MULTILOCUS SPECIES DELIMITATION AND SPECIES TREE INFERENCE WITHIN THE WESTERN RATTLESNAKE (CROTALUS VIRIDIS) SPECIES COMPLEX.

Transcription

1 MULTILOCUS SPECIES DELIMITATION AND SPECIES TREE INFERENCE WITHIN THE WESTERN RATTLESNAKE (CROTALUS VIRIDIS) SPECIES COMPLEX A Thesis Presented to the Faculty of San Diego State University In Partial Fulfillment of the Requirements for the Degree Master of Science in Biology by Julianne R. Goldenberg Summer 23

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3 iii Copyright 23 by Julianne R. Goldenberg All Rights Reserved

4 iv ABSTRACT OF THE THESIS Multilocus Species Delimitation and Species Tree Inference Within the Western Rattlesnake (Crotalus viridis) Species Complex by Julianne R. Goldenberg Master of Science in Biology San Diego State University, 23 The recent renaissance in the development of multilocus coalescent-based species tree inference methods has transformed the study of systematics; however, coalescent-based methods require a priori knowledge of species limits. A variety of methods of multilocus species delimitation are now available which provide potentially objective approaches to assign individuals to putative species; however, these methods may require knowledge of the species tree. This dichotomy illustrates the necessity of studying species delimitation and species tree inference in concert. Here I demonstrate a method of simultaneous multilocus coalescent-based species delimitation and species tree inference that does not require prior assumption of species limits or the species tree. This method uses the Bayes factor to compare the fit of competing hypotheses of species delimitation to the data, and can be used to compare non-nested hypotheses. The multilocus Bayesian species tree is inferred under each competing hypothesis while the fit of the hypothesis to the data is quantified using marginal likelihood estimation. Marginal likelihood scores (as estimated using path sampling, stepping stone, and the smoothed harmonic mean estimator) are then compared using the Bayes factor. Here I apply this method to the Western Rattlesnake (Crotalus viridis) species complex, a group for which the species limits are contentious and the species tree is unknown. I collected DNA sequence data for six loci (five nuclear introns and one mitochondrial coding gene) and 63 ingroup individuals. Hypotheses of species limits were generated using () historical subspecific designations and (2) clades on a guide mitochondrial gene tree that were iteratively clustered into increasingly inclusive groupings. For each hypothesis, the species tree and marginal likelihood were estimated (under three competing marginal likelihood estimators) using *BEAST. Resulting marginal likelihood scores were compared to one another using the Bayes factor. BPP was also used to delimit species within the C. viridis complex for comparison. Contrary to currently recognized taxonomy, I recovered very strong support using both the Bayes factor method and BPP that the C. viridis complex contains six independently evolving species, including cryptic species within the Northern Pacific Rattlesnake (currently C. o. oreganus). I applied this resulting taxonomy to infer the first dated multilocus species tree of the C. viridis complex, which is topologically discordant from the mitochondrial gene tree. This study successfully demonstrated a novel method of Bayesian multilocus species delimitation. The results presented here warrant revision of the taxonomy within the C. viridis complex and dramatically revise our understanding of the evolutionary history of this group.

5 v TABLE OF CONTENTS PAGE ABSTRACT... iv LIST OF TABLES... vii LIST OF FIGURES... viii ACKNOWLEDGEMENTS... ix CHAPTER INTRODUCTION... Simultaneous Multilocus Species Tree Inference and Species Delimitation...3 The Crotalus viridis Species Complex...5 Objectives MATERIALS AND METHODS...8 Taxon Sampling and Data Collection...8 Gene Tree Inference...9 Generation of Hypotheses of Species Delimitation... Bayes Factor Hypothesis Testing... Species Delimitation Using BPP...2 Species Concept...3 Dated Multilocus Phylogeny of the C. viridis Species Complex RESULTS...5 Data Collection...5 Gene Tree Inference...5 Generation of Hypotheses of Species Delimitation...7 Method (): Traditional Subspecies as Species...7 Method (2): Mitochondrial Clades...7 Method (3): Multilocus Nuclear Clustering Using POFAD...2 Hypothesis Testing via Marginal Likelihood Estimation...2 Species Trees...2 Hypothesis Testing...23

6 Species Delimitation Using BPP...24 Dated Multilocus Phylogeny of the C. viridis Species Complex DISCUSSION...28 Coalescent Species Delimitation...28 Using the Bayes Factor for Species Delimitation...29 Comparison to BPP...3 Species Limits and Phylogeny Within the Crotalus viridis Species Complex...3 Taxonomic Recommendations...35 Conclusions...36 REFERENCES...38 APPENDIX A SUPPLEMENTARY TABLES...45 B SPECIES DESIGNATIONS APPLIED A PRIORI FOR EACH HYPOTHESIS OF SPECIES DELIMITATION TESTED...49 C SUPPLEMENTARY FIGURES...55 vi

7 vii LIST OF TABLES PAGE Table. Models of Molecular Evolution and Locus Variability... Table 2. Hypotheses of Species Delimitation Generated using Methods () and (2)...9 Table 3. Marginal Likelihoods Estimated Using Mitochondrial and Nuclear Data for Each Hypothesis Tested (Table 2)...2 Table 4. Individuals Sampled for This Study...46 Table 5. Primer Information...48 Table 6. Hypotheses H -H Table 7. Hypotheses H 8 -H

8 viii LIST OF FIGURES PAGE Figure. Range of Crotalus viridis species complex Figure 2. Starting tree for species delimitation hypothesis generation using method (2)....8 Figure 3. Marginal likelihoods of hypotheses H -H 4, estimated via path sampling (PS), stepping stone (SS), and the smoothed harmonic mean estimator (shme) Figure 4. Discordant guide trees used as starting trees for analysis with BPP Figure 5. Time-calibrated multilocus species tree of the Crotalus viridis species complex, with outgroups C. scutulatus and C. adamanteus Figure 6. Individual gene trees inferred within a Bayesian framework using MrBayes Figure 7. Individual gene trees inferred within a maximum likelihood framework using RAxML Figure 8. Morphology of individual UT_nunt_ Figure 9. Results of POFAD analysis (i.e., method [3])....7 Figure. Species trees inferred using *BEAST under each hypothesis of species delimitation with all data included Figure. Species trees inferred using *BEAST under each hypothesis of species delimitation without mitochondrial data....77

9 ix ACKNOWLEDGEMENTS I thank the members of my thesis committee, Marshal Hedin and Juanjuan Fan, for discussion that greatly improved this work. I am also grateful to the SDSU Evolutionary Biology faculty and students (past and present) who, both formally in a classroom setting and informally during vital theoretical and practical discussion, helped lead me to the conclusions presented here. I thank my undergraduate assistant, Narina Brothers, for extensive help with DNA sequence data collection. Principally, though, I thank my thesis advisor, Tod Reeder, for formative and invaluable mentorship during the pursuit of my Masters degree. All tissue samples used in this study were generously loaned to me by the following individuals and institutions: Bradford Hollingsworth (San Diego Natural History Museum [SDNHM]), Chris R. Feldman (University of Nevada at Reno [UNR]), Carol L. Spencer (Museum of Vertebrate Zoology [MVZ]), Curtis Schmidt and Travis Taggart (Sternberg Museum of Natural History, Fort Hays State University [FHSM]), Edward A. Myers (City University of New York [CUNY]), Jens Vindum (California Academy of Sciences [CAS]), Melissa Amarello (Arizona State University [ASU]), Robert E. Espinoza (California State University, Northridge [CSUN]), Wolfgang Wüster (Bangor University), Donna L. Dittmann and Robb T. Brumfield (LSU Museum of Natural Science Collection of Genetic Resources), and Bryan Hamilton (National Parks Service). I gratefully acknowledge funding from the Herpetologists League Jones-Lovich Grant in Southwestern Herpetology, the Theodore Roosevelt Memorial Fund of the American Museum of Natural History, and the Harry E. Hamber Memorial Scholarship, without which this study would not have been possible.

10 CHAPTER INTRODUCTION No one definition has yet satisfied all naturalists; yet every naturalist knows vaguely what he means when he speaks of a species. Generally the term includes the unknown element of a distinct act of creation. The term variety is almost equally difficult to define; but here community of descent is almost universally implied. --Charles R. Darwin 859, p. 44 It is quite true that, in the great majority of cases, what we term species are so well marked and definite that there is no difference of opinion about them; but as the test of a true theory is, that it accounts for, or at the very least is not inconsistent with, the whole phenomena and apparent anomalies of the problem to be solved, it is reasonable to ask that those who deny the origin of species by variation and selection should grapple with the facts in detail, and show how the doctrine of the distinct origin and permanence of species will explain and harmonize them. --Alfred R. Wallace 865, p. 2 A complete understanding of the evolutionary history of a species involves knowledge of both contemporary species limits and the history of speciation (i.e., the species tree). As discussed by Darwin (859), a species is a real entity known to exist in nature. However defining the limits of these species, as opposed to discussing the existence of varieties or subspecies, has led to heated debate within both scientific and non-scientific communities. Wallace (865) discussed five levels of organization below the species level, using these extremely fine divisions to illustrate the futility associated with categorizing a gradient of relatedness. In a detailed review of species concepts, de Queiroz (27) explained the difference between species concepts and criteria of species delimitation. Alternate species concepts are generally concordant in defining a species as a separately evolving metapopulation lineage. However, species concepts disagree on what criteria signify that speciation has occurred, or is occurring, during the process of lineage divergence or cladogenesis. Because the process of speciation, and the definition and delimitation of species are inseparable, species tree inference and species delimitation must be studied in concert.

11 2 The past few years have brought about a renaissance in the development of coalescent-based species tree inference methods that rely on information from multiple independent loci (e.g., STEM, Kubatko et al. 29; BEST, Liu 28; *BEAST, Heled and Drummond 2). As these approaches to species tree estimation assume that gene tree/species tree discordance is entirely attributed to incomplete lineage sorting, it follows that terminal taxa (i.e., species or independently evolving populations) have been reproductively isolated from one another since speciation and that each represents a fully interbreeding metapopulation. Therefore, in order to utilize such coalescent-based methods, it is necessary to a priori designate each sampled individual to a defined species/population before species tree inference can be undertaken. This is problematic for groups where species limits are viewed as contentious or uncertain. The recent advent of a variety of multilocus methods of species delimitation has provided potentially objective approaches to assign individuals to putative species. Among these are population genetic (e.g., Structurama, Huelsenbeck and Andolfatto 27, sensu Rittmeyer and Austin 22), non-coalescent (e.g., approximate Bayesian computing, sensu Camargo et al. 22), and coalescent-based (e.g., BPP, Yang and Rannala 2) methods. Population genetic and non-coalescent approaches to species delimitation are free from certain assumptions imposed by coalescent-based methods, though the coalescent-based BPP approach has outperformed these methods in multiple recent simulation studies (e.g., Leaché and Rannala 2; Rittmeyer and Austin 22; Camargo et al. 22; Zhang et al. 2;). To delimit species BPP uses reverse-jump Markov-chain Monte Carlo (rjmcmc), which allows the number of parameters θ to change during Markov chain Monte Carlo (MCMC) moves. This allows for nodes to be collapsed and resolved along the chain, a type of move that is not possible under classic MCMC. Because of this, BPP requires a guide tree on which to collapse/resolve nodes. Further, this restricts BPP to only testing nested hypotheses of species delimitation. This is problematic for groups where the species tree is unknown.

12 3 SIMULTANEOUS MULTILOCUS SPECIES TREE INFERENCE AND SPECIES DELIMITATION Here I demonstrate a method of simultaneous multilocus species tree inference and species delimitation via hypothesis testing that utilizes a Bayesian approach to compare models of species evolution (Grummer, submitted). A hypothesis testing approach provides the advantage of evaluating statistical support favoring the best-fitting explanation of the data over alternative hypotheses or models of speciation. Knowles and Carstens (27) present a hypothesis testing approach to species delimitation where the probabilities that gene trees were evolved under competing models of speciation history (e.g., a speciation event resulting in species A and species B vs. no speciation event resulting in lumped species AB) are compared using a likelihood ratio test (LRT, Matz and Nielsen 25). However in order to compute such probabilities, this method requires prior knowledge of the species tree. Further, as this is a maximum likelihood approach, uncertainty in gene tree estimation is not taken into account. Ence and Carstens (2) propose another hypothesis testing approach to species delimitation (SpedeSTEM) where a maximum likelihood species tree is calculated from all hierarchical arrangements of species limits, and the fits of these arrangements to the data are compared using the Akaike Information Criterion (AIC, Akaike 973). Importantly, this method does not require prior knowledge of a species tree, but still does not take into account uncertainty in phylogenetic estimation of individual gene trees. Similarly, two methods proposed by O Meara (2) simultaneously infer species delimitations and the species tree, but both methods take as input fully resolved gene trees, thereby not accounting for uncertainty in estimation of gene trees. The method demonstrated in this study (Grummer, submitted) similarly compares competing models of species limits but allows for uncertainty in phylogenetic estimation, does not require prior knowledge of the species tree, and can compare non-nested hypotheses. Here, competing models of speciation are applied as a priori species delimitations for multilocus Bayesian species tree inference that takes as input DNA sequence alignments from multiple genes. The resulting posterior distributions associated with each competing hypothesis are then used to estimate the fit of each hypothesis to the data, quantified as the marginal likelihood (also termed the integrated likelihood, normalizing constant, or harmonic mean identity) of the model (Raftery et al. 27; Kass and Raftery

13 4 995). The marginal likelihoods of competing hypotheses are directly comparable across analyses if the dataset is held constant, and can be compared for statistical support using the Bayes factor, a ratio of marginal likelihoods (Kass and Raftery 995; Lartillot and Philippe 26; Raftery et al. 27; Xie et al. 2; Baele et al. 22A;). The marginal likelihood represents the fit of a model to the data integrated over the posterior distribution; but just as the posterior distribution of parameters must be estimated using MCMC for practical purposes, so must the marginal likelihood of a model be estimated (Lartillot and Philippe 26; Raftery et al. 27; Xie et al. 2; Baele et al. 22A). The estimation of the marginal likelihood of a model has a history of computational difficulty (Suchard et al. 2). The method of the harmonic mean estimator (HME, Newton and Raftery 994) presents a simple and consistent approach where the harmonic mean of the likelihoods of samples drawn from the posterior distribution is computed as a representation of the marginal likelihood. Unfortunately this estimator may have infinite variance across simulations, even in very simple situations, which results in rampant inaccuracy (Lartillot and Philippe 26; Raftery et al. 27; Xie et al. 2). Further, this estimator has been shown to systematically overestimate the marginal likelihood of a model (Xie et al. 2, Baele et al. 22A ). One method proposed to stabilize the variance of the HME is the smoothed HME (shme, Suchard et al. 23), which includes samples from both the posterior and prior distributions in harmonic mean calculation. Though the shme has been demonstrated to be an improvement over the HME (Suchard et al. 23; Lartillot and Philippe 26), it is still highly inaccurate (Lartillot and Philippe 26). Another Bayesian approach to model selection that utilizes information from the posterior distribution is via a shifted gamma estimator, as in the AICM, a MCMC-based adaptation of the AIC (Raftery et al. 27). Here the gamma shape of the posterior distribution is used to compute a maximum achievable log-likelihood for the model, which is then used to penalize the mean of loglikelihoods computed for samples drawn from the posterior distribution. Note that the AICM is not an estimator of marginal likelihood, though does still quantify the fit of a model to the data. This method of hypothesis testing outperforms the HME (Xie et al. 2; Baele et al. 22A), but has yet to be compared with the shme. However, as with the HME and shme, the AICM may be an unreliable representation of the goodness of fit of a model to the data (Xie et al. 2; Baele et al. 22A).

14 5 The development of thermodynamic integration (TI) (e.g., path sampling [PS], Lartillot and Philippe 26) has vastly improved marginal likelihood estimation, though this method can be computationally expensive if a dataset is large or if a large number of parameters must be estimated. The PS approach to TI relies on inferring a secondary MCMC chain relating the posterior distribution to the prior distribution and integrating likelihood over this resulting secondary distribution. To accommodate for large datasets, the recently developed stepping stone (SS) method (Xie et al. 2) combines the accuracy of PS with the computational ease of the HME. Here, a secondary MCMC chain is again inferred, but subsamples are drawn from this secondary distribution, as in the HME, and the marginal likelihood is inferred from the resulting subsample. This method has been shown to be as accurate as PS, but is computationally easier to implement (Xie et al. 2). THE CROTALUS VIRIDIS SPECIES COMPLEX The rattlesnakes of the Crotalus viridis species complex (currently consisting of C. viridis, C. cerberus, and C. oreganus [Crother et al. 22]) have the most extensive distribution of any venomous reptile in North America, ranging from southern Canada to northern Mexico and from the Pacific Coast to the mid-western United States (Figure ). Historically (Klauber 93, 943, 956; Foote and MacMahon 977; Aird 984; Quinn 987) and until relatively recently (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22), the polytypic C. viridis complex included as many as nine geographically and morphologically distinct subspecies contained within a single widespread species C. viridis (C. viridis sensu lato throughout). Among these are such varied forms as the markedly melanistic Arizona Black Rattlesnake (C. cerberus), the dwarfed and particularly neurotoxic Midget Faded Rattlesnake (C. o. concolor), the island endemic Coronado Island Rattlesnake (C. o. caliginis), and the dwarfed Hopi Rattlesnake (C. v. nuntius) famous for its role in the Hopi Snake Dance (Klauber 997). Attempts to infer the evolutionary relationships within this complex (e.g., Foote and MacMahon 977; Aird 984; Quinn 987; Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22) have resulted in controversial and contradictory taxonomic recommendations, and despite these efforts an understanding of the phylogeny remains a source of contention (e.g., Parker and Anderson 27; Mackessy 2; Oyler-McCance and Parker 2).

15 6 Figure. Range of Crotalus viridis species complex. Range map is adapted from Stebbins (23). Ranges of nine subspecies are colored according to legend. Individuals sampled for this study are indicated by black dots (see Table 4 in Appendix A for specific sampling localities). Prior to studies utilizing DNA sequence data, the few taxonomic revisions of Crotalus viridis sensu lato that advocated one or more subspecies be elevated to specific rank (e.g., Aird 984; Quinn 987) had generally not been formally accepted (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22). Three nearly coincident phylogenetic reconstructions of C. viridis sensu lato based on mitochondrial DNA (mtdna) sequence data (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22) were largely concordant with one another. All three mtdna-based studies of C. viridis sensu lato recovered a phylogenetic split between an eastern (C. v. viridis + C. v. nuntius) and western (all

16 7 remaining subspecies) clade, and found C. cerberus to be sister to all remaining western clade subspecies. Though the first of these 22species: C. viridis would contain C. v. viridis and C. v. nuntius, while C. oreganus would encompass all remaining western subspecies. The authors noted that it is likely that C. cerberus may represent an evolutionary species, but conservatively did not elevate this taxon to full species status. Douglas et al. (22) liberally applied the phylogenetic species concept (Cracraft 983) to define seven species within the C. viridis complex: C. viridis, C. oreganus, C. cerberus, C. helleri, C. concolor, C. lutosus, and C. abyssus. Remaining subspecies (C. v. nuntius and C. o. caliginis) were synonimized with C. viridis and C. helleri, respectively. While these three studies were largely concordant with regard to the discovery of distinct mtdna lineages, their taxonomic conclusions differed dramatically. As a result, the current taxonomy generally followed reflects an amalgamation of these recommendations, designating species status to C. viridis (including C. v. viridis and C. v. nuntius), C. cerberus, and C. oreganus (including C. o. oreganus, C. o. helleri, C. o. caliginis, C. o. lutosus, C. o. abyssus, and C. o. concolor) (Crother et al. 22). OBJECTIVES In order to apply coalescent-based species tree inference methods, putative species must be designated a priori. Likewise, in order to apply BPP and other coalescent-based methods of multilocus species delimitation, a guide species tree must be designated a priori. Here I apply a method of simultaneous species delimitation and species tree inference to the Crotalus viridis (Western Rattlesnake) species complex, a group for which the species tree is unknown, the species limits are contentious, and the interrelationships among distinct populations (e.g., subspecies) are uncertain. In such a situation, it is inappropriate to apply a method that requires the input of a guide tree that supposedly reflects the relationships among populations or putative species. However, I apply BPP as well, both to compare the simultaneous species tree inference and species delimitation method demonstrated here to this widely accepted method of multilocus species delimitation and to explore the process and impact of imposing a guide tree on a system for which the species-level/population-level phylogeny is unknown.

17 8 CHAPTER 2 MATERIALS AND METHODS Here, I first discuss my taxon sampling and data collection. I then describe methodology for gene tree inference. Next, I discuss generation of competing hypotheses of species delimitation. I then compare these competing hypotheses using the Bayes factor. I compare this novel Bayes factor approach to species delimitation to the widely implemented BPP method. Finally, I infer a dated multilocus species tree of the Crotalus viridis complex from these combined approaches. TAXON SAMPLING AND DATA COLLECTION DNA sequence data were collected from 63 individuals. Every subspecies of C. viridis sensu lato was represented by at least three individuals, with the exception of C. o. concolor and C. o. abyssus, each of which were represented by a single individual, and the insular C. o. caliginis, which was not represented in this study (Figure, Table 4 in Appendix A). As previous mtdna-based studies have found C. o. abyssus and C. o. caliginis to be nested within C. o. lutosus and C. o. helleri respectively (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22), and have found C. o. concolor to be closely related to C. o. lutosus (Ashton and de Queiroz 2; Douglas et al. 22), the impact of this sparse sampling is expected to be minimal. Additionally, single individuals of C. adamanteus and C. scutulatus were included as outgroups, as previous mitochondrial and multilocus studies place C. scutulatus as sister to C. viridis sensu lato, and place C. adamanteus outside of the C. scutulatus + C. viridis sensu lato clade (Murphy et al. 22; Castoe and Parkinson 26; Pyron et al. 23). Genomic DNA was extracted from frozen or ethanol preserved tissues using a NucleoSpin Tissue extraction kit (Macherey-Nagel Inc., Bethlehem, PA). Amplification of the mitochondrial ND2 protein-coding gene and introns of the nuclear genes BZW, RP4, RPS8, SELT, and TBP2 was carried out using standard PCR methods (Table 5 in Appendix A). Purified PCR products were sequenced by Macrogen USA (Rockville, MD) using an

18 9 ABI 373xl DNA Analyzer (Applied Biosystems, Inc., Carlsbad, CA). Sequences were edited and contigs assembled using Geneious Pro 5..4 (Drummond et al. 2), and aligned using MUSCLE (Edgar 24). Haplotypes of heterozygous individuals were inferred using PHASE 2.. (Stephens et al. 24) under the recombination model. Haplotypes inferred with less than 9% certainty were left as ambiguous, which yielded,, 32,, and 2 ambiguous sites remaining within alignments of BZW, RP4, RPS8, SELT, and TBP2, respectively. Each haplotype inference analysis was repeated twice with different random starting seeds to ensure consistent results. Each nuclear locus was tested for recombination using the DSS Analysis within Topali v2.5 (Milne et al. 24). GENE TREE INFERENCE Single gene trees were inferred for each locus within a maximum likelihood framework using RAxML v7.2.8 Black Box (Stamatakis 26) through the Cyberinfrastructure for Phylogenetic Research (CIPRES, Miller et al. 2). The mitochondrial ND2 gene was partitioned by codon position, and nuclear introns were left unpartitioned. For all RAxML analyses, a GTR+I+ model of molecular evolution was applied to each locus and partition. Each maximum likelihood analysis was repeated twice to ensure consistent results. Results of all likelihood analyses were confirmed within a Bayesian inference framework using MrBayes v3..2 (Ronquist et al. 22) through CIPRES. For Bayesian analyses, the appropriate model of molecular evolution, as determined using jmodeltest v.. (Posada 28; Gascuel 23) under the AIC, was applied to each locus and partition (Table ). All Metropolis-coupled MCMC (MC 3 ) analyses were run for 5 million generations, sampled every 5, generations. Convergence of runs was assessed by observation of ESS values in Tracer v.5 (Rambaut and Drummond 29), and appropriate burnin was removed. For each locus, the resulting most probable tree was used for comparison with RAxML results. Each Bayesian analysis (consisting of two independent runs) was repeated twice to ensure convergence onto the same posterior distributions.

19 Table. Models of Molecular Evolution and Locus Variability With Outgroups Ingroup Only Parsimony Parsimony Locus Model Clock model Length (bp) Variabl e Sites Informative Sites Variable Sites Informative Sites BZW GTR+Γ Relaxed a RP4 GTR+I Strict RPS8 GTR+I+Γ Strict SELT HKY+I Strict TBP2 HKY+I Strict ND2 (all) Strict ND2 (pos ) GTR+I+Γ ND2 (pos 2) GTR+I+Γ ND2 (pos 3) GTR+I+Γ a relaxed uncorrelated lognormal clock GENERATION OF HYPOTHESES OF SPECIES DELIMITATION Alternative hypotheses of species delimitation were generated in three ways: () historic morphology- and geography-based subspecific ranks were treated as species, (2) major nodes on the mtdna gene tree inferred from the ND2 dataset were collapsed iteratively, and (3) a multilocus clustering algorithm (POFAD, Joly and Bruneau 26) was implemented utilizing only the nuclear intron data in order to explore the possible presence of additional genetic groupings not suggested by the morphological and/or mitochondrial data. For method (), where available, pre-existing morphology-based museum data designating individuals to subspecies were used to group specimens into putative species. If this information was unavailable, or if this information was equivocal, the locality of each specimen was compared to previously published range maps for the C. viridis complex (Klauber 956, 976, 997; Stebbins 23), and individuals were re-designated to subspecies based on range and/or morphology. For method (2), a starting tree was generated by the abovementioned gene tree inference methods. The most-split starting tree was generated by collapsing strongly supported (bootstrap support [BS] 7 and posterior probability [PP].95) reciprocally monophyletic groups into putative species. The nodes of this resulting tree were iteratively collapsed to create competing models of speciation. POFAD combines allelic data from multiple independently evolving loci, each of which is represented by a separate distance matrix relating alleles, to create a single distance

20 matrix relating a given set of individuals. For hypothesis generation method (3), input distance matrices were generated for each nuclear intron using PAUP* v4.b (Swofford 22). After execution of matrices in POFAD, the resulting output matrix was used to create a neighbor-joining network using SplitsTree (Hudson and Bryant 26). The resulting network was inspected by eye for identification of any genetic clustering of individuals. BAYES FACTOR HYPOTHESIS TESTING Here I quantify the fit of each hypothesis of species delimitation (=model of speciation) to the data using estimated marginal likelihoods, and I compare the relative fits of these hypotheses to the data using the Bayes factor. This method combines the advantages of other coalescent-based methods of testing hypotheses of species delimitation while removing significant disadvantages: () unlike maximum likelihood-based methods (e.g., SpedeSTEM; LRT), this method takes uncertainty in phylogenetic estimation into account, (2) unlike BPP, this method does not require prior knowledge of a guide tree, and (3) unlike BPP, this method can compare non-nested hypotheses. All marginal likelihood estimation was carried out using *BEAST (Heled and Drummond 2) implemented in BEAST v.7.2 (Drummond and Rambaut, 27/22), run in parallel using Beagle (Ayres et al. 22) through CIPRES. The inference of species trees for the alternative species delimitation hypotheses differed only by a priori species groupings (i.e., by the input traits file for *BEAST analyses). This method differs from the rjmcmc-based node-collapsing algorithm implemented by BPP in that the guide tree topology is not fixed across analyses. By re-inferring the optimal species tree during each analysis, the topology can change across analyses. The removal of this topological constraint is expected to affect the likelihoods of hypotheses tested (i.e., maximize the estimated likelihoods). *BEAST analyses were executed with and without mitochondrial data included. For each analysis, substitution models, clock models, and trees were unlinked among loci. Initially an uncorrelated lognormal relaxed clock was assigned to each locus, and analyses were rerun under a strict clock if loci were found to evolve in a clock-like manner (i.e., if the standard deviation of the uncorrelated lognormal relaxed clock parameter [ucld.stdev] was estimated to be less than ). The appropriate model of molecular evolution was applied to

21 2 each nuclear intron and to each codon position partition of the mitochondrial ND2. Each analysis was run for 2 million generations, sampled every 2, generations. Convergence of runs was assessed by observation of ESS values in Tracer, and appropriate burnin was removed. Each species tree analysis was repeated twice to ensure convergence onto the same posterior distribution. Posterior distributions of replicate runs were combined using LogCombiner v.7.2 (Rambaut and Drummond 2), and a maximum clade credibility tree was constructed from the resulting combined posterior distribution using TreeAnnotator v.7.2 (Rambaut and Drummond 22). Here I compare three estimators of marginal likelihood: the shme, PS, and SS estimation. To estimate marginal likelihood using the shme, samples were drawn from the posterior distribution after the appropriate burnin was removed, and the harmonic mean was calculated using these samples along with samples drawn from the prior distribution. To estimate marginal likelihood using both PS and SS, a secondary distribution of power posteriors was inferred after each *BEAST run. The sampling scheme of powers followed a Beta (.3,.) distribution, after Xie et al. (2). Power posteriors from each replicate run were pooled before marginal likelihood estimation, resulting in one marginal likelihood score per species delimitation model. Both the PS and SS estimates were calculated from this secondary distribution. It is expected that the shme will overestimate marginal likelihood, compared with the more accurate PS and SS methods (Lartillot and Philippe 26; Xie et al. 2; Baele et al. 22A). All XML code for marginal likelihood estimation is credited to Baele et al. (22A and 22B), made publically available on the BEAST website ( Resulting marginal likelihood scores were compared pairwise using the Bayes factor, calculated as ln(l) A -ln(l) B =ln(bf) AB. Significance was assessed in accordance with Kass and Raftery (995), where 2ln(BF) AB < 2 is considered insignificant, 2 < 2ln(BF) AB < 6 is considered positive, 6 < 2ln(BF) AB < is considered strong, < 2ln(BF) AB is considered very strong. Final species delimitation decisions were made following the level of very strong support, or < 2ln(BF) AB. SPECIES DELIMITATION USING BPP To compare the Bayes factor hypothesis testing method demonstrated in this study with a widely implemented method of multilocus species delimitation, BPP was also used to

22 3 infer species limits within the Crotalus viridis complex. As both species delimitation and phylogeny within this complex are uncertain, guide tree choice is problematic. Sources for guide trees in previous studies that have utilized BPP for species delimitation have varied from mitochondrial gene trees (e.g., Setiadi et al. 2) to multilocus concatenated species trees (e.g., Burbrink et al. 2) to multilocus coalescent species trees (e.g., Niemiller et al. 2; Ramiro et al. 22; Martínez-Solano et al. 22; Camargo et al. 22). Leaché and Fujita (2) demonstrated that the choice of guide tree in BPP analyses has a dramatic impact on results, and that the use of a topologically inaccurate guide tree may lead to oversplitting, stating that even moderate changes to the guide tree can impact support for models, (p. 375). To explore the potential impact of uncertainty in guide tree within the C. viridis complex, BPP was first run using the mitochondrial gene tree as a guide tree, and was rerun using the multilocus phylogeny inferred under the most-split hypothesis of species limits generated using method (2) as a guide tree, if this phylogeny was found to be topologically discordant from the mitochondrial gene tree. BPP analyses were run both including and excluding mitochondrial data. The following priors were applied for all BPP analyses: the gamma distribution priors for both θ and τ were set to G (, 2), θ was held constant across all nuclear loci and was rescaled appropriately for mitochondrial ND2, and automatic fine tune adjustments by the program were allowed. Analyses were repeated using both species delimitation algorithms. For algorithm, analyses were repeated with ε=2, 5,, or 2. For algorithm, analyses were repeated with α=,.5 or 2 and m=.5,, or 2. SPECIES CONCEPT The approach to species delimitation demonstrated here assumes that any gene tree discordance is entirely the result of incomplete lineage sorting, rather than gene flow. This is an assumption shared by all coalescent-based species tree inference and species delimitation methods (e.g., BPP, *BEAST, STEM). However, it has been demonstrated that many of such methods are robust to low levels of gene flow (Eckert and Carstens 28; Ence and Carstens 2; Camargo et al. 22). Based on this, the method demonstrated here, BPP, SpedeSTEM, and any other coalescent-based method of species delimitation operate according to the supposition that if gene flow between species A and species B is sufficient

23 4 (i.e., to the point that A and B are no longer independently-evolving metapopulation lineages), these methods should favor lumping species A and species B into the single species AB. This species concept, objectively defined by the method itself, is directly compatible with the evolutionary species concept (Simpson 96; Wiley 978; Frost and Hillis 99) where a species is a lineage of ancestor-descendent populations that maintains its identity from other such lineages and has its own evolutionary tendencies and historical fate. DATED MULTILOCUS PHYLOGENY OF THE C. VIRIDIS SPECIES COMPLEX To infer a dated multilocus phylogeny, the best-fitting hypothesis of species limits, as determined using the Bayes factor hypothesis testing method, was applied to the dataset for analysis using *BEAST. Substitution models, clock models, and trees were unlinked among loci. The appropriate model of molecular evolution was applied to each nuclear intron, and to each codon position partition of ND2. If loci were found to evolve in a clocklike manner, a strict clock was applied to each locus. Reliable fossil calibrations for the C. viridis complex are unavailable. Therefore, a squamate rate of sequence evolution was used to calibrate a molecular clock. The rate of.65% changes per million years (Macey et al. 998) was applied to ND2 (a widely employed standard in dating squamate phylogenies; Avila-Pires et al. 22; Campbell-Staton et al. 22; Werneck et al. 22) and all other clocks were estimated based on this rate. Species were constrained into nesting clades, based on the topology recovered by the previously executed *BEAST analysis used for marginal likelihood estimation, in order to infer the time to the most recent common ancestor of each clade.

24 5 CHAPTER 3 RESULTS Here, I first summarize the results of my data collection. I then walk through my six independent gene trees, inferred within both maximum likelihood and Bayesian frameworks. I then discuss the results of three approaches to hypothesis generation. Next, I infer the species tree under these generated competing hypotheses while simultaneously estimating a goodness of fit associated with each hypothesis, and I compare these fits using the Bayes factor. I then demonstrate that my results are concordant with those of analysis using BPP. Finally, I present the first multilocus species tree of the Crotalus viridis species complex. DATA COLLECTION Individual locus alignments ranged from 4 to 26 base pairs, and consisted of a 64% complete (combined) dataset of 3852 base pairs. All loci were found to confidently reject a significant level of recombination. Table lists variable and parsimony informative sites for each locus included in this study, both including and excluding outgroups. Sequence alignments of nuclear introns contained from 4 to 35 ingroup parsimony informative sites. The three ND2 data partitions contained many more parsimony informative sites. GENE TREE INFERENCE Individual gene trees inferred using MrBayes and RAxML were highly similar in topology and nodal support (Figures 6 and 7 in Appendix C). Subsequent results reported here refer to RAxML gene trees (Figure 7 in Appendix C). There was generally little topological congruence across loci, as is expected within recently diverged groups (Maddison and Knowles 26; Carstens and Knowles 27; Knowles and Carstens 27; Edwards 29). Interestingly, alleles from the closest outgroup taxon C. scutulatus were found to be nested (though with weak support) within the C. viridis complex in gene trees of RPS8 and TBP2 (Figures 7C and 7E in Appendix C), reflecting expected incomplete lineage sorting associated with a recent divergence. Though reciprocal monophyly of subspecies was not

25 6 prevalent in any nuclear gene trees, some general subspecific groupings were present. Within the BZW gene tree, all Idaho and most Utah individuals of C. o. lutosus were placed within a strongly supported clade (BS = 98, Figure 7A in Appendix C). Locus RPS8 recovered a clade containing all samples of C. o. oreganus from Oregon and Washington, though this clade was weakly supported (BS = 35, Figure 7C in Appendix C). This locus also recovered a weakly supported clade containing all individuals of C. cerberus (BS = 3, Figure 7C in Appendix C). Within the TBP2 gene tree, a strongly supported clade containing C. v. viridis and C. v. nuntius was recovered (BS = 99, Figure 7E in Appendix C), and a weakly supported clade containing many individuals of C. o. lutosus was recovered (BS = 65). Clades were generally weakly supported within the RP4 and SELT gene trees (Figures 7B and 7D in Appendix C), likely due to low variability within these loci (Table ). The inferred mitochondrial ND2 gene tree was highly concordant with previous mitochondrial studies of the C. viridis complex (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22), recovering numerous reciprocally monophyletic subspecific clades. Crotalus viridis sensu lato was strongly supported as monophyletic (BS = 99, Figure 7F in Appendix C). As in previous studies, a strongly supported western clade (BS = 75) containing the species C. cerberus and C. oreganus (as currently recognized) was recovered, and was found to be sister to C. viridis. Importantly, one individual of C. cerberus (AZ_cerb_25, Figure 7F in Appendix C) was found to be nested within the otherwise monophyletic C. v. nuntius clade, which was nested within C. viridis. This individual was geographically and morphologically confirmed as C. cerberus, so was removed from subsequent species tree analyses due to the possibility of introgression hinted by its placement in the mtdna gene tree. As in previous studies, C. cerberus was recovered as sister to a strongly supported C. oreganus (BS = 96), but monophyly of C. cerberus was not strongly supported (BS = 59). Because of my fine-scale sampling, phylogenetic structure not detectable in previous mtdna studies was observable in the ND2 gene tree inferred in this study. Within C. cerberus, a split possibly pre-dating diversification within C. oreganus was detected. Within C. oreganus, C. o. oreganus was found to be sister to a strongly supported clade containing all remaining members of C. oreganus (BS = 73), but monophyly of C. o. oreganus was not strongly supported (BS = 63). C. o. oreganus was further split into two clades, representing a geographic separation between sampled California individuals (C. o.

26 7 oreganus B) and Oregon and Washington individuals (C. o. oreganus A) (see Figure and Table 4 in Appendix A for localities of sampled individuals). This structure may indicate the existence of a cryptic species within the currently recognized C. o. oreganus. A monophyletic C. o. helleri (BS = 93) was found to be sister to a strongly supported clade containing C. o. lutosus, C. o. abyssus, and C. o. concolor (BS = 9), though only a single individual represented each of these latter two subspecies. GENERATION OF HYPOTHESES OF SPECIES DELIMITATION Competing hypotheses of species delimitation were generated using three approaches. First, historical morphological subspecies were treated as species. Second, the mitochondrial gene tree was used as guide for treating increasingly inclusive mitochondrial clades as species. Third, nuclear genetic clustering was explored to look for any additional groupings. Method (): Traditional Subspecies as Species With one exception (UT_nunt_2), subspecific designation was unequivocal for all individuals included in this study, after the removal of the sample of C. cerberus mentioned previously (AZ_cerb_25). Individual UT_nunt_2 had originally been designated as C. o. concolor. Mitochondrially, this individual appeared to be more closely related to C. v. nuntius than to C. o. concolor. Upon closer morphological and geographic examination, this specimen was reclassified as C. v. nuntius, based on a combination of the mitochondrial evidence, sympatry of this individual with other sampled C. v. nuntius, and head scalation of this individual compared with other C. v. nuntius and C. o. concolor (Figure 8 in Appendix C). After this reclassification, a total of eight putative species were tested under this hypothesis, deemed hypothesis H 4 : C. viridis, C. nuntius, C. cerberus, C. oreganus, C. helleri, C. lutosus, C. concolor, and C. abyssus, representing individuals of subspecies and species C. viridis viridis, C. v. nuntius, C. cerberus, C. oreganus oreganus, C. o. helleri, C. o. lutosus, C. o. concolor, and C. o. abyssus. Method (2): Mitochondrial Clades Figure 2 shows the starting ML gene tree inferred from the mitochondrial ND2 gene. Table 2 lists the 2 speciation hypotheses generated by iteratively collapsing nodes on the

27 Figure 2. Starting tree for species delimitation hypothesis generation using method (2). Tree topology is identical to RAxML mitochondrial ND2 gene tree pictured in Figure S2F. Plus signs at nodes indicate bootstrap support 7. Asterisks at nodes indicate posterior probability.95. Strongly supported reciprocally monophyletic clades are boxed. Letters at nodes correspond to Table 2. Boxes are colored by historic subspecific designation: C. viridis viridis + C. v. nuntius (red); C. cerberus (grey); C. oreganus oreganus (green); C. o. helleri (blue); C. o. lutosus + C. o. concolor + C. o. abyssus (yellow). 8

28 9 Table 2. Hypotheses of Species Delimitation Generated using Methods () and (2) Hypothesis Nodes Collapsed f Number of Putative Species b H A c H 2 B 2 a H 3 C, D 3 H 4 C 4 H 5 D, E, F 4 H 6 D, E 5 H 7 D, F 5 H 8 E, F 5 H 9 D 6 H E 6 H F 6 H 2 None 7 d H 3 see text 7 e H 4 see text 8 Note: For Hypotheses H through H, Nodes to be Collapsed are Indicated by Letter in Column 2. Letters Correspond to Figure 2. Column 3 Displays the Number of Putative Species that Result from Collapsing the Lettered Nodes Listed in Column 2. a current taxonomy, after Crother et al. 22 b taxonomic recommendation of Pook et al. 2 c taxonomic recommendation of Ashton and de Queiroz 2 d taxonomic recommendation of Douglas et al. 22 e historic subspecies treated as putative species f see Figure 2 for nodes referenced mtdna gene tree to create increasingly inclusive putative species, as well as two additional hypotheses. The most-split hypothesis tested (H2) represents a situation in which each denoted major clade in Figure 2 is treated as a species, resulting in seven putative species: C. viridis (containing C. v. viridis and C. v. nuntius), C. cerberus A, C. cerberus B, C. oreganus A, C. oreganus B, C. helleri, and C. lutosus (containing C. l. lutosus, C. l. concolor, and C. l. abyssus). For hypotheses H through H, nodes to be collapsed are indicated in Table 2. For example, in H, node A is collapsed; thus, every terminal individual traced to node A will be grouped into one putative species. The total number of putative ingroup species for each hypothesis is indicated in Table 2. Importantly, hypothesis H3 represents current taxonomy, after Crother et al. (22). H3 describes the taxonomic recommendations of Douglas et al. (22), subsuming C. v. nuntius within C. v. viridis while treating all other sampled subspecies as species. H 4 denotes the hypothesis generated by method ().

29 2 Method (3): Multilocus Nuclear Clustering Using POFAD The results of the POFAD analysis did not show any notable genetic clusters (Figure 9 in Appendix C), so no hypotheses were generated from these results. HYPOTHESIS TESTING VIA MARGINAL LIKELIHOOD ESTIMATION The estimated marginal likelihood scores (with and without mtdna) associated with each hypothesis tested are provided in Table 3 and are plotted in Figure 3. Species trees inferred under each competing hypothesis of species delimitation when both nuclear and mitochondrial data were included in analyses are shown in Figure in Appendix C, while inferred species trees based on nuclear intron data only are shown in Figure in Appendix C. Table 6 & 7 in Appendix B shows the species designations applied for each hypothesis tested. Analyses differed only by these a priori species designations. Subsequent use of specific epithets will refer to putative species, as applied in each hypothesis tested (e.g., if a given hypothesis specifies that C. o. oreganus, C. o. lutosus, and C. o. helleri are grouped into one putative species, the species will be called C. oreganus when discussing this hypothesis; likewise, if a given hypothesis specifies that C. o. abyssus is one putative species, this taxon will be called C. abyssus when discussing this hypothesis; Table 6 & 7in Appendix B). SPECIES TREES Strongly supported topological discordance between analyses utilizing all the data (Figure in Appendix C) and analyses utilizing only nuclear data (Figure in Appendix C) was not present. As a result, subsequent discussion will focus on species tree analyses utilizing all the data (Figure in Appendix C). When comparing species trees inferred under alternative hypotheses of species delimitation, in general the species trees were largely topologically concordant. However discordance was present when possibly non-sister taxa were grouped into putative species. Figure N in Appendix C depicts the species tree inferred by treating historic subspecies as species, as per method (). Under this hypothesis (H4), a strongly supported C. concolor + C. abyssus + C. lutosus clade was recovered (PP =.). Crotalus oreganus was weakly recovered as sister to C. helleri (PP =.82). A C. oreganus + C. helleri + C. concolor + C. abyssus + C. lutosus clade was recovered with

30 Table 3. Marginal Likelihoods Estimated Using Mitochondrial and Nuclear Data for Each Hypothesis Tested (Table 2) Nuclear and Mitochondrial Data PS SS shme ln(marginal Likelihood) 2ln(Bayes Factor) a ln(marginal Likelihood) 2ln(Bayes Factor) a ln(marginal Likelihood) 2ln(Bayes Factor) a H * - H H H H H H H H * * H H H H H Nuclear Data Only PS SS shme ln(marginal Likelihood) 2ln(Bayes Factor) a ln(marginal Likelihood) 2ln(Bayes Factor) a ln(marginal Likelihood) 2ln(Bayes Factor) a H * - H H H H H H H H H H H * * H H Note: Marginal Likelihood was Estimated using the Path Sampling (PS) Method, the Stepping Stone (SS) Method, and the Smoothed Harmonic Mean Estimator (shme). See Appendix B; Figure for Nuclear and Mitochondrial Data, Figure for Nuclear Data only. *best-fitting hypothesis, under each estimator apairwise Bayes Factor comparison between hypothesis Hn and best-fitting hypothesis* +strong support for best-fitting hypothesis (6 < 2ln[BF] <, (Kass and Raftery 995) ++very strong support for best-fitting hypothesis ( < 2ln[BF], Kass and Raftery 995) 2

31 22 A All Data B Nuclear Data Only Marginal Likelihood Marginal Likelihood PS SS shme PS SS shme Hypotheses Hypotheses Figure 3. Marginal likelihoods of hypotheses H-H4, estimated via path sampling (PS), stepping stone (SS), and the smoothed harmonic mean estimator (shme). (A). Marginal likelihood estimated from all the data. (B). Marginal likelihood estimated from nuclear data only (i.e., without mitochondrial data). See Table 3 for marginal likelihood values and Bayes factors. See Figures and for corresponding species trees.

32 23 proposed by Douglas et al. (22; hypothesis H3). This phylogeny was fully concordant with the species tree inferred under hypothesis H4. In all analyses where C. viridis and C. cerberus were treated as separate species (H 3 - H 4, Table 2, Figures C-N in Appendix C), these taxa were recovered as sister lineages. However, this relationship was weakly supported in all cases (.48 < PP <.55). Note that this sister relationship was recovered despite the removal of individual AZ_cerb_25, the individual of C. cerberus which was nested within the C. v. viridis + C. v. nuntius mtdna clade (Figure 7F in Appendix C). In all analyses where C. cerberus was split into two lineages (C. cerberus A and C. cerberus B; H 4, H 8, H -H 2, Table 2, Figures D, H, J- L in Appendix C), these putative species were strongly recovered (PP =.) as sister lineages. Further, the split between these putative species was very shallow, relative to all other divergences in the species trees. In all analyses, a clade containing C. oreganus, C. lutosus, and C. helleri was recovered with strong support (.94 < PP <.), but the interrelationships within this clade varied when possibly non-sister taxa were lumped into putative species (e.g., if C. helleri and C. oreganus B are sister taxa, hypotheses that group C. oreganus A and C. oreganus B. into a single species would not allow this relationship to exist). In analyses where C. oreganus was split into two lineages (C. oreganus A and C. oreganus B; H 7, H 9, H -H 2, Table 2, Figures G, I, K, L in Appendix C), these taxa were not recovered as sister to one another, with C. oreganus B always placed as sister to C. helleri with moderate support (.84<PP<.9). Finally, in analyses where C. o. helleri and C. o. lutosus were treated as separate species, these taxa were not recovered as sister to one another (H 6, H 9, H, H 2 -H 4 ) (contrary to relationships recovered in the mitochondrial gene tree). Hypothesis Testing When comparing the estimated marginal likelihoods (Table 3, Figure 3) for each of the 4 hypotheses tested (with and without mitochondrial data included), as expected, PS and SS yielded largely similar results, with SS yielding slightly higher marginal likelihood estimates. In all cases, the shme method tended to dramatically overestimate the marginal likelihoods of all hypotheses tested. Further, the ordering of the fit of hypotheses to the data differed between the shme and the two other marginal likelihood estimators (Table 3, Figure

33 24 3). Interestingly, even though the shme yielded quite different results when mitochondrial data were excluded (Table 3B, Figure 3B), hypothesis H, which considers C. viridis sensu lato to be one widespread species, was favored using this estimator both with and without mitochondrial data (2. < 2ln[BF] < 62. with mitochondrial data; 9. < 2ln[BF] <47.8 without mitochondrial data; Table 3). Subsequent discussion of marginal likelihood estimates refer to scores estimated using PS and SS, as these estimators yielded similar results to one another and yielded similar Bayes factor (BF) results both with and without mitochondrial data. When mitochondrial data were included, hypothesis H 9 was optimal (i.e., best fits the data) and Bayes factor analysis provided very strong support favoring this hypothesis over all other hypotheses (25. < 2ln[BF] < 527. using PS; 26.8 < 2ln[BF] < using SS; Table 3A, Figure 3A). The second best fitting hypothesis to the data (H 2 ) differed from H 9 only in the splitting of C. cerberus into putative species C. cerberus A and C. cerberus B (Table 3A, Figures 3A, I, and L). When mitochondrial data were excluded (i.e., nuclear data only), H 2 best fitted the data and there was very strong support for this speciation hypothesis over all other hypotheses (.6 < 2ln[BF] < using PS;.6 < 2ln[BF] < 4.4 using SS; Table 3B, Figure 3B). The second best fitting hypothesis to the nuclear data was hypothesis H 9 (Table 3B, Figure 3B). Thus, the single effect of excluding mitochondrial data here is the resulting inclination to split C. cerberus into two lineages (C. cerberus A and C. cerberus B). As strongly supported discordance between species trees generated using all the data and species trees generated using only nuclear intron data was not detected (Figures and in Appendix C), I follow the Bayes factor results based on all the DNA sequence data, which support the recognition of the following six species within the C. viridis complex: C. viridis (including C. v. viridis and C. v. nuntius), C. cerberus, C. oreganus A, C. oreganus B, C. helleri, and C. lutosus (including C. l. lutosus, C. l. abyssus, and C. l. concolor). Species Delimitation Using BPP By using the mitochondrial gene tree and the *BEAST species tree inferred under the most-split hypothesis generated using method (2), two alternate topologically discordant trees were evaluated as guide trees in BPP analyses (Figure 4). BPP analyses initiated with

34 25 A Mitochondrial Gene Tree outgroups viridis cerberusb cerberusa oreganusb oreganusa lutosus helleri Multilocus Species Tree outgroups viridis cerberusb cerberusa oreganusa lutosus oreganusb helleri Figure 4. Discordant guide trees used as starting trees for analysis with BPP. (A). Mitochondrial ND2 gene tree (identical to Figures 2 and 7F). (B). Multilocus species tree inferred using *BEAST under the most-split hypothesis of species delimitation generated under method (2) (hypothesis H 2 ) (identical to Figure L). the different starting trees, different prior values, and different datasets (with and without mitochondrial data included) yielded identical species delimitation results. In all BPP analyses, the presence of every node was supported with PP >.99, with one exception: the node leading to C. cerberus A and C. cerberus B was supported with.52 < PP <.58 (i.e., the splitting of C. cerberus into two lineages was not supported). In summary, BPP did not recover identical results as the Bayes factor species delimitation method. The Bayes factor method favored splitting C. cerberus into two lineages when mitochondrial data were excluded, but BPP did not favor splitting this lineage when mitochondrial data were excluded. In this case, BPP was demonstrated to be robust to varying starting trees because the sister relationship between C. cerberus A and C. cerberus B was present in both starting trees. B DATED MULTILOCUS PHYLOGENY OF THE C. VIRIDIS SPECIES COMPLEX Figure 5 depicts the phylogeny of the C. viridis species complex. This dated multilocus species-level phylogeny was inferred using hypothesis H 9, as this hypothesis of species limits was very strongly supported over all others in the Bayes factor analyses (25. < 2ln[BF] < 527. using PS; 26.8 < 2ln[BF] < using SS; Table 3), and was also strongly supported using BPP. This multilocus phylogeny differs topologically from the mitochondrial gene tree. In this multilocus-based phylogeny, C. viridis and C. cerberus are

35 26 found to be sister species (though with low support; PP =.52), and this clade is sister to a strongly supported clade comprised of all other species within the complex. Within this more exclusive western clade, C. oreganus A is sister to a weakly supported clade containing C. lutosus, C. oreganus B, and C. helleri (PP =.62). The clock models indicated in Table were applied to each locus. If the standard deviation of the uncorrelated lognormal relaxed clock parameter was estimated to be less than, a strict clock could not be rejected and was therefore applied. Using an estimated rate of evolution of.65% for ND2, divergence dates within the C. viridis complex fall within the Pliocene and Pleistocene epochs. The six species of the Crotalus viridis complex last shared a common ancestor approximately 2.93 million years ago (Ma). Crotalus cerberus and C. viridis last shared a common ancestor approximately 2.26 Ma. The more exclusive western clade last shared a common ancestor 2. Ma. Crotalus lutosus diverged from C. oreganus B + C. helleri approximately.4 Ma. Most recently, C. oreganus B and C. helleri last shared a common ancestor approximately.72 Ma (Figure 5).

36 27 Crotalus adamanteus. Crotalus scutulatus Crotalus cerberus Crotalus viridis.98 Crotalus oreganus A.95 Crotalus lutosus.62 Crotalus oreganus B.87 Crotalus helleri Miocene Neogene Pliocene 2.6 Pleistocene Quaternary Figure 5. Time-calibrated multilocus species tree of the Crotalus viridis species complex, with outgroups C. scutulatus and C. adamanteus. Values at nodes represent posterior probabilities. Bars represent 95% highest posterior density intervals of node ages.

37 28 CHAPTER 4 DISCUSSION This study successfully demonstrates the ability of a Bayes factor hypothesis testing approach to simultaneously infer species limits and the species tree of a group of organisms from multilocus data. Further I have verified the accuracy of this approach by validating my findings using a widely implemented method of multilocus coalescent-based species delimitation. The method demonstrated here is applicable to any species complex where the species tree and species limits are contentious or uncertain. The results of this study revise our current understanding of speciation and evolution within the Crotalus viridis complex and reveal a potential early (cryptic) speciation event in the process. COALESCENT SPECIES DELIMITATION Recent debate regarding genetic species detection and coalescent-based species delimitation reflects disagreement pertaining to the role of these new methodologies in taxonomic revision and species description (Leaché and Fujita 2; Bauer et al. 2; Fujita and Leaché 2). Researchers appear to agree that an integrative approach to species delimitation, where multiple lines of evidence support species limits, is advantageous (Bauer et al. 2; Fujita and Leaché 2; Fujita et al. 22; Camargo and Sites 23). However, if highly genetically divergent populations are detected within a seemingly morphologically homogenous species (i.e., if cryptic species are discovered), multiple lines of evidence supporting the distinctiveness of these divergent populations may not yet be available. Bauer et al. (2) maintain that in order for a species to be described, identification of unifying characters is paramount to a proposal of novel species delimitation. However Fujita and Leaché (2) argue that Bayesian species delimitation improves objectivity with regard to species detection, as the subjectivity associated with the act of noting morphological distinctiveness is removed. Pertaining to this study, morphologically and geographically distinct taxa have previously been designated as subspecies within the C. viridis species complex. Using Bayesian species delimitation, I have demonstrated that a number of these subspecies

38 29 represent independently evolving lineages, and therefore represent evolutionary species. Here the morphological characters associated with historic subspecies may serve to assist with species description. However, I have also exposed the presence of a cryptic species within this complex. Within the historic subspecies C. o. oreganus, I have detected the presence of two strongly divergent lineages. There are no previously recorded morphological traits differentiating these species, and the historic distribution of subspecies C. o. oreganus appears to be continuous across the probable contact zone of these species. This scenario perfectly illustrates the beneficial objectivity associated with Bayesian species delimitation. It would be biased to elevate independently evolving lineages exhibiting defined morphological characteristics (i.e., previously recognized as subspecies) to species status without recognizing the species status of this newly detected cryptic species, which does not appear to have defining morphological characteristics. It is also important to consider that the detection of cryptic species using molecular methods has many times led to the subsequent discovery of subtle morphological characters that help to differentiate these cryptic taxa (e.g., Randi et al. 22; Xu and Amason 996; Brown et al. 27). USING THE BAYES FACTOR FOR SPECIES DELIMITATION As a useful tool for Bayesian model selection, the Bayes factor has been applied to a wide variety of model testing scenarios, including comparison of demographic and molecular clock models (Baele et al. 22A, 22B). The Bayes factor represents a ratio comparing the marginal likelihoods of two models. The marginal likelihood of a model represents the fit of that model to the data. If the dataset is held constant, the marginal likelihoods of any two models can be compared. Here, I compared the fit of competing models of speciation (species delimitation) to the data. For Bayesian coalescent-based species tree inference, the dataset consists of DNA sequence data from independently evolving loci, and the model that is applied to the data consists of prior parameter restrictions placed on the analyses. Along with nucleotide substitution and clock models, as well as demographic parameters, these restrictions include a priori species groupings (i.e., a pre-determined explanation of the speciation history). In the case of Bayes factor species delimitation, because only these species groupings (speciation models) are adjusted across analyses, the comparative fit of the model to the data (quantified as the marginal likelihood) is a direct reflection of the fit of the

39 3 proposed explanation of speciation history to the data. Further, analyses carried out under competing hypotheses of species groupings are directly comparable, even if the models to be compared are non-nesting or differ in the number of parameters to be estimated. If the marginal likelihood is known with certainty, this approach to species delimitation is highly advantageous over methods for which assumptions must be placed on the topology of the species tree (e.g., BPP) or methods that assume gene trees have been inferred without error (e.g., SpedeSTEM, LRT). However, the marginal likelihood of a model must be estimated for practical purposes. Until relatively recently, only fairly inaccurate and unpredictable estimators of the marginal likelihood have been widely implementable (e.g., HME, shme). Computational advances have allowed for more accurate and consistent estimation of the marginal likelihood of a model (e.g., via PS or SS). Here I compared these recently implementable methods of marginal likelihood estimation with the inaccurate shme in an empirical study. As expected based on previous studies (Baele et al. 22b; Xie et al. 2), the shme greatly overestimated marginal likelihood, compared with the PS and SS methods (Table 3, Figure 3). Further, while the removal of mitochondrial data only slightly affected the ordering of the fit of hypotheses to the data when marginal likelihood was estimated using the PS and SS methods, the ordering of hypotheses based on estimates by the shme were significantly impacted (Table 3, Figure 3). Previous studies have shown Bayes factor model selection to generally favor more parameter rich models (Fan et al. 2; Xie et al. 2), especially when a harmonic mean estimator (i.e., HME or shme) is applied for marginal likelihood estimation. However, in this study, Bayes factor analysis based on the shme very strongly favored the least parameter rich model (hypothesis H ), regardless of the inclusion or exclusion of mitochondrial data. Further, the optimal or best fitting model (hypothesis H 9 ) to the data as determined by the PS and SS based marginal likelihoods was not the most parameter rich model evaluated. These results are therefore discordant with previous thought that marginal likelihood estimation tends to favor more parameter rich models. COMPARISON TO BPP Since its introduction (Yang and Rannala 2), the BPP method of species delimitation has been used to delimit species in a wide variety of systems (e.g., Leaché and

40 3 Fujita 2; Leavitt et al. 2; Niemiller et al. 2; Zhou et al. 22). Here I compared the Bayes factor species delimitation method demonstrated in this study to the widely implemented rjmcmc-based BPP approach. BPP tests for lineage independence by exploring the probability that a node should be collapsed vs. resolved on a user-specified guide tree of species or populations. This process of species delimitation translates to exploring whether sister taxa A and B contain haplotypes that have sorted enough for the taxa to be considered independently evolving metapopulation lineages (i.e., species A and species B), or whether these sister taxa should be collapsed into a single species instead (i.e., species AB). Consider a scenario in which an inaccurate guide tree is provided, where sister taxa A and B are not placed as sister to one another. The node relating these two taxa does not exist on this guide tree; therefore the hypotheses in which taxon A and taxon B are grouped into species AB will not be tested via the node collapsing algorithm of BPP. Rather, the hypothesis in which taxon A and a lineage that is not sister to taxon A are grouped into a single species will be tested, and likely rejected. As a result, the lineage independence of species A and species B will be supported in this situation. For this reason, the input of inaccurate guide trees where truly sister taxa are not placed as sister can potentially lead to oversplitting in BPP analyses. Here I supplied two different guide trees as input for BPP analyses: a maximum likelihood mitochondrial gene tree (Figure 4A) and the most split multilocus species tree as generated using method (2) (Figure 4B). Though these trees differed topologically, BPP analyses yielded identical results. This is because the only node that was favored to collapse (C. cerberus A + C. cerberus B) was present in both the multilocus species tree and the mitochondrial gene tree. BPP analyses yielded identical results as the Bayes factor method demonstrated here, bolstering confidence in both methods and thereby illustrating the merit of applying multiple methods of species delimitation for increased certainty in results. SPECIES LIMITS AND PHYLOGENY WITHIN THE CROTALUS VIRIDIS SPECIES COMPLEX The results of this study revise our current understanding of the evolutionary history of the Crotalus viridis species complex. I recovered very strong statistical support for the presence of six species within the complex, including one previously unrecognized cryptic species. These findings raise phylogeographic questions relating to Pleistocene speciation

41 32 throughout Western North America, and provide a novel framework through which interspecific relationships within the complex can be explored in finer detail. The multilocus coalescent-based species phylogeny of the C. viridis complex recovered in this study differs topologically from the mitochondrial gene tree previously accepted as a representation of the evolutionary history of the group. This finding underscores the importance of including multiple independently evolving genes in phylogenetic analysis. The mitochondrial ND2 gene tree recovered in this study was largely concordant with previous studies, recovering a sister relationship between an eastern clade (C. v. viridis + C. v. nuntius) and a clade containing all remaining subspecies within the complex, and recovering a sister relationship between C. cerberus and C. oreganus, as they are currently recognized (Crother et al. 22) (Figures 2, 6F, 7F in Appendix C). However, the multilocus species tree places C. cerberus as sister to C. viridis, though with weak support (Figures 5, 6I in Appendix C). Though the monophyly of C. cerberus + C. oreganus is strongly supported in the mitochondrial gene tree, the weakly supported sister relationship of C. cerberus and C. viridis recovered by the multilocus data may be explained by either a rapid or nearly concurrent divergence of the three lineages leading to C. viridis, C. cerberus, and all remaining species (resulting in deep coalescence, which may explain the recovered weak nodal support if this relationship is correct), or may be explained by contemporary gene flow between C. cerberus and C. v. nuntius (convoluting true species relationships). One line of evidence supporting this latter hypothesis is the mitochondrial nesting of individual AZ_cerb_25 within the C. v. nuntius mtdna clade, which is within the C. viridis mtdna clade, a hallmark of mitochondrial introgression (Figures 2 and 6F, 7F in Appendix C). Though this individual was removed from subsequent multilocus species tree analyses, the sister relationship between C. viridis and C. cerberus was still recovered. However in support of the former hypothesis, the divergence between C. viridis and C. cerberus would likely be more shallow if contemporary or recent gene flow was explaining this sister relationship. It is important to note that C. cerberus is morphologically distinct and geographically isolated from C. v. nuntius, so contemporary gene flow is not expected. Further fine-scale population genetic and phylogeographic studies of C. cerberus and C. v. nuntius would greatly assist with explaining the relationships among these lineages.

42 33 The currently recognized species C. oreganus (sensu Crother et al. 22) was recovered as monophyletic in both the mitochondrial gene tree and the multilocus species tree with strong support (Figures 2, 5, and 6F, 7F, I in Appendix C). However relationships within this group differed. Within the mitochondrial gene tree, C. o. helleri was recovered as sister to C. o. lutosus, which contained subspecies C. o. abyssus and C. o. concolor, though only one individual was sampled for each of these subspecies. This clade was found to be sister to C. o. oreganus, which was found to be comprised of two strongly supported mitochondrial clades, though the monophyly of C. o. oreganus was weakly supported by the mitochondrial data. In the multilocus species tree, these two C. o. oreganus mtdna clades were not found to be each other s closest relatives. Rather, C. oreganus B, consisting of individuals sampled as far north as approximately the San Francisco Bay Area, was found to be sister to C. helleri, a geographically logical relationship (Figure ). Crotalus oreganus A, consisting of individuals from a disjunct distribution across Oregon and Washington, was found to be sister to the clade containing C. oreganus B, C. helleri, and C. lutosus. These surprising relationships necessitate a reconsideration of the phylogeography of this western portion of the C. viridis species complex. To investigate possible mechanisms that may explain the inferred phylogenetic relationships in the C. viridis complex, divergence dates were estimated on the multilocus species tree using a defined squamate rate of DNA sequence evolution for the mitochondrial ND2. Crotalus scutulatus had previously been found to be sister to the C. viridis species complex (Castoe and Parkinson 26), though this relationship was based on mitochondrial data. In this study, I estimated that C. scutulatus last shared a common ancestor with the C. viridis complex in the late Miocene. It is thought that the Sierra Nevada range and the western Great Basin, features currently impacting the distribution of species within the C. viridis complex, were formed by the end of the Eocene (Cassel et al. 29). The six delimited species within the C. viridis complex last shared a common ancestor in the late Pliocene. Subsequent diversification occurred during the Pleistocene, likely affected by climatic changes during this time period. Given this information, historic niche modeling would greatly assist with reconstructing possible refugia utilized by these taxa during climatic fluctuations.

43 34 The detection of a genetically distinct cryptic species within C. oreganus was a surprising result. I found that C. o. oreganus (sensu Crother et al. 22) is likely comprised of two morphologically similar but genetically distinct species that are not each other s closest relatives. Importantly, Douglas et al. (22) similarly recovered two mtdna clades of C. o. oreganus, though sampling in their study did not allow for further exploration of this finding. Douglas et al. (22) noted, however, that their northern sampled C. o. oreganus likely represents an undescribed C. oreganus-like form, and further sampling and analysis will be required before it can be formally described, (p. 29). Additionally, Ashton and de Queiroz (2) recovered two mtdna clades of C. o. oreganus (one northern clade and one California clade), though they did not discuss an explanation for this structure. Though these previous studies detected the possible presence of cryptic diversity using mitochondrial data, there have been no subsequent efforts to reveal or evaluate these putative cryptic species. Unfortunately, in this study, the geographic boundary separating the two species within C. o. oreganus lies within a large sampling gap throughout northern California. Fine scale sampling throughout northern California is essential to identifying range limits of these two species. Dense sampling throughout southern California allowed for verification of the range limits of C. oreganus B and C. helleri. Here, I detected a clear biogeographic separation that coincides with current range estimates (Stebbins 23). I did not detect any evidence of introgression across this separation, despite sampling extensively near the contact zone of these two species. This break corresponds to the extremely complex Transverse Ranges of southern California, a biogeographic boundary for many squamate reptiles (Rodriguez- Robles et al. 999; Stebbins 23; Feldman and Spicer 26). Even though sampling within C. lutosus included individuals from three extreme geographic edges of this taxon s range, this species was recovered as mitochondrially exclusive except for the nested inclusion of the single individuals of C. o. concolor and C. o. abyssus. Previous studies have placed C. o. abyssus within C. lutosus (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22). However, no previous studies have found C. o. concolor to be nested within C. lutosus. Because this subspecies differs tremendously in morphology and venom composition from other taxa within the C. viridis complex, the results of this study are not sufficient for determining the evolutionary history of this taxon.

44 35 Improved sampling for C. o. concolor is imperative to determining species status and proper placement of this population in the species tree. Results pertaining to the geographic distribution and species status of C. viridis and C. cerberus are compatible with currently recognized taxonomy. I found that C. v. nuntius is mitochondrially nested within C. viridis. Therefore I consider this genetically similar yet morphologically distinct form to be a geographic variant of the metapopulation lineage C. viridis. Though I recovered mitochondrial structure within C. cerberus, the methods employed here support the inclusion of all populations of C. cerberus as a single species. TAXONOMIC RECOMMENDATIONS The purpose of recommending taxonomic revision here is to encourage that taxonomy represents the true evolutionary history and lineage diversity within the Crotalus viridis complex. Previous taxonomy of the C. viridis complex significantly understates lineage diversity, grouping populations into three species. Based on the results of this study, I recommend that six species be recognized within the C. viridis complex, though sampling within certain subspecies is too sparse to confidently recommend that these taxa are not truly independently evolving lineages. Crotalus cerberus. Arizona Black Rattlesnake. No taxonomic revision is proposed for this species. Crotalus helleri. C. h. helleri. Southern Pacific Rattlesnake. C. h. caliginis. Coronado Island Rattlesnake. This study did not include any individuals of this subspecies, but based on the results of previous studies (Pook et al. 2; Ashton and de Queiroz 2; Douglas et al. 22), I recommend that the taxon previously recognized as C. oreganus caliginis is a subspecies of C. helleri. While this insular taxon is mitochondrially nested within C. h. helleri, it appears to be morphologically distinct (Klauber 997; Stebbins 23) and geographically isolated from its mainland relative; thus, further research should evaluate the genetic relationship between the insular and mainland populations, and potentially assess the age of this isolated population. Even though this taxon is geographically isolated from the mainland C. h. helleri, and therefore is no longer sharing genes with this related taxon, recognition of C. h. caliginis and C. h. helleri as ecological species would render C. h. helleri paraphyletic. Crotalus lutosus. C. l. lutosus. Great Basin Rattlesnake.

45 C. l. concolor. Midget Faded Rattlesnake C. l. abyssus. Grand Canyon Rattlesnake. Crotalus oreganus. Northern Pacific Rattlesnake. As the type locality of Crotalus oreganus is within the range of the northern species recovered within the historic C. o. oreganus clade (Holbrook 84; Klauber 997), I recommend that the northern species (C. oreganus A in this study) retain the specific epithet Crotalus oreganus (Klauber 956). Based on the biogeography of other terrestrial vertebrates, I hypothesize that the southern range limit of this species in California is near the latitude of the Murray fracture zone. Crotalus oreganus Sp. Nov. I propose that the southern species (Crotalus oreganus B in this study) recovered within the historic C. o. oreganus clade be recognized and named as a distinct species, as the type locality of C. oreganus falls within the range of the northern species. Based on the biogeography of other terrestrial vertebrates, I hypothesize that the northern range limit of this species is near the latitude of the Murray fracture zone. Crotalus viridis. C. v. viridis. Prairie Rattlesnake C. v. nuntius. Hopi Rattlesnake. Though current taxonomy (after Crother et al. 22, as advocated by Douglas et al. 22) does not recognize the subspecies C. v. nuntius, I recommend the use of this subspecific epithet due to the morphological and geographic distinctiveness of this variant (Klauber et al. 997; Stebbins 23). Further, I found C. v. nuntius to be mitochondrially monophyletic, nested within C. v. viridis. These lines of evidence indicate that this is a geographic variant of C. v. viridis that may be in the process of lineage divergence or speciation. CONCLUSIONS This study successfully demonstrated a novel method of Bayesian multilocus species delimitation, elucidating with confidence the evolutionary history and species limits within the Crotalus viridis species complex. The methods demonstrated in this study provide a framework for simultaneous inference of phylogeny and species limits that incorporates uncertainty in gene tree estimation, is free from the assumptions imposed by a guide tree, and provides measures of statistical support for non-nested competing hypotheses of speciation. Additionally, the model testing approach applied here can be expanded to compare any historic demographic parameters associated with phylogeny (e.g., historic population size fluctuations, constraints on divergence dates, etc.). The results of this study demonstrate the ability of this method to detect the presence of cryptic species, concomitantly recovering the phylogenetic history of the newly discovered species. As species limits and speciation history 36

46 37 are interlaced, concomitant Bayes factor species delimitation and species tree inference represents a significant step in the pursuit of an integrative taxonomy.

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54 45 APPENDIX A SUPPLEMENTARY TABLES

55 46 Table 4. Individuals Sampled for This Study Specimen ID Voucher Genus Species Subspecies Ctry. St. Co. Latitude Longitude adamanteus_ CAS 2447 Crotalus adamanteus US FL Leon AZ_cerb_3 LEB 55 Crotalus cerberus US AZ Gila AZ_cerb_4 WW 33 Crotalus cerberus US AZ Gila AZ_cerb_5 WW 27 Crotalus cerberus US AZ Graham AZ_cerb_6 MA Crotalus cerberus US AZ Gila AZ_cerb_7 MA 2 Crotalus cerberus US AZ Yavapai AZ_cerb_8 MA 3 Crotalus cerberus US AZ Cochise AZ_cerb_9 MA 4 Crotalus cerberus US AZ Cochise AZ_cerb_2 MA 5 Crotalus cerberus US AZ Gila AZ_cerb_2 MA 6 Crotalus cerberus US AZ Yavapai AZ_cerb_22 MA 7 Crotalus cerberus US AZ Yavapai AZ_cerb_24 MA 9 Crotalus cerberus US AZ Coconino AZ_cerb_25 TWR 677 Crotalus cerberus US AZ Mohave AZ_abys_ FHSM 6372 Crotalus oreganus abyssus US AZ Coconino UT_conc_2 CAS Crotalus oreganus concolor US UT Carbon CA_hell_2 SD Field 2837 Crotalus oreganus helleri US CA Riverside CA_hell_3 SD Field 285 Crotalus oreganus helleri US CA San Diego CA_hell_6 CSUN 322 Crotalus oreganus helleri US CA Los Angeles CA_hell_9 CSUN 242 Crotalus oreganus helleri US CA Los Angeles CA_hell_24 DAW 4 Crotalus oreganus helleri US CA Los Angeles MX_hell_ SD Field 77 Crotalus oreganus helleri MX Baja MX_hell_2 SD Field 39 Crotalus oreganus helleri MX Baja MX_hell_3 SD Field 228 Crotalus oreganus helleri MX Baja ID_luto_ Sc 26 Crotalus oreganus lutosus US ID Bonneville ID_luto_2 Sc 2 Crotalus oreganus lutosus US ID Bonneville ID_luto_3 Sc 69 Crotalus oreganus lutosus US ID Bonneville ID_luto_4 Sc 7 Crotalus oreganus lutosus US ID Bonneville ID_luto_6 RCAV 4 Crotalus oreganus lutosus US ID Bonneville ID_luto_7 RCAV 88 Crotalus oreganus lutosus US ID Bonneville NV_luto_3 CSUN 499 Crotalus oreganus lutosus US NV Storey NV_luto_4 UNR 6999 Crotalus oreganus lutosus US NV Lyon NV_luto_5 UNR 794 Crotalus oreganus lutosus US NV Mineral NV_luto_6 UNR 795 Crotalus oreganus lutosus US NV Mineral NV_luto_9 UNR 7283 Crotalus oreganus lutosus US NV Washoe (table continues)

56 47 Table 4. (continued) Specimen ID Voucher Genus Species Subspecies Ctry. St. Co. Latitude Longitude UT_luto_2 JQR 85 Crotalus oreganus lutosus US UT Kane UT_luto_3 BTH 57 Crotalus oreganus lutosus US UT Garfield UT_luto_4 BTH 57 Crotalus oreganus lutosus US UT Garfield CA_oreg_6 JQR 47 Crotalus oreganus oreganus US CA Kern CA_oreg_ MVZ 2894 Crotalus oreganus oreganus US CA Alameda CA_oreg_3 MVZ 5247 Crotalus oreganus oreganus US CA Alameda CA_oreg_4 MVZ 5248 Crotalus oreganus oreganus US CA Alameda CA_oreg_5 MVZ Crotalus oreganus oreganus US CA San Benito CA_oreg_6 MVZ Crotalus oreganus oreganus US CA Fresno CA_oreg_7 MVZ Crotalus oreganus oreganus US CA Tuolumne CA_oreg_8 MVZ Crotalus oreganus oreganus US CA Tuolumne CA_oreg_2 MVZ Crotalus oreganus oreganus US CA Tuolumne CA_oreg_2 MVZ Crotalus oreganus oreganus US CA Tuolumne CA_oreg_22 CSUN 48 Crotalus oreganus oreganus US CA San Luis Obispo CA_oreg_23 CSUN 486 Crotalus oreganus oreganus US CA San Luis Obispo OR_oreg_ EAM 33 Crotalus oreganus oreganus US OR Josephine OR_oreg_2 EAM 58 Crotalus oreganus oreganus US OR Jackson OR_oreg_3 EAM 78 Crotalus oreganus oreganus US OR Josephine WA_oreg_3 EAM 2 Crotalus oreganus oreganus US WA Whitman scutulatus_2 TWR 747 Crotalus scutulatus US AZ_nunt_3 WW 32 Crotalus viridis nuntius US AZ Coconino UT_nunt_ TWR 777 Crotalus viridis nuntius US UT San Juan Co UT_nunt_2 CAS 746 Crotalus viridis nuntius US UT San Juan CO_viri_2 FHSM 433 Crotalus viridis viridis US CO Baca CO_viri_5 WW 55 Crotalus viridis viridis US CO Moffat KS_viri_ FHSM 883 Crotalus viridis viridis US KS Barber KS_viri_4 FHSM 5742 Crotalus viridis viridis US KS Logan KS_viri_5 FHSM 548 Crotalus viridis viridis US KS Stanton NM_viri_2 WW 86 Crotalus viridis viridis US NM Dona Ana

57 48 Table 5. Primer Information Locus Source Primers BZW Fujita et al. 2 (modified by J. Goldenberg) AmpF: GATGCTTCTGGRGCAAARCTT AmpR: TGCATCGTTTCTAGGTCYTCY SeqF: GAGGAGGAAAAGGGGAAGAA SeqR: CTGGTTTACCAGATCATCTTT RP4 Friesen et al. 999 (modified by D. Leavitt) F: ATGTGGTGGATGYTGGCTCGT R: GCTTCTCAGCWGCRGCCTGC RPS8 D. Leavitt, pers. comm. F: CGGAAAAAGAATGCYAAGATCAGTAG R: GTAGCCATCTGCTCGGCCACATTGTCC SELT D. Leavitt, pers. comm. F: GTTATYAGCCAGCGGTACCCAGACATCCG R: GCCTATTAAYACTAGTTTGAAGACTGACAG TBP2 Kubatko et al. 2 (modified by J. Goldenberg) AmpF: CCTTTACCAGGAACCACACC AmpR: CGAAGGGCAATGGTTTTTAG SeqF: AGGGTCTTTGCAATTTA SeqR: GGTTTGGCCACCTAATGAGA ND2 D. Leavitt, pers. comm. F: AAGCTYGGCCCATACCCCGA R: GTTAATTAATTDTTTAYGGGATCRAGGCCC

58 49 APPENDIX C SPECIES DESIGNATIONS APPLIED A PRIORI FOR EACH HYPOTHESIS OF SPECIES DELIMITATION TESTED

59 5 Table 6. Hypotheses H-H7. Sample ID H H2 H3 H4 H5 H6 H7 AZ_cerb_7 C. viridis C. oreganus C. cerberus C. cerberus A C. cerberus C. cerberus C. cerberus AZ_cerb_2 C. viridis C. oreganus C. cerberus C. cerberus A C. cerberus C. cerberus C. cerberus AZ_cerb_3 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_4 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_5 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_6 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_8 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_9 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_2 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_22 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus AZ_cerb_24 C. viridis C. oreganus C. cerberus C. cerberus B C. cerberus C. cerberus C. cerberus CA_hell_2 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus CA_hell_3 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus CA_hell_6 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus CA_hell_9 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus CA_hell_24 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus MX_hell_ C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus MX_hell_2 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus MX_hell_3 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. helleri C. lutosus AZ_abys_ C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus UT_conc_2 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus UT_luto_2 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus UT_luto_3 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus UT_luto_4 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus ID_luto_ C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus ID_luto_2 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus ID_luto_3 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus (table continues)

60 5 Table 6. (continued) Sample ID H H2 H3 H4 H5 H6 H7 ID_luto_4 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus ID_luto_6 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus ID_luto_7 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus NV_luto_3 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus NV_luto_4 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus NV_luto_5 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus NV_luto_6 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus NV_luto_9 C. viridis C. oreganus C. oreganus C. oreganus C. lutosus C. lutosus C. lutosus OR_oreg_ C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus A OR_oreg_2 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus A OR_oreg_3 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus A WA_oreg_3 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus A CA_oreg_6 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_ C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_3 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_4 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_5 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_6 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_7 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_8 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_2 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_2 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_22 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CA_oreg_23 C. viridis C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus C. oreganus B CO_viri_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis CO_viri_5 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis KS_viri_ C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis KS_viri_4 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis (table continues)

61 52 Table 6. (continued) KS_viri_5 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis NM_viri_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis UT_nunt_ C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis UT_nunt_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis AZ_nunt_3 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis

62 53 Table 7. Hypotheses H8-H4. Sample ID H8 H9 H H H2 H3 H4 AZ_cerb_7 C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A AZ_cerb_2 C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A C. cerberus A AZ_cerb_3 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_4 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_5 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_6 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_8 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_9 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_2 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_22 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B AZ_cerb_24 C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B C. cerberus B CA_hell_2 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri CA_hell_3 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri CA_hell_6 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri CA_hell_9 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri CA_hell_24 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri MX_hell_ C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri MX_hell_2 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri MX_hell_3 C. lutosus C. helleri C. helleri C. lutosus C. helleri C. helleri C. helleri AZ_abys_ C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. abyssus C. abyssus UT_conc_2 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. concolor C. concolor UT_luto_2 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus UT_luto_3 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus UT_luto_4 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus ID_luto_ C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus ID_luto_2 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus ID_luto_3 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus ID_luto_4 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus ID_luto_6 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus (table continues)

63 54 Table 7. (continued) ID_luto_7 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus NV_luto_3 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus NV_luto_4 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus NV_luto_5 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus NV_luto_6 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus NV_luto_9 C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus C. lutosus OR_oreg_ C. oreganus C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A OR_oreg_2 C. oreganus C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A OR_oreg_3 C. oreganus C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A WA_oreg_3 C. oreganus C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A C. oreganus A CA_oreg_6 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_ C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_3 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_4 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_5 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_6 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_7 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_8 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_2 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_2 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_22 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CA_oreg_23 C. oreganus C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B C. oreganus B CO_viri_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis CO_viri_5 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis KS_viri_ C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis KS_viri_4 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis KS_viri_5 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis NM_viri_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis UT_nunt_ C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. nuntius UT_nunt_2 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. nuntius AZ_nunt_3 C. viridis C. viridis C. viridis C. viridis C. viridis C. viridis C. nuntius

64 55 APPENDIX D SUPPLEMENTARY FIGURES

65 Figure 6. Individual gene trees inferred within a Bayesian framework using MrBayes. Values at nodes represent posterior probabilities. 56

66 57 adamanteus_a adamanteus_b AZ_cerb_5a AZ_cerb_5b AZ_cerb_8a AZ_cerb_8b AZ_cerb_2b AZ_cerb_9a AZ_cerb_9b AZ_cerb_2a AZ_cerb_2a AZ_cerb_2b AZ_cerb_22a AZ_cerb_22b CA_hell_2a CA_hell_2b CA_hell_3a CA_hell_3b CA_hell_6a ID_luto_3a ID_luto_3b ID_luto_4a ID_luto_4b ID_luto_6a ID_luto_6b ID_luto_7a ID_luto_7b NN_cerb_a AZ_cerb_25b NV_luto_9a NV_luto_9b UT_luto_3a UT_luto_3b UT_luto_4a UT_luto_4b UT_nunt_2a UT_nunt_2b CA_hell_6b CA_hell_9a CA_hell_9b CA_hell_24a CA_hell_24b CA_oreg_6a CA_oreg_6b CA_oreg_3a CA_oreg_3b CA_oreg_4a CA_oreg_4b CA_oreg_5a CA_oreg_5b CA_oreg_7a CA_oreg_7b CA_oreg_8a CA_oreg_8b CA_oreg_2a CA_oreg_2b CA_oreg_2a CA_oreg_2b CA_oreg_23a CA_oreg_23b MX_cali_a MX_cali_b MX_hell_a MX_hell_b MX_hell_3a MX_hell_3b MX_hell_2a MX_hell_2b NM_viri_2a NM_viri_2b NV_luto_3a NV_luto_3b NV_luto_5a NV_luto_5b NV_luto_6a NV_luto_6b OR_oreg_a OR_oreg_b OR_oreg_2a OR_oreg_2b OR_oreg_3a OR_oreg_3b scutulatus_2a scutulatus_2b UT_conc_2a UT_conc_2b UT_luto_2a UT_luto_2b WA_oreg_3a WA_oreg_3b BZW A

67 58 adamanteus_a adamanteus_b AZ_abys_a AZ_abys_b AZ_cerb_25a AZ_cerb_25b CA_hell_2a CA_hell_2b CA_hell_3a CA_hell_3b CA_hell_6a CA_hell_6b CA_hell_9a CA_hell_9b CA_hell_24a CA_hell_24b CA_oreg_6a CA_oreg_6b CA_oreg_a CA_oreg_b CA_oreg_3a CA_oreg_3b CA_oreg_4a CA_oreg_4b CA_oreg_23a CA_oreg_23b ID_luto_a ID_luto_b ID_luto_2a ID_luto_2b ID_luto_3a ID_luto_3b ID_luto_4a ID_luto_4b NV_luto_3b ID_luto_6a ID_luto_6b MX_hell_a MX_hell_b MX_hell_2a MX_hell_2b MX_hell_3a MX_hell_3b NV_luto_3a NV_luto_5a NV_luto_5b NV_luto_6a NV_luto_6b scutulatus_2a scutulatus_2b UT_luto_2a UT_luto_2b UT_nunt_a UT_nunt_b RP4 B

68 59 adamanteus_a adamanteus_b AZ_abys_a AZ_abys_b AZ_cerb_3a AZ_cerb_3b AZ_cerb_24b AZ_cerb_4a AZ_cerb_4b AZ_cerb_5a AZ_cerb_5b AZ_cerb_2a AZ_cerb_2b AZ_cerb_24a AZ_cerb_6a AZ_cerb_2a AZ_cerb_22a AZ_cerb_25b AZ_cerb_25a AZ_cerb_6b AZ_cerb_2b AZ_cerb_22b CA_hell_24a CA_hell_24b CA_oreg_23a MX_hell_2a MX_hell_2b AZ_cerb_7a AZ_cerb_7b AZ_cerb_8a AZ_cerb_8b AZ_cerb_9a AZ_cerb_9b AZ_nunt_3a AZ_nunt_3b CO_viri_5a CO_viri_5b ID_luto_a ID_luto_b ID_luto_4b KS_viri_a KS_viri_b KS_viri_4a KS_viri_4b NV_luto_4b NV_luto_9b UT_conc_2a UT_conc_2b CA_hell_2a MX_hell_a MX_hell_3a CA_hell_2b CA_hell_3a CA_hell_3b CA_hell_6a CA_hell_6b CA_hell_9a CA_hell_9b CA_oreg_b CA_oreg_3b MX_hell_b CA_oreg_6a CA_oreg_6b CA_oreg_a CA_oreg_3a CA_oreg_4a CA_oreg_4b OR_oreg_a OR_oreg_b OR_oreg_2a OR_oreg_2b OR_oreg_3a OR_oreg_3b WA_oreg_3a WA_oreg_3b CA_oreg_5a CA_oreg_5b CA_oreg_7a CA_oreg_7b CA_oreg_8a CA_oreg_8b CA_oreg_2a CA_oreg_2b CA_oreg_2a CA_oreg_2b CA_oreg_22a CA_oreg_22b CA_oreg_23b ID_luto_2a ID_luto_2b ID_luto_3a ID_luto_3b ID_luto_4a ID_luto_6a ID_luto_6b ID_luto_7a ID_luto_7b MX_cali_a MX_cali_b MX_hell_3b NM_viri_2a NM_viri_2b NV_luto_3a NV_luto_3b NV_luto_4a NV_luto_5a NV_luto_5b NV_luto_6a NV_luto_6b NV_luto_9a scutulatus_2a scutulatus_2b UT_luto_3a UT_luto_3b UT_luto_4a UT_luto_4b UT_nunt_a UT_nunt_b RPS8 C

69 D SELT 6 adamanteus_b adamanteus_a.98 UT_nunt_2b UT_nunt_2a UT_nunt_b UT_nunt_a UT_luto_2b UT_luto_2a UT_conc_2b UT_conc_2a NV_luto_5b NV_luto_5a MX_hell_3b MX_hell_3a MX_hell_b MX_hell_a ID_luto_7b ID_luto_7a ID_luto_6b ID_luto_6a ID_luto_4b ID_luto_4a ID_luto_2b ID_luto_2a CA_oreg_6b CA_oreg_6a CA_hell_24b CA_hell_24a CA_hell_9b CA_hell_9a CA_hell_6b CA_hell_6a CA_hell_3b CA_hell_3a CA_hell_2b CA_hell_2a AZ_cerb_25b AZ_cerb_25a AZ_cerb_3b AZ_cerb_3a.82 scutulatus_2b scutulatus_2a

70 6 adamanteus_a adamanteus_b AZ_cerb_3a AZ_cerb_3b UT_nunt_a UT_nunt_b KS_viri_4a KS_viri_4b AZ_cerb_25a AZ_cerb_25b CA_hell_9a CA_hell_9b CA_oreg_6a CA_oreg_6b ID_luto_2a ID_luto_2b ID_luto_6a ID_luto_6b ID_luto_7a ID_luto_7b NV_luto_3a NV_luto_3b NV_luto_5a NV_luto_5b NV_luto_6a NV_luto_6b UT_luto_2a UT_luto_2b CA_oreg_2b CA_hell_3a CA_hell_3b scutulatus_2a scutulatus_2b CA_hell_2a CA_hell_2b CA_hell_24b CA_hell_24a CA_oreg_a CA_oreg_b CA_oreg_3b CA_oreg_5a CA_oreg_2a MX_hell_2a MX_hell_2b MX_hell_3a MX_hell_3b CA_oreg_3a CA_oreg_5b CA_oreg_7a CA_oreg_7b CA_oreg_23a CA_oreg_23b TBP2 E

71 62 adamanteus scutulatus_2 AZ_abys_ UT_luto_2 UT_luto_3 UT_luto_4 ID_luto_ ID_luto_2 ID_luto_3 ID_luto_4 ID_luto_6 ID_luto_7 NV_luto_3 NV_luto_4 NV_luto_5 NV_luto_6 NV_luto_9 UT_conc_2 CA_hell_2 CA_hell_3 CA_hell_9 CA_hell_24 MX_hell_ MX_hell_2 MX_hell_3 CA_hell_6 CA_oreg_6 CA_oreg_ CA_oreg_3 CA_oreg_4 MX_cali_ CA_oreg_5 CA_oreg_6 CA_oreg_22 CA_oreg_23 CA_oreg_7 CA_oreg_8 CA_oreg_2 CA_oreg_2 OR_oreg_ OR_oreg_2 OR_oreg_3 WA_oreg_3 AZ_cerb_3 AZ_cerb_5 AZ_cerb_8 AZ_cerb_9 AZ_cerb_4 AZ_cerb_6 AZ_cerb_2 AZ_cerb_7 AZ_cerb_2 AZ_cerb_22 AZ_cerb_25 UT_nunt_2 AZ_nunt_3 CO_viri_2 KS_viri_ CO_viri_5 KS_viri_4 KS_viri_5 UT_nunt_ NM_viri_ ND2 F

72 Figure 7. Individual gene trees inferred within a maximum likelihood framework using RAxML. Values at nodes represent bootstrap support. 63

73 64 adamanteus_a scutulatus_2b scutulatus_2a NV_luto_3a NV_luto_3b UT_conc_2b UT_conc_2a NV_luto_5a NV_luto_6a NV_luto_6b MX_hell_2a AZ_cerb_9b AZ_cerb_2a AZ_cerb_22b CA_oreg_7a CA_oreg_2b AZ_cerb_9a AZ_cerb_22a AZ_cerb_2a UT_luto_2b UT_luto_2a CA_oreg_7b CA_oreg_2b AZ_cerb_2b CA_oreg_2a CA_oreg_5a AZ_cerb_8a AZ_cerb_8b AZ_cerb_2b MX_hell_a MX_hell_b MX_hell_3a MX_hell_3b NM_viri_2a NM_viri_2b CA_oreg_5b OR_oreg_b OR_oreg_a AZ_cerb_5b AZ_cerb_5a CA_hell_9b CA_hell_9a CA_hell_24a CA_hell_24b CA_hell_2a CA_hell_2b OR_oreg_3b WA_oreg_3b WA_oreg_3a CA_hell_3a CA_hell_3b OR_oreg_2a OR_oreg_2b OR_oreg_3a CA_oreg_6a CA_oreg_4b CA_oreg_4a CA_oreg_2a CA_oreg_6b CA_oreg_23a CA_oreg_23b AZ_cerb_25b AZ_cerb_25a ID_luto_3b ID_luto_3a UT_nunt_2a NV_luto_9a NV_luto_9b ID_luto_7b ID_luto_7a UT_luto_3a UT_luto_4a ID_luto_6a UT_luto_4b UT_luto_3b ID_luto_6b ID_luto_4b ID_luto_4a UT_nunt_2b CA_hell_6a CA_hell_6b CA_oreg_3b CA_oreg_3a CA_oreg_8a CA_oreg_8b MX_hell_2b NV_luto_5b adamanteus_b BZW A

74 65 adamanteus_a scutulatus_2b scutulatus_2a CA_oreg_4a CA_oreg_4b MX_hell_b MX_hell_a AZ_abys_a AZ_abys_b CA_oreg_b CA_hell_9a CA_hell_2a CA_hell_2b CA_hell_6a CA_hell_6b ID_luto_6a ID_luto_6b CA_oreg_23a MX_hell_3a MX_hell_3b CA_oreg_23b CA_hell_9b NV_luto_5b NV_luto_5a CA_hell_3a CA_hell_3b CA_oreg_a MX_hell_2b ID_luto_2b ID_luto_b MX_hell_2a ID_luto_a ID_luto_2a CA_hell_24b CA_hell_24a NV_luto_3a NV_luto_6a NV_luto_6b UT_nunt_b UT_nunt_a NV_luto_3b ID_luto_4a ID_luto_4b ID_luto_3a ID_luto_3b CA_oreg_3a CA_oreg_3b UT_luto_2b UT_luto_2a AZ_cerb_25a AZ_cerb_25b CA_oreg_6a CA_oreg_6b adamanteus_b RP4 B

75 66 adamanteus_a MX_hell_3b CA_hell_2a MX_hell_a MX_hell_3a CA_oreg_b CA_oreg_3b CA_hell_9b MX_hell_b CA_oreg_6b scutulatus_2b MX_hell_2b CA_hell_24b CA_hell_24a AZ_cerb_6b AZ_cerb_22b AZ_cerb_2b MX_hell_2a CA_oreg_23a scutulatus_2a CA_hell_2b CA_oreg_2b CA_hell_3a CA_hell_3b CA_oreg_22b CA_oreg_22a CA_oreg_2a CA_oreg_2b AZ_cerb_9b UT_nunt_b UT_nunt_a ID_luto_6b NV_luto_5a AZ_abys_b AZ_abys_a NV_luto_4a NV_luto_9a NV_luto_3a ID_luto_7a CA_oreg_5a CA_oreg_4a ID_luto_2a AZ_cerb_4b AZ_cerb_4a UT_luto_3b ID_luto_3a CA_oreg_3a AZ_cerb_9a UT_luto_3a NV_luto_6a AZ_cerb_7b AZ_cerb_7a AZ_cerb_3b AZ_cerb_3a AZ_cerb_24b AZ_cerb_24a AZ_cerb_2a AZ_cerb_2b AZ_cerb_5b AZ_cerb_5a AZ_cerb_25b AZ_cerb_2a AZ_cerb_6a AZ_cerb_22a AZ_cerb_25a AZ_cerb_8a AZ_cerb_8b CA_oreg_23b ID_luto_3b ID_luto_2b ID_luto_6a ID_luto_4a ID_luto_7b CA_oreg_a UT_luto_4a UT_luto_4b CA_hell_6a UT_conc_2b UT_conc_2a ID_luto_4b ID_luto_a NV_luto_4b ID_luto_b NV_luto_9b AZ_nunt_3b AZ_nunt_3a KS_viri_b KS_viri_4b KS_viri_4a KS_viri_a CO_viri_5a CO_viri_5b CA_oreg_2a CA_oreg_5b CA_oreg_7b CA_oreg_7a CA_oreg_6a NM_viri_2b NM_viri_2a OR_oreg_a CA_oreg_4b WA_oreg_3b WA_oreg_3a OR_oreg_3a OR_oreg_3b OR_oreg_2b OR_oreg_2a OR_oreg_b CA_hell_6b NV_luto_5b CA_oreg_8b CA_oreg_8a CA_hell_9a NV_luto_3b NV_luto_6b adamanteus_b RPS8 C

76 67 adamanteus_a scutulatus_2a scutulatus_2b MX_hell_b MX_hell_a MX_hell_3a MX_hell_3b CA_hell_9a CA_hell_9b UT_luto_2a ID_luto_7b NV_luto_5a ID_luto_4a UT_conc_2b ID_luto_6b ID_luto_2b CA_hell_24a ID_luto_4b CA_hell_24a AZ_cerb_25b AZ_cerb_25a ID_luto_6a UT_conc_2a ID_luto_2a CA_hell_24b NV_luto_5b ID_luto_7a CA_hell_24b CA_oreg_6a CA_oreg_6b AZ_cerb_3b AZ_cerb_3a CA_hell_2a CA_hell_2b CA_hell_3a UT_nunt_2b UT_nunt_2a UT_luto_2b UT_nunt_b UT_nunt_a CA_hell_3b adamanteus_b SELT D

77 68 adamanteus_a CA_oreg_23a CA_oreg_23b CA_oreg_5b CA_oreg_2a CA_hell_2a CA_hell_2b CA_hell_24b MX_hell_3b CA_oreg_3b CA_hell_24a CA_oreg_5a CA_oreg_a CA_oreg_b MX_hell_3a MX_hell_2a MX_hell_2b UT_nunt_a AZ_cerb_3a AZ_cerb_3b UT_nunt_b KS_viri_4b KS_viri_4a CA_oreg_7b CA_oreg_7a CA_oreg_2b CA_hell_9a UT_luto_2a AZ_cerb_25a CA_oreg_6b CA_oreg_6a ID_luto_2b NV_luto_3a CA_hell_9b ID_luto_6b NV_luto_5a NV_luto_6b AZ_cerb_25b ID_luto_7a ID_luto_7b NV_luto_6a NV_luto_5b ID_luto_6a UT_luto_2b NV_luto_3b ID_luto_2a CA_hell_3a CA_hell_3b scutulatus_2a scutulatus_2b CA_oreg_3a adamanteus_b TBP2 E

78 69 adamanteus_ scutulatus_2 AZ_abys_ UT_luto_2 UT_luto_3 UT_luto_4 ID_luto_ ID_luto_2 ID_luto_3 ID_luto_4 ID_luto_6 ID_luto_7 NV_luto_3 NV_luto_4 NV_luto_5 NV_luto_6 NV_luto_9 UT_conc_2 CA_hell_2 CA_hell_3 CA_hell_9 CA_hell_24 MX_hell_ MX_hell_2 MX_hell_3 CA_hell_6 CA_oreg_6 CA_oreg_ CA_oreg_3 CA_oreg_4 MX_cali_ CA_oreg_5 CA_oreg_6 CA_oreg_22 CA_oreg_23 CA_oreg_7 CA_oreg_8 CA_oreg_2 CA_oreg_2 OR_oreg_ OR_oreg_2 OR_oreg_3 WA_oreg_3 AZ_cerb_3 AZ_cerb_5 AZ_cerb_8 AZ_cerb_9 AZ_cerb_4 AZ_cerb_6 AZ_cerb_2 AZ_cerb_7 AZ_cerb_2 AZ_cerb_22 AZ_cerb_25 UT_nunt_2 AZ_nunt_3 CO_viri_2 KS_viri_ CO_viri_5 KS_viri_4 KS_viri_5 UT_nunt_ NM_viri_ ND2 F

79 7 A B C D E Figure 8. Morphology of individual UT_nunt_2. A. Top left: Crotalus viridis nuntius paratype; bottom left: C. v. nuntius paratype; middle: UT_nunt_2; right: C. oreganus concolor. B. Close-up of top left C. v. nuntius from panel (A). C. Close-up of bottom left C. v. nuntius from panel (A). C. Close-up of right C. o. concolor from panel (A). D. Close-up of UT_nunt_2, now reassigned from C. o. concolor to C. v. nuntius.