TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE


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1 Evolution, 57(4), 23, pp TETING FOR HYOGENETIC IGNA IN COARATIVE DATA: BEHAVIORA TRAIT ARE ORE ABIE ION. BOBERG, 1 THEODORE GARAND, JR., 1,2 AND ANTHONY R. IVE 3,4 1 Department of Biology, University of California, Riverside, California Department of Zoology, University of Wisconsin, adison, Wisconsin Astract. The primary rationale for the use of phylogenetically ased statistical methods is that phylogenetic signal, the tendency for related species to resemle each other, is uiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, ut general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuousvalued characters that can e implemented with either phylogenetically independent contrasts or generalized leastsquares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal.5 and good power (.8) for simulated datasets with 2 or more species. econd, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two iologically motivated ranchlength transformations, one ased on the Ornstein Uhleneck (OU) model of stailizing selection, the other ased on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). aximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal ecause an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also e preferale for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 2 or more species, 92% of traits exhiited significant phylogenetic signal (randomization test), including ehavioral and ecological ones that are thought to e relatively evolutionarily malleale (e.g., highly adaptive) and/or suject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (ut not ody size, on average) showed less signal than expected given the topology, ranch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may e attriuted to adaptation and/or measurement error in the road sense (including errors in estimates of phenotypes, ranch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that ehavioral traits exhiit lower signal than ody size, morphological, lifehistory, or physiological traits. In addition, physiological traits (corrected for ody size) showed less signal than did ody size itself. For trees with 2 or more species, the estimated OU (25% of traits) and/or ACDC (4%) transformation parameter differed significantly from oth zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original etter fit the data and so could e preferred for comparative analyses. Key words. Adaptation, ehavior, ody size, ranch lengths, comparative method, constraint, physiology. A great triumph of iology has een the demonstration that organisms are descended from common ancestors and hence are related in a hierarchical fashion (ayr 1982). An oservation of almost equal importance is that phylogenetically related organisms tend to resemle each other for most aspects of the phenotype (e.g., hummingirds look like hummingirds, elephants look like elephants). We term this resemlance phylogenetic signal to avoid such terms as phylogenetic inertia or constraint, which are inconsistently defined, oth conceptually and operationally (e.g., see Antonovics and van Tienderen 1991; ckitrick 1993; Wagner and chwenk 2; Orzack and oer 21; Reeve and herman 21) and whose original use was rather different from current use (Blomerg and Garland 22). As argued elsewhere (Blomerg and Garland 22), we elieve that the evolutionary processes implied y phylogenetic inertia and constraint are difficult, if not impossile, to estimate from comparative data alone (ut see Hansen 1997; Orzack and oer 21; chwenk and Wagner 21). Therefore, we use phylogenetic signal simply to descrie a tendency (pattern) for evolutionarily related organisms to resemle each other, with no implication as to the mechanism that might cause such resemlance (process). This use of phylogenetic signal 23 The ociety for the tudy of Evolution. All rights reserved. Received arch 29, 22. Accepted Novemer 27, is consistent with recent usage in systematic iology (e.g., Hillis and Huelseneck 1992) and is also similar to the phylogenetic effect of Derrickson and Ricklefs (1988) and the phylogenetic conservatism of Ashton (21). We do not use phylogenetic conservatism, however, ecause it seems to connote less change than might e expected from the phylogenetic structure of the taxa in question; as argued elow, the existence of phylogenetic signal does not require the existence of such processes as that may imply. We did not use the phylogenetic correlation of Gittleman et al. (1996a) ecause they used statistical approaches different from those presented here and ecause our methods do not involve correlation per se. A crucial point that has not een sufficiently appreciated in the literature is that phylogenetic signal will occur to some extent under most simple, stochastic models of character evolution along a tree with any amount of hierarchical structure. Under such models as Brownian motion, evolutionary changes are simply added to values present in the previous generation or at the previous node on a phylogenetic tree. Thus, memers of lineages that have only recently diverged will necessarily (on average) tend to e similar, as compared with more distantly related lineages. This effect carries on to the
2 718 ION. BOBERG ET A. values for taxa at the tips of a phylogenetic tree (e.g., extant species). The presence of phylogenetic signal does not require a failure to evolve (Wake 1991) along certain ranches, ut only that the ranch lengths of a phylogenetic tree are proportional to the expected variance of evolution for the character in question. Hence, significant phylogenetic signal does not require appeal to such processes as inertia or constraint (Blomerg and Garland 22). Although phylogenetic signal is a necessary property of stochastic evolution along a hierarchical tree, whether phylogenetic signal can e detected for a given trait will, of course, depend on the sample size of a dataset, the power of the statistical test, the accuracy of the phylogenetic tree (e.g., topological errors will tend to oscure the signal), and the accuracy of the trait data (high measurement error will also tend to oscure signal). oreover, a test for phylogenetic signal can also e viewed as a test for hierarchical tree structure, if one assumes something like Brownian motion to have een in effect. Or, it can e interpreted as a test for Brownian motion if one assumes the phylogenetic topology and ranch lengths are known without error. (Related to the issue of detecting phylogenetic signal is the issue of quantifying and comparing evolutionary rates [e.g., see Garland 1992; artins 1994]. For example, traits that evolve slowly are often identified as having phylogenetic inertia or eing suject to phylogenetic constraint [review in Gittleman et al. 1996a; see also Blomerg and Garland 22].) Although it is simply a necessary consequence of stochastic evolution along a hierarchical tree, the existence of phylogenetic signal has important practical consequences. First, the characteristics (either phenotypic or genetic) of a species that has not yet een studied can often e rather well predicted simply from knowledge of its phylogenetic position and the characteristics of some close relatives. For example, the aility to detect species that deviate significantly from general allometric equations can e greatly enhanced y the use of phylogenetically informed statistical procedures (Garland and Ives 2). econd, average values for the phenotypes of a set of species generally do not represent independent and identically distriuted datapoints for a statistical analysis. (They also typically do not represent random samples from the population of all organisms or even of a more restricted lineage.) Hence, conventional statistical methods are usually inappropriate for the analysis of comparative data (e.g., Ridley 1983; Felsenstein 1985; Grafen 1989; Harvey and agel 1991; Garland et al. 1993, 1999; Reynolds and ee 1996; artins and Hansen 1997; Ackerly 2; Rohlf 21; widerski 21). This realization has led to a large ody of work aimed at the development of phylogenetically ased statistical methods, spurred in particular y the pulication of two different methods in 1985 (Cheverud et al. 1985; Felsenstein 1985). Both analytical and simulation studies have shown that, under a fairly wide range of phylogenies and models of character evolution, related species do tend to resemle each other and that this tendency will inflate Type I error rates for testing hypotheses (e.g., whether two traits are correlated across species) with comparative data (e.g., Grafen 1989; artins and Garland 1991; urvis et al. 1994; DíazUriarte and Garland 1996; Harvey and Ramaut 1998; artins et al. 22). Theory is one thing, ut the real world is another. Hierarchical phylogenetic relationships and a tendency for relatives to resemle each other are not without exception. For example, interspecific hyridization and horizontal gene transfer violate the general idea that phylogenetic trees are always divergent. Convergent evolution leads to distantly related organisms resemling each other more closely than expected (Wake 1991), whereas character displacement causes closely related organisms to resemle each other less than expected (osos 2). Other modes of evolution can also lead to amounts of phylogenetic signal that are lower than expected from a given topology and ranch lengths, including limits to evolution (e.g., see Garland et al. 1993; DíazUriarte and Garland 1996) and the OrnsteinUhleneck process (which models stailizing selection that can oliterate the resemlance of descendant species and their ancestors, e.g., Felsenstein 1988; Garland et al. 1993; Hansen and artins 1996; artins et al. 22). Therefore, several workers have suggested that it might not always e necessary to incorporate phylogenetic information in statistical analyses of comparative data (e.g., Westoy et al. 1995; Bjorklund 1997; rice 1997; azer 1998; osos 1999; Ashton 21). For a given set of species and a given phenotypic trait, the amount of phylogenetic signal might not e very strong. Thus, perhaps one should first test for phylogenetic signal in a dataset and apply a phylogenetically ased statistical method only if it is statistically significant (e.g., Gittleman and Kot 199; Gittleman et al. 1996a; Aouheif 1999). As a relatively early example, Gittleman and uh (1992, p. 41) concluded that it is mandatory initially to diagnose the comparative data at hand.... If phylogenetic correlation is not oserved, then comparative method procedures should not e adopted. Following this suggestion, some empirical papers have not used phylogenetically ased statistical methods, after one or more diagnostic tests indicated a lack of significant phylogenetic signal (e.g., Irschick et al. 1997). This perspective suggests a dichotomy etween phylogenetic and nonphylogenetic analyses, ut this dichotomy is oth illusory and unnecessary. As noted previously (e.g., urvis and Garland 1993), a conventional (nonphylogenetic) statistical analysis is just one that assumes the special case of a star phylogeny, that is, a hard polytomy with equallength ranches (and implicitly assumed character evolution y something like Brownian motion with stochastically equal rates along each ranch). The three estjustified phylogenetically ased statistical methods can all e applied with a star phylogeny, in which case they yield results that are exactly the same as a conventional analysis (phylogenetically independent contrasts: Felsenstein 1985; generalized least squares: Grafen 1989; artins and Hansen 1997; Garland and Ives 2; Rohlf 21; onte Carlo simulations: artins and Garland 1991; Garland et al. 1993; Reynolds and ee 1996). oreover, as emphasized here (see also Freckleton et al. 22), various types of ranch lengthtransformations can allow one to conduct analyses on trees that vary continuously from a star with contemporaneous tips to the original candidate tree and on to a tree with ranches transformed to imply even more phylogenetic signal than implied y the original candidate tree. The optimal ranchlength transformation can e chosen ojectively y such criteria as maxi
3 HYOGENETIC IGNA 719 mum likelihood (; est fit to data; e.g., Grafen 1989), thus allowing what amounts to a simultaneous test for phylogenetic signal (see also ynch 1991; ooers et al. 1999; agel 1999; Harvey and Ramaut 2; artins et al. 22), and/ or analyses can e conducted across a range of ranchlength transformations as a sensitivity analysis (e.g., Butler et al. 2). Given the limitations of existing methods (see Discussion), our first purpose is to provide a new test for phylogenetic signal in continuousvalued characters, ased on simple randomization procedures. This test allows us to address how common phylogenetic signal really is. econd, we provide a descriptive statistic, K, to gauge the amount of phylogenetic signal. uch a statistic is important ecause it allows comparisons of different traits across different trees and hence the possiility of discovering general patterns of relative evolutionary laility across trait types (cf. de Queiroz and Wimerger 1993; Gittleman et al. 1996a,; Wimerger and de Queiroz 1996; Ackerly and Donoghue 1998; Ackerly and Reich 1999; BöhningGaese and Oerrath 1999; orales 2; rinzing et al. 21). Third, we implement two iologically motivated transformations of ranch lengths, one of which corresponds to the OrnsteinUhleneck (OU) process and the other to a new proposed model of accelerating versus decelerating rates of character evolution (ACDC). aximum likelihood estimation of the OU or ACDC transformation parameter provides an alternative approach to testing for phylogenetic signal and can e used to guide the choice of ranch lengths for a comparative analysis. As descried in Felsenstein s (1985) seminal paper, the ranch lengths used in phylogenetically ased statistical methods are assumed to e proportional to expected variance of character evolution for the trait(s) in question. uch ranch lengths cannot e known directly, except perhaps in some laoratory organisms with known histories, generation s, mutation rates, and selective regimes. Therefore, reasonale approximations must e employed. tarter ranch lengths may e real, such as estimated genetic distances or divergence s, or they may e aritrary, as derived from a simple setting rule (e.g., Cheverud et al. 1985; Grafen 1989; agel 1992; ). Real ranch lengths oviously can enhance the iological relevance of statistical inferences, ut aritrary ranch lengths do not necessarily negate iological interpretations (e.g., see Grafen 1989; Garland 1992; Cloert et al. 1998). Whatever starter ranch lengths are employed, performance of phylogenetically ased statistical methods, in terms of Type I error rate, can often e improved y transformation of the ranch lengths (Grafen 1989; Garland et al. 1992; DíazUriarte and Garland 1996, 1998; artins et al. 22). Transformation of ranch lengths will also affect the interpretation of parameters estimated from comparative data, including the degree of phylogenetic signal and the form of the relationship etween traits (e.g., see Gittleman and Kot 199; artins 1996; Garland et al. 1999). Therefore, we also review various ways of transforming ranch lengths. Finally, we apply all of our proposed procedures to empirical examples taken from the literature. We ask how common significant phylogenetic signal really is, compare the amount of phylogenetic signal among different types of traits, and compare results of the randomization and ranchlengthtransformation tests for signal. ARANDOIZATION TET FOR HYOGENETIC IGNA A randomization procedure can e used to test whether a given set of comparative data exhiit a significant tendency for related species to resemle each other. This test makes no assumption aout the model of evolution that produced the data oserved at the tips (terminal nodes) of the phylogeny, except to the extent that (possily aritrary) ranch lengths are used in the calculations and they imply something aout how much variance in character evolution occurs etween nodes (e.g., Felsenstein 1985; DíazUriarte and Garland 1996, 1998). The asic idea is to ask whether a given tree (topology and ranch lengths) etter fits a set of tip data as compared with the fit otained when the data have een randomly permuted across the tips of the tree, thus destroying any phylogenetic signal that may have existed. The test can e implemented via a G approach (Grafen 1989; artins and Hansen 1997; Garland et al. 1999; agel 1999; Butler et al. 2; Garland and Ives 2; Rohlf 21), for which we have written the ata program HYIG. (see Appendix 1; this and other programs are availale at edu/faculty/garland/hyig.html). Alternatively, it can e implemented via phylogenetically independent contrasts, for which existing modules of our henotypic Diversity Analysis rogram (DA: DA.html) can e used. With independent contrasts, the method works as follows. For a given set of real data, one computes standardized phylogenetically independent contrasts in the usual way (Felsenstein 1985; Garland et al. 1992; urvis and Garland 1993). The variance of the contrasts is then computed as an index of how well the tree fits the data. If related species tend to have similar values for a given trait (i.e., they exhiit phylogenetic signal), then the variance of contrasts will tend to e low. To determine whether the oserved phylogenetic signal is statistically significant, the variance of contrasts for the real data can e compared with the values otained after the data have een permuted randomly across the tips of the tree without regard to phylogenetic relationships. If, say, 95% of the permuted datasets show variances of contrasts that are greater than that for the data in their correct positions, then we reject the null hypothesis of no phylogenetic signal. This would e appropriate for a onetailed test. The test can also detect cases for which the resemlance of relatives is actually lower than expected (which we might term phylogenetic antisignal ). Here, the permuted data would have lower variances than for the data in their original, correct positions on the tree. The procedure for computing the test with DA modules (Garland et al. 1999; Garland and Ives 2; apointe and Garland 21) is descried in its accompanying documentation (DITRW.DOC). In G mode, the mean squared error (E) is used as the test statistic, rather than the variance of contrasts, ut the test is performed in exactly the same fashion as descried aove and the results will e identical (following from the results in Appendices 4 and 5).
4 72 ION. BOBERG ET A. TYE IERROR RATE AND OWER OF THE RANDOIZATION TET To calculate the approximate Type I error rate of our proposed randomization test for phylogenetic signal, we simulated 1 datasets under the Brownian motion assumption (DIU of Garland et al. 1993), using a star phylogeny (contemporaneous tips) with species (one large polytomy with equal ranch lengths). For these datasets, species have no relatives that are closer than any other, and hence no phylogenetic signal can exist. Therefore, under the null hypothesis and holding at.5, 5% of the simulated datasets should appear to exhiit significant phylogenetic signal when analyzed on any given hierarchical phylogenetic tree. Thus, for each of the simulated datasets, we used HYIGER. to conduct the randomization test as descried aove (ut using only 1 permutations for each of the 1 simulated datasets), using three different hierarchical trees: a symmetrical tree with contemporaneous tip heights, a laddershaped (pectinate) phylogeny with contemporaneous tip heights, and a laddershaped tree with all ranch lengths set equal to one. In each case, we calculated how many of the 1 simulated datasets appeared to show significant phylogenetic signal (i.e., 95 or more of the permuted datasets showed greater variance than for the data in their original positions on the tree), resulting in a onetailed estimate for the Type I error rate. In future studies, it would e of interest to examine the effects of different evolutionary models on Type I error rates, as well as power to detect phylogenetic signal. We expected the power (1 Type II error rate) of the randomization test to depend on oth tree size and shape. Therefore, we considered the same three tree shapes as descried in the previous paragraph. For the symmetrical trees, we used sample sizes of eight, 16,, and 64 species. For the laddershaped trees, we used those sizes plus 12, 24, and 48. For each tree, we simulated 1 datasets under Brownian motion. For each of the 1 simulated datasets, we used HYIGER. (with 1 permutations) to determine the proportion for which we rejected the null hypothesis (for.5) of no phylogenetic signal. Given that the null hypothesis was indeed false in all cases (i.e., the simulations were produced on a hierarchical tree), this proportion is an estimate of the power of the test. For very small trees, there are few distinct possile permutations of the data. For example, on a symmetrical tree with four tips, the data can only e permuted in, at most (assuming all values are unique), three ways that yield distinct values of E. Thus, it is simply not possile to get a statistically significant result (i.e., detect phylogenetic signal at.5) with a fourtipped tree. The numer of possile distinct E categories depends on tree topology in addition to size and on the numer of unique tip values. Therefore, when analyzing real data with small trees (e.g., N 7), the user should check the histogram of E values for the permuted data that is provided y HYIG. and make sure that it is possile to detect a signal at.5. All else eing equal, one would expect that character evolution along a strongly hierarchical tree would lead to a greater degree of phylogenetic signal than would e the case for evolution along a tree that was more starlike in shape (i.e., a rapid radiation involving multiple, nearly simultaneous speciation events, followed y a relatively long period of independent evolution leading to the tip species). To examine the effect of such variation in tree shape, we used a phylogeny of 49 Carnivora and ungulates as a starter tree (from fig. 1 of Garland et al. 1993). We then transformed that tree using the OU transformation (which can mimic stailizing selection; see elow), with parameter values of 1.5, 1.,.95,.9,.8,.7,.6,.5,.4, and.2, the latter six values producing successively more starlike versions of the starter tree (Fig. 1 shows some examples). We then proceeded as efore, simulating 1 datasets under the Brownian motion model for each of the seven trees, and conducting the randomization test on each dataset, with 1 permutations for each test. Type I error rates for the randomization test were close or equal to the nominal value of.5 (.48 for the symmetrical tree,.5 for the laddershaped tree,.51 for the laddershaped tree with all ranch lengths equal to one). ower increases dramatically with sample size, with a value of.8 reached at approximately 2 species (Fig. 2). ower also varies somewhat with tree topology and/or ranch lengths: oth symmetrical trees and ladders with equal ranch lengths tend to show slightly higher power at all sample sizes. Finally, as expected, transformation of the 49species tree to e less hierarchical (Fig. 1) reduced power to detect phylogenetic signal (Fig. 3). EIRICA EXAE OF TETING FOR HYOGENETIC IGNA For this initial application of our proposed randomization test for phylogenetic signal (and methods of ranchlength transformation as discussed elow), we did not do an exhaustive survey of pulished phylogenetically ased comparative studies, ut we did endeavor to include a wide range of organisms and trait types. We favored studies that have used DA (Garland et al. 1993, 1999; Garland and Ives 2) simply ecause we were often ale to otain the original input files from the original authors, hence expediting our analyses and reducing the possiility of introducing errors when reentering data. In some studies that presented data on multiple traits, some of the traits were redundant (e.g., multiple measures of ody size), so we somes analyzed only a suset of the traits presented. For simplicity, we used the topology and ranch lengths as reported in the original paper and did not try to either update phylogenetic information or transform ranch lengths (see elow). In total, we analyzed 9 traits that were associated with 34 different phylogenies; sample size ranged from six to 254 (Appendix 6). Datasets included organisms that ranged widely in phylogenetic position and scope, including plants, verterates, mammals, irds, lizards, salamanders, fish, and Drosophila. For traits that varied closely with ody size, we computed sizecorrected values as follows. First, we logtransformed the trait as well as the measure of size (mass or snoutvent length). econd, we computed standardized phylogenetically independent contrasts for oth traits. Third, we computed a leastsquares linear regression through the origin for the contrasts and noted the slope, (allometric exponent).
5 HYOGENETIC IGNA 721 FIG. 2. Relationship etween statistical power of the permutation test for detecting phylogenetic signal and sample size (numer of species in the tree) as well as tree shape. Irrespective of tree shape, power is good (.8) when sample size is greater than aout 2 species. As a heuristic, a smoothed line is fitted to the data for the laddershaped tree. than 2 showed signal, whereas signal was detected for only 41% (27 of 66) of the traits with N less than 2 (Appendix 6). For N 2, the four traits that did not show significant phylogenetic signal were: log (male/female) ody mass of genera of ats (.89; Nee s aritrary ranch lengths, Hutcheon 21), log masscorrected maximal metaolic rate of 47 irds (.26; DNA hyridization ranch lengths, Rezende et al. 22), rhytidome allometric coefficient of inus spp. (.9;, Jackson et al. 1999), and log FIG. 1. Examples of the phylogenies used for simulations to illustrate the effect on statistical power of relative starness. Top is phylogeny with ranch lengths proportional to estimated divergence s for 49 species of Carnivora and ungulates (fig. 1 of Garland et al. 1993). iddle is the same tree ut transformed with a value of d.7, where d is the restraining parameter in the Ornstein Uhleneck transformation (see text and Appendix 2). Bottom is tree with d.2. imulations were conducted on trees with d 1.5 (making it more hierarchical), 1. (original tree),.95,.9,.8,.7,.6,.5,.4, and.2 (see Fig. 3 for results). Finally, we computed sizecorrected values for the original trait (not contrasts) as: log[trait/(size )]. As expected from the power simulations, whether a trait showed significant (.5) phylogenetic signal was strongly related to the numer of species included in the study: 92% (49 of 53) of the traits with sample sizes greater FIG. 3. ower of the permutation test in relation to tree starness. d is the restraining parameter in the OrnsteinUhleneck transformation; d 1.5 makes the tree more hierarchical, d 1 represents the original tree, as shown in the top of Figure 1, and successively smaller values of d represent increasing starness. (A dvalue of zero would yield a star phylogeny, i.e., a single polytomy with equal ranch lengths.) As a heuristic, a smoothed line is fitted to the data.
6 722 ION. BOBERG ET A. FIG. 4. Example in which significant phylogenetic signal is detected for one trait ut not for another. (A) referred ody temperature of some Australian skinks (.1, K.453). (B) Optimal ody temperature for sprinting (.167, K.). Data and tree are from Huey and Bennett (1987) and Garland et al. (1991), respectively. of masscorrected group size of 28 macropod marsupials (.161;, Fisher and Owens 2). Importantly, irrespective of sample size, we found no case in which the permuted data showed significantly less variance (.95) than the real data, that is, no case in which the data showed what might e termed antisignal. Figure 4 shows an example in which phylogenetic signal is detected for one trait ut not another within a given study. Data on ody temperatures of Australian skinks are plotted aove their corresponding phylogeny (as used in a pulished comparative analysis y Garland et al. 1991). For preferred ody temperature, close relatives tend to e similar: phylogenetic signal is detected (.1). For the optimal temperature for sprinting, however, relatives are not significantly more similar than if placed randomly on the tree (.167). QUANTIFYING HYOGENETIC IGNA The randomization test descried aove provides a method wherey the degree of resemlance among relatives can e distinguished from random. Here, we derive a descriptive statistic that indicates the strength of phylogenetic signal, as compared with an analytical expectation ased only on the tree structure (topology and ranch lengths) and assuming Brownian motion character evolution. A statistic to quantify phylogenetic signal egins with the ratio of the mean squared error of the tip data, measured from the phylogenetically correct mean (E ), divided y the mean squared error of the data calculated using the variancecovariance matrix derived from the candidate tree (E). If the candidate tree accurately descries the variancecovariance pattern oserved in the data, then the value of E will e relatively small, leading to a large value of E /E. Conversely, if there is little covariance within the tip data that is explained y the candidate tree, then E will e relatively small, leading to a smaller value of E /E. Thus, high values of E /E imply more phylogenetic signal. E is calculated simply as: (X â) (X â) E, (1) n 1 where â is the estimate of the phylogenetically correct mean (equal to the estimated trait value at the root node of the tree; e.g., see Garland et al. 1999) and X is the data vector containing n values. E is calculated as: (U â) (U â) E, (2) n 1 where U DX is the transformed X vector otained from the generalized leastsquares procedure. The matrix D satisfies the equation: DVD I, where V is the variancecovariance matrix and I is the identity matrix (Garland et al. 1999). The ratio E /E is calculated from the data, and although relatively large values imply more phylogenetic signal, values of E /E from different phylogenetic trees are not directly comparale. For such comparisons, however, it is possile to scale the value of E /E y that value that is predicted under the assumption of Brownian motion evolution along the specified tree. pecifically, the expected ratio under Brownian motion is: E 1 n trv 1, (3) E n 1 V where trv is the trace (sum of diagonal elements) of V, and V 1 is the sum of all elements of the inverse matrix of V. Note that it is not possile to extract separately the numerator and denominator of this expectation. The expected E /E is a useful descriptor of how hierarchical a given tree is, with larger values corresponding to greater degrees of hierarchy. Figure 5 shows that, for the 35 trees represented in Appendix 6, the expected E /E varies widely and also shows a positive dependence on tree size. For several trees, the expected E /E is rather far aove or elow the general trend, and consideration of their ranch lengths makes it apparent why. For example, the largest tree in our sample, with 254 species, is for irds (Reynolds and ee 1996) and is ased primarily on the DNA hyridization phylogeny of iley and Ahlquist (199). any ifurcations occur near the ase (root), leading to many long ranches (this
7 HYOGENETIC IGNA 723 (X ā) (X ā) E*, (5) n 1 FIG. 5. Effect of tree size on expected amount of phylogenetic signal (expected E /E) for 35 phylogenetic trees used in pulished comparative studies. Note the strong positive effect of tree size and that some values deviate from the general trend ecause of unusual ranch lengths (see text for discussion). tree is shown in fig. 5A of Garland and Ives 2) and hence relatively less expected phylogenetic resemlance (E / E 3.54) than would e the case for a tree of equal size ut heights of nodes more uniformly distriuted etween the root and the top of the tree. The same tree with agel s (1992) aritrary ranch lengths yields a value of 6.37, with Grafen s (1989) aritrary ranch lengths the value is 3.69, and with all ranch lengths set equal to one the value is Also consider two trees aove the general trend. The tree with N 12 (estimated divergence s, as shown in Fig. 4) has few long, internal ranches and many short ranches at or near the top of the tree, yielding an expected E /E of With Grafen s aritrary ranch lengths the value is 2., with agel s aritrary the value is 1.47, and for all ranch lengths set equal to one the value is 1.5. erry and Garland (22; N 18) used Grafen s aritrary ranch lengths, which tend to result in long ranches at the ase of the tree, yielding an expected E /E of 1.2. Expected E / E using agel s (1992) ranch lengths is 4.6, more in line with the general trend (with all ranch lengths equal to one, E /E 3.54). Given that the expected value of E /E depends on tree size and shape, it is natural to scale the oserved E / E ratio y the expected ratio, thus allowing comparisons of traits regardless of tree characteristics. Therefore, we used the following statistic, E E K oserved expected. (4) E E A K less than one implies that relatives resemle each other less than expected under Brownian motion evolution along the candidate tree. This could e caused y either departure from Brownian motion evolution, such as adaptive evolution that is uncorrelated with the phylogeny (i.e., homoplasy), or measurement error in the road sense (see Discussion). A K greater than one implies that close relatives are more similar than expected under Brownian motion evolution. An alternative to K involves the ratio E*/E, where E* is calculated from the oserved data on a star phylogeny with contemporaneous tips: where ā is the estimate of the mean of the raw data X, rather than the phylogenetically correct mean â as used in the calculation of E. The ratio E*/E has the expected value under the assumption of Brownian motion of: E* 1 V trv. (6) E n 1 n Dividing oserved y expected values yields a statistic that we term K*. Here, we only present results for K, which we feel has greater heuristic value. However, K and K* are highly correlated, so results using the latter would e similar. EIRICA EXAE OF QUANTIFYING HYOGENETIC IGNA We considered the same set of traits and trees as aove, plus one study involving only four species of irds (which is too few species for the randomization test). Hence, the total numer of traits was 121, from 35 trees (Appendix 6). Considering all of these datasets, K ranged from.84 for the duration of head os in Cyclura lizards (a ehavioral trait; artins and amont 1998) to 4.2 for ody mass of female macropod marsupials (Fisher and Owens 2). As aove in the analysis using the randomization test for signal, we used the topology and ranch lengths as reported in the original paper and did not transform ranch lengths. As shown in Figure 6, K does not vary in relation to tree size, ut K is less than one for most traits, indicating a general tendency for less signal than expected under Brownian motion along the specified tree. To compare the amount of phylogenetic signal across traits of different types, we categorized them as adult ody size, life history, morphological, physiological, ehavioral, or ecological. Categorizing traits is not always straightforward (see also Gittleman et al. 1996a; tirling et al. 22), and we view the following analyses primarily as a heuristic. We would encourage others to perform similar analyses with larger and more comprehensive datasets and alternative categorization schemes. Adult ody size (N 24) was treated as a separate category ecause it has somes een viewed as a morphological trait, yet is often highly correlated with various lifehistory and physiological traits (Calder 1984; Roff 1992; Gittleman et al. 1996a), and is the end result of the ontogenetic growth trajectory, which would usually e considered a lifehistory trait. Our morphological category (N 35) included such traits as sizecorrected lim length, ill length, rain size, testes mass, and sperm length, as well as individual leaf area and petiole length. ifehistory traits (N 2) included age at maturity, length of nestling period, adult mortality, clutch size, log male/female ody size (an index of sexual dimorphism), seedling height, and seed size and viaility. hysiological traits (N 21) included metaolic rates, maximal sprint speed, stride frequency, critical thermal minimum and maximum, and enzyme activities. Behavioral traits (N ) included daily movement distance, prey size, display characteristics, and preferred ody temperature. Only four traits were in our ecological category: seasonality of
8 724 ION. BOBERG ET A. FIG. 6. Empirical relationship etween tree size (numer of species) and K, a statistic that indicates the amount of phylogenetic signal oserved in a set of comparative data divided y the amount expected under Brownian motion character evolution along the specified tree topology and ranch lengths. ost traits show less signal than expected, that is, K less than one. Analysis of covariance indicates that log K does not vary systematically with log tree size ut does differ significantly among trait types (see text).
9 HYOGENETIC IGNA 725 environments of 52 Carnivora (coefficient of variation of actual evapotranspiration averaged across year and across geographic range; K., Ferguson and ariviere 22), rainfall in haitats of 28 macropods (corrected for correlation with ody mass; K 1.1, Fisher and Owens 2), mean annual temperature in the waters of 15 Fundulus fish (K., ierce and Crawford 1997), latitude of ranges of 15 Drosophila (K.73, Giert and Huey 21). (The datasets analyzed y Freckleton et al. [22] include a large numer of ecological traits.) After exclusion of the four traits in the ecological category owing to small sample size, data were analyzed y a conventional oneway ANOVA, following log transformation to homogenize variances. The datasets included clearly do not meet the assumption of independence some of them include the same taxa and even some of the same species ut we could not envision any simple way to deal with the situation. In any case, as noted aove, we present the analyses primarily as a heuristic. Trait categories differed significantly in mean log 1 Kvalue (F 4.97, df 4, 2,.1). evene s test indicated no significant difference in variance among categories (twotailed.266). Based on Duncan s multiple range comparison (.5, and using harmonic mean cell sample size of ), ehavioral traits showed significantly lower log 1 K than did the other four types of traits. In addition, physiological traits showed lower log 1 K than did ody size. ean values (95% CI) for log 1 K were: ody size.8 (.2,.3); morphology.15 (.23,.7); life history.2 (.35,.5); physiology.27 (.39,.14); and ehavior.45 (.63,.27). To verify that log 1 K was uncorrelated with tree size, we repeated the analysis as an ANCOVA and found that log 1 tree size was not a significant covariate (F 1.88, df 1, 1,.3), whereas trait type remained highly significant (F 5.22, df 4, 1,.1). We were concerned that the finding of lower Kvalues for ehavioral traits could e misleading if most of the ehavioral traits were from a small numer of studies. That is not the case, however, as 1 studies included ehavioral traits (see Appendix 6, Fig. 6). Five of the ehavioral traits were from a single study of nine lizards (artins and amont 1998). One of these traits exhiited the lowest K of any trait that we examined (K.8), ut the other four traits exhiited Kvalues that were not unusual (range..79; Appendix 6, Fig. 6). Therefore, we do not think that our results are iased y particular studies. Nevertheless, another way to compare traits is to consider only studies that include multiple types of traits. Given that the ANOVA indicated ehavioral traits to have lower levels of phylogenetic signal as compared with other traits, it was of interest to examine those studies that included oth ehavioral and other traits. Unfortunately, this amounted to only seven of the 35 studies listed in Appendix 6. Averaging values within categories for the seven studies yielded: 18 lizards, ehavior., ody size.2; 75 antelope, ehavior.5, ody size 1.23; 49 Carnivora ungulates, ehavior.58, ody size.57; 28 macropod marsupials, ehavior.49, mean of two other traits 3.84 (one ecological trait excluded, as in overall ANOVA descried aove); 26 hystricognath rodents, mean of two ehavior traits.9, ody size 1.3; 27 swallows, ehavior.26, mean of three other traits.27; and 12 skinks, ehavior.45, mean of three physiological traits.29. Thus, for this susample of studies, ehavioral traits still tend to exhiit lower K, ut a paired ttest of the logarithms of these seven values indicates no statistical difference (t 8, twotailed.187). We suspect that this lack of significance reflects mainly a lack of power caused y small sample size. ROBABIITY OF DETECTING IGNA DEEND ON BOTH N AND K Whether phylogenetic signal, as judged y the randomization test, is detected for a given trait would e predicted to depend on oth sample size (numer of species) and the amount of signal for that trait. Therefore, as a heuristic, we performed a multiple regression of log (value 1) on log N and log K. The overall model was highly significant (multiple r 2.4, F 38., df 2,6,.1) and oth independent variales were significant negative predictors (log N: F 44.6,.1; log K: F 36.9,.1; the correlation etween log N and log K was.74). (The logtransformed values exhiit a nonnormal and truncated distriution, ecause only 1 permutations were used to generate them, ut nonetheless residuals did not deviate excessively from normality.) TATITICA AND BIOOGICA AROACHE TO BRANCHENGTH TRANFORATION As noted in the introduction, ranch lengths are an integral part of comparative analyses, including the proposed randomization test for phylogenetic signal and the descriptive Kstatistic. oreover, certain ranchlength transformations can allow an alternative test of signal y comparing fits to data on a series that includes a star as one end of the continuum (e.g., Grafen 1989; next section). The choice of ranch lengths for phylogenetically ased statistical analysis involves several issues, including: (1) the type of starter ranch lengths employed (e.g., whether they are aritrary or ased on data); (2) types of transformations to e considered; and (3) the inferences that may or may not e appropriately drawn from analyses that employ a given type of ranch length and/or transformation. Ideally, ranch lengths may e derived from data, such as genetic distances or estimates of divergence s derived from the fossil record. any comparative analyses, however, have employed aritrary ranch lengths, such as setting all segments equal to one or aligning tip species at the top of the tree and setting the depths of internal nodes y an aritrary algorithm (Grafen 1989; agel 1992;. Nee cited in urvis 1995). Typically, this is done ecause real ranch lengths are unavailale, ut some workers actually seem to prefer aritrary ranch lengths (especially all equal to one) as somehow making fewer assumptions aout the data or analysis. Whatever the reason, approximately onethird of the studies that we have analyzed employed aritrary ranch lengths (13 of 35 trees, 55 of 121 traits). However starter ranch lengths are otained, they should e checked for statistical adequacy ased on one or more
10 726 ION. BOBERG ET A. diagnostics (Grafen 1989; Garland et al. 1991, 1992; urvis and Ramaut 1995; DíazUriarte and Garland 1996, 1998; Reynolds and ee 1996; Garland and DíazUriarte 1999; Harvey and Ramaut 2). If they fail these diagnostics, then transformations of the ranch lengths (or of the tip data) may e employed as a remedial measure. ost commonly, the type of ranchlength transformation used has een chosen aritrarily and from a purely statistical perspective. Examples include log transformation, raising all ranches to a power, and setting all ranches equal to one (equivalent to raising ranch lengths to a power of zero). In addition, ranch lengths may e transformed proportional to the estimated relative amount of evolution (character variance) that has occurred aove and elow them on the tree, using Grafen s (1989) (see also agel 1994, 1999). Branch lengths may also e transformed y extending only the terminal ranches, which may e appropriate in the presence of measurement error in the tip data (T. Garland and A. R. Ives, unpul. data). Finally, all ranch segments etween internal nodes may e collapsed to zero and ranches leading to tip species (terminal taxa) set to e equal in length, thus enforcing a star topology on the tree. This can e viewed as one end of the continuum of possile transformations, one which is appropriate under the assumption that all traits evolved independently from a common ancestor, as is implicit when conventional statistical methods are applied to comparative data (e.g., see Grafen 1989; urvis and Garland 1993; Garland et al. 1999). Note also that if the tips of the eginning phylogenetic tree are contemporaneous, then using Grafen s (1989) transformation with also results in a star phylogeny. Alternatively, if ranch lengths are viewed as entities that may contain important iological information in their own right, then transformations of ranch lengths according to some explicit model of evolution and estimation of the parameters in the model may provide useful information aout underlying evolutionary processes (artins 1994; ooers et al. 1999; artins et al. 22). (Of course, this argument has greatest weight if the starter ranch lengths are real rather than aritrary.) To date, only one iologically motivated transformation has een suggested, the OrnsteinUhleneck (OU) process, which has een proposed as a model of stailizing selection (Felsenstein 1988; Garland et al. 1993; Hansen and artins 1996; artins et al. 22). Testing for significance of the parameter associated with the OU model can thus e viewed as a test for stailizing selection (artins 1994). Very strong stailizing selection can oliterate the effects of history such that phylogenetic signal disappears; accordingly, one end of the continuum of an OU transformation results in a star phylogeny. imilarly, as we show here, a transformation can e developed that models the acceleration or deceleration of Brownian motion evolution from the root to the tips of the tree (hereafter ACDC). Rapidly accelerating rates of evolution can also erase the effects of descent with modification, and again one end of the ACDC transformation results in a phylogeny that is virtually a star (very little hierarchical structure). Even if ranch lengths and transformations of them are aritrary, iological inferences can somes still e made, provided traits are compared with respect to the same set(s) of ranch lengths. For example, if multiple traits are considered, then traitspecific differences in the rate of evolution etween two clades may e apparent (e.g., Cloert et al. 1998), although it may not e clear which clade is evolving relatively faster. Along these lines, agel (1999) and Freckleton et al. (22) employ a transformation parameter ( ) that is not ased on an explicit model of evolution, ut they argue that various iological interpretations may nonetheless e possile when the estimate differs significantly from unity. It is also possile to estimate the length of each ranch segment from the tip data themselves (Garland et al. 1992, 1999), and ooers et al. (1999) used an approach that allowed them to draw iological inferences in comparisons of different types of traits. Whatever type of starter ranch lengths are used and whatever types of transformations one is willing to consider, the decision as to whether a transformation should actually e applied is not always clearcut. The prolem can e descried as a continuum etween two philosophies. At one end is the perspective that transformations should always e applied to satisfy assumptions of the ensuing statistical test as closely as possile. At the other end of the continuum is the idea that transformations should only e performed when asolutely necessary, for example, when diagnostics show statistically significant prolems; minor departures from the assumptions of ensuing statistical tests can e ignored ecause most are roust to at least some departure from their assumptions (see discussion in DíazUriarte and Garland 1996). TRANFORATION OF BRANCH ENGTH UNDER EVOUTIONARY ODE The OU model descries the motion of a species in the phenotypic space wherey the species moves randomly within the space, ut is influenced y a central tendency such that large deviations from the central optimum receive a stronger force ack toward the optimum (Felsenstein 1988; Garland et al. 1993; artins 1994; Hansen and artins 1996; Butler et al. 2; artins et al. 22). We implement a simple version of the OU process, in which the covariance relationship among the characters can e represented y the formulae: 2( 1 d ij i) 2 V{X i} and (7a) 1 d 2 1 d 2 ij i j 2 cov{x i, X j} d, (7) 1 d 2 where 2 denotes the rate of evolutionary divergence through, and d governs the strength of the central tendency of the OU process, with d 1 corresponding to Brownian motion evolution and lower values of d giving stronger stailizing selection to a central mean for all species. The nodetotip ranch length for species i is denoted i, and ij denotes the shared ranch length etween tips i and j. The resulting trait values from this OU process follow a multivariate normal distriution with covariance matrix V given y equation (7a). The derivation of these expressions given in Appendix 2 is comparale to that presented in the appendix of Hansen (1997) and to simulations under DIU (Garland et al. 1993; see DINTRW.DOC). The ACDC model is new. It descries evolution that either
11 HYOGENETIC IGNA 727 increases (accelerates, AC) or decreases (decelerates, DC) in rate over. The covariance relationship among the characters can e represented y the formulae: ( 1 g i ij) 2 V{X i} and (8a) 1 g 1 1 g ij 2 i j cov{x, X }, (8) 1 g 1 where g is the overall rate of acceleration (g less than one) or deceleration (g greater than one), and other variales are as for the OU model (Appendix 3). The resulting distriution of trait values is multivariate normal with covariance matrix V given y y equation (8a). The OU and ACDC parameters, d and g, can e estimated using in G mode (HYIGOU., HYIGACDC.), and Appendix 4 demonstrates that the estimates are also the estimated generalized leastsquares estimates. These parameters can also e estimated using independent contrasts, as descried in Appendix 5. ingleparameter transformations, such as OU and ACDC, can provide information aout the degree of phylogenetic structure in the data, y direct inspection of the estimates of the OU and/or ACDC parameters. For example, the OU transformation has one parameter (d). When d* 1, the original candidate tree est fits the data (the asterisk refers to the estimate of the parameter). When d* is etween zero and one, the data show less structure than would e expected from the original candidate topology and ranch lengths (e.g., see Fig. 1). For d* greater than one, the data show more structure than expected, given the original tree (as is also true for the of agel 1999; Freckleton et al. 22). When d*, the estfitting tree is estimated to e a star phylogeny. The ACDC transformation also has one parameter (g), and interpretation is similar. When g* is less than one, the transformed tree is more starlike than the original candidate. Unlike d, g can never equal zero; however, very small values of g (for estimation in ata, the smallest possile value of g is ) can yield matrices that are virtually the identity matrix (e.g., mathematically indistinguishale in terms of E), and limits on numerical precision in some programs may cause rounding or truncation that leads to an identity matrix. A g* greater than one yields a tree that is more hierarchical than the original candidate tree. A test of whether d is significantly different from zero provides information that is somewhat similar to our proposed randomization test for phylogenetic signal (see aove), ut different in an important way. If the est estimate of d for a given dataset is zero, then a star phylogeny etter fits the data than does the original candidate tree or any OU transformations of it. The randomization test attempts to detect resemlance among relatives, ut does not actually compare the candidate tree with a star. Nevertheless, results of the two procedures would e expected to e correlated (at least for trees with N 2): if no phylogenetic signal is detected, then we would proaly estimate an optimal d that is close to zero. The same would e true for g, the ACDC transformation parameter, except g can never equal exactly zero. To examine the effect of tree size on the estimation of d and g, we simulated data (DIU) under Brownian motion along three sets of trees with four, eight, 16, and tips: symmetrical with contemporaneous tip heights, laddershaped (pectinate) with contemporaneous tip heights, and laddershaped with all ranch lengths set equal to one. We did 1 simulations for each of the 12 trees. We then calculated d and g for each simulated tree (using our program HYIGDG.) and plotted the frequency distriution for each statistic at each sample size. As shown in Figure 7, sampling distriutions for d and g were not normal and contain many values that are at (d) or near (g) zero, particularly at small sample sizes. Thus, d and g cannot e estimated relialy for trees of fewer than aout 2 tips. The highly nonnormal distriutions for estimates of d and g are prolematic for construction of traditional confidence intervals. Therefore, we devised a randomization procedure to determine whether the given set of real data could have produced a d orgvalue as much greater than zero as that oserved, if there were actually no phylogenetic signal in the data (HYIGOU., HYIGACDC.). First, we calculated the oserved d (or g) for the original tree and data. econd, data were permuted equiproaly across the tips of the tree (1 s), and then the estimate of the parameter d (or g) was calculated for each permuted dataset, along with the corresponding E. The numer of cases for which E of the permuted data was greater than the value for the original data in their correct positions was tallied. If many cases (e.g., 95%) were found for which the Es for permuted data were greater than the E for the real data, then the hypothesis that d is zero could e rejected. Interpretation is similar for estimation of g (the ACDC parameter), except that, as noted aove, g can never actually take on a value of zero. Freckleton et al. (22) recently presented an approach to testing for phylogenetic signal that is similar to that just descried. Their analysis is ased on agel s (1999), which is a parameter multiplied to each of the offdiagonal elements of the variancecovariance matrix V. Although is not associated with an explicit model of evolution, it is nonetheless a straightforward way to decrement the candidate phylogenetic structure of the data y uniformly decrementing all covariances. Thus, when, the tree has a single polytomy at the asal node for all species, whereas when 1 the original candidate tree is recovered. tatistical tests for phylogenetic signal are performed under the null hypothesis that, and tests for less signal than the candidate tree are performed under the null hypothesis that 1. As we do for d and g of the OU and ACDC processes, Freckleton et al. (22) estimate the estfitting using. In contrast to our use of randomization tests for statistical inference (otaining values), Freckleton et al. (22) use a loglikelihood ratio test. We prefer randomization tests, ecause loglikelihood ratio tests are only asymptotically valid, and for small sample sizes (where, unfortunately, what is meant y small depends on the structure of the data, ut is generally fewer than 3) loglikelihood ratio tests can give noticealy imprecise values. We can also test the null hypothesis that d (or g) 1 (i.e., that the original candidate tree adequately fits the data). For this, we devised another randomization procedure. For any
12 728 ION. BOBERG ET A. FIG. 7. Distriution of estimated OrnsteinUhleneck (OU; d) or accelerationdeceleration (ACDC; g) ranchlength transformation parameters in relation to tree size for a laddershaped (pectinate) tree with four, eight, 16, or tips. Data were simulated under simple Brownian motion (DIU.EXE; see Garland et al. 1993). For all panels, N 1, and values of d or g greater than 1.4 are placed into a single in. Results were similar for laddershaped trees with all ranch lengths set equal and for symmetrical trees with contemporaneous tips (results not shown). Estimation of d and g is unstale for smaller trees, so we recommend relying on these types of ranchlength transformations only for trees with aout 2 or more species. value of d, the fit of the OU model can e judged y examining the E. To test the null hypothesis that d 1, one can calculate the difference etween the E when d 1, denoted E d 1, and the E under the estimate of d, denoted E d d. The difference E d 1 E d d measures the decrease in E (and hence the degree of fit) of the model when d equals the value compared to when d 1. tatistical confidence in distinguishing the estimate of d from d 1 requires estimating the proaility of otaining the oserved difference E d 1 E d d under the null hypothesis that d 1. If the oserved value of the difference E d 1 E d d falls outside the 95% interval of values of E d 1 E d d otained from permutation of the data under the assumption that d 1, then d differs from one with 95% confidence. To create a permutation dataset of values of E d 1 E d d under
13 HYOGENETIC IGNA 729 the assumption that d 1, we first fit the model with d 1. In G mode, this fitting produces residuals that, under the assumption that d 1, are independent in the data space defined y transforming the data according to the covariance matrix V of the phylogenetic tree; these residuals are denoted in Garland and Ives (2). We then permuted these residuals equiproaly and acktransformed the permuted values to create a new permutation dataset (Efron and Tishirani 1993). The permutation dataset was analyzed in the same way as the true dataset: the value of the estimate of d was calculated, and the fit of the resulting model was compared to the case of d 1 using the difference E d 1 E d d. By repeating this procedure many s (e.g., 1 permutation datasets), we otained a collection of equiproale values of E d 1 E d d under the assumption that d 1. If the oserved value of E d 1 E d d for the real data falls outside the 95% confidence interval of the permutation distriution, then we concluded that d 1. Note that ecause E d 1 E d d will always e positive, the 95% confidence interval is onetailed. The test for whether g 1 under the ACDC transformation is conducted in an identical way. These calculations are performed in HYOUHd.m and HYACDCHg.m. Finally, note that this procedure can e used to test the statistical significance of any hypothesized value of d, not just the hypothesis d 1, y setting d to the tested value efore constructing the permutation datasets. EIRICA EXAE OF BRANCHENGTH TRANFORATION As noted aove, estimation of the OU transformation parameter, d, for a given set of data on a given candidate tree is equivalent to testing for the est fitting tree along the continuum from a star (with contemporaneous tips; d ) to the original tree (d 1) and on to a tree that is even more hierarchical than the original (d 1). For the 2 trees with more than 2 tips (see Appendix 6), the null hypothesis that d was equal to zero was rejected (.5) for 89% of the traits considered (47 of 53). Thus, a star phylogeny usually could e rejected in favor of a hierarchical tree. For 1 cases, the estimate of d was significantly greater than zero ut less than one, indicating that the estfitting tree is neither the original candidate nor a star, ut rather something intermediate in terms of amount of hierarchical structure. For 19 of 53 traits on trees with more than 2 tips, the estimate of d was greater than one, and it was significantly greater than one in three of these: plasma osmolarity of 2 verterates (starting ranch lengths, d 1.27,.1; Garland et al. 1997); log ody mass of 75 antelope (divergence ranch lengths, d 1.,.1; Brashares et al. 2); log masscorrected ill length of 58 irds (, d 1.25,.49; zékely et al. 2). Thus, occasionally a tree more hierarchical than the original etter fit the data. imilar to estimation of the OU transformation, estimates of the ACDC parameter, g, were significantly greater than zero for 49 of 53 cases (two of the cases that were not greater than zero were the same as for d, ut two were different). Thus, again a star phylogeny usually could e rejected in favor of a hierarchical tree. (For the trees with all ranch lengths 1, estimation of g was somes prolematic [e.g., irregular likelihood surface]; hence, results for such trees should e interpreted with caution.) For 16 cases, the estimate of g was significantly greater than zero ut less than one. Figure 8 shows an example for which oth d and g were significantly different from oth zero and unity, and for which the ACDCtransformed tree yielded the est fit to the data (lowest E). For 19 of the 53 cases, g was greater than one, and five of these were significantly so: plasma osmolarity of 2 verterates (Garland et al. 1997); log female ody mass, log masscorrected wing length, log masscorrected ill length of 58 irds (zékely et al. 2); log masscorrected tail length of 23 anguid lizards (uence ranch lengths; Wiens and lingluff 21). AGREEENT BETWEEN RANDOIZATION AND BRANCHENGTH TRANFORATION AROACHE TO TETING FOR HYOGENETIC IGNA Over 9% of the traits with sample sizes greater than 2 (49 of 53) showed statistically significant (.5) phylogenetic signal ased on our randomization test, and results of that test were in close accord with estimation of the optimal d parameter for the OU ranchlength transformation. pecifically, in 46 of the 49 cases that showed significant signal, d was significantly greater than zero, implying that a star phylogeny did not adequately fit the data (Appendix 6). Only three cases showed a (small) discrepancy etween the two test procedures. For the log(male/female) ody mass of genera of ats (Nee s aritrary ranch lengths; data and topology from Hutcheon 21), signal was not detected (.89) ut d was greater than zero (.31). For log(age at maturity) (corrected for snoutvent length) of 9 lizards (all ranch lengths 1; Cloert et al. 1998), signal was detected (.1) ut d was not greater than zero (.l61). For log(female home range area) (corrected for ody mass) of 28 macropod marsupials (all ranch lengths 1; Fisher and Owens 2), signal was detected (.33) ut d was not greater than zero (.184). Estimation of the optimal ACDC parameter, g, also agreed closely with the randomization test for phylogenetic signal, as again 46 of 49 cases showed g that were significantly greater than zero. Furthermore, as would e expected, nonsignificant results from the randomization test were generally associated with low (close to zero) values for the estimate of oth d and g. Given that the optimal value of d is chosen ased on the est fit of the tree to the data, we may expect that in some cases phylogenetic signal will not e detected with the original candidate ranch lengths ut will e detected after transformation. For trees with more than 2 tips, two such cases ( switched from.5 to.5) occurred: log(male/ female) ody mass of at genera (Nee s aritrary ranch lengths; data from Hutcheon 21); rhytidome allometric coefficient for inus spp. (all ranch lengths 1; Jackson et al. 1999). A third case was close: log of masscorrected group size of 28 macropods ( changed from.161 to.59; Fisher and Owens 2). With the ACDC transformation, the same two traits showed significant signal after ranchlength
14 73 ION. BOBERG ET A. transformation, as did macropod group size ( changed to.36). DICUION We have devised a simple randomization test for the presence of phylogenetic signal in continuousvalued traits. A survey of pulished datasets indicates that phylogenetic signal can e detected (.5) in most cases, especially for studies that involve 2 or more species (92%). This result demonstrates that phylogenetic signal is uiquitous and provides empirical justification for the claim that estimates of mean values for species generally cannot e assumed to represent independent pieces of information in statistical analyses. We have also developed a descriptive statistic (K) that can e used to gauge the amount of phylogenetic signal relative to the amount expected for a character undergoing Brownian motion evolution along the specified topology and ranch lengths. The literature survey indicates that on average most traits, other than ody size, showed less signal than expected (i.e., K 1). Analysis of variance and multiplerange comparison shows that, as a group, ehavioral traits exhiit significantly lower signal than ody size, morphological, lifehistory, or physiological traits (all corrected for ody size) and that physiological traits show less signal than does ody size. This result provides evidence consistent with the longstanding idea that ehavior is relatively laile evolutionarily. An alternative approach to testing for phylogenetic signal involves comparison of the fit of a series of trees whose ranch lengths vary as dictated y a transformation parameter, one extreme of which yields a star phylogeny. Accordingly, we have also studied the ehavior of two iologically motivated ranchlength transformations, one ased on the OU model of character evolution and the other ased on a new model of accelerating/decelerating rates of evolution (ACDC). We have shown how to fit oth models to data to otain trees that etter descrie the data (lower E in G models or, equivalently, lower variance of phylogenetically independent contrasts). For trees with 2 or more species, tests of whether the OU and ACDC transformation parameters (d and g, respectively) are significantly greater than zero (which implies the presence of phylogenetic signal) are consistent with results of the randomization test: when the randomization test detects signal, the estimated d and g are also greater than zero (a estimate that equaled zero or near zero would imply that a star phylogeny est fit the data). These two transformations provide different iological perspectives from which the degree of phylogenetic signal in the data can e examined. Application to real datasets indicates that the ACDC model is at least as useful as the OU FIG. 8. Example of a real tree (estimated divergence s for 15 species of primates; from mith and Cheverud 22) transformed y the maximum likelihood estimate of the OrnsteinUhleneck (OU) parameter (d) and of the accelerationdeceleration (ACDC) parameter (g) to otain est fit to data on log(male/female) ody mass (a measure of sexual dimorphism). Both parameters differ significantly from zero (oth.1) and from unity (.28 and.2, respectively); hence, oth transformed trees fit the data etter than does either a star phylogeny (no hierarchical structure, E.54441) or the original candidate tree (E.3422). Comparison of the Es indicates that ACDCtransformed tree (E ) fits the data etter than does the OUtransformed tree (E.27769).
15 HYOGENETIC IGNA 731 model in terms of otaining a set of ranch lengths that provides a good fit to data. For trees with more than 2 tips, the estimated OU (25% of cases) and/or ACDC (4%) transformation parameter differed significantly from oth one and zero, indicating that a tree less (or occasionally more) hierarchical than the original provided a etter fit to the tip data and so could e preferred for comparative analyses. Empirical Results: resence of hylogenetic ignal and Transformation of Branch engths Regardless of sample size, our randomization test detected no traits that showed a significant tendency for relatives not to resemle each other. In other words, we found no cases in which relatives were less similar than if placed on the phylogeny at random (phylogenetic antisignal). Thus, for the examples that we considered, we found no evidence to suggest the presence of a process such as phylogenywide character displacement etween close relatives. This is not to suggest that such a process did not occur etween or among some close relatives, just that, if it did occur, its effects were swamped y the more general tendency for relatives to resemle each other. The fit of any tree to data, regardless of whether its ranch lengths have een transformed, can e compared y the E (under the constraint that the height of all trees have een scaled so that the determinant of the variancecovariance matrix equals unity; see Appendix 4). Two comparisons are instructive. First, in how many cases does a star phylogeny etter fit the data than the original candidate tree? econd, in how many cases does a tree with ranch lengths transformed according to the OU or ACDC process etter fit the data than does a star phylogeny? Note that the transformed tree always must fit the data etter than or as well as the original tree. Also, note that other types of ranchlength transformations could e considered, such as raising the length of each ranch segment to a power or taking the log (Garland et al. 1992; DíazUriarte and Garland 1996, 1998; Reynolds and ee 1996; Garland and DíazUriarte 1999) or employing agel s (agel 1999; Freckleton et al. 22). For trees with more than 2 tips, a star phylogeny etter fit the data than did the candidate tree in 12 of 53 cases (23%). For these traittree cominations, one might thus e tempted to employ conventional statistical analyses in a comparative study of, for example, character correlations. However, in all 12 of those cases, the OU and/or the ACDCtransformed tree etter fit the data (lower E) than did the star. Hence, for statistical analyses, the transformed tree would e a etter choice. Comparing the transformations, in 25 cases the est fit was otained y the OU model and in 28 y the ACDC model. Thus, the new ACDC model appears to e at least as useful as the OU model. For the 53 traits, ranks of the estimates of d and g were highly correlated (pearman s r.824,.1). (Note that the ACDC transformation is highly nonlinear: very small values can still imply sustantial hierarchical structure.) Finally, it is important to note that such models as OU or ACDC may provide improved fit to a set of tip data yet still e a poor representation of the real evolutionary processes that have een at work. Thus, inferences aout the presence of OU or ACDClike processes should e made with caution. Empirical Results: Amount of hylogenetic ignal and Comparison of Trait Types A Kvalue of one indicates that a trait shows exactly the amount of phylogenetic signal expected under Brownian motion evolution along the specified tree. For the 24 measures of adult ody size, the mean log 1 K was.8 with a 95% confidence interval that included zero (.2 to.3), or unity on the arithmetic scale. Thus, on average, ody size exhiits neither more nor less phylogenetic signal than expected under a simple stochastic model of character evolution. This result is somewhat remarkale, given the various sources of error that will tend to reduce phylogenetic signal (see introduction and next paragraph). ost traits, however, exhiited K less than unity (see Fig. 6), and for all other trait types the 95% confidence interval excludes zero on the log scale, indicating that, as a group, they tend to exhiit less signal than expected (see Fig. 6). Values of K less than unity may e caused y deviation from Brownian motion and/or measurement error, and variation in either factor could account for differences in K among individual traits or among types of traits. One ovious way that trait evolution may deviate from simple Brownian motion is if adaptation to a particular environmental factor occurs in some ut not all memers of a set of species. A simple hypothetical example of this is shown in figure 1 of Blomerg and Garland (22). easurement error can come from three sources: error in the measurement of the tip data, errors in ranch lengths, or errors in tree topology. All three errors will generally make close relatives appear less similar than expected under Brownian motion, hence lowering K. We may expect the tip data to contain sustantial measurement error in many of the datasets, given that values are often ased on the measurement of few individuals, many studies include data taken y different investigators, and many of them were not ased on commongarden studies, so genotypeenvironment interactions may have had unpredictale effects (see also Garland and Adolph 1991; Garland et al. 1992). The last of these represents an intractale prolem for many comparative studies, especially ones that include a large range of ody sizes and/or kinds of organisms (see also Garland 21). For example, one simply cannot employ the same housing conditions nor feed the same food to a hummingird and an ostrich. oreover, many of the ehavioral and ecological traits considered (e.g., home range size) exist only under natural conditions. Errors in ranch lengths may also e large, especially given that approximately onethird of the studies that we have analyzed employed aritrary ranch lengths. Finally, topological errors are almost certain to exist in trees with relatively large numers of species. When analyzed on the original trees (without transformation of ranch lengths), different types of traits showed significant variation in the amount of phylogenetic signal. ean log E values ranked as ody size morphology life history physiology ehavior, and a multiple range comparison indicated that ehavioral traits showed significantly lower E than did all other trait types, and physi
16 7 ION. BOBERG ET A. ology had lower K than did ody size. At least three nonexclusive hypotheses could explain this result. First, the studies included in our analysis may e a iased sample, unrepresentative of traits in general. As noted in the Results, however, the ehavioral traits are from 1 studies, and those with particularly low Kvalues (on the order of.1) are from four different studies. econd, although we do not have direct information on pure measurement error, we would hypothesize that it is lower for ody size and for other morphological traits (e.g., lim length) than for the other types of traits considered. At the opposite extreme, ehavioral traits are notorious for eing strongly suject to oth immediate environmental effects and acclimation. As well, ehavioral traits measured in the field (e.g., home range size) may show large seasonal or yeartoyear variation. Third, and most interesting, ehavioral traits may e more evolutionary laile than other types of traits, as has often een suggested (e.g., ayr 1963, 1982; Bush 1986; Huey and Bennett 1987; lomin 199; Gittleman et al. 1996a,). any examples are known in which ehavioral traits seem to have evolved further than have some of the underlying morphological and physiological traits that would facilitate the ehavior, such as in the foraging ehavior of some anuran amphiians (Taigen and ough 1985, p. 991) or the water ouzels (dippers), which dive ut seem to have few morphological or physiological specializations that would facilitate diving aility (Futuyma 1986, p. 257). In other cases, changes in ehavior may alter the selective environment of other traits and drive their evolution (lotkin 1988; Wcislo 1989). Gittleman et al. (1996a,) used the true R 2 and phylogenetic autocorrelograms (Gittleman and Kot 199; see next section), as well as estimates of evolutionary rates in darwins, to compare two morphological traits (rain size, ody size), two lifehistory traits (gestation length, irth weight), and two ehavioral traits (home range size, group size) across eight mammalian datasets ranging in size from to 39 species. They found that the two ehavioral traits (especially group size) generally showed the lowest R 2 and the weakest relationship with phylogeny in autocorrelograms, as well as higher estimated evolutionary rates. orphological traits generally showed the highest R 2, and lifehistory traits were intermediate. Thus, their results are generally consistent with ours. (Note, however, that they did not correct the other traits for correlations with ody size.) Given the mathematical and conceptual prolems that have come to light with the autocorrelation method (Rohlf 21), it would e of interest to reanalyze their datasets with our methods. Different results emerge from comparisons among traits for oth consistency indices and retention indices: ehavioral characters showed no more homoplasy than did morphological characters (de Queiroz and Wimerger 1993; Wimerger and de Queiroz 1996). However, as noted y Wimerger and de Queiroz (1996), the ehavioral traits that they considered were chosen y systematists specifically for potential utility in reconstructing phylogenetic relationships, and hence do not necessarily reflect properties of ehavior and morphology in general. oreover, they studied discrete rather than continuousvalued traits. Finally, studies of the heritaility estimates for ehavioral, lifehistory, physiological, and morphological traits show that ehavioral and physiological traits have relatively low heritaility, similar to lifehistory traits, and much lower than morphological traits (ousseau and Roff 1987; tirling et al. 22). In principle, low heritailities could reduce evolutionary laility, ut the heritaility estimates for ehavioral traits are still generally high enough to allow rapid response to selection. Other Approaches to hylogenetic ignal A numer of previous techniques have een used, either explicitly or implicitly, to study phylogenetic signal in continuousvalued characters. ome of these have een used in attempts to test for phylogenetic inertia (see Blomerg and Garland 22). Here, we riefly outline some of the more prominent methods. everal techniques, ased on nested analysis of variance, have een used to examine the relative proportion of variance in a trait that can e partitioned among taxonomic levels (e.g., tearns 1983; Harvey and Clutton Brock 1985; rinzing et al. 21; see Harvey and agel 1991 for review). We do not consider these methods ecause taxonomic levels are aritrary and the methods cannot accommodate full phylogenetic information (any topology and ranch lengths). Cheverud et al. (1985) proposed the first fully phylogenetic method that has een used to gauge the degree of phylogenetic urden or inertia in continuousvalued characters. They modified the tools of spatial autocorrelation and developed two statistics that might indicate the degree of phylogenetic signal, the autocorrelation coefficient ( ) and the true R 2 (see Garland et al. 1999). Gittleman and Kot (199) extended the method y allowing, in effect, for transformation of phylogenetic ranch lengths; if this parameter is allowed to vary, then interpretation of and the true R 2 would also e affected. Gittleman and Kot (199) also proposed the use of oran s I as another indicator of phylogenetic signal. usequently, Gittleman and coworkers have applied the phylogenetic autocorrelation method to a large numer of comparative datasets (e.g., Geffen et al. 1996; Gittleman et al. 1996a,; Gittleman and Van Valkenurgh 1997), as have others (e.g., orales 2; mith and Cheverud 22). Unfortunately, Rohlf (21) has recently shown that the autocorrelation coefficient has not een estimated correctly in previous studies and, moreover, that the method has several prolems that limit its usefulness in comparative studies. In particular, the interpretation of is uncertain ecause it is not constrained etween 1 and is somes consideraly less than minus one. Also, oth large positive and large negative values imply that most trait variation is at the ase of the tree, whereas values closer to zero imply that variation is at the tips of the tree. arge values of do not necessarily imply a etter fit of the model to the data. Hence, significance tests of are difficult to interpret. The use of the true R 2 as a measure of the variance explained y phylogeny is also prolematic ecause a high R 2 value only signifies trait variation somewhere on the tree. Finally, the estimation of does not correspond to the maximization of R 2 (Rohlf 21). The other major test for phylogenetic signal in comparative data (continuousvalued or categorical traits) is y Aouheif (1999), who uses a test for serial independence, originally developed y von Neumann et al. (1941) in a nonphyloge
17 HYOGENETIC IGNA 733 netic context. The von Neumann et al. test works y comparing the variation in the difference of successive oservations to the sum of squares of the oservations. It can e used to test for serial independence in any dataset, ut it requires some modification for application to phylogenetic data. pecifically, Aouheif (1999) deals with the prolem that any single tree topology can e represented in 2 (n 1) different ways, ecause ranches can e rotated at nodes. Rotating ranches at nodes results in a different ordering of the data in the dataset and changes the calculation of the statistic for serial independence. Aouheif s solution is to calculate C (the statistic for serial independence) on a large numer of randomly generated trees that maintain the same relationships among species as the original tree topology. A mean Cvalue is then calculated. This process is then used in a permutation test to generate a null distriution of mean Cvalues to use in tests of significance. The main limitation of Aouheif s (1999) proposed test is that it does not incorporate ranch length information. This is prolematic ecause ranch lengths provide important information aout expected species similarity that cannot e gained from the topology alone. In addition, he provides no analysis of Type I or Type II error rates. A test similar to our randomization procedure, ut for discrete characters, was proposed y addison and latkin (1991). They also randomized character values equiproaly across the tree, ut instead of using the variance of phylogenetically independent contrasts (or the E in G mode) as a measure of fit of the tree to the data, they use the Fitch Farris character optimization algorithm to calculate the minimum numer of character changes necessary on the tree. A low numer of character changes (low FitchFarris score), compared with the distriution of minimum character changes generated y permuting the species on the tree, implies that the traits show significant historical inertia. The addisonlatkin method in effect assumes that all ranch segments are of equal length. The quantitative convergence index (QVI) of Ackerly and Donoghue (1998) can also e interpreted as an indicator of phylogenetic signal. This statistic is ased on linear parsimony algorithms for continuousvalued characters. It is equivalent to one minus the retention index (Farris 1989) and measures the degree to which sister taxa are similar (QVI ) or different (i.e., QVI 1 when the most distant taxa are most similar, and thus convergent evolution is maximized). Randomization methods are used to provide null distriutions for testing the statistical significance of the QVI (see also Ackerly and Reich 1999; rinzing et al. 21). Another method that partitions phylogenetic effects among different phylogenetic levels was developed y egendre et al. (1994; for an application see BöhningGaese and Oerrath 1999). This method relies on the comparison of a matrix of trait dissimilarities with a matrix of phylogenetic distances. It can e used to compare different classes of traits, as in the present paper, ut it is not ased on any explicit model of evolution, and it must make implicit assumptions depending on the way matrices are constructed (artins and Hansen 1996). Jalonski (1987) analyzed the crossspecies heritailities (which might e viewed as a measure of phylogenetic signal) of geographical range in fossil mollusks. However, his analysis is unusual in that a detailed fossil record allowed identification of ancestor (parent)descendent (offspring) relationships among species. These conditions are not normally met with other comparative datasets. Ashton (21) proposed a similar test for phylogenetic conservatism, ased on the correlation oserved in a ivariate scatterplot of traits values for sister taxa (with ancestral values computed as simple averages of the sister taxa). This test did not use information on phylogenetic ranch lengths, however, and its statistical properties are unknown. Grafen (1989), who first proposed G methods for comparative data, also included estimation of a parameter (not the same as the likenamed autocorrelation coefficient in Gittleman and Kot 199) that served to stretch/compress the internal nodes of a phylogenetic tree, much like the OU and ACDC transformations. He did so from the presumed starting point that only aritrary ranch lengths would e availale and that the topology would contain soft polytomies. He implemented a method for estimating an optimal ranchlength transformation, simultaneously with estimating the form of the relationship etween two or more traits in a G model. If, and only if, the starting ranch lengths have contemporaneous tips, then a of zero will yield a star phylogeny. Thus, estimation of could e interpreted as providing a test for phylogenetic signal. However, the ranchlength modifications implied y values of that differed from unity were not ased on a model of evolution, and Grafen viewed it simply as a nuisance parameter. agel (1999) and Freckleton et al. (22) employed a similar transformation parameter ( ), also not ased on a model of evolution, ut they suggest it can e used to differentiate among various modes of evolution. One might conjecture that noniologically motivated ranchlength transformations would tend to fit real data less well than do their counterparts that are ased on plausile iological models. However, it should also e noted that many real datasets show clear deviations from such simple models as OU and ACDC (e.g., asolute physical limits to character evolution, unequal rates of evolution across lineages [Garland 1992; Garland and Ives 2], lineagespecific effects [sensu Arnold 1994]) and many studies use aritrary ranch lengths as a starting point, so it is possile that these iologically ased transformations would fare little etter than, for example, Grafen s (1989) or direct exponential or logarithmic transformation of each ranch segment (Garland et al. 1992; DíazUriarte and Garland 1996, 1998; Reynolds and ee 1996; Garland and DíazUriarte 1999). This is an empirical question that could e examined y application of oth types of transformations to a large numer of real and simulated datasets (cf. ooers et al. 1999). It would, of course, e possile to apply transformations that represent even more complex models of evolution than OU and ACDC. Transformations with multiple parameters (e.g., the free model of ooers et al. 1999) can e prolematic, as the position of the tree in the multiparameter space must e considered and the meaning of the parameters can ecome uncertain (as in the phylogenetic autocorrelation method, see aove). For example, the free model requires one parameter for each ranch length (edge) in the tree (2N
18 734 ION. BOBERG ET A. 2 parameters for N species in the tree). It is not clear how to simultaneously interpret all of the parameters in the model. Furthermore, estimating each ranch length from the data can provide a description of the est set of ranch lengths given the trait data, ut does not incorporate any external information on ranch lengths for the given tree, such as may e availale from molecular or fossil data, although the information in the tree topology is used. However, intermediate transformations those that affect only a suset of the ranch lengths on the tree may e very useful. One ad hoc empirical example is y Garland and Ives (2), who descrie differing rates of evolution in passerine and nonpasserine irds. They scaled the height of the ranches in the passerine suclade separately from the rest of the tree in an attempt to enforce the equivalence of contrast variances across the tree as a whole. uch rescaling can e viewed as an intermediate transformation etween singleparameter transformations, such as our implementation of OU or ACDC and the free model of ooers et al. (1999), in which the numer of parameters is equal to the numer of ranches in the tree ecause the rescaling transformation has a parameter corresponding to the different values of the contrast variances for passerines and nonpasserines. Finally, the mixedeffects model of ynch (1991) can e used to gauge phylogenetic signal. Analogous to quantitativegenetic analyses with pedigrees, this method attempts to decompose the total phenotypic variance among species (tips on a phylogeny) into components associated with heritale versus nonheritale effects. The method is still under development and seems to e quite demanding of sample size (artins et al. 22; E. A. Housworth and. ynch, pers. comm.). Discussion of its potential is premature and eyond the scope of the present paper (see also Blomerg and Garland 22). Recommendations for Analysis of Comparative Data We recommend presentation of the following along with any univariate analysis of comparative data: (1) the tip data analyzed; (2) the actual phylogeny (topology and ranch lengths) used for analyses; (3) the expected E /E as an index of how hierarchical the tree is (e.g., see Fig. 5); (4) the oserved E /E and the Kstatistic, the latter to allow direct comparisons among traittree cominations; (5) the E of the trait(s) on the tree used for analyses and on a star phylogeny; (6) results of the randomization test for phylogenetic signal; and (7) results of the traditional diagnostic test for adequacy of ranch lengths (earson correlation [not through origin] of asolute values of standardized contrasts versus their standard deviations; Garland et al. 1991, 1992), along with comments aout any notale outlier contrasts that may heavily influence the distriution. In addition, one should check for clade differences in this diagnostic plot that may indicate heterogeneity in rate of evolution, and hence the need for more complex ranchlength transformations or statistical analytical models (e.g., see Garland 1992; Garland and Ives 2). We view all of the foregoing as essential. Additional important information depends on what types of ranchlength transformations, if any, have een pursued. Whatever strategy is followed, a description of the procedures used to arrive at a final set of ranch lengths for analysis should e presented. If optimal transformations are estimated, such as under the OU and ACDC models, then one should present the estimated transformation parameter, the E for the trait(s) on the transformed tree, and the randomization tests for whether d or g differs significantly from oth zero and unity. In addition, one should generally sutract degrees of freedom during any susequent comparative analyses (e.g., independent contrasts tests for a correlation etween two traits) to account for estimation of the additional parameters (e.g., see DíazUriarte and Garland 1996, 1998; Garland and DíazUriarte 1999). The randomization test for phylogenetic signal in comination with tests of whether d or g are different from zero allow one to choose ojectively the ranch lengths for a comparative analysis, such as y the method of phylogenetically independent contrasts, which is equivalent to G models (see also Freckleton et al. 22). One possile outcome is that a star phylogeny fits the data as well as or etter than other sets of ranches. For traits with more than 2 species (N 53), our empirical survey revealed only one such case. For the log of masscorrected maximal metaolic rates of 47 species of irds (Rezende et al. 22), estimates of oth the OU and ACDC transformation parameter were close to zero (1 1 8 and 1 1 3, respectively), yielding a tree that was mathematically indistinguishale from a star for the OU transform, ut that had some small ifurcations remaining for the ACDC transform. In addition, no significant phylogenetic signal was detected y the randomization test on either the original tree (.26) or the ACDCtransformed tree (.65). Thus, for this trait, a star phylogeny would e a justifiale choice for statistical analyses. In agreement with our finding of no phylogenetic signal for maximal metaolic rate, Rezende et al. (22) found no significant difference in a phylogenetic ANCOVA comparing passerines with nonpasserines. For these same irds, however, log(ody mass) showed strong signal (randomization.1) and was est fit y the original tree (estimate of d.99, g 2.3; neither significantly different from unity). Independent contrast analyses of multivariate or multivariale relationships can e performed with different sets of ranch lengths, or different transformations thereof, for different traits (e.g., Bonine and Garland 1999). Because independent contrasts are a special case of G computations (Rohlf 21), the latter may also employ different ranches for different traits, although this is often computationally more awkward than when using independent contrasts (Garland and Ives 2). An alternative is to use a single set of ranch lengths for analyses that involve multiple traits (e.g., ivariate correlation, principal components analysis, Cloert et al. 1998; multiple regression, see Grafen 1989). The most appropriate strategy will depend on the assumptions that one is willing to make aout evolution of the traits in the analysis as well as the assumptions inherent to the statistical model (e.g., independence and normality in a ivariate relationship, in multivariate space, for residuals from a regression). As noted elsewhere, multipleregression type analyses often include methodological nuisance variales (such as calculation method for home range area, length of study; e.g., Wolf et al. 1998; erry and Garland 22) that are clearly nonphy
19 HYOGENETIC IGNA 735 logenetic and so may est e analyzed as if on a star phylogeny, which can e done easily with independent contrasts. For trees with fewer than 2 tips, our simulations indicate relatively low power to detect phylogenetic signal y the proposed randomization test (Fig. 2) and also unreliale estimation of the OU and ACDC ranchlength transformation parameters (Fig. 7), which can also serve as a test for signal. Consistent with these simulation results, our empirical survey found that a star etter fit the data, as compared with the hierarchical tree, for 38 of 68 traits in studies with fewer than 2 species. Following transformation, the estfitting tree was: star, ; OU, 24; ACDC, 27 (the star was credited with est fit if the OU or ACDC parameter was estimated as zero). The fact that a star phylogeny somes etter fits the data for trees with relatively few species may e attriutale to the lower possile amount of phylogenetic signal on smaller trees (Fig. 5) and/or the difficulty in estimation for smaller trees (Fig. 7). For analysis of real data with small sample sizes, a finding that a star phylogeny etter fits the data than do various hierarchical trees should not e taken as a directive to apply and favor the results of conventional statistical analyses. However, one would e well advised to apply analyses with several sets of ranch lengths, whether aritrary or ased on iological models, and view the exercise as a sensitivity analysis (e.g., see fig. 2 in Garland et al. 1999; Butler et al. 2). Conclusions and Future Directions Our empirical survey indicates that phylogenetic signal is pervasive in crossspecies datasets, even for some ehavioral traits that are expected to e evolutionarily malleale (e.g., highly adaptive) and/or quite suject to nongenetic environmental effects, such as home range size and group size (see also rinzing et al. [21] on niche position of plants). This result reinforces the importance of phylogenetically ased statistical methods for analyses of comparative datasets. Our results also emphasize the importance of exploring ranchlength transformations, whether iologically motivated or not. An important area for future research will e determining, for a wide range of evolutionary models, which types of transformations are most effective and roust with respect to the performance of susequent statistical analyses, such as testing for correlated character evolution (e.g., Grafen 1989; artins and Garland 1991; agel 1994, 1999; urvis et al. 1994; DíazUriarte and Garland 1996, 1998; rice 1997; Harvey and Ramaut 1998, 2; Garland and DíazUriarte 1999; artins et al. 22). Another important topic is how the various diagnostics that have een suggested previously for choosing ranchlength transformations from a purely statistical perspective (Grafen 1989; Garland et al. 1991, 1992; DíazUriarte and Garland 1996, 1998; Reynolds and ee 1996; Garland and DíazUriarte 1999; Harvey and Ramaut 2) relate to the alternate optimality criterion of minimizing the E, as used here and y others who have implemented G approaches. Either kind of optimality criterion could e used to choose from within families of either statistically or iologically motivated ranchlength transformations, ut this has not yet een pursued. Our preliminary analyses, oth analytical and empirical (results not shown), indicate that the traditional diagnostic test for adequacy of ranch lengths (earson correlation [not through origin] of asolute values of standardized contrasts versus their standard deviations: Garland et al. 1991, 1992) often suggests the same tree as does the criterion of minimum E. Although the OU and ACDC transformations may cover a fairly road range of what might e termed wellehaved evolutionary models (see also Garland et al. 1993; Hansen and artins 1996; Butler et al. 2; artins et al. 22), they are clearly too simple for many real traits, such as those exhiiting strong lineagespecific effects (e.g., plasma osmolarity of verterates, Garland et al. 1997), clade differences in rates of evolution (Garland 1992; Barosa 1993; Cloert et al. 1998; Barosa and oreno 1999; Garland and Ives 2), or asolute limits to evolution (see Garland et al. 1993). uch cases will often exhiit prolems of estimation that may e detected y inspection of a plot of the E versus the parameter value, as is provided in our ata programs (see also comments in Grafen 1989). When an irregular likelihood surface is noted, or when a more complicated evolutionary model is suspected, our randomization test is proaly much more roust for simply detecting the presence of phylogenetic signal as compared with testing whether d or g differs significantly from zero. Our Kstatistic is a useful descriptor of the amount of phylogenetic signal for any trait on any tree, regardless of how complicated its evolution may have een, ut values much larger or smaller than unity do not offer any insight as to how evolution may have deviated from Brownian motion (even if we assume that measurement errors are negligile). Another way to detect complicated trait evolution is y inspection of, for example, ivariate scatterplots of traits versus ody mass, oth for raw data and for phylogenetically independent contrasts, including rawdata plots that are coded y major lineage (to detect grade shifts) and/or that have phylogenetically correct allometric lines superimposed (see also Garland et al. 1993; Ackerly and Donoghue 1998; Ackerly 1999; Ackerly and Reich 1999; Garland and Ives 2; Nunn and Barton 2; Garland 21). Insight to adaptive radiation may also e gained y comparing the amount of signal (or the rate of evolution: Garland 1992; Garland and Ives 2) present in different suclades. For example, a low amount of signal or high rate of evolution suggests that the clade in question may possess a key innovation. Of course, a single such clade represents only an anecdote, and the attriution of key innovations is exceedingly difficult if not logically impossile in the asence of replicate lineages that show oth the putative key innovation and a high rate of evolution (e.g., see osos and iles 22). The tools employed in this paper should e applied to a much wider range of comparative datasets. Our preliminary results are encouraging in that they reveal differences in the amount of phylogenetic signal (as indicated y our Kstatistic) among different types of traits. Consistent with some previous work (Gittleman et al. 1996a,), we find that ehavioral traits tend to show less phylogenetic signal than do other types of traits. Another topic of interest would e to explore the effects of using various types of real and/or aritrary ranch lengths, plus ranchlength transformations, on comparisons of the amount of signal across trait types
20 736 ION. BOBERG ET A. (see also ooers et al. 1999). For example, if a consistent set of ranch lengths were imposed for all trees and traits, with or without the use of ranchlength transformations, would we still find differences in phylogenetic signal across trait types? Neither the descriptive statistic (K) nor the tests for phylogenetic signal (randomization; estimates of d and g vs. zero) attempt to take into account effects of adaptation (e.g., see fig. 1 in Blomerg and Garland 22). In comparative studies, the usual way of inferring adaptation is y correlating character variation with continuousvalued or categorical descriptors of environmental factors that are thought to indicate variation in selective regime. Thus, it will e important to develop methods that can relialy estimate correlations of characters with environmental variales while simultaneously estimating the degree of phylogenetic signal. uch methods can e prolematic in practice ecause variation in selective regime may itself e confounded with phylogenetic position; that is, the environmental predictor variales may themselves exhiit phylogenetic signal. uch confounding can lead to prolems of oth estimation and interpretation, similar to quantitativegenetic studies in which genotypes and environments are confounded. Also important for such attempts will e explicit incorporation of information on measurement error of various types. uch a comprehensive and practical method does not yet exist, ut steps are eing made in that direction (e.g., see Gittleman and Kot 199, p. 231; ynch 1991; Butler et al. 2; Cornillon et al. 2; Baum and Donoghue 21; Housworth and artins 21; Orzack and oer 21; artins et al. 22; Y. Desdevises,. egendre,. Azouzi, and. orand, pers. comm.). The effect of such simultaneousinference procedures on the apparent amount of phylogenetic signal in traits is difficult to predict. ACKNOWEDGENT This work was supported y National cience Foundation grant DEB to TG and ARI. B was supported in part y a ichael Guyer postdoctoral fellowship from the University of Wisconsin adison Department of Zoology. We thank all of our colleagues who provided data files for analyses. We also thank B. J. Crespi, A. de Queiroz, C.. Nunn, and R. J. mith for comments on the manuscript. ITERATURE CITED Aouheif, E A method for testing the assumption of phylogenetic independence in comparative data. Evol. Ecol. Res. 1: Ackerly, D. D Comparative plant ecology and the role of phylogenetic information. p in. C. ress, J. D. choles, and. G. Braker, eds. hysiological plant ecology. The 39th symposium of the British Ecological ociety held at the University of York, 7 9 eptemer Blackwell cience, Oxford, U.K.. 2. Taxon sampling, correlated evolution, and independent contrasts. Evolution 54: Ackerly, D. D., and. J. Donoghue eaf size, sapling allometry, and Corner s rules: phylogeny and correlated evolution in maples (Acer). Am. Nat. 152: Ackerly, D. D., and. B. Reich Convergence and correlations among leaf size and function in seed plants: a comparative test using independent contrasts. Am. J. Bot. 86: Antonovics, J., and. H. van Tienderen Ontoecogenophyloconstraints? The chaos of constraint terminology. Trends Ecol. Evol. 6: Arnold, E. N Investigating the origins of performance advantage: adaptation, exaptation and lineage effects. p in. Eggleton and R. I. VaneWright, eds. hylogenetics and ecology. innean ociety ymposium eries no.. Academic ress, ondon. Ashton, K. G. 21. Body size variation among mainland populations of the western rattlesnake (Crotalus viridis). Evolution 55: Barosa, A orphometric variation of the hindlim of waders and its evolutionary implications. Ardeola 1993: Barosa, A., and E. oreno Evolution of foraging strategies in shoreirds: an ecomorphological approach. Auk 6: Baum, D. A., and. J. Donoghue. 21. A likelihood framework for the phylogenetic analysis of adaptation. p in. Orzack and E. oer, eds. Adaptationism and optimality. Camridge Univ. ress, Camridge, U.K. Bjorklund, Are comparative methods always necessary? Oikos 8: Blomerg,.., and T. Garland Jr. 22. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J. Evol. Biol. 15: BöhningGaese, B., and R. Oerrath hylogenetic effects on morphological, lifehistory, ehavioural and ecological traits of irds. Evol. Ecol. Res. 1: Bonine, K. E., and T. Garland Jr print performance of phrynosomatid lizards, measured on a highspeed treadmill, correlates with hindlim length. J. Zool. (ond.) 248: Bonine, K. E., T. T. Gleeson, and T. Garland Jr. 21. Comparative analysis of fiertype composition in the iliofiularis muscle of phrynosomatid lizards (auria). J. orphol. 25: Brashares, J., T. Garland Jr., and. Arcese. 2. hylogenetic analysis of coadaptation in ehavior, diet, and ody size in the African antelope. Behav. Ecol. : Bush, G Evolutionary ehavior genetics. p. 1 5 in. D. Huettel, ed. Evolutionary genetics of inverterate ehavior, progress and prospects. lenum ress, New York. Butler,. A., T. W. choener, and J. B. osos. 2. The relationship etween sexual size dimorphism and haitat use in Greater Antillean Anolis lizards. Evolution 54: Calder, W. A., III ize, function, and life history. Harvard Univ. ress, Camridge, A. Chevalier, C. D Aspects of thermoregulation and energetics in the rocyonidae (ammalia: Carnivora). h.d. diss., University of California, Irvine, CA. Cheverud, J.., and.. Dow An autocorrelation analysis of genetic variation due to lineal fission in social groups of rhesus macaques. Am. J. hys. Anthropol. 67: Cheverud, J..,.. Dow, and W. eutenegger The quantitative assessment of phylogenetic constraints in comparative analyses: sexual dimorphism in ody weight among primates. Evolution 39: Cloert, J., T. Garland Jr., and R. Barault The evolution of demographic tactics in lizards: a test of some hypotheses concerning life history evolution. J. Evol. Biol. : Cornillon,.A., D. ontier, and.j. Rochet. 2. Autoregressive models for estimating phylogenetic and environmental effects: accounting for withinspecies variations. J. Theor. Biol. 22: CruzNeto, A.., T. Garland Jr., and A.. Ae. 21. Diet, phylogeny, and asal metaolic rate in phyllostomid ats. Zoology 14: Derrickson, E.., and R. E. Ricklefs Taxondependent diversification of lifehistory traits and the perception of phylogenetic constraints. Funct. Ecol. 2: de Queiroz, A., and. H. Wimerger The usefulness of ehavior for phylogeny estimation: levels of homoplasy in ehavioral and morphological characters. Evolution 47:46 6. DíazUriarte, R., and T. 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