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202
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 57 (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 37 (2 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 34 (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.
A phylogenetic model for investigating correlated evolution of substitution rates and continuous phenotypic characters
"... The comparative approach is routinely used to test for possible correlations between phenotypic or lifehistory traits. To correct for phylogenetic inertia, the method of independent contrasts assumes that continuous characters evolve along the phylogeny according to a multivariate Brownian process. ..."
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Cited by 24 (3 self)
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The comparative approach is routinely used to test for possible correlations between phenotypic or lifehistory traits. To correct for phylogenetic inertia, the method of independent contrasts assumes that continuous characters evolve along the phylogeny according to a multivariate Brownian process. Brownian diffusion processes have also been used to describe time variations of the parameters of the substitution process, such as the rate of substitution or the ratio of synonymous to nonsynonymous substitutions. Here, we develop a probabilistic framework for testing the coupling between continuous characters and parameters of the molecular substitution process. Rates of substitution and continuous characters are jointly modeled as a multivariate Brownian diffusion process of unknown covariance matrix. The covariancematrix, the divergence times and the phylogenetic variationsof substitution rates and continuous characters are all jointly estimated in a BayesianMonte Carlo framework, imposing on the covariance matrix a prior conjugate to the Brownian process so as to achieve a greater computational efficiency. The coupling between rates and phenotypes is assessed by measuring the posterior probability of positive or negative covariances, whereas divergence dates and phenotypic variations are marginally reconstructed in the context of the joint analysis. As an illustration, we apply the model to a set of 410 mammalian cytochrome b sequences. We observe a negative correlation between the rate of substitution and mass and longevity, which was previously observed. We also find a positive correlation between ω = dN/dS and mass and longevity, which we interpret as an indirect effect of variations of effective population size, thus in partial agreement with the nearly neutral theory. The method can easily be extended to any parameter of the substitution process and to any continuous phenotypic or environmental character.
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 15 (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
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 13 (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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Cited by 13 (1 self)
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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
Testing and quantifying phylogenetic signals and homoplasy in morphometric data
 Syst Biol
, 2010
"... Abstract.—The relationship between morphometrics and phylogenetic analysis has long been controversial. Here we propose an approach that is based on mapping morphometric traits onto phylogenies derived from other data and thus avoids the pitfalls encountered by previous studies. This method treats ..."
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Cited by 10 (0 self)
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Abstract.—The relationship between morphometrics and phylogenetic analysis has long been controversial. Here we propose an approach that is based on mapping morphometric traits onto phylogenies derived from other data and thus avoids the pitfalls encountered by previous studies. This method treats shape as a single, multidimensional character. We propose a test for the presence of a phylogenetic signal in morphometric data, which simulates the null hypothesis of the complete absence of phylogenetic structure by permutation of the shape data among the terminal taxa. We also propose 2 measures of the fit of morphometric data to the phylogeny that are direct extensions of the consistency index and retention index used in traditional cladistics. We apply these methods to a small study of the evolution of wing shape in the Drosophila melanogaster subgroup, for which a very strongly supported phylogeny is available. This case study reveals a significant phylogenetic signal and a relatively low degree of homoplasy. Despite the low homoplasy, the shortest tree computed from landmark data on wing shape is inconsistent with the wellsupported phylogenetic tree from molecular data, underscoring that morphometric data may not provide reliable information for inferring phylogeny. [Drosophilidae; geometric morphometrics; homoplasy; phylogeny; Procrustes superposition; shape; squaredchange parsimony.] Understanding the evolution of organismal form is
Phylogenetic dependency networks: Inferring patterns of adaptation in HIV
, 2009
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ANALYSIS OF COMPARATIVE DATA WITH HIERARCHICAL AUTOCORRELATION
, 2008
"... The asymptotic behavior of estimates and information criteria in linear models are studied in the context of hierarchically correlated sampling units. The work is motivated by biological data collected on species where autocorrelation is based on the species ’ genealogical tree. Hierarchical autocor ..."
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Cited by 9 (0 self)
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The asymptotic behavior of estimates and information criteria in linear models are studied in the context of hierarchically correlated sampling units. The work is motivated by biological data collected on species where autocorrelation is based on the species ’ genealogical tree. Hierarchical autocorrelation is also found in many other kinds of data, such as from microarray experiments or human languages. Similar correlation also arises in ANOVA models with nested effects. I show that the best linear unbiased estimators are almost surely convergent but may not be consistent for some parameters such as the intercept and lineage effects, in the context of Brownian motion evolution on the genealogical tree. For the purpose of model selection I show that the usual BIC does not provide an appropriate approximation to the posterior probability of a model. To correct for this, an effective sample size is introduced for parameters that are inconsistently estimated. For biological studies, this work implies that treeaware sampling design is desirable; adding more sampling units may not help ancestral reconstruction and only strong lineage effects may be detected with high power.