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135
Phylogenetic approaches in comparative physiology
, 2005
"... Over the past two decades, comparative biological analyses have undergone profound changes with the incorporation of rigorous evolutionary perspectives and phylogenetic information. This change followed in large part from the realization that traditional methods of statistical analysis tacitly assum ..."
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Cited by 44 (7 self)
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Over the past two decades, comparative biological analyses have undergone profound changes with the incorporation of rigorous evolutionary perspectives and phylogenetic information. This change followed in large part from the realization that traditional methods of statistical analysis tacitly assumed independence of all observations, when in fact biological groups such as species are differentially related to each other according to their evolutionary history. New phylogenetically based analytical methods were then rapidly developed, incorporated into ‘the comparative method’, and applied to many physiological, biochemical, morphological and behavioral investigations. We now review the rationale for including phylogenetic information in comparative studies and briefly discuss three methods for doing this (independent contrasts, generalized leastsquares models, Summary and Monte Carlo computer simulations). We discuss when and how to use phylogenetic information in comparative studies and provide several examples in which it has been helpful, or even crucial, to a comparative analysis. We also consider some difficulties with phylogenetically based statistical methods, and of comparative approaches in general, both practical and theoretical. It is our personal opinion that the incorporation of phylogeny information into comparative studies has been highly beneficial, not only because it can improve the reliability of statistical inferences, but also because it continually emphasizes the potential importance of past evolutionary history in determining current form and function.
WithinSpecies Variation and Measurement Error in Phylogenetic Comparative Methods
, 2007
"... Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Withinspecies variation has several components. Differences among p ..."
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Cited by 29 (1 self)
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Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Withinspecies variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Withinpopulation variation also contributes to withinspecies variation and includes sampling variation, instrumentrelated error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include withinspecies variation, or “measurement error ” as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of “phylogenetic signal, ” (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of “functional relations, ” such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression,
Phylogenetic signal, evolutionary process, and rate
 Systematic Biology
, 2008
"... This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan or sublicensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express ..."
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Cited by 19 (3 self)
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This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan or sublicensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Estimation of ancestral states of continuous characters: a computer simulation study. Systematic Biology
 Syst. Biol
, 1999
"... In systematics, we usually estimate ancestral phenotypes of morphological and molecular characters measured as categorical or “state ” variables. Ancestral estimation has also been important in the studies of animal behavior, physiology, ecology, and other areas of biology, where the characters are ..."
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Cited by 13 (0 self)
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In systematics, we usually estimate ancestral phenotypes of morphological and molecular characters measured as categorical or “state ” variables. Ancestral estimation has also been important in the studies of animal behavior, physiology, ecology, and other areas of biology, where the characters are usually measured as continuous rather than categorical variables. In the past, the most common way of estimating the ancestral states of continuous characters was to use a parsimony algorithm (Farris, 1970; Swofford and Maddison, 1987; Maddison, 1991). Recently, two new methods (Martins and Hansen, 1997; Schluter et al., 1997) have
Geometric Morphometrics and Phylogeny
, 2000
"... This paper reviews some of the important properties of geometric morphometric shape variables and discusses the advantages and limitations of the use of such data in studies of phylogeny. A method for fitting morphometric data to a phylogeny (i.e., estimating ancestral states of the shape variables) ..."
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Cited by 7 (0 self)
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This paper reviews some of the important properties of geometric morphometric shape variables and discusses the advantages and limitations of the use of such data in studies of phylogeny. A method for fitting morphometric data to a phylogeny (i.e., estimating ancestral states of the shape variables) is presented using the squaredchange parsimony criterion for estimation. These results are then used to illustrate shape change along a phylogeny as a deformation of the shape of any other node on the tree (e.g., the estimated root of the tree). In addition, a method to estimate the digitized image of an ancestor is given that uses averages of unwarped images. An example dataset with 18 wing landmarks for 11 species of mosquitoes is used to illustrate the methods.
2002 Phylogeny shape and the phylogenetic comparative method. Syst
 Biol
, 2003
"... Abstract.—We explored the impact of phylogeny shape on the results of interspecific statistical analyses incorporating phylogenetic information. In most phylogenetic comparative methods (PCMs), the phylogeny can be represented as a relationship matrix, and the hierarchical nature of interspecific ph ..."
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Cited by 6 (0 self)
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Abstract.—We explored the impact of phylogeny shape on the results of interspecific statistical analyses incorporating phylogenetic information. In most phylogenetic comparative methods (PCMs), the phylogeny can be represented as a relationship matrix, and the hierarchical nature of interspecific phylogenies translates into a distinctive blocklike matrix that can be described by its eigenvectors (topology) and eigenvalues (branch lengths). Thus, differences in the eigenvectors and eigenvalues of different relationship matrices can be used to gauge the impact of possible phylogeny errors by comparing the actual phylogeny used in a PCM analysis with a second phylogenetic hypothesis that may be more accurate. For example, we can use the sum of inverse eigenvalues as a rough index to compare the impact of phylogenies with different branch lengths. Topological differences are better described by the eigenvectors. In general, phylogeny errors that involve deep splits in the phylogeny (e.g., moving a taxon across the base of the phylogeny) are likely to have much greater impact than will those involving small perturbations in the fine structure near the tips. Small perturbations, however, may have more of an impact if the phylogeny structure is highly dependent (with many recent splits near the tips of the tree). Unfortunately, the impact of any phylogeny difference on the results of a PCM depends on the details of the data being considered. Recommendations regarding the choice, design, and statistical power of interspecific analyses are also made. [Comparative method; eigenvalues; eigenvectors; evolution; phylogeny; principal components; theory.] In recent years, it has become generally accepted that statistical analyses of interspecific data should be conducted in a phylogenetic context using the phylogenetic comparative method (PCM; for description and
Do early branching lineages signify ancestral traits? Trends in Ecology and Evolution
, 2005
"... A reverence for ancestors that has preoccupied humans since time immemorial persists to the present. Reconstructing ancestry is the focus ofmany biological studies but failure to distinguish between presentday descendants and longdead ancestors has led to incorrect interpretation of phylogeneti ..."
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Cited by 6 (0 self)
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A reverence for ancestors that has preoccupied humans since time immemorial persists to the present. Reconstructing ancestry is the focus ofmany biological studies but failure to distinguish between presentday descendants and longdead ancestors has led to incorrect interpretation of phylogenetic trees. This has resulted in erroneous reconstruction of traits such as morphology and ancestral areas. Misinterpretation becomes evident when authors use the terms ‘basal ’ or ‘early diverging’ to refer to extant taxa. Here, we discuss the correct interpretation of trees and methods for reconstructing the ancestral features of organisms using recently developed statistical models. These models can be inaccurate unless they use information that is independent of phylogenies, such as genetics, molecular and developmental biology, functional morphology, geological and climatic processes, and the fossil record.
Phylogenetic dependency networks: Inferring patterns of adaptation in HIV
, 2009
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Phylogenetic Logistic Regression for Binary Dependent Variables
"... Abstract.—We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an ev ..."
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Cited by 4 (1 self)
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Abstract.—We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an evolutionary model of binary traits in which trait values switch between 0 and 1 as species evolve up a phylogenetic tree. The more frequently the trait values switch (i.e., the higher the rate of evolution), the more rapidly correlations between trait values for phylogenetically related species break down. Therefore, the statistical methods also give a way to estimate the phylogenetic signal of binary traits. More generally, the methods can be applied with continuous and/or discretevalued independent variables. Using simulations, we assess the statistical properties of the methods, including bias in the estimates of the logistic regression coefficients and the parameter that estimates the strength of phylogenetic signal in the dependent variable. These analyses show that, as with the case for continuousvalued dependent variables, phylogenetic logistic regression should be used rather than standard logistic regression when there is the possibility of phylogenetic correlations among species. Standard logistic regression does not properly account for the loss of information caused by resemblance of relatives and as a result is likely to give inflated type I error rates, incorrectly identifying regression parameters as statistically significantly different from zero when they are not.
STATISTICAL APPROACH TO TESTS INVOLVING PHYLOGENIES
"... This chapter reviews statistical testing involving phylogenies. We present both the classical framework with the use of sampling distributions involving the bootstrap and permutation tests and the Bayesian approach using posterior distributions. We give some examples of direct tests for deciding whe ..."
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This chapter reviews statistical testing involving phylogenies. We present both the classical framework with the use of sampling distributions involving the bootstrap and permutation tests and the Bayesian approach using posterior distributions. We give some examples of direct tests for deciding whether the data support a given tree or trees that share a particular property, comparative analyses using tests that condition on the phylogeny being known are also discussed. We introduce a continuous parameter space that enables one to avoid the delicate problem of comparing exponentially many possible models with a finite amount of data. This chapter contains a review of the literature on parametric tests in phylogenetics and some suggestions of nonparametric tests. We also present some open questions that have to be solved by mathematical statisticians to provide the theoretical justification of both current testing strategies and as yet underdeveloped areas of statistical testing in