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Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations

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Abstract

Life on earth spans a size range of around 21 orders of magnitude across species and can span a range of more than 6 orders of magnitude within species of animal. The effect of size on physiology is, therefore, enormous and is typically expressed by how physiological phenomena scale with massb. When b ≠ 1 a trait does not vary in direct proportion to mass and is said to scale allometrically. The study of allometric scaling goes back to at least the time of Galileo Galilei, and published scaling relationships are now available for hundreds of traits. Here, the methods of scaling analysis are reviewed, using examples for a range of traits with an emphasis on those related to metabolism in animals. Where necessary, new relationships have been generated from published data using modern phylogenetically informed techniques. During recent decades one of the most controversial scaling relationships has been that between metabolic rate and body mass and a number of explanations have been proposed for the scaling of this trait. Examples of these mechanistic explanations for metabolic scaling are reviewed, and suggestions made for comparing between them. Finally, the conceptual links between metabolic scaling and ecological patterns are examined, emphasizing the distinction between (1) the hypothesis that size‐ and temperature‐dependent variation among species and individuals in metabolic rate influences ecological processes at levels of organization from individuals to the biosphere and (2) mechanistic explanations for metabolic rate that may explain the size‐ and temperature‐dependence of this trait. © 2014 American Physiological Society. Compr Physiol 4:231‐256, 2014.

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Figure 1. Figure 1. Scaling of surface area with body mass (M, kg) in mammals (data from 333). The solid line is the phylogenetically informed scaling relationship (surface area = 0.092 M0.67) (333). The lower dashed line is the relationship between the surface area and volume of a sphere with a density of 1.08 g cm−3, a rough estimate of a mixture of muscle, fat, and bone: muscle has a density of 1.06 g cm−3; bone has a density of 2.00 g cm−3; and fat has a density of 0.93 g cm−3 (7).
Figure 2. Figure 2. (A) Standard deviation of 10,000 scaling exponents (b, where Y = aMb) calculated for mass ranges of 0.2 to 6 orders of magnitude. (B) Standard deviation of 10,000 scaling exponents (b, where Y = aMb) calculated for sample sizes of 10 to 500 “species.” For each of the 10,000 simulations in (A) 100 values of log(M) spanning the appropriate range were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. For each of the 10,000 simulations in (B), values of log(M) spanning two orders of magnitude were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. Values of b were then calculated as the slope of the relationship between log(Y) and log(M).
Figure 3. Figure 3. The scaling of mean arterial blood pressure (P, mmHg) in mammals (data and analysis from 448). Solid line is the significant three parameter power equation: P = 6.9 M0.24 + 93. The scaling exponent is significantly greater than 0, and not significantly different from the scaling exponent of 0.33 predicted by geometric scaling of the vertical distance between the heart and head (448).
Figure 4. Figure 4. Scaling of skeletal mass (Ms) and relative skeletal mass with body mass (M) in mammals. Relative skeletal mass is calculated by dividing skeletal mass by body mass. Data are for 73 species and were compiled from published sources (21,241,319,322,335), and matched to a supertree of mammals (28). Data were analyzed using phylogenetic generalized least squares (PGLS) (128,146,251) in the APE (Analysis of Phylogenetics and Evolution) package (310) within R (189) according to established procedures (98,156,432). In addition to the dated branch lengths associated with the supertree, a range of branch length transformations were compared: star, loge, punctuated, Grafen's (146), Nee's (324), and Pagel's (308). For each of these models, a measure of phylogenetic correlation, λ (119,306), was estimated by fitting PGLS models with different values of λ and finding the value that maximizes the log likelihood. The degree to which trait evolution deviates from Brownian motion (λ = 1) was accommodated by modifying the covariance matrix using the maximum likelihood value of λ, which is a multiplier of the off‐diagonal elements of the covariance matrix (i.e., those quantifying the degree of relatedness between species). All models were compared on the basis of Akaike's Information Criterion (AIC) as a measure of model fit (49). The solid line is a significant second‐order polynomial regression relating log(Ms) to log(M) and [log(M) + 4]2: the best model included a phylogeny with all branch lengths equal to 1 (wi = 0.18, λ = 0.32); log(Ms) = −1.53 + 0.87 log(M) + 0.02 [log(M) + 4]2. Dashed line is the best model for data excluding Loxodonta africana and Elephas maximas and including a phylogeny with all branch lengths equal to 1 (wi = 0.68, λ = 0.37), which is a linear model: log(Ms) = −1.19 + 1.02 log(M). The 95% confidence interval of the scaling exponent for the linear model includes 1 (95%CI: 0.98‐1.06).
Figure 5. Figure 5. Scaling of field metabolic rate (FMR) with body mass (M) in arid (filled symbols, solid line) and nonarid (unfilled symbols, dashed line) birds (data from 405). Phylogenetically informed relationships: arid birds, FMR = 5.24 (2.86‐9.59) M0.691 (0.610‐0.772); non‐arid birds, FMR = 9.31 (7.79‐11.12) M0.676 (0.383‐0.969); values in parentheses are 95% confidence limits. Analyzed using independent contrasts (110), the scaling exponent of FMR does not differ among arid and non‐arid birds (t = −1.57, P = 0.12), whereas environment (desert or non‐desert) does have a significant effect (t = 2.11, P < 0.04) (405).
Figure 6. Figure 6. Scaling of heart mass with body mass for birds (unfilled symbols, dashed line) and mammals (filled symbols, dashed line) (data from 362). The scaling exponents of heart mass are different for birds (b = 0.91) and mammals (b = 1.06). Application of the Johnson‐Neyman technique (196) demonstrates that at P = 0.05, regression elevations are not significantly different at masses above 4.26 kg (427), thus confirming the conclusion that the hearts of flightless birds are not significantly larger than those of similarly sized mammals (362). The vertical dashed line represents the lower limit of the region of nonsignificance. Within the region of nonsignificance there is no significant difference in elevation between the scaling relationships, so in this example the groups differ significantly in elevation to the left of the vertical line. Birds: heart mass (g) = 8.08 M0.91, mammals: heart mass = 4.04 M1.06.
Figure 7. Figure 7. Scaling of heart rate (fH, bpm) with body mass (M, kg) for mammals. Data are for 58 species compiled by White and Seymour (444), matched to a supertree of mammals (28), and analyzed according to the PGLS methods described in the legend to Figure 3. Solid line is the scaling relationship fitted by PGLS with all branch lengths equal and equal to 1, with a maximum likelihood λ of 0.71 (fH = 150 M−0.26 [95% CI: −0.30 to −0.22]).
Figure 8. Figure 8. Interspecific relationship between heart rate (fH, bpm) and basal metabolic rate (BMR, mL h−1) assessed using phylogenetic generalized least squares. Data are for 58 species (444) matched to a supertree of mammals (28). The relationship between BMR and fH was then assessed by comparing the fit of statistical models that either included BMR (logfH ∼ logM + logBMR) or did not include BMR (logfH ∼ logM). The best model was chosen on the basis of its Akaike weight (wi, the probability that a model is the best of a candidate set, given the data). The best model included logBMR and logM (wi = 0.19) with a maximum likelihood λ of 0.64 and all branch lengths set equal to one (a punctuated model of evolution). Summed over all evolutionary models (dated, punctuated, Grafen's, Nee's and Pagel's), the probability that the best model for logfH includes logBMR in addition to logM is 0.66 (see legend to Figure 3 for full details of analysis procedure). Residuals are shown to account for the relationships between logM and both logBMR and logfH, but the analysis of the relationship between fH and BMR was not assessed using residuals; the solid line is the parameter estimate for logBMR from the best model (logfH ∼ logM + logBMR) plotted through the bivariate mean of residuals.
Figure 9. Figure 9. (A) scaling of basal metabolic for 148 species of murid rodent. Data adapted, with permission, from White and Seymour (443) synonymized to match the supertree of Bininda‐Emonds et al. (28); see White et al. (432) for details. Solid blue line is the ordinary least squares relationship; dashed blue lines are the 95% prediction interval of the ordinary least squares (OLS) relationship. Solid red line is the relationship for Notomys alexis estimated by independent contrasts (PIC) (110) following Garland and Ives (128); dotted red lines are the 95% prediction interval for the phylogenetically informed regression. (B) BMR measured for 11 individual Notomys alexis (mean mass 33 g) using indirect calorimetry (440) shown ± SEM and compared with predicted BMR for a 33 g murid rodent (shown ± SEE) for the OLS and PIC regressions presented in (A). Error bounds of BMR value predicted by OLS encompass the measured value, but absolute BMR is overestimated by 29%, and the OLS relationship estimates BMR with considerable uncertainty. The PIC estimate of BMR for Notomys alexis is more accurate and overestimates BMR by only 4%, an error similar to the measurement error associated with experimental determination of metabolic rate by indirect calorimetry (e.g., 439), but the error bounds associated with the PIC estimate of BMR are wider than those of OLS. Note that the error bars for predicted BMR are asymmetric because of back‐transformation from log‐transformed data.
Figure 10. Figure 10. Mean coefficient of determination (r2, filled diamonds and solid line) and standard error of estimate (filled squares and dashed line) for 10,000 scaling exponents (b, where Y = aMb) calculated for mass ranges of 0.2 to 6 orders of magnitude. For each of the 10,000 simulations, 100 values of log(M) spanning the appropriate range were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. The value of b was then calculated as the slope of the relationship between log(Y) and log(M). For a given quantity of residual variation, the coefficient of determination increases with mass range but the standard error of estimate does not.
Figure 11. Figure 11. Scaling of (A) maximum metabolic rate (V·O2max, filled diamonds and solid black line), (B) field metabolic rate (FMR, filled and unfilled circles, blue and red solid lines), and (C) basal metabolic rate (BMR, unfilled diamonds, blue and red dashed lines) with body mass (M) for mammals. V·O2max = 3300 M0.87 (see also 93 for a recent analysis including additional species that reported a scaling exponent of 0.85, data adapted, with permission, from 445). Data for FMR are shown for terrestrial (filled circles) and aquatic (unfilled circles) animals (2,289,385); blue and red solid lines in (B) are maximum sustained metabolic rates predicted by the Heat Dissipation Limit theory for endotherms at 10 and 30°C, respectively (385). Blue and red dashed lines in (B) and (C) are predicted BMR for tropical/xeric/desert and widespread species, respectively, and are curvilinear on log‐log axes (429).
Figure 12. Figure 12. Size dependence of (A) factorial aerobic scope ( = maximum aerobic metabolic rate, V·O2max, divided by basal metabolic rate, BMR) and (B) factorial activity scope ( = field metabolic rate, FMR, divided by BMR) for bats (unfilled symbols) and other mammals (filled symbols). Both aerobic and activity scope show a triangular pattern, with a wider range of aerobic scopes observed for large mammals than small ones, and a wider range of activity scopes observed for small animals than large ones. Aerobic and activity scopes were calculated using published data for V·O2max (198,234,412,445), FMR (385), and BMR (101,267).


Figure 1. Scaling of surface area with body mass (M, kg) in mammals (data from 333). The solid line is the phylogenetically informed scaling relationship (surface area = 0.092 M0.67) (333). The lower dashed line is the relationship between the surface area and volume of a sphere with a density of 1.08 g cm−3, a rough estimate of a mixture of muscle, fat, and bone: muscle has a density of 1.06 g cm−3; bone has a density of 2.00 g cm−3; and fat has a density of 0.93 g cm−3 (7).


Figure 2. (A) Standard deviation of 10,000 scaling exponents (b, where Y = aMb) calculated for mass ranges of 0.2 to 6 orders of magnitude. (B) Standard deviation of 10,000 scaling exponents (b, where Y = aMb) calculated for sample sizes of 10 to 500 “species.” For each of the 10,000 simulations in (A) 100 values of log(M) spanning the appropriate range were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. For each of the 10,000 simulations in (B), values of log(M) spanning two orders of magnitude were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. Values of b were then calculated as the slope of the relationship between log(Y) and log(M).


Figure 3. The scaling of mean arterial blood pressure (P, mmHg) in mammals (data and analysis from 448). Solid line is the significant three parameter power equation: P = 6.9 M0.24 + 93. The scaling exponent is significantly greater than 0, and not significantly different from the scaling exponent of 0.33 predicted by geometric scaling of the vertical distance between the heart and head (448).


Figure 4. Scaling of skeletal mass (Ms) and relative skeletal mass with body mass (M) in mammals. Relative skeletal mass is calculated by dividing skeletal mass by body mass. Data are for 73 species and were compiled from published sources (21,241,319,322,335), and matched to a supertree of mammals (28). Data were analyzed using phylogenetic generalized least squares (PGLS) (128,146,251) in the APE (Analysis of Phylogenetics and Evolution) package (310) within R (189) according to established procedures (98,156,432). In addition to the dated branch lengths associated with the supertree, a range of branch length transformations were compared: star, loge, punctuated, Grafen's (146), Nee's (324), and Pagel's (308). For each of these models, a measure of phylogenetic correlation, λ (119,306), was estimated by fitting PGLS models with different values of λ and finding the value that maximizes the log likelihood. The degree to which trait evolution deviates from Brownian motion (λ = 1) was accommodated by modifying the covariance matrix using the maximum likelihood value of λ, which is a multiplier of the off‐diagonal elements of the covariance matrix (i.e., those quantifying the degree of relatedness between species). All models were compared on the basis of Akaike's Information Criterion (AIC) as a measure of model fit (49). The solid line is a significant second‐order polynomial regression relating log(Ms) to log(M) and [log(M) + 4]2: the best model included a phylogeny with all branch lengths equal to 1 (wi = 0.18, λ = 0.32); log(Ms) = −1.53 + 0.87 log(M) + 0.02 [log(M) + 4]2. Dashed line is the best model for data excluding Loxodonta africana and Elephas maximas and including a phylogeny with all branch lengths equal to 1 (wi = 0.68, λ = 0.37), which is a linear model: log(Ms) = −1.19 + 1.02 log(M). The 95% confidence interval of the scaling exponent for the linear model includes 1 (95%CI: 0.98‐1.06).


Figure 5. Scaling of field metabolic rate (FMR) with body mass (M) in arid (filled symbols, solid line) and nonarid (unfilled symbols, dashed line) birds (data from 405). Phylogenetically informed relationships: arid birds, FMR = 5.24 (2.86‐9.59) M0.691 (0.610‐0.772); non‐arid birds, FMR = 9.31 (7.79‐11.12) M0.676 (0.383‐0.969); values in parentheses are 95% confidence limits. Analyzed using independent contrasts (110), the scaling exponent of FMR does not differ among arid and non‐arid birds (t = −1.57, P = 0.12), whereas environment (desert or non‐desert) does have a significant effect (t = 2.11, P < 0.04) (405).


Figure 6. Scaling of heart mass with body mass for birds (unfilled symbols, dashed line) and mammals (filled symbols, dashed line) (data from 362). The scaling exponents of heart mass are different for birds (b = 0.91) and mammals (b = 1.06). Application of the Johnson‐Neyman technique (196) demonstrates that at P = 0.05, regression elevations are not significantly different at masses above 4.26 kg (427), thus confirming the conclusion that the hearts of flightless birds are not significantly larger than those of similarly sized mammals (362). The vertical dashed line represents the lower limit of the region of nonsignificance. Within the region of nonsignificance there is no significant difference in elevation between the scaling relationships, so in this example the groups differ significantly in elevation to the left of the vertical line. Birds: heart mass (g) = 8.08 M0.91, mammals: heart mass = 4.04 M1.06.


Figure 7. Scaling of heart rate (fH, bpm) with body mass (M, kg) for mammals. Data are for 58 species compiled by White and Seymour (444), matched to a supertree of mammals (28), and analyzed according to the PGLS methods described in the legend to Figure 3. Solid line is the scaling relationship fitted by PGLS with all branch lengths equal and equal to 1, with a maximum likelihood λ of 0.71 (fH = 150 M−0.26 [95% CI: −0.30 to −0.22]).


Figure 8. Interspecific relationship between heart rate (fH, bpm) and basal metabolic rate (BMR, mL h−1) assessed using phylogenetic generalized least squares. Data are for 58 species (444) matched to a supertree of mammals (28). The relationship between BMR and fH was then assessed by comparing the fit of statistical models that either included BMR (logfH ∼ logM + logBMR) or did not include BMR (logfH ∼ logM). The best model was chosen on the basis of its Akaike weight (wi, the probability that a model is the best of a candidate set, given the data). The best model included logBMR and logM (wi = 0.19) with a maximum likelihood λ of 0.64 and all branch lengths set equal to one (a punctuated model of evolution). Summed over all evolutionary models (dated, punctuated, Grafen's, Nee's and Pagel's), the probability that the best model for logfH includes logBMR in addition to logM is 0.66 (see legend to Figure 3 for full details of analysis procedure). Residuals are shown to account for the relationships between logM and both logBMR and logfH, but the analysis of the relationship between fH and BMR was not assessed using residuals; the solid line is the parameter estimate for logBMR from the best model (logfH ∼ logM + logBMR) plotted through the bivariate mean of residuals.


Figure 9. (A) scaling of basal metabolic for 148 species of murid rodent. Data adapted, with permission, from White and Seymour (443) synonymized to match the supertree of Bininda‐Emonds et al. (28); see White et al. (432) for details. Solid blue line is the ordinary least squares relationship; dashed blue lines are the 95% prediction interval of the ordinary least squares (OLS) relationship. Solid red line is the relationship for Notomys alexis estimated by independent contrasts (PIC) (110) following Garland and Ives (128); dotted red lines are the 95% prediction interval for the phylogenetically informed regression. (B) BMR measured for 11 individual Notomys alexis (mean mass 33 g) using indirect calorimetry (440) shown ± SEM and compared with predicted BMR for a 33 g murid rodent (shown ± SEE) for the OLS and PIC regressions presented in (A). Error bounds of BMR value predicted by OLS encompass the measured value, but absolute BMR is overestimated by 29%, and the OLS relationship estimates BMR with considerable uncertainty. The PIC estimate of BMR for Notomys alexis is more accurate and overestimates BMR by only 4%, an error similar to the measurement error associated with experimental determination of metabolic rate by indirect calorimetry (e.g., 439), but the error bounds associated with the PIC estimate of BMR are wider than those of OLS. Note that the error bars for predicted BMR are asymmetric because of back‐transformation from log‐transformed data.


Figure 10. Mean coefficient of determination (r2, filled diamonds and solid line) and standard error of estimate (filled squares and dashed line) for 10,000 scaling exponents (b, where Y = aMb) calculated for mass ranges of 0.2 to 6 orders of magnitude. For each of the 10,000 simulations, 100 values of log(M) spanning the appropriate range were randomly generated and values of Y were calculated as M0.75 plus a normal deviate with a mean of 0 and a standard deviation equal to 20% of M0.75. The value of b was then calculated as the slope of the relationship between log(Y) and log(M). For a given quantity of residual variation, the coefficient of determination increases with mass range but the standard error of estimate does not.


Figure 11. Scaling of (A) maximum metabolic rate (V·O2max, filled diamonds and solid black line), (B) field metabolic rate (FMR, filled and unfilled circles, blue and red solid lines), and (C) basal metabolic rate (BMR, unfilled diamonds, blue and red dashed lines) with body mass (M) for mammals. V·O2max = 3300 M0.87 (see also 93 for a recent analysis including additional species that reported a scaling exponent of 0.85, data adapted, with permission, from 445). Data for FMR are shown for terrestrial (filled circles) and aquatic (unfilled circles) animals (2,289,385); blue and red solid lines in (B) are maximum sustained metabolic rates predicted by the Heat Dissipation Limit theory for endotherms at 10 and 30°C, respectively (385). Blue and red dashed lines in (B) and (C) are predicted BMR for tropical/xeric/desert and widespread species, respectively, and are curvilinear on log‐log axes (429).


Figure 12. Size dependence of (A) factorial aerobic scope ( = maximum aerobic metabolic rate, V·O2max, divided by basal metabolic rate, BMR) and (B) factorial activity scope ( = field metabolic rate, FMR, divided by BMR) for bats (unfilled symbols) and other mammals (filled symbols). Both aerobic and activity scope show a triangular pattern, with a wider range of aerobic scopes observed for large mammals than small ones, and a wider range of activity scopes observed for small animals than large ones. Aerobic and activity scopes were calculated using published data for V·O2max (198,234,412,445), FMR (385), and BMR (101,267).
References
 1. Abensperg‐Traun M , Dickman CR , Boer ESD . Patch use and prey defence in a mammalian myrmecophage, the echidna (Tachyglossus aculeatus) (Monotremata: Tachyglossidae): A test of foraging efficiency in captive and free‐ranging animals. J Zool 225: 481‐493, 1991.
 2. Acquarone M , Born EW , Speakman JR . Field metabolic rates of walrus (Odobenus rosmarus) measured by the doubly labeled water method. Aquat Mamm 32: 363‐369, 2006.
 3. Adams DC , Church JO . Amphibians do not follow Bergmann's rule. Evolution 62: 413‐420, 2008.
 4. Addo‐Bediako A , Chown SL , Gaston KJ . Metabolic cold adaptation in insects: A large‐scale perspective. Funct Ecol 16: 332‐338, 2002.
 5. Agutter PS , Tuszynski JA . Analytic theories of allometric scaling. J Exp Biol 214: 1055‐1062, 2011.
 6. Agutter PS , Wheatley DN . Metabolic scaling: Consensus or controversy? Theor Biol Med Model 1: 13 (http://www.tbiomed.com/content/1/1/13), 2004.
 7. Alexander RM. Locomotion of Animals. Glasgow: Blackie, 1982.
 8. Algar AC , Kerr JT , Currie DJ . A test of Metabolic theory as the mechanism underlying broad‐scale species‐richness gradients. Glob Ecol Biogeogr 16: 170‐178, 2007.
 9. Allen AP , Brown JH , Gillooly JF . Global biodiversity, biochemical kinetics, and the energetic‐equivalence rule. Science 297: 1545‐1548, 2002.
 10. Anderson JF , Hall‐Martin A , Russell DA . Long‐bone circumference and weight in mammals, birds and dinosaurs. J Zool 207: 53‐61, 1985.
 11. Anderson KJ , Jetz W . The broad‐scale ecology of energy expenditure of endotherms. Ecol Lett 8: 310‐318, 2005.
 12. Arens JR , Sheldon JC . Seasonal and diurnal variation in metabolism and ventilation in house sparrows. The Condor 107: 433‐444, 2005.
 13. Artacho P , Nespolo RF . Natural selection reduces energy metabolism in the garden snail, Helix aspersa (Cornu aspersum). Evolution 63: 1044‐1050, 2009.
 14. Ashton KG . Patterns of within‐species body size variation of birds: Strong evidence for Bergmann's rule. Glob Ecol Biogeogr 11: 505‐523, 2002.
 15. Ashton KG , Tracy MC , de Queiroz A . Is Bergmann's rule valid for mammals? Am Nat 156: 390‐415, 2000.
 16. Badeer HS . Is the flow in the giraffe's jugular vein a “free” fall? Comp Biochem Physiol A 118: 573‐576, 1997.
 17. Banavar JR , Damuth J , Maritan A , Rinaldo A . Allometric cascades. Nature 421: 713‐714, 2003.
 18. Banavar JR , Damuth J , Maritan A , Rinaldo A . Supply‐demand balance and metabolic scaling. Proc Natl Acad Sci U S A 99: 10506‐10509, 2002.
 19. Banavar JR , Maritan A , Rinaldo A . Size and form in efficient transportation networks. Nature 399: 130‐131, 1999.
 20. Banavar JR , Moses ME , Brown JH , Damuth J , Rinaldo A , Sibly RM , Maritan A . A general basis for quarter‐power scaling in animals. Proc Natl Acad Sci U S A 107: 15816‐15820, 2010.
 21. Barclay RMR . Constraints on reproduction by flying vertebrates: Energy and calcium. Am Nat 144: 1021‐1031, 1994.
 22. Bednekoff PA , Biebach H , Krebs J . Great tit fat reserves under unpredictable temperatures. J Avian Biol 25: 156‐160, 1994.
 23. Benedict FG. Vital Energetics: A Study in Comparative Basal Metabolism. Washington, D.C.: Carnegie Institution of Washington, 1938.
 24. Bergmann C. Üeber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien 3: 595‐708, 1847.
 25. Berke SK , Jablonski D , Krug AZ , Roy K , Tomasovych A . Beyond Bergmann's rule: Size–latitude relationships in marine Bivalvia world‐wide. Glob Ecol Biogeogr 22: 173‐183, 2012.
 26. Bertram JEA , Biewener AA . Differential scaling of the long bones in the terrestrial carnivora and other mammals. J Morphol 204: 157‐169, 1990.
 27. Bininda‐Emonds ORP . The evolution of supertrees. Trends Ecol Evol 19: 315‐322, 2004.
 28. Bininda‐Emonds ORP , Cardillo M , Jones KE , MacPhee RDE , Beck RMD , Grenyer R , Price SA , Vos RA , Gittleman JL , Purvis A . The delayed rise of present‐day mammals. Nature 446: 507‐512, 2007.
 29. Biro PA , Stamps JA . Do consistent individual differences in metabolic rate promote consistent individual differences in behavior? Trends Ecol Evol 25: 653‐659, 2010.
 30. Bishop CM . The maximum oxygen consumption and aerobic scope of birds and mammals: Getting to the heart of the matter. Proc Roy Soc B‐Biol Sci 266: 2275‐2281, 1999.
 31. Black JL , Mullan BP , Lorschy ML , Giles LR . Lactation in the sow during heat stress. Livest Prod Sci 35: 153‐170, 1993.
 32. Blackburn TM , Hawkins BA . Bergmann's rule and the mammal fauna of northern North America. Ecography 27: 715‐724, 2004.
 33. Blackmer AL , Mauck RA , Ackerman JT , Huntington CE , Nevitt GA , Williams JB . Exploring individual quality: Basal metabolic rate and reproductive performance in storm‐petrels. Behav Ecol 16: 906‐913, 2005.
 34. Blomberg SP , Garland T, Jr , Ives AR . Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57: 717‐745, 2003.
 35. Bochdansky AB , Grønkjær P , Herra TP , Leggett WC . Experimental evidence for selection against fish larvae with high metabolic rates in a food limited environment. Mar Biol 147: 1413‐1417, 2005.
 36. Boratyński Z , Koskela E , Mappes T , Oksanen TA . Sex‐specific selection on energy metabolism – selection coefficients for winter survival. J Evol Biol 23: 1969‐1978, 2010.
 37. Boratyński Z , Koteja P . The association between body mass, metabolic rates and survival of bank voles. Funct Ecol 23: 330‐339, 2009.
 38. Brice PH . Thermoregulation in monotremes: Riddles in a mosaic. Aust J Zool 57: 255‐263, 2009.
 39. Brody S. Bioenergetics and growth. New York: Reinhold Publishing Corporation, 1945, p. 1023.
 40. Brody S , Proctor RC . Relation between basal metabolism and mature body weight in different species of mammals and birds. Univ. Missouri Agric. Exp. Stn. Res. Bull. 166: 89‐101, 1932.
 41. Brøndum E , Hasenkam JM , Secher NH , Bertelsen MF , Grøndahl C , Petersen KK , Buhl R , Aalkjær C , Baandrup U , Nygaard H , Smerup M , Stegmann F , Sloth E , Østergaard KH , Nissen P , Runge M , Pitsillides K , Wang T . Jugular venous pooling during lowering of the head affects blood pressure of the anesthetized giraffe. Am J Physiol 297: R1058‐R1065, 2009.
 42. Brown JH , Gillooly JF , Allen AP , Savage VM , West GB . Toward a metabolic theory of ecology. Ecology 85: 1771‐1789, 2004.
 43. Brown JH , Marquet PA , Taper ML . Evolution of body size: Consequences of an energetic definition of fitness. Am Nat 142: 573‐584, 1993.
 44. Brown JH , West GB editors. Scaling in Biology. New York: Oxford University Press, 2000, p. 352.
 45. Brown JH , West GB , Enquist BJ . Yes, West, Brown and Enquist's model of allometric scaling is both mathematically correct and biologically relevant. Funct Ecol 19: 735‐738, 2005.
 46. Buckley LB , Rodda GH , Jetz W . Thermal and energetic constraints on ectotherm abundance: A global test using lizards. Ecology 89: 48‐55, 2008.
 47. Buffenstein R , Jarvis JUM . Thermoregulation and metabolism in the smallest African gerbil, Gerbillus pusillus . J Zool 205: 107‐121, 1985.
 48. Bundle MW , Hoppeler H , Vock R , Tester JM , Weyand PG . High metabolic rates in running birds. Nature 397: 31‐32, 1999.
 49. Burnham KP , Anderson DR . Model Selection and Multi‐Model Inference: A Practical Information‐Theoretic Approach. New York: Springer, 2010, p. 488.
 50. Burton T , Killen SS , Armstrong JD , Metcalfe NB . What causes intraspecific variation in resting metabolic rate and what are its ecological consequences? Proc R Soc Lond B Biol Sci 278: 3465‐3473, 2011.
 51. Butler PJ , Green JA , Boyd IL , Speakman JR . Measuring metabolic rate in the field: The pros and cons of the doubly labelled water and heart rate methods. Funct Ecol 18: 168‐183, 2004.
 52. Calder WA, III . Size, Function, and Life History. Cambridge: Harvard University Press, 1984, p. 431.
 53. Capellini I , Venditti C , Barton RA . Phylogeny and metabolic scaling in mammals. Ecology 91: 2783‐2793, 2010.
 54. Cardoso JFMF , van der Veer HW , Kooijman SALM . Body‐size scaling relationships in bivalve species: A comparison of field data with predictions by the Dynamic Energy Budget (DEB) theory. J Sea Res 56: 125‐139, 2006.
 55. Careau V , Garland T, Jr . Performance, personality, and energetics: Correlation, causation, and mechanism. Physiol Biochem Zool 85: 543‐571, 2012.
 56. Careau V , Thomas D , Humphries MM , Réale D . Energy metabolism and animal personality. Oikos 117: 641‐653, 2008.
 57. Carey N , Sigwart JD , Richards JG . Economies of scaling: More evidence that allometry of metabolism is linked to activity, metabolic rate and habitat. J Exp Mar Biol Ecol 439: 7‐14, 2013.
 58. Cassemiro FAS , Diniz‐Filho JAF . Deviations from predictions of the metabolic theory of ecology can be explained by violations of assumptions. Ecology 91: 3729‐3738, 2010.
 59. Caviedes‐Vidal E , McWhorter TJ , Lavin SR , Chediack JG , Tracy CR , Karasov WH . The digestive adaptation of flying vertebrates: High intestinal paracellular absorption compensates for smaller guts. Proc Natl Acad Sci U S A 104: 19132‐19137, 2007.
 60. Cawley GC , Janacek GJ . On allometric equations for predicting body mass of dinosaurs. J Zool 280: 355‐361, 2010.
 61. Chappell R . Fitting bent lines to data, with applications to allometry. J Theor Biol 138: 235‐256, 1989.
 62. Cheverud JM . Relationships among ontogenetic, static, and evolutionary allometry. Am J Phys Anthropol 59: 139‐149, 1982.
 63. Cheverud JM , Dow MM . An autocorrelation analysis of genetic variation due to lineal fission in social groups of rhesus macaques. Am J Phys Anthropol 67: 113‐121, 1985.
 64. Cheverud JM , Dow MM , Leutengger W . The quantitative assessment of phylogenetic constraints in comparative analyses: Sexual dimorphism in body weight among primates. Evolution 39: 1335‐1351, 1985.
 65. Chown SL , Gaston KJ . Macrophysiology for a changing world. Proc Roy Soc B‐Biol Sci 275: 1469‐1478, 2008.
 66. Chown SL , Gaston KJ . Body size variation in insects: A macroecological perspective. Biol Rev 85: 139‐169, 2010.
 67. Chown SL , Gaston KJ , Kleunen M , Clusella‐Trullas S . Population responses within a landscape matrix: A macrophysiological approach to understanding climate change impacts. Evol Ecol 24: 601‐616, 2010.
 68. Chown SL , Gaston KJ , Robinson D . Macrophysiology: Large‐scale patterns in physiological traits and their ecological implications. Funct Ecol 18: 159‐167, 2004.
 69. Chown SL , Marais E , Terblanche JS , Klok CJ , Lighton JRB , Blackburn TM . Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct Ecol 21: 282‐290, 2007.
 70. Clarke A , Gaston KJ . Climate, energy and diversity. Proc Roy Soc B‐Biol Sci 273: 2257‐2266, 2006.
 71. Clarke A , Rothery P , Isaac NJB . Scaling of basal metabolic rate with body mass and temperature in mammals. J Anim Ecol 79: 610‐619, 2010.
 72. Clemente CJ , Thompson GG , Withers PC . Evolutionary relationships of sprint speed in Australian varanid lizards. J Zool 278: 270‐280, 2009.
 73. Cochran WG . Analysis of covariance: Its nature and uses. Biometrics 13: 261‐281, 1957.
 74. Cooper CE , Withers PC . Numbats and aardwolves—how low is low? A re‐affirmation of the need for statistical rigour in evaluating regression predictions. J Comp Physiol A 176: 623‐629, 2006.
 75. Cresswell W . Diurnal and seasonal mass variation in blackbirds Turdus merula: Consequences for mass‐dependent predation risk. J Anim Ecol 67: 78‐90, 1998.
 76. Currie DJ . What shape is the relationship between body size and population density? Oikos 66: 353‐358, 1993.
 77. Currie DJ , Mittelbach GG , Cornell HV , Field R , Guégan J‐F , Hawkins BA , Kaufman DM , Kerr JT , Oberdorff T , O'Brien E , Turner JRG . Predictions and tests of climate‐based hypotheses of broad‐scale variation in taxonomic richness. Ecol Lett 7: 1121‐1134, 2004.
 78. D'Alonzo KT . The Johnson‐Neyman procedure as an alternative to ANCOVA. Western J Nurs Res 26: 804‐812, 2004.
 79. Daan S , Masman D , Strijkstra A , Verhulst S . Intraspecific allometry of basal metabolic rate: Relations with body size, temperature, composition, and circadian phase in the kestrel, Falco tinnunculus . J Biol Rhythms 4: 267‐283, 1989.
 80. Damuth J . Interspecific allometry of population density in mammals and other animals: The independence of body mass and population energy use. Biol J Linn Soc 31: 193‐246, 1987.
 81. Damuth J . Population density and body size in mammals. Nature 290: 699‐700, 1981.
 82. Darveau CA , Suarez RK , Andrews RD , Hochachka PW . Allometric cascade as a unifying principle of body mass effects on metabolism. Nature 417: 166‐170, 2002.
 83. Dawson TJ , Hulbert AJ . Standard metabolism, body temperature, and surface areas of Australian marsupials. Am J Physiol 218: 1233‐1238, 1970.
 84. de Castro F , Gaedke U . The metabolism of lake plankton does not support the metabolic theory of ecology. Oikos 117: 1218‐1226, 2008.
 85. de Queiroz A , Ashton KG . The phylogeny of a species‐level tendency: Species heritability and possible deep origins of Bergmann's rule in tetrapods. Evolution 58: 1674‐1684, 2004.
 86. Dell AI , Pawar S , Savage VM . Systematic variation in the temperature dependence of physiological and ecological traits. Proc Natl Acad Sci U S A 108: 10591‐10596, 2011.
 87. DeLong JP , Okie JG , Moses ME , Sibly RM , Brown JH . Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life. Proc Natl Acad Sci U S A 107: 12941‐12945, 2010.
 88. Demetrius L . The origin of allometric scaling laws in biology. J Theor Biol 243: 455‐467, 2006.
 89. Demetrius L , Tuszynski JA . Quantum metabolism explains the allometric scaling of metabolic rates. J Royal Soc Interface 7: 507‐514, 2010.
 90. Díaz‐Uriarte R , Garland T, Jr . Effects of branch length errors on the performance of phylogenetically independent contrasts. Syst Biol 47: 654‐672, 1998.
 91. Díaz‐Uriarte R , Garland T, Jr . Testing hypotheses of correlated evolution using phylogenetically independent contrasts: Sensitivity to deviations from Brownian motion. Syst Biol 45: 24‐47, 1996.
 92. Diniz JAF , De Sant'ana CER , Bini LM . An eigenvector method for estimating phylogenetic inertia. Evolution 52: 1247‐1262, 1998.
 93. Dlugosz EM , Chappell MA , Meek TH , Szafrańska PA , Zub K , Konarzewski M , Jones JH , Bicudo JEPW , Nespolo RF , Careau V , Garland T, Jr . Phylogenetic analysis of mammalian maximal oxygen consumption during exercise. J Exp Biol 2013. [Epub ahead of print].
 94. Dodds PS . Optimal form of branching supply and collection networks. Phys Rev Lett 104: 048702, 2010.
 95. Dodds PS , Rothman DH , Weitz JS . Re‐examination of the “3/4‐law” of metabolism. J Theor Biol 209: 9‐27, 2001.
 96. Dorsey SG , Soeken KL . Use of the Johnson‐Neyman technique as an alternative to analysis of covariance. Nurs Res 45: 363‐366, 1996.
 97. Dubois E . Sur le rapport du poids de l'encéphale avec la grandeur du corps chez les mammifères. Bull Mem Soc Anthrop Paris 8: 337‐376, 1897.
 98. Duncan RP , Forsythe DM , Hone J . Testing the metabolic theory of ecology: Allometric scaling exponents in mammals. Ecology 88: 324‐333, 2007.
 99. Economos AC . On the origin of biological similarity. J Theor Biol 94: 25‐60, 1982.
 100. Egger M , Smith GD , Schneider M , Minder C . Bias in meta‐analysis detected by a simple, graphical test. BMJ 315: 629‐634, 1997.
 101. Eisenberg JF. The Mammalian Radiations. Chicago: The University of Chicago Press, 1981.
 102. Elia M . Organ and tissue contributions to metabolic rate. In: Kinney JM , Tucker HN , editors. Energy Metabolism: Tissue Determinants and Cellular Corollaries. New York: Raven, 1992.
 103. Ellison GTH . Thermoregulatory responses on cold acclimated fat mice (Steatomys pratensis). J Mammal 76: 240‐247, 1995.
 104. Engqvist L . The mistreatment of covariate interaction terms in linear model analyses of behavioural and evolutionary ecology studies. Anim Behav 70: 967‐971, 2005.
 105. Fairbairn DJ , Preziosi RF . Sexual selection and the evolution of allometry for sexual size dimorphism in the water strider, Aquarius remigis . Am Nat 144: 101‐118, 1994.
 106. Falconer DS , Mackay TFC . Introduction to Quantitative Genetics. Edinburgh: Longman, 1996.
 107. Farrell‐Gray CC , Gotelli NJ . Allometric exponents support a 3/4‐power scaling law. Ecology 86: 2083‐2087, 2005.
 108. Feldman HA . On the allometric mass exponent, when it exists. J Theor Biol 172: 187‐197, 1995.
 109. Feldman HA , McMahon TA . The 3/4 mass exponent for energy metabolism is not a statistical artifact. Respir Physiol 52: 149‐164, 1983.
 110. Felsenstein J . Phylogenies and the comparative method. Am Nat 125: 1‐15, 1985.
 111. Fick A. Über die Messung des Blutquantums in den Herzventrikeln. Verh Phys Med Ges Würzburg 2: 16, 1870.
 112. Finney DJ . Was this in your statistics textbook? V. Transformations of data. Exp Agric 25: 165‐175, 1989.
 113. Foster WK , Taggart DA . Gender and parental influences on the growth of a sexually dimorphic carnivorous marsupial. J Zool 275: 221‐228, 2008.
 114. Franz R , Hummel J , Kienzle E , Kölle P , Gunga H‐C , Clauss M . Allometry of visceral organs in living amniotes and its implications for sauropod dinosaurs. Proc Roy Soc B‐Biol Sci 276: 1731‐1736, 2009.
 115. Frappell PB , Butler PJ . Minimal metabolic rate, what it is, its usefulness, and its relationship to the evolution of endothermy: A brief synopsis. Physiol Biochem Zool 77: 865‐868, 2004.
 116. Frappell PB , Hinds DS , Boggs DF . Scaling of respiratory variables and the breathing pattern in birds: An allometric and phylogenetic approach. Physiol Biochem Zool 74: 75‐89, 2001.
 117. Freckleton RP . On the misuse of residuals in ecology: Regression of residuals vs. multiple regression. J Anim Ecol 71: 542‐545, 2002.
 118. Freckleton RP , Cooper N , Jetz W . Comparative methods as a statistical fix: The dangers of ignoring an evolutionary model. Am Nat 178: E10‐E17, 2011.
 119. Freckleton RP , Harvey PH , Pagel M . Phylogenetic analysis and comparative data: A test and review of evidence. Am Nat 160: 712‐726, 2002.
 120. Freckleton RP , Jetz W . Space versus phylogeny: Disentangling phylogenetic and spatial signals in comparative data. Proc Roy Soc B‐Biol Sci 276: 21‐30, 2009.
 121. Fritz SA , Rahbek C . Global patterns of amphibian phylogenetic diversity. J Biogeogr 39: 1373‐1382, 2012.
 122. Fuquay JW . Heat stress as it affects animal production. J Anim Sci 52: 164‐174, 1981.
 123. García‐Berthou E . On the misuse of residuals in ecology: Testing regression residuals vs. the analysis of covariance. J Anim Ecol 70: 708‐711, 2001.
 124. Garland T, Jr , Adolph SC . Why not to do two species comparative studies: Limitations on inferring adaptation. Physiol Biochem Zool 67: 797‐828, 1994.
 125. Garland T, Jr , Bennett AF , Rezende EL . Phylogenetic approaches in comparative physiology. J Exp Biol 208: 3015‐3035, 2005.
 126. Garland T, Jr , Diaz‐Uriarte R . Polytomies and phylogenetically independent contrasts: Examination of the bounded degrees of freedom approach. Syst Biol 48: 547‐558, 1999.
 127. Garland T, Jr . The relation between maximal running speed and body mass in terrestrial mammals. J Zool 199: 157‐170, 1983.
 128. Garland T, Jr. , Ives AR . Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods. Am Nat 155: 346‐364, 2000.
 129. Gartner GEA , Hicks JW , Andrade DV , Secor SM , Garland T, Jr . Reply to “Heart position in snakes”. Physiol Biochem Zool 84: 102‐106, 2011.
 130. Gartner GEA , Hicks JW , Manzani PR , Andrade DV , Abe AS , Wang T , Secor SM , Garland T, Jr . Phylogeny, ecology, and heart position in snakes. Physiol Biochem Zool 83: 43‐54, 2010.
 131. Gaston KJ , Chown SL , Calosi P , Bernado J , Bilton DT , Clarke A , Clusella‐Trullas S , Ghalambor CK , Konarzewski M , Peck LS , Porter WP , Pörtner HO , Rezende EL , Schulte PM , Spicer JI , Stillman JH , Terblanche JS , van Kleunen M . Macrophysiology: A conceptual reunification. Am Nat 174: 595‐612, 2009.
 132. Gillanders BM . Comparison of growth rates between estuarine and coastal reef populations of Achoerodus viridis (Pisces: Labridae). Mar Ecol Prog Ser 146: 283‐287, 1997.
 133. Gillooly JF , Allen AP . Changes in body temperature influence the scaling of Vo2max and aerobic scope in mammals. Biol Letters 3: 99‐102, 2007.
 134. Gillooly JF , Brown JH , West GB , Savage VM , Charnov EL . Effects of size and temperature on metabolic rate. Science 293: 2248‐2251, 2001.
 135. Gingerich PD . Arithmetic or geometric normality of biological variation: An empirical test of theory. J Theor Biol 204: 201‐221, 2000.
 136. Gingerich PD , Smith BH , Rosenberg K . Allometric scaling in the dentition of primates and prediction of body weight from tooth size in fossils. Am J Phys Anthropol 58: 81‐100, 1982.
 137. Ginzburg L , Damuth J . The space‐lifetime hypothesis: Viewing organisms in four dimensions, literally. Am Nat 171: 125‐131, 2008.
 138. Glazier DS . Beyond the ‘3/4‐power law’: Variation in the intra‐ and interspecific scaling of metabolic rate in animals. Biol Rev 80: 1‐52, 2005.
 139. Glazier DS . Effects of metabolic level on the body size scaling of metabolic rate in birds and mammals. P Roy Soc B‐Biol Sci 22: 1405‐1410, 2008.
 140. Glazier DS . Activity affects intraspecific body‐size scaling of metabolic rate in ectothermic animals. J Comp Physiol B 179: 821‐828, 2009.
 141. Glazier DS . Metabolic level and size scaling of rates of respiration and growth in unicellular organisms. Funct Ecol 23: 963‐968, 2009.
 142. Glazier DS . Ontogenetic body‐mass scaling of resting metabolic rate covaries with species‐specific metabolic level and body size in spiders and snakes. Comp Biochem Physiol A 153: 403‐407, 2009.
 143. Glazier DS . A unifying explanation for diverse metabolic scaling in animals and plants. Biol Rev 85: 111‐138, 2010.
 144. Glazier DS , Butler EM , Lombardi SA , Deptola TJ , Reese AJ , Satterthwaite EV . Ecological effects on metabolic scaling: Amphipod responses to fish predators in freshwater springs. Ecol Monogr 81: 599‐618, 2011.
 145. Gould SJ . Allometry and size in ontogeny and phylogeny. Biol Rev 44: 587‐640, 1966.
 146. Grafen A . The phylogenetic regression. Philos T R Soc Lon B 326: 119‐157, 1989.
 147. Green B , Griffiths M , Newgrain K . Seasonal patterns in water, sodium and energy turnover in free‐living echidnas, Tachyglossus aculeatus (Mammalia: Monotremata). J Zool 227: 351‐365, 1992.
 148. Green JA . The heart rate method for estimating metabolic rate: Review and recommendations. Comp Biochem Physiol A 158: 287‐304, 2011.
 149. Green JA , White CR , Butler PJ . Allometric estimation of metabolic rate from heart rate in penguins. Comp Biochem Physiol A 142: 478‐484, 2005.
 150. Günther B . Dimensional analysis and theory of biological similarity. Physiol Rev 55: 659‐699, 1975.
 151. Günther B , León de la Barra B . A unified theory of biological similarities. J Theor Biol 13: 48‐59, 1966.
 152. Günther B , Morgado E . Theory of biological similarity revisited. J Theor Biol 96: 543‐560, 1982.
 153. Hadfield JD , Nakagawa S . General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi‐trait models for continuous and categorical characters. J Evol Biol 23: 494‐508, 2010.
 154. Hails CJ . The metabolic rate of tropical birds. Condor 85: 61‐65, 1983.
 155. Halsey LG . The challenge of measuring energy expenditure: Current field and laboratory methods. Comp Biochem Physiol A 158: 247‐251, 2011.
 156. Halsey LG , Butler PJ , Blackburn TM . A phylogenetic analysis of the allometry of diving. Am Nat 167: 276‐287, 2006.
 157. Halsey LG , White CR , Enstipp MR , Jones DR , Martin GR , Butler PJ . When cormorants go fishing: The differing costs of hunting for sessile and motile prey. Biol Letters 3: 574‐576, 2007.
 158. Hartikainen H , Humphries S , Okamura B . Form and metabolic scaling in colonial animals. J Exp Biol doi: 10.1242/jeb.093484, 2013.
 159. Harvey PH . On rethinking allometry. J Theor Biol 95: 37‐41, 1982.
 160. Harvey PH , Pagel MD . The Comparative Method in Evolutionary Biology. New York: Oxford University Press, 1991, p. 239.
 161. Hawkins BA , Albuquerque FS , Araújo MB , Beck J , Bini LM , Cabrero‐Sañudo FJ , Castro‐Parga I , Diniz‐Filho JAF , Ferrer‐Castán D , Field R , Gómez JF , Hortal J , Kerr JT , Kitching IJ , León‐Cortés JL , Lobo JM , Montoya D , Moreno JC , Ollalla‐Tárraga MÁ , Pausas JG , Qian H , Rahbek C , Rodríguez MÁ , Sanders NJ , Williams P . A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients. Ecology 88: 1877‐1888, 2007.
 162. Hawkins BA , Diniz‐Filho JAF , Bini LM , Araújo MB , Field R , Hortal J , Kerr JT , Rahbek C , Rodríguez MÁ , Sanders NJ . Metabolic theory and diversity gradients: Where do we go from here? Ecology 88: 1898‐1902, 2007.
 163. Hayes JP , Shonkwiler JS . Allometry, antilog transformations, and the perils of prediction on the original scale. Physiol Biochem Zool 79: 665‐674, 2006.
 164. Hayes JP , Shonkwiler JS . Analyzing mass‐independent data. Physiol Zool 69: 974‐980, 1996.
 165. Hayssen V , Lacy RC . Basal metabolic rates in mammals: Taxonomic differences in the allometry of BMR and body mass. Comp Biochem Physiol A 81: 741‐754, 1985.
 166. Hechinger RF , Lafferty KD , Dobson AP , Brown JH , Kuris AM . A common scaling rule for abundance, energetics, and production of parasitic and free‐living species. Science 333: 445‐448, 2011.
 167. Hemmingsen AM . Energy metabolism as related to body size and respiratory surfaces, and its evolution. Rep. Steno Meml Hosp. Nordisk Insulinlab. 9: 1‐110, 1960.
 168. Heusner AA . Energy metabolism and body size: 1. Is the 0.75 mass exponent of Kleibers equation a statistical artifact? Respir Physiol 48: 1‐12, 1982.
 169. Heusner AA . Energy metabolism and body size: 2. Dimensional analysis and energetic nonsimilarity. Respir Physiol 48: 13‐26, 1982.
 170. Heusner AA . Size and power in mammals. J Exp Biol 160: 25‐54, 1991.
 171. Heymsfield SB , Childers D , Beetsch J , Allison DB , Pietrobelli A . Body size and human energy requirements: Reduced mass‐specific resting energy expenditure in tall adults. J Appl Physiol 103: 1543‐1550, 2007.
 172. Heymsfield SB , Gallagher D , Kotler ZW , Allison DB , Heshka S . Body‐size dependence of resting energy expenditure can be attributed to nonenergetic homogeneity of fat‐free mass. Am J Physiol 282: E132‐E138, 2005.
 173. Heymsfield SB , Thomas D , Bosy‐Westphal A , Shen W , Peterson CM , Müller MJ . Evolving concepts on adjusting human resting energy expenditure measurements for body size. Obes Rev 13: 1001‐1014, 2012.
 174. Hicks JW , Badeer HS . Gravity and the circulation: “open” vs. “closed” systems. Am J Physiol 262: R725‐R732, 1992.
 175. Hicks JW , Badeer HS . Siphon mechanism in collapsible tubes: Application to circulation of the giraffe head. Am J Physiol 256: R567‐R571, 1989.
 176. Hölker F . Effects of body size and temperature on metabolism of bream compared to sympatric roach. Anim Biol 56: 23‐37, 2006.
 177. Hongo Y . Evolution of male dimorphic allometry in a population of the Japanese horned beetle Trypoxylus dichotomus septentrionalis . Behav Ecol Sociobiol 62: 245‐253, 2007.
 178. Hoppeler H , Weibel ER . Scaling functions to body size: Theories and facts. J Exp Biol 208: 1573‐1574, 2005.
 179. Hou C , Bolt KM , Bergman A . A general model for ontogenetic growth under food restriction. Proc Roy Soc B‐Biol Sci 278: 2881‐2890, 2011.
 180. Hou C , Zuo W , Moses ME , Woodruff WH , Brown JH , West GB . Energy uptake and allocation during ontogeny. Science 322: 736‐739, 2008.
 181. Housworth EA , Martins EP , Lynch M . The phylogenetic mixed model. Am Nat 163: 84‐96, 2004.
 182. Howland HC , Merola S , Basarab JR . The allometry and scaling of the size of vertebrate eyes. Vision Res 44: 2043‐2065, 2004.
 183. Hui C , Terblanche JS , Chown SL , McGeoch MA . Parameter landscapes unveil the bias in allometric prediction. Meth Ecol Evol 1: 69‐74, 2010.
 184. Hui D , Jackson RB . Uncertainty in allometric exponent estimation: A case study in scaling metabolic rate with body mass. J Theor Biol 249: 168‐177, 2007.
 185. Huitema BE. The Analysis of Covariance and Alternatives. New York: John Wiley and Sons, 1980, p. 445.
 186. Humphries MM , Boutin S , Thomas DW , Ryan JD , Selman C , McAdam AG , Berteaux D , Speakman JR . Expenditure freeze: The metabolic response of small mammals to cold environments. Ecol Lett 8: 1326‐1333, 2005.
 187. Humphries MM , Careau V . Heat for nothing or activity for free? Evidence and implications of activity‐thermoregulatory heat substitution. Integr Comp Biol 51: 419‐431, 2011.
 188. Huxley JS. Problems of Relative Growth. London: Methuen & Co., 1932.
 189. Ihaka R , Gentleman R . R: A language for data analysis and graphics. J Comput Graph Stat 5: 299‐314, 1996.
 190. Isaac NJB , Carbone C . Why are metabolic scaling exponents so controversial? Quantifying variance and testing hypotheses. Ecol Lett 33: 728‐735, doi: 10.1111/j.1461‐0248.2010.01461.x, 2010.
 191. Ives AR , Garland T, Jr . Phylogenetic logistic regression for binary dependent variables. Syst Biol 59: 9‐26, 2010.
 192. Ives AR , Midford PE , Garland T, Jr . Within‐species variation and measurement error in phylogenetic comparative methods. Syst Biol 56: 252‐270, 2007.
 193. Jackson DM , Trayhurn P , Speakman JR . Associations between energetics and over‐winter survival in the short‐tailed field vole Microtus agrestis . J Anim Ecol 70: 633‐640, 2001.
 194. Jetz W , Freckleton RP , McKechnie AE . Environment, migratory tendency, phylogeny and basal metabolic rate in birds. PLoS ONE 3: e3261, 2007.
 195. Jetz W , Thomas GH , Joy JB , Hartmann K , Mooers AO . The global diversity of birds in space and time. Nature 491: 444‐448, 2012.
 196. Johnson PO , Neyman J . Tests of certain linear hypotheses and their application to some educational problems. Stat. Res. Mem. 1: 57‐93, 1936.
 197. Jolicoeur P . A simplified model for bivariate complex allometry. J Theor Biol 140: 41‐49, 1989.
 198. Jones JH , Taylor CR , Lindholm A , Straub R , Longworth KE , Karas RH . Blood gas measurements during exercise: Errors due to temperature correction. J Appl Physiol 67: 879‐884, 1989.
 199. Jones KE , Bielby J , Cardillo M , Fritz SA , O'Dell J , Orme CDL , Safi K , Sechrest W , Boakes EH , Carbone C , Connolly C , Cutts MJ , Foster JK , Grenyer R , Habib M , Plaster CA , Price SA , Rigby EA , Rist J , Teacher A , Bininda‐Emonds ORP , Gittleman JL , Mace GM , Purvis A . PanTHERIA: A species‐level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90: 2648, 2009.
 200. Kabat AP , Blackburn TM , McKechnie AE , Butler PJ . Phylogenetic analysis of the allometric scaling of therapeutic regimes for birds. J Zool 275: 359‐367, 2008.
 201. Kaiyala KJ , Ramsay DS . Direct animal calorimetry, the underused gold standard for quantifying the fire of life. Comp Biochem Physiol A 158: 252‐264, 2011.
 202. Kearney MR , White CR . Testing metabolic theories. Am Nat 180: 546‐565, 2012.
 203. Kerkhoff AJ , Enquist BJ . Multiplicative by nature: Why logarithmic transformation is necessary in allometry. J Theor Biol 257: 519‐521, 2009.
 204. Killen SS , Atkinson D , Glazier DS . The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecol Lett 13: 184‐193, 2010.
 205. Killen SS , Costa I , Brown JA , Gamperl AK . Little left in the tank: Metabolic scaling in marine teleosts and its implications for aerobic scope. Proc Roy Soc B‐Biol Sci 274: 431‐438, 2007.
 206. Kleiber M . Body size and metabolism. Hilgardia 6: 315‐353, 1932.
 207. Kleiber M. The Fire of Life. New York, London: John Wiley & Sons, Inc., 1961, p. 454.
 208. Knight MH . Thermoregulation in the largest African cricetid, the giant rat Cricetomys gambianus . Comp Biochem Physiol A 89: 705‐708, 1988.
 209. Kodric‐Brown A , Sibly RM , Brown JH . The allometry of ornaments and weapons. Proc Natl Acad Sci U S A 103: 8733‐8738, 2006.
 210. Kolokotrones T , Savage VM , Deeds EJ , Fontana W . Curvature in metabolic scaling. Nature 464: 753‐756, 2010.
 211. Konarzewski M , Książek A . Determinants of intra‐specific variation in basal metabolic rate. J Comp Physiol B 183: 27‐41, 2013.
 212. Kooijman SALM. Dynamic Energy and Mass Budgets in Biological Systems. Cambridge: Cambridge University Press, 2000.
 213. Kooijman SALM. Dynamic Energy Budget Theory for Metabolic Organisation. Cambridge: Cambridge University Press, 2010.
 214. Kooijman SALM . Energy budgets can explain body size relations. J Theor Biol 121: 269‐282, 1986.
 215. Kooijman SALM . Waste to hurry: Dynamic energy budgets explain the need of wasting to fully exploit blooming resources. Oikos 122: 348‐357, 2013.
 216. Kottelat M , Britz R , Hui TH , Witte K‐E . Paedocypris, a new genus of Southeast Asian cyprinid fish with a remarkable sexual dimorphism, comprises the world's smallest vertebrate. Proc Roy Soc B‐Biol Sci 273: 895‐899, 2006.
 217. Kozłowski J , Konarzewski M . Is West, Brown and Enquist's model of allometric scaling mathematically correct and biologically relevant? Funct Ecol 18: 283‐289, 2004.
 218. Kozłowski J , Konarzewski M . West, Brown and Enquist's model of allometric scaling again: The same questions remain. Funct Ecol 19: 739‐743, 2005.
 219. Kozłowski J , Konarzewski M , Gawelczyk AT . Cell size as a link between noncoding DNA and metabolic rate scaling. Proc Natl Acad Sci U S A 100: 14080‐14085, 2003.
 220. Kozłowski J , Konarzewski M , Gawelczyk AT . Intraspecific body size optimization produces intraspecific allometries. In: Blackburn TM , Gaston KJ , editors. Macroecology: Concepts and Consequences. Malden: Blackwell Science Ltd, 2003, pp. 299‐320.
 221. Krogh A. Respiratory Exchange of Animals and Man. London: Longmans, Green and Co., 1916.
 222. Król E , Murphy RW , Speakman JR . Limits to sustained energy intake. X. Effects of fur removal on reproductive performance in laboratory mice. J Exp Biol 240: 4233‐4243, 2007.
 223. Kühn I , Nobis MP , Durka W . Combining spatial and phylogenetic eigenvector filtering in trait analysis. Glob Ecol Biogeogr 18: 745‐758, 2009.
 224. Larivée ML , Boutin S , Speakman JR , McAdam AG , Humphries MM . Associations between over‐winter survival and resting metabolic rate in juvenile North American red squirrels. Funct Ecol 24: 597‐607, 2010.
 225. Lavin Shana R , Karasov William H , Ives Anthony R , Middleton Kevin M , Theodore G, Jr . Morphometrics of the avian small intestine compared with that of nonflying mammals: A phylogenetic approach. Physiol Biochem Zool 81: 526‐550, 2008.
 226. LeBarbera M . Analyzing body size as a factor in ecology and evolution. Annu Rev Ecol Syst 20: 97‐117, 1989.
 227. Leon AC , Portera L , Lowell K , Rheinheimer D . A strategy to evaluate a covariate by group interaction in an analysis of covariance. Psychopharmacol Bull 34: 805‐809, 1998.
 228. Lighton JRB. Measuring Metabolic Rates: A Manual for Scientists. Oxford: Oxford University Press, 2008.
 229. Lighton JRB , Halsey LG . Flow‐through respirometry applied to chamber systems: Pros and cons, hints and tips. Comp Biochem Physiol A 158: 265‐275, 2011.
 230. Lillywhite H , Albert J . Evolutionary physiology, comparative data, and phylogenetic methods. In: Morris S , Vosloo A , editors. Proceedings of the 4th CPB Meeting in Africa: MARA 2008 Molecules to Migration: The Pressures of Life. Pianoro: Medimond, 2009, pp. 613‐620.
 231. Lillywhite HB , Albert JS , Sheehy CM, III , Seymour RS . Gravity and the evolution of cardiopulmonary morphology in snakes. Comp Biochem Physiol A 161: 230‐242, 2012.
 232. Lillywhite HB , Seymour RS . Heart position in snakes: Response to “Phylogeny, ecology, and heart position in snakes”. Physiol Biochem Zool 84: 99‐101, 2011.
 233. Lindinger MI . Exercise in the heat: Thermoregulatory limitations to performance in humans and horses. Can J Appl Physiol 24: 152‐163, 1999.
 234. Lindstedt SL , Hokanson JF , Wells DJ , Swain SD , Hoppeler H , Navarro V . Running energetics in the pronghorn antelope. Nature 353: 748‐750, 1991.
 235. Lindstedt SL , Schaeffer PJ . Use of allometry in predicting anatomical and physiological parameters of mammals. Lab Anim 36: 1‐19, 2002.
 236. Lockyear C . Body weights of some species of large whales. J. Cons. Int. Explor. Mer 36: 259‐273, 1976.
 237. Losos JB . Seeing the forest for the trees: The limitations of phylogenies in comparative biology. Am Nat 177: 709‐727, 2011.
 238. Lovegrove BG . The influence of climate on the basal metabolic rate of small mammals: A slow‐fast metabolic continuum. J Comp Physiol B 173: 87‐112, 2003.
 239. Lovegrove BG . The metabolism of social subterranean rodents: Adaptation to aridity. Oecologia 69: 551‐555, 1986.
 240. Lovegrove BG . The zoogeography of mammalian basal metabolic rate. Am Nat 156: 201‐219, 2000.
 241. Lydersen C , Ryg MS , Hammill MO , O'Brien PJ . Oxygen stores and aerobic dive limit of ringed seals (Phoca hispida). Can J Zool 70: 458‐461, 1992.
 242. Lynch M . Methods for the analysis of comparative data in evolutionary biology. Evolution 45: 1065‐1080, 1991.
 243. Lynch M , Walsh B . Genetics and Analysis of Quantitative Traits. Sunderland: Sinauer Associates, 1998.
 244. Maddison WP , Maddison DR . Mesquite: A modular system for evolutionary analysis. Version 2.75. http://mesquiteproject.org. 2011.
 245. Magnusson William E . Significance versus magnitude: Use of the Johnson‐Neyman technique in comparative biology. Physiol Biochem Zool 78: 105, 2005.
 246. Mahmood I . Prediction of clearance, volume of distribution and half‐life by allometric scaling and by use of plasma concentrations predicted from pharmacokinetic constants: A comparative study. J Pharm Pharmacol 51: 905‐910, 1999.
 247. Mahmood I , Martinez M , Hunter RP . Interspecies allometric scaling. Part I: Prediction of clearance in large animals. J Vet Pharmacol Ther 29: 415‐423, 2006.
 248. Mahoney SA . Cost of locomotion and heat balance during rest and running from 0 to 55 degrees C in a patas monkey. J Appl Physiol 49: 789‐800, 1980.
 249. Maino JL , Kearney MR , Nisbet RM , Kooijman SALM . Reconciling theories for metabolic scaling. J Anim Ecol DOI: 10.1111/1365‐2656.12085, 2013. [Epub ahead of print].
 250. Makarieva AM , Gorshkov VD , Li B‐L , Chown SL , Reich PB , Gavrilov VM . Mean mass‐specific metabolic rates are strikingly similar across life's major domains: Evidence for life's metabolic optimum. Proc Natl Acad Sci USA 105: 16994‐16999, 2008.
 251. Martins EP , Hansen TF . Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data. Am Nat 149: 646‐667, 1997.
 252. McArdle BH . Lines, models, and errors: Regression in the field. Limnol Oceanogr 48: 1363‐1366, 2003.
 253. McArdle BH . The structural relationship: Regression in biology. Can J Zool 66: 2329‐2339, 1988.
 254. McFeeters BJ , Xenopoulos MA , Spooner DE , Wagner ND , Frost PC . Intraspecific mass‐scaling of field metabolic rates of a freshwater crayfish varies with stream land cover. Ecosphere 2: art13, 2011.
 255. McKechnie AE , Freckleton RP , Jetz W . Phenotypic plasticity in the scaling of avian basal metabolic rate. Proc Roy Soc B‐Biol Sci 273: 931‐937, 2006.
 256. McKechnie AE , Wolf BO . The allometry of avian basal metabolic rate: Good predictions need good data. Physiol Biochem Zool 77: 502‐521, 2004.
 257. McMahon T . Size and shape in biology. Science 179: 1201‐1204, 1973.
 258. McMahon TA , Bonner JT . On Size and Life. New York: Scientific American, 1983.
 259. McNab BK . Climatic adaptation in the energetics of heteromyid rodents. Comp Biochem Physiol A 62: 813‐820, 1979.
 260. McNab BK . Physiological convergence amongst ant‐eating and termite‐eating mammals. J Zool 203: 485‐510, 1984.
 261. McNab BK . Complications inherent in scaling the basal rate of metabolism in mammals. Q Rev Biol 63: 25‐54, 1988.
 262. McNab BK . On the utility of uniformity in the definition of basal rate of metabolism. Physiol Zool 70: 718‐720, 1997.
 263. McNab BK . The standard energetics of mammalian carnivores: Felidae and Hyaenidae. Can J Zool 78: 2227‐2239, 2000.
 264. McNab BK . Sample size and the estimation of physiological parameters in the field. Funct Ecol 17: 82‐86, 2003.
 265. McNab BK . Standard energetics of phyllostomid bats: The inadequacies of phylogenetic‐contrast analyses. Comp Biochem Physiol A 135: 357‐368, 2003.
 266. McNab BK . The evolution of energetics in eutherian “insectivorans”: An alternate approach. Acta Theriol 51: 113‐128, 2006.
 267. McNab BK . An analysis of the factors that influence the level and scaling of mammalian BMR. Comp Biochem Physiol A 151: 5‐28, 2008.
 268. McNab BK . Ecological factors affect the level and scaling of avian BMR. Comp Biochem Physiol A 152: 22‐45, 2009.
 269. McNab BK . Energy expenditure cannot be effectively analyzed with phylogenetically based techniques. In: Morris S , Vosloo A , editors. Proceedings of the 4th CPB Meeting in Africa: MARA 2008 Molecules to Migration: The Pressures of Life. Pianoro: Medimond, 2009, pp. 621‐626.
 270. McNab BK . Resources and energetics determined dinosaur maximal size. Proc Natl Acad Sci USA 106: 12184‐12188, 2009.
 271. McNab BK , Eisenberg JF . Brain size and its relation to the rate of metabolism in mammals. Am Nat 133: 157‐167, 1989.
 272. McNab BK , Morrison P . Body temperature and metabolism in subspecies of Peromyscus from arid and mesic environments. Ecol Monogr 33: 63‐82, 1963.
 273. Meehan TD . Energy use and animal abundance in litter and soil communities. Ecology 87: 1650‐1658, 2006.
 274. Meehan TD , Jetz W , Brown JH . Energetic determinants of abundance in winter landbird communities. Ecol Lett 7: 532‐537, 2004.
 275. Meiri S , Dayan T . On the validity of Bergmann's rule. J Biogeogr 30: 331‐351, 2003.
 276. Michard‐Picamelot D , Zorn T , Gendner JP , Mata AJ , Le Maho Y . Body protein does not vary despite seasonal changes in fat in the white stork Ciconia ciconia . Ibis 144: E1‐E10, 2002.
 277. Midford PE , Garland T, Jr , Maddison W . PDAP Package for Mesquite. Version 1.16. 2011.
 278. Mitchell G , Maloney SK , Mitchell D , Keegan DJ . The origin of mean arterial and jugular venous blood pressures in giraffes. J Exp Biol 209: 2515‐2524, 2006.
 279. Mollet HF , Cailliet GM . Using allometry to predict body mass from linear measuremetns of the white shark. In: Klimley AP , Ainley DG , editors. Great White Sharks: The Biology of Carcharodon carcharias. San Diego: Academic Press, 1996, pp. 81‐89.
 280. Moran D , Wells RMG . Ontogenetic scaling of fish metabolism in the mouse‐to‐elephant mass magnitude range. Comp Biochem Physiol A 148: 611‐620, 2007.
 281. Mueller P , Diamond J . Metabolic rate and environmental productivity: Well‐provisioned animals evolved to run and idle fast. Proc Natl Acad Sci U S A 98: 12551‐12554, 2001.
 282. Muller‐Landau HC , Condit RS , Chave J , Thomas SC , Bohlman SA , Bunyavejchewin S , Davies S , Foster R , Gunatilleke S , Gunatilleke N , Harms KE , Hart T , Hubbell SP , Itoh A , Kassim AR , LaFrankie JV , Lee HS , Losos E , Makana J‐R , Ohkubo T , Sukamar R , Sun I‐F , Supardi N , Tan S , Thompson J , Valencia R , Muñoz GV , Wills C , Yamakura T , Chuyong G , Dattaraja HS , Esufali S , Hall P , Hernandez C , Kenfack D , Kiratiprayoon S , Suresh HS , Thomas D , Vallejo MI , Ashton P . Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol Lett 9: 575‐588, 2006.
 283. Müller DWH , Codron D , Werner J , Fritz J , Hummell J , Griebeler EM , Clauss M . Dichotomy of eutherian reproduction and metabolism. Oikos 121: 102‐115, 2012.
 284. Müller MJ , Langemann D , Gehrke I , Later W , Heller M , Glüer CC , Heymsfield SB , Bosy‐Westphal A . Effect of constitution on mass of individual organs and their association with metabolic rate in humans—a detailed view on allometric scaling. PLoS ONE 6: e22732, 2011.
 285. Munch SB , Salinas S . Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc Natl Acad Sci U S A 106: 13860‐13864, 2009.
 286. Muñoz‐Garcia A , Williams JB . Basal metabolic rate in carnivores is associated with diet after controlling for phylogeny. Physiol Biochem Zool 78: 1039‐1056, 2005.
 287. Nagilla R , Ward KW . A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans. J Pharm Sci 93: 2522‐2534, 2004.
 288. Nagy KA . Field metabolic rate and body size. J Exp Biol 208: 1621‐1625, 2005.
 289. Nagy KA , Girard IA , Brown TK . Energetics of free‐ranging mammals, reptiles and birds. Annu Rev Nutr 19: 247‐277, 1999.
 290. Nakaya F , Saito Y , Motokawa T . Experimental allometry: Effect of size manipulation on metabolic rate of colonial ascidians. Proc Roy Soc B‐Biol Sci 272: 1963‐1969, 2005.
 291. Nespolo RF , Franco M . Whole‐animal metabolic rate is a repeatable trait: A meta‐analysis. J Exp Biol 210: 2000‐2005, 2007.
 292. Nevill AM , Holder RL . Scaling, normalizing, and per ratio standards: An allometric modelling approach. J Appl Physiol 79: 1027‐1031, 1995.
 293. Nisbet RM , Muller EB , Lika K , Kooijman SALM . From molecules to ecosystems through dynamic energy budget models. J Anim Ecol 69: 913‐926, 2000.
 294. Nowak RM. Walker's Mammals of the World. Baltimore: Johns Hopkins University Press, 1999, p. 1936.
 295. O'Conner MP , Agosta SJ , Hansen F , Kemp SJ , Sieg AE , McNair JN , Dunham AE . Phylogeny, regression, and the allometry of physiological traits. Am Nat 170: 431‐442, 2007.
 296. Ohlberger J , Mehner T , Staaks G , Hölker F . Temperature‐related physiological adaptations promote ecological divergence in a sympatric species pair of temperate freshwater fish, Coregonus spp. Funct Ecol 22: 501‐508, 2008.
 297. Orme D , Freckleton RP , Thomas GH , Petzoldt Y , Fritz S , Isaac N , Pearse W . Caper: Comparative Analyses of Phylogenetics and Evolution in R package version 0.5. 2012.
 298. Packard GC . On the use of logarithmic transformations in allometric analyses. J Theor Biol 257: 515‐518, 2009.
 299. Packard GC , Birchard GF . Traditional allometric analysis fails to provide a valid predictive model for mammalian metabolic rates. J Exp Biol 211: 3581‐3587, 2008.
 300. Packard GC , Birchard GF , Boardman TJ . Fitting statistical models in bivariate allometry. Biol Rev 83: 254‐563, 2011.
 301. Packard GC , Boardman TJ . The misuse of ratios to scale physiological data that vary allometrically with body size. In: Feder ME , Bennett AF , Burggren WW , Huey RB , editors. New Directions in Ecological Physiology. Cambridge: Cambridge University Press, 1987, pp. 216‐239.
 302. Packard GC , Boardman TJ . The misuse of ratios, indices and percentages in ecophysiological research. Physiol Zool 61: 1‐9, 1988.
 303. Packard GC , Boardman TJ . The use of percentages and size‐specific indices to normalize physiological data for variation in body size: Wasted time, wasted effort? Comp Biochem Physiol A 122: 37‐44, 1999.
 304. Packard GC , Boardman TJ . Model selection and logarithmic transformation in allometric analysis. Physiol Biochem Zool 81: 496‐507, 2008.
 305. Packard GC , Boardman TJ . A comparison of methods for fitting allometric equations to field metabolic rates of animals. J Comp Physiol A 179: 175‐182, 2009.
 306. Pagel M . Inferring the historical patterns of biological evolution. Nature 401: 877‐884, 1999.
 307. Pagel M , Harvey PH . The taxon‐level problem in the evolution of mammalian brain size: Facts and artifacts. Am Nat 132: 344‐359, 1988.
 308. Pagel MD . A method for the analysis of comparative data. J Theor Biol 156: 431‐442, 1992.
 309. Painter PR . Data from necropsy studies and in vitro tissue studies lead to a model for allometric scaling of basal metabolic rate. Theor Biol Med Model 2: 39, 2005.
 310. Paradis E , Claude J , Strimmer K . APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20: 289‐290, 2004.
 311. Patterson JL , Goetz RH , Doyle JT , Warren JV , Gauer OH , Detweiler DK , Said SI , Hoernicke H , McGregor M , Keen EN , Smith MH , Hardie EL , Reynolds M , Flatt WP , Waldo DR . Cardiorespiratory dynamics in the ox and giraffe, with comparative observations on man and other mammals. Ann N Y Acad Sci 127: 393‐413, 1965.
 312. Patterson MR . A mass transfer explanation of metabolic scaling relations in some aquatic invertebrates and algae. Science 255: 1421‐1423, 1992.
 313. Pélabon C , Bolstad GH , Egset CK , Cheverud JM , Pavlicev M , Rosenqvist G . On the relationship between ontogenetic and static allometry. Am Nat 181: 195‐212, 2013.
 314. Peters RH. The Ecological Implications of Body Size. Cambridge: Cambridge University Press, 1983, p. 329.
 315. Phillipson J . Bioenergetic options and phylogeny. In: Townsend CR , Calow P , editors. Physiological Ecology: An Evolutionary Approach to Resource Use. Sunderland: Sinauer Associates, 1981, pp. 20‐45.
 316. Pitts GC , Bullard TR . Some interspecific aspects of body composition in mammals. In: Reid JT , Bensadoun A , Bull LS , editors. Body composition in animals and man. Washington, DC: National Academy of Science, 1968.
 317. Porter WP , Kearney M . Size, shape, and the thermal niche of endotherms. Proc Natl Acad Sci U S A 106: 19666‐19672, 2009.
 318. Portugal SJ , Green JA , Butler PJ . Annual changes in body mass and resting metabolism in captive barnacle geese (Branta leucopsis): The importance of wing moult. J Exp Biol 210: 1391‐1397, 2007.
 319. Potter B . The allometry of primate skeletal weight. Int J Primatol 7: 457‐466, 1986.
 320. Potthoff R . On the Johnson‐Neyman technique and some extensions thereof. Psychometrika 29: 241–256, 1964.
 321. Powers DR , Getsinger PW , Tobalske BW , Wethington SM , Powers SD , Warrick DR . Respiratory evaporative water loss during hovering and forward flight in hummingbirds. Comp Biochem Phys A 161: 279‐285, 2012.
 322. Prange HD , Anderson JF , Rahn H . Scaling of skeletal mass to body mass in birds and mammals. Am Nat 113: 103‐122, 1979.
 323. Price CA , Enquist BJ , Savage VM . A general model for allometric covariation in botanical form and function. Proc Natl Acad Sci USA 104: 13204‐13209, 2007.
 324. Purvis A . A composite estimate of primate phylogeny. Philos T R Soc Lon B 348: 405‐421, 1995.
 325. Purvis A , Garland T, Jr . Polytomies in comparative analyses of continuous characters. Syst Biol 42: 569‐575, 1993.
 326. Purvis A , Gittleman JL , Luh H‐K . Truth or consequences: Effects of phylogenetic accuracy on two comparative methods. J Theor Biol 167: 293‐300, 1994.
 327. Pyron RA , Burbrink FT , Wiens JJ . A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol Biol 13: 93, 2013.
 328. Pyron RA , Wiens JJ . A large‐scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol Phylogenet Evol 61: 543‐583, 2011.
 329. Quinn GP , Keough MJ . Experimental Design and Data Analysis for Biologists. Cambridge: Cambridge University Press, 2002.
 330. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2013.
 331. Rahbek C , Gotelli NJ , Colwell RK , Entsminger GL , Rangel TFLVB , Graves GR . Predicting continental‐scale patterns of bird species richness with spatially explicit models. Proc Roy Soc B‐Biol Sci 274: 165‐174, 2007.
 332. Redford KH , Dorea JG . The nutritional value of invertebrates with emphasis on ants and termites as food for mammals. J Zool 203: 385‐395, 1984.
 333. Reynolds PS . Phylogenetic analysis of surface areas of mammals. J Mammal 78: 859‐868, 1997.
 334. Reynolds WW . Skeleton weight allometry in aquatic and terrestrial vertebrates. Hydrobiologia 56: 35‐37, 1977.
 335. Reynolds WW , Karlotski WJ . The allometric relationship of skeleton weight to body weight in teleost fishes: A preliminary comparison with birds and mammals. Copeia 1977: 160‐163, 1977.
 336. Rezende EL , Diniz‐Filho JAF . Phylogenetic analyses: Comparing species to infer adaptations and physiological mechanisms. Compr Physiol 2: 639‐674, 2012.
 337. Riveros AJ , Enquist BJ . Metabolic scaling in insects supports the predictions of the WBE model. J Insect Physiol DOI: 10.1016/j.jinsphys.2011.1001.1011, 2011.
 338. Rixon RH , Stevenson JAF . Factors influencing survival of rats in fasting. Metabolic rate and body weight loss. Am J Physiol 188: 332‐336, 1957.
 339. Roberts MF , Lightfoot EN , Porter WP . A new model for the body size–metabolism relationship. Physiol Biochem Zool 83: 395‐405, 2010.
 340. Roberts MF , Lightfoot EN , Porter WP . Basal metabolic rate of endotherms can be modeled using heat‐transfer principles and physiological concepts: Reply to “Can the basal metabolic rate of endotherms be explained by biophysical modeling?”. Physiol Biochem Zool 84: 111‐114, 2011.
 341. Robinson WR , Peters RH , Zimmermann J . The effects of body size and temperature on metabolic rate of organisms. Can J Zool 61: 281‐288, 1983.
 342. Rogowitz GL , Chappell MA . Energy metabolism of eucalyptus‐boring beetles at rest and during locomotion: Gender makes a difference. J Exp Biol 203: 1131‐1139, 2000.
 343. Rohlf FJ . Comparative methods for the analysis of continuous variables: Geometric interpretations. Evolution 55: 2143‐2160, 2001.
 344. Rombouts I , Beaugrand G , Ibaňez F , Chiba S , Legendre L . Marine copepod diversity patterns and the metabolic theory of ecology. Oecologia 166: 349‐355, 2011.
 345. Rowland JM , Emlen DJ . Two thresholds, three male forms result in facultative male trimorphism in beetles. Science 323: 773‐776, 2009.
 346. Rubner M . Über den Einfluss der Körpergrösse auf Stoff‐ und Kraftwechsel. Zeischrift für Biologie 19: 536‐562, 1883.
 347. Runciman S , Seymour RS , Baudinette RV , Pearson JT . An allometric study of lung morphology during development in the Australian pelican, Pelicanus conspicillatus, from embryo to adult. J Anat 207: 365‐380, 2005.
 348. Ruxton GD , Houston DC . Could Tyrannosaurus rex have been a scavenger rather than a predator? An energetics approach. Proc Roy Soc B‐Biol Sci 270: 731‐733, 2003.
 349. Savage VM , Deeds EJ , Fontana W . Sizing up allometric scaling theory. PLoS Computational Biology 4: e1000171, 2008.
 350. Savage VM , Gillooly JF , Woodruff WH , West GB , Allen AP , Enquist BJ , Brown JH . The predominance of quarter‐power scaling in biology. Funct Ecol 18: 257‐282, 2004.
 351. Schimpf NG , Matthews PGD , White CR . Cockroaches that exchange respiratory gases discontinuously survive food and water restriction. Evolution 66: 597‐604, 2012.
 352. Schimpf NG , Matthews PGD , White CR . Standard metabolic rate is associated with gestation duration, but not clutch size, in speckled cockroaches Nauphoeta cinerea . Biol Open 1: 1185‐1191, 2012.
 353. Schlader ZJ , Stannard SR , Mündel T . Human thermoregulatory behavior during rest and exercise—A prospective review. Physiol Behav 99: 269‐275, 2010.
 354. Schmid J , Andersen NA , Speakman JR , Nicol SC . Field energetics of free‐living, lactating and non‐lactating echidnas (Tachyglossus aculeatus). Comp Biochem Physiol A 136: 903‐909, 2003.
 355. Schmidt‐Nielsen K . Locomotion: Energy cost of swimming, flying and running. Science 172: 222‐228, 1972.
 356. Schmidt‐Nielsen K . Scaling in biology: The consequences of size. J Exp Zool 194: 287‐307, 1975.
 357. Schmidt‐Nielsen K. Scaling: Why is Animal Size so Important? Cambridge: Cambridge University Press, 1984, p. 241.
 358. Seebacher F . A new method to calculate allometric length‐mass relationships of dinosaurs. J Vertebr Paleontol 21: 51‐60, 2001.
 359. Seebacher F , Grigg GC , Beard LA . Crocodiles as dinosaurs: Behavioural thermoregulation in very large ectotherms leads to high and stable body temperatures. J Exp Biol 202: 77‐86, 1999.
 360. Seim E , Sæther B‐E . On rethinking allometry: Which regression model to use? J Theor Biol 104: 161‐168, 1983.
 361. Seymour RS . Raising the sauropod neck: It costs more to get less. Biol Letters 5: 317‐319, 2009.
 362. Seymour RS , Blaylock AJ . The principle of Laplace and scaling of ventricular wall stress and blood pressure in mammals and birds. Physiol Biochem Zool 73: 389‐405, 2000.
 363. Seymour RS , Gienger CM , Brien ML , Tracy CR , Manolis SC , Webb GJW , Christian KA . Scaling of standard metabolic rate in estuarine crocodiles Crocodylus porosus . J Comp Physiol B 183: 491‐500, 2013.
 364. Seymour RS , Hargens AR , Pedley TJ . The heart works against gravity. Am J Physiol 265: R715‐R720, 1993.
 365. Seymour RS , Lillywhite HB . Blood pressure in snakes from different habitats. Nature 264: 664‐666, 1976.
 366. Seymour RS , Lillywhite HB . Hearts, neck posture and metabolic intensity of sauropod dinosaurs. Proc R Soc Lond B 267: 1883‐1887, 2000.
 367. Seymour RS , Runciman S , Baudinette RV , Pearson JT . Developmental allometry of pulmonary structure and function in the altricial Australian pelican Pelecanus conspicillatus . J Exp Biol 207: 2663‐2669, 2004.
 368. Seymour RS , Smith SL , White CR , Henderson DM , Schwarz‐Wings D . Blood flow to long bones indicates activity metabolism in mammals, reptiles and dinosaurs. Proc Roy Soc B‐Biol Sci 279: 451‐456, 2012.
 369. Seymour RS , White CR . Can the basal metabolic rate of endotherms be explained by biophysical modeling? Response to “A new model for the body size–metabolism relationship”. Physiol Biochem Zool 84: 107‐110, 2011.
 370. Shaffer SA . A review of seabird energetics using the doubly labeled water method. Comp Biochem Physiol A 158: 315‐322, 2011.
 371. Shingleton AW , Frankino WA , Flatt T , Nijhout HF , Emlen DJ . Size and shape: The developmental regulation of static allometry in insects. BioEssays 29: 536‐548, 2007.
 372. Shipley B. Cause and Correlation in Biology. Cambridge: Cambridge University Press, 2000.
 373. Sibly RM , Brown JH , Kodric‐Brown A editors. Metabolic Ecology: A Scaling Approach. Wiley Blackwell, 2012, p. 392.
 374. Sieg AE , O'Conner MP , McNair JN , Grant BW , Agosta SJ , Dunham AE . Mammalian metabolic allometry: Do intraspecific variation, phylogeny, and regression models matter? Am Nat 174: 720‐733, 2009.
 375. Silva M . Allometric scaling of body length: Elastic or geometric similarity in mammalian design. J Mammal 79: 20‐32, 1998.
 376. Silva M , Downing JA . The allometric scaling of density and body mass: A nonlinear relationship for terrestrial mammals. Am Nat 145: 704‐727, 1995.
 377. Simons MJP , Reimert I , van der Vinne V , Hambly C , Vaanholt LM , Speakman JR , Gerkema MP . Ambient temperature shapes reproductive output during pregnancy and lactation in the common vole (Microtus arvalis): A test of the heat dissipation limit theory. J Exp Biol 214: 38‐49, 2011.
 378. Smith RJ . Allometric scaling in comparative biology: Problems of concept and method. Am J Physiol 246: R152‐R160, 1984.
 379. Smith RJ . Rethinking allometry. J Theor Biol 87: 97‐111, 1980.
 380. Smith RJ . Use and misuse of the reduced major axis for line‐fitting. Am J Phys Anthropol 140: 476‐486, 2009.
 381. Snell O . Die Abhängigkeit des Hirngewichtes von dem Körpergewicht und den geistigen Fähigkeiten. Arch Psychiat Nervenkr 23: 436‐446, 1891.
 382. Snelling EP , Seymour RS , Matthews PGD , Runciman S , White CR . Scaling of resting and maximum hopping metabolic rate throughout the life cycle of the locust Locusta migratoria . J Exp Biol 214: 3218‐3224, 2011.
 383. Sokal RR , Rohlf FJ . Biometry. W H Freeman and Co., 1995.
 384. Speakman JR. Doubly Labelled Water: Theory and Practice. London: Chapman and Hall, 1997.
 385. Speakman JR , Król E . Maximal heat dissipation capacity and hyperthermia risk: Neglected key factors in the ecology of endotherms. J Anim Ecol 79: 726‐746, 2010.
 386. Stahl WR . Organ weights in primates and other mammals. Science 150: 1039‐1042, 1965.
 387. Stahl WR . Scaling of respiratory variables in mammals. J Appl Physiol 22: 453‐460, 1967.
 388. Stern DL , Emlen DJ . The developmental basis for allometry in insects. Development 126: 1091‐1101, 1999.
 389. Streicher JW , Cox CL , Birchard GF . Non‐linear scaling of oxygen consumption and heart rate in a very large cockroach species (Gromphadorhina portentosa): Correlated changes with body size and temperature. J Exp Biol 215: 1137‐1143, 2012.
 390. Suarez R . The biology of energy expenditure. J Exp Biol 214: 163‐163, 2011.
 391. Swanson DL , Garland T, Jr . The evolution of high summit metabolism and cold tolerance in birds and its impact on present‐day distributions. Evolution 63: 184‐194, 2009.
 392. Swanson DL , Liknes ET . A comparative analysis of thermogenic capacity and cold tolerance in small birds. J Exp Biol 209: 466‐474, 2006.
 393. Symonds MRE . Life history of the Insectivora: The role of phylogeny, metabolism and sex differences. J Zool 249: 315‐337, 1999.
 394. Symonds MRE . The effects of topological inaccuracy in evolutionary trees on the phylogenetic comparative method of independent contrasts. Syst Biol 51: 541‐553, 2002.
 395. Taggart DA , Schultz D , White C , Whitehead P , Underwood G , Phillips K . Cross‐fostering, growth and reproductive studies in the brush‐tailed rock‐wallaby, Petrogale penicillata (Marsupialia : Macropodidae): Efforts to accelerate breeding in a threatened marsupial species. Aust J Zool 53: 313‐323, 2005.
 396. Tang H , Hussain A , Leal M , Fluhler E , Mayersohn M . Controversy in the allometric application of fixed‐ versus varying‐exponent models: A statistical and mathematical perspective. J Pharm Sci 100: 402‐410, 2011.
 397. Tang H , Hussain A , Leal M , Mayersohn M , Fluhler E . Interspecies prediction of human drug clearance based on scaling data from one or two animal species. Drug Metab Dispos 35: 1886‐1893, 2007.
 398. Tang H , Mayersohn M . Controversies in allometric scaling for predicting human drug clearance: An historical problem and reflections on what works and what does not. Curr Top Med Chem 11: 340‐350, 2011.
 399. Taylor CR , Dmi'el R , Shkolnik A , Baharav D , Borut A . Heat balance of running gazelles: Strategies for conserving water in the desert. Am J Physiol 226: 439‐442, 1974.
 400. Taylor CR , Rowntree VJ . Temperature regulation and heat balance in running cheetahs: A strategy for sprinters? Am J Physiol 224: 848‐851, 1973.
 401. Taylor CR , Schmidt‐Nielsen K , Dmi'el R , Fedak MA . Effect of hyperthermia on heat balance during running in the African hunting dog. Am J Physiol 220: 823‐827, 1971.
 402. Terblanche JS , Janion C , Chown SL . Variation in scorpion metabolic rate and rate‐temperature relationships: Implications for the fundamental equation of the metabolic theory of ecology. J Evol Biol 20: 1602‐1612, 2007.
 403. Terribile LC , Diniz‐Filho JAF . Spatial patterns of species richness in New World coral snakes and the metabolic theory of ecology. Acta Oecol 35: 163‐173, 2009.
 404. Thompson DW. On Growth and Form. Cambridge: Cambridge University Press, 1917.
 405. Tieleman BI , Williams JB . The adjustment of avian metabolic rates and water fluxes to desert environments. Physiol Biochem Zool 73: 461‐479, 2000.
 406. van der Meer J . Metabolic theories in ecology. Trends Ecol Evol 21: 136‐140, 2006.
 407. van der Veer HW , Kooijman SALM , van der Meer J . Body size scaling relationships in flatfish as predicted by Dynamic Energy Budgets (DEB theory): Implications for recruitment. J Sea Res 50: 257‐272, 2003.
 408. Vogt JT , Appel AG . Standard metabolic rate of the fire ant, Solenopsis invicta Buren: Effects of temperature, mass, and caste. J Insect Physiol 45: 655‐666, 1999.
 409. Wang Z , Ying Z , Bosy‐Westphal A , Zhang J , Heller M , Later W , Heymsfield SB , Müller MJ . Evaluation of specific metabolic rates of major organs and tissues: Comparison between men and women. Am J Hum Biol 23: 333‐338, 2011.
 410. Wang Z , Ying Z , Bosy‐Westphal A , Zhang J , Heller M , Later W , Heymsfield SB , Müller MJ . Evaluation of specific metabolic rates of major organs and tissues: Comparison between nonobese and obese women. Obesity 20: 95‐100, 2012.
 411. Weibel ER. Symmorphosis: On Form and Function in Shaping Life. Cambridge: Harvard University Press, 2000.
 412. Weibel ER , Bacigalupe LD , Schmidt B , Hoppeler H . Allometric scaling of maximal metabolic rate in mammals: Muscle aerobic capacity as a determinant factor. Resp Physiol Neurobi 140: 115‐132, 2004.
 413. Weibel ER , Hoppeler H . Exercise‐induced maximal metabolic rate scales with muscle aerobic capacity. J Exp Biol 208: 1635‐1644, 2005.
 414. Weibel ER , Taylor CR , Bolis L editors. Principles of Design: The Optimization and Symmorphosis Debate. Cambridge: Cambridge University Press, 1998.
 415. Weibel ER , Taylor CR , Hoppeler H . The concept of symmorphosis: A testable hypothesis of structure‐function relationship. Proc Natl Acad Sci U S A 88: 10357‐10361, 1991.
 416. Weiner J , Górecki A . Standard metabolic rate and thermoregulation in 5 species of Mongolian small mammals. J Comp Physiol B 145: 127‐132, 1981.
 417. Welch KC, Jr . The power of feeder‐mask respirometry as a method for examining hummingbird energetics. Comp Biochem Physiol A 158: 276‐286, 2011.
 418. West GB , Brown JH . The origin of allometric scaling laws in biology from genomes to ecosystems: Towards a quantitative unifying theory of biological structure and organization. J Exp Biol 208: 1575‐1592, 2005.
 419. West GB , Brown JH , Enquist BJ . A general model for the origin of allometric scaling laws in biology. Science 276: 122‐126, 1997.
 420. West GB , Brown JH , Enquist BJ . A general model for the structure and allometry of plant vascular systems. Nature 400: 664‐667, 1999.
 421. West GB , Brown JH , Enquist BJ . The fourth dimension of life: Fractal geometry and allometric scaling of organisms. Science 284: 1677‐1679, 1999.
 422. West GB , Woodruff WH , Brown JH . Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. Proc Natl Acad Sci U S A 99: 2473‐2478, 2002.
 423. Westerterp KR , Speakman JR . Physical activity energy expenditure has not declined since the 1980s and matches energy expenditures of wild mammals. Int J Obes 32: 1256‐1263, 2008.
 424. Westoby M , Leishman M , Lord J . Further remarks on phylogenetic correction. J Ecol 83: 727‐729, 1995.
 425. Westoby M , Leishman M , Lord J . Issues of interpretation after relating comparative datasets to phylogeny. J Ecol 83: 892‐893, 1995.
 426. Westoby M , Leishman MR , Lord JM . On misinterpreting the phylogenetic correction. J Ecol 83: 531‐534, 1995.
 427. White CR . Allometric analysis beyond heterogeneous regression slopes: Use of the Johnson‐Neyman technique in comparative biology. Physiol Biochem Zool 76: 135‐140, 2003.
 428. White CR . The influence of foraging mode and arid adaptation on the basal metabolic rate of burrowing mammals. Physiol Biochem Zool 76: 122‐134, 2003.
 429. White CR . Allometric estimation of metabolic rates in animals. Comp Biochem Physiol A 158: 346‐357, 2011.
 430. White CR , Alton LA , Frappell PB . Metabolic cold adaptation in fish occurs at the level of whole animal, mitochondria, and enzyme. Proc R Soc Lond B Biol Sci 279: 1740‐1747, 2012.
 431. White CR , Blackburn TM , Martin GR , Butler PJ . Basal metabolic rate of birds is associated with habitat temperature and precipitation, not primary productivity. Proc Roy Soc B‐Biol Sci 274: 287‐293, 2007.
 432. White CR , Blackburn TM , Seymour RS . Phylogenetically informed analysis of the allometry of mammalian basal metabolic rate supports neither geometric nor quarter‐power scaling. Evolution 63: 2658‐2667, 2009.
 433. White CR , Blackburn TM , Terblanche JS , Marais E , Gibernau M , Chown SL . Evolutionary responses of discontinuous gas exchange in insects. Proc Natl Acad Sci USA 104: 8357‐8361, 2007.
 434. White CR , Cassey P , Blackburn TM . Allometric exponents do not support a universal metabolic allometry. Ecology 88: 315‐323, 2007.
 435. White CR , Cassey P , Schimpf NG , Halsey LG , Green JA , Portugal SJ . Implantation reduces the negative effects of bio‐logging devices on birds. J Exp Biol 216: 537‐542, 2013.
 436. White CR , Frappell PB , Chown SL . An information‐theoretic approach to evaluating the size and temperature dependence of metabolic rate. Proc Roy Soc B‐Biol Sci 279: 3616‐3621, 2012.
 437. White CR , Kearney MR . Determinants of inter‐specific variation in basal metabolic rate. J Comp Physiol B 183: 1‐26, 2013.
 438. White CR , Kearney MR , Matthews PGD , Kooijman SALM , Marshall DJ . A manipulative test of competing theories for metabolic scaling. Am Nat 178: 746‐754, 2011.
 439. White CR , Martin GR , Butler PJ . Pedestrian locomotion energetics and gait characteristics of a diving bird, the great cormorant, Phalacrocorax carbo . J Comp Physiol B 178: 745‐754, 2008.
 440. White CR , Matthews PGD , Seymour RS . Balancing the competing requirements of saltatorial and fossorial specialisation: Burrowing costs in the spinifex hopping mouse, Notomys alexis . J Exp Biol 209: 2103‐2113, 2006.
 441. White CR , Phillips NF , Seymour RS . The scaling and temperature dependence of vertebrate metabolism. Biol Letters 2: 125‐127, 2006.
 442. White CR , Schimpf NG , Cassey P . The repeatability of metabolic rate declines with time. J Exp Biol 216: 1763‐1765, 2013.
 443. White CR , Seymour RS . Mammalian basal metabolic rate is proportional to body mass2 /3 . Proc Natl Acad Sci U S A 100: 4046‐4049, 2003.
 444. White CR , Seymour RS . Does BMR contain a useful signal? Mammalian BMR allometry and correlations with a selection of physiological, ecological and life‐history variables. Physiol Biochem Zool 77: 929‐941, 2004.
 445. White CR , Seymour RS . Allometric scaling of mammalian metabolism. J Exp Biol 208: 1611‐1619, 2005.
 446. White CR , Seymour RS . Sample size and mass range effects on the allometric exponent of basal metabolic rate. Comp Biochem Physiol A 142: 74‐78, 2005.
 447. White CR , Seymour RS . Physiological functions that scale to body mass in fish. In: Farrell AP , editor. Encyclopedia of Fish Physiology: From Genome to Environment. San Diego: Academic Press, 2011, pp. 1573‐1582.
 448. White CR , Seymour RS . The role of gravity in the evolution of mammalian blood pressure. In Review, 2013. [Epub ahead of print].
 449. White CR , Terblanche JS , Kabat AP , Blackburn TM , Chown SL , Butler PJ . Allometric scaling of maximum metabolic rate: The influence of temperature. Funct Ecol 22: 616‐623, 2008.
 450. Wiersma P , Muñoz‐Garcia A , Walker A , Williams JB . Tropical birds have a slow pace of life. Proc Natl Acad Sci U S A 104: 9340‐9345, 2007.
 451. Wieser W . A distinction must be made between the ontogeny the phylogeny of metabolism in order to understand the mass exponent of energy metabolism. Respir Physiol 55: 1‐9, 1984.
 452. Wikelski M , Spinnery L , Schelsky W , Scheuerlein A , Gwinner E . Slow pace of life in tropical sedentary birds: A common‐garden experiment on four stonechat populations from different latitudes. Proc Roy Soc B‐Biol Sci 270: 2383‐2388, 2003.
 453. Withers PC . Design, calibration and calculation for flow‐through respirometry systems. Aust J Zool 49: 445‐461, 2001.
 454. Xiao X , White EP , Hooten MB , Durham SL . On the use of log‐transformation vs. nonlinear regression for analyzing biological power‐laws. Ecology 92: 1887‐1894, 2011.
 455. Xiao Y . What are the units of the parameters in the power function for the length–weight relationship? Fish Res 35: 247‐249, 1998.
 456. Zar JH. Biostatistical Analysis. Upper Saddle River: Pearson, 2010.
 457. Zerbe GO , Archer PG , Banchero N , Lechner AJ . On comparing regression lines with unequal slopes. Am J Physiol 242: R178‐R180, 1982.
 458. Zhao Z‐J . Energy budget during lactation in striped hamsters at different ambient temperatures. J Exp Biol 214: 988‐995, 2011.
 459. Zhao Z‐J , Król E , Moille S , Gamo Y , Speakman JR . Limits to sustained energy intake. XV. Effects of wheel running on the energy budget during lactation. J Exp Biol 216: 2316‐2327, 2013.
 460. Zub K , Fletcher QE , Szafrańska PA , Konarzewski M . Male weasels decrease activity and energy expenditure in response to high ambient temperatures. PLoS ONE 8: e72646, 2013.
 461. Zuo W , Moses ME , Hou C , Woodruff WH , West GB , Brown JH . Response to comments on “Energy uptake and allocation during ontogeny”. Science 325: 1206‐c, 2009.
 462. Zuo W , Moses ME , West GB , Hou C , Brown JH . A general model for effects of temperature on ectotherm ontogenetic growth and development. Proc Roy Soc B‐Biol Sci 279: 1840‐1846, 2012.

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Craig R. White, Michael R. Kearney. Metabolic Scaling in Animals: Methods, Empirical Results, and Theoretical Explanations. Compr Physiol 2014, 4: 231-256. doi: 10.1002/cphy.c110049