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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. References Armbruster, W.S. & Schwaegerle, K.E. (1996). Causes of covariation of phenotypic traits among populations. J. Evol. Biol. 9, 261–276. 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. Brzek, P., Bielawska, K., Ksiazek, A. & Konarzewski, M. (2007). Anatomic and molecular correlates of divergent selection for basal metabolic rate in laboratory mice. Physiol. Biochem. Zool. 80, 491–499. Crawley, M.J. (1993). GLIM for ecologists. Oxford: Blackwell Scientific Publications. Daan, S., Masman, D. & Groenewold, A. (1990). Avian basal metabolic rates: their association with body composition and energy expenditure in nature. Am. J. Physiol. 259, R333–R340. Deerenberg, C., Overkamp, G.J.F., Visser, G.H. & Daan, S. (1998). Compensation in resting metabolism for experimentally increased activity. J. Comp. Physiol. B 186, 507–512. Drent, R.H. & Daan, S. (1980). The prudent parent: energetic adjustments in avian breeding. Ardea 68, 225–252. Falconer, D.S. & Mackay, T.F.C. (1996). Introduction to quantitative genetics. New York: Longman. Gilmour, A.R., Gogel, B.J., Cullis, B.R., Welham, S.J. & Thompson, R. (2006). ASReml user guide. Release 1.0. Hemel Hempstead, UK: VSN International. Gwinner, E., König, S. & Haley, C.S. (1995). Genetic and environmental factors influencing clutch size in equatorial and temperate-zone stonechats (Saxicola torquata axillaris and S. t. rubicola): an experimental study. Auk 112, 748–755. Hadfield, J.D., Nutall, A., Osorio, D. & Owens, I.P.F. (2007). Testing the phenotypic gambit: phenotypic, genetic and environmental correlations of colour. J. Evol. Biol. 20, 549–557. Helm, B. & Gwinner, E. (1999). Timing of postjuvenal molt in African (Saxicola torquata axillaris) and European (Saxicola torquata rubicola) stonechats: effects of genetic and environmental factors. Auk 116, 589–603. Houle, D. (1992). Comparing evolvability and variability of quantitative traits. Genetics 130, 195–204. Johnson, M.S. & Speakman, J.R. (2007). Heritability of resting metabolic rate in short-tailed field voles, Microtus agrestis. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 148, S21. King, J.R. (1974). Seasonal allocation of time and energy resources in birds. In Avian energetics: 4–85, 1st edn. Paynter, R.A. Jr (Ed.). Cambridge, MA: Nuttall Ornithological Club. Klaassen, M. (1995). Moult and basal metabolic costs in males of two subspecies of stonechats: the European Saxicola torquata rubicula and the East African S. t. axillaris. Oecologia 104, 424–432. Kruuk, L.E.B. (2004). Estimating genetic parameters in natural populations using the ‘animal model’. Philos. Trans. Roy. Soc. B 359, 873–890. Ksiazek, A., Konarzewski, M. & Lapo, I.B. (2004). Anatomic and energetic correlates of divergent selection for basal metabolic rate in laboratory mice. Physiol. Biochem. Zool. 77, 890–899. Genetics of metabolism in birds Lacy, R.C. & Lynch, C.B. (1979). Quantitative genetic analysis of temperature regulation in Mus musculus. I. Partitioning of variance. Genetics 91, 743–753. Lande, R. (1982). A quantitative genetic theory of life-history evolution. Ecology 63, 607–615. Lande, R. & Arnold, S.J. (1983). The measurement of selection on correlated characters. Evolution 37, 1210–1226. McKechnie, A.E., Freckleton, R.P. & Jetz, W. (2006). Phenotypic plasticity in the scaling of avian basal metabolic rate. Proc. Roy. Soc. Lond. Ser. B 273, 931–937. McNab, B.K. (1988). Food habits and the basal rate of metabolism in birds. Oecologia 77, 343–349. Nagy, K.A. (1987). Field metabolic rate and food requirement scaling in mammals and birds. Ecol. Monogr. 57, 111–128. Nespolo, R.F., Bacigalupe, L.D. & Bozinovic, F. (2007). Heritability of energetics in a wild mammal, the leaf-eared mouse (Phyllotis darwini). Evolution 57, 1679–1688. Nespolo, R.F., Bustamante, D.M., Bacigalupe, L.D. & Bozinovic, F. (2005). Quantitative genetics of bioenergetics and growth-related traits in the wild mammal, Phyllotis darwini. Evolution 59, 1829–1837. Pease, C.M. & Bull, J.J. (1988). A critique of methods for measuring life-history trade-offs. J. Evol. Biol. 1, 293–303. Rasbash, J., Steele, F., Browne, W. & Prosser, B. (2005). A User’s Guide to MlwiN version 2.0. Centre for Multilevel Modelling, University of Bristol, Bristol. Rödl, T., Goymann, W., Schwabl, I. & Gwinner, E. (2004). Excremental androgen metabolite concentrations and gonad sizes in temperate zone vs. tropical Stonechats (Saxicola torquata ssp.). Gen. Comp. Endocrinol. 139, 124–130. Roff, D.A. (1997). Evolutionary quantitative genetics. New York: Chapman and Hall. Roff, D.A. (2001). The threshold model as a general purpose normalizing transformation. Heredity 89, 404–411. Rønning, B., Jensen, H., Moe, B. & Bech, C. (2007). Basal metabolic rate: heritability and genetic correlations with morphological traits in the zebra finch. J. Evol. Biol. 20, 1815–1822. Sadowska, E.T., Labocha, M.K., Baliga, K., Stanisz, A., Wroblewska, A.K., Jagusiak, W. & Koteja, P. (2005). Genetic correlations between basal and maximum metabolic rates in a wild rodent: consequences for evolution of endothermy. Evolution 59, 672–681. Shaw, R.G. (1991). The comparison of quantitative geneticparameters between populations. Evolution 45, 143–151. Speakman, J.R. (2005). Body size, energy metabolism and lifespan. J. Exp. Biol. 208, 1717–1730. Speakman, J.R., Selman, C., McLaren, J.S. & Harper, E.J. (2002). Living fast, dying when? The link between aging and energetics. J. Nutr. 132, 1583S–1597S. Steppan, S.J., Phillips, P.C. & Houle, D. (2002). Comparative quantitative genetics: evolution of the G matrix. Trends Ecol. Evol. 17, 320–327. c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London Journal of Zoology 279 (2009) 129–136 135 Genetics of metabolism in birds B. I. Tieleman et al. Szafranska, P.A., Zub, K. & Konarzewski, M. (2007). Longterm repeatability of body mass and resting metabolic rate in free-living weasels, Mustela nivalis. Funct. Ecol. 21, 731–737. Tieleman, B.I. (2007). Differences in the physiological responses to temperature among stonechats from three populations reared in a common environment. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 146, 194–199. Tieleman, B.I., Dijkstra, T.H., Lasky, J.R., Mauck, R.A., Visser, G.H. & Williams, J.B. (2006). Physiological and behavioural correlates of life-history variation: a comparison between tropical and temperate zone house wrens. Func. Ecol. 20, 491–499. Tieleman, B.I., Versteegh, M.A., Fries, A., Helm, B., Dingemanse, N.J., Gibbs, H.L. & Williams, J.B. (2009). Genetic modulation of energy metabolism in birds through mitochondrial function. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 276, 1685–1693. Tieleman, B.I. & Williams, J.B. (2000). The adjustment of avian metabolic rates and water fluxes to desert environments. Physiol.Biochem. Zool. 73, 461–479. Tieleman, B.I., Williams, J.B. & Bloomer, P. (2003). Adaptation of metabolism and evaporative water loss along 136 an aridity gradient. Proc. Roy. Soc. Lond. Ser. B 270, 207–214. Versteegh, M.A., Heim, B., Dingemanse, N.J. & Tieleman, B.I. (2008). Repeatability and individual correlates of basal metabolic rate and total evaporative water loss in birds: a case study in European stonechats. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 150, 452–457. Wiersma, P., Munoz-Garcia, A., Walker, A. & Williams, J.B. (2007). Tropical birds have a slow pace of life. Proc. Natl. Acad. Sci. USA 104, 9340–9345. Wikelski, M., Spinney, L., Schelsky, W., Scheuerlein, A. & Gwinner, E. (2003). Slow pace of life in tropical sedentary birds: a common-garden experiment on four stonechat populations from different latitudes. Proc. Roy. Soc. Lond. Ser. B 270, 2383–2388. Woog, F., Wink, M., Rastegar-Pouyani, E., Gonzalez, J. & Helm, B. (2008). Distinct taxonomic position of the Madagascar stonechat (Saxicola torquatus sibilla) revealed by nucleotide sequences of mitochondrial DNA. J. Ornithol. 149, 423–430. Zar, J.H. (1996). Biostatistical analysis, 3rd edn. Englewood Cliffs, NJ: Prentice Hall Inc. c 2009 The Authors. Journal compilation c 2009 The Zoological Society of London Journal of Zoology 279 (2009) 129–136