• basal metabolic rate;
  • constitutive immunity;
  • corticosterone;
  • Pace-of-Life;
  • stonechats


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Variation in demographic and physiological attributes of life history is thought to fall on one single axis, a phenomenon termed the Pace-of-Life. A slow Pace-of-Life is characterized by low annual reproduction, long life span and low metabolic rate, a fast Pace-of-Life by the opposite characteristics. The existence of a single axis has been attributed to constraints among physiological mechanisms that are thought to restrict evolutionary potential. In that case, physiological traits should covary in the same fashion at the levels of individual organisms and species. We examined covariation at the levels of individual and subspecies in three physiological systems (metabolic, endocrine and immune) using four stonechat subspecies with distinct life-history strategies in a common-garden set-up. We measured basal metabolic rate, corticosterone as endocrine measure and six measures of constitutive immunity. Metabolic rate covaried with two indices of immunity at the individual level, and with corticosterone concentrations and one index of immunity at the subspecies level, but not with other measures. The different patterns of covariation among individuals and among subspecies demonstrate that links among physiological traits are loose and suggest that these traits can evolve independent of each other.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Variation in demographic and physiological attributes of life-history strategies is thought to fall on one dominant slow–fast Pace-of-Life axis, with low reproductive rates, long life spans and low metabolic rates at one end, and the opposite traits at the other end (Promislow & Harvey, 1990; Ricklefs & Wikelski, 2002). This single dominant axis has been reported at different levels of organization, ranging from studies that include a wide array of different species (Promislow & Harvey, 1990; Ricklefs, 2000) to those that investigate among-individual variation within a single species (Ricklefs, 2000; Johnson, 2001; Tieleman et al., 2005). Traditionally, most studies connecting life-history and physiology measure a standardized level of metabolic rate (e.g. Pearl, 1928; Drent & Daan, 1980; Ricklefs & Wikelski, 2002; Wikelski et al., 2003; Speakman, 2005; Tieleman et al., 2006), but more recently, the endocrine system and the immune system have been included as well (Sheldon & Verhulst, 1996; Ricklefs & Wikelski, 2002; Tieleman et al., 2005; Lee, 2006). The idea behind this view is that the different physiological systems (e.g. immune, endocrine and metabolic) are intrinsically linked (e.g. Dhabhar & McEwen, 1997; Lochmiller & Deerenberg, 2000; Speakman, 2005; Landys et al., 2006), in addition to each playing its own role in the balance between reproductive effort and adult self-maintenance. Because physiology mediates the relationship between organism and environment, Ricklefs & Wikelski (2002) suggest that the organization of physiological mechanisms may constrain individual responses to the environment, thereby limiting life-history variation. Constraints at the individual level would result in limited evolutionary potential (Lande, 1979; Schluter, 1996; Duckworth, 2010; Ketterson & Nolan, 1999). Yet, the existence of these constraints remains to be revealed.

When evaluating whether and how physiological constraints play a role in shaping life-history variation, patterns of covariation of traits at different levels of organization can provide complementary insights: (i) if the connections among physiological mechanisms have restricted the independent evolutionary potential of physiological traits, one might expect that these traits covary in the same way within and among species. Moreover, if physiological mechanisms restrict the evolutionary potential of life-history traits, one would expect that these physiological traits, including immune and endocrine variables, covary with the single dominant axis described for demographic and metabolic traits. (ii) If patterns of covariation of physiological traits within species differ from those among species, links between physiological traits are unlikely to constrain the evolutionary potential of these traits, or life-history traits. In fact, the absence of correlations among two or more traits at the individual level would strongly indicate that these traits are not forced to covary through mechanistic connections. The presence, in contrast, of a strong correlation among traits at the individual level does not necessarily confirm the existence of physiological constraints, which could lead to forced coevolution, but could also reflect individual condition or quality. (iii) Finally, patterns of covariation among individuals can also stem from environmental conditions selectively favouring certain combinations of traits, potentially resulting in different patterns of covariation within and among species (Lande, 1986; Ketterson & Nolan, 1999; Duckworth, 2010). Therefore, in studies of the physiology/life-history nexus, the role of the environment must be explicitly considered as well.

Environmental conditions affect life history and physiology at different evolutionary timescales, and as a result, phenotypic variation in life history and physiology reflects a mixture of different evolutionary phenomena. These phenomena include phenotypic plasticity, genetic differences based on adaptation and genetic differences resulting from historical evolutionary pathways. Their contributions can be largely disentangled when raising organisms from different environments in a common environment (Falconer & Mackay, 1996). Such a common-garden experiment minimizes environmental components of variation and highlights the genetic component of the adaptive response to different environments. When the experiment is restricted to closely related taxa, the effects of historical pathways are minimized.

Stonechats (Saxicola torquata) are an ideal study system to address evolutionary questions related to variation in life history, physiology and environment, because of their widespread geographic range covering a variety of habitats, their well-documented variation in life-history and physiological traits (Table 1) and their ease of handling and acclimation to captivity (Gwinner et al., 1995; Raess, 2005). When raised and kept in a common garden, different subspecies of stonechats maintain differences in clutch size, metabolic rate, moult, development of the young and behaviour (Gwinner et al., 1995; Klaassen, 1995; König & Gwinner, 1995; Starck et al., 1995; Helm & Gwinner, 1999, 2001; Wikelski et al., 2003; Tieleman, 2007; Helm, 2009; Tieleman et al., 2009; Helm et al., 2009). Many of these traits are correlated with factors characterizing a subspecies' original environment and migratory strategy. The existing knowledge of stonechats combined with the common-garden set-up allows a powerful integrative and broad study of physiology and life history.

Table 1. Life-history traits of the studied stonechat subspecies (after Helm, 2003; Baldwin et al., 2010)
Migratory statusSedentaryPartially short distanceShort distanceLong distance
Migration distance (km)00; 120017002600
Present on the breeding groundYear-roundYear-round; migrants: February–SeptemberMarch to mid-OctoberEarly May to early September
Number of clutches1–23–42–31
Number of eggs per clutch3556
Time of hatchingRelated to rainy seasonEarly April–AugustMid-April–AugustLate May/June

In this study, we evaluate the hypothesis that variation in innate immunity, baseline and stress-induced corticosterone concentrations and metabolic rate falls along a single axis within and among four subspecies of stonechats kept in a common garden. We chose to measure six indices of innate and acquired immunity to capture the complexity of the immune system (Lee, 2006; Buehler et al., 2011). We measured corticosterone concentration because of its functional relationships with immunity and energy mobilization (McEwen et al., 1997; Sapolsky et al., 2000; Landys et al., 2004). We explore whether physiological constraints among the traits at the level of individual birds (the evolutionary potential) can explain variation in physiology and life histories at the level of subspecies (the evolutionary product). Specifically, we test how immune function, corticosterone response and metabolic rate are associated within and among four subspecies of stonechats. Metabolic rate correlates well with some life-history traits, such as migratory disposition and clutch size (Table 1; Wikelski et al., 2003; Tieleman et al., 2009). Therefore, traits that correlate with metabolic rate are likely to correlate with these life-history traits. In this study, we investigate the following scenarios: (i) if we find that physiological traits show the same patterns of covariation within and among subspecies, constraints among these traits may underlie limited evolution. (ii) If, however, the patterns of covariation of physiological traits differ within and among subspecies, it is unlikely that physiological constraints at the individual level have limited the evolutionary potential and have led to the variation in physiological and life-history traits we find at the subspecies level.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Birds and measurement period

Stonechats are small passerines with a geographic distribution ranging from 71°N to 35°S (Urquhart, 2002). We studied four geographically distinct stonechat subspecies: the Kenyan S. torquata axillaris, the Irish S. torquata hibernans, the European S. torquata torquata and the Kazakh S. torquata maura. Kenyan stonechats show life-history and metabolic traits characteristic for a slow Pace-of-Life and Kazakh stonechats for a fast Pace-of-Life; Irish and European stonechats show metabolic rate and some life history for an intermediate (i.e. development time, clutch size, migratory disposition; Table 1) and some for a fast Pace-of-Life (i.e. annual reproduction; Table 1; Klaassen, 1995; Wikelski et al., 2003). We took measurements from a total of 123 hand-raised birds (Max Planck Institute for Ornithology, Andechs, Germany). Captive-bred birds were one (n = 31), two (n = 36) or three (n = 23) generations removed from the wild. Wild birds (n = 33) were taken as nestlings from the field, moved to the institute and hand-raised (Gwinner et al., 1987). All individuals were fully grown and ranged in age from 0 to 8 years (average 1.8 years), except one bird of age 12. We included this bird in the analyses because it did not show extreme values for any of the physiological traits, and excluding it minimally influenced the effect-size of the explanatory variables. Birds were housed individually in cages. Eight to fourteen cages were placed in rooms that were maintained at constant temperature (20–22 °C) and under an artificial light/dark cycle, which matched natural day length of the European population in winter at 40°N.

We measured multiple innate constitutive immune defences, baseline and stress-induced corticosterone concentrations, body mass and basal metabolic rate (BMR) during winter between late November and late February. Winter is a quiescent period for the four subspecies in captivity: none of the birds were moulting, showing migratory restlessness or breeding. We measured immune and metabolic parameters and body mass in 14 Kenyan, 16 Irish, 19 European and 15 Kazakh birds. For comparison of metabolic rate and body mass, we added the measurements of 26 European and 2 Kazakh stonechats from the studies of repeatability (Versteegh et al., 2008) and heritability (Tieleman et al., 2009) of BMR to the data set. Therefore, we have one (n = 58), two (n = 28), three (n = 2) or four (n = 1) measurements of body mass and BMR for various individuals. We collected < 225 μL blood per collection and collected blood a maximum of three times per bird: once for microbicidal ability assays, once for haemagglutination, haemolysis and haptoglobin assays and once for the corticosterone assay. When sufficient blood was left over from the microbicidal ability assays, we used the plasma to perform haemagglutination, haemolysis and/or haptoglobin assay duplicates. Therefore, we have two measurements of haemagglutination and haemolysis for 46 birds and of haptoglobin for 26 birds. We measured corticosterone concentrations in a subset of individuals (12 Kenyan, 7 European and 12 Kazakh stonechats; no Irish stonechats) that were not bled for immune function.

Baseline and stress-induced corticosterone levels

To determine baseline and stress-induced levels of corticosterone, we drew two blood samples per bird. The first sample was used to determine baseline corticosterone concentration. To minimize the rise in circulating corticosterone in the blood as a result of handling stress, this sample was collected within 3 min (mean ±  SD = 2.11 ± 0.60 min) after entering the room in which the target birds were housed (Wingfield et al., 1982; Romero & Reed, 2005). Target birds were then placed in cloth bags for 30 min, after which time the second blood sample was taken to determine stress-induced corticosterone concentrations (Wingfield et al., 1992). To minimize the effects of circadian fluctuations, all birds were sampled between 13.00 and 14.30 h (Joseph & Meier, 1973). Blood sample was kept on ice and centrifuged within 90 min after sampling. For the baseline samples, we recorded the handling time, which was the number of minutes between entering the room and completing the first blood draw.

Samples were analysed using the radioimmunoassay protocol of Goymann et al. (2006). The extraction efficiencies were 90 ± 4% for baseline samples and 91 ± 5% for stress-induced samples. Each sample was measured in duplicate. Intra- and inter-assay variations were 3.1% and 18.8%, respectively; the detection limit was 0.06 ng mL−1 for both baseline and stress-induced concentrations.

Indices of constitutive immunity

Microbicidal capacity

We tested microbicidal ability of stonechat blood against three microorganisms: Escherichia coli, Candida albicans and Staphylococcus aureus. Sterile blood samples were collected within 3 min of entering a room with birds. Assays were performed in a laminar flow hood within 1 h after blood sampling, following Tieleman et al. (2005) and Millet et al. (2007). Details about stock solutions and assay protocols for microbicidal ability against E. coli and C. albicans are described elsewhere (Tieleman et al., 2010). Microbicidal ability against S. aureus has not been reported for stonechats before, and details about stock solutions and assays are as follows. We reconstituted lyophilized pellets of S. aureus (Epower Microorganisms, ATCC no. 6538 MicroBioLogics no. 0485E7, MicroBioLogics, St. Cloud, MN, USA;) following manufacturer's instructions. We took subsamples daily from this stock solution to make a working solution as described in Tieleman et al. (2010). We added 30 μL of this working solution to 75 μL blood mixed with 225 μL CO2 independent medium (Invitrogen, Carlsbad, CA, USA; Gibco media no. 18045) + 4 mm l-Glutamine. We used incubation times of 15 min (E. coli) and 240 min (C. albicans and S. aureus) based on an earlier study on stonechats (B.I. Tieleman, unpublished). S. aureus is not always killed by stonechat blood (Tieleman et al., 2009), but also shows growth. However, the amount of growth differs among individuals (ranging from 72.1% killed to 171% growth) and we interpret it as a measure of how well stonechat blood inhibits growth.

Complement and natural antibodies

We centrifuged blood samples within 1 h after collection to separate plasma and cellular fractions; we stored the plasma at −80 °C until further analysis. We quantified natural antibody and complement activity following the haemagglutination–haemolysis assay described by Matson et al. (2005) with modifications described by Mauck et al. (2005). We scored haemagglutination and haemolysis titres as the last well at which the rabbit's red blood cells were agglutinated or lysed. Wells that showed partial agglutination or lysis were assigned half scores. All scoring was made by a single person (MAV).

Haptoglobin-like proteins

We measured haptoglobin concentrations or its functional equivalents in plasma with a commercial kit following the manufacturer's instructions (Tridelta Development Ltd., Maynooth, Ireland). The kit is a functional assay that colorimetrically quantifies the concentration of haptoglobin-like protein (from now on referred to as haptoglobin). We mixed plasma and reagents in 96-well plates and recorded the absorbance at 630 nm after 5 min using a Molecular Devices Spectra Max 340 plate reader (Matson et al., 2012).

Body mass and metabolic measurements

Respirometry equipment and setup, measurement protocol and data analysis are described elsewhere (Tieleman, 2007; Versteegh et al., 2008). We calculated mass-specific BMR by dividing BMR by body mass.

Statistical analyses

We used the program r 2.8.0 for all statistical analyses (R Development Core Team, 2008). We first examined response variables for normality using Shapiro tests. Microbicidal ability against E. coli was left-skewed and was Box–Cox transformed. We analysed transformed and untransformed data and found that transformation of the data did not affect the significance of fixed effects; we only report the results of analyses on transformed data. First, we tested for differences among subspecies in metabolic measures, corticosterone concentrations and indices of constitutive immunity, for each trait separately. Subsequently, we investigated how the traits covaried among subspecies and among individuals, using discriminant analysis and principle component analysis (PCA).

Comparing each physiological trait separately among subspecies

For comparisons among subspecies of each physiological trait, we used regression models with subspecies as fixed effect. We had one observation per individual for baseline corticosterone concentration, stress-induced corticosterone concentration and microbicidal ability, and we used linear models (lm in r). Additionally, we tested the effect of treatment on corticosterone with a mixed effects model with time spent in the cloth bag (0 or 30 min) as fixed effect and individual as random effect. Time spent in the bag had a significant effect on corticosterone concentration (F1,23 = 77.32, P ≤ 0.001). For body mass, metabolic measurements, haemagglutination, haemolysis and haptoglobin, we had multiple observations per individual, and we used mixed effect models with individual as random effect (lme in r; package lme4). Additionally, to the fixed effect subspecies, we report significance of sex, age and the subspecies × sex interaction in all analyses. Some individuals were taken as nestlings from the field and moved to the institute, whereas others were born in captivity, and we included origin (wild or captive born) in the analysis. We also tested the interaction subspecies × age. Neither origin nor the interaction subspecies × age changed the significance of the other fixed effects, and we do not report the results. Model simplification was based on backward elimination with P < 0.05 as selection criterion (Crawley, 2007).

Because immunological indices (Buehler et al., 2008a; Matson et al., 2006a) and corticosterone concentrations (Wingfield et al., 1982; Romero & Reed, 2005) can change quickly in response to disturbance, we included the number of minutes elapsed between entering a room and bleeding birds as a covariate in the analyses of these response variables. Because we made a fresh stock solution of microbes once a week, we also included age in days of the microbial stock solution as a covariate in the analysis of the microbial assays.

We performed mixed effects models on BMR and body-mass-independent BMR. We investigated BMR independent of body mass using mass-specific BMR as the response variable and by including body mass as a covariate in models with BMR as the response variable. Additionally, we performed a linear mixed model on log-transformed BMR with log-transformed body mass as covariate. Results from transformed and untransformed data did not differ, and we only report the results from untransformed data.

Covariation of physiological traits among subspecies

The relationship between mass-specific BMR and life-history traits has been studied in stonechat subspecies before (Table 1; Klaassen, 1995; Wikelski et al., 2003), and it is known to correlate with life-history traits in other species as well (see Speakman, 2005). By correlating immune indices and corticosterone concentration with mass-specific BMR, we explored among-subspecies patterns of covariation between physiological traits and life-history traits. Because we measured only four subspecies, we were unable to perform PCA on the subspecies level, like we did on the individual level. Therefore, we performed a discriminant analysis on the six immune measures (lda in r; package mass) to reduce the number of immune variables and to additionally investigate covariation within constitutive immunity. Discriminant analysis explores whether subspecies can be distinguished by placing the physiological traits on a fixed number of functions (in this case, three functions, based on four subspecies), taking covariation among individuals into account. Significance of a discriminant function indicates that the traits with a high loading on this function differ among subspecies. High loadings on the same discriminant function indicate covariation (Crawley, 2007; Bartels & Bartels 2009). Discriminant analysis has the advantage above PCA that it looks at all individuals, so you do not have to calculate means. To investigate the relationships between immunity and mass-specific BMR, we correlated the resulting discriminant functions with subspecies' means of mass-specific BMR. Discriminant analysis takes into account covariation of traits among individuals. In the separate comparison of each immune trait among subspecies, we did not take into account how the traits covaried among individuals. This may lead to differences in the results of the two methods.

Covariation of physiological traits among individuals

To investigate covariation of immune indices and metabolic rate among individuals, we statistically removed variation among subspecies from these traits by calculating the residuals of the traits using regression models with subspecies as fixed effect. We constructed a correlation matrix of these residuals of immune measures. A Kaiser–Meyer–Olkin test of sampling adequacy and a Bartlett's test of sphericity showed that the matrix was appropriate for performing a principal component analysis (Kaiser–Meyer–Olkin = 0.55; Bartlett's test of sphericity: χ215 = 29.50, P = 0.01). To reduce the number of immune variables and to investigate covariation among immune indices, we then performed a varimax-rotated PCA on the correlation matrix (principal in r; package psych). High loadings on the same principal component indicate covariation. Subsequently, we correlated residual mass-specific BMR with the principal components with an eigenvalue larger than one. Corticosterone concentrations were measured in a different set of individuals and could not be correlated at the individual level with mass-specific BMR.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

When we compared each physiological trait separately among the four stonechat subspecies, two general patterns of variation emerged: one that did not correlate with variation in mass-specific BMR and another one that did. The first general pattern included all immune indices except haptoglobin. Kenyan or Kazakh stonechats, the subspecies that differed most in mass-specific BMR and life-history traits, showed intermediate, and Irish or European showed the highest or lowest values compared with the other subspecies (Fig. 1a–e). Although the pattern was qualitatively present in all immune indices except haptoglobin, differences between subspecies were statistically significant only for haemolysis and microbicidal capacity against E. coli (Table 2; Fig. 1a,e). The second pattern, which did correspond to mass-specific BMR (Fig. 2e), consisted of a gradual change from Kenyan to Irish and European to Kazakh stonechats in haptoglobin (decrease, Fig. 1f), stress-induced corticosterone levels (increase, Fig. 2b), and body mass (decrease, Fig. 2c). In other words, for these indices, the Kenyan and Kazakh subspecies differed most from each other, and Irish and European subspecies were intermediate. Again, the pattern was qualitatively present in all indices, but differences between subspecies were statistically significant only for body mass and mass-specific BMR (Table 2).


Figure 1. Residuals of immune indices of four subspecies of stonechats. Microbicidal capacity of blood against (a) Escherichia coli, (b) Candida albicans and (c) Staphylococcus aureus. (d) haemagglutination, (e) haemolysis and (f) haptoglobin concentration in plasma. The residuals are calculated from linear models with covariates and all significant fixed effects except subspecies. Bars and whiskers refer to mean values ± standard error. Letters refer to significant differences among subspecies. Numbers refer to sample sizes (number of individuals).

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Figure 2. Residuals of (a) baseline and (b) stress-induced corticosterone, (c) body mass, (d) basal metabolic rate (BMR) and (e) mass-specific BMR of four subspecies of stonechats. Residuals are calculated from linear models with covariates and all significant fixed effects except subspecies. Bars and whiskers refer to mean values ± standard error. Letters refer to significant differences among subspecies. Numbers refer to sample sizes (number of individuals).

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Table 2. Results of the linear models for the fixed effects subspecies, sex and age, and the interaction subspecies × sex on measures of body mass, metabolic rate, immune function and corticosterone concentration of four subspecies of stonechats from different environments. Results are from mixed effects or linear regression models after backward elimination of nonsignificant terms (P > 0.05)
 SubspeciesSexAgeSubspecies × sex
P F d.f. P F d.f. P F d.f. P F d.f.
  1. a

    Included as covariate in the model is the time between entering the room and completion of sampling.

  2. b

    Included as covariates in the model are time between entering the room and completion of sampling, and age of the microbial stock solution.

Mass< 0.00127.133,84< 0.00114.091,840.301.091,340.062.503,81
Basal metabolic rate (BMR)0.321.183,840.025.501,840.580.311,340.373.163,81
BMR with mass as covariate0.0492.733,850.311.041,840.710.141,330.131.923,81
Mass-specific BMR< 0.0017.083,840.171.921,840.770.091,340.013.873,81
Baseline corticosteronea 0.580.562,220.083.431,200.790.071,190.400.962,17
Stress-induced corticosteronea 0.132.212,260.600.291,250.035.421,260.630.482,23
Haemagglutinationa 0.072.513,350.860.031,550.122.531,560.281.353,32
Haemolysisa 0.033.213,420.132.311,56< 0.00114.611,570.470.843,39
Haptoglobina 0.281.313,550.770.091,530.330.981,540.740.423,50
Microbicidal ability against
Escherichia coli b < 0.0016.483,500.610.261,500.181.851,490.0084.353,50
Candida albicans b 0.680.513,500.035.161,500.870.031,490.990.043,46
Staphylococcus aureus b 0.510.783,510.034.731,510.720.131,500.710.473,47

To explore the patterns of covariation of physiological traits at the level of subspecies, we correlated corticosterone and immune measures with mass-specific BMR (Fig. 3). We correlated subspecies' means of mass-specific BMR with residual values of baseline and stress-induced corticosterone concentrations. Mass-specific BMR was not significantly correlated with residual baseline levels of corticosterone (t 1 = 1.12, P = 0.47), nor, despite the positive trend, with residual stress-induced levels of corticosterone (t 1 = 5.50, P = 0.11) (Fig. 3a). Discriminant analysis of the immune measures resulted in one significant discriminant function and two nonsignificant discriminant functions (Table 3). The significant first discriminant function showed that Kenyan and Kazakh subspecies were most different and that Irish and European stonechats had intermediate values, a result consistent with the Pace-of-Life hypothesis (Fig. 3b). The correlation coefficient between this first function and mass-specific BMR was marginally nonsignificant (F 1,2 = 3.50, P = 0.07). Because haemagglutination was the immune variable that contributed mostly to the first function, and because this function was significant, we interpret this result as a significant difference in haemagglutination among subspecies. The nonsignificant second and third discriminant functions describe the variation in the other immune indices, including haemolysis, haptoglobin and microbicidal capacity against E. coli, C. albicans and S. aureus (Table 3; Fig. 3c,d). The second and third functions did not significantly correlate with mass-specific BMR, and in contrast to the predictions regarding the Pace-of-Life, scores for the Kenyan and Kazakh subspecies were similar for the second and third functions.


Figure 3. Subspecies level correlations between (a) residual baseline (black circles) and stress-induced (grey circles) corticosterone and (b–d) the three discriminant functions of constitutive immunity and the indicator for Pace-of-Life, mass-specific basal metabolic rate. Residual corticosterone is calculated from linear models with covariates and all significant fixed effects except subspecies. Dots and whiskers refer to mean values ± standard error. P-values of the correlation are shown in the graphs. If the correlation showed a trend (P < 0.1) a regression line was drawn.

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Table 3. Results of the discriminant analysis of six measures of constitutive immune function among four subspecies of stonechats. The highest loading for each physiological trait shows to which function that trait contributes the largest explanatory value (compare values within rows, highest value is bolded). Significance of each discriminant function indicates whether that function can be significantly discriminated among subspecies. High loadings on the same discriminant function indicate correlation between physiological traits
  Function 1Function 2Function 3
Haemagglutination 0.757 −0.0670.223
Haemolysis−0.097−0.374 0.388
Haptoglobin0.101 0.290 0.131
Microbial ability against
Escherichia coli 0.1250.714 0.474
Candida albicans −0.0710.249 0.905
Staphylococcus aureus −0.1800.106 0.188
Prop. variance explained (%)56.9435.937.14
Cumulative prop. variance explained (%)56.9492.86100.00
χ2 29.7213.452.44

The principal component analysis at the level of individual revealed grouping of physiological traits that was dissimilar from the result of the discriminant analysis at the level of subspecies (Tables 4 vs. 3). The first principle component was highly correlated with haemagglutination and haemolysis. This principal component showed a significant negative correlation with mass-specific BMR (F 1,44 = −2.45, P = 0.02; Fig. 4a). The second principle component explained most of the variation in microbicidal capacity against E. coli and C. albicans. The third principal component explained most of the variation in microbicidal capacity against S. aureus and haptoglobin (Table 4). These two principal components did not significantly correlate with mass-specific BMR (F 1,44 > −1.50, P > 0.14; Fig. 4b–c).


Figure 4. Individual-level correlations between the indicator for Pace-of-Life, mass-specific basal metabolic rate (BMR) and (a) PC1, (b) PC2 and (c) PC3 resulting from the principal component analysis on indices of immunity. The y-axes are labelled with the indices that had the highest loadings on the PCs. Mass-specific BMR was statistically corrected for subspecies. Correlation coefficients are calculated with pooled subspecies, but subspecies are represented by different symbols. Significance of the correlation is shown in the graphs, and if the correlation was significant (P < 0.05) a regression line was drawn.

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Table 4. Results of principal component analysis after varimax rotation for constitutive immune indices of individual stonechats pooled across the four subspecies. Loadings on the first three principal components (PC's) had an eigenvalue larger than 1. Loadings larger than 0.50 are bolded. Variables are correlated if they have a high loading on the same PC
Haemagglutination 0.836 −0.0920.042
Haemolysis 0.849 0.1680.093
Microbicidal ability against
Escherichia coli 0.268 0.720 −0.135
Candida albicans −0.213 0.822 0.130
Staphylococcus aureus 0.2970.200 0.789
Prop. variance explained (%)282418
Cumulative prop. variance explained (%)285270


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Immune function, corticosterone response and BMR did not fall on a single dominant axis in stonechats, neither among subspecies nor among individuals (Figs 3 and 4). Our finding of dissimilar patterns of covariation of immune, endocrine and metabolic traits on the levels of subspecies and individual birds does not support the hypothesis that intrinsic connections between these physiological systems constrain the evolution of physiological attributes. Because we used different methods to describe the patterns of covariation among and within subspecies, a formal statistical comparison was not possible. Yet, the striking difference in patterns within and among subspecies make it unlikely that constraints among immune, endocrine and metabolic traits have limited the evolution of life-history variation (Ricklefs & Wikelski, 2002). If the strength of environmental selection factors is strong enough, it can uncouple originally constrained traits over evolutionary time (Ketterson & Nolan, 1999; Duckworth, 2010). This can lead to a lack of correlations at the level of subspecies between traits that are constrained within individuals. In our study, covariation among some physiological traits at the level of individuals is present and potentially points to such constraints. Recent related studies (discussed below), however, find that these same traits can also vary independently of each other. Synthesizing our results we conclude that the phenomenon of a single Pace-of-Life axis does not result from constraints among the physiological traits we measured. Combining our results with results of other studies shows that universal constraints are unlikely to exist among these traits. A possible alternative hypothesis is that environmental factors, such as food availability, select sets of covarying physiological traits and life-history characteristics, potentially leading to constrained evolution. Other environmental factors, like pathogen pressure, might select traits (e.g. immune and endocrine) which do not covary with life-history characteristics.

Physiological variation among subspecies

We found that only some physiological traits covaried with mass-specific BMR at the level of stonechat subspecies. Because previous studies demonstrate a link between life-history and metabolic traits in stonechats, we can use mass-specific BMR as an index for Pace-of-Life (Table 1; Klaassen, 1995; Wikelski et al., 2003; Tieleman et al., 2009). Concentrations of corticosterone after an acute stressor have been hypothesized to be high in birds with a slow Pace-of-Life, because of the hormone's role in favouring self-maintenance over current reproduction (Wingfield et al., 1995; Wikelski & Ricklefs, 2001). Therefore, we expected stress-induced corticosterone to be highest in Kenyan and lowest in Kazakh stonechats. Our results do not support this prediction: concentrations of stress-induced corticosterone were higher in fast-living Kazakh stonechats than in relatively slow-living Kenyan stonechats, and intermediate in European stonechats. Levels of baseline corticosterone concentration were also higher in Kazakh than in Kenyan stonechats. These results corroborate several other studies that find high levels of baseline (Martin et al., 2005; Bókony et al., 2009; Hau et al., 2010) or stress-induced corticosterone concentrations (Martin et al., 2005) in fast-living species, subspecies or populations. However, other studies, including one on free-living stonechats during the breeding season (Wingfield et al., 1995; Silverin et al., 1997; Breuner et al., 2003; Goymann et al., 2006; Bókony et al., 2009), offer support for the hypothesized link between higher corticosterone concentrations with a slower Pace-of-Life. A third set of studies finds no relationship between corticosterone concentrations and Pace-of-Life characteristics (Breuner et al., 2003; Lindström et al., 2005; Hau et al., 2010). Therefore, we conclude that the corticosterone response does not overlay the single axis of variation fitting with the Pace-of-Life syndrome.

Birds with longer life spans – often indicated by lower metabolic rates – might benefit from more robust immune defences (Boots & Bowers, 2004; Lee et al., 2008; Horrocks et al., 2011). Stonechat subspecies, however, show no clear covariation among constitutive immunity and metabolic rate. When each index was analysed separately, haptoglobin was the only index that covaried with metabolic rate. When immune indices were collapsed in a discriminant analysis, only haemagglutination covaried with metabolic rate. Earlier studies that measured constitutive immunity in relation to metabolic rate or other Pace-of-Life indicators show ambiguous patterns. Among Neotropical birds, species with a low metabolic rate had higher microbicidal ability against E. coli than species with a high metabolic rate (Tieleman et al., 2005). In contrast, a population of Garter snakes (Thamnophis elegans) with a slow Pace-of-Life (i.e. with small litter size and a long life span) had lower microbicidal ability against E. coli than a fast-living population (Sparkman & Palacios, 2009). The slow-living population also had comparatively lower haemagglutination and haemolysis titres. In an inter-specific study of seven Peromyscus mice species, life-history strategy was not associated with microbicidal ability against E. coli or with other immune measures (Martin et al., 2007). Investigations of induced immune responses in a life-history context also show mixed results (Tella et al., 2002; Ardia, 2005; Palacios & Martin, 2006; Martin et al., 2007). The ambiguous patterns arising from previous studies suggest that variation in immune function exhibited by different taxonomic units does not clearly align with a single life-history/physiology (i.e. Pace-of-Life) axis. Our study shows that even within closely related subspecies physiological constraints do not lead to a single general axis among immunity, corticosterone and metabolic rate.

Physiological variation among individuals

At the among-individual (i.e. within subspecies) level, only haemagglutination and haemolysis were associated with mass-specific BMR, a proxy for Pace-of-Life. That is, only the first, and not the second or third, principal components covaried with mass-specific BMR (Fig. 4, Table 4). Studies investigating the correlation between indices of constitutive immunity and Pace-of-Life characteristics at the individual level are still scarce, and results are mixed. In contrast to our current findings, Tieleman et al. (2005) report that microbicidal ability against E. coli correlates with mass-corrected BMR in tropical house wrens (Troglodytes aedon). Rubenstein et al. (2008), however, find no relationship between investment in breeding, another proxy for Pace-of-Life, and microbicidal ability against E. coli in superb starlings (Lamprotornis superbus). Ots & Horak (1996) found that great tits (Parus major) with high breeding investment have higher heterophil/lymphocyte ratios as compared to great tits with low breeding investment, suggesting a decrease in health status. Investigations of induced immunity and Pace-of-Life characteristics at the individual level also lead to equivocal results (Nordling et al., 1998; Ilmonen et al., 2002; Apanius & Nisbet, 2006). The set-up that we used, with a common environment with one set of environmental conditions (day length, temperature), reflects only one point along the reaction norms of each subspecies (Nussey et al., 2007). If the reactions norms of the different subspecies differ, this may have influenced the relationships among traits that we found (Stearns, 1989; Nussey et al., 2007). However, the ambiguous picture painted by the diversity of associations between immune indices and Pace-of-Life characteristics at the individual level in our study as well as in the literature implies that at the individual level other factors than constraints must be important; immunity does not simply overlay the Pace-of-Life axis.

Although our data cast doubt on the generality of a relationship between immunity, corticosterone and metabolic rate, the correlations among immune indices which we identified at the individual level may still represent constraints within the immune system. We found correlations between haemagglutination and haemolysis, between microbicidal ability against E. coli and C. albicans and between microbicidal ability against S. aureus and haptoglobin. If these correlations represent universal physiological constraints (i.e. intrinsic mechanistic connections between indices), then we would expect to find the same correlations in other species. Red knots (Calidris canutus) and several species of waterfowl show the same correlations between haemagglutination and haemolysis (Matson et al., 2006b; Buehler et al., 2008b,2008c, 2011). Furthermore, these correlations are in line with expectations based on the underlying physiological mechanism: natural antibodies, a central player in haemagglutination, interact functionally with lytic enzymes, like complement, to cause haemolysis in vitro (Ochsenbein & Zinkernagel, 2000; Janeway et al., 2004). However, there are also two studies that report insignificant correlations between haemagglutination and haemolysis (Mendes et al., 2006; Parejo & Silva, 2009), suggesting that natural antibodies and complement can also operate, at least partly, independently from each other. Of the three studies of red knots, microbicidal ability against E. coli and C. albicans is correlated in only one (Buehler et al., 2008b,2008c, 2009). In the previous studies, the correlation between haptoglobin and microbicidal ability against S. aureus was consistently nonsignificant (Matson et al., 2006b; Buehler et al., 2009, 2011). This panoply of results begs for more studies but also leads us to tentatively conclude that the correlations that we identified among immune indices at the level of individual birds are not universal and, therefore, unlikely to result from physiological constraints. Therefore, environmental factors are likely to have influenced the evolution of correlated traits and should be taken into account. For example, recently, ecological immunologists have stressed the importance of measurement of pathogen pressure in studies of the immune system (Horrocks et al., 2011; Pedersen & Babayan, 2011), and measurement of such environmental factors will improve our understanding of the role of physiology in life-history evolution.

Evolutionary consequences

After exploring variation in indices from three major physiological systems to determine the potential presence and role of physiological constraints in explaining life-history traits, we validate that metabolic rate covaries with traditional demographic life-history traits, but conclude that corticosterone and immune system do not. Therefore, if physiological constraints limit the evolutionary potential of life-history traits (Sheldon & Verhulst, 1996; Ricklefs & Wikelski, 2002; Lee, 2006; Hau et al., 2010), then these constraints have to be at least partly located in the metabolic system. Metabolic rate summarizes the physico-chemical substrate of life, and life-history activities, such as breeding, are likely to be tightly linked to availability of metabolic energy (Brown et al., 2004). Moreover, metabolic constraints continue to be at the centre of studies of metabolic damage and other molecular mechanisms of ageing (Speakman, 2005; Dowling & Simmons, 2009). Alternative, more ecologically based hypotheses can also explain the covariation among traits that characterize the Pace-of-Life syndrome. Combinations of environmental factors (e.g. food availability, predation risk, pathogen pressure) play simultaneous selective roles and may result in particular combinations of traits (Lande, 1986; Duckworth, 2010). The enormous global diversity in combinations with environmental selective factors might help explain why the metabolic system, and not the endocrine and immune systems, covaries with a more general (slow–fast) life-history axis. On the one hand, environmental factors that impact energy metabolism (e.g. temperature, moisture, food availability (Tieleman et al., 2003, 2004) might covary with factors that impact demographic traits (e.g. effects of food availability, nest predation or seasonality on clutch size or mortality (Ashmole, 1963; Chalfoun & Martin, 2007; Biancucci & Martin, 2010; Griebeler et al., 2010)). In this case, metabolic and demographic traits might covary without the presence of constraints. On the other hand, environmental factors that impact immune system (e.g. pathogen pressure (Guernier et al., 2004; Buehler et al., 2008c; Horrocks et al., 2011)) or endocrine function (e.g. environmental predictability) might vary in a way that is unrelated or tangentially related to metabolism and demographic life-history traits. We therefore advocate including environmental factors in investigations of the links between life history and the immune and endocrine systems.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

We thank B. Helm, L. Trost, S. Kuhn, M. Trappschuh, W. Jensen, E. Koch and W. Goymann, in addition to the late E. Gwinner for support. E. Croese, S. Engel, A. Lohrentz, C. Muck, J. Partecke, M. Raess, C. Schmidt-Wellenburg, L. Trost, A. Wittenzellner and E. Yohannes helped collect the blood samples, and J. Leenders, A. Foucher and M. Versteegh helped analyse the blood samples. We also thank M. Visser and J. Tinbergen for helpful discussions. D. Buehler, W. Goymann, K. Matson and R. Mauck and five anonymous referees provided valuable comments on the manuscript. This work was supported by the Netherlands Organization for Scientific Research (Veni 863.04.023, BIT) and an E-bird grant (MAV).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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Data deposited at Dryad: doi: 10.5061/dryad.kf320