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Journal of Zoology
Journal of Zoology. Print ISSN 0952-8369
Quantitative genetics parameters show partial independent
evolutionary potential for body mass and metabolism in
stonechats from different populations
B. I. Tieleman1,2, M. A. Versteegh1, B. Helm2 & N. J. Dingemanse1,3
1 Animal Ecology Group, Centre for Ecological and Evolutionary Studies, University of Groningen, AA Haren, The Netherlands
2 Max Planck Institute for Ornithology, Andechs, Germany
3 Department of Behavioural Biology, Centre for Behaviour and Neurosciences, University of Groningen, AA Haren, The Netherlands
Keywords
basal metabolic rate (BMR); body mass;
heritability; evolvability; stonechat; bird;
genetic correlation.
Correspondence
B. Irene Tieleman, Animal Ecology Group,
Centre for Ecological and Evolutionary
Studies, University of Groningen, PO Box
14, 9750 AA Haren, The Netherlands. Tel:
+31 50 3638096; Fax: +31 50 3635205
Email: [email protected]
Editor: Jean-Nicolas Volff
Abstract
Phenotypic variation in physiological traits, such as energy metabolism, is
commonly subjected to adaptive interpretations, but little is known about the
heritable basis or genetic correlations among physiological traits in non-domesticated species. Basal metabolic rate (BMR) and body mass are related in complex
ways. We studied the quantitative genetics of BMR, residual BMR (on body
mass), mass-specific BMR and body mass of stonechats originating from four
different populations and bred in captivity. Heritabilities ranged from 0.2 to 0.7.
The genetic variance–covariance structure implied that BMR, mass-specific BMR
and body mass can in part evolve independently of each other, because we found
genetic correlations deviating significantly from one and minus one. BMR, massspecific BMR and body mass further differed among populations at the phenotypic level; differences in the genetic correlation among populations are discussed.
Received 13 January 2009; revised 9 April
2009; accepted 7 May 2009
doi:10.1111/j.1469-7998.2009.00597.x
Introduction
Phenotypic variation in physiological traits, such as energy
metabolism and measures of aging, is commonly subject to
adaptive interpretations in both comparative and intraspecific studies (Drent & Daan, 1980; Nagy, 1987; Speakman
et al., 2002). Little, however, is known about the heritable
basis of these traits in non-domesticated species, especially
birds, despite few studies on small rodents (Lacy & Lynch,
1979; Ksiazek, Konarzewski & Lapo, 2004; Nespolo et al.,
2005; Sadowska et al., 2005; Brzek et al., 2007; Johnson &
Speakman, 2007; Nespolo, Bacigalupe & Bozinovic, 2007;
Szafranska, Zub & Konarzewski, 2007) and one study on
birds (Rønning et al., 2007). In addition, little information is
available about genetic connections among metabolic traits
and between metabolism and body mass, a trait that
correlates with considerable variation in metabolism at the
phenotypic level (e.g. Rønning et al., 2007).
Adaptive hypotheses concerning relationships among
physiological traits are often evaluated by assessing their
phenotypic correlations. Implicit in this approach is the
assumption that phenotypic correlations reflect the underlying genetic correlations (Hadfield et al., 2007). However,
this assumption is not necessarily met for traits with moderate heritabilities (Lande, 1982; Hadfield et al., 2007). Therefore, for physiological traits, we need information on genetic
correlations, to help predict evolutionary change in response
to selection, hence adaptation (Pease & Bull, 1988; Hadfield
et al., 2007).
Genetic correlations provide only a snapshot of the
genetic relationships between traits at present (Steppan,
Phillips & Houle, 2002), because they themselves might
evolve either due to stochastic events (Armbruster &
Schwaegerle, 1996) or in response to selection acting on trait
correlations [‘correlational selection’ (Lande & Arnold,
1983)]. As a result of different evolutionary histories, different genetic connections among traits may exist in different
populations. Therefore, comparative studies of phenotypic
variation of energy metabolism among populations or
species that seek to make adaptive explanations are
strengthened by knowledge of genetic correlations among
traits.
Energy metabolism has attracted widespread interest
over the past century, in disciplines ranging from evolutionary ecology to comparative physiology, biomedicine and
aging. Each application uses its own preferred measure of
c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London
Journal of Zoology 279 (2009) 129–136 129
Genetics of metabolism in birds
B. I. Tieleman et al.
metabolism, resulting in a suite of traits, all derived from
measurements of basal metabolic rate (BMR). BMR itself is
defined as the minimum energy expenditure of a postabsorptive animal measured during the rest phase and at
thermoneutral temperatures (King, 1974), and is thought to
represent an animal’s minimum energetic maintenance
costs. In this paper, we highlight three common measures
used in different contexts: first, whole-organism BMR,
expressed in kJ day 1 animal 1, is related to the overall daily
energy expenditure in the field (e.g. Daan, Masman &
Groenewold, 1990), activity (e.g. Deerenberg et al., 1998),
food availability and diet (e.g. McNab, 1988; Tieleman,
Williams & Bloomer, 2003), and used in an ecological
context as a predictor of the energetic costs of living.
Second, residual BMR (expressed in residuals of the relationship between log BMR and log body mass, or in
kJ day 1 g x, where x is the slope of this relationship) is
mostly used in comparative studies of physiology where the
general increase of BMR with size needs to be incorporated
(e.g. Tieleman & Williams, 2000; Wiersma et al., 2007).
Third, mass-specific BMR calculated as BMR divided by
body mass (in kJ day 1 g 1) is commonly applied in studies
evaluating tissue-level processes, such as mitochondrial
function, production of free radicals and other measures
related to aging or intrinsic mortality (e.g. Speakman, 2005).
Body mass is often considered a confounding factor in
studies of metabolic measures, but the exact nature of the
connection is unresolved, and the variability of the relationship between metabolic rate and mass within and among
species is large (McKechnie, Freckleton & Jetz, 2006).
Exploring the genetic connections between body mass and
the various measures of BMR, and among the measures of
BMR themselves, is interesting for two reasons. First, BMR,
residual BMR and mass-specific BMR are mathematically
related, through body mass, but we do not know whether
they are coded by the same or different sets of genes. We
emphasize that the correlation between mass-specific BMR
and mass does not have to be confounded by autocorrelation because mass-specific BMR is commonly used as a
single trait (related to tissue-level processes), and because
the two traits (mass and BMR), from which it is calculated,
can potentially evolve independently. Second, one might
expect that these measures are subjected to different selective pressures: some factors, such as food availability, could
act as a selection pressure on whole-organism BMR or body
mass, whereas at the same time other factors, such as
extrinsic mortality by predation, could act as a selection
pressure on mass-specific BMR (Tieleman et al., 2006). Yet,
we do not know whether body mass, whole-organism BMR,
residual BMR and mass-specific BMR can evolve independently of each other. Therefore, documenting whether
associations among these traits can in principle be uncoupled is essential for evolutionary interpretations of these
traits.
We studied four populations of stonechats, collected
from different environments, and kept and bred under
identical conditions in captivity. The differences among the
environments of origin are reflected in different life-history
130
strategies and annual cycles (Gwinner, König & Haley,
1995; Helm & Gwinner, 1999; Rödl et al., 2004). In previous
studies, this common-garden setup has led to interpretation
of differences in metabolic physiology among populations in
the light of genetic differences (Klaassen, 1995; Wikelski
et al., 2003; Tieleman, 2007; Tieleman et al., 2009).
In this study, we aimed to understand the genetic basis
underlying phenotypic (co)variation in three measures of
BMR and body mass in birds to explore the evolutionary
potential of these traits independently and in concert. We
estimated the heritability of body mass, whole-organism
BMR, residual BMR and mass-specific BMR, and the
genetic correlations among these traits, in stonechats that
originated from four wild populations. To illustrate the
contribution of quantitative genetics knowledge to comparative studies, we compared the phenotypic means of
body mass and the metabolic measures among the four
populations, and we explored differences in additive genetic
variances, heritabilities and genetic correlations among
these populations.
Methods
Birds and housing
We made 123 measurements on 89 birds belonging to four
populations [considered (sub)species by some but not others
(Woog et al., 2008)] of stonechats originating from Kenya
(Saxicola torquata axillaris, n= 14), Austria (Saxicola torquata rubicola, n = 45), Kazakhstan (Saxicola torquata.
maura, n = 15) and Ireland (Saxicola torquata hibernans,
n = 15). All birds were hand-raised in Andechs, Germany,
after being bred in captivity or being collected in the field as
nestlings (Gwinner et al., 1995). After fledging, they were
housed in individual cages under constant temperatures of
20–22 1C and day length conditions mimicking those of
Andechs (481N111E). All birds were measured during February–March 2005 and/or November 2005–February 2006,
during their quiescent winter phase.
Pedigrees
Over a timespan of 15 years, wild-caught birds were brought
into captivity. We assumed that wild-caught nestlings originally collected from different nests were not related to
each other, and that nestlings collected from the same nest
had the same genetic father and mother. In captivity, stonechats bred with conspecifics within their population of
origin, in aviaries that contained a single pair of birds; thus,
for all offspring the genetic father and mother were known.
The breeding scheme was not designed for the purpose of a
quantitative genetics study. As a result, we did not have a
standard breeding setup per generation, and instead had
four populations with known relationships among birds,
including crosses among generations. The resulting pedigrees included 50 Kenyan, 127 Austrian, 53 Kazakh and
55 Irish individuals. The birds that we measured
c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London
Journal of Zoology 279 (2009) 129–136 B. I. Tieleman et al.
Genetics of metabolism in birds
Table 1 Estimates of additive genetic variance (VA), residual variance (VR), heritability (h2), coefficient of additive genetic variation (CVA) and
coefficient of residual variation (CVR) of body mass and different measures of metabolic rate in stonechats from (a) Austria, (b) Kenya and (c)
Kazakhstan
VA (SE)
P (VA)
VR (SE)
h2 (SE)
(a) Austrian stonechats (n =72, N = 45)
Body mass
14.3 (1.73)
BMR
22.0 (2.34)
Residual BMR
0 (1)
Mass-specific BMR
1.55 (0.18)
0.548 (0.445)
3.245 (1.419)
0.506 (0.242)
0.0130 (0.0071)
0.17
0.002
0.003
0.02
1.711 (0.405)
2.606 (0.707)
0.554 (0.143)
0.0194 (0.0049)
(b) Kenyan stonechats (n =18, N = 14)
Body mass
18.0 (2.43)
BMR
22.3 (1.83)
Residual BMR
0 (1)
Mass-specific BMR
1.26 (0.14)
4.741 (3.289)
0.737 (1.542)
0.197 (0.364)
0.0084 (0.0086)
0.17
0.54
0.46
0.23
(c) Kazakh stonechats (n = 18, N =15)
Body mass
13.1 (1.40)
Mass-specific BMR
1.67 (0.22)
1.318 (0.772)
0.0181 (0.0269)
0.01
0.47
Trait
Phenotypic mean (SD)
CVA
CVR
0.243 (0.177)
0.555 (0.152)
0.477 (0.160)
0.401 (0.170)
5.2
8.2
–
7.4
9.2
7.3
–
9.0
2.157 (1.373)
2.915 (1.450)
0.767 (0.360)
0.0107 (0.0058)
0.687 (0.245)
0.202 (0.391)
0.204 (0.347)
0.441 (0.343)
12.1
3.9
–
7.3
8.2
7.7
–
8.2
0.485 (0.344)
0.0315 (0.0212)
0.731 (0.213)
0.365 (0.467)
8.8
8.1
5.3
10.6
P values indicate significance of difference from zero for estimates of VA. Total phenotypic variance was partitioned into VA and VR. Sex was
included as fixed factor for all traits. Note that we only show traits for which models converged. Phenotypic means (SD) for traits that were not
included in the table because models did not converge: Kazakh stonechats BMR = 21.8 (2.97) kJ day 1; Irish stonechats’ (n =15, N = 15) body
mass = 15.2 (1.73) g, BMR = 23.2 (1.66) kJ day 1, mass-specific BMR = 1.55 (0.186) kJ day 1 g 1).
BMR, basal metabolic rate; n, number of measurements; N, number of individuals measured.
phenotypically belonged to generations 0 (wild caught) to 4,
with the largest numbers from generations 1 and 2.
Laboratory setup and metabolic
measurements
Laboratory setup, measurement protocol and data analysis
to determine metabolic rate and body mass are described
elsewhere (Tieleman, 2007; Versteegh et al., 2008; Tieleman
et al., 2009). Standardized residuals from the relationship
between log BMR and log body mass were calculated
separately for each population, using SPSS v. 14.0.
Estimation of genetic parameters and
statistical analysis
We estimated variance components, heritabilities and genetic correlations with restricted maximum likelihood models
(Falconer & Mackay, 1996; Kruuk, 2004) using the program
ASReml 2.0 (Gilmour et al., 2006). For each population and
trait, we used a univariate animal model to partition the
total phenotypic variance (VP) into additive genetic variance
(VA) and residual variance (VR). Models included sex as a
fixed effect, accounting for differences between sexes in
phenotypic means and animal as an additional random
effect, taking into account repeated measurements that were
available for one-third of the birds. The common environment experienced by offspring from the same brood or with
the same social parents may bias the estimates of additive
genetic variances and covariances. We ran models that
included permanent environment effects (Austrian population only), nest effects, environmental and genetic maternal
and paternal effects for the Austrian and Kenyan populations, but found no significant contributions of any of these
effects to the total phenotypic variance. Therefore, we
decided not to include these effects. We caution that the
sample sizes and the statistical power are limited and
emphasize that estimates of VA and VR may be biased
depending on inclusion of other variance components. Some
models, especially for the Irish and Kazakh populations, did
not converge, possibly due to the combination of variation
in traits and structure of the pedigree (A. Gilmour, pers.
comm.), explaining missing values in Tables 1 and 2. Heritability (h2) was calculated as the ratio of VA over VP
(Falconer & Mackay, 1996). We used bivariate animal
models to estimate additive and residual covariances, and
correlations. We present genetic correlations (rA) only when
the geometric mean heritability of two traits was above 0.15
to avoid spurious results (following Roff, 2001).
To test whether VA was significantly larger than zero, we
compared the 2 loglikelihood of a model that estimated
VA with one that did not [i.e. one-tailed test (Shaw, 1991)].
To test whether estimates of correlation were significantly
different from zero (and from 1 or 1), we compared the
2 loglikelihood of models with VA fixed at the value
estimated by an unconstrained model, and rA either fixed at
zero (or 1 or 1) or unconstrained. For combinations of
traits that generally show positive phenotypic correlations
(i.e. all combinations of mass, BMR and mass-specific
BMR, except the combination mass and mass-specific
BMR), we tested rA against 1, whereas for the combination
of traits with a negative phenotypic correlation (i.e. mass
and mass-specific BMR), we tested rA against 1 (Speakman, 2005). We used two-tailed P values from a w2 distribution for comparisons with zero, and one-tailed values for
comparisons with 1 or 1.
To compare additive genetic and residual variances
across traits, we calculated coefficients of additive genetic
c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London
Journal of Zoology 279 (2009) 129–136 131
Genetics of metabolism in birds
B. I. Tieleman et al.
Table 2 Phenotypic correlations (rP SE, in italics) and additive genetic correlations (rA SE, underlined) between body mass and three measures
of metabolic rate in stonechats from (a) Austria, (b) Kenya, (c) Kazakhstan and (d) Ireland
Body mass (g)
(a) Austrian stonechats
Body mass (g)
–
BMR (kJ day 1)
Residual BMR
Mass-specific BMR (kJ day
1
g 1)
(b) Kenyan stonechats
Body mass (g)
–
BMR (kJ day 1)
Residual BMR
Mass-specific BMR (kJ day
1
g 1)
(c) Kazakh stonechats
Body mass (g)
BMR (kJ day 1)
Residual BMR
Mass-specific BMR (kJ day
(d) Irish stonechats
Body mass (g)
BMR (kJ day 1)
Residual BMR
Mass-specific BMR (kJ day
0.370 (0.119)
0.400 (0.349)
0.001 (0.136)
0.125 (0.428)
0.504 (0.102)
0.326 (0.397)
0.586 (0.185)
0.780 (0.360)
0.090 (0.280)
0.068 (0.867)
0.795 (0.103)
0.914 (0.141)
BMR (kJ day 1)
P0 = 0.28
P1 = 0.01
–
0.927 (0.020)
0.968 (0.030)
0.611 (0.088)
0.781 (0.180)
P0 = 0.10
P1 = 0.19
–
0.859 (0.073)
0.680 (0.504)
0.026 (0.278)
0.443 (0.740)
Residual BMR
P0o0.0001
P1 = 0.01
P0o0.0001
P1 = 0.07
–
0.857 (0.037)
0.909 (0.084)
P0 = 0.94
P1 = 0.10
P0 = 0.08
P1 = 0.06
–
0.535 (0.200)
0.375 (0.784)
–
1
1
g 1)
g 1)
0.409 (0.210)
0.040 (0.250)
0.391 (0.168)
1.0 (0.361)
–
0.257 (0.259)
0.068 (0.276)
0.802 (0.099)
Mass-specific
BMR (kJ day 1 g 1)
P0 =0.40
P 1 = 0.005
P0 =0.005
P1 =0.09
P0o0.0001
P1 =0.12
–
P0 =0.005
P 1 = 0.19
P0 =0.54
P1 =0.045
P0 =0.63
P1 =0.18
–
P0 =0.025
P 1 = 0.81
–
0.897 (0.049)
0.655 (0.143)
–
0.919 (0.039)
–
–
Not estimable
0.364 (0.241)
–
0.535 (0.198)
–
Note that we only show estimates of rA for two traits with geometric mean heritabilities 40.15 (see Table 1). Above the diagonal, P values denote
if rA differed from 0 (P0), 1 (P1) or 1 (P 1); we tested against 1 or 1 depending on the sign of the expected phenotypic correlation. Sample sizes
include Austrian stonechats (n = 72, N = 45), Kenyan stonechats (n = 18, N = 14), Kazakh stonechats (n =18, N = 15) and Irish stonechats (n =15,
N =15).
BMR, basal metabolic rate.
variance (CVA) and residual variance (CVR) following
Houle (Houle, 1992).
We compared the phenotypic means and estimates of VA,
VR and h2 among populations. Phenotypic means for mass,
BMR and mass-specific BMR were compared using a mixed
model with individual entered as a random effect (using
MLwiN 2.02, Rasbash et al., 2005). We included sex and
population (and body mass in a model for BMR) as fixed
effects, and used backward elimination of non-significant
terms (P40.05) as the selection procedure (Crawley, 1993).
We used multivariate animal models to compare VA of the
same trait among populations. In these models, the trait of
interest was entered as a separate y-variable for each
population included, and the additive genetic and residual
covariances between the y-variables were constrained to
zero. Next, we calculated the w2-distributed difference between the 2 loglikelihoods of a multivariate animal
model where the populations were constrained to have
132
identical VA’s versus one where VA was estimated independently for each population. We compared estimates of h2
and rA among populations using z-scores (Zar, 1996).
Comparisons of h2 among populations can be problematic,
but, in combination with comparisons of VA and VR, can be
readily interpreted (Houle, 1992). Implicit in this approach
is the assumption that phylogenetic relationships among
populations do not bias our results.
Results
Heritabilities
Estimates of h2 for body mass ranged from 0.24 to 0.73, for
BMR from 0.20 to 0.56, for residual BMR from 0.20 to 0.48
and for mass-specific BMR from 0.37 to 0.44 among stonechat populations (Table 1). For Austrian birds, h2 differed
significantly from zero for all physiological traits but not for
c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London
Journal of Zoology 279 (2009) 129–136 B. I. Tieleman et al.
body mass, whereas for Kenyan and Kazakh stonechats, h2
was not significantly different from zero for the physiological traits but significant for body mass.
Correlations among traits
At the phenotypic level, correlations of body mass with
BMR and mass-specific BMR followed expectations based
on general patterns in the literature (e.g. Speakman, 2005),
for all populations: the correlation with body mass was
positive for BMR and negative for mass-specific BMR
(Table 2). The phenotypic correlations between mass and
residual BMR were all zero, befitting the definition of
residual BMR. All equivalent genetic correlations appeared
to be stronger than their phenotypic counterparts, except for
the correlation between body mass and mass-specific BMR
in the Austrian stonechats that had a lower value than its
phenotypic counterpart (Table 2). This implies that the
environment (residual correlation) counteracted the genetic
link between mass and the three metabolic measures, but in
the case of the Austrian birds it enhanced the link between
mass and mass-specific BMR. Estimates of rA deviated from
0, 1 or 1 for several trait combinations in the different
populations (Table 2).
The phenotypic correlations with BMR were positive for
residual and mass-specific BMR in all populations, with the
exception of a zero correlation for mass-specific BMR in the
Kenyans (Table 2). The genetic correlations of BMR with
residual and mass-specific BMR were stronger than the
phenotypic ones in the Austrian population, but weaker for
residual BMR and even of a different sign for mass-specific
BMR in the Kenyans (Table 2). Most estimates of rA
deviated significantly from either 0, 1 or 1 (Table 2).
Phenotypic and genetic differences among
populations
Detailed analyses of phenotypic differences among populations are described elsewhere (Wikelski et al., 2003; Tieleman, 2007). Briefly, in the current dataset (phenotypic
means reported in Table 1, Versteegh et al., unpubl. data),
populations differed in body mass (w2 = 49.5, d.f. =3,
Po0.0001), with significant differences between all populations, except the Austrian and Irish ones, based on post hoc
analyses. BMR did not differ significantly among populations although it showed a trend (w2 = 7.4, d.f. =3,
P= 0.06), a result that did not change when body mass was
included as a covariate in the model (mass w2 = 4.5, d.f. = 1,
P= 0.03; population w2 = 6.9, d.f.= 3, P= 0.07). Yet, massspecific BMR differed highly significantly among populations (w2 = 37.7, d.f. = 3, Po0.001), with post hoc analyses
showing significant differences between all populations,
except Austrian and Irish birds.
To explore the differences in evolutionary potential
among populations, we compared h2, VA and VR, in addition to the genetic correlations between body mass and the
metabolic measures. Although h2 for body mass appeared to
be low in the Europeans in comparison with the Kenyans
Genetics of metabolism in birds
and Kazakhs (Table 1), none of the pairwise comparisons
revealed statistically significant differences (all zo1.77, all
P40.08). Likewise, differences among Austrian, Kenyan
and Kazakh (only for BMR) in VA or VR for mass, BMR
and mass-specific BMR were not significant (all P40.29).
The genetic correlations between mass and BMR or massspecific BMR appeared to be higher in the Kenyan population than in the Austrian population, but these differences
did not reach statistical significance (all P40.1 in pairwise
comparisons using z-scores). Even the estimates of rA
between BMR and mass-specific BMR did not differ significantly between the Kenyan and the Austrian populations, despite rA being negative in the Kenyan population
and positive in the Austrian population (z = 1.61, P= 0.10).
The only significantly different rA was the one relating mass
and mass-specific BMR in the Kazakh versus Kenyan
population (z = 2.38, P= 0.017), with a stronger correlation
in the latter population.
Discussion
Whole-organism BMR, residual BMR (adjusted for body
mass), mass-specific BMR and body mass were heritable
traits, although not all estimates of additive genetic variance
were significantly larger than zero in all the studied populations of stonechats. Genetic correlations deviating from 1
and 1 showed the potential for these four traits to evolve in
part independently of each other. In agreement with earlier
studies, four populations of stonechats, originating from
four different environments, differed in body mass, BMR
and mass-specific BMR. We speculate that this variation
may partly result from evolution of different genetic correlations among body mass and metabolic measures in different
populations.
This study has implications for evolutionary interpretations of different measures of energy metabolism and their
presumed co-evolution with body mass. We have shown
that body mass, whole-organism BMR and mass-specific
BMR can at least partially evolve independently of each
other, but can also be (partially) genetically linked, depending on the population. This finding emphasizes that there is
no single ideal fashion to take into account the effect of
body mass when evaluating different measures of BMR in
light of evolutionary questions. The use of whole-organism
BMR, residual BMR and mass-specific BMR is context
dependent, and the fact that these traits may evolve independently of each other needs to be considered in the
interpretation of future studies. Moreover, our findings
illustrate that it is dangerous to ‘correct’ for body mass in
evolutionary studies of metabolic rate without considering
their evolutionary potential independently and in concert.
Because patterns of genetic correlation between traits may
vary among populations or species, information about the
genetics underlying phenotypic variation and covariation
provides a valuable, and perhaps crucial, complement to
adaptive explanations from comparative studies at the
phenotypic level.
c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London
Journal of Zoology 279 (2009) 129–136 133
Genetics of metabolism in birds
B. I. Tieleman et al.
We caution that our results need to be interpreted in light
of the assumptions made in the animal model analysis. The
total phenotypic variance was partitioned in additive and
residual variance only, and other variance components, such
as permanent environment effects, maternal and paternal
genetic effects or maternal and paternal environmental
effects, were not included.
The paucity of data for birds, and the bias in the nonhuman, non-domestic mammal literature toward small rodents emphasize the need for more studies with a quantitative
genetics approach to metabolism. It also restricts the possibility to place the stonechat estimates of heritability of BMR,
residual BMR and mass-specific BMR in a broader perspective, applicable to all endotherms. The only other study on
birds reports for captive zebra finches that heritabilities equal
0.34 for body mass and 0.25 for BMR (Rønning et al., 2007).
In the small mammal literature, heritability estimates for
BMR are generally between zero and 0.2 and non-significant
(Lacy & Lynch, 1979; Nespolo et al., 2005, 2007, but see
Johnson & Speakman, 2007). Estimates of heritability for
residual BMR range from 0.4 to 0.6, (Sadowska et al., 2005;
Johnson & Speakman, 2007; Szafranska et al., 2007), while
estimates for mass-specific BMR are not available.
The presence of genetic correlations different from 1 and
1 (Table 2) illustrate the potential for partially independent evolution of body mass, BMR and mass-specific BMR
in birds. In Kenyan stonechats, BMR and mass-specific
BMR were strongly genetically correlated with body mass,
suggesting that the evolution of these physiological traits
depends on natural selection acting on body mass, and vice
versa. However, in Austrian stonechats weaker genetic
correlations of BMR and mass-specific BMR with body
mass, significantly smaller than 1 and larger than 1,
respectively, illustrate that independent evolution of all
three traits might be possible (Roff, 1997). Hence, we
speculate that the strong genetic correlations in the Kenyans
are unlikely to result from genetic constraints, and should
thus result either from selection on the genetic connections
themselves, past evolutionary bottlenecks or other stochastic processes (Armbruster & Schwaegerle, 1996). A strong
genetic correlation between BMR and body mass
(rA = 0.914 0.081, not significantly different from 1 when
tested against a normal distribution: z= 1.06, P= 0.14) was
also reported in the study on zebra finches, leading Rønning
et al. (2007) to conclude that the evolution of BMR was
closely tied to body mass in their population. Unfortunately,
despite high heritabilities reported for body mass in some
studies on mammals, genetic correlations between body mass
and metabolic measures are not reported. Hence, we were
unable to verify our interpretations about the potential for
independent evolution of body mass and metabolic measures
with knowledge based on mammals. We would like to
encourage colleagues to include investigations of genetic
correlations among physiological traits in future works.
To illustrate how knowledge of quantitative genetics
characters can provide an interesting complement to comparative studies in ecological and evolutionary physiology,
we explored possible interpretations of trends resulting from
134
the comparison between the four populations of stonechats.
We realized that the power of comparison is limited with
only four populations, and modest sample sizes per population, but intended to provide an example that stimulates
future studies. The comparison of physiological traits and
body mass among the four stonechat populations confirmed
results from previous studies (Klaassen, 1995; Wikelski
et al., 2003; Tieleman, 2007; Versteegh et al., unpubl. data):
Kenyan stonechats have the largest body mass and the
lowest mass-specific BMR, Kazakhs have the smallest body
mass and the highest mass-specific BMR and Austrian and
Irish birds show intermediate values. Whole-organism BMR
values are almost indistinguishable among populations.
These findings can be placed in the context of life-history
evolution, with selection in the tropical resident (Kenyan)
population for a long life span, slow aging and reduced
reproductive output per year (Wikelski et al., 2003). In
contrast, selection in the Kazakh stonechats putatively has
resulted in the opposite characteristics because birds breed
in an environment with a high seasonal variation, inducing
them to migrate over long distances and to work hard
during their reproductive effort. The Kazakh life history
and annual cycle are thought to be best supported by higher
levels of BMR relative to body mass (Wikelski et al., 2003).
We caution again that the limited sample sizes do not allow
firm conclusions. Yet, the non-significant trends in the
differences in heritabilities and genetic correlations between
the Kenyan and the Austrian population can be viewed as
tentative support for high adaptive values of a relatively low
mass-specific BMR in the tropics and of relatively high
potential workload, that is whole-organism BMR, in the
temperate population. If mass-specific levels of metabolism
are the primary target of selection, as hypothesized for
tropical birds that age slowly (Speakman, 2005; Tieleman
et al., 2006), one can imagine that strong genetic links evolve
between mass and BMR. In contrast, if whole-organism
BMR is the primary trait under selection – related to workload for a large brood size (Drent & Daan, 1980) – then
genetic correlations with mass may be weak.
Acknowledgments
We thank W. Jensen, L. Trost, E. Koch, C. Schmidt-Wellenburg and other staff at the Max Planck Institute for Ornithology in addition to the late E. Gwinner for support. We
gratefully acknowledge D. Garant, A. Gilmour, D. Réale and
D. Roff for advice on animal-model analyses, and D. Buehler
for comments on an earlier draft. Funding was provided by
grants from the Netherlands Organization for Scientific Research to B.I.T. (Veni 863.04.023) and N.J.D. (Veni 863.05.002)
and the Max Planck Institute for Ornithology.
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