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 2013

iii

Copyright © 2013

by

Julianne R. Goldenberg

All Rights Reserved

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, 2013

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 (1) 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.

v

TABLE OF CONTENTS

PAGE

ABSTRACT ............................................................................................................................. iv

LIST OF TABLES .................................................................................................................. vii

LIST OF FIGURES ............................................................................................................... viii

ACKNOWLEDGEMENTS ..................................................................................................... ix

CHAPTER

1 INTRODUCTION .........................................................................................................1 

Simultaneous Multilocus Species Tree Inference and Species Delimitation ...........3 

The Crotalus viridis Species Complex ....................................................................5 

Objectives ................................................................................................................7 

2 MATERIALS AND METHODS ...................................................................................8 

Taxon Sampling and Data Collection ......................................................................8 

Gene Tree Inference .................................................................................................9 

Generation of Hypotheses of Species Delimitation ...............................................10 

Bayes Factor Hypothesis Testing...........................................................................11 

Species Delimitation Using BPP ...........................................................................12 

Species Concept .....................................................................................................13 

Dated Multilocus Phylogeny of the C. viridis Species Complex...........................14 

3 RESULTS ....................................................................................................................15 

Data Collection ......................................................................................................15 

Gene Tree Inference ...............................................................................................15 

Generation of Hypotheses of Species Delimitation ...............................................17 

Method (1): Traditional Subspecies as Species ...............................................17 

Method (2): Mitochondrial Clades...................................................................17 

Method (3): Multilocus Nuclear Clustering Using POFAD ............................20 

Hypothesis Testing via Marginal Likelihood Estimation ......................................20 

Species Trees .........................................................................................................20 

Hypothesis Testing...........................................................................................23 

vi

Species Delimitation Using BPP .....................................................................24 

Dated Multilocus Phylogeny of the C. viridis Species Complex...........................25 

4 DISCUSSION ..............................................................................................................28 

Coalescent Species Delimitation ............................................................................28 

Using the Bayes Factor for Species Delimitation ..................................................29 

Comparison to BPP ................................................................................................30 

Species Limits and Phylogeny Within the Crotalus viridis Species Complex .................................................................................................................31 

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 

vii

LIST OF TABLES

PAGE

Table 1. Models of Molecular Evolution and Locus Variability .............................................10 

Table 2. Hypotheses of Species Delimitation Generated using Methods (1) and (2) ..............19 

Table 3. Marginal Likelihoods Estimated Using Mitochondrial and Nuclear Data for Each Hypothesis Tested (Table 2) ..............................................................................21 

Table 4. Individuals Sampled for This Study ..........................................................................46 

Table 5. Primer Information ....................................................................................................48 

Table 6. Hypotheses H1-H7. .....................................................................................................50 

Table 7. Hypotheses H8-H14. ....................................................................................................53 

viii

LIST OF FIGURES

PAGE

Figure 1. Range of Crotalus viridis species complex.. ..............................................................6 

Figure 2. Starting tree for species delimitation hypothesis generation using method (2). ................................................................................................................................18 

Figure 3. Marginal likelihoods of hypotheses H1-H14, estimated via path sampling (PS), stepping stone (SS), and the smoothed harmonic mean estimator (sHME). .......................................................................................................................22 

Figure 4. Discordant guide trees used as starting trees for analysis with BPP. .......................25 

Figure 5. Time-calibrated multilocus species tree of the Crotalus viridis species complex, with outgroups C. scutulatus and C. adamanteus. .......................................27 

Figure 6. Individual gene trees inferred within a Bayesian framework using MrBayes.. .....................................................................................................................56 

Figure 7. Individual gene trees inferred within a maximum likelihood framework using RAxML. .............................................................................................................63 

Figure 8. Morphology of individual UT_nunt_02. ..................................................................70 

Figure 9. Results of POFAD analysis (i.e., method [3]). .........................................................71 

Figure 10. Species trees inferred using *BEAST under each hypothesis of species delimitation with all data included. ..............................................................................72 

Figure 11. Species trees inferred using *BEAST under each hypothesis of species delimitation without mitochondrial data. .....................................................................77 

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.

1

CHAPTER 1

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 1859, 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 1865, p. 12

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 (1859), 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 (1865) 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 (2007) 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.

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. 2009; BEST, Liu 2008; *BEAST, Heled and

Drummond 2010). 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 2007, sensu

Rittmeyer and Austin 2012), non-coalescent (e.g., approximate Bayesian computing, sensu

Camargo et al. 2012), and coalescent-based (e.g., BPP, Yang and Rannala 2010) 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 2011; Rittmeyer and Austin 2012; Camargo et al. 2012; Zhang et al. 2011;). 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.

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 (2007) 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 2005). 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 (2011) 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 1973). 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 (2010) 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. 2007; Kass and Raftery

4

1995). 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 1995; Lartillot and Philippe

2006; Raftery et al. 2007; Xie et al. 2011; Baele et al. 2012A;).

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 2006; Raftery et al. 2007; Xie et al. 2011; Baele et al. 2012A). The

estimation of the marginal likelihood of a model has a history of computational difficulty

(Suchard et al. 2001). The method of the harmonic mean estimator (HME, Newton and

Raftery 1994) 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 2006; Raftery et al. 2007; Xie et al. 2011). Further, this estimator has been

shown to systematically overestimate the marginal likelihood of a model (Xie et al. 2011,

Baele et al. 2012A ). One method proposed to stabilize the variance of the HME is the

smoothed HME (sHME, Suchard et al. 2003), 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. 2003; Lartillot and

Philippe 2006), it is still highly inaccurate (Lartillot and Philippe 2006). 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. 2007). 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 log-

likelihoods 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. 2011; Baele et al.

2012A), 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. 2011; Baele et al. 2012A).

5

The development of thermodynamic integration (TI) (e.g., path sampling [PS],

Lartillot and Philippe 2006) 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. 2011) 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. 2011).

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. 2012]) 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 1).

Historically (Klauber 1930, 1943, 1956; Foote and MacMahon 1977; Aird 1984; Quinn

1987) and until relatively recently (Pook et al. 2000; Ashton and de Queiroz 2001; Douglas

et al. 2002), 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 1997). Attempts to infer the evolutionary relationships within

this complex (e.g., Foote and MacMahon 1977; Aird 1984; Quinn 1987; Pook et al. 2000;

Ashton and de Queiroz 2001; Douglas et al. 2002) 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 2007; Mackessy 2010;

Oyler-McCance and Parker 2010).

6

Figure 1. Range of Crotalus viridis species complex. Range map is adapted from Stebbins (2003). 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 1984; Quinn 1987) had generally not been formally accepted (Pook et al. 2000;

Ashton and de Queiroz 2001; Douglas et al. 2002). Three nearly coincident phylogenetic

reconstructions of C. viridis sensu lato based on mitochondrial DNA (mtDNA) sequence data

(Pook et al. 2000; Ashton and de Queiroz 2001; Douglas et al. 2002) 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

7

remaining subspecies) clade, and found C. cerberus to be sister to all remaining western

clade subspecies. Though the first of these 2012species: 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. (2002) liberally

applied the phylogenetic species concept (Cracraft 1983) 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. 2012).

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.

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 1, 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. 2000; Ashton and

de Queiroz 2001; Douglas et al. 2002), and have found C. o. concolor to be closely related to

C. o. lutosus (Ashton and de Queiroz 2001; Douglas et al. 2002), 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. 2002; Castoe and Parkinson 2006;

Pyron et al. 2013).

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 BZW1, RP40,

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

9

ABI 3730xl DNA Analyzer (Applied Biosystems, Inc., Carlsbad, CA). Sequences were

edited and contigs assembled using Geneious Pro 5.0.4 (Drummond et al. 2011), and aligned

using MUSCLE (Edgar 2004). Haplotypes of heterozygous individuals were inferred using

PHASE 2.1.1 (Stephens et al. 2004) under the recombination model. Haplotypes inferred

with less than 90% certainty were left as ambiguous, which yielded 0, 0, 32, 0, and 2

ambiguous sites remaining within alignments of BZW1, RP40, 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. 2004).

GENE TREE INFERENCE

Single gene trees were inferred for each locus within a maximum likelihood

framework using RAxML v7.2.8 Black Box (Stamatakis 2006) through the

Cyberinfrastructure for Phylogenetic Research (CIPRES, Miller et al. 2010). 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 v3.1.2 (Ronquist et al. 2012) through CIPRES. For Bayesian

analyses, the appropriate model of molecular evolution, as determined using jModelTest

v0.1.1 (Posada 2008; Gascuel 2003) under the AIC, was applied to each locus and partition

(Table 1). All Metropolis-coupled MCMC (MC3) analyses were run for 50 million

generations, sampled every 5,000 generations. Convergence of runs was assessed by

observation of ESS values in Tracer v1.5 (Rambaut and Drummond 2009), 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.

10

Table 1. Models of Molecular Evolution and Locus Variability

With Outgroups Ingroup Only

Locus Model Clock model

Length (bp)

Variable Sites

Parsimony Informative

Sites Variable Sites

Parsimony Informative

Sites

BZW1 GTR+Γ Relaxeda 776 64 62 37 35 RP40 GTR+I Strict 411 15 14 9 9 RPS8 GTR+I+Γ Strict 551 25 25 21 21 SELT HKY+I Strict 491 10 10 4 4 TBP2 HKY+I Strict 597 29 29 22 22 ND2 (all) Strict 1026 221 142 176 135 ND2 (pos 1) GTR+I+Γ 342 93 68 77 64 ND2 (pos 2) GTR+I+Γ 342 48 25 36 25 ND2 (pos 3) GTR+I+Γ 342 80 49 63 46 arelaxed uncorrelated lognormal clock

GENERATION OF HYPOTHESES OF SPECIES

DELIMITATION

Alternative hypotheses of species delimitation were generated in three ways: (1)

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 2006) 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 (1), 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 1956, 1976, 1997; Stebbins 2003), 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] ≥ 70 and posterior probability [PP] ≥ 0.95) 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

11

matrix relating a given set of individuals. For hypothesis generation method (3), input

distance matrices were generated for each nuclear intron using PAUP* v4.0b10 (Swofford

2002). After execution of matrices in POFAD, the resulting output matrix was used to create

a neighbor-joining network using SplitsTree (Hudson and Bryant 2006). 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: (1) 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 2010) implemented in BEAST v1.7.2 (Drummond and Rambaut, 2007/2012),

run in parallel using Beagle (Ayres et al. 2012) 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 1). The appropriate model of molecular evolution was applied to

12

each nuclear intron and to each codon position partition of the mitochondrial ND2. Each

analysis was run for 200 million generations, sampled every 20,000 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 v1.7.2 (Rambaut and Drummond 2011), and a maximum clade

credibility tree was constructed from the resulting combined posterior distribution using

TreeAnnotator v1.7.2 (Rambaut and Drummond 2012).

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 100 power

posteriors was inferred after each *BEAST run. The sampling scheme of powers followed a

Beta (0.3, 1.0) distribution, after Xie et al. (2011). 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 2006; Xie et al.

2011; Baele et al. 2012A). All XML code for marginal likelihood estimation is credited to

Baele et al. (2012A and 2012B), made publically available on the BEAST website

(http://beast.bio.ed.ac.uk). 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 (1995), where 2ln(BF)AB < 2 is considered insignificant, 2

< 2ln(BF)AB < 6 is considered “positive”, 6 < 2ln(BF)AB < 10 is considered “strong”, 10 <

2ln(BF)AB is considered “very strong”. Final species delimitation decisions were made

following the level of “very strong” support, or 10 < 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

13

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. 2011) to multilocus concatenated species

trees (e.g., Burbrink et al. 2011) to multilocus coalescent species trees (e.g., Niemiller et al.

2011; Ramiro et al. 2012; Martínez-Solano et al. 2012; Camargo et al. 2012). Leaché and

Fujita (2010) 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. 3075). 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 τ0 were set to G (1, 2000), 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 0, analyses were repeated with ε=2, 5, 10, or 20. For algorithm 1, analyses were

repeated with α=1, 1.5 or 2 and m=0.5, 1, 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 2008; Ence and Carstens

2011; Camargo et al. 2012). 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”

14

(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 1961; Wiley 1978; Frost and

Hillis 1990) 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 0.65% changes per million years (Macey et al. 1998) was

applied to ND2 (a widely employed standard in dating squamate phylogenies; Avila-Pires et

al. 2012; Campbell-Staton et al. 2012; Werneck et al. 2012) 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.

15

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 411 to 1026 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 1 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 2006; Carstens and Knowles 2007; Knowles and Carstens 2007; Edwards

2009). 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

16

prevalent in any nuclear gene trees, some general subspecific groupings were present. Within

the BZW1 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 RP40 and SELT gene trees (Figures 7B

and 7D in Appendix C), likely due to low variability within these loci (Table 1).

The inferred mitochondrial ND2 gene tree was highly concordant with previous

mitochondrial studies of the C. viridis complex (Pook et al. 2000; Ashton and de Queiroz

2001; Douglas et al. 2002), 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.

17

oreganus B) and Oregon and Washington individuals (C. o. oreganus A) (see Figure 1 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 = 91), 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 (1): Traditional Subspecies as Species

With one exception (UT_nunt_02), 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_02 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 H14: 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 12 speciation hypotheses generated by iteratively collapsing nodes on the

18

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 ≥ 70. Asterisks at nodes indicate posterior probability ≥ 0.95. 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).

19

Table 2. Hypotheses of Species Delimitation Generated using Methods (1) and (2)

Hypothesis Nodes Collapsedf Number of Putative Species

H1b A 1

H2c B 2

H3a C, D 3

H4 C 4 H5 D, E, F 4 H6 D, E 5 H7 D, F 5 H8 E, F 5 H9 D 6 H10 E 6 H11 F 6 H12 None 7 H13

d see text 7 H14

e see text 8 Note: For Hypotheses H1 through H11, 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. acurrent taxonomy, after Crother et al. 2012 btaxonomic recommendation of Pook et al. 2000 ctaxonomic recommendation of Ashton and de Queiroz 2001 dtaxonomic recommendation of Douglas et al. 2002 ehistoric subspecies treated as putative species fsee 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 (H12) 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 H1 through H11, nodes to be collapsed are indicated in Table 2. For

example, in H1, 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. (2012). H13 describes the taxonomic recommendations of Douglas et al.

(2002), subsuming C. v. nuntius within C. v. viridis while treating all other sampled

subspecies as species. H14 denotes the hypothesis generated by method (1).

20

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 10 in Appendix C, while

inferred species trees based on nuclear intron data only are shown in Figure 11 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 10 in Appendix C) and analyses utilizing only nuclear data (Figure 11 in Appendix

C) was not present. As a result, subsequent discussion will focus on species tree analyses

utilizing all the data (Figure 10 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 10N in Appendix C depicts the species tree

inferred by treating historic subspecies as species, as per method (1). Under this hypothesis

(H14), a strongly supported C. concolor + C. abyssus + C. lutosus clade was recovered (PP =

1.0). Crotalus oreganus was weakly recovered as sister to C. helleri (PP = 0.82). A C.

oreganus + C. helleri + C. concolor + C. abyssus + C. lutosus clade was recovered with

21

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

H1 -9653.0 527.0++ -9665.5 534.4++ -8736.5* - H2 -9608.4 437.8++ -9621.7 446.8++ -8737.5 2 H3 -9532.4 285.8++ -9542.1 287.6++ -8743.2 13.4++ H4 -9528.2 277.4++ -9538.8 281.0++ -8745.7 18.4++ H5 -9483.3 187.6++ -9494.3 192.0++ -8767.5 62.0++ H6 -9412.5 46.0++ -9421.5 46.4++ -8763.1 53.2++ H7 -9454.8 130.6++ -9465.1 133.6++ -8766.3 59.6++ H8 -9494.3 209.6++ -9504.7 212.8++ -8766.7 60.4++ H9 -9389.5* - -9398.3* - -8759.8 46.6++ H10 -9418.9 58.8++ -9429.3 62.0++ -8765.5 58.0++ H11 -9454.1 129.2++ -9463.6 130.6++ -8765.7 58.4++ H12 -9402.0 25.0++ -9411.7 26.8++ -8763.3 53.6++ H13 -9404.4 29.8++ -9413.5 30.4++ -8764.9 56.8++ H14 -9402.0 25.0++ -9412.7 28.8++ -8765.5 58.0++ 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

H1 -6026.3 392.8++ -6037.2 400.4++ -5338.1* - H2 -5984.7 309.6++ -5993.8 313.6++ -5342.6 9+ H3 -5949.8 239.8++ -5958.2 242.4++ -5346.2 16.2++ H4 -5948.4 237.0++ -5956.0 238.0++ -5346.6 17.0++ H5 -5919.3 178.8++ -5928.2 182.4++ -5360.3 44.4++ H6 -5849.1 38.4++ -5856.1 38.2++ -5360.2 44.2++ H7 -5879.2 98.6++ -5886.5 99.0++ -5357.0 37.8++ H8 -5903.8 147.8++ -5911.9 149.8++ -5360.5 44.8++ H9 -5835.7 11.6++ -5842.3 10.6++ -5360.0 43.8++ H10 -5851.3 42.8++ -5858.9 43.8++ -5362.0 47.8++ H11 -5907.8 155.8++ -5915.9 157.8++ -5358.4 40.6++ H12 -5829.9* - -5837* - -5359.9 43.6++ H13 -5847.0 34.2++ -5854.9 35.8++ -5361.6 47.0++ H14 -5844.0 28.2++ -5851.5 29.0++ -5360.4 44.6++

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 10 for Nuclear and Mitochondrial Data, Figure 11 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] < 10, (Kass and Raftery 1995) ++very strong support for best-fitting hypothesis (10 < 2ln[BF], Kass and Raftery 1995)

22

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proposed by Douglas et al. (2002; hypothesis H13). This phylogeny was fully concordant

with the species tree inferred under hypothesis H14.

In all analyses where C. viridis and C. cerberus were treated as separate species (H3-

H14, Table 2, Figures 10C-10N in Appendix C), these taxa were recovered as sister lineages.

However, this relationship was weakly supported in all cases (0.48 < PP < 0.55). 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; H4, H8, H10-H12, Table 2, Figures 10D, 10H, 10J-

10L in Appendix C), these putative species were strongly recovered (PP = 1.0) 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 (0.94 < PP < 1.0), 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; H7, H9, H11-H12, Table 2, Figures 10G, 10I, 10K, 10L 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 (0.84<PP<0.91). 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 (H6, H9, H10, H12-H14) (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 14 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

24

3). Interestingly, even though the sHME yielded quite different results when mitochondrial

data were excluded (Table 3B, Figure 3B), hypothesis H1, which considers C. viridis sensu

lato to be one widespread species, was favored using this estimator both with and without

mitochondrial data (2.0 < 2ln[BF] < 62.0 with mitochondrial data; 9.0 < 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 H9 was optimal (i.e., best fits the

data) and Bayes factor analysis provided “very strong” support favoring this hypothesis over

all other hypotheses (25.0 < 2ln[BF] < 527.0 using PS; 26.8 < 2ln[BF] < 534.4 using SS;

Table 3A, Figure 3A). The second best fitting hypothesis to the data (H12) differed from H9

only in the splitting of C. cerberus into putative species C. cerberus A and C. cerberus B

(Table 3A, Figures 3A, 10I, and 10L). When mitochondrial data were excluded (i.e., nuclear

data only), H12 best fitted the data and there was very strong support for this speciation

hypothesis over all other hypotheses (11.6 < 2ln[BF] < 392.8 using PS; 10.6 < 2ln[BF] <

400.4 using SS; Table 3B, Figure 3B). The second best fitting hypothesis to the nuclear data

was hypothesis H9 (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

10 and 11 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

25

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 H12) (identical to Figure 10L).

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 > 0.99, with one exception: the

node leading to C. cerberus A and C. cerberus B was supported with 0.52 < PP < 0.58 (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.

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 H9, as this hypothesis of

species limits was very strongly supported over all others in the Bayes factor analyses (25.0 <

2ln[BF] < 527.0 using PS; 26.8 < 2ln[BF] < 534.4 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

outgroups

helleri

lutosus

oreganusA

oreganusB

cerberusA

cerberusB

viridis

Mitochondrial Gene TreeAoutgroups

helleri

oreganusB

lutosus

oreganusA

cerberusA

cerberusB

viridis

Multilocus Species TreeB

26

found to be sister species (though with low support; PP = 0.52), 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 = 0.62). The clock models indicated in Table 1

were applied to each locus. If the standard deviation of the uncorrelated lognormal relaxed

clock parameter was estimated to be less than 1, a strict clock could not be rejected and was

therefore applied. Using an estimated rate of evolution of 0.65% 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

2.00 Ma. Crotalus lutosus diverged from C. oreganus B + C. helleri approximately 1.41 Ma.

Most recently, C. oreganus B and C. helleri last shared a common ancestor approximately

0.72 Ma (Figure 5).

27

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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 2010; Bauer et al. 2011; Fujita

and Leaché 2011). Researchers appear to agree that an integrative approach to species

delimitation, where multiple lines of evidence support species limits, is advantageous (Bauer

et al. 2011; Fujita and Leaché 2011; Fujita et al. 2012; Camargo and Sites 2013). 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. (2011) 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 (2011) 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

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. 2002; Xu and Amason 1996; Brown et al. 2007).

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. 2012A, 2012B). 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

30

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. 2012b; Xie et al. 2011), 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. 2011; Xie et al. 2011),

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 H1), regardless of the inclusion

or exclusion of mitochondrial data. Further, the optimal or best fitting model (hypothesis H9)

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 2010), the BPP method of species

delimitation has been used to delimit species in a wide variety of systems (e.g., Leaché and

31

Fujita 2010; Leavitt et al. 2011; Niemiller et al. 2011; Zhou et al. 2012). 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

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. 2012) (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.

33

The currently recognized species C. oreganus (sensu Crother et al. 2012) was

recovered as monophyletic in both the mitochondrial gene tree and the multilocus species

tree with strong support (Figures 2, 5, and 6F, 7F, 10I 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 1). 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 2006), 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. 2009). 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.

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. 2012) is likely comprised

of two morphologically similar but genetically distinct species that are not each other’s

closest relatives. Importantly, Douglas et al. (2002) 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. (2002) 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 (2001) 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 2003). 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. 1999; Stebbins 2003; Feldman and Spicer 2006).

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. 2000;

Ashton and de Queiroz 2001; Douglas et al. 2002). 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.

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. 2000; Ashton and de Queiroz 2001; Douglas et al. 2002), 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 1997; Stebbins 2003) 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.

36

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 1840; Klauber 1997), I recommend that the northern species (C. oreganus A in this study) retain the specific epithet Crotalus oreganus (Klauber 1956). 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. 2012, as advocated by Douglas et al. 2002) 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. 1997; Stebbins 2003). 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

37

are interlaced, concomitant Bayes factor species delimitation and species tree inference

represents a significant step in the pursuit of an integrative taxonomy.

38

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45

APPENDIX A

SUPPLEMENTARY TABLES

46

Tab

le 4

. In

div

idu

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Sam

ple

d f

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133

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127

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47

Tab

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Cro

talu

s or

egan

us

oreg

anus

U

S C

A

Tuo

lum

ne

37.9

166

-119

.958

7 C

A_o

reg_

18

MV

Z

2458

43

Cro

talu

s or

egan

us

oreg

anus

U

S C

A

Tuo

lum

ne

37.9

162

-119

.656

3 C

A_o

reg_

20

MV

Z

2458

45

Cro

talu

s or

egan

us

oreg

anus

U

S C

A

Tuo

lum

ne

37.9

167

-119

.642

2 C

A_o

reg_

21

MV

Z

2458

46

Cro

talu

s or

egan

us

oreg

anus

U

S C

A

Tuo

lum

ne

37.9

172

-119

.649

3 C

A_o

reg_

22

CSU

N

1481

C

rota

lus

oreg

anus

or

egan

us

US

CA

S

an L

uis

Obi

spo

35.3

859

-119

.987

8 C

A_o

reg_

23

CSU

N

1486

C

rota

lus

oreg

anus

or

egan

us

US

CA

S

an L

uis

Obi

spo

35.2

193

-119

.897

7 O

R_o

reg_

01

EA

M

33

Cro

talu

s or

egan

us

oreg

anus

U

S O

R

Jose

phin

e 42

.298

0 -1

23.7

499

OR

_ore

g_02

E

AM

58

C

rota

lus

oreg

anus

or

egan

us

US

OR

Ja

ckso

n 42

.741

2 -1

22.7

124

OR

_ore

g_03

E

AM

78

C

rota

lus

oreg

anus

or

egan

us

US

OR

Jo

seph

ine

42.6

578

-123

.546

5 W

A_o

reg_

03

EA

M

2 C

rota

lus

oreg

anus

or

egan

us

US

WA

W

hitm

an

46.6

344

-117

.378

0 sc

utul

atus

_02

TW

R

747

Cro

talu

s sc

utul

atus

US

AZ

_nun

t_03

W

W

132

Cro

talu

s vi

ridi

s nu

ntiu

s U

S A

Z

Coc

onin

o 35

.196

0 -1

11.3

040

UT

_nun

t_01

T

WR

17

77

Cro

talu

s vi

ridi

s nu

ntiu

s U

S U

T

San

Jua

n C

o.

38.3

311

-109

.877

6 U

T_n

unt_

02

CA

S 17

0416

C

rota

lus

viri

dis

nunt

ius

US

UT

S

an J

uan

38.2

231

-109

.544

7 C

O_v

iri_

02

FH

SM

14

033

Cro

talu

s vi

ridi

s vi

ridi

s U

S C

O

Bac

a 37

.470

8 -1

02.3

414

CO

_vir

i_05

W

W

55

Cro

talu

s vi

ridi

s vi

ridi

s U

S C

O

Mof

fat

37.9

989

-105

.910

0 K

S_vi

ri_0

1 F

HS

M

1188

3 C

rota

lus

viri

dis

viri

dis

US

KS

Bar

ber

37.0

229

-98.

9527

K

S_vi

ri_0

4 F

HS

M

1574

2 C

rota

lus

viri

dis

viri

dis

US

KS

Log

an

38.8

915

-100

.970

7 K

S_vi

ri_0

5 F

HS

M

1154

8 C

rota

lus

viri

dis

viri

dis

US

KS

Sta

nton

37

.527

5 -1

01.9

985

NM

_vir

i_02

W

W

86

Cro

talu

s vi

ridi

s vi

ridi

s U

S N

M

Don

a A

na

32.4

858

-106

.723

5

48

Tab

le 5

. Pri

mer

In

form

atio

n L

ocus

S

ourc

e P

rim

ers

B

ZW

1 F

ujita

et a

l. 20

10 (

mod

ifie

d by

J. G

olde

nber

g)

Am

pF:

GA

TG

CT

TC

TG

GR

GC

AA

AR

CT

T

A

mpR

: T

GC

AT

CG

TT

TC

TA

GG

TC

YT

CY

SeqF

: G

AG

GA

GG

AA

AA

GG

GG

AA

GA

A

S

eqR

: C

TG

GT

TT

AC

CA

GA

TC

AT

CT

TT

R

P40

F

ries

en e

t al.

1999

(m

odif

ied

by D

. Lea

vitt)

F

: A

TG

TG

GT

GG

AT

GY

TG

GC

TC

GT

R:

GC

TT

CT

CA

GC

WG

CR

GC

CT

GC

R

PS

8 D

. Lea

vitt,

per

s. c

omm

. F

: C

GG

AA

AA

AG

AA

TG

CY

AA

GA

TC

AG

TA

G

R

: G

TA

GC

CA

TC

TG

CT

CG

GC

CA

CA

TT

GT

CC

S

EL

T

D. L

eavi

tt, p

ers.

com

m.

F:

GT

TA

TY

AG

CC

AG

CG

GT

AC

CC

AG

AC

AT

CC

G

R

: G

CC

TA

TT

AA

YA

CT

AG

TT

TG

AA

GA

CT

GA

CA

G

TB

P2

Kub

atko

et a

l. 20

11 (

mod

ifie

d by

J. G

olde

nber

g)

Am

pF:

CC

TT

TA

CC

AG

GA

AC

CA

CA

CC

Am

pR:

CG

AA

GG

GC

AA

TG

GT

TT

TT

AG

Seq

F:

AG

GG

TC

TT

TG

CA

AT

TT

A

S

eqR

: G

GT

TT

GG

CC

AC

CT

AA

TG

AG

A

ND

2 D

. Lea

vitt,

per

s. c

omm

. F

: A

AG

CT

YG

GC

CC

AT

AC

CC

CG

A

R

: G

TT

AA

TT

AA

TT

DT

TT

AY

GG

GA

TC

RA

GG

CC

C

49

APPENDIX C

SPECIES DESIGNATIONS APPLIED A PRIORI

FOR EACH HYPOTHESIS OF SPECIES

DELIMITATION TESTED

50

Tab

le 6

. Hyp

oth

eses

H1-

H7.

Sam

ple

ID

H1

H2

H3

H4

H5

H6

H7

AZ

_cer

b_17

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s A

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s A

Z_c

erb_

21

C. v

irid

is

C. o

rega

nus

C. c

erbe

rus

C. c

erbe

rus

A

C. c

erbe

rus

C. c

erbe

rus

C. c

erbe

rus

AZ

_cer

b_03

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s B

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s A

Z_c

erb_

14

C. v

irid

is

C. o

rega

nus

C. c

erbe

rus

C. c

erbe

rus

B

C. c

erbe

rus

C. c

erbe

rus

C. c

erbe

rus

AZ

_cer

b_15

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s B

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s A

Z_c

erb_

16

C. v

irid

is

C. o

rega

nus

C. c

erbe

rus

C. c

erbe

rus

B

C. c

erbe

rus

C. c

erbe

rus

C. c

erbe

rus

AZ

_cer

b_18

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s B

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s A

Z_c

erb_

19

C. v

irid

is

C. o

rega

nus

C. c

erbe

rus

C. c

erbe

rus

B

C. c

erbe

rus

C. c

erbe

rus

C. c

erbe

rus

AZ

_cer

b_20

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s B

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s A

Z_c

erb_

22

C. v

irid

is

C. o

rega

nus

C. c

erbe

rus

C. c

erbe

rus

B

C. c

erbe

rus

C. c

erbe

rus

C. c

erbe

rus

AZ

_cer

b_24

C

. vir

idis

C

. ore

ganu

s C

. cer

beru

s C

. cer

beru

s B

C

. cer

beru

s C

. cer

beru

s C

. cer

beru

s C

A_h

ell_

12

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. h

elle

ri

C. l

utos

us

CA

_hel

l_13

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. hel

leri

C

. lut

osus

C

A_h

ell_

16

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. h

elle

ri

C. l

utos

us

CA

_hel

l_19

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. hel

leri

C

. lut

osus

C

A_h

ell_

24

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. h

elle

ri

C. l

utos

us

MX

_hel

l_01

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. hel

leri

C

. lut

osus

M

X_h

ell_

02

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. h

elle

ri

C. l

utos

us

MX

_hel

l_03

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. hel

leri

C

. lut

osus

A

Z_a

bys_

01

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

UT

_con

c_02

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. lut

osus

C

. lut

osus

U

T_l

uto_

02

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

UT

_lut

o_03

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. lut

osus

C

. lut

osus

U

T_l

uto_

04

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

01

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

02

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

03

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

(tab

le c

onti

nu

es)

51

Tab

le 6

. (co

nti

nu

ed)

Sam

ple

ID

H1

H2

H3

H4

H5

H6

H7

ID_l

uto_

04

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

06

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

07

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_03

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. lut

osus

C

. lut

osus

N

V_l

uto_

04

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_05

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. lut

osus

C

. lut

osus

N

V_l

uto_

06

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_09

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. lut

osus

C

. lut

osus

C

. lut

osus

O

R_o

reg_

01

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

A

OR

_ore

g_02

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s A

O

R_o

reg_

03

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

A

WA

_ore

g_03

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s A

C

A_o

reg_

06

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_11

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

A_o

reg_

13

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_14

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

A_o

reg_

15

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_16

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

A_o

reg_

17

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_18

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

A_o

reg_

20

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_21

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

A_o

reg_

22

C. v

irid

is

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

C. o

rega

nus

B

CA

_ore

g_23

C

. vir

idis

C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s C

. ore

ganu

s B

C

O_v

iri_

02

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

CO

_vir

i_05

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

K

S_vi

ri_0

1 C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

K

S_vi

ri_0

4 C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

(tab

le c

onti

nu

es)

52

Tab

le 6

. (co

nti

nu

ed)

KS_

viri

_05

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

NM

_vir

i_02

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

U

T_n

unt_

01

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

UT

_nun

t_02

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

A

Z_n

unt_

03

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

53

Tab

le 7

. Hyp

oth

eses

H8-

H14

.

Sam

ple

ID

H8

H9

H10

H11

H12

H

13H

14

AZ

_cer

b_17

C

. cer

beru

s A

C

. cer

beru

s A

C

. cer

beru

s A

C

. cer

beru

s A

C

. cer

beru

s A

C

. cer

beru

s A

C

. cer

beru

s A

A

Z_c

erb_

21

C. c

erbe

rus

A

C. c

erbe

rus

A

C. c

erbe

rus

A

C. c

erbe

rus

A

C. c

erbe

rus

A

C. c

erbe

rus

A

C. c

erbe

rus

A

AZ

_cer

b_03

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

A

Z_c

erb_

14

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

AZ

_cer

b_15

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

A

Z_c

erb_

16

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

AZ

_cer

b_18

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

A

Z_c

erb_

19

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

AZ

_cer

b_20

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

A

Z_c

erb_

22

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

C. c

erbe

rus

B

AZ

_cer

b_24

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

. cer

beru

s B

C

A_h

ell_

12

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. h

elle

ri

CA

_hel

l_13

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. hel

leri

C

A_h

ell_

16

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. h

elle

ri

CA

_hel

l_19

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. hel

leri

C

A_h

ell_

24

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. h

elle

ri

MX

_hel

l_01

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. hel

leri

M

X_h

ell_

02

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. l

utos

us

C. h

elle

ri

C. h

elle

ri

C. h

elle

ri

MX

_hel

l_03

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. lut

osus

C

. hel

leri

C

. hel

leri

C

. hel

leri

A

Z_a

bys_

01

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. a

byss

us

C. a

byss

us

UT

_con

c_02

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. con

colo

r C

. con

colo

r U

T_l

uto_

02

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

UT

_lut

o_03

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

U

T_l

uto_

04

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

01

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

02

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

03

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

04

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

ID_l

uto_

06

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

(tab

le c

onti

nu

es)

54

Tab

le 7

. (co

nti

nu

ed)

ID_l

uto_

07

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_03

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

N

V_l

uto_

04

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_05

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

N

V_l

uto_

06

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

C. l

utos

us

NV

_lut

o_09

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

C

. lut

osus

O

R_o

reg_

01

C. o

rega

nus

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

OR

_ore

g_02

C

. ore

ganu

s C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

O

R_o

reg_

03

C. o

rega

nus

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

C. o

rega

nus

A

WA

_ore

g_03

C

. ore

ganu

s C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

. ore

ganu

s A

C

A_o

reg_

06

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_11

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

A_o

reg_

13

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_14

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

A_o

reg_

15

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_16

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

A_o

reg_

17

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_18

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

A_o

reg_

20

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_21

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

A_o

reg_

22

C. o

rega

nus

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

C. o

rega

nus

B

CA

_ore

g_23

C

. ore

ganu

s C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

. ore

ganu

s B

C

O_v

iri_

02

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

CO

_vir

i_05

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

K

S_vi

ri_0

1 C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

K

S_vi

ri_0

4 C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

K

S_vi

ri_0

5 C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

N

M_v

iri_

02

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

C. v

irid

is

UT

_nun

t_01

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. nun

tius

UT

_nun

t_02

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. nun

tius

AZ

_nun

t_03

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. vir

idis

C

. nun

tius

55

APPENDIX D

SUPPLEMENTARY FIGURES

56

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

57

adamanteus_01aadamanteus_01b

AZ_cerb_15aAZ_cerb_15b

AZ_cerb_18aAZ_cerb_18bAZ_cerb_20b

AZ_cerb_19aAZ_cerb_19bAZ_cerb_20aAZ_cerb_21aAZ_cerb_21bAZ_cerb_22aAZ_cerb_22b

CA_hell_12aCA_hell_12b

CA_hell_13aCA_hell_13b

CA_hell_16a

ID_luto_03aID_luto_03b

ID_luto_04aID_luto_04bID_luto_06aID_luto_06b

ID_luto_07aID_luto_07b

NN_cerb_01aAZ_cerb_25b

NV_luto_09aNV_luto_09b

UT_luto_03aUT_luto_03bUT_luto_04aUT_luto_04b

UT_nunt_02a

UT_nunt_02b

CA_hell_16b

CA_hell_19aCA_hell_19bCA_hell_24aCA_hell_24b

CA_oreg_06aCA_oreg_06b

CA_oreg_13aCA_oreg_13b

CA_oreg_14aCA_oreg_14bCA_oreg_15a

CA_oreg_15bCA_oreg_17aCA_oreg_17b

CA_oreg_18aCA_oreg_18b

CA_oreg_20aCA_oreg_20bCA_oreg_21aCA_oreg_21bCA_oreg_23aCA_oreg_23bMX_cali_01aMX_cali_01b

MX_hell_01aMX_hell_01bMX_hell_03aMX_hell_03b

MX_hell_02aMX_hell_02b

NM_viri_02aNM_viri_02b

NV_luto_03aNV_luto_03b

NV_luto_05aNV_luto_05bNV_luto_06aNV_luto_06bOR_oreg_01aOR_oreg_01bOR_oreg_02aOR_oreg_02bOR_oreg_03aOR_oreg_03b

scutulatus_02ascutulatus_02b

UT_conc_02aUT_conc_02b

UT_luto_02aUT_luto_02bWA_oreg_03aWA_oreg_03b

1.00

0.78

0.79

1.00

0.96

0.90

0.89

0.54

0.90

0.84

0.81

1.00

0.58

1.00

0.82

1.00

0.80

BZW1A

58

adamanteus_01aadamanteus_01b

AZ_abys_01aAZ_abys_01b

AZ_cerb_25aAZ_cerb_25bCA_hell_12aCA_hell_12bCA_hell_13aCA_hell_13b

CA_hell_16aCA_hell_16b

CA_hell_19aCA_hell_19bCA_hell_24aCA_hell_24bCA_oreg_06aCA_oreg_06bCA_oreg_11aCA_oreg_11b

CA_oreg_13aCA_oreg_13b

CA_oreg_14aCA_oreg_14b

CA_oreg_23aCA_oreg_23bID_luto_01aID_luto_01bID_luto_02aID_luto_02b

ID_luto_03aID_luto_03b

ID_luto_04aID_luto_04bNV_luto_03b

ID_luto_06aID_luto_06b

MX_hell_01aMX_hell_01b

MX_hell_02aMX_hell_02b

MX_hell_03aMX_hell_03b

NV_luto_03aNV_luto_05aNV_luto_05bNV_luto_06aNV_luto_06b

scutulatus_02ascutulatus_02b

UT_luto_02aUT_luto_02b

UT_nunt_01aUT_nunt_01b

0.99

0.69

0.56

0.66

0.82

1.00

0.98

0.59

RP40B

59

adamanteus_01aadamanteus_01b

AZ_abys_01aAZ_abys_01b

AZ_cerb_03aAZ_cerb_03bAZ_cerb_24b

AZ_cerb_14aAZ_cerb_14b

AZ_cerb_15aAZ_cerb_15b

AZ_cerb_21aAZ_cerb_21b

AZ_cerb_24aAZ_cerb_16aAZ_cerb_20a

AZ_cerb_22aAZ_cerb_25b

AZ_cerb_25a

AZ_cerb_16bAZ_cerb_20bAZ_cerb_22bCA_hell_24aCA_hell_24bCA_oreg_23a

MX_hell_02aMX_hell_02b

AZ_cerb_17aAZ_cerb_17b

AZ_cerb_18aAZ_cerb_18b

AZ_cerb_19aAZ_cerb_19b

AZ_nunt_03aAZ_nunt_03bCO_viri_05aCO_viri_05bID_luto_01aID_luto_01bID_luto_04bKS_viri_01aKS_viri_01bKS_viri_04aKS_viri_04bNV_luto_04bNV_luto_09bUT_conc_02a

UT_conc_02b

CA_hell_12aMX_hell_01aMX_hell_03a

CA_hell_12bCA_hell_13aCA_hell_13bCA_hell_16aCA_hell_16bCA_hell_19a

CA_hell_19bCA_oreg_11bCA_oreg_13bMX_hell_01b

CA_oreg_06aCA_oreg_06b

CA_oreg_11aCA_oreg_13aCA_oreg_14a

CA_oreg_14bOR_oreg_01a

OR_oreg_01b

OR_oreg_02aOR_oreg_02b

OR_oreg_03aOR_oreg_03b

WA_oreg_03aWA_oreg_03b

CA_oreg_15aCA_oreg_15bCA_oreg_17aCA_oreg_17bCA_oreg_18aCA_oreg_18bCA_oreg_20aCA_oreg_20bCA_oreg_21aCA_oreg_21b

CA_oreg_22aCA_oreg_22b

CA_oreg_23bID_luto_02aID_luto_02bID_luto_03aID_luto_03bID_luto_04aID_luto_06a

ID_luto_06bID_luto_07aID_luto_07bMX_cali_01a

MX_cali_01bMX_hell_03b

NM_viri_02aNM_viri_02b

NV_luto_03aNV_luto_03b

NV_luto_04aNV_luto_05aNV_luto_05b

NV_luto_06aNV_luto_06bNV_luto_09ascutulatus_02ascutulatus_02bUT_luto_03aUT_luto_03bUT_luto_04aUT_luto_04b

UT_nunt_01aUT_nunt_01b

1.00

0.66

0.54

0.570.92

0.830.98

0.99 0.96

0.58

0.700.72

0.71

0.84

0.78

0.66

0.880.55

0.77

0.56

1.000.750.84

RPS8C

60

adamanteus_01a

adamanteus_01b

AZ_cerb_03a

AZ_cerb_03b

AZ_cerb_25a

AZ_cerb_25b

CA_hell_12a

CA_hell_12b

CA_hell_13a

CA_hell_13b

CA_hell_16a

CA_hell_16b

CA_hell_19a

CA_hell_19b

CA_hell_24a

CA_hell_24b

CA_oreg_06a

CA_oreg_06b

ID_luto_02a

ID_luto_02b

ID_luto_04a

ID_luto_04b

ID_luto_06a

ID_luto_06b

ID_luto_07a

ID_luto_07b

MX_hell_01a

MX_hell_01b

MX_hell_03a

MX_hell_03b

NV_luto_05a

NV_luto_05b

scutulatus_02a

scutulatus_02b

UT_conc_02a

UT_conc_02b

UT_luto_02a

UT_luto_02b

UT_nunt_01a

UT_nunt_01b

UT_nunt_02a

UT_nunt_02b

0.98

0.82

SELTD

61

adamanteus_01aadamanteus_01b

AZ_cerb_03aAZ_cerb_03bUT_nunt_01a

UT_nunt_01bKS_viri_04aKS_viri_04b

AZ_cerb_25aAZ_cerb_25b

CA_hell_19aCA_hell_19b

CA_oreg_06aCA_oreg_06bID_luto_02aID_luto_02bID_luto_06aID_luto_06bID_luto_07aID_luto_07b

NV_luto_03aNV_luto_03b

NV_luto_05aNV_luto_05bNV_luto_06aNV_luto_06bUT_luto_02aUT_luto_02b

CA_oreg_21bCA_hell_13aCA_hell_13b

scutulatus_02ascutulatus_02b

CA_hell_12aCA_hell_12bCA_hell_24b

CA_hell_24aCA_oreg_11aCA_oreg_11b

CA_oreg_13bCA_oreg_15a

CA_oreg_21aMX_hell_02aMX_hell_02bMX_hell_03a

MX_hell_03b

CA_oreg_13aCA_oreg_15b

CA_oreg_17aCA_oreg_17b

CA_oreg_23aCA_oreg_23b

1.00

1.00

0.89

0.67

0.61

0.55

0.590.78

0.99

0.89

0.62

0.94

1.00

TBP2E

62

adamanteus_scutulatus_02

AZ_abys_01UT_luto_02

UT_luto_03UT_luto_04

ID_luto_01ID_luto_02

ID_luto_03ID_luto_04

ID_luto_06ID_luto_07NV_luto_03NV_luto_04NV_luto_05NV_luto_06

NV_luto_09UT_conc_02

CA_hell_12CA_hell_13

CA_hell_19CA_hell_24

MX_hell_01

MX_hell_02MX_hell_03

CA_hell_16

CA_oreg_06

CA_oreg_11CA_oreg_13

CA_oreg_14MX_cali_01CA_oreg_15

CA_oreg_16CA_oreg_22CA_oreg_23

CA_oreg_17CA_oreg_18CA_oreg_20CA_oreg_21

OR_oreg_01OR_oreg_02

OR_oreg_03WA_oreg_03

AZ_cerb_03

AZ_cerb_15AZ_cerb_18AZ_cerb_19

AZ_cerb_14

AZ_cerb_16AZ_cerb_20

AZ_cerb_17

AZ_cerb_21AZ_cerb_22

AZ_cerb_25UT_nunt_02AZ_nunt_03

CO_viri_02KS_viri_01

CO_viri_05

KS_viri_04KS_viri_05

UT_nunt_01NM_viri_02

0.79

1.00

1.00

0.89

1.00

0.99

0.73

1.00

1.00

0.61

1.001.00

0.88

0.890.95

0.72

1.00

0.78

0.81

0.93

0.97

0.94

0.85

0.90

1.00

0.62

1.00

1.000.93

0.771.00

1.001.00

1.00

0.790.64

0.84

1.000.88

0.830.97

ND2F

63

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

64

adamanteus_01a

scutulatus_02bscutulatus_02a

NV_luto_03aNV_luto_03b

UT_conc_02bUT_conc_02a

NV_luto_05aNV_luto_06aNV_luto_06b

MX_hell_02aAZ_cerb_19bAZ_cerb_21a

AZ_cerb_22bCA_oreg_17a

CA_oreg_21b

AZ_cerb_19aAZ_cerb_22a

AZ_cerb_20aUT_luto_02bUT_luto_02aCA_oreg_17b

CA_oreg_20bAZ_cerb_21bCA_oreg_20aCA_oreg_15a

AZ_cerb_18aAZ_cerb_18bAZ_cerb_20b

MX_hell_01aMX_hell_01bMX_hell_03aMX_hell_03b

NM_viri_02aNM_viri_02b

CA_oreg_15bOR_oreg_01bOR_oreg_01a

AZ_cerb_15bAZ_cerb_15a

CA_hell_19bCA_hell_19aCA_hell_24aCA_hell_24b

CA_hell_12aCA_hell_12b

OR_oreg_03b

WA_oreg_03bWA_oreg_03aCA_hell_13aCA_hell_13b

OR_oreg_02aOR_oreg_02b

OR_oreg_03a

CA_oreg_06aCA_oreg_14bCA_oreg_14aCA_oreg_21a

CA_oreg_06bCA_oreg_23aCA_oreg_23b

AZ_cerb_25bAZ_cerb_25a

ID_luto_03bID_luto_03a

UT_nunt_02a

NV_luto_09aNV_luto_09b

ID_luto_07bID_luto_07a

UT_luto_03aUT_luto_04a

ID_luto_06aUT_luto_04bUT_luto_03bID_luto_06b

ID_luto_04bID_luto_04aUT_nunt_02b

CA_hell_16aCA_hell_16b

CA_oreg_13bCA_oreg_13a

CA_oreg_18aCA_oreg_18b

MX_hell_02b

NV_luto_05b

adamanteus_01b

10099

3972

5075

12

1

1

1

6

0

7100000000015

2

0

0

7430

06375

79

109 89

374

9397

94

0

94

4

10102185

0

38811

0310

51

34

98

57

3957

77

313

80

84

9795

9

410

10

95

39

70

97

BZW1A

65

adamanteus_01a

scutulatus_02bscutulatus_02a

CA_oreg_14aCA_oreg_14b

MX_hell_01bMX_hell_01a

AZ_abys_01aAZ_abys_01b

CA_oreg_11b

CA_hell_19aCA_hell_12aCA_hell_12b

CA_hell_16aCA_hell_16b

ID_luto_06aID_luto_06b

CA_oreg_23a

MX_hell_03aMX_hell_03b

CA_oreg_23bCA_hell_19bNV_luto_05bNV_luto_05a

CA_hell_13aCA_hell_13b

CA_oreg_11a

MX_hell_02bID_luto_02b

ID_luto_01bMX_hell_02a

ID_luto_01aID_luto_02aCA_hell_24bCA_hell_24a

NV_luto_03a

NV_luto_06aNV_luto_06b

UT_nunt_01bUT_nunt_01a

NV_luto_03b

ID_luto_04aID_luto_04b

ID_luto_03aID_luto_03b

CA_oreg_13aCA_oreg_13b

UT_luto_02bUT_luto_02a

AZ_cerb_25aAZ_cerb_25bCA_oreg_06aCA_oreg_06b

adamanteus_01b

97

96

17

1

78

22

88

7

3

77

26453521

1

0

1

11

137

73

551121

28

3

0

1

05

5

23

9

2154

89

8

25

5490

83

5

136

91

2378

56

RP40B

66

adamanteus_01a

MX_hell_03b

CA_hell_12aMX_hell_01aMX_hell_03a

CA_oreg_11b

CA_oreg_13bCA_hell_19b

MX_hell_01bCA_oreg_06b

scutulatus_02bMX_hell_02b

CA_hell_24bCA_hell_24a

AZ_cerb_16b

AZ_cerb_22bAZ_cerb_20b

MX_hell_02aCA_oreg_23ascutulatus_02a

CA_hell_12bCA_oreg_20b

CA_hell_13aCA_hell_13b

CA_oreg_22bCA_oreg_22a

CA_oreg_21aCA_oreg_21bAZ_cerb_19b

UT_nunt_01bUT_nunt_01a

ID_luto_06bNV_luto_05a

AZ_abys_01bAZ_abys_01a

NV_luto_04a

NV_luto_09aNV_luto_03a

ID_luto_07aCA_oreg_15a

CA_oreg_14aID_luto_02a

AZ_cerb_14bAZ_cerb_14aUT_luto_03bID_luto_03aCA_oreg_13aAZ_cerb_19aUT_luto_03a

NV_luto_06a

AZ_cerb_17bAZ_cerb_17a

AZ_cerb_03bAZ_cerb_03aAZ_cerb_24b

AZ_cerb_24aAZ_cerb_21a

AZ_cerb_21bAZ_cerb_15bAZ_cerb_15a

AZ_cerb_25b

AZ_cerb_20aAZ_cerb_16a

AZ_cerb_22a

AZ_cerb_25a

AZ_cerb_18aAZ_cerb_18b

CA_oreg_23b

ID_luto_03b

ID_luto_02bID_luto_06a

ID_luto_04aID_luto_07bCA_oreg_11aUT_luto_04aUT_luto_04b

CA_hell_16aUT_conc_02b

UT_conc_02aID_luto_04b

ID_luto_01aNV_luto_04bID_luto_01b

NV_luto_09b

AZ_nunt_03bAZ_nunt_03a

KS_viri_01bKS_viri_04b

KS_viri_04aKS_viri_01a

CO_viri_05aCO_viri_05b

CA_oreg_20a

CA_oreg_15b

CA_oreg_17bCA_oreg_17a

CA_oreg_06a

NM_viri_02bNM_viri_02a

OR_oreg_01aCA_oreg_14b

WA_oreg_03bWA_oreg_03a

OR_oreg_03aOR_oreg_03bOR_oreg_02b

OR_oreg_02a

OR_oreg_01b

CA_hell_16b

NV_luto_05b

CA_oreg_18bCA_oreg_18a

CA_hell_19a

NV_luto_03bNV_luto_06b

adamanteus_01b

99

0

0

0

0

0

0

443850

0

19415237

1

7

00

076

0063

1

132669

97

000

0

94

0

0

0

319

1894

0

0

0

0

0

10

93

13316

12211

8

0 83

3

1234

3684

300 64

2442

75 87

87

300

02827

79

0

21

272439

147

103047

38671116

22

0

0

03731

173

164

53585

1178

45

03

1657

21

RPS8C

67

adamanteus_01a

scutulatus_02a

scutulatus_02b

MX_hell_01b

MX_hell_01a

MX_hell_03a

MX_hell_03b

CA_hell_19a

CA_hell_19b

UT_luto_02a

ID_luto_07b

NV_luto_05a

ID_luto_04a

UT_conc_02b

ID_luto_06b

ID_luto_02b

CA_hell_24a

ID_luto_04b

CA_hell_24a

AZ_cerb_25b

AZ_cerb_25a

ID_luto_06a

UT_conc_02a

ID_luto_02a

CA_hell_24b

NV_luto_05b

ID_luto_07a

CA_hell_24b

CA_oreg_06a

CA_oreg_06b

AZ_cerb_03b

AZ_cerb_03a

CA_hell_12a

CA_hell_12b

CA_hell_13a

UT_nunt_02b

UT_nunt_02a

UT_luto_02b

UT_nunt_01b

UT_nunt_01a

CA_hell_13b

adamanteus_01b

100

98

95

25

1

8

18

9

97

20

4

13

0

0

0

0

0

0

0

0

06

13

27

0

00

24

9

1

4

1

0

0

82

6

1242

SELTD

68

adamanteus_01a

CA_oreg_23aCA_oreg_23b

CA_oreg_15bCA_oreg_21a

CA_hell_12aCA_hell_12bCA_hell_24b

MX_hell_03b

CA_oreg_13bCA_hell_24aCA_oreg_15aCA_oreg_11aCA_oreg_11b

MX_hell_03a

MX_hell_02aMX_hell_02b

UT_nunt_01a

AZ_cerb_03aAZ_cerb_03b

UT_nunt_01bKS_viri_04bKS_viri_04a

CA_oreg_17bCA_oreg_17a

CA_oreg_21b

CA_hell_19aUT_luto_02aAZ_cerb_25a

CA_oreg_06bCA_oreg_06a

ID_luto_02b

NV_luto_03aCA_hell_19b

ID_luto_06b

NV_luto_05aNV_luto_06bAZ_cerb_25b

ID_luto_07aID_luto_07bNV_luto_06aNV_luto_05b

ID_luto_06aUT_luto_02b

NV_luto_03bID_luto_02a

CA_hell_13aCA_hell_13b

scutulatus_02ascutulatus_02b

CA_oreg_13a

adamanteus_01b

100

12

34

97

20

41

9

209437

8

1

3020

81

3191

99

654793

78

27

12

98

61

25

52

65

595129

414848

61

43

8

3

06

181

11

6786

99

TBP2E

69

adamanteus_01scutulatus_02

AZ_abys_01UT_luto_02

UT_luto_03UT_luto_04

ID_luto_01ID_luto_02

ID_luto_03ID_luto_04

ID_luto_06ID_luto_07NV_luto_03NV_luto_04NV_luto_05NV_luto_06

NV_luto_09UT_conc_02

CA_hell_12CA_hell_13

CA_hell_19CA_hell_24

MX_hell_01

MX_hell_02MX_hell_03

CA_hell_16

CA_oreg_06

CA_oreg_11CA_oreg_13

CA_oreg_14MX_cali_01CA_oreg_15

CA_oreg_16CA_oreg_22CA_oreg_23

CA_oreg_17CA_oreg_18CA_oreg_20CA_oreg_21

OR_oreg_01OR_oreg_02

OR_oreg_03WA_oreg_03

AZ_cerb_03

AZ_cerb_15AZ_cerb_18AZ_cerb_19

AZ_cerb_14

AZ_cerb_16AZ_cerb_20

AZ_cerb_17

AZ_cerb_21AZ_cerb_22

AZ_cerb_25UT_nunt_02AZ_nunt_03

CO_viri_02KS_viri_01

CO_viri_05

KS_viri_04KS_viri_05

UT_nunt_01NM_viri_02

0.79

1.00

1.00

0.89

1.00

0.99

0.73

1.00

1.00

0.61

1.001.00

0.88

0.890.95

0.72

1.00

0.78

0.81

0.93

0.97

0.94

0.85

0.90

1.00

0.62

1.00

1.000.93

0.771.00

1.001.00

1.00

0.790.64

0.84

1.000.88

0.830.97

ND2F

70

Figure 8. Morphology of individual UT_nunt_02. A. Top left: Crotalus viridis nuntius paratype; bottom left: C. v. nuntius paratype; middle: UT_nunt_02; 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_02, now re-assigned from C. o. concolor to C. v. nuntius.

E D

C B

A

71

F

igu

re 9

. Res

ult

s of

PO

FA

D a

nal

ysis

(i.e

., m

eth

od [

3]).

72

Figure 10. Species trees inferred using *BEAST under each hypothesis of species delimitation with all data included. Trees were inferred using five nuclear genes and one mitochondrial gene. Values at nodes represent posterior probability.

73

adamanteus

scutulatus

viridis

1

0.98

Hypothesis H1Aadamanteus

oreganus

scutulatus

viridis

1

0.98

0.98

Hypothesis H2B

adamanteus

cerberus

oreganus

scutulatus

viridis

1

0.98

0.98

0.55

Hypothesis H3Cadamanteus

cerberusA

cerberusB

oreganus

scutulatus

viridis

1

0.97

0.98

0.54

1

Hypothesis H4D

74

adamanteus

cerberus

lutosus

oreganus

scutulatus

viridis

1

0.98

0.99

0.48

0.99

Hypothesis H5Eadamanteus

cerberus

helleri

lutosus

oreganus

scutulatus

viridis

1

0.97

0.99

0.52

0.98

0.78

Hypothesis H6F

adamanteus

cerberus

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.97

0.97

0.96

0.84

0.5

Hypothesis H7Gadamanteus

cerberusA

cerberusB

lutosus

oreganus

scutulatus

viridis

1

0.97

0.98

1

0.53

1

Hypothesis H8H

75

adamanteus

cerberus

helleri

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.97

0.98

0.95

0.62

0.87

0.52

Hypothesis H9Iadamanteus

cerberusA

cerberusB

helleri

lutosus

oreganus

scutulatus

viridis1

0.96

0.98

1

0.75

0.54

1

Hypothesis H10J

adamanteus

cerberusA

cerberusB

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.96

0.98

0.52

1

0.94

0.87

Hypothesis H11Kadamanteus

cerberusA

cerberusB

helleri

lutosus

oreganusA

oreganusB

scutulatus

viridis1

0.97

0.98

0.95

0.63

0.91

0.55

1

Hypothesis H12L

76

abyssus

adamanteus

cerberus

concolor

helleri

lutosus

oreganus

scutulatus

viridis1

0.95

0.98

0.52

0.97

0.82

1

0.78

Hypothesis H13M

abyssus

adamanteus

cerberus

concolor

helleri

lutosus

nuntius

oreganus

scutulatus

viridis

1

0.96

0.99

0.51

1

0.96

1

0.77

0.82

Hypothesis H14N

77

Figure 11. Species trees inferred using *BEAST under each hypothesis of species delimitation without mitochondrial data. Trees were inferred using five nuclear genes. Values at nodes represent posterior probability.

78

adamanteus

scutulatus

viridis

1

0.8

Hypothesis H1Aadamanteus

oreganus

scutulatus

viridis

1

0.86

0.96

Hypothesis H2B

adamanteus

cerberus

oreganus

scutulatus

viridis

1

0.85

0.88

0.66

Hypothesis H3Cadamanteus

cerberusA

cerberusB

oreganus

scutulatus

viridis

1

0.83

0.86

0.69

0.94

Hypothesis H4D

79

adamanteus

cerberus

lutosus

oreganus

scutulatus

viridis

1

0.84

0.91

0.99

0.66

Hypothesis H5Eadamanteus

cerberus

helleri

lutosus

oreganus

scutulatus

viridis

1

0.75

0.96

0.56

0.37

0.98

Hypothesis H6F

adamanteus

cerberus

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.84

0.71

0.4

0.97

0.65

Hypothesis H7Gadamanteus

cerberusA

cerberusB

lutosus

oreganus

scutulatus

viridis

1

0.84

0.88

0.68

0.93

1

Hypothesis H8H

80

adamanteus

cerberus

helleri

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.76

0.88

0.42

0.43

0.43

0.96

Hypothesis H9Iadamanteus

cerberusA

cerberusB

helleri

lutosus

oreganus

scutulatus

viridis

1

0.73

0.97

0.61

0.35

0.92

0.99

Hypothesis H10J

adamanteus

cerberusA

cerberusB

helleri

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.77

0.84

0.41

0.96

0.49

0.37

0.89

Hypothesis H11Kadamanteus

cerberusA

cerberusB

helleri

lutosus

oreganusA

oreganusB

scutulatus

viridis

1

0.77

0.84

0.41

0.96

0.49

0.37

0.89

Hypothesis H12L

81

abyssus

adamanteus

cerberus

concolor

helleri

lutosus

oreganus

scutulatus

viridis

1

0.73

0.94

0.5

0.3

0.29

0.74

0.99

Hypothesis H13M

abyssus

adamanteus

cerberus

concolor

helleri

lutosus

nuntius

oreganus

scutulatus

viridis

1

0.73

0.91

0.99

0.47

0.36

0.13

0.73

0.29

Hypothesis H14N