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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 27 (6 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 13 (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,
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 5 (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 Interpretations Of Comparative Methods For The Analysis Of Continuous Variables
"... : This study is concerned with statistical methods used for the analysis of comparative data (data in which observations are not expected to be independent because they are sampled across phylogenetically related species). The phylogenetically independent contrasts (PIC), phylogenetic generalized le ..."
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Cited by 2 (1 self)
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: This study is concerned with statistical methods used for the analysis of comparative data (data in which observations are not expected to be independent because they are sampled across phylogenetically related species). The phylogenetically independent contrasts (PIC), phylogenetic generalized leastsquares (PGLS), and phylogenetic autocorrelation (PA), methods are compared. While the independent contrasts are not orthogonal they are independent if the data conform to the Brownian motion model of evolution on which they are based. It is shown that uncentered correlations and regressions through the origin using the PIC method are identical to those obtained using PGLS with an intercept included in the model. Thus the PIC method is a special case of PGLS. The treatment of trees with multifurcations is discussed and shown to be an algorithmic rather than a statistical problem. Some of the relationships among the methods are shown graphically using the multivariate space in which varia...
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 2 (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.
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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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.
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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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.
The Journal of Experimental Biology 208, 30153035 Published by The Company of Biologists 2005
"... doi:10.1242/jeb.01745 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 ..."
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doi:10.1242/jeb.01745 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, Studies of organismal form and function rely on multiple types of scientific investigation, including theory, description, experimentation and comparison. Comparing species is an ancient human enterprise, done for a variety of reasons (Sanford et al., 2002). Since Charles Darwin, the ‘comparative method ’ – comparing populations, species or higher taxa – has been the most common and productive means of elucidating past evolutionary processes (Harvey and Pagel, 1991; Brooks and McClennan, 2002). Comparative methods have been used extensively to infer evolutionary adaptation, that is, changes in response to natural selection (for alternate physiological meanings of ‘adaptation’, see Garland and Adolph, 1991; Bennett, 1997). They are most often promoted and criticized (e.g. Leroi et al., 1994) within this context. However, comparative methods are not used to infer adaptation alone (Garland and Adolph, 1994; Sanford et al., 2002), but are also employed to analyze the effects of sexual selection (e.g.
BMC Evolutionary Biology BioMed Central
, 2008
"... Research article Accelerated evolutionary rates in tropical and oceanic parmelioid lichens (Ascomycota) ..."
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Research article Accelerated evolutionary rates in tropical and oceanic parmelioid lichens (Ascomycota)