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Syst. Biol. 51(1):4468, 2002

Molecular Systematics of the Eastern Fence Lizard(Sceloporus undulatus): A Comparison of Parsimony, Likelihood,

and Bayesian Approaches

ADAM D. LEACHE1 AND TOD W. REEDER

Department of Biology, San Diego State University, San Diego, California 92182-4614 , USA;E-mail: [email protected]

Abstract.Phylogenetic analysis of large datasets using complex nucleotide substitution models un-der a maximum likelihood framework can be computationally infeasible, especially when attemptingto infer condence values by way of nonparametric bootstrapping. Recent developments in phyloge-netics suggest the computational burden can be reduced by using Bayesian methods of phylogeneticinference. However, few empirical phylogenetic studies exist that explore the efciency of Bayesiananalysis of large datasets. To this end, we conducted an extensive phylogenetic analysis of the wide-ranging and geographically variable Eastern Fence Lizard (Sceloporus undulatus). Maximum parsi-mony, maximum likelihood, and Bayesian phylogenetic analyses were performed on a combinedmitochondrial DNA dataset (12S and 16S rRNA, ND1 protein-coding gene, and associated tRNA;3,688 bp total) for 56 populations ofS. undulatus (78 total terminals including other S. undulatus groupspecies and outgroups). Maximum parsimony analysis resulted in numerous equally parsimonioustrees (82,646 from equally weighted parsimony and 335 from weighted parsimony). The majorityrule consensus tree derived from the Bayesian analysis was topologically identical to the single bestphylogeny inferred from the maximum likelihood analysis, but required 80% less computationaltime. The mtDNA data provide strong support for the monophyly of the S. undulatus group andthe paraphyly of S. undulatus with respect to S. belli, S. cautus, and S. woodi. Parallel evolution ofecomorphs within S. undulatus has masked the actual number of species within this group. Thisevidence, along with convincing patterns of phylogeographic differentiation suggests S. undulatusrepresents at least four lineages that should be recognized as evolutionary species. [Bayesian analy-sis; ecomorph; maximum likelihood; molecular systematics; mitochondrial DNA; phylogeography;Sceloporus undulatus; species limits.]

Phylogenies have become essential toolsfor elucidating patterns of lineage diversi-cation at the population level (Avise, 2000).Inferring such patterns for large polytypicspecies often results in disagreement withtraditional classications and has formedthe basis for revised taxonomies (Zamudioet al., 1997; Wiens et al., 1999; Burbrinket al., 2000; Rodriguez-Robles and de JesusEscobar, 2000; Wilgenbusch and de Queiroz,2000; Wiens and Penkrot, 2002). This followsthe prediction of Frost and Hillis (1990) thatmost wide-ranging polytypic species would

be found to consist of several evolutionaryspecies. To address these species limits prob-lems, the utility of a phylogeny is greatly en-hanced if large numbers of populations areconsidered in the analysis. Large datasets,however, offer a challenge to systematists be-

cause the number of possible phylogenetictrees increases exponentially with the ad-

1Present address (and address for correspondence):Department of Biological Sciences and Museum ofNatural Science, 119 Foster Hall, Louisiana State Uni-versity, Baton Rouge, Louisiana 70803, USA; E-mail:[email protected]

dition of each terminal taxon (Felsenstein,1978), limiting our ability to thoroughly ex-plore the tree space (Swofford et al., 1996).Computational limitations are particularlysevere when model-based methods of phylo-genetic inference are applied (Sanderson andKim, 2000).

Bayesian methods of phylogeny recon-struction enable quick and efcient analy-sis of large datasets while allowing the useof complex nucleotide substitution modelswithin a parametric statistical framework(Larget and Simon, 1999). A fundamentaldistinction between maximum likelihood(ML) and Bayesian inference is that aBayesian approach provides probabilities forhypothesesnot probabilities of data, givena hypothesis (reviewed by Lewis, 2001).Bayesian inference of phylogeny generates

an approximation of the posterior probabilitydistribution of all parameters (i.e., tree topol-ogy, branch lengths, and substitution modelparameter estimates) in a phylogenetic anal-ysis by using Markov chain Monte Carlo(MCMC) methods (Mau, 1996; Rannala andYang, 1996; Mauand Newton, 1997; Yang andRannala, 1997; Mau et al., 1999). Sampling

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2002 LEACHE AND REEDERSYST EMATICS OF THE EASTERN FENCE LIZARD 45

from the posterior probability distribution atstationarity (that is, samples taken when theMarkov chain has forgotten its starting val-ues) allows the direct quantication of sta-tistical measures for each model parameterin the Bayesian analysis (Huelsenbeck and

Bollback, 2001). Bayesian inference of phy-logeny by using MCMC requires fewer com-putational resources than standard ML anal-ysis because the Bayesian method does notnecessarily attempt to nd the globally op-timal ML score. In addition, the estimationof branch support accompanies tree estima-tion, thereby eliminating the need to sepa-rately conduct time-intensive nonparametric

bootstrap analyses (Larget and Simon, 1999).Despite these advantages, however, the effec-tiveness of Bayesian inference has not beendemonstrated on a large dataset (see alsoHuelsenbeck and Imennov, 2002).

The advantages of a ML approach are welldocumented. ML provides an objective wayofestimatingandchoosingcharacterweights(Felsenstein, 1981) and incorporates impor-tant aspects of molecular evolution that aredifcult to implement in parsimony analy-ses (e.g., among-site rate variation, unequal

base frequencies, and limited nonindepen-dence of substitutions). Also, likelihood isdemonstrably a consistent and efcient es-timator of phylogenies under a variety ofsimulated conditions where other methods(i.e., maximum parsimony [MP] and distancemethods) are expected to fail and is robust toperturbations of model and model parame-ters (Huelsenbeck, 1995; Yang, 1996). How-ever, computational limitations make it dif-cult to perform robust ML analyses with

large numbers of taxa (Sanderson and Kim,2000). MP and the various distance-basedphylogenetic methods are less hindered bylarge numbers of taxa (Hillis, 1996), but the

benets of a parametric statistical frameworkfor analyzing DNA sequence data may out-weigh the costs of applying these methods.Bayesian inference extracts information fromthe data through the likelihood function and,with uniform prior probabilities, is expected

to generate results similar or identical tothose obtained with ML when applying thesame substitution modelsbut all in a sub-stantially shorter time (Larget and Simon,1999). Nonetheless, the degree of congruence

between topologies, levels of support (pos-terior probabilities vs. nonparametric boot-

strap values), and nucleotide substitutionmodel parameter estimates derived from MLand Bayesian methods remains to be ex-plored through an analysis of large numbersof taxa. In this study, we compare the re-sults of Bayesian phylogenetic inference with

those of MP and ML methods in the recon-struction of a population-level phylogeny forthe diverse and wide-ranging lizard speciesSceloporus undulatus (Eastern Fence Lizard).

The Model: Sceloporus undulatusSpecies Group

In addition to Sceloporus undulatus, the un-dulatus species group also includes S. belli, S.cautus, S. exsul, S. occidentalis, S. virgatus, andS. woodi. This diverse radiation of phrynoso-matid lizards is endemic to the United Statesand north-central Mexico (Smith, 1938; Siteset al., 1992). Variability among S. undulatuspopulations is remarkable and includes dif-ferences in behavior (Vinegar, 1975; Smithet al., 1992), morphology and color pattern(Smith, 1938), sexual dimorphism (Smithet al., 1992), life history (Niewiarowski, 1994;Smith et al., 1996), demography (Tinkle

and Dunham, 1986), reproductive ecology(Gillis and Ballinger, 1992), and chromosomestructure (Cole, 1972). This geographicvariability has led to the description of10 subspecies within S. undulatus, differingprimarily in squamation and dorsal and ven-tral coloration patterns (Smith et al., 1992).

Several classications have been proposedto account for the geographic variabil-ity among Sceloporus undulatus populations.Ferguson et al. (1980), in an attempt to

explain life history variation, partitionedS. undulatus populations by habitat typescorresponding to eastern woodlands, cen-tral grasslands, and western canyons. Smithet al. (1992, 1995) provided a phylogeneticconcept for S. undulatus based on the over-all morphological and behavioral similarityamong subspecies and recognized three ex-erges (subspecies groups): (1) undulatus ex-erge, forested woodland subspecies in the

eastern United States with bark-matchingphenotypes and arboreal behavior; (2) con-sobrinus exerge, grassland-, prairie-, andsand-dwelling subspecies exhibiting curso-rial (adapted for running) phenotypes; and(3) tristichus exerge, canyon and plateau sub-species of the western United States (Fig. 1).



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46 S YSTEMATIC BIOLOGY VOL. 51

FIGURE 1. Three ecomorphs ofSceloporus undulatus adapted from Smith et al. (1992, 1995). Exerge (subspeciesgroup) names applied by Smith et al. (1992, 1995) are shown in bold. Prairie ecomorphs are distinguishable by theirterrestrial habits, smaller body size (

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FIGURE 2. Generalized ranges ofSceloporus undulatus subspecies in the United States and north-central Mexicobased on Smith et al. (1991, 1999) and Lemos-Espinal et al. (1999). Numbers correspond to sampled populations ofS. undulatus listed in Appendix 1. The sampling locality and range of S. belli is shown because of suspicions asto whether this taxon is distinct from S. undulatus (Lemos-Espinal et al., 1998). Sampling localities of S. woodi(Florida, USA) and S. cautus (Nuevo Leon, Mexico) are also provided.

populations of S. undulatus were sampled,representing all 10 described subspecies(Fig. 2). Subspecies were identied by us-ing the best estimates of range approxima-tions from Smith et al. (1991, 1999) andLemos-Espinal et al. (1999), a taxonomic key(Smith et al., 1995), and reference to mu-seum collection records. The proper identi-cation ofS. undulatus subspecies was criti-cal because the ecomorph representing eachpopulation was determined solely on the

basis of its subspecic designation (Fig. 1).Lemos-Espinal et al. (1998) considered S. u.belli a full species, based on a specimen foundin apparent sympatry with S. u. speari. In theabsence of discrete character differences, we

believe this observation requires conrma-tion by rigorous data analysis.We considerthe elevation ofS. u. belli suspect, and furtherinvestigation is needed on the status of thistaxon.

Individuals from six closely related speciesgroups in Sceloporus were used as outgrouptaxa, based on the observations of Wiens andReeder (1997): S. graciosus (graciosus group),S. horridus (spinosus group), S. magister (mag-ister group), S. megalepidurus (megalepidu-rus group), S. mucronatus (torquatus group),

and S. olivaceus (olivaceus group). All treeswere rooted with S. graciosus because ev-idence is strong that this is the most dis-tantly related species included in this study(Wiens and Reeder, 1997). Such a rootingstrategy allowed the positions of the otheroutgroup species to remain unconstrainedwith respect to the ingroup. This is our pre-ferred method of rooting because the interre-lationships among the remaining Sceloporusspecies groups are only weakly supported,and the closest relative to the undulatus groupremains unclear (Wiens and Reeder, 1997).

Molecular Methods

Total genomic DNA was isolated fromsmall amounts of liver according to thephenolchloroform extraction protocol ofHillis et al. (1996). Three portions of the mi-tochondrial genome were amplied and se-quenced by the polymerase chain reaction

(PCR). Fragment one (950 bp) containedpart of the phenylalanine tRNA and most ofthe 12S rRNA gene (12S). The second frag-ment (1,500 bp) contained the valine tRNAgene and a large portion of the 16S rRNAgene (16S). Sequence data from this secondfragment was generated only for a subset



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TABLE 1. Oligonucleotide primers used in this study. Positions correspond to the 3 nucleotide position in thehuman mtDNA sequence of Anderson et al. (1981).

Primer name Sequence (53) Position Source

Fragment 1 (950 bp)tPhe AAAGCAC(A/G)GCACTGAAGATGC 618 Wiens and Reeder, 199712e GT(A/G)CGCTTACC(A/T)TGTTACGACT 1,558 Wiens and Reeder, 1997

12g TATCGATTATAGGACAGGCTCCTCTA 1,220 This study12a AAACTGGGATTAGATACCCCACTAT 1,091 Kocher et al., 1989a

12bR ACACACCGCCCGTCACCCTC 1,497 Kocher et al., 1989b

Fragment 2 (1,500 bp)12eR GGCAAGTCGTAACA(A/T)GGTAAGCGCAC 1,579 This study16f GTAGCTCACTTGATTTCGGG 1,903 This study16fR CCCGAAATCAAGTGAGCTAC 1,922 This study16g GGCTGATTTACAGTTGTGCG(T/G)AGAG 2,317 This study16aR CCCG(A/C)CTGTTTACCAAAAACA 2,509 Reeder, 1995a

16d CTCCGGTCTGAACTCAGATCACGTAG 3,057 Reeder, 199516e ATTTAGAAGACAAGTGATTACGCTACCT 2,591 This study

Fragment 3 (1400 bp)

16dR CTACGTGATCTGAGTTCAGACCGGAG 3,082 This studytMet ACCAACATTTTCGGGGTATGGGC 4,429 This studyND1a TCCTAGAACG(T/A)AAAATCCTAGG 3,392 This studyND1b GATGCTCGTAC(T/C)CA(T/C)AGGAATC 4,142 This study

aOriginal primer sequence has been modied.b Original primer sequence has been reversed and modied.

of 15 individuals (Appendix 1). Fragmentthree (1,400 bp) contained a small portionof the 3 end of the 16S gene, the entire ND1

protein-coding gene (ND1), and the leucine,isoleucine, and glutamine tRNA, as well as aportion of the methionine tRNA. Fragmentsone and three were sequenced for all indi-viduals included in the study. Primers usedfor PCR and sequencing are given in Table 1.Approximately 50200 ng of total DNA wasused as template for PCR in a nal volume of50L containing 10 mM Tris (pH 8.8), 50 mMKCl, 1.5 mM MgCl2, 0.1% Triton X-100, 0.10.2 mM of each dNTP, 0.4 M of each primer,and 1.25 units of Taq polymerase. SufcientPCR product for direct sequencing was gen-erated after 3540 cycles (fragment one: 94Cfor30sec,53Cfor30sec,72Cfor30sec;frag-ments two and three: 94Cfor1min,55C for1 min, 72C for 1 min). PCR products werepuried by polyethylene glycol (PEG) pre-cipitation (20% PEG 8000/2.5 M NaCl). Puri-ed templates were sequenced by using dye-labeled dideoxy terminator cycle sequencing

(Applied Biosystems, Inc.) and run on an ABI373 or ABI 377 automated DNA sequencer.

Sequence Alignments

Contiguous sequences of DNA werelinked and edited with SequencherTM 3.0.

Multiple sequence alignments were gener-ated by using Clustal W (Thompson et al.,1994). Those portions of data missing for taxa

were coded as ?. Initially, default multiplealignment parameters were used (gap open-ing penalty D 10, gap extension penalty D 5,delay divergent sequences D 40%). Multi-ple sequence alignments using gap-openingpenalties of 6 and 12 were also examined. Re-gions that were identically aligned across dif-ferent gap penalty values were retained forphylogenetic analysis. Nucleotide positionsthat could not be unambiguously alignedwere excluded from phylogenetic analyses

because of their uncertain positional ho-mology (Gatesy et al., 1993). Positional ho-mology of rDNA sequence alignments wasconstrained by using secondary structuremodels, according to the protocols described

by Titus and Frost (1996) and Wiens andReeder (1997). Only 60 nucleotide positionsfrom the 16S could not be aligned unam-

biguously. The tRNA sequence data lackedproblematic insertions/deletions (as did the

protein-coding ND1 region) and was easilyaligned without the use of structural mod-els. Overall, the combined data contained3,688 unambiguously aligned nucleotide po-sitions. All DNA sequences are depositedin GenBank (accession numbers AF440018-AF440095).



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Separate Analyses

Separate phylogenetic analyses were con-ducted on the 12S rRNA and ND1 protein-coding genes to investigate phylogeneticcongruence among the sampled data. Sep-arate analyses were not conducted for thetRNA and 16S rRNA genes because ofcomplications involving limited charactersampling or incomplete data. We restrictedour comparison of phylogenetic methods tothe combined-data analyses and conductedthese separate 12S and ND1 analyses onlyunder a Bayesian framework (see Bayesiananalyses section below). These separate anal-yses were conducted to detect potential areasof strongly supported incongruence (where

combined analysis may fail; Wiens, 1998), asindicated by conicting nodes with posteriorprobability values 95%.

Combined-Data Analyses

Maximum parsimony.We conductedequally and differentially weighted MPheuristic searches with 100 random se-quence addition replicates and TBR branch-swapping, using PAUP v4.0b8 (Swofford,

2001). A two-parameter step matrix wasused to differentially weight transitions andtransversions. ML was used to estimate thetransition:transversion ratio for the com-

bined data under the HKY85 C I C 0(Hasegawa et al., 1985; Gu et al., 1995)model of nucleotide substitution on a ran-domly chosen equally weighted parsimonytree. Nonparametric bootstrap analyses(Felsenstein, 1985) with 100 pseudorepli-cates and 10 random sequence additions

were conducted on the equally and differen-tially weighted data.

Maximum likelihood.The general time re-versible model with some sites assumed to

be invariable and with variable sites as-sumed to follow a discrete gamma distri-

bution (i.e., GTR C I C 0; Yang 1994a) wasselected as the best-t model of nucleotidesubstitution (ModelTest v3.04; Posada andCrandall, 1998) for the mtDNA sequences.

The gamma distribution was separated intove discrete rate classes to better accom-modate rate heterogeneity (Yang, 1994b). Aheuristic ML analysis was implemented byusing PAUP v4.0b8, with a starting tree ob-tained by way of neighbor-joining (uncor-rected p distances), and the model param-eters under GTR C I C 0 were optimized

on this topology. All ML heuristic searchesimplemented TBR branch-swapping. A suc-cessive approach was used in which modelparameters were reestimated on the result-ing tree and were then used in a subse-quent heuristic search (Swofford et al., 1996;

Wilgenbusch and de Queiroz, 2000). Thisprocess was repeated until the same MLvalue was obtained by sequential heuristicsearches.

Because of the computational limitationsimposed by ML estimation, we were unableto perform a simultaneous ML bootstrap ofour entire dataset. Therefore, we conductedseparate ML bootstrap analyses on the majorclades recovered with strong support fromthe MP and separate and combined Bayesiananalyses (see Results). This resulted in sixseparate nonparametric bootstrap analyses,including an analysis of the internodes con-nectingouraprioridenedclades(Fig.3).Alldata were included in each bootstrap anal-ysis, but the relationships among taxa out-side of the clade being bootstrapped wereconstrained to the ML topology with branchlengths left free to vary. All ML bootstrapanalyses incorporated 100 pseudoreplicates,

TBR branch-swapping, and starting trees ob-tained by neighbor-joining. Although thisprocedure did not provide bootstrap valuesfor the six constrained clades, it did pro-vide support values for the 70 remainingnodes.

Bayesian analyses.Bayesian phylogeneticanalyses were conducted with MrBayes 2.0(Huelsenbeck and Ronquist, 2001). Again,the GTR C I C 0 model was used in all anal-yses. Specic nucleotide substitution model

parameter values were not dened a pri-ori for analyses. Instead, model parameterswere treated as unknown variables with uni-form priors and were estimated as part of theanalysis. All Bayesian analyses were initiatedwith random starting trees and were run for2.0 106 generations. Sampling the Markovchains at intervals of 100 generations thinnedthe data to 20,000 sample points.

A critical aspect of Bayesian analysis is to

ensure that the Markov chain has reachedstationarity. All sample points prior to reach-ing stationarity are essentially random andshould be discarded as burn-in samples

because they do not contain useful infor-mation about the parameters. We plottedthe log-likelihood scores of sample pointsagainst generation time and determined



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FIGURE 3. Phylogeny illustrating the six nodes that were collapsed and constrained to expedite the ML bootstrapanalyses.

that stationarity was achieved when thelog-likelihood values of the sample pointsreached a stable equilibrium value (Huelsen-

beck and Ronquist, 2001). Because thesestationarity samples collectively form ourapproximation of the posterior probabilitydistribution, we deem it better to be cautiousand discard useful samples when determin-ing stationarity than to unknowingly retain

burn-in samples.We used several methods to assure that our

analyses were not trapped on local optima.First, analyses were run independently atleast twice, beginning with different starting

trees, and their apparent stationarity levelswere compared for convergence (Huelsen-

beck and Bollback, 2001). Independent anal-yses were considered to have convergedif their log-likelihood values approachedsimilar mean values. Second, Metropolis-coupled Markov chain Monte Carlo wasused to enhance the tree-climbing capabili-ties of the Markov chains (Huelsenbeck andRonquist, 2001). This method generates in-

crementally heated Markov chains, whichenables a more thorough exploration of pa-rameter space (Marinari and Parisi, 1992;Geyer and Thompson, 1995). The random ex-change of parameter values between heatedchains and the chain of interest effectively de-creases the distance between optimal peaksin parameter space as a mechanism to avoid

being trapped on local optima. We usedfour incrementally heated Markov chains,utilizing the default heating values. Third,

the posterior probabilities for individualclades obtained from separate analyses werecompared for congruence (Huelsenbeck andImennov, 2002)given the possibility thattwo analyses could appear to converge onthe same log-likelihood value while actuallysupporting incongruent phylogenetic trees.Directly comparing levels of support for in-dividual nodes further ensures convergenceof analyses.

After discarding burn-in samples (all sam-

ples preceding stationarity) and evaluatingconvergence, the remaining samples were re-tained for further analysis and data process-ing. Each sample includes a tree topologythat includes branch lengths and substitu-tion model parameter values. The topolo-gies were used to generate a 50% major-ity rule consensus tree, with the percentageof samples recovering any particular claderepresenting that clades posterior probabil-

ity (Huelsenbeck and Ronquist, 2001). Un-like nonparametric bootstrap support val-ues, these are the true probabilities of theclades under the assumed models (Rannalaand Yang, 1996). Consequently, we considerprobabilities of 95% or greater to be signi-cantly supported. The set of substitution pa-rameter values are analyzed to determine the



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mean, variance, and 95% credibility inter-val (CI).

Congruence of Methods and HypothesisTesting

The congruence of MP and ML with re-spect to Bayesian inference was evaluated

by assessing the number of shared nodesand the congruence between the estimatedmeasures of support (bootstrap vs. poste-rior probabilities). We were particularly in-terested in comparing the results of the MLand Bayesian analyses, which were expectedto provide similar results.

The ShimodairaHasegawa test statisticwas used to statistically compare alterna-

FIGURE 4. (a) Burn-in plots of the separate 12S Bayesian analyses. Letters AD represent four independentanalyses (each involving four incrementally heated Markov chains) beginning with random starting trees. (b) Burn-in plots of the separate ND1 Bayesian analyses. The results of the two independent analyses are superimposed,illustrating that the log-likelihood scores converged on similar values.

tive phylogenetic hypotheses (Shimodairaand Hasegawa, 1999; Goldman et al., 2000).ShimodairaHasegawa tests were conductedusing PAUP v4.0b8, with RELL (resamplingestimated log-likelihood) optimization and10,000 bootstrap replicates.

RESULTS

Separate Bayesian Analyses

The 12S data contained 897 unambigu-ously aligned nucleotide positions. Two ini-tial independent Bayesian analyses of thesedata under the GTR C I C 0 model failedto converge on similar log-likelihood scores(analyses A and B; Fig. 4a). Two additionalanalyses were run (analyses C and D), which



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converged with analysis B at intervals of2.5 105 generations (Fig. 4a). Analysis Cwas run for an additional 3.0 106 gener-ations (sampled at intervals of 1,000) andthe log-likelihood score remained stable (plotnot shown), further suggesting that station-

arity had been reached for analyses B, C, andD by at least 1.0 106 generations. The sam-ples obtained with analyses B, C, and D werecombined and the consequent phylogeneticresults were compared with those obtainedfrom analysis A. The phylogenetic relation-ships inferred from analysis A versus anal-yses B, C, and D were not signicantly in-congruent, but the 95% CI of the likelihoodscores and alpha shape parameter estimatesdo not overlap, which does suggest a signif-icant difference (Table 2). The 50% majorityrule consensus tree of analyses B, C, and Dcombined supported 27 nodes with signi-cance levels 95% (Fig. 5a).

The ND1 data alignment contained 969positions and exhibited no length variation.Two independent Bayesian analyses of thesedata under the GTR C I C0 model convergedon similar log-likelihood scores (Fig. 4b). Allsamples preceding generation number 5.0

105

were discarded as burn-in, and the re-maining samples were combined. The 50%

TABLE 2. Nucleotide substitution model parameter estimates (GTR C I C 0) for two stationarity levels reachedduring Bayesian analyses of the 12S data. Upper values in each pair correspond to analysis A; lower values corre-spond to a combination of analyses B, C, and D (Fig. 4a).

Mean Variance 95% CI

ln L 4910.486630 310.481792 4948.02, 4879.794755.715244 166.841017 4782.08, 4731.91

rCT 30.727221 29.187001 20.502879, 39.68503345.025011 227.596693 14.940798, 77.246475rAG 15.179506 11.981245 8.447808, 21.986699

18.715500 41.911734 5.996052, 33.522341rAT 2.497647 0.459963 1.286195, 3.938626

4.440543 2.763438 1.284080, 7.886315rCG 1.007537 0.219843 0.296376, 2.084873

1.430581 0.667038 0.265578, 3.392998rAC 2.546959 0.416793 1.437499, 3.947036

4.360079 2.475638 1.351498, 7.623224A 0.386064 0.000230 0.356224, 0.415929

0.372929 0.000219 0.344801, 0.402884C 0.259127 0.000160 0.235031, 0.284552

0.253982 0.000155 0.230038, 0.278660G 0.145137 0.000108 0.126109, 0.165862

0.161500 0.000140 0.138758, 0.185055T 0.209671 0.000129 0.189230, 0.233054

0.211599 0.000138 0.189092, 0.235047 0.209520 0.000241 0.181472, 0.241943

0.471552 0.008370 0.326483, 0.686532Pinvar. 0.497819 0.000708 0.443709, 0.548427

0.467468 0.002234 0.365898, 0.550697

majority rule consensus tree of these com-bined samples supported 40 nodes with sig-nicance levels 95% (Fig. 5b). No areas ofstrongly supported conict were identied

between this tree and the 12S tree.

Combined-Data AnalysesThe combined dataset consists of 3,688

unambiguously aligned nucleotide posti-tions from the 12S, ND1, 16S, and tRNA(phenylalanine, valine, leucine, isoleucine,glutamine, and methionine) genes.

Maximum parsimony.Equally weightedparsimony analysis of the 801 parsimony-informative characters (656 for the ingroup)resulted in 82,646 most-parsimonious trees.Bootstrap analysis of these data resulted in42 ingroup nodes (out of 71) with 70%support (Appendix 2). Differentially weight-ing transitions and transversions by a factorof 4.3:1 (ML estimation with the HKY85 CI C 0 model) resulted in 335 equally parsi-monious trees, and a bootstrap analysis pro-vided 70% support for 47 ingroup nodes(Appendix 2).

Maximum likelihood.ML analysis of thecombined data under the GTR C I C0 model

resulted in a topology with ln L D 22086.42and model parameter estimates within the



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FIGURE 5. The 50% majority rule consensus trees from the separate Bayesian analyses of the 12S (a) and ND1(b) data. Numbers on nodes represent posterior probability values.

95% CI obtained in the Bayesian analysis(Table 3). Two successive searches wereneeded to obtain the optimal ML phylogenyand required 700 hr of computational time

TABLE 3. Nucleotide substitution model parameter estimates (GTR + I + 0) from the Bayesian analysis of thecombined data. ML parameter estimates were calculated on the combined-data Bayesian tree (Fig. 7).

Mean Variance 95% CI ML estimate

ln L 22160.977941 108.449522 22182.380000, 22141.580000 22086.418rCT 19.873457 16.933814 13.354946 , 29.15972 1 15.966051rAG 20.297484 18.271686 13.477004 , 29.80507 2 16.107469

rAT 2.052029 0.218075 1.299985, 3.192565 1.641191rCG 1.100560 0.105623 0.583899, 1.850187 0.826086rAC 2.541306 0.307020 1.640349, 3.741008 2.061876A 0.381994 0.000048 0.368958, 0.396138 0.381490C 0.261211 0.000036 0.249498, 0.272753 0.261115G 0.129720 0.000026 0.120049, 0.139890 0.130429T 0.227075 0.000035 0.215589, 0.238535 0.226967 0.645388 0.005916 0.509200, 0.809334 0.579430Pinvar. 0.529845 0.000525 0.481909, 0.572336 0.515555

on a 450 MHz Macintosh G4 computerwith 896 MB of RAM. ML bootstrap anal-ysis yielded 40 unconstrained nodes with70% support (Appendix 2). Whereas a



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FIGURE 6. (a) Burn-in plots of the combined-data (12S, 16S, ND1, and tRNA) Bayesian analyses. The results oftwo independent analyses are superimposed, illustrating that the log-likelihood scores converged on similar values.(b) Comparison of clade probabilities from the two independent Bayesian analyses, illustrating the congruence

between the resulting posterior probability values.

conventional ML bootstrap analysis of thecombined data would require several yearsto nish, our modied approach required

150 hr.Bayesian analysis.Bayesiananalysisofthetotal combined data under the GTR C I C 0model for 2.0 106 generations resulted in aposterior probability distribution containing2.0 104 samples per analysis. Two inde-pendent analyses converged on similar log-likelihood scores and reached stationarity

at no later than 500,000 generations (Fig. 6a).The initial 500,000 samples from each anal-ysis were discarded, leaving a total of 3.0

106

combined samples. The posterior prob-ability values supporting congruent nodesbetween these analyses were highly corre-lated (Fig. 6b), further indicating that theanalyses converged. A majority rule consen-sus tree of the 3.0 106 combined sam-ples resulted in a tree containing 46 ingroupnodes with a signicance level>95% (Fig. 7).



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FIGURE 7. The 50% majority rule consensus tree from the Bayesian analysis of the combined data. Numbers onnodes represent posterior probability values.



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TABLE 4. ShimodairaHasegawa test results for comparisons of alternative hypotheses.

Hypothesis ln L Difference ln L P

Equal weights MP 22178.515 92.096 0.4756Weighted MP 22118.087 31.669 0.5531Bayesian and ML 22086.418 S. woodi C Western 22086.727 0.308 0.8931

S. undulatus monophyly 31406.550 9320.131 0.0000

Ecomorph monophyly 30009.876 7923.458 0.0000

P < 0:05.

The ShimodairaHasegawa test failed to re-ject any of the alternative topologies fromthe MP, ML, and separate Bayesian analy-ses (Table 4). The ML and Bayesian topolo-gies were identical and therefore received the

same likelihood scores. The computationaltime required by the Bayesian analysis was125 hr, including the time for estimating

branch support.

Congruence of Methods

Our Bayesian analyses supplied posteriorprobabilities for many nodes shared with theMP andML bootstrap analyses (Appendix 2).Overall, MP bootstrap scores were notstrongly correlated with posterior probabil-ities values (Fig. 8a, b). However, the corre-lation was tighter when comparing ML andBayesian support values (Fig. 8c).

Phylogenetic Relationships

Phylogenetic analyses using MP, ML, andBayesian methods all provide strong sup-port for Sceloporus undulatus group mono-phyly and place S. occidentalis and S.virgatus as basal to the remaining members

of the undulatus group. The ML andBayesian analyses provide strong supportfor a sister group relationship between S.virgatus and a more exclusive undulatusgroup clade that contains S. belli, S. cautus,S. undulatus, and S. woodi. All analysesstrongly support S. undulatus paraphylywith respect to S. belli, S. cautus, and S. woodi.A ShimiodairaHasegawa test rejects themonophyly of S. undulatus (Table 4). The

major mtDNA lineages recovered withinS. undulatus exhibit a strong pattern ofphylogeographic structure and effectivelypartition S. undulatus into geographicalportions of their range. These groupings areinconsistent with the current subspecic tax-onomy (Fig. 9). These groups, which weinformally refer to as the Central, Eastern,

Southwestern, and Western clades, corre-sponding to their relative geographic posi-tions, are strongly supported by all separateand combined analyses.

The Southwestern clade contains Scelo-porus cautus, S. belli, a n d S. undulatuspopulations from Arizona, New Mexico,and north-central Mexico. Sceloporus belli isnested deep within this clade as the sistertaxon to S. u. speari. Although the South-west clade (including S. cautus) is stronglysupported, the basal relationships within thislineage, which includes S. cautus and twoChihuahuan Desert S. undulatus popula-tions (2 and 6), are weakly supported. Theplacement ofS. cautus within the Southwest-

ern clade may be an artifact of the combined-data analysis. The ND1 phylogeny (Fig. 5b)placed S. cautus as the sister species toa monophyletic Southwestern clade. Ourcombined-data phylogeny may be obscuringthe relationships among these basal lineagesof the Southwestern clade. Although S. u.tristichus population 42b is nested withinthe Southwestern clade, a second individualfrom this population (42a) is nested withinthe Western clade. The Western and South-western clades are not sister taxa despite thenonexclusivity of haplotypes from popula-tion 42. The Southwestern clade is stronglysupported as the sister taxon to a stronglysupported clade containing S. woodi and theCentral, Western, and Eastern clades. How-ever, the ML and Bayesian analyses provideonly weak support for the interrelationshipsamong the Western, Central, and Easternclades, as well as the placement of S. woodi

as the sister species of the Eastern clade.

DISCUSSION

Posterior Probabilities and NonparametricBootstrap Proportions

Phylogenetic analyses of large datasetsusing complex substitution models remain



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FIGURE 8. Comparisons of Bayesian posterior probabilities with (a) equally weighted MP, (b) weighted MP, and(c) ML analyses. Dotted lines represent where the 70% bootstrap corresponds to a 95% CI.



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FIGURE

9.MonophyleticmtDNAlineagessupportedbythecombined-data(12S,

16S,

ND1,andtRNA)Bayesianphylogeny.Numbersco

rrespond

toScelop

orusundulatuspopulationsliste

dinAppendix1.

Circumscribed

populationsarethosesupporte

dwith95%posteriorprobabilitybythe

combined

-dataBayesiananalysis.Population42ineast-centralArizonaco

ntainshaplotypesfoundintheWesternandSouthwesternclades.Central

cladepop

ulations18,

22,

32,

37,and38formastronglysupportedmonophyleticgroupbutwerenotcircledforeaseofillustration.

Relatio

nshipsof

S.cautusandS.woodiarealsonotshown.

58



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computationally prohibitive for standardML searches. Because the Bayesian analysisproduced a tree similar to that produced byML and provided posterior probability val-ues for all nodes in a substantially shortertime (days vs. years), it is important to deter-

mine how posterior probabilities providedby Bayesian analysis compare with boot-strap proportions. Without knowing the truephylogeny, it is not possible to determinewhich, if either, estimate of phylogeneticcondence is more accurate. However, be-cause the vast majority of phylogenetic stud-ies rely heavily on MP and ML bootstrapproportions, we believe a comparison of

bootstrap proportions and posterior proba-bilities is warranted. Based on known phylo-genies and simulations (Penny and Hendy,1986; Zharkikh and Li, 1992; Hillis and Bull,1993), bootstrap values 50% are generallyunderstood to be underestimates of trueclade probabilities, and values 70% corre-spond to a 95% CI. Our Bayesian analysessupply posterior probabilities for numerousnodes shared with the MP and ML boot-strap analyses (Appendix 2), thereby allow-ing comparisons of posterior probabilities

and bootstrap values in the context of ourempirical dataset. We do not nd a strong re-lationship between bootstrap values of 70%corresponding to posterior probabilities of95%. Differences in models (parsimony vs.likelihood), stochastic error, problems withour ML bootstrap procedure, or some com-

bination of these could be causing the dif-ferences seen among our analyses. Furtherstudies of the correspondence between thesesupport values are needed.

Local Optima

Maddison (1991) demonstrated within aMP framework that a data matrix can containseveral distinct classes of tree islands. OurBayesian analyses of the 12S rRNA data ap-peared to have difculties reaching station-arity. Each of our four independent analysesof these data was trapped on local optima

for some time (Fig. 4a). Although the topol-ogy recovered from the local optimum didnot differ signicantly from that recoveredat the putative global optimum, clearly theunderlying substitution model parameter es-timates differ, and the alpha value at leastdiffered signicantly (Table 2). This observa-tion contradicts the nding of Sullivan et al.

(1996) on cytochrome b sequences from ro-dents, in which only major rearrangementsin topology were found to affect alpha val-ues. Our 12S analyses demonstrate that lo-cal optima can confound substitution modelparameter estimates; these results highlight

why one should take care to ensure station-arity has been reached when using Bayesianinference.

Phylogeny and Evolution of theSceloporus undulatus Group

The MP, ML, and Bayesian analysessupport the monophyly of the Sceloporusundulatus group. Contrary to most otherworks (Dixon et al., 1972; Sites et al., 1992;Wiens and Reeder, 1997), Smith et al. (1992)regarded both S. cautus and S. exsul as mem-

bers of the spinosus group, based on overallsimilarities in morphology and behavior. Allof our analyses are congruent with Wiens andReeder (1997), placing S. cautus well within aparaphyletic S. undulatus. The morpholog-ical data of Wiens and Reeder (1997) also sup-ported S. exsul as a member of the undulatusgroup, placing it as the sister species of

S. virgatus. Thus, all explicit phylogeneticanalyses have supported S. cautus andS. exsul as members of the undulatus group.

Smith et al. (1992, 1995) provided a phy-logenetic hypothesis for Sceloporus undu-latus based on the overall morphologicaland behavioral similarities among taxa, inwhich they recognized three exerges (Fig.1). Our phylogenetic analyses support theparaphyly of the exerges hypothesized bySmith et al. (1992, 1995), and a Shimodaira

Hasegawa test rejects the hypothesis ofmonophyly of S. undulatus and these ex-erges (Table 4). Subspecies color patterns and

behaviors have multiple independent ori-gins, suggesting that ecological adaptationhas played a critical role in the evolution ofthis group (Fig. 10). Our data suggest thatthe traditional subspecies within S. undula-tus represent ecomorphs and that the par-allel evolution of these ecomorphs and their

recognition as distinct taxa have masked thetrue number of species within this complexgroup. Despite the paraphyly of these eco-morphs, theEastern and Western clades com-prise only the Forest and Canyon ecomorphpopulations, respectively (Fig. 10).

The phylogeny recovered in this study par-titions Sceloporus undulatus geographically



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FIGURE 10. Evolution of ecomorphs within the Sceloporus undulatus species group.Boxes lled with black, white,or gray indicate habitat types as shown by the inset drawings. Support values (MP, ML, and Bayesian) for thenumbered nodes are provided in Appendix 2.



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in ways that are concordant with divisionsfound in other taxa. The Eastern and Cen-tral clades appear to be separated by MobileBay, and a strongly supported basal divisionwithin the Central clade separates popula-tions from opposite sides of the Mississippi

River. In addition, a basal division withinthe Eastern clade strongly supports a lin-eage restricted to the region east of the Ap-palachian Mountains. These geographic fea-tures have represented effective barriers togene ow in other organisms (Avise, 1996;Burbrink et al., 2000; Rodriguez-Robles andde Jesus-Escobar, 2000). Although the his-torical factors that produced the initial deepdivisions among the Western, Southwestern,Central, and Eastern clades are unclear, wecannot reject that contemporary processesof gene ow may be occurring among anyof these clades, solely on the basis of ourmtDNA data and current sampling. How-ever, the Western and Southwestern cladesare clearly in contact in northeast Arizona(population 42).

MostSceloporus undulatus subspecies arediagnosed by overlapping ranges of scala-tion characters, and zones of intergradation

have been assumed to exist at the contact be-tween the ranges of some subspecies (Smith,1938; Lemos-Espinal et al., 1998). An area ofintergradation is thought to exist at the con-tact between the ranges of S. u. elongatusand S. u. tristichus (Smith, 1938; Lemos-Espinal et al., 1998) north of the vicinityof population 42, but the gene ow occur-ring between the Southwestern and Westernclades (if any) is between highly divergentlineages that clearly do not represent these

subspecies. Our phylogeny does not supportany of the wide-ranging subspecies bound-aries, suggesting that the suspected zonesof intergradation may actually represent ar-eas of clinal variation between weakly de-ned taxa. Although none of these zoneshas been rigorously studied, our results havehelped further elucidate where future mor-phological or molecular studies (or both) areneeded.

Phylogeography of Sceloporus woodi

The phylogenetic placement of Sceloporuswoodi has been addressed previously inthe literature and has interesting phylogeo-graphic implications (Jackson, 1973; Wiens

and Reeder, 1997; Clark et al., 1999). Jackson(1973) suggested S. woodi may have origi-nated from S. undulatus populations fromthe southwestern United States or north-central Mexico, given their overall similari-ties in morphology and behavior. Through-

out the Pleistocene, the Circumferential GulfCoast Corridor allowed for the dispersalof ora and fauna from the southwesternUnited States and Mexico to the FloridaPeninsula, thus providing a mechanism forgene ow between these now allopatric pop-ulations (Auffenberg and Milstead, 1965).However, dispersal of S. woodi is currentlyimpeded by restriction to, and presumedreliance on, a single plant community, thesand-pine scrub (Pinus clausaQuercus asso-ciation). Wiens and Reeder (1997) providedweak support for a southwestern afnity ofS. woodi, using a phylogenetic analysis ofmorphological data. However, all phyloge-netic analyses of molecular data (Wiens andReeder, 1997; Clark et al., 1999; this study)contradict this hypothesis and place S. woodisister to more geographically proximate east-ern S. undulatus populations; nonetheless,no analysis has provided strong support for

the placement ofS. woodi. Our Bayesian anal-ysis provides a posterior probability of 0.62for a S. woodi C Eastern S. undulatus clade.However, a ShimodairaHasegawa test failsto reject a S. woodi C Western clade relation-ship (Table 4) and thus fails to reject the pos-sibility of this hypothesis of Jackson (1973).

Taxonomic Implications

Species denitions are diverse in opin-

ion, and supercially there appears to bediscord among concepts. Despite the philo-sophical differences among concepts, deQueiroz (1998) advocated that all modernspecies criteria (i.e., biological [Mayr, 1942,1963], evolutionary [Simpson, 1961; Frostand Hillis, 1990], and phylogenetic [Cracraft,1983]) are in agreement with a general lin-eage concept of species. The general lin-eage concept denes species as segments

of population level evolutionary lineages(de Queiroz, 1998) Under this unifying ter-minology, contemporary species denitions(D species concepts) differ only with re-spect to the particular threshold criterionused as evidence to infer lineage status dur-ing the time-extended process of speciation



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(i.e., reproductive incompatibility, coales-cence, and so forth). We agree with the gen-eral lineage concept of species (de Queiroz,1998) and for philosophical reasons con-sider the evolutionary species denitionto be the most appropriate for dening

lineages.To recognize Sceloporus undulatus as a

natural group, we must not exclude any de-scendants of the ancestor of this group. Thisensures that statements about taxonomic or-dering and evolutionary history are logi-cally consistent (Frost and Hillis, 1990; deQueiroz and Gauthier, 1992; de Queiroz,1997). Such criteria are not met with regard toS. undulatus. We suggest that the Western,Southwestern, Central, and Eastern lineagesof S. undulatus each represent evolutionaryspecies.

Our taxonomic recommendations willhave positive impacts on comparative stud-ies of Sceloporus undulatus, which com-monly use the current taxonomy as a com-parative framework. It is important forfuture comparative studies to appreciate thatmuch of the geographic variation withinS. undulatus is not attributable to intraspe-

cic variation alone and that some pop-ulations are more closely related to otherspecies (e.g., S. cautus, S. woodi) than toother S. undulatus populations. A com-parative analysis of life history variationwithin S. undulatus that takes into ac-count our phylogeny is currently in progress(Niewiarowski et al., unpubl.).

We recognize that justifying the delimita-tion of species solely on the basis of mtDNAis controversial because of the potential

problems associated with incomplete lin-eage sorting, introgression/hybridization,and male-biased dispersal (Avise, 1994).However, Wiens and Penkrot (2002), dis-cussing the advantages of using mtDNAto delimit species, provided an objec-tive framework for species delimitationthrough a DNA haplotype phylogeny. Us-ing their framework, we have concluded thatSceloporus undulatus represents multiple

species, based on our interpretation that thecontact between the Western and Southwest-ern lineages (population 42) is inadequateevidence of gene ow to warrant the recog-nition of S. undulatus as a single, nonex-clusive (i.e., paraphyletic) lineage. We havedemonstrated that lineages of S. undulatus

are obfuscated by the parallel evolutionof ecomorphs, and our conclusion thatpopulation 42 represents secondary con-tact between cryptic nonsister species is

based largely on the phylogenetic results,indicating that these haplotypes belong

to highly divergent evolutionary lineages.We await additional evidence from nu-clear molecular markers or morphology(or both) to potentially corroborate orfalsify the following species limits sug-gested by our mitochondrial DNA data.We tentatively propose the following tax-onomic arrangement and explain why fur-ther sampling is important for the renementand corroboration of these revised specieslimits:

Sceloporus undulatus (Latreille, in Sonniniand Latreille, 1802). This species includesall populations belonging to the Easternclade. The western limit of this speciesrange approaches Mobile Bay, but fur-ther sampling is required in Alabama,Mississippi, and Tennessee. The stronglysupported subdivision within this lin-eage along the Appalachian Mountains

suggests the presence of possibly twoseparate evolutionary species. However,this conclusion is contingent on fur-ther taxon sampling between these twosubclades.

Sceloporus consobrinus (Baird and Girard,1853). This species includes all popula-tions in the Central clade. Eventually,additional geographic sampling may re-quire that priority be given to S. thayerii(Baird and Girard, 1852) if the type lo-

cality (Indianola, Calhoun Co., Texas) isfound to group within this species. Theeastern extent of this species distributionapproaches Mobile Bay and the westernlimits are the grasslands of eastern NewMexico and Colorado. Populations east ofthe Mississippi River are basal and exclu-sive within this species; whether they rep-resent another distinct evolutionary lin-eage requires further sampling along the

Mississippi River.Sceloporus tristichus (Cope in Yarrow, 1875).This species includes the populationsgrouping in the Western clade, includ-ing the type locality of S. u. tristichus.The nature of gene ow (if any) betweenS. tristichus and the genetically divergent



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Southwestern lineage is unclear and is cur-rently being investigated (Leache, Reeder,and Cole, unpubl. data).

Sceloporus cowlesi (Lowe and Norris, 1956).This species includes populations group-ing in the Southwestern clade (including

the type locality of S. u. cowlesi) andis distributed throughout the ChihuahuanDesert and north-central Mexico. Phylo-genetic analysis of the combined mtDNAdata suggests this species is nonexclusivewith respect to S. cautus. However, that re-sult is weakly supported, and one partition(i.e., ND1) recovers S. cowlesi as exclusive(monophyletic), with S. cautus as its sisterspecies. Further geographic sampling is re-quired throughout the Chihuahuan Desertto obtain a better understanding of the re-lationship between S. cowlesi and S. cau-tus. Such sampling will also allow the fur-ther evaluation of the status of the weaklydifferentiated and poorly dened S. belli.Until then, we consider S. belli to be con-specic with S. cowlesi.

Currently, we are unaware of any diagnos-tic characters that would corroborate the ex-

istence of our newly proposed evolutionaryspecies. As previously stated, each of thesespecies contains populations that were pre-viously allocated to multiple subspecies ofSceloporus undulatus (sensu lato). Thus, theintraspecic morphological variation is asgreat (if not greater) than the variation be-tween these species. The lack of diagnosticcharacters and extensive intraspecic varia-tion is similar to that found by Wiens et al.(1999) and Wiens and Penkrot (2002) in two

species (S. minor and S. oberon) of the torqua-tus species group. Unfortunately, an exten-sive and rigorous morphological analysis ofS. undulatus (sensu lato), as conducted byWiens and Penkrot (2002) for the S. jarroviicomplex, hasnot beenundertaken. However,such a study may ultimately identify diag-nostic characters for some (if not all) of theseevolutionary species and provide a better un-derstanding of the species limits within thisdiverse group of lizards.

ACKNOWLEDGEMENTS

The following individuals made this project possi-ble by assisting in collecting lizards or providing tis-sue samples: R. Andrews, M. Angilletta, K. Ashton,L. Bell, A. Buto, G. Clark, J. Cole, J. Collins, B. Conrod,N. Crochet, S. Echernacht, S. Fishman-Armstrong,

L. Fitzgerald, A. Hebbert, M. Hedin, T. Hibbits, S. Kelly,S. Mackessy, J. Matter, D. Mulcahy, P. Niewiarowski, S.Oberle, D. Orange, A. Pece, T. Persons, J. Richmond,

J. Reichert, J. Sites, Jr., H. Smith, L. Vitt, M.Warburton, J. Wiens, and D. Wood. Funding wasprovided to A.D.L. by the following institutionsand programs: Theodore Roosevelt Memorial Fund

(American Museum of Natural History), McNairScholars Program (San Diego State University [SDSU]),Evolutionary Biology Graduate Scholarship (SDSU),Hafner Award (Louisiana State University Museum ofNatural Science), and a Sally Casanova Pre-DoctoralScholarship (California State University). Financialsupport was also provided by a National ScienceFoundation grant (DEB-9707428) to T.W.R. We thankF. Burbrink, T. Devitt, R. Etheridge, J. Huelsenbeck,

J. Light, J. McGuire, S. Messenger, D. Pollock,B. Rannala, D. Reed, C. Simon, J. Sites, Jr., and J. Wiensfor their insightful comments on various versions of thismanuscript. We also thank the students and faculty of

the SDSU Evolutionary Biology Program Area and theLouisiana State University Museum of Natural Sciencefor their support. Finally, we thank Anne Leache for herillustrations of Sceloporus undulatus.

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