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Testing the Fitness Consequences of the Thermoregulatory and Parental Care Models for the Origin of Endothermy

Testing the Fitness Consequences of the Thermoregulatory and Parental Care Models for the Origin of Endothermy

  • Sabrina Clavijo-Baque, 
  • Francisco Bozinovic
PLOS
x

Abstract

The origin of endothermy is a puzzling phenomenon in the evolution of vertebrates. To address this issue several explicative models have been proposed. The main models proposed for the origin of endothermy are the aerobic capacity, the thermoregulatory and the parental care models. Our main proposal is that to compare the alternative models, a critical aspect is to determine how strongly natural selection was influenced by body temperature, and basal and maximum metabolic rates during the evolution of endothermy. We evaluate these relationships in the context of three main hypotheses aimed at explaining the evolution of endothermy, namely the parental care hypothesis and two hypotheses related to the thermoregulatory model (thermogenic capacity and higher body temperature models). We used data on basal and maximum metabolic rates and body temperature from 17 rodent populations, and used intrinsic population growth rate (Rmax) as a global proxy of fitness. We found greater support for the thermogenic capacity model of the thermoregulatory model. In other words, greater thermogenic capacity is associated with increased fitness in rodent populations. To our knowledge, this is the first test of the fitness consequences of the thermoregulatory and parental care models for the origin of endothermy.

Introduction

Continuous endothermy is an exclusive feature of birds and mammals, although endothermic traits are present in several groups of plants, invertebrates, and other vertebrates [1]. Strictly speaking, endothermy is the maintenance of high and constant body temperatures (Tb) through the production and conservation of metabolic heat [2]. Birds and mammals may produce heat through internal organs such as brain, liver, heart, kidneys and gut, while in other endothermic organisms heat generation occurs only through muscular contraction [3], [4]. In comparison to ectotherms, mammals and birds exhibit a higher Tb, because they maintain high metabolic rates and exhibit low thermal conductance [1], [2], [5][7]. One major benefit of continuous endothermy (hereafter endothermy) is independence from the environment [8], which could account for the relative number and diversity of mammal and avian species in climatically extreme environments [9]. Moreover, endothermic organisms can sustain high levels of activity due to their high capacity for aerobic metabolism. This has important ecological benefits such as the ability to escape from predators or to search for food under a wider range of environmental conditions than ectotherms [10].

Endothermic organisms have high daily costs, for example basal (BMR) or resting metabolic rates (RMR) nearly 20 times higher than metabolic rates of reptiles of similar body size [11], [12]. As a consequence, birds and mammals spend about 30% of their total energy budget on maintenance [1]. Understanding the evolutionary history of these thermal adaptations and the high costs of going from an ectothermic condition to the extant endothermic condition, has been an elusive and controversial topic [13], [14]. The evolution of endothermy, while one of the most important evolutionary steps in vertebrate history, is a puzzling evolutionary event [15].

Several competing hypotheses have been suggested to explain the evolution of endothermy, namely the aerobic capacity model [6], the thermoregulatory model [8], [16] and the parental care model [5], [17]. The aerobic capacity model posits that natural selection favoured sustained activity, a condition related to aerobic capacity during exercise. Aerobic capacity is usually measured as maximum rate of oxygen consumption, a proxy for maximum metabolic rate (MMR). Assuming a structural coupling between MMR and RMR, directional selection on MMR would have generated a correlated response in RMR [6]. The aerobic capacity model assumes that MMR and BMR are heritable and also genetically correlated [18][22].

The thermoregulatory model comprises several hypotheses which assume body temperature was the target of natural selection [16], [23]. In one scenario, the thermogenic capacity model, natural selection acted directly on Tb, but only after selection for the ability to maintain stable Tb under different environmental conditions [8]. When proto-mammals became nocturnal their thermal adaptation allowed them to expand their thermal niche, with the resulting colonization of new environments [8]. After this, they returned to diurnal activity, with the consequent increase in metabolic rate and Tb [15], [24].

Alternatively, one of the first hypotheses proposed for the origin of endothermy was the adaptation to higher Tb values [25] to maximize performance. For example, enzymatic reactions are maximal at particular temperatures, and proto-endotherms probably had high temperature set points, for which increases in Tb were selected [25]. We call this version of the thermoregulatory model the “higher body temperature model”. Experimental tests attempting to increase Tb in reptiles by increasing metabolic rate have failed to support this higher temperature model. In particular, tripling or even quadrupling the standard metabolic rate (SMR) of experimental subjects during digestion results in an increase in Tb of less than 1°C [7]. However, it has recently been suggested that the thermoregulatory profits of an increased RMR could play an important role in the “aerobic capacity” scenario [24].

Finally, the parental care model postulates that the increase in BMR was a by-product of natural selection for parental care [17]. Parental care entails high activity, higher daily energy expenditure (DEE), larger internal organs and, ultimately, a high BMR. Koteja [1] argued that selection on parental care–which increases juvenile survival relative to adults–would be strong (but see [26]). In contrast, the parental care model proposed by Farmer [5] is linked to the thermoregulatory hypothesis. Farmer’s model [5] posits that natural selection acted on incubation temperature to increase developmental stability with a consequent increase in hatchling growth rate. Thus, Farmer’s parental care based hypothesis is not necessarily distinct from the thermoregulatory model, as it relies on an additional benefit of high Tb [1], [27].

Several studies have tested the various models for the origin of endothermy by analyzing assumptions of the models such as the relationship between BMR and MMR [7], [18], [19], [23]. Nevertheless, a key challenge is to determine the links between each factor that has been proposed as a major functional determinant in the evolution of endothermy and fitness. To resolve this problem it is necessary to identify the functional traits that are important to proto-endotherms and the strength of selection to which they were subjected. Clearly, this information is not available, but we suggest that evidence gathered from extant populations may shed light on what happened in the past. Knowing how energetic traits are associated with fitness in the present is a first step towards understanding the evolution of endothermy [22]. Furthermore, apart from the relationship between the target trait and fitness, each model for the evolution of endothermy assumes different causal relationships among metabolic traits, which have not yet been analyzed together. Consequently, the aim of our study was to test the thermoregulatory and Koteja’s parental care models for the origin of endothermy. To this end, we carried out a cause-effect analysis of the relative effects of BMR, MMR and Tb on fitness. This allowed us to infer the target of natural selection on endothermy in extant populations. To do this we estimated fitness as the intrinsic growth population rate (Rmax) reported for different species’ populations, which includes processes of both reproduction and survival. The strength of association was evaluated using Structural Equation Modeling (SEM; also referred to as path modeling). These statistical models represent a series of hypothesized cause-effect relationships, which can be viewed as a composite hypothesis concerning patterns of statistical dependencies [28]. Once a hypothesis has been proposed, it can be tested against empirical data using SEM. Then, it is possible to construct a set of candidate models which represent different theoretical models, or competing hypotheses, and compare their viability given the available data. The relative strength of each hypothesis was evaluated using an information criterion such as the Bayesian Information Criterion (BIC).

Predicitons of Endothermy Models

All proposed models for the origin of endothermy, unfortunately, are verbal and there is no mathematical representation of them. This makes it difficult to test them rigorously (Angilleta, 2010). Hence, we translate the verbal models to mathematical ones (path models) including the assumptions and relationships which represent the proposed mechanism for the origin of endothermy (Figure 1, Table 1). First, the higher body temperature model posits that selection favoured a higher Tbs [25]. Therefore, an increase in this variable should be associated with an increase in Rmax. Thus, we expected to find that path b6 was significantly different from zero (Figure 1). The responsible mechanism to increase Tb is not clear; some authors suggest that Tb increases could be due to increase in BMR [2]. Therefore, we represented this relationship with path b8, from BMR to Tb (Figure 1, Table 1). However, experimental evidence suggests that significant increases in BMR do not increase Tb [7]; furthermore, increases in Tb may be because of changes in conductance [13]. In this case, path b8 could not be considered to test the higher body temperature model.

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Figure 1. Path diagrams of the evaluated causal models.

The chart without shading shows all considered cause-effect relationships, colored arrows are relationships related to the tested models for endothermy while black arrows are unrelated relationships. Arrows have their corresponding number and variables. The shaded charts show the 12 models evaluated in this work, highlighting which endothermy models are represented and the involved variables. mb = body mass, BMR = basal metabolic rate, MMR = maximum metabolic rate, Rmax = intrinsic population growth rate; Tb = body temperature. Note that for testing Koteja’s parental care model, path b5 is a correlation (indicated by bidirectional arrows), because the relationship between both variables is mediated by parental care, as proposed by the model.

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Table 1. Models tested in this work are presented as structural equations (for graphical representation see Figure 2).

https://doi.org/10.1371/journal.pone.0037069.t001

The thermogenic capacity model says that selection acted on the capacity to maintain constant body temperature, during cold exposure [2]. Then, increases in MMR should be positively related to an increase in fitness, represented by path 4 (Figure 1). The higher body temperature model further assumes that Tb increases, whereas the thermogenic capacity model does not explicitly explain how higher Tb was achieved [25]. So, path b8 was not included in path models representing the thermogenic capacity model (Figure 1).

Koteja’s parental care model is similar to the aerobic capacity model in that it suggests selection favoured increased locomotor activity [17]. However, the main difference between the parental care and aerobic capacity model is that the parental model suggests this increase in locomotor capacity may have been necessary for the evolution of enhanced parental care [17]. Additionally, the parental care model posits that BMR increased as a by-product of selection for enhanced parental care [17]. Therefore, increasing BMR should be correlated with increasing Rmax (b5) (Figure 1). Path b5 has a double headed arrow, meaning that this relationship in our model is correlational [29] and may be mediated by other variables not included in our data set (i.e. parental care and DEE).

Finally, the hypotheses are not mutually exclusive [22], [24], therefore, we also tested some combined models which have the relevant relationships for higher body temperature, thermogenic capacity and parental care models (Figure 1). We further included paths to account for the relationship between metabolic rates (BMR and MMR) and mb (b1 and b2, respectively), BMR and MMR (b3), and mb and Rmax (b7). The relationship between MMR and BMR is an assumption of the aerobic capacity model, which we did not test; however, we consider that it is accurately represented in our models because the genetic correlation between MMR and BMR has been reported for several species [18][22]. The relationship between mb and Rmax (b7) has been reported previously for mammals [30], so we include it in our models.

Results

We found no phylogenetic signal linked to any variables tested (Table 2), thus we did not correct our data for phylogenetic trends.

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Table 2. Estimation of phylogenetic signal in physiological and population level variables using the K and λ parameters.

https://doi.org/10.1371/journal.pone.0037069.t002

The best fit to the available data was attained with model VI (Table 3), which only included a path associated with the thermogenic capacity model (Figure 2). This model explained 18% of the variance in Rmax whereas the other related model with thermogenic capacity (model V), explained 23% of the variance (Table 3), but with fewer degrees of freedom (Figure 1; Table 3). Degrees of freedom decrease when the number of parameters estimated (i.e. paths included in the model) increase, while the explained variance increases with number of parameters in the model [29].

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Figure 2. Schematic of thermogenic capacity model (model VI), the best fitting model.

The parameter estimated for each path and their associated probability are indicated above arrows (*** = P<0.001 with ML, ** = P<0.05 with ML, * = distinct from 0 based on bootstrap). The arrows’ thickness is proportional to the estimated path’s coefficient. mb = body mass, BMR = basal metabolic rate, MMR = maximum metabolic rate, Rmax = intrinsic population growth rate; Tb = body temperature.

https://doi.org/10.1371/journal.pone.0037069.g002

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Table 3. Indices used for model selection and percentage of variance explained for the response variable.

https://doi.org/10.1371/journal.pone.0037069.t003

Models including paths associated with the higher body temperature model (I-IV) and the parental care model (VII-VIII) explained a relatively small proportion of variances, and yielded higher values of BIC (Table 3 and 4).

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Table 4. Structural equations for the most representative theoretical models.

https://doi.org/10.1371/journal.pone.0037069.t004

The thermogenic capacity model (model VI) had a path coefficient for b4 (MMR to Rmax) which was marginally significant using maximum likelihood (ML) (Table 4), and significant based on bootstrapping. We compared strength of the cause-effect relationships by comparing the estimated path coefficients in our path models. In this sense, path b3, from MMR to BMR, had lower estimate than the other paths in model VI. Nevertheless, b3 was implicated as an important link because it was significant in the best model, and all models that did not include this path fit the data poorly (Table 3). In contrast, path b7 (mb to Rmax) was not significant in any model; moreover, models that included this path were inferior (BIC>−16). Finally, path b8 (BMR to Tb) was poorly supported (Table 3).

Discussion

The thermogenic capacity model, represented by model VI, best describes the data and assumes a direct functional relationship between MMR and fitness (Table 3; Figure 2). Our path analysis revealed that the best fit to the data was obtained with a model representing the thermogenic capacity hypothesis. This is not the first time that the relationship between metabolic rates and fitness, or some of its components (survival and reproduction), have been tested [31]. Nonetheless, until now there has been insufficient data regarding the strength of natural selection on metabolic rates to understand the probability of it explaining the origin of endothermy [22].

Our results suggest that MMR, not BMR, determines the relationship between generation time and reproductive rate, as previously suggested [32][34]. An advantage of our work is that we tested causal relationships, with mass affecting both metabolic rates. Further agreement with previous information is the observation that both mass-independent BMR and MMR were correlated [35][39]. This connection was supported by the observation that path b3 was significantly distinct from zero (Table 4).

The best fitting model was the thermogenic capacity model (model VI), given both ML and bootstrap analyses. This model represents the thermogenic capacity model, and path b4 (from MMR to Rmax) was significant and positive. Even though the percentage of Rmax explained was only 18%, this is considerable given that Rmax is a global estimate which is also influenced by several other factors, such as environmental productivity, life cycle and phylogeny [34].

While our analysis may be criticized on the basis of a small sample size (we only had access to 17 species for which data for all parameters were available), it is important to note that Model VI fit (see Table 3) with high statistical power (RMSEA index close to zero) [40]. Furthermore, we can differentiate the thermogenic capacity model (model VI) from all other models using BIC, since it allows us to discriminate between competing models penalizing for small sample size. Although the data we used come from populations belonging to a derived group among mammals, this is, as far as we know, the first time that a global estimator of fitness, like Rmax, has been used to examine the fitness consequences for the origin of endothermy.

Also noteworthy was that path b7 did not occur in our best model. While this relationship (from mb to Rmax) has been reported for mammals [30], it was not significant. Likely, to be due to the fact we studied rodent populations with a smaller range of body sizes. Moreover, b6 (Tb to Rmax) was poorly supported (Table 3) and any model that contained only this relationship poorly described the data, explaining less than 1% of the variance in Rmax (Table 3). Perhaps, it was not significant because Tb varies too little. As a result, it is not possible to reject the higher temperature model; rather, it has the poorest experimental evidence [7], an absence of fossil evidence [14], and a lack of statistical support from natural populations. Taken together, these findings imply that endothermy arose as a mechanism to expand thermogenic capacity.

Finally, since metabolic rates set the pace of life, measurements and analysis of their variability and evolution have been, and continue to be, of paramount importance to several contemporary evolutionary and ecological theories, which attempt to link animal energetics to traits such as species richness, species distribution, life-history strategies and evolutionary processes. Now, based on a bioenergetics approach, we provide support for the thermogenic capacity model for the origin of endothermy. Clearly, this assumes that the processes currently operating were similar to those that operated in the past [9], [41] and that inter-specific rodent’s variability represents at least part of the proto-endotherms variability. In this sense, similar studies on reptile and bird populations are still needed to evaluate the generality of our results. It is important to note that we did not test the aerobic capacity model [42].

Materials and Methods

Source of Data

We considered the following physiological variables: BMR, Tb, and MMR. Body mass (mb) was also included through its effects on metabolic rates [2]. We used MMR obtained during exposure to cold temperatures and in He-O2 atmospheres [43], [44].

To perform path analyses we selected all species of rodents where data were available on metabolic rates measured in the same individuals, together with the corresponding data of population dynamics. We conducted path analysis [29] with data obtained for 17 rodent species from North and South America, Australia and Europe, covering a size range from 6 g to 900 g (for more details see Table S1). We used inter-specific comparisons assuming that the interspecific variation represents intraspecific variability in physiological traits evolved through natural selection. In other words, after several generations of positive selection acting on these traits, it is more likely that any extant species has the physiological variability comparable to proto-endotherms variability. Therefore, we preferred inter-specific over intra-specific comparisons. Physiological data were taken from the literature, and we chose articles where all measurements came from the same set of individuals.

Although interesting, most of the studies which analyzed relationships among metabolic rates and fitness used proxies of fitness based on only one of its components [31], [45]. We used Rmax as a proxy of fitness, which is a more inclusive measurement since it includes both reproduction and survival. In short, Rmax is an estimate of how long an average individual lives and how many descendants it leaves in the population [46]. In spite of its accuracy, it is not frequently used since estimations of Rmax require several years of population data. Data on intrinsic population growth rates (Rmax) were usually obtained from studies different from those reporting physiological measurements. However, we chose reports of Rmax obtained from populations inhabiting geographically or environmental similar habitats relative to those of studies used as sources of metabolic variables. We also selected this procedure to avoid noise and variation between populations owing to local adaptation. Whenever direct estimates of Rmax were not available, we calculated Rmax estimates from data on time series through cubic splines to avoid problems of convergence [47]. This method finds a different equation for every pair of adjacent points, and selects the equations such that the overall curve is smooth [48].

Phylogenetic analysis

To examine whether evolutionary relationships among species could confound our analysis [49][51], we quantified the phylogenetic signal associated with each variable [52]. To do so, we first built a phylogeny of the species based on DNA sequences for interphotoreceptor retinoid-binding (IRPB), which were gathered from GenBank®. When sequences for target species were unavailable, we employed sequences from closely related species based on previous and unrelated phylogenetic analysis (Table S2). Sequence alignment was conducted online with a Muscle Alignment and “A la carte” mode [53]; maximum-likelihood searches were carried out with PhyML [54]. The phylogenetic tree used to estimate phylogenetic signal is shown in Figure S1. This tree is congruent with most previously published phylogenetic relationships across the main families of Rodentia [55][57]. We then tested for phylogenetic signal on all variables using the packages of Picante and Geiger for the R platform [58][60]. We calculated the “K” and “λ” parameters [49]. Since we did not find phylogenetic signal linked to any of the examined variables (Table 2), we did not correct our data for phylogenetic relatedness.

Modeling

We used SEM to evaluate functional relationships between fitness and the physiological traits that have been proposed as critical targets of natural selection in the alternative models for the evolution of endothermy. This method allows the evaluation of a cause-effect relationship between variables, and also allows us to contrast theoretical models [28]. In SEM, the relationships (arrows) are described by parameters (path coefficients) that indicate the magnitude of the effect (which can be direct or indirect, or a combination) of the independent variables on the dependent variables, (see [28]). We conducted a path analysis with the aim of testing some critical co-variations predicted by alternative models for the evolution of endothermy (Figure 1) [29].

The variables were log transformed when it was necessary to meet normality assumptions. Additionally, all variables were standardized using the correlation matrix in path analyses so that all estimated coefficients could be compared. We used Maximum-likelihood (ML) tools to estimate path coefficients and their associated probability values in structural equations [29]. Afterwards, we used bootstrapping to calculate confidence intervals associated with each path coefficient. This approach is powerful for examining small data bases and provides an adequate evaluation of evolutionary and ecological hypotheses [61]. We used “sem” and “boot” packages in Program R [62], [63]. Model selection was conducted using χ2 (P>0.05 model could not be rejected), BIC (comparatively lower values indicate a better model), index root means square error approximation (RMSEA, near to 0 is considered a good fit), and variance explained to determine that the best fitted model was accurate to explain the data [29], [61]. We used BIC since it allows us to discriminate between competing models when penalizing for small sample size [29], [61].

Supporting Information

Figure S1.

Phylogenetic tree resulting from the maximum-likelihood analysis of the IRPB gene sequences of 17 rodent species and 1 outgroup (Lepus crawshayi).

https://doi.org/10.1371/journal.pone.0037069.s001

(TIF)

Table S1.

Alphabetical listing of metabolic and population data compiled for 17 species of rodents with references.

https://doi.org/10.1371/journal.pone.0037069.s002

(DOCX)

Table S2.

IRBP gene sequences used in phylogenetic reconstruction and its GenBank code.

https://doi.org/10.1371/journal.pone.0037069.s003

(DOCX)

Acknowledgments

We acknowledge C. Dickman, S. Estay, A. Farias, B. Fox, L. Ebensperger, M. Lima, F. Labra, P. Koteja and E. Rezende for useful discussions and comments.

Author Contributions

Conceived and designed the experiments: SC FB. Performed the experiments: SC FB. Analyzed the data: SC FB. Contributed reagents/materials/analysis tools: SC FB. Wrote the paper: SC FB.

References

  1. 1. Koteja P (2004) The evolution of concepts on the evolution of endothermy in birds and mammals. Physiol Biochem Zool 77: 1043–1050.
  2. 2. McNab BK (2002) The physiological ecology of vertebrates: a view from energetics. New York: Cornell University Press. 576 p.
  3. 3. Hulbert A, Else P (2004) Basal metabolic rate: history, composition, regulation, and usefulness. Physiol Biochem Zool 77: 869–876.
  4. 4. Else PL, Turner N, Hulbert A (2004) The evolution of endothermy: Role for membranes and molecular activity. Physiol Biochem Zool 77: 950–958.
  5. 5. Farmer CG (2000) Parental care: the key to understanding endothermy and other convergent features in birds and mammals. Am Nat 155: 326–334.
  6. 6. Bennett AF, Ruben JA (1979) Endothermy and activity in vertebrates. Science 206: 649–654.
  7. 7. Bennett AF, Hicks JW, Cullum AJ (2000) An experimental test of the thermoregulatory hypothesis for the evolution of endothermy. Evolution 54: 1768–1773.
  8. 8. Crompton AW, Taylor CR, Jagger JA (1978) Evolution of homeothermy in mammals. Nature 272: 333–336.
  9. 9. Lomolino MV, Riddle BR, Brown JH (2005) Biogeography. Sunderland: Sinauer Associates. 978 p.
  10. 10. Hillenius WJ, Ruben JA (2004) The evolution of endothermy in terrestrial vertebrates: Who? When? Why? Physiol Biochem Zool 77: 1019–1042.
  11. 11. Koteja P (1991) On the relation between basal and field metabolic rates in birds and mammals. Funct Ecol 5: 56–64.
  12. 12. Ricklefs RE, Konarzewski M, Daan S (1996) The relationship between basal metabolic rate and daily energy expenditure in birds and mammals. Am Nat 147: 1047–1071.
  13. 13. Hayes JP, Garland T (1995) The evolution of endothermy: testing the aerobic capacity model. Evolution 49: 836–847.
  14. 14. Ruben J (1995) The evolution of endothermy in mammals and birds: from physiology to fossils. Annu Rev Physiol 57: 69–95.
  15. 15. Kemp TS (2006) The origin of mammalian endothermy: a paradigm for the evolution of complex biological structure. Zool J Linn Soc-Lond 147: 473–488.
  16. 16. McNab BK (1978) The evolution of endothermy in the phylogeny of mammals. Am Nat 112: 1–21.
  17. 17. Koteja P (2000) Energy assimilation, parental care and the evolution of endothermy. P Roy Soc B-Biol Sci 267: 479–484.
  18. 18. Sadowska ET, Baliga-Klimczyk K, Labocha MK, Koteja P (2009) Genetic correlations in a wild rodent: grass-eaters and fast-growers evolve high basal metabolic rates. Evolution 63: 1530–1539.
  19. 19. Sadowska ET, Labocha MK, Baliga K, Stanisz A, Jagusiak W (2005) Genetic correlations between basal and maximum metabolic rates in a wild rodent: consequences for evolution of endothermy. Evolution 59: 672–681.
  20. 20. Nespolo RF, Bacigalupe LD, Bozinovic F (2003) Heritability of energetics in a wild mammal, the leaf-eared mouse (Phyllotis darwini). Evolution 57: 1679–1688.
  21. 21. Nespolo RF, Bustamante DM, Bacigalupe LD, Bozinovic F (2005) Quantitative genetics of bioenergetics and growth-related traits in the wild mammal, Phyllotis darwini. Evolution 59: 1829–1837.
  22. 22. Hayes JP (2010) Metabolic rates, genetic constraints, and the evolution of endothermy. J Evolution Biol 23: 1868–1877.
  23. 23. Grigg GC, Beard LA, Augee ML (2004) The evolution of endothermy and its diversity in mammals and birds. Physiol Biochem Zool 77: 982–997.
  24. 24. Clarke A, Pörtner HO (2010) Temperature, metabolic power and the evolution of endothermy. Biol Rev 85: 703–727.
  25. 25. Heinrich B (1977) Why have some animals evolved to regulate a high body temperature? Am Nat 111: 623–640.
  26. 26. Caswell H (2001) Matrix population models. Massachusetts: Sinauer Associates Sunderland. 722 p.
  27. 27. Angilletta MJ , Sears MW (2003) Is parental care the key to understanding endothermy in birds and mammals? Am Nat 162: 821–825.
  28. 28. Shipley B (2000) A new inferential test for path models based on directed acyclic graphs. Struct Equ Modeling 7: 206–218.
  29. 29. Shipley B (2002) Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference. New York: Cambridge University Press. 317 p.
  30. 30. Hennemann WW (1983) Relationship among body mass, metabolic rate and the intrinsic rate of natural increase in mammals. Oecologia 56: 104–108.
  31. 31. Boratynski Z, Koteja P (2009) The association between body mass, metabolic rates and survival of bank voles. Funct Ecol 23: 330–339.
  32. 32. Stearns S (1992) The evolution of life histories. New York: Oxford University Press. 249 p.
  33. 33. Lovegrove BG (2003) The influence of climate on the basal metabolic rate of small mammals: a slow-fast metabolic continuum. J Comp Physiol B 173: 87–112.
  34. 34. Mueller P, Diamond J (2001) Metabolic rate and environmental productivity: well-provisioned animals evolved to run and idle fast. Proc Natl Acad Sci U S A 98: 12550–12554.
  35. 35. Bozinovic F (1992) Scaling of basal and maximum metabolic rate in rodents and the aerobic capacity model for the evolution of endothermy. Physiol Zool 65: 921–932.
  36. 36. Dutenhoffer MS, Swanson DL (1996) Relationship of basal to summit metabolic rate in passerine birds and the aerobic capacity model for the evolution of endothermy. Physiol Zool 69: 1232–1254.
  37. 37. Vezina F, Jalvingh KM, Dekinga A, Piersma T (2006) Acclimation to different thermal conditions in a northerly wintering shorebird is driven by body mass-related changes in organ size. J Exp Biol 209: 3141–3154.
  38. 38. Koteja P (1987) On the relation between basal and maximum metabolic rate in mammals. Comp Biochem Phys A 87: 205–208.
  39. 39. Bozinovic F (2007) Long-term repeatability of body mass and body temperature (but not basal metabolism) in the free-ranging leaf-eared mouse. Evol Ecol Res 9: 547–554.
  40. 40. Pugesek BH, Tomer A (2003) Structural equation modeling: Applications in ecological and evolutionary biology. New York: Cambridge University Press. 409 p.
  41. 41. Gould SJ (1965) Is uniformitarianism necessary? Is uniformitarianism necessary? 263: 223–228.
  42. 42. Nespolo RF, Bacigalupe LD, Figueroa CC, Koteja P, Opazo JC (2011) Using new tools to solve an old problem: the evolution of endothermy in vertebrates. Trends Ecol Evol 26: 414–423.
  43. 43. Rosenmann M, Morrison P (1974) Maximum oxygen consumption and heat loss facilitation in small homeotherms by He-O2. Am J Physiol 226: 490–495.
  44. 44. Bozinovic F, Rosenmann M (1989) Maximum metabolic rate of rodents: physiological and ecological consequences on distributional limits. Funct Ecol 3: 173–181.
  45. 45. Boratynski Z, Koteja P (2010) Sexual and natural selection on body mass and metabolic rates in free living bank voles. Funct Ecol 24: 1252–1261.
  46. 46. Berryman AA (1999) Principles of population dynamics and their application. Cheltenham: Stanley Thornes. 243 p.
  47. 47. Estay SA, Clavijo-Baquet S, Lima M, Bozinovic F (2010) Beyond average: an experimental test of temperature variability on the population dynamics of Tribolium confusum. Popul Ecol 53: 1–6.
  48. 48. Motulsky HJ, Ransnas LA (1987) Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J 1: 365–374.
  49. 49. Pagel M (1999) Inferring the historical patterns of biological evolution. Nature 401: 877–884.
  50. 50. Blomberg SP, Theodore Garland JR, Ives AR (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57: 717–745.
  51. 51. Garland T Jr, Bennett AF, Rezende EL (2005) Phylogenetic approaches in comparative physiology. J Exp Biol 208: 3015–3035.
  52. 52. Garland T Jr, Harvey PH, Ives AR (1992) Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst Biol 41: 18–32.
  53. 53. Dereeper A, Guignon V, Blanc G, Audic S, Buffet S, et al. (2008) Phylogeny. fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res 36: 465–469.
  54. 54. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52: 696–704.
  55. 55. Jansa SA, Goodman SM, Tucker PK (1999) Molecular phylogeny and biogeography of the native rodents of Madagascar (Muridae: Nesomyinae): a test of the single-origin hypothesis. Cladistics 15: 253–270.
  56. 56. Jansa SA, Weksler M (2004) Phylogeny of muroid rodents: relationships within and among major lineages as determined by IRBP gene sequences. Mol Phylogenet Evol 31: 256–276.
  57. 57. Jansa SA, Giarla TC, Lim BK (2009) The phylogenetic position of the rodent genus Typhlomys and the geographic origin of Muroidea. J Mammal 90: 1083–1094.
  58. 58. Paradis E (2006) Analysis of phylogenetics and evolution with R. New York: Springer Verlag. 211 p.
  59. 59. Kembel SW, Ackerly DD, Blomberg SP, Cornwell WK, Cowan PD, et al. (2009) Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26: 1463–1464.
  60. 60. Harmon LJ, Weir JT, Brock CD, Glor RE, Challenger W (2008) GEIGER: investigating evolutionary radiations. Bioinformatics 24: 129–131.
  61. 61. Shipley B (1997) Exploratory path analysis with applications in ecology and evolution. Am Nat 149: 1113–1138.
  62. 62. Fox J (2006) Teacher’s corner: Structural Equation Modeling with the sem Package in R. Struct Equ Modeling 13: 465–486.
  63. 63. Davison AC, Hinkley DV, Schechtman E (1986) Efficient bootstrap simulation. Biometrika 73: 555–566.