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Phylogenetic Analyses: Comparing Species to Infer Adaptations and Physiological Mechanisms

Enrico L. Rezende, José Alexandre F. Diniz‐Filho

10.1002/cphy.c100079

Source: Volume 2, Issue 1, January 2012

Published online: January 2012

Full Article on Wiley Online Library



Abstract

Comparisons among species have been a standard tool in animal physiology to understand how organisms function and adapt to their surrounding environment. During the last two decades, conceptual and methodological advances from different fields, including evolutionary biology and systematics, have revolutionized the way comparative analyses are performed, resulting in the advent of modern phylogenetic statistical methods. This development stems from the realization that conventional analytical methods assume that observations are statistically independent, which is not the case for comparative data because species often resemble each other due to shared ancestry. By taking evolutionary history explicitly into consideration, phylogenetic statistical methods can account for the confounding effects of shared ancestry in interspecific comparisons, improving the reliability of standard approaches such as regressions or correlations in comparative analyses. Importantly, these methods have also enabled researchers to address entirely new evolutionary questions, such as the historical sequence of events that resulted in current patterns of form and function, which can only be studied with a phylogenetic perspective. Here, we provide an overview of phylogenetic approaches and their importance for studying the evolution of physiological processes and mechanisms. We discuss the conceptual framework underlying these methods, and explain when and how phylogenetic information should be employed. We then outline the difficulties and limitations inherent to comparative approaches and discuss potential problems researchers may encounter when designing a comparative study. These issues are illustrated with examples from the literature in which the incorporation of phylogenetic information has been useful, or even crucial, for inferences on how species evolve and adapt to their surrounding environment. © 2012 American Physiological Society. Compr Physiol 2:639‐674, 2012.

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Figure 1. Figure 1.

A hypothetical phylogeny representing the evolutionary relationships among five species, and its consequences at the level of phenotypic variation. The tips of the phylogenetic tree represent extant species and nodes depict the most recent common ancestor of a clade, that is, a hierarchically arranged, monophyletic group of species. For illustrative purposes, only the separation between clades A and B are shown, but note that these two clades belong to a larger clade that encompasses all species in the phylogeny, with a common ancestor known as the root node of the tree. Given the hierarchical patterns of relatedness among species, phenotypic data in comparative studies may not necessarily provide independent sources of information, as shown for the two pairs of closely related species that are phenotypically very similar. Consequently, patterns of phenotypic resemblance may be interpreted as evidence of evolutionary convergence (adaptation) when in fact they reflect common ancestry. For this particular example, phenotypic evolution proceeded as a random walk (i.e., a Brownian motion model of evolution).
Figure 2. Figure 2.

Evolution of osmotic tolerance across populations of Eurytemora affinis, a copepod that invaded freshwater environments repeated times. Phylogenetic information suggests that tolerance to low osmotic pressures has evolved at least three different times from a marine ancestral lineage, and this adaptive response resulted in divergence between close relatives and convergence across distantly related species. Adapted from Lee et al. 172 with permission of the University of Chicago Press.
Figure 3. Figure 3.

The evolutionary steps behind the origin of the swim bladder can be traced onto the phylogeny of jawed vertebrates. Some fishes present complex vascular counter‐current systems known as retia mirabilia that are, among other functions, involved in the secretion of gases by blood acidification (Root effect). Phylogenetic analyses support a single origin for the choroid rete mirabile, suggesting that the physiology behind oxygen secretion first evolved within the ray‐finned fishes to maintain a metabolically active retina. This preceded the evolution of the swimbladder rete mirabile, which occurred in four independent lineages (gray arrows) and enabled them to control buoyancy by physiological means. Symbols at the tips of the phylogeny indicate the presence of the choroid or the swimbladder retia mirabilia in extant species, and different branches illustrate the hypothesized state of ancestral lineages according to parsimony (evolutionary losses are not shown in the phylogeny for clarity, but can be inferred from the tip data). Modified, with permission, from Berenbrink 22.
Figure 4. Figure 4.

The problem of analyzing phylogenetically structured data with conventional statistical methods. Ignoring phylogeny, one would conclude that X and Y are positively correlated (Pearson r = 0.48, 2‐tailed P = 0.034), when in fact this relationship emerges primarily from the high divergence in X and Y between the two clades at the root of the phylogeny. Modified from Felsenstein 86, with permission of the University of Chicago Press.
Figure 5. Figure 5.

Increased type I error rates of conventional statistics in analyses of interspecific data. When two traits evolve independently along a phylogeny according to Brownian motion, the probability of rejecting the null hypothesis of no correlation (type I error) increases with the amount of phylogenetic structure of the data. The shaded area represents simulations where the resulting ordinary Pearson coefficient falls above the tabular critical value of +0.476 (11 degrees of freedom), which would incorrectly suggest that the two traits are correlated. Simulations with a star phylogeny result in the error rates of 5%, which is the expected type I error rate if conventional (nonphylogenetic) analyses are used. Type I error rates can be higher than 25% if the data shows a strong phylogenetic structure (for one obvious example where the correlation between two traits is incorrectly inferred, see Fig. 4). Modified, with permission, from Garland et al. 101.
Figure 6. Figure 6.

Branch lengths in comparative analyses. The branches of a phylogeny indicate the elapsed time between speciation events, but the degree of phenotypic similarity expected among species (which is the main concern in a comparative dataset) will depend on the elapsed time and on the evolutionary model of character evolution. Under an evolutionary model of Brownian motion, the “expected variance of character change” is proportional to elapsed time. Alternatively, the expected variance is not directly proportional to time when rates of character change accelerate (gray arrow) or decelerate in time (black arrow), which affects the relative contribution of recent versus past evolution to the overall distribution of phenotypes. Accelerating evolutionary rates results in less phylogenetic signal than expected from Brownian motion because most phenotypic variation reflects recent evolutionary history, whereas decelerating rates generate the opposite pattern. A star phylogeny depicts the extreme case in which all effects of shared ancestry and past evolution have been blurred by recent phenotypic evolution (at the tips of the phylogeny). Many evolutionary processes, such as divergent or stabilizing selection, can account for a nonlinear association between character change and time. Modified, with permission, from Diniz‐Filho 71.
Figure 7. Figure 7.

Randomization analysis to test for significant phylogenetic signal in continuous and categorical traits, illustrated with the distribution of body mass and diet categories (white = omnivore, gray = granivore, black = herbivore) of rodent species. Phylogenetic similarity was estimated as the variance of phylogenetic independent contrasts for the continuous trait (log10‐transformed body mass) 29 and as the minimum number of transitions according to unrestrained parsimony (i.e., all transitions are possible) for the categorical trait diet 196. These indexes are then calculated after shuffling the phenotypic characters across the tips of the phylogeny, breaking any phylogenetic structure and providing a null random distribution of n replicates (n = 999 in this example) where phylogenetic signal is absent. The histograms illustrate how both indexes calculated in the real dataset (represented by the arrows) fall consistently below randomizations, hence one can conclude that the distribution of body mass and diet shows significant phylogenetic signal across rodent species (P < 0.001 in both cases). Data and phylogeny, with permission, from Rezende et al. 261.
Figure 8. Figure 8.

Branch length transformation analyses to test for the presence of phylogenetic signal attempt to find the phylogenetic structure that best fits the data. A phylogeny can be described as a matrix of variance‐covariance describing the expected residual distribution of the comparative data. The diagonals depict the expected phenotypic variance (i.e., how species are expected to differ from the overall mean) that, under Brownian motion, corresponds to the total distance from the root to the tips. The off‐diagonals provide the expected phenotypic covariance among species (or how they are expected to resemble each other due to shared ancestry), which corresponds to the total amount of evolutionary history shared by each pair of species. Because the amount of phylogenetic signal essentially encapsulates the amount of phenotypic covariance among species, parameters such as λ that affect the degree of hierarchy of a phylogeny by manipulating the length of the internal branches can be employed to estimate which phylogeny best fits the data (see Fig. 9). In this hypothetical example, the phylogeny that best fits trait A is very hierarchical (λ = 1) suggesting that close relatives tend to resemble each other for this trait. Conversely, the phylogeny that best fits the distribution of trait B shows no hierarchy (λ = 0), suggesting that signal in trait B is negligible. A log‐likelihood ratio test comparing the likelihoods of models when λ = 0 versus λ = 1 can be employed for significance testing (not shown). Adapted from Freckleton et al. 93 (see also 256), with permission of the University of Chicago Press.
Figure 9. Figure 9.

Varying degrees of hierarchy in phylogenetic trees expressed as matrices of phenotypic variance‐covariance among species, illustrated for the phylogeny in Figure 8. The expected amount of phenotypic similarity due to shared ancestry decreases as λ approaches zero, and as when λ = 0 the matrix of phenotypic variance‐covariance converges to the identity matrix (i.e., the expected residual distribution of conventional statistical analyses). In other words, comparative studies employing conventional analyses inherently assume that the data does not exhibit phylogenetic signal and species provide independent sources of information, which may or may not be true (compare traits A and B in Fig. 8).
Figure 10. Figure 10.

Calculation of phylogenetic independent contrasts for two hypothetical variables X and Y. Contrasts estimate the amount of phenotypic divergence across sister lineages standardized by the amount of time they had to diverge (the square root of the sum of the two branches). The algorithm runs iteratively from the tips to the root of the phylogeny, transforming n phenotypic measurements that are not independent in n–1 contrast that are statistically independent. Because phenotypic estimates at intermediate nodes (X' and Y') are not measured, but inferred from the tip data, divergence times employed to calculate these contrasts include an additional component of variance that reflects the uncertainty associated with these estimates. In practice, this involves lengthening the branches (dashed lines) by an amount that, assuming Brownian motion, can be calculated as (daughter branch length 1 × daughter branch length 2) / (daughter branch length 1 + daughter branch length 2). As a result, the association between the hypothetical phenotypic variables X and Y analyzed employing conventional statistics and independent contrasts may seem remarkably different, as shown in the bottom panels. Because independent contrasts estimate phenotypic divergence after speciation and are expressed as deviations from zero (i.e., the daughter lineages were initially phenotypically identical), correlation and regression analyses employing contrasts do not include an intercept term and must be always calculated through the origin 82,105,175. Note that the sign of each contrast is arbitrary; hence many studies have adopted the convention to give a positive sign to contrasts in the x‐axis and invert the sign of the contrast in the y‐axis accordingly (this procedure does not affect regression or correlation analyses through the origin; see 105). Even though the classic algorithm to calculate contrasts neglects important sources of uncertainty such as individual variation and measurement error, recent methods can account for these sources of error 87,157,206.
Figure 11. Figure 11.

Correlated evolution and grade shifts in comparative data, plotted in raw dimensions and employing independent contrasts. The semicircular canal system in mammals (Rod = rodentia, Prim = primates, Cet = cetacea, Art = artiodactyla, Car = carnivora, Chir = chiroptera) contributes to stabilization and balances during locomotion and varies positively with body size. The highly derived system of cetaceans (close symbols) seems to have evolved as an adaptation to an aquatic environment, and not taking this fact into account (i.e., pooling all species during analyses) would result in an underestimation of the allometric slope of the semicircular canal system across mammals. A regression with independent contrasts controls for this problem during scaling analyses and also extracts the region of the phylogeny where the grade shift has occurred (gray symbol), since the node separating cetaceans from their artiodactyl sister lineage falls outside the 99% prediction interval for a new observation when it is removed from the analysis. Modified, with permission, from Spoor et al. 289, the phylogeny was built employing the mammalian supertree reconstructed by Beck et al. 19.
Figure 12. Figure 12.

The general approach to study the evolution of categorical traits. Different evolutionary models can be emulated by varying the rates of transitions q1 and q2 between categorical states: evolution is reversible when transitions in both directions are possible (i.e., the probability that a transition occurs is different from zero for q1 and q2), and irreversible when the probability of a transition in one of the directions is constrained. Different set of rules about transformations between states can have a major impact on the analytical results, as shown for the ancestral reconstruction performed with maximum likelihood assuming a Markov model of evolution (see text). In the first case, transition rates are assumed to be identical (q1 = q2), resulting in equiprobable ancestral states in all nodes, which contrasts dramatically with the outcome of the same analysis assuming that evolution is irreversible.
Figure 13. Figure 13.

The statistical power to detect associations will depend on the degree of overlap between the independent variable of study and phylogeny relations among species. Left panel. Hypothesized phylogenetic relationships between arctic (open symbols) and tropical (black symbols) mammalian species 276, where it is relatively simple to discriminate between phylogenetic and environmental effects. Right panel. The worst‐case scenario for a comparative analysis, because all carnivore species (black symbols) are clustered within Carnivora while all herbivores (open symbols) are ungulates 98 (adapted, with permission, from the University of Chicago Press). In this case, any potential effect of diet will be highly confounded with phylogeny.
Figure 14. Figure 14.

Relationship between maximum metabolic rates during thermogenesis (MMR), body mass, and environmental temperature for rodents across the world. The residual variation in MMR—obtained from a regular regression because this trait did not exhibit phylogenetic signal—is significantly correlated with ambient temperature, suggesting that interspecific variation in this trait may be partly explained by thermal adaptation. Modified, with permission, from Rezende et al. 261.
Figure 15. Figure 15.

Phylogenetic analyses can detect differences in rates of phenotypic evolution between clades, as illustrated for body mass in passerines (open symbols) and nonpasserines (black symbols). Passerines show a more homogeneous distribution (i.e., lower variance) of body mass than nonpasserines, which is supported by statistical comparisons between absolute standardized contrasts (P < 0.001) after excluding the contrast between passerines and their nonpasserine sister clade (gray symbol). Assuming that branch lengths accurately reflect elapsed times, this pattern supports a lower rate of evolution of body mass in passerines. Modified from Garland and Ives 107, with permission of the University of Chicago Press, original data from Reynolds and Lee 260.


Figure 1.

A hypothetical phylogeny representing the evolutionary relationships among five species, and its consequences at the level of phenotypic variation. The tips of the phylogenetic tree represent extant species and nodes depict the most recent common ancestor of a clade, that is, a hierarchically arranged, monophyletic group of species. For illustrative purposes, only the separation between clades A and B are shown, but note that these two clades belong to a larger clade that encompasses all species in the phylogeny, with a common ancestor known as the root node of the tree. Given the hierarchical patterns of relatedness among species, phenotypic data in comparative studies may not necessarily provide independent sources of information, as shown for the two pairs of closely related species that are phenotypically very similar. Consequently, patterns of phenotypic resemblance may be interpreted as evidence of evolutionary convergence (adaptation) when in fact they reflect common ancestry. For this particular example, phenotypic evolution proceeded as a random walk (i.e., a Brownian motion model of evolution).



Figure 2.

Evolution of osmotic tolerance across populations of Eurytemora affinis, a copepod that invaded freshwater environments repeated times. Phylogenetic information suggests that tolerance to low osmotic pressures has evolved at least three different times from a marine ancestral lineage, and this adaptive response resulted in divergence between close relatives and convergence across distantly related species. Adapted from Lee et al. 172 with permission of the University of Chicago Press.



Figure 3.

The evolutionary steps behind the origin of the swim bladder can be traced onto the phylogeny of jawed vertebrates. Some fishes present complex vascular counter‐current systems known as retia mirabilia that are, among other functions, involved in the secretion of gases by blood acidification (Root effect). Phylogenetic analyses support a single origin for the choroid rete mirabile, suggesting that the physiology behind oxygen secretion first evolved within the ray‐finned fishes to maintain a metabolically active retina. This preceded the evolution of the swimbladder rete mirabile, which occurred in four independent lineages (gray arrows) and enabled them to control buoyancy by physiological means. Symbols at the tips of the phylogeny indicate the presence of the choroid or the swimbladder retia mirabilia in extant species, and different branches illustrate the hypothesized state of ancestral lineages according to parsimony (evolutionary losses are not shown in the phylogeny for clarity, but can be inferred from the tip data). Modified, with permission, from Berenbrink 22.



Figure 4.

The problem of analyzing phylogenetically structured data with conventional statistical methods. Ignoring phylogeny, one would conclude that X and Y are positively correlated (Pearson r = 0.48, 2‐tailed P = 0.034), when in fact this relationship emerges primarily from the high divergence in X and Y between the two clades at the root of the phylogeny. Modified from Felsenstein 86, with permission of the University of Chicago Press.



Figure 5.

Increased type I error rates of conventional statistics in analyses of interspecific data. When two traits evolve independently along a phylogeny according to Brownian motion, the probability of rejecting the null hypothesis of no correlation (type I error) increases with the amount of phylogenetic structure of the data. The shaded area represents simulations where the resulting ordinary Pearson coefficient falls above the tabular critical value of +0.476 (11 degrees of freedom), which would incorrectly suggest that the two traits are correlated. Simulations with a star phylogeny result in the error rates of 5%, which is the expected type I error rate if conventional (nonphylogenetic) analyses are used. Type I error rates can be higher than 25% if the data shows a strong phylogenetic structure (for one obvious example where the correlation between two traits is incorrectly inferred, see Fig. 4). Modified, with permission, from Garland et al. 101.



Figure 6.

Branch lengths in comparative analyses. The branches of a phylogeny indicate the elapsed time between speciation events, but the degree of phenotypic similarity expected among species (which is the main concern in a comparative dataset) will depend on the elapsed time and on the evolutionary model of character evolution. Under an evolutionary model of Brownian motion, the “expected variance of character change” is proportional to elapsed time. Alternatively, the expected variance is not directly proportional to time when rates of character change accelerate (gray arrow) or decelerate in time (black arrow), which affects the relative contribution of recent versus past evolution to the overall distribution of phenotypes. Accelerating evolutionary rates results in less phylogenetic signal than expected from Brownian motion because most phenotypic variation reflects recent evolutionary history, whereas decelerating rates generate the opposite pattern. A star phylogeny depicts the extreme case in which all effects of shared ancestry and past evolution have been blurred by recent phenotypic evolution (at the tips of the phylogeny). Many evolutionary processes, such as divergent or stabilizing selection, can account for a nonlinear association between character change and time. Modified, with permission, from Diniz‐Filho 71.



Figure 7.

Randomization analysis to test for significant phylogenetic signal in continuous and categorical traits, illustrated with the distribution of body mass and diet categories (white = omnivore, gray = granivore, black = herbivore) of rodent species. Phylogenetic similarity was estimated as the variance of phylogenetic independent contrasts for the continuous trait (log10‐transformed body mass) 29 and as the minimum number of transitions according to unrestrained parsimony (i.e., all transitions are possible) for the categorical trait diet 196. These indexes are then calculated after shuffling the phenotypic characters across the tips of the phylogeny, breaking any phylogenetic structure and providing a null random distribution of n replicates (n = 999 in this example) where phylogenetic signal is absent. The histograms illustrate how both indexes calculated in the real dataset (represented by the arrows) fall consistently below randomizations, hence one can conclude that the distribution of body mass and diet shows significant phylogenetic signal across rodent species (P < 0.001 in both cases). Data and phylogeny, with permission, from Rezende et al. 261.



Figure 8.

Branch length transformation analyses to test for the presence of phylogenetic signal attempt to find the phylogenetic structure that best fits the data. A phylogeny can be described as a matrix of variance‐covariance describing the expected residual distribution of the comparative data. The diagonals depict the expected phenotypic variance (i.e., how species are expected to differ from the overall mean) that, under Brownian motion, corresponds to the total distance from the root to the tips. The off‐diagonals provide the expected phenotypic covariance among species (or how they are expected to resemble each other due to shared ancestry), which corresponds to the total amount of evolutionary history shared by each pair of species. Because the amount of phylogenetic signal essentially encapsulates the amount of phenotypic covariance among species, parameters such as λ that affect the degree of hierarchy of a phylogeny by manipulating the length of the internal branches can be employed to estimate which phylogeny best fits the data (see Fig. 9). In this hypothetical example, the phylogeny that best fits trait A is very hierarchical (λ = 1) suggesting that close relatives tend to resemble each other for this trait. Conversely, the phylogeny that best fits the distribution of trait B shows no hierarchy (λ = 0), suggesting that signal in trait B is negligible. A log‐likelihood ratio test comparing the likelihoods of models when λ = 0 versus λ = 1 can be employed for significance testing (not shown). Adapted from Freckleton et al. 93 (see also 256), with permission of the University of Chicago Press.



Figure 9.

Varying degrees of hierarchy in phylogenetic trees expressed as matrices of phenotypic variance‐covariance among species, illustrated for the phylogeny in Figure 8. The expected amount of phenotypic similarity due to shared ancestry decreases as λ approaches zero, and as when λ = 0 the matrix of phenotypic variance‐covariance converges to the identity matrix (i.e., the expected residual distribution of conventional statistical analyses). In other words, comparative studies employing conventional analyses inherently assume that the data does not exhibit phylogenetic signal and species provide independent sources of information, which may or may not be true (compare traits A and B in Fig. 8).



Figure 10.

Calculation of phylogenetic independent contrasts for two hypothetical variables X and Y. Contrasts estimate the amount of phenotypic divergence across sister lineages standardized by the amount of time they had to diverge (the square root of the sum of the two branches). The algorithm runs iteratively from the tips to the root of the phylogeny, transforming n phenotypic measurements that are not independent in n–1 contrast that are statistically independent. Because phenotypic estimates at intermediate nodes (X' and Y') are not measured, but inferred from the tip data, divergence times employed to calculate these contrasts include an additional component of variance that reflects the uncertainty associated with these estimates. In practice, this involves lengthening the branches (dashed lines) by an amount that, assuming Brownian motion, can be calculated as (daughter branch length 1 × daughter branch length 2) / (daughter branch length 1 + daughter branch length 2). As a result, the association between the hypothetical phenotypic variables X and Y analyzed employing conventional statistics and independent contrasts may seem remarkably different, as shown in the bottom panels. Because independent contrasts estimate phenotypic divergence after speciation and are expressed as deviations from zero (i.e., the daughter lineages were initially phenotypically identical), correlation and regression analyses employing contrasts do not include an intercept term and must be always calculated through the origin 82,105,175. Note that the sign of each contrast is arbitrary; hence many studies have adopted the convention to give a positive sign to contrasts in the x‐axis and invert the sign of the contrast in the y‐axis accordingly (this procedure does not affect regression or correlation analyses through the origin; see 105). Even though the classic algorithm to calculate contrasts neglects important sources of uncertainty such as individual variation and measurement error, recent methods can account for these sources of error 87,157,206.



Figure 11.

Correlated evolution and grade shifts in comparative data, plotted in raw dimensions and employing independent contrasts. The semicircular canal system in mammals (Rod = rodentia, Prim = primates, Cet = cetacea, Art = artiodactyla, Car = carnivora, Chir = chiroptera) contributes to stabilization and balances during locomotion and varies positively with body size. The highly derived system of cetaceans (close symbols) seems to have evolved as an adaptation to an aquatic environment, and not taking this fact into account (i.e., pooling all species during analyses) would result in an underestimation of the allometric slope of the semicircular canal system across mammals. A regression with independent contrasts controls for this problem during scaling analyses and also extracts the region of the phylogeny where the grade shift has occurred (gray symbol), since the node separating cetaceans from their artiodactyl sister lineage falls outside the 99% prediction interval for a new observation when it is removed from the analysis. Modified, with permission, from Spoor et al. 289, the phylogeny was built employing the mammalian supertree reconstructed by Beck et al. 19.



Figure 12.

The general approach to study the evolution of categorical traits. Different evolutionary models can be emulated by varying the rates of transitions q1 and q2 between categorical states: evolution is reversible when transitions in both directions are possible (i.e., the probability that a transition occurs is different from zero for q1 and q2), and irreversible when the probability of a transition in one of the directions is constrained. Different set of rules about transformations between states can have a major impact on the analytical results, as shown for the ancestral reconstruction performed with maximum likelihood assuming a Markov model of evolution (see text). In the first case, transition rates are assumed to be identical (q1 = q2), resulting in equiprobable ancestral states in all nodes, which contrasts dramatically with the outcome of the same analysis assuming that evolution is irreversible.



Figure 13.

The statistical power to detect associations will depend on the degree of overlap between the independent variable of study and phylogeny relations among species. Left panel. Hypothesized phylogenetic relationships between arctic (open symbols) and tropical (black symbols) mammalian species 276, where it is relatively simple to discriminate between phylogenetic and environmental effects. Right panel. The worst‐case scenario for a comparative analysis, because all carnivore species (black symbols) are clustered within Carnivora while all herbivores (open symbols) are ungulates 98 (adapted, with permission, from the University of Chicago Press). In this case, any potential effect of diet will be highly confounded with phylogeny.



Figure 14.

Relationship between maximum metabolic rates during thermogenesis (MMR), body mass, and environmental temperature for rodents across the world. The residual variation in MMR—obtained from a regular regression because this trait did not exhibit phylogenetic signal—is significantly correlated with ambient temperature, suggesting that interspecific variation in this trait may be partly explained by thermal adaptation. Modified, with permission, from Rezende et al. 261.



Figure 15.

Phylogenetic analyses can detect differences in rates of phenotypic evolution between clades, as illustrated for body mass in passerines (open symbols) and nonpasserines (black symbols). Passerines show a more homogeneous distribution (i.e., lower variance) of body mass than nonpasserines, which is supported by statistical comparisons between absolute standardized contrasts (P < 0.001) after excluding the contrast between passerines and their nonpasserine sister clade (gray symbol). Assuming that branch lengths accurately reflect elapsed times, this pattern supports a lower rate of evolution of body mass in passerines. Modified from Garland and Ives 107, with permission of the University of Chicago Press, original data from Reynolds and Lee 260.

 1. Abouheif E. A method for testing the assumption of phylogenetic independence in comparative data. Evol Ecol Res 1: 895‐909, 1999.
 2. Ackerly D. Conservatism and diversification of plant functional traits: evolutionary rates versus phylogenetic signal. Proc Natl Acad Sci U S A 106: 19699‐19706, 2009.
 3. Ackerly DD. Taxon sampling, correlated evolution, and independent contrasts. Evolution 54: 1480‐1492, 2000.
 4. Adams DC. Phylogenetic meta‐analysis. Evolution 62: 567‐572, 2008.
 5. Al‐kahtani MA, Zuleta C, Caviedes‐Vidal E, Garland T. Kidney mass and relative medullary thickness of rodents in relation to habitat, body size, and phylogeny. Physiol Biochem Zool 77: 346‐365, 2004.
 6. Alerstam T, Rosen M, Backman J, Ericson PGP, Hellgren O. Flight speeds among bird species: allometric and phylogenetic effects. Plos Biol 5: 1656‐1662, 2007.
 7. Alexander HJ, Taylor JS, Wu SST, Breden F. Parallel evolution and vicariance in the guppy (Poecilia reticulata) over multiple spatial and temporal scales. Evolution 60: 2352‐2369, 2006.
 8. Alfaro ME, Holder MT. The posterior and the prior in Bayesian phylogenetics. Annu Rev Ecol Evol S 37: 19‐42, 2006.
 9. Alfaro ME, Huelsenbeck JP. Comparative performance of Bayesian and AIC‐based measures of phylogenetic model uncertainty. Syst Biol 55: 89‐96, 2006.
 10. Alstrom P, Ericson PGP, Olsson U, Sundberg P. Phylogeny and classification of the avian superfamily Sylvioidea. Mol Phylogenet Evol 38: 381‐397, 2006.
 11. Angilletta MJ. Thermal Adaptation: A Theoretical and Empirical Synthesis New York: Oxford University Press, 2009.
 12. Arnold C, Nunn CL. Phylogenetic targeting of research effort in evolutionary biology. Am Nat 176: 601‐612, 2010.
 13. Arnold SJ. Morphology, performance and fitness. Am Zool 23: 347‐361, 1983.
 14. Ashton KG. Comparing phylogenetic signal in intraspecific and interspecific body size datasets. J Evol Biol 17: 1157‐1161, 2004.
 15. Banavar JR, Damuth J, Maritan A, Rinaldo A. Ontogenetic growth: modelling universality and scaling. Nature 420: 626‐626, 2002.
 16. 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.
 17. Barker FK, Cibois A, Schikler P, Feinstein J, Cracraft J. Phylogeny and diversification of the largest avian radiation. Proc Natl Acad Sci U S A 101: 11040‐11045, 2004.
 18. Bauwens D, Garland T, Castilla AM, Vandamme R. Evolution of sprint speed in lacertid lizards: morphological, physiological, and behavioral covariation. Evolution 49: 848‐863, 1995.
 19. Beck RMD, Bininda‐Emonds ORP, Cardillo M, Liu FGR, Purvis A. A higher‐level MRP supertree of placental mammals. BMC Evol Biol 6: 93, 2006.
 20. Bennett AF. Adaptation and the evolution of physiological characters. In: Dantzler WH, editor. Handbook of Physiology: Comparative Physiology, New York: Oxford University Press, 1997, sect. 13, pp. 3‐16.
 21. Bennett AF, Ruben JA. Endothermy and activity in vertebrates. Science 206: 649‐654, 1979.
 22. Berenbrink M. Historical reconstructions of evolving physiological complexity: O2 secretion in the eye and swimbladder of fishes. J Exp Biol 210: 1641‐1652, 2007.
 23. Berenbrink M, Koldkjaer P, Kepp O, Cossins AR. Evolution of oxygen secretion in fishes and the emergence of a complex physiological system. Science 307: 1752‐1757, 2005.
 24. Bininda‐Emonds ORP, Gittleman JL, Purvis A. Building large trees by combining phylogenetic information: a complete phylogeny of the extant Carnivora (Mammalia). Biol Rev 74: 143‐175, 1999.
 25. Birdsey GM, Lewin J, Holbrook JD, Simpson VR, Cunningham AA, Danpure CJ. A comparative analysis of the evolutionary relationship between diet and enzyme targeting in bats, marsupials and other mammals. Proc R Soc B 272: 833‐840, 2005.
 26. Bishop CM. The maximum oxygen consumption and aerobic scope of birds and mammals: getting to the heart of the matter. Proc R Soc B 266: 2275‐2281, 1999.
 27. Block BA, Finnerty JR, Stewart AFR, Kidd J. Evolution of endothermy in fish: mapping physiological traits on a molecular phylogeny. Science 260: 210‐214, 1993.
 28. Blomberg SP, Garland T. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J Evol Biol 15: 899‐910, 2002.
 29. Blomberg SP, Garland T, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57: 717‐745, 2003.
 30. Bonine KE, Gleeson TT, Garland T. Comparative analysis of fiber‐type composition in the lliofibularis muscle of Phrynosomatid lizards (Squamata). J Morphol 250: 265‐280, 2001.
 31. Bozinovic F, Rosenmann M. Maximum metabolic‐rate of rodents: physiological and ecological consequences on distributional limits. Funct Ecol 3: 173‐181, 1989.
 32. Breuner CW, Lynn SE, Julian GE, Cornelius JM, Heidinger BJ, Love OP, Sprague RS, Wada H, Whitman BA. Plasma‐binding globulins and acute stress response. Horm Metab Res 38: 260‐268, 2006.
 33. Brooks DR, McLennan DA. Phylogeny, Ecology and Behavior: A Research Program in Comparative Biology. Chicago: The University of Chicago Press, 1991.
 34. Buchwalter DB, Cain DJ, Martin CA, Xie L, Luoma SN, Garland T. Aquatic insect ecophysiological traits reveal phylogenetically based differences in dissolved cadmium susceptibility. Proc Natl Acad Sci U S A 105: 8321‐8326, 2008.
 35. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information‐Theoretic Approach. New York: Springer, 2002.
 36. Butler MA, King AA. Phylogenetic comparative analysis: a modeling approach for adaptive evolution. Am Nat 164: 683‐695, 2004.
 37. Butler MA, Losos JB. Testing for unequal amounts of evolution in a continuous character on different branches of a phylogenetic tree using linear and squared‐change parsimony: an example using lesser antillean Anolis lizards. Evolution 51: 1623‐1635, 1997.
 38. Butler MA, Schoener TW, Losos JB. The relationship between sexual size dimorphism and habitat use in Greater Antillean Anolis Lizards. Evolution 54: 259‐272, 2000.
 39. Calder WA. Size, Function, and Life‐History. Cambridge: Harvard University press, 1996.
 40. Capellini I, Venditti C, Barton RA. Phylogeny and metabolic scaling in mammals. Ecology 91: 2783‐2793, 2010.
 41. Cardillo M, Bininda‐Emonds ORP, Boakes E, Purvis A. A species‐level phylogenetic supertree of marsupials. J Zool 264: 11‐31, 2004.
 42. Careau V, Morand‐Ferron J, Thomas D. Basal metabolic rate of canidae from hot deserts to cold arctic climates. J Mammal 88: 394‐400, 2007.
 43. Chappell MA, Hayes JP, Snyder LRG. Hemoglobin polymorphisms in deer mice (Peromyscus maniculatus): physiology of beta‐globin variants and alpha‐globin recombinants. Evolution 42: 681‐688, 1988.
 44. Chappell MA, Snyder LRG. Biochemical and physiological correlates of deer mouse alpha‐chain hemoglobin polymorphisms. Proc Natl Acad Sci U S A 81: 5484‐5488, 1984.
 45. Cheverud JM, Dow MM. An auto‐correlation analysis of genetic variation due to lineal fission in social groups of Rhesus macaques. Am J Phys Anthropol 67: 113‐121, 1985.
 46. Cheverud JM, Dow MM, Leutenegger W. The quantitative assessment of phylogenetic constraints in comparative analyses: sexual dimorphism in body‐weight among primates. Evolution 39: 1335‐1351, 1985.
 47. Chown SL, Jumbam KR, Sorensen JG, Terblanche JS. Phenotypic variance, plasticity and heritability estimates of critical thermal limits depend on methodological context. Funct Ecol 23: 133‐140, 2009.
 48. Claude J, Pritchard PCH, Tong HY, Paradis E, Auffray JC. Ecological correlates and evolutionary divergence in the skull of turtles: a geometric morphometric assessment. Syst Biol 53: 933‐948, 2004.
 49. Clauss M, Hofmann RR, Fickel J, Streich WJ, Hummel J. The intraruminal papillation gradient in wild ruminants of different feeding types: implications for rumen physiology. J Morphol 270: 929‐942, 2009.
 50. Clusella‐Trullas S, Terblanche JS, Blackburn TM, Chown SL. Testing the thermal melanism hypothesis: a macrophysiological approach. Funct Ecol 22: 232‐238, 2008.
 51. Coddington JA. Cladistic tests of adaptational hypotheses. Cladistics 4: 3‐22, 1988.
 52. Collar DC, Near TJ, Wainwright PC. Comparative analysis of morphological diversity: does disparity accumulate at the same rate in two lineages of centrarchid fishes? Evolution 59: 1783‐1794, 2005.
 53. Cooper N, Purvis A. Body size evolution in mammals: complexity in tempo and mode. Am Nat 175: 727‐738, 2010.
 54. Corl A, Davis AR, Kuchta SR, Comendant T, Sinervo B. Alternative mating strategies and the evolution of sexual size dimorphism in the side‐blotched lizard, Uta stansburiana: a population‐level comparative analysis. Evolution 64: 79‐96, 2010.
 55. Cubo J, Ponton F, Laurin M, de Margerie E, Castanet J. Phylogenetic signal in bone microstructure of sauropsids. Syst Biol 54: 562‐574, 2005.
 56. Cunningham CW, Omland KE, Oakley TH. Reconstructing ancestral character states: a critical reappraisal. Trends Ecol Evol 13: 361‐366, 1998.
 57. Darst CR, Menendez‐Guerrero PA, Coloma LA, Cannatella DC. Evolution of dietary specialization and chemical defense in poison frogs (Dendrobatidae): a comparative analysis. Am Nat 165: 56‐69, 2005.
 58. 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.
 59. Davis EB. Comparison of climate space and phylogeny of Marmota (Mammalia: Rodentia) indicates a connection between evolutionary history and climate preference. Proc R Soc B 272: 519‐526, 2005.
 60. 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.
 61. de Queiroz A, Gatesy J. The supermatrix approach to systematics. Trends Ecol Evol 22: 34‐41, 2007.
 62. Demere TA, Mcgowen MR, Berta A, Gatesy J. Morphological and molecular evidence for a stepwise evolutionary transition from teeth to baleen in mysticete whales. Syst Biol 57: 15‐37, 2008.
 63. Desdevises Y, Legendre P, Azouzi L, Morand S. Quantifying phylogenetically structured environmental variation. Evolution 57: 2647‐2652, 2003.
 64. Dial KP. Wing‐assisted incline running and the evolution of flight. Science 299: 402‐404, 2003.
 65. Dial KP, Jackson BE, Segre P. A fundamental avian wing‐stroke provides a new perspective on the evolution of flight. Nature 451: U985‐U983, 2008.
 66. Dial KP, Randall RJ, Dial TR. What use is half a wing in the ecology and evolution of birds? Bioscience 56: 437‐445, 2006.
 67. Diaz GB, Ojeda RA, Rezende EL. Renal morphology, phylogenetic history and desert adaptation of South American hystricognath rodents. Funct Ecol 20: 609‐620, 2006.
 68. Diaz‐Uriarte R, Garland T. Testing hypotheses of correlated evolution using phylogenetically independent contrasts: sensitivity to deviations from Brownian motion. Syst Biol 45: 27‐47, 1996.
 69. Diaz‐Uriarte R, Garland T. Effects of branch length errors on the performance of phylogenetically independent contrasts. Syst Biol 47: 654‐672, 1998.
 70.Diniz‐Filho JAF. Phylogenetic autocorrelation under distinct evolutionary processes. Evolution 55: 1104‐1109, 2001.
 71. Diniz‐Filho JAF. Phylogenetic diversity and conservation priorities under distinct models of phenotypic evolution. Conserv Biol 18: 698‐704, 2004.
 72. Diniz‐Filho JAF, De Sant'ana CER, Bini LM. An eigenvector method for estimating phylogenetic inertia. Evolution 52: 1247‐1262, 1998.
 73. Diniz‐Filho JAF, Bini LM, Rangel TF, Morales‐Castilla I, Olalla‐Tarraga MA, Rodriguez MA, Hawkins BA. On the selection of phylogenetic eigenvectors for ecological analyses. Ecography (in press), 2011. DOI: 10.1111/j.1600‐0587.2011.06949.x.
 74. Diniz‐Filho JAF, Torres NM. Phylogenetic comparative methods and the geographic range size: body size relationship in new world terrestrial carnivora. Evol Ecol 16: 351‐367, 2002.
 75. Doughty P. Statistical analysis of natural experiments in evolutionary biology: comments on recent criticisms of the use of comparative methods to study adaptation. Am Nat 148: 943‐956, 1996.
 76. Dumont JPC, Robertson RM. Neuronal circuits: an evolutionary perspective. Science 233: 849‐853, 1986.
 77. Dunbar RIM. Adaptation, fitness, and the evolutionary tautology. In: Sociobiology KsC., editor. Current Problems in Sociobiology, Cambridge: Cambridge University Press, 1982, pp. 9‐28.
 78. Duncan RP, Forsyth DM, Hone J. Testing the metabolic theory of ecology: allometric scaling exponents in mammals. Ecology 88: 324‐333, 2007.
 79. Dutenhoffer MS, Swanson DL. Relationship of basal to summit metabolic rate in passerine birds and the aerobic capacity model for the evolution of endothermy. Physiol Zool 69: 1232‐1254, 1996.
 80. Elgar MA, Harvey PH. Basal metabolic rates in mammals: allometry, phylogeny and ecology. Funct Ecol 1: 25‐36, 1987.
 81. Feder ME, Bennett AF, Huey RB. Evolutionary physiology. Annu Rev Ecol Syst 31: 315‐341, 2000.
 82. Felsenstein J. Phylogenies and the comparative method. Am Nat 125: 1‐15, 1985.
 83. Felsenstein J. Phylogenies and quantitative characters. Annu Rev Ecol Syst 19: 445‐471, 1988.
 84. Felsenstein J. Contrasts for a within‐species comparative method. In: Slatkin M, Veuille M, editors. Modern Developments in Theoretical Population Genetics. Oxford: Oxford University Press, 2002, pp. 118‐129.
 85. Felsenstein J. Inferring Phylogenies. Sunderland, MA: Sinauer Associates, Inc, 2004.
 86. Felsenstein J. Using the quantitative genetic threshold model for inferences between and within species. Philos Trans R Soc B 360: 1427‐1434, 2005.
 87. Felsenstein J. Comparative methods with sampling error and within‐species variation: contrasts revisited and revised. Am Nat 171: 713‐725, 2008.
 88. Finarelli JA, Flynn JJ. Ancestral state reconstruction of body size in the Caniformia (Carnivora, Mammalia): the effects of incorporating data from the fossil record. Syst Biol 55: 301‐313, 2006.
 89. Finarelli JA, Flynn JJ. Brain‐size evolution and sociality in Carnivora. Proc Natl Acad Sci U S A 106: 9345‐9349, 2009.
 90. Fisher DO, Owens IPF. The comparative method in conservation biology. Trends Ecol Evol 19: 391‐398, 2004.
 91. 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.
 92. Freckleton RP, Harvey PH. Detecting non‐Brownian trait evolution in adaptive radiations. Plos Biol 4: 2104‐2111, 2006.
 93. Freckleton RP, Harvey PH, Pagel M. Phylogenetic analysis and comparative data: a test and review of evidence. Am Nat 160: 712‐726, 2002.
 94. Freckleton RP, Phillimore AB, Pagel M. Relating traits to diversification: a simple test. Am Nat 172: 102‐115, 2008.
 95. Freeman S, Herron JC. Evolutionary Analysis. Upper Saddle River, New Jersey: Pearson/Prentice Hall, 2004.
 96. Fry BG, Roelants K, Norman JA. Tentacles of venom: toxic protein convergence in the kingdom animalia. J Mol Evol 68: 311‐321, 2009.
 97. Fry BG, Vidal N, Norman JA, Vonk FJ, Scheib H, Ramjan SFR, Kuruppu S, Fung K, Hedges SB, Richardson MK, Hodgson WC, Ignjatovic V, Summerhayes R, Kochva E. Early evolution of the venom system in lizards and snakes. Nature 439: 584‐588, 2006.
 98. Garland T. Rate tests for phenotypic evolution using phylogenetically independent contrasts. Am Nat 140: 509‐519, 1992.
 99. Garland T. Phylogenetic comparison and artificial selection: two approaches in evolutionary physiology. Adv Exp Med Biol 502: 107‐132, 2001.
 100. Garland T, Adolph SC. Why not to do 2‐species comparative studies: limitations on inferring adaptation. Physiol Zool 67: 797‐828, 1994.
 101. Garland T, Bennett AF, Rezende EL. Phylogenetic approaches in comparative physiology. J Exp Biol 208: 3015‐3035, 2005.
 102. Garland T, Carter PA. Evolutionary physiology. Annu Rev Physiol 56: 579‐621, 1994.
 103. Garland T, Diaz‐Uriarte R. Polytomies and phylogenetically independent contrasts: examination of the bounded degrees of freedom approach. Syst Biol 48: 547‐558, 1999.
 104. Garland T, Dickerman AW, Janis CM, Jones JA. Phylogenetic analysis of covariance by computer simulation. Syst Biol 42: 265‐292, 1993.
 105. Garland T, Harvey PH, Ives AR. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst Biol 41: 18‐32, 1992.
 106. Garland T, Huey RB, Bennett AF. Phylogeny and coadaptation of thermal physiology in lizards: a reanalysis. Evolution 45: 1969‐1975, 1991.
 107. Garland T, Ives AR. Using the past to predict the present: confidence intervals for regression equations in phylogenetic comparative methods. Am Nat 155: 346‐364, 2000.
 108. Garland T, Janis CM. Does metatarsal femur ratio predict maximal running speed in cursorial mammals? J Zool 229: 133‐151, 1993.
 109. Garland T, Midford PE, Ives AR. An introduction to phylogenetically based statistical methods, with a new method for confidence intervals on ancestral values. Am Zool 39: 374‐388, 1999.
 110. Gartner GEA, Hicks JW, Manzani PR, Andrade DV, Abe AS, Wang T, Secor SM, Garland T. Phylogeny, ecology, and heart position in snakes. Physiol Biochem Zool 83: 43‐54, 2010.
 111. Giannini NP, Goloboff PA. Delayed‐response phylogenetic correlation: an optimization‐based method to test covariation of continuous characters. Evolution 64: 1885‐1898, 2010.
 112. Gillooly JF, Allen AP. Changes in body temperature influence the scaling of VO2max and aerobic scope in mammals. Biol Lett 3: 99‐102, 2007.
 113. Gillooly JF, Allen AP, Charnov EL. Dinosaur fossils predict body temperatures. Plos Biol 4: 1467‐1469, 2006.
 114. Gittleman JL, Kot M. Adaptation: statistics and a null model for estimating phylogenetic effects. Syst Zool 39: 227‐241, 1990.
 115. Gittleman JL, Luh HK. On comparing comparative methods. Annu Rev Ecol Syst 23: 383‐404, 1992.
 116. Glazier DS. Beyond the ‘3/4‐power law’: variation in the intra‐ and interspecific scaling of metabolic rate in animals. Biol Rev 80: 611‐662, 2005.
 117. Glazier DS. Effects of metabolic level on the body size scaling of metabolic rate in birds and mammals. Proc R Soc B 275: 1405‐1410, 2008.
 118. Glor RE. Phylogenetic insights on adaptive radiation. Annu Rev Ecol Syst 41: 251‐270, 2010.
 119. Goldberg EE, Igic B. On phylogenetic tests of irreversible evolution. Evolution 62: 2727‐2741, 2008.
 120. Golding GB, Dean AM. The structural basis of molecular adaptation. Mol Biol Evol 15: 355‐369, 1998.
 121. Gomes FR, Rezende EL, Grizante MB, Navas CA. The evolution of jumping performance in anurans: morphological correlates and ecological implications. J Evol Biol 22: 1088‐1097, 2009.
 122. Gould SJ, Lewontin RC. Spandrels of San Marco and the panglossian paradigm: a critique of the adaptationist program. Proc R Soc B 205: 581‐598, 1979.
 123. Gould SJ, Vrba ES. Exaptation: a missing term in the science of form. Paleobiology 8: 4‐15, 1982.
 124. Grafen A. The phylogenetic regression. Philos Trans R Soc B 326: 119‐157, 1989.
 125. Grafen A. The uniqueness of the phylogenetic regression. J Theor Biol 156: 405‐423, 1992.
 126. Grafen A, Ridley M. Statistical tests for discrete cross‐species data. J Theor Biol 183: 255‐267, 1996.
 127. Grueber CE, Nakagawa S, Laws RJ, Jamieson IG. Multimodel inference in ecology and evolution: challenges and solutions. J Evol Biol 24: 699‐711, 2011.
 128. Hahn TP, MacDougall‐Shackleton SA. Adaptive specialization, conditional plasticity and phylogenetic history in the reproductive cue response systems of birds. Philos Trans R Soc B 363: 267‐286, 2008.
 129. Halsey LG, Butler PJ, Blackburn TM. A phylogenetic analysis of the allometry of diving. Am Nat 167: 276‐287, 2006.
 130. Hansen TF. Stabilizing selection and the comparative analysis of adaptation. Evolution 51: 1341‐1351, 1997.
 131. Hansen TF, Martins EP. Translating between microevolutionary process and macroevolutionary patterns: the correlation structure of interspecific data. Evolution 50: 1404‐1417, 1996.
 132. Hansen TF, Pienaar J, Orzack SH. A comparative method for studying adaptation to a randomly evolving environment. Evolution 62: 1965‐1977, 2008.
 133. Harmon LJ, Glor RE. Poor statistical performance of the Mantel test in phylogenetic comparative analyses. Evolution 64: 2173‐2178, 2010.
 134. Harmon LJ, Kolbe JJ, Cheverud JM, Losos JB. Convergence and the multidimensional niche. Evolution 59: 409‐421, 2005.
 135. Harmon LJ, Losos JB. The effect of intraspecific sample size on type I and type II error rates in comparative studies. Evolution 59: 2705‐2710, 2005.
 136. Harmon LJ, Losos JB, Davies TJ, Gillespie RG, Gittleman JL, Jennings WB, Kozak KH, McPeek MA, Moreno‐Roark F, Near TJ, Purvis A, Ricklefs RE, Schluter D, Schulte JA, Seehausen O, Sidlauskas BL, Torres‐Carvajal O, Weir JT, Mooers AO. Early bursts of body size and shape evolution are rare in comparative data. Evolution 64: 2385‐2396, 2010.
 137. Harvey PH, Pagel MD. The Comparative Method in Evolutionary Biology. Oxford: Oxford University Press, 1991.
 138. Harvey PH, Purvis A. Comparative methods for explaining adaptations. Nature 351: 619‐624, 1991.
 139. Harvey PH, Rambaut A. Comparative analyses for adaptive radiations. Philos Trans R Soc B 355: 1599‐1605, 2000.
 140. Hochachka PW, Somero GN. Biochemical Adaptation: Mechanism and Process in Physiological Evolution. New York: Oxford University Press, 2002.
 141. Hodges WL. Evolution of viviparity in horned lizards (Phrynosoma): testing the cold‐climate hypothesis. J Evol Biol 17: 1230‐1237, 2004.
 142. Hohenlohe PA, Arnold SJ. MIPoD: a hypothesis‐testing framework for microevolutionary inference from patterns of divergence. Am Nat 171: 366‐385, 2008.
 143. Holmes DJ, Kristan DM. Comparative and alternative approaches and novel animal models for aging research. Age 30: 63‐73, 2008.
 144. Hopkins SR, Powell FL. Common themes of adaptation to hypoxia: insights from comparative physiology. Adv Exp Med Biol 502: 153‐167, 2001.
 145. Housworth EA, Martins EP. Random sampling of constrained phylogenies: conducting phylogenetic analyses when the phylogeny is partially known. Syst Biol 50: 628‐639, 2001.
 146. Housworth EA, Martins EP, Lynch M. The phylogenetic mixed model. Am Nat 163: 84‐96, 2004.
 147. Huelsenbeck JP, Rannala B. Detecting correlation between characters in a comparative analysis with uncertain phylogeny. Evolution 57: 1237‐1247, 2003.
 148. Huelsenbeck JP, Rannala B, Masly JP. Accommodating phylogenetic uncertainty in evolutionary studies. Science 288: 2349‐2350, 2000.
 149. Huey RB. Phylogeny, history, and the comparative method. New Directions in Ecological Physiology. New York: Cambridge University Press, 1987, pp. 79‐98.
 150. Huey RB, Bennett AF. A comparative approach to field and laboratory studies in evolutionary biology. In: Predator‐Prey Relationships: Perspectives and Approaches from the Study of Lower Vertebrates. Chicago: Chicago University Press, 1986, pp. 82‐98.
 151. Huey RB, Bennett AF. Phylogenetic studies of coadaptation: preferred temperatures versus optimal performance temperatures of lizards. Evolution 41: 1098‐1115, 1987.
 152. Hulbert AJ, Else PL. Basal metabolic rate: history, composition, regulation, and usefulness. Physiol Biochem Zool 77: 869‐876, 2004.
 153. Hulbert AJ, Else PL. Membranes and the setting of energy demand. J Exp Biol 208: 1593‐1599, 2005.
 154. Hulbert AJ, Faulks S, Buttemer WA, Else PL. Acyl composition of muscle membranes varies with body size in birds. J Exp Biol 205: 3561‐3569, 2002.
 155. Hunter JP, Jernvall J. The hypocone as a key innovation in mammalian evolution. Proc Natl Acad Sci U S A 92: 10718‐10722, 1995.
 156. Ives AR, Garland T. Phylogenetic logistic regression for binary dependent variables. Syst Biol 59: 9‐26, 2010.
 157. Ives AR, Midford PE, Garland T. Within‐species variation and measurement error in phylogenetic comparative methods. Syst Biol 56: 252‐270, 2007.
 158. Johnson JB, Omland KS. Model selection in ecology and evolution. Trends Ecol Evol 19: 101‐108, 2004.
 159. Johnston IA, Fernandez DA, Calvo J, Vieira VLA, North AW, Abercromby M, Garland T. Reduction in muscle fibre number during the adaptive radiation of notothenioid fishes: a phylogenetic perspective. J Exp Biol 206: 2595‐2609, 2003.
 160. Kelchner SA, Thomas MA. Model use in phylogenetics: nine key questions. Trends Ecol Evol 22: 87‐94, 2007.
 161. Kelly SA, Panhuis TM, Stoehr AM. Phenotypic plasticity: molecular mechanisms and adaptive significance. Comp Physiol (in press), 2012.
 162. Koteja P, Weiner J. Mice, voles and hamsters: metabolic rates and adaptive strategies in muroid rodents. Oikos 66: 505‐514, 1993.
 163. Kozak KH, Larson AA, Bonett RM, Harmon LJ. Phylogenetic analysis of ecomorphological divergence, community structure, and diversification rates in dusky salamanders (Plethodontidae: Desmognathus). Evolution 59: 2000‐2016, 2005.
 164. Kram R, Taylor CR. Energetics of running: a new perspective. Nature 346: 265‐267, 1990.
 165. Krebs HA. Krogh's principle: for many problems there is an animal on which it can be most conveniently studied. J Exp Zool 194: 221‐226, 1975.
 166. Lajeunesse MJ. Meta‐analysis and the comparative phylogenetic method. Am Nat 174: 369‐381, 2009.
 167. Lambert AJ, Boysen HM, Buckingham JA, Yang T, Podlutsky A, Austad SN, Kunz TH, Buffenstein R, Brand MD. Low rates of hydrogen peroxide production by isolated heart mitochondria associate with long maximum lifespan in vertebrate homeotherms. Aging Cell 6: 607‐618, 2007.
 168. Lauder GV. Form and function: structural analysis in evolutionary morphology. Paleobiology 7: 430‐442, 1981.
 169. Lauder GV, Leroi AM, Rose MR. Adaptations and history. Trends Ecol Evol 8: 294‐297, 1993.
 170. Laurin M. The evolution of body size, Cope's rule and the origin of amniotes. Syst Biol 53: 594‐622, 2004.
 171. Lavin SR, Karasov WH, Ives AR, Middleton KM, Garland T. Morphometrics of the avian small intestine compared with that of nonflying mammals: a phylogenetic approach. Physiol Biochem Zool 81: 526‐550, 2008.
 172. Lee CE. Rapid and repeated invasions of fresh water by the copepod. Evolution 53: 1423‐1434, 1999.
 173. Lee CH, Blay S, Mooers AO, Singh A, Oakley TH. CoMET: a mesquite package for comparing models of continuous character evolution on phylogenies. Evol Bioinform 2: 183‐186, 2006.
 174. Lee MSY. Molecular evidence and marine snake origins. Biol Lett 1: 227‐230, 2005.
 175. Legendre P, Desdevises Y. Independent contrasts and regression through the origin. J Theor Biol 259: 727‐743, 2009.
 176. Leroi AM, Rose MR, Lauder GV. What does the comparative method reveal about adaptation? Am Nat 143: 381‐402, 1994.
 177. Lewis PO. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst Biol 50: 913‐925, 2001.
 178. Liefting M, Hoffmann AA, Ellers J. Plasticity versus environmental canalization: population differences in thermal responses along a latitudinal gradient in Drosophila serrata. Evolution 63: 1954‐1963, 2009.
 179. Lindenfors P, Revell LJ, Nunn CL. Sexual dimorphism in primate aerobic capacity: a phylogenetic test. J Evol Biol 23: 1183‐1194, 2010.
 180. Losos JB. Uncertainty in the reconstruction of ancestral character states and limitations on the use of phylogenetic comparative methods. Anim Behav 58: 1319‐1324, 1999.
 181. Losos JB. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol Lett 11: 995‐1003, 2008.
 182. Losos JB. Seeing the forest for the trees: the limitations of phylogenies in comparative biology. Am Nat 177: 709‐727, 2011.
 183. Losos JB, Miles DB. Adaptation, constraint, and the comparative method: phylogenetic issues and methods. In: Wainwright PC, Reilly SM, editors. Ecological Morphology. Chicago: The University of Chicago Press, 1994, pp. 60‐98.
 184. Lovegrove BG. The zoogeography of mammalian basal metabolic rate. Am Nat 156: 201‐219, 2000.
 185. Ludden TM, Beal SL, Sheiner LB. Comparison of the Akaike information criterion, the Schwarz criterion and the F‐test as guides to model selection. J Pharmacokinet Biop 22: 431‐445, 1994.
 186. Lutzoni F, Pagel M, Reeb V. Major fungal lineages are derived from lichen symbiotic ancestors. Nature 411: 937‐940, 2001.
 187. Lynch M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45: 1065‐1080, 1991.
 188. MacDougall‐Shackleton SA, Stevenson TJ, Watts HE, Pereyra ME, Hahn TP. The evolution of photoperiod response systems and seasonal GnRH plasticity in birds. Integr Comp Biol 49: 580‐589, 2009.
 189. Mace R, Pagel M. The comparative method in anthropology. Curr Anthropol 35: 549‐564, 1994.
 190. Maddison DR, Maddison WP. MacClade Version 4: Analysis of Phylogeny and Character Evolution. Sunderland, MA: Sinauer Associates, 2000.
 191. Maddison WP. A method for testing the correlated evolution of 2 binary characters: are gains or losses concentrated on certain branches of a phylogenetic tree? Evolution 44: 539‐557, 1990.
 192. Maddison WP. Squared‐change parsimony reconstructions of ancestral states for continuous‐valued characters on a phylogenetic tree. Syst Zool 40: 304‐314, 1991.
 193. Maddison WP. Calculating the probability distributions of ancestral states reconstructed by parsimony on phylogenetic trees. Syst Biol 44: 474‐481, 1995.
 194. Maddison WP. Testing character correlation using pairwise comparisons on a phylogeny. J Theor Biol 202: 195‐204, 2000.
 195. Maddison WP. Confounding asymmetries in evolutionary diversification and character change. Evolution 60: 1743‐1746, 2006.
 196. Maddison WP, Slatkin M. Null models for the number of evolutionary steps in a character on a phylogenetic tree. Evolution 45: 1184‐1197, 1991.
 197. Martin RD, Genoud M, Hemelrijk CK. Problems of allometric scaling analysis: examples from mammalian reproductive biology. J Exp Biol 208: 1731‐1747, 2005.
 198. Martins EP. A comparative study of the evolution of Sceloporus push‐up displays. Am Nat 142: 994‐1018, 1993.
 199. Martins EP. Estimating the rate of phenotypic evolution from comparative data. Am Nat 144: 193‐209, 1994.
 200. Martins EP. Phylogenies, spatial autoregression, and the comparative method: a computer simulation test. Evolution 50: 1750‐1765, 1996.
 201. Martins EP. Estimation of ancestral states of continuous characters: a computer simulation study. Syst Biol 48: 642‐650, 1999.
 202. Martins EP, Diniz JAF, Housworth EA. Adaptive constraints and the phylogenetic comparative method: a computer simulation test. Evolution 56: 1‐13, 2002.
 203. Martins EP, Garland T. Phylogenetic analyses of the correlated evolution of continuous characters: a simulation study. Evolution 45: 534‐557, 1991.
 204. Martins EP, Hansen TF. A microevolutionary link between phylogenies and comparative data. In: Harvey PH, Leigh Brown AJ, Maynard Smith J, Nee S, editors. New Uses for New Phylogenies. Oxford: Oxford University Press, 1996, pp. 273‐288.
 205. Martins EP, Hansen TF. The statistical analysis of interspecific data: a review and evaluation of phylogenetic comparative methods. In: Martins E, editor. Phylogenies and the Comparative Method in Animal Behavior. Oxford: Oxford University Press, 1996, pp. 22‐75.
 206. 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.
 207. Martins EP, Housworth EA. Phylogeny shape and the phylogenetic comparative method. Syst Biol 51: 873‐880, 2002.
 208. McCracken KG, Barger CP, Bulgarella M, Johnson KP, Sonsthagen SA, Trucco J, Valqui TH, Wilson RE, Winker K, Sorenson MD. Parallel evolution in the major haemoglobin genes of eight species of Andean waterfowl. Mol Ecol 18: 3992‐4005, 2009.
 209. McKechnie AE, Freckleton RP, Jetz W. Phenotypic plasticity in the scaling of avian basal metabolic rate. Proc R Soc B 273: 931‐937, 2006.
 210. McKechnie AE, Wolf BO. The allometry of avian basal metabolic rate: good predictions need good data. Physiol Biochem Zool 77: 502‐521, 2004.
 211. Mcnab BK. Climatic adaptation in the energetics of heteromyid rodents. Comp Biochem Physiol A 62: 813‐820, 1979.
 212. McNab BK. The Physiological Ecology of Vertebrates: A View from Energetics. New York: Cornell University Press, 2002.
 213. Mcnab BK. Ecological factors affect the level and scaling of avian BMR. Comp Biochem Physiol A 152: 22‐45, 2009.
 214. Mcnab BK, Morrison P. Body temperature and metabolism in subspecies of Peromyscus from arid and mesic environments. Ecol Monogr 33: 63‐82, 1963.
 215. McPeek MA. Testing hypotheses about evolutionary change on single branches of a phylogeny using evolutionary contrasts. Am Nat 145: 686‐703, 1995.
 216. Meredith RW, Pires MN, Reznick DN, Springer MS. Molecular phylogenetic relationships and the evolution of the placenta in Poecilia (Micropoecilia) (Poeciliidae: Cyprinodontiformes). Mol Phylogenet Evol 55: 631‐639, 2010.
 217. Miles DB, Dunham AE. Historical perspectives in ecology and evolutionary biology: the use of phylogenetic comparative analyses. Annu Rev Ecol Syst 24: 587‐619, 1993.
 218. Montes L, Le Roy N, Perret M, De Buffrenil V, Castanet J, Cubo J. Relationships between bone growth rate, body mass and resting metabolic rate in growing amniotes: a phylogenetic approach. Biol J Linn Soc 92: 63‐76, 2007.
 219. Mooers AO, Schluter D. Reconstructing ancestor states with maximum likelihood: support for one‐ and two‐rate models. Syst Biol 48: 623‐633, 1999.
 220. Mooers AO, Vamosi SM, Schluter D. Using phylogenies to test macroevolutionary hypotheses of trait evolution in Cranes (Gruinae). Am Nat 154: 249‐259, 1999.
 221. Mora C, Maya MF. Effect of the rate of temperature increase of the dynamic method on the heat tolerance of fishes. J Therm Biol 31: 337‐341, 2006.
 222. Morey S, Reznick D. A comparative analysis of plasticity ln larval development in three species of spadefoot toads. Ecology 81: 1736‐1749, 2000.
 223. Munoz‐Garcia A, Williams JB. Basal metabolic rate in carnivores is associated with diet after controlling for phylogeny. Physiol Biochem Zool 78: 1039‐1056, 2005.
 224. Nagy KA. Field bioenergetics of mammals: what determines field metabolic rates? Aust J Zool 42: 43‐53, 1994.
 225. Nishikawa KC. Evolutionary convergence in nervous systems: insights from comparative phylogenetic studies. Brain Behav Evolut 59: 240‐249, 2002.
 226. Nunn CL. The Comparative Method in Evolutionary Anthropology and Biology. Chicago: University of Chicago Press, 2011.
 227. Nunn CL, Barton RA. Allometric slopes and independent contrasts: a comparative test of Kleiber's law in primate ranging patterns. Am Nat 156: 519‐533, 2000.
 228. O'Connor 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.
 229. O'Meara BC, Ane C, Sanderson MJ, Wainwright PC. Testing for different rates of continuous trait evolution using likelihood. Evolution 60: 922‐933, 2006.
 230. Oakley TH, Cunningham CW. Independent contrasts succeed where ancestor reconstruction fails in a known bacteriophage phylogeny. Evolution 54: 397‐405, 2000.
 231. Pagel M. Seeking the evolutionary regression coefficient: an analysis of what comparative methods measure. J Theor Biol 164: 191‐205, 1993.
 232. Pagel M. Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proc R Soc B 255: 37‐45, 1994.
 233. Pagel M. Inferring the historical patterns of biological evolution. Nature 401: 877‐884, 1999.
 234. Pagel M, Meade A. Bayesian analysis of correlated evolution of discrete characters by reversible‐jump Markov chain Monte Carlo. Am Nat 167: 808‐825, 2006.
 235. Pagel M, Meade A, Barker D. Bayesian estimation of ancestral character states on phylogenies. Syst Biol 53: 673‐684, 2004.
 236. Pagel M, Venditti C, Meade A. Large punctuational contribution of speciation to evolutionary divergence at the molecular level. Science 314: 119‐121, 2006.
 237. Pagel MD. A method for the analysis of comparative data. J Theor Biol 156: 431‐442, 1992.
 238. Paradis E, Claude J. Analysis of comparative data using generalized estimating equations. J Theor Biol 218: 175‐185, 2002.
 239. Pavoine S, Ollier S, Pontier D, Chessel D. Testing for phylogenetic signal in phenotypic traits: new matrices of phylogenetic proximities. Theor Popul Biol 73: 79‐91, 2008.
 240. Pierce VA, Crawford DL. Phylogenetic analysis of glycolytic enzyme expression. Science 276: 256‐259, 1997.
 241. Piersma T, van Gils JAM. The Flexible Phenotype: A Body‐Centered Integration of Ecology, Physiology, and Behaviour. Oxford: Oxford University Press, 2011.
 242. Polly PD. Paleontology and the comparative method: ancestral node reconstructions versus observed node values. Am Nat 157: 596‐609, 2001.
 243. Pough FH. Advantages of ectothermy for tetrapods. Am Nat 115: 92‐112, 1980.
 244. Powers DA, Ropson I, Brown DC, Vanbeneden R, Cashon R, Gonzalezvillasenor LI, Dimichele JA. Genetic variation in Fundulus heteroclitus: geographic distribution. Am Zool 26: 131‐144, 1986.
 245. Poyart C, Wajcman H, Kister J. Molecular adaptation of hemoglobin function in mammals. Resp Physiol 90: 3‐17, 1992.
 246. Price T. Correlated evolution and independent contrasts. Philos Trans R Soc B 352: 519‐529, 1997.
 247. Purvis A. A composite estimate of primate phylogeny. Philos Trans R Soc B 348: 405‐421, 1995.
 248. Purvis A, Garland T. Polytomies in comparative analyses of continuous characters. Syst Biol 42: 569‐575, 1993.
 249. Purvis A, Gittleman JL, Luh HK. Truth or consequences: effects of phylogenetic accuracy on 2 comparative methods. J Theor Biol 167: 293‐300, 1994.
 250. Rabosky DL, Donnellan SC, Talaba AL, Lovette IJ. Exceptional among‐lineage variation in diversification rates during the radiation of Australia's most diverse vertebrate clade. Proc R Soc B 274: 2915‐2923, 2007.
 251. Ramirez JM, Folkow LP, Blix AS. Hypoxia tolerance in mammals and birds: from the wilderness to the clinic. Annu Rev Physiol 69: 113‐143, 2007.
 252. Ree RH. Detecting the historical signature of key innovations using stochastic models of character evolution and cladogenesis. Evolution 59: 257‐265, 2005.
 253. Reiss MJ. The Allometry of Growth and Reproduction. Cambridge: Cambridge University Press, 1989.
 254. Revell LJ. On the analysis of evolutionary change along single branches in a phylogeny. Am Nat 172: 140‐147, 2008.
 255. Revell LJ. Size‐correction and principal components for interspecific comparative studies. Evolution 63: 3258‐3268, 2009.
 256. Revell LJ. Phylogenetic signal and linear regression on species data. Methods Ecol Evol 1: 319‐329, 2010.
 257. Revell LJ, Harmon LJ, Collar DC. Phylogenetic signal, evolutionary process, and rate. Syst Biol 57: 591‐601, 2008.
 258. Revell LJ, Harrison AS. PCCA: a program for phylogenetic canonical correlation analysis. Bioinformatics 24: 1018‐1020, 2008.
 259. Revell LJ, Johnson MA, Schulte JA, Kolbe JJ, Losos JB. A phylogenetic test for adaptive convergence in rock‐dwelling lizards. Evolution 61: 2898‐2912, 2007.
 260. Reynolds PS, Lee RM. Phylogenetic analysis of avian energetics: passerines and nonpasserines do not differ. Am Nat 147: 735‐759, 1996.
 261. Rezende EL, Bozinovic F, Garland T. Climatic adaptation and the evolution of basal and maximum rates of metabolism in rodents. Evolution 58: 1361‐1374, 2004.
 262. Rezende EL, Swanson DL, Novoa FF, Bozinovic F. Passerines versus nonpasserines: so far, no statistical differences in the scaling of avian energetics. J Exp Biol 205: 101‐107, 2002.
 263. Rezende EL, Tejedo M, Santos M. Estimating the adaptive potential of critical thermal limits: methodological problems and evolutionary implications. Funct Ecol 25: 111‐121, 2011.
 264. Reznick DN, Mateos M, Springer MS. Independent origins and rapid evolution of the placenta in the fish genus Poeciliopsis. Science 298: 1018‐1020, 2002.
 265. Rheindt FE, Grafe TU, Abouheif E. Rapidly evolving traits and the comparative method: how important is testing for phylogenetic signal? Evol Ecol Res 6: 377‐396, 2004.
 266. Ricklefs RE, Starck JM. Applications of phylogenetically independent contrasts: a mixed progress report. Oikos 77: 167‐172, 1996.
 267. Rohlf FJ. Comparative methods for the analysis of continuous variables: geometric interpretations. Evolution 55: 2143‐2160, 2001.
 268. Rohlf FJ. A comment on phylogenetic correction. Evolution 60: 1509‐1515, 2006.
 269. Ross CF, Henneberg M, Ravosa MJ, Richard S. Curvilinear, geometric and phylogenetic modeling of basicranial flexion: is it adaptive, is it constrained? J Hum Evol 46: 185‐213, 2004.
 270. Ruben J. The evolution of endothermy in mammals and birds: from physiology to fossils. Annu Rev Physiol 57: 69‐95, 1995.
 271. Sanford GM, Lutterschmidt WI, Hutchison VH. The comparative method revisited. Bioscience 52: 830‐836, 2002.
 272. 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.
 273. Schluter D, Price T, Mooers AO, Ludwig D. Likelihood of ancestor states in adaptive radiation. Evolution 51: 1699‐1711, 1997.
 274. Schmidt‐Nielsen K. Animal Physiology: Adaptation and Environment. Cambridge: Cambridge University Press, 1997.
 275. Schmidt‐Nielsen K. Scaling: Why is Animal Size So Important? Cambridge: Cambridge University Press, 1999.
 276. Scholander PF, Walters V, Hock R, Irving L. Body insulation of some arctic and tropical mammals and birds. Biol Bull 99: 225‐236, 1950.
 277. Schulz TM, Bowen WD. The evolution of lactation strategies in pinnipeds: a phylogenetic analysis. Ecol Monogr 75: 159‐177, 2005.
 278. Seebacher F. Dinosaur body temperatures: the occurrence of endothermy and ectothermy. Paleobiology 29: 105‐122, 2003.
 279. Sibley CG, Ahlquist JE. Phylogeny and Classification of Birds: A Study in Molecular Evolution. New Haven: Yale University Press, 1990.
 280. Sieg AE, O'Connor 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.
 281. Singer MA. Comparative Physiology, Natural Animal Models and Clinical Medicine Insights Into Clinical Medicine from Animal Adaptations. London: Imperial College Press, 2007.
 282. Snyder LRG. Deer mouse hemoglobins: is there genetic adaptation to high‐altitude? Bioscience 31: 299‐304, 1981.
 283. Snyder LRG. Low P50 in deer mice native to high‐altitude. J Appl Physiol 58: 193‐199, 1985.
 284. Snyder LRG, Hayes JP, Chappell MA. Alpha‐chain hemoglobin polymorphisms are correlated with altitude in the deer mouse, Peromyscus maniculatus. Evolution 42: 689‐697, 1988.
 285. Sol D, Duncan RP, Blackburn TM, Cassey P, Lefebvre L. Big brains, enhanced cognition, and response of birds to novel environments. Proc Natl Acad Sci U S A 102: 5460‐5465, 2005.
 286. Sol D, Price TD. Brain size and the diversification of body size in birds. Am Nat 172: 170‐177, 2008.
 287. Speakman JR. The cost of living: field metabolic rates of small mammals. Adv Ecol Res 30: 177‐297, 2000.
 288. Speakman JR, Krol E. Maximal heat dissipation capacity and hyperthermia risk: neglected key factors in the ecology of endotherms. J Anim Ecol 79: 726‐746, 2010.
 289. Spoor F, Bajpal S, Hussaim ST, Kumar K, Thewissen JGM. Vestibular evidence for the evolution of aquatic behaviour in early cetaceans. Nature 417: 163‐166, 2002.
 290. Spoor F, Garland T, Krovitz G, Ryan TM, Silcox MT, Walker A. The primate semicircular canal system and locomotion. Proc Natl Acad Sci U S A 104: 10808‐10812, 2007.
 291. Springer MS, Cleven GC, Madsen O, deJong WW, Waddell VG, Amrine HM, Stanhope MJ. Endemic African mammals shake the phylogenetic tree. Nature 388: 61‐64, 1997.
 292. Springer MS, Stanhope MJ, Madsen O, de Jong WW. Molecules consolidate the placental mammal tree. Trends Ecol Evol 19: 430‐438, 2004.
 293. Stoks R, McPeek MA. A tale of two diversifications: reciprocal habitat shifts to fill ecological space along the pond permanence gradient. Am Nat 168: S50‐S72, 2006.
 294. Stone EA. Why the phylogenetic regression appears robust to tree misspecification. Syst Biol 60: 245‐260, 2011.
 295. Stone GN, Nee S, Felsenstein J. Controlling for non‐independence in comparative analysis of patterns across populations within species. Phil Trans R Soc B 366: 1410‐1424, 2011.
 296. Storz JF. Hemoglobin function and physiological adaptation to hypoxia in high‐altitude mammals. J Mammal 88: 24‐31, 2007.
 297. Storz JF, Runck AM, Sabatino SJ, Kelly JK, Ferrand N, Moriyama H, Weber RE, Fago A. Evolutionary and functional insights into the mechanism underlying high‐altitude adaptation of deer mouse hemoglobin. Proc Natl Acad Sci U S A 106: 14450‐14455, 2009.
 298. Storz JF, Sabatino SJ, Hoffmann FG, Gering EJ, Moriyama H, Ferrand N, Monteiro B, Nachman MW. The molecular basis of high‐altitude adaptation in deer mice. Plos Genet 3: 448‐459, 2007.
 299. Swanson DL, Garland T. The evolution of high summit metabolism and cold tolerance in birds and its impact on present‐day distributions. Evolution 63: 184‐194, 2009.
 300. Swofford DL, Maddison WP. Reconstructing ancestral character states under Wagner parsimony. Math Biosci 87: 199‐229, 1987.
 301. Symonds MRE. The effects of topological inaccuracy in evolutionary trees on the phylogenetic comparative method of independent contrasts. Syst Biol 51: 541‐553, 2002.
 302. Symonds MRE, Elgar MA. Phylogeny affects estimation of metabolic scaling in mammals. Evolution 56: 2330‐2333, 2002.
 303. Taylor CR, Heglund NC, Maloiy GMO. Energetics and mechanics of terrestrial locomotion 1. Metabolic energy‐consumption as a function of speed and body size in birds and mammals. J Exp Biol 97: 1‐21, 1982.
 304. Terblanche JS, Deere JA, Clusella‐Trullas S, Janion C, Chown SL. Critical thermal limits depend on methodological context. Proc R Soc B 274: 2935‐2942, 2007.
 305. Thomas GH, Freckleton RP, Szekely T. Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds. Proc R Soc B 273: 1619‐1624, 2006.
 306. Thornton JW, DeSalle R. Gene family evolution and homology: genomics meets phylogenetics. Annu Rev Genom Hum G 1: 41‐73, 2000.
 307. Tieleman BI, Williams JB. The adjustment of avian metabolic rates and water fluxes to desert environments. Physiol Biochem Zool 73: 461‐479, 2000.
 308. Tieleman BI, Williams JB, Bloomer P. Adaptation of metabolism and evaporative water loss along an aridity gradient. Proc R Soc B 270: 207‐214, 2003.
 309. Turkheimer F, Hinz R, Cunningham V. Model averaging using Akaike weights. Neuroimage 16: S60‐S60, 2002.
 310. Turkheimer FE, Hinz R, Cunningham VJ. On the undecidability among kinetic models: from model selection to model averaging. J Cerebr Blood F Met 23: 490‐498, 2003.
 311. Van Buskirk J. A comparative test of the adaptive plasticity hypothesis: relationships between habitat and phenotype in anuran larvae. Am Nat 160: 87‐102, 2002.
 312. Vonk FJ, Admiraal JF, Jackson K, Reshef R, de Bakker MAG, Vanderschoot K, van den Berge I, van Atten M, Burgerhout E, Beck A, Mirtschin PJ, Kochva E, Witte F, Fry BG, Woods AE, Richardson MK. Evolutionary origin and development of snake fangs. Nature 454: 630‐633, 2008.
 313. Webster AJ, Purvis A. Testing the accuracy of methods for reconstructing ancestral states of continuous characters. Proc R Soc B 269: 143‐149, 2002.
 314. Weibel ER. Understanding the limitation of O2 supply through comparative physiology. Resp Physiol 118: 85‐93, 1999.
 315. Weibel ER, Bacigalupe LD, Schmitt B, Hoppeler H. Allometric scaling of maximal metabolic rate in mammals: muscle aerobic capacity as determinant factor. Resp Physiol Neurobi 140: 115‐132, 2004.
 316. Weibel ER, Hoppeler H. Exercise‐induced maximal metabolic rate scales with muscle aerobic capacity. J Exp Biol 208: 1635‐1644, 2005.
 317. West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science 276: 122‐126, 1997.
 318. West GB, Brown JH, Enquist BJ. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284: 1677‐1679, 1999.
 319. Westneat MW. Feeding, function, and phylogeny: analysis of historical biomechanics in labrid fishes using comparative methods. Syst Biol 44: 361‐383, 1995.
 320. Westoby M, Leishman MR, Lord JM. On misinterpreting the phylogenetic correction. J Ecol 83: 531‐534, 1995.
 321. White CR. The influence of foraging mode and arid adaptation on the basal metabolic rates of burrowing mammals. Physiol Biochem Zool 76: 122‐134, 2003.
 322. White CR. Allometric estimation of metabolic rates in animals. Comp Biochem Phys A 158: 346‐357, 2011.
 323. White CR, Blackburn TM, Martin GR, Butler PJ. Basal metabolic rate of birds is associated with habitat temperature and precipitation, not primary productivity. Proc R Soc B 274: 287‐293, 2007.
 324. 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.
 325. White CR, Blackburn TM, Terblanche JS, Marais E, Gibernau M, Chown SL. Evolutionary responses of discontinuous gas exchange in insects. P Natl Acad Sci USA 104: 8357‐8361, 2007.
 326. White CR, Seymour RS. Mammalian basal metabolic rate is proportional to body mass(2/3). P Natl Acad Sci USA 100: 4046‐4049, 2003.
 327. 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.
 328. Whiting MF, Bradler S, Maxwell T. Loss and recovery of wings in stick insects. Nature 421: 264‐267, 2003.
 329. Whitney KD, Garland T. Did genetic drift drive increases in genome complexity? Plos Genet 6, 2010.
 330. Wiens JJ. Character analysis in morphological phylogenetics: problems and solutions. Syst Biol 50: 689‐699, 2001.
 331. Wiens JJ, Ackerly DD, Allen AP, Anacker BL, Buckley LB, Cornell HV, Damschen EI, Davies TJ, Grytnes JA, Harrison SP, Hawkins BA, Holt RD, McCain CM, Stephens PR. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol Lett 13: 1310‐1324, 2010.
 332. Wiens JJ, Graham CH. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu Rev Ecol Evol S 36: 519‐539, 2005.
 333. Wiens JJ, Graham CH, Moen DS, Smith SA, Reeder TW. Evolutionary and ecological causes of the latitudinal diversity gradient in hylid frogs: Treefrog trees unearth the roots of high tropical diversity. Am Nat 168: 579‐596, 2006.
 334. Wiens JJ, Kuczynski CA, Duellman WE, Reeder TW. Loss and re‐evolution of complex life cycles in marsupial frogs: Does ancestral trait reconstruction mislead? Evolution 61: 1886‐1899, 2007.
 335. Wiersma P, Chappell MA, Williams JB. Cold‐ and exercise‐induced peak metabolic rates in tropical birds. Proc Natl Acad Sci U S A 104: 20866‐20871, 2007.
 336. Withers PC, Cooper CE, Larcombe AN. Environmental correlates of physiological variables in marsupials. Physiol Biochem Zool 79: 437‐453, 2006.
 337. Wolf CM, Garland T, Griffith B. Predictors of avian and mammalian translocation success: reanalysis with phylogenetically independent contrasts. Biol Conserv 86: 243‐255, 1998.
 338. Yanoviak SP, Kaspari M, Dudley R. Gliding hexapods and the origins of insect aerial behaviour. Biol Lett 5: 510‐512, 2009.
 339. Zheng L, Ives AR, Garland T, Larget BR, Yu Y, Cao KF. New multivariate tests for phylogenetic signal and trait correlations applied to ecophysiological phenotypes of nine Manglietia species. Funct Ecol 23: 1059‐1069, 2009.
Further Reading
 1. http://en.wikipedia.org/wiki/Phylogenetic_comparative_methods
 2.Lists of software packages
 3. http://evolution.genetics.washington.edu/phylip/software.html
 4. http://bioinfo.unice.fr/biodiv/Tree_editors.html
 5. http://cran.r‐project.org/web/views/Phylogenetics.html
 6. http://mesquiteproject.org/mesquite/mesquite.html
 7. Discussion forums, courses and miscelaneous information
 8. http://bodegaphylo.wikispot.org/
 9. http://informatics.nescent.org/wiki/Main_Page
 10. http://phytools.blogspot.com/
 11. http://www.r‐phylo.org/wiki/Main_Page
 12.Mailing lists (R‐phylo and Mesquite)
 13. https://stat.ethz.ch/mailman/listinfo/r‐sig‐phylo
 14. http://mesquiteproject.org/mailman/listinfo/mesquitelist

Phylogenetic statistical methods are becoming increasingly flexible and complex, and considerable information regarding these approaches, available softwares and forums of discussion, is currently available of the internet. Even though these sources are not peer-reviewed, they can be very helpful for researchers and students that are not familiar with the field and look for some guidance. Here we list a few web pages that may be useful in this context.

 

Introduction

http://en.wikipedia.org/wiki/Phylogenetic_comparative_methods

 

Lists of software packages

http://evolution.genetics.washington.edu/phylip/software.html 

http://bioinfo.unice.fr/biodiv/Tree_editors.html

http://cran.r-project.org/web/views/Phylogenetics.html

http://mesquiteproject.org/mesquite/mesquite.html

 

Discussion forums, courses and miscelaneous information

http://bodegaphylo.wikispot.org/

http://informatics.nescent.org/wiki/Main_Page

http://phytools.blogspot.com/

http://www.r-phylo.org/wiki/Main_Page

 

Mailing lists (R-phylo and Mesquite)

https://stat.ethz.ch/mailman/listinfo/r-sig-phylo

http://mesquiteproject.org/mailman/listinfo/mesquitelist

 


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Article Title: Phylogenetic Analyses: Comparing Species to Infer Adaptations and Physiological Mechanisms
 

How to Cite

Enrico L. Rezende, José Alexandre F. Diniz‐Filho. Phylogenetic Analyses: Comparing Species to Infer Adaptations and Physiological Mechanisms. Compr Physiol 2012, 2: 639-674. doi: 10.1002/cphy.c100079