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Phylogenetic Analyses: Comparing Species to Infer Adaptations and Physiological Mechanisms

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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.

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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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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