We are very grateful to Gerard Overkamp,
Ger Veltman, Pawel Koteja and Martijn Salomons for assistance
with respirometry measurements and calculations, to Andy
Gosler, Julian Howe, Jane Carpenter and Samantha Patrick for
their help in the field, to David Wilson for taking excellent care
of the birds while being in captivity, and to Phil Smith for
managing the field station. Fieldwork was supported by grants
from the Schure-Beijerinck-Popping Fund and the Dutch Science
Foundation (NWO) to SB, who was also supported by an NWO
Rubicon grant and ERC grant AdG 250164 to BCS during
writing. SV was funded by an NWO Vici-grant.
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