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 least-squares 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.

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. Within-species 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). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related 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 within-species 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,

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

: 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 least-squares (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...

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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 squared-change 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.

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 least-squares 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.

Research article Accelerated evolutionary rates in tropical and oceanic parmelioid lichens (Ascomycota)

Microarray platforms are used increasingly to make comparative inferences through genome-wide surveys of gene expression. While recent studies focus on describing the evidence for natural selection using estimates of the within and between taxa mutational variances, these methods do not often explicitly or flexibly account for predicted non-independence due to phylogenetic associations between measurements. In the interest of parsing the effects of selection, we introduce a mixture model for the comparative analysis of variation in gene expression across multiple taxa. This class of models isolates the phylogenetic signal from the non-phylogenetic and the heritable signal from the non-heritable while measuring the proper amount of correction. As a result, the mixture model resolves outstanding differences between existing models and relates different ways to estimate the across taxa variance. We investigate by simulation and application the feasibility and utility of estimation of the required parameters. We

comparative methods; constraint; independent contrasts; natural selection; phylogenetic inertia; statistics. Before the Evolutionary Synthesis, ‘phylogenetic inertia ’ was associated with theories of orthogenesis, which claimed that organisms possessed an endogenous perfecting principle. The concept in the modern literature dates to Simpson (1944), who used ‘evolutionary inertia ’ as a description of pattern in the fossil record. Wilson (1975) used ‘phylogenetic inertia ’ to describe population-level or organismal properties that can affect the course of evolution in response to selection. Many current authors now view phylogenetic inertia as an alternative hypothesis to adaptation by natural selection when attempting to explain interspecific variation, covariation or lack thereof in phenotypic traits. Some phylogenetic comparative methods have been claimed to allow quantification and testing of phylogenetic inertia. Although some existing methods do allow valid tests of whether related species tend to resemble each other, which we term ‘phylogenetic signal’, this is simply pattern recognition and does not imply any underlying process. Moreover, comparative data sets generally do not include information that would allow rigorous inferences concerning causal processes underlying such patterns. The concept of phylogenetic inertia needs to be defined and studied with as much care as ‘adaptation’.