Population Structure of Yarrow's Spiny Lizard, Sceloporus jarrovii, and its Malarial Parasite, Plasmodium chiricahuae

Item Type text; Electronic Dissertation

Authors Kaplan, Matthew Ezra

Publisher The University of Arizona.

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POPULATION STRUCTURE OF YARROW'S SPINY LIZARD, SCELOPORUS JARROVII, AND ITS MALARIAL PARASITE, PLASMODIUM CHIRICAHUAE

by

Matthew Ezra Kaplan

______

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF ECOLOGY AND EVOLUTIONARY BIOLOGY

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2011

2

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Matthew E. Kaplan entitled Population Structure of Yarrow's Spiny Lizard, Sceloporus jarrovii, and its Malarial Parasite, Plasmodium chiricahuae and recommend that it be accepted as fulfilling the dissertation requirement for the

Degree of Doctor of Philosophy

______Date: December 5, 2011 Dr. J. Bruce Walsh

______Date: December 5, 2011 Dr. Michael Sanderson

______Date: December 5, 2011 Dr. Cecil Schwalbe

______Date: December 5, 2011 Dr. Noah Whiteman

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: December 5, 2011 Dissertation Director: Dr. J. Bruce Walsh

3

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Matthew E. Kaplan 4

ACKNOWLEDGEMENTS

This dissertation is the product of what has been a long voyage that would never have been completed without the support of many people that have helped me on the way and this dissertation truly embodies the sentiment “It takes a village”. I owe an immeasurable debt to my advisor, Dr. J. Bruce Walsh, who never lost faith in my ability to complete this work. I also would like to thank my committee: Dr. Michael Sanderson, Dr. Cecil Schwalbe, and Dr. Noah Whiteman who’s input and generosity of their time made it possible for me to bring this work into final form. There were also many other EEB faculty that gave their time and inspiration over the years, including: Dr. Wayne Maddison, Dr. Michael Nachman, Dr. Michael Hammer, Dr. Dan Papaj, Dr. William Birky, Dr. Judith Bronstein, Dr. Margaret Kidwell, Dr. Lucinda McDade, and Dr.Michael Worobey. I would also like to acknowledge the late Dr. Michael Cusanovich, who served as a role model for me and greatly shaped my view of the type of scientist I hope to become. I would also like to thank William Tyler Lansden for invaluable assistance in the laboratory. Durring my initial years of graduate school I was supported by the NSF Research Training Group In The Analysis Of Biological Diversification.

I would also like to thank my family without who’s support and assistance this work would not have been completed. My parents Allan and Cynthia and my sister Susanne and brother Nevin who have all been there for me, and had patience with me durring the harder times. I would like to give special thanks to my father who spent countless hours revising drafts of this work over the last number of years and this dissertation is much better for it.

I would never have made it through to the end without the support of a collection of great friends many of whom have been from the start: Taylor Edwards, Dr. Harold Greeney, Dr. John Stireman, Dr. Caleb Gordon, Dr. Hans-Werner Herrmann, Dr. Christopher Tillquist, Dr. Tasha Altheide, Dr. Greta Binford, Dr. Caren Goldberg, Dr. Kevin Bonine, Dr. D. Patrick Abbot, Dr. Mike Singer, Dr. Maya Metni Pilkington, Dr. Susan Masta, Dr. Patrick O'Grady, Dr. Brent Burt, and Dr. Marshall Hedin, for endless hours of scientific discussion that certainly improved my thinking of the concepts and practices that have made this work what it is today. I would also like to thank all of my friends that provided emotional support and necessary distraction throughout this journey: Chance Agrella, Lynn Davis, Barbara Fransway, Ryan Sprissler, Jon Galina- Melman, Nirav Merchant, Cindi Noshay, Cori Dolan, Joe Rush, Pauline Rush, Matthew Taleck, Kristin Taleck, Gawain Douglas, Jenny Barber Douglas, Amy Conroy Raymond, Kim Powers, John King, Dan Hahn, Jen Weeks, Stacey Forsyth, Matt Johnston, Dana Brentt Johnston, Eileen Hebets, Margy Green, Andrew Bergmann, Melanie Roberts, Jamie Platto, and Arthur Beach.

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DEDICATION

This dissertation is dedicated to all of my friends and family who never lost faith in me…and to my father’s allergy to mammals, without which I might never have realized my interest in lizards.

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TABLE OF CONTENTS

LIST OF FIGURES ...... 8

LIST OF TABLES...... 9

ABSTRACT...... 10

CHAPTER 1: POPULATION STRUCTURE OF THE MOUNTAIN SPINY LIZARD

(SCELOPORUS JARROVII) IN SOUTHERN ARIZONA ...... 11

Introduction...... 11

The Study System ...... 19

Materials and Methods...... 20

Sample collection...... 20

Molecular techniques...... 23

Sequence Analysis ...... 25

Population genetic analysis...... 25

Phylogenetic analysis...... 29

Results...... 33

Discussion...... 50

Population structure of Sceloporus jarrovii ...... 50

Biogeography of Southern Arizona ...... 56

Phylogeography of Sceloporus ...... 58

CHAPTER 2: THE POPULATION STRUCTURE AND ORIGIN OF THE

SQUAMATE HAEMOSPORIDIAN PARASITE, PLASMODIUM CHIRICAHUAE.... 60

Introduction...... 60 7

TABLE OF CONTENTS (continued)

The Study System ...... 62

Materials and Methods...... 65

Sample collection...... 65

Molecular techniques...... 67

Population genetic analysis...... 68

Phylogenetic analysis...... 71

Results...... 74

Discussion...... 89

Population structure of Plasmodium chiricahuae ...... 89

Origin of Plasmodium chiricahuae ...... 99

CHAPTER 3: HOST GENETIC DIVERSITY AND THE PREVALENCE OF THE

LIZARD MALARIAL PARASITE, PLASMODIUM CHIRICAHUAE, IN THE SKY

ISLAND POPULATIONS OF SCELOPORUS JARROVII ...... 100

Introduction...... 100

Methods ...... 103

Sample collection...... 103

Molecular techniques...... 105

Statistical analysis...... 108

Results...... 109

Discussion...... 119

REFERENCES ...... 125 8

LIST OF FIGURES

Figure 1.1 Map of S. jarrovii sampling localities. ……………………………………...22 Figure 1.2 Median Joining networks of S. jarrovii haplotypes. ………………………..34 Figure 1.3 Mantel test of Log(M’) vs. Log(geographic distance). ………………….…..43 Figure 1.4 Mantel test of Log(Da) vs. Log(geographic distance). ……………….……..44 Figure 1.5 PAUP Parsimony Tree. ……………………………………………………...46 Figure 1.6 RAxML Maximum Likelihood Tree. ………...……………………………..47 Figure 1.7 MRBAYES Bayesian Tree. …………………………..……………………..48 Figure 1.8 BEAST Bayesian Tree. ……………………………………………………...49

Figure 2.1 Map of P. chiricahuae sampling localities. ……………………………….....66 Figure 2.2 Network of P. chiricahuae cytochrome b haplotypes. ………………….…...77 Figure 2.3 Phylogenetic reconstruction of P. chiricahuae cytochrome b sequences. …..84 Figure 2.4 Results of phylogenetic reconciliation. …………………………….…...…...86 Figure 2.5 Phylogenetic reconstruction of North American lizard Plasmodia.……...…..88

Figure 3.1 Map of population sampling localities with sample sizes and number of infected lizards. …………………………………………………………………..…….104 Figure 3.2 Electropherograms of multiclonal and single clone infections. ………...….107 Figure 3.3 Graphs of Lizard population genetic diversity vs. population prevalence of malaria infection. …………………………………………………………...………….112 Figure 3.4 Graphs of Malaria prevalence by lizard population by lizard sex. ………...113 Figure 3.5 Graphs of Malaria prevalence by lizard tail condition. …………………….114 Figure 3.6 Graphs of Malaria prevalence by lizard size. ……………………...……….115 Figure 3.7 Graphs of Malaria prevalence by level of lizard mite infestation. ………....116

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LIST OF TABLES

Table 1.1 Summary of Sky Island population studies. ………………………..………...15 Table 1.2 GenBank accession numbers for outgroup sequences. …………………..…...30 Table 1.3 Population haplotype distributions by locus. …………………………………35 Table 1.4 Population sequence diversity by locus. ……………………………………...38 Table 1.5 Interpopulation net sequence divergence (Da) by locus. ……………...……...39

Table 1.6 Pairwise ΦST values by locus. ……………………..………………..………...41 Table 1.7 Population geographic distances. ……………………………………...……..52

Table 2.1 Prevalence of P. chiricahuae infection in S. jarrovii populations. …………...75 Table 2.2 Distribution of P. chiricahuae haplotypes. …………………………………..78 Table 2.3 P. chiricahuae sequence diversity. …………...………………………….…..79 Table 2.4 Pairwise P. chiricahuae population Da values. ……………………….……..80

Table 2.5 P. chiricahuae population pairwise ΦST values. ……………….………...…..82

Table 3.1 Prevalence of Plasmodium chiricahuae infection in Sceloporus jarrovii populations. …………………………………………………………………………….110 Table 3.2 Frequency of multiclonal infections in populations. ………………….…….118

10

ABSTRACT

Estimates from radiocarbon-dated packrat middens indicate that the high elevation woodland communities of the Madrean Sky Islands were continuous as recently as 8,000 to 12,000 years ago. A number of population studies on a diverse collection of taxa have investigated the extent to which the Madrean Sky Island system has limited gene flow among mountain ranges. The results of several of these studies indicate that population divergences may be more ancient than the Holocene. Yarrow’s spiny lizards,

Sceloporus jarrovii, were sampled from eight sites representing seven mountain ranges.

The populations of S. jarrovii are host to the malarial parasite, Plasmodium chiricahuae.

DNA sequences from the lizards and their malarial parasites were used to reconstruct the evolutionary relationships and estimate the ages of the populations for both host and parasite. The findings of these analyses indicate that the sky island populations of S. jarrovii have been isolated for hundreds of thousands of years and did not experience gene flow during the last woodland expansion. In contrast, the results indicate that the malarial infection occurred more recently, possibly during the Holocene woodland expansion.

In addition, the prevalence of the malarial infection was compared to multiple attributes of the lizards. This analysis revealed a negative relationship between the genetic diversity of the lizard populations and the prevalence of infection. Furthermore, lizard populations with lower prevalence of infection have a lower frequency of multi- clonal infections. 11

CHAPTER 1: POPULATION STRUCTURE OF THE MOUNTAIN SPINY LIZARD

(SCELOPORUS JARROVII) IN SOUTHERN ARIZONA

Introduction

A major focus of population genetics is to understand the forces that drive and constrain population differentiation. Mutation, natural selection and genetic drift cause differentiation among-populations, while gene flow acts to homogenize them (Wright,

1931). The balance between these opposing forces determines the degree to which individual populations will become distinct.

Barriers to gene flow limit the homogenization of populations. The length of time that these barriers exist is one factor that determines their importance in the evolution of populations. Understandably if a barrier persists long enough for differentiation to accumulate, it will allow populations to establish different evolutionary trajectories. In contrast, if a barrier is present for a short period, minor amounts of population differentiation that may accumulate may be lost upon the removal of this barrier. Long- standing barriers to gene flow are the foundation of allopatric speciation.

Island systems present ideal opportunities to investigate the effects of barriers to gene flow. Darwin (1859), in his seminal work “On the Origin of Species”, was the first to explore the impact of isolated islands on species differentiation. From the population genetics perspective, however, the definition of islands has been expanded well beyond merely a landmass surrounded by water (Gillespie and Roderick, 2002). The central feature to all island systems is isolation. Islands can include habitats within a landmass 12

that are isolated from similar habitats by inhospitable terrain.

The definition of “habitat islands” must be extrapolated to be different for different species (Gillespie and Roderick, 2002). What defines a habitat island for taxa that fly will be vastly different from what defines one for taxa that must traverse the features of the landscape. The definition of islands must be relevant to the taxa that

“experience” them. This taxon-specific island concept is dependent on the dispersal capacity of the individual species in consideration. Dispersal may not only be limited by obvious physical barriers; it may also be dictated by habitat preferences or specialties that vary among seemingly similar taxa (Branch et al., 2003). To some lizard species, sandy xeric patches of scrub may be islands within a more mesic environment (Branch et al.,

2003; Clark et al., 1999; Hokit et al., 2010), while to small insects a single oak tree may constitute an island (Opler, 1974). It may even be possible to extend this concept to include islands in time for species sharing the same geographic range but appearing at different times as in the case of the 13- and 17-year cicadas (Simon, 1988).

The mountain “sky islands” of the southwestern United States are an important set of terrestrial islands in North America (Warshall, 1995). They are isolated patches of high elevation oak-pine woodland divided by a “sea” of short-grass prairie, subtropical thornscrub, and subtropical desert (Marshall, 1957). This basin and range mountain system was formed by two tectonic events the first being the Laramide uplift that began approximately 70 MYA and ended approximately 50 MYA, and the second was the

Neogene uplift that began approximately 15 MYA and ended approximately 7 MYA

(Wilson and Pitts, 2010). 13

Estimates from radiocarbon-dated packrat middens indicate that the woodland communities of the sky islands, that are currently restricted to higher elevations, had been a continuous habitat as recently as 8,000 to 12,000 years ago (Van Devender, 1977; Van

Devender and Spaulding, 1979). These findings provide evidence that the pinyon-juniper woodlands occurred at elevations from 550 to 1525 meters as recently as 11,000 years ago in areas now occupied by desert scrub.

An increasing number of population studies have investigated the extent to which the Madrean Sky island system has had an impact on a diverse group of taxa (Table 1.1).

This literature includes numerous studies of plant species: the lemon lily, Lilium parryi

(Linhart and Premoli, 1994), three species of columbine, Aquilegia (Strand et al., 1996), the Rincon Indian paintbrush, Castilleja austromontana (Slentz et al., 1999), the giant trumpet, Macromeria viridiflora (Boyd, 2002), and the alpine woodsorrel, Oxalis alpina

(Perez-Alquicira et al., 2010; Weller et al., 2007). There are also a number of studies focused on invertebrate species: the snail-eating ground beetle, Scaphinotus petersi (Ball,

1966), the talus snails, Sonorella (Miller, 1967; Weaver et al., 2010), the lizard malaria parasite, Plasmodium chiricahuae (Mahrt, 1987), the jumping spider, Habronattus pugillis (Elias et al., 2006; Maddison and McMahon, 2000; Masta, 2000; Masta and

Maddison, 2002), the grape galling insect, Daktulosphaira vitifoliae (Downie, 2004), the mycophagous fly, Drosophila innubila and its Wolbachia endosymbiont (Dyer and

Jaenike, 2005), the longhorn cactus beetle, Moneilema appressum (Smith and Farrell,

2005), and the giant water bug, Abedus herberti (Finn et al., 2007). Additional work has focused on vertebrate species: the canyon tree frog Hyla arenicolor (Barber, 1999a; 14

Barber, 1999b), the New Mexico ridge-nosed rattlesnake, Crotalus willardi obscurus

(Holycross and Douglas, 2007), the striped plateau lizard, Sceloporus virgatus

(Tennessen and Zamudio, 2008) and the Mexican jay, Aphelocoma ultramarina

(McCormack et al., 2008). 15

Table 1.1 Summary of Sky Island population studies.

16

Some of these studies found little evidence of population differentiation among mountain ranges (Downie, 2004; Linhart and Premoli, 1994; Mahrt, 1987), but the vast majority have found evidence of differentiation among different ranges (Ball, 1966;

Barber, 1999b; Boyd, 2002; Dyer and Jaenike, 2005; Elias et al., 2006; Finn et al., 2007;

Holycross and Douglas, 2007; Maddison and McMahon, 2000; Masta and Maddison,

2002; McCormack et al., 2008; Perez-Alquicira et al., 2010; Slentz et al., 1999; Smith and Farrell, 2005; Strand et al., 1996; Tennessen and Zamudio, 2008; Weaver et al.,

2010; Weller et al., 2007). Some authors have postulated that research on additional species will help to determine if a general pattern can be deduced relating to how the particular configuration of extant sky islands has affected the dispersal of different taxa

(Tennessen and Zamudio, 2008). My review of this literature has not uncovered a generalizable pattern, except that most studies have found that the sky islands facilitate genetic isolation within many species of plant, invertebrate and vertebrate.

One group of studies has observed differences among populations on the basis of north–south latitudes either in the form of clinal variation of morphological characters

(Boyd, 2002), or in the form of clades in phylogenetic reconstructions (Barber, 1999b;

Masta, 2000). A sharp north-south biogeographic boundary line in the region has been described as the “Madrean Line” (Lowe, 1992). The Madrean Line was deduced from a comparison of the presence / absence of species in seemingly similar habitat in mountain ranges in north and south of the region and, divides the Madrean Archipelago in approximately the present location of Interstate Highway I-10 in southeastern Arizona and adjacent southwestern New Mexico. A more rigorous investigation of this 17

phenomenon was performed using geographic information systems (GIS) to map and analyze species distributional patterns and found further support for this biogeographic boundary (Swann et al., 2004).

Another group of studies found an east-west division either as differentiation among populations (McCormack et al., 2008; Smith and Farrell, 2005) or in the species distribution of their taxa solely in the eastern mountain ranges of the region (Holycross and Douglas, 2007; Tennessen and Zamudio, 2008) or solely in the western mountain ranges (Masta, 2000). The GIS species distribution analysis of Swan and collaborators

(2004) also found support for an east-west biogeographic boundary in the region.

McCormack and collaborators (2008) showed that the connection of the woodland communities 8,000 to 12,000 years ago, as proposed by the packrat midden data (Van

Devender, 1977; Van Devender and Spaulding, 1979), provided an opportunity for gene flow among the populations of Mexican jays. The question should be asked if this event should be expected to have impacted all of the oak woodland taxa equally. Several studies addressing morphological and behavioral characters have questioned if the diversity that they observed among sky island populations could have evolved within such a short time span (Ball, 1966; Boyd, 2002; Elias et al., 2006; Maddison and

McMahon, 2000; Weller et al., 2007). The incongruity between the degree of divergence of these characters and the length of the time interval between when these populations were connected (supported by the packrat midden data) suggests that many taxa currently restricted to higher elevation habitat may not have expanded their ranges down into the basins, when the woodland habitat was last contiguous. Maddison and McMahon (2000) 18

questioned the reliability of the midden timeline to explain the extent of the morphological and behavioral differentiation that they observed among spiders populations.

Several studies using molecular data have been able to estimate divergence times.

The sky island populations of jumping spiders appear to have been isolated from 30,000 years to over a million years (Masta, 2000). Similarly, successive studies have estimated deep divergence times of equivalent scale in other species (Dyer and Jaenike, 2005; Finn et al., 2007; Smith and Farrell, 2005; Tennessen and Zamudio, 2008).

In sum, different taxa appear to “experience” these islands differently depending on habitat specificity and dispersal capacity. Although the most authors investigating the taxa of the region presume that gene flow during the last glacial maximum is the appropriate initial hypothesis, this is not supported by the majority of the existing data

(Table 1.1). Maddison and McMahon (2000) postulated that the alluvial plains separating mountain ranges would likely provide unsuitable habitat for the jumping spider even if the tree composition were correct. In recent work, Templeton and collaborators (2011) concluded that a variety of factors including microclimate, prey availability, and line of sight were likely causes of reduced dispersal of collared lizards between habitat patches in the Missouri Ozarks. It can be concluded from the groups of studies displaying north- south and east-west differentiation that the geography of the region impacted the dispersal of various taxa in different ways. Also, evidence for dispersal during the last glacial maximum has been observed in the Mexican jay (McCormack et al., 2008), but many studies estimated that isolation occurred for more extended time spans (Dyer and 19

Jaenike, 2005; Finn et al., 2007; Smith and Farrell, 2005; Tennessen and Zamudio,

2008). It is possible that the generality anticipated by Tennessen and Zamudio (2008) may be more elusive than expected.

The Study System

The Arizona populations of the mountain spiny lizard, Sceloporus jarrovii, are currently restricted to the sky islands (Smith, 1939). This species is a saxicolous lizard occurring in southeastern Arizona, southwestern New Mexico, and Mexico between elevations of 1370-3550 meters (Jones and Lovich, 2009; Stebbins, 2003). It is a highly territorial species, with both males and females holding and defending territories from

May to October when they then abandon their territories and move to local hibernacula for the winter until they emerge in the spring (Ruby, 1978). Both sexes defend territories for the entire season of activity and their dispersal is therefore greatly restricted. Long- term studies of populations of S. jarrovii (Ballinger, 1973; Ruby, 1986) have estimated migration rates among rock outcrops to be extremely low. The rocky habitat that the lizards occupy is distributed in disparate patches that further restrict successful dispersal.

In fact, Ruby (1986) hypothesized that the risk to a lizard of searching for a suitable location outside of their local area may outweigh any benefits of a move.

The extent of the isolation between the mountain populations of S. jarrovii has been examined in the past using variation in allozymes (Frankel and Middendorf III,

1991). The results of this investigation showed some evidence supporting isolation of the different mountain ranges, but due to the small number of loci examined and the low levels of variation at these loci, a clear evaluation of the population structure was not 20

possible.

The study described in this chapter tests the hypothesis that the populations of S. jarrovii have been isolated long enough due to Sky Island geography to become genetically distinct. This hypothesis is evaluated by measuring the degree of differentiation among the Arizona populations of S. jarrovii using both mitochondrial and nuclear DNA sequence data. This investigation also evaluates the impact that the geographic barriers of the region and/or the vicariant events associated with the receding oak woodland forests over the last 10K years, have had on the gene flow of S. jarrovii.

Estimation of the population divergence times will be used to test if the Holocene woodland expansion that occurred 8,000 to 12,000 years ago provided an opportunity for gene flow.

Materials and Methods

Sample collection

Adult lizards were sampled from eight populations representing seven mountain ranges (Figure 1.1). Lizards were collected using a noosing pole within approximately

300-m2 areas between 1600–2500 meters in elevation. Blood samples were collected from each lizard using a heparinized capillary tube to draw 50-200 µl of blood from the post-orbital sinus. The blood was stored in 1.5-ml tubes with 50 µl of 1X STE buffer (10 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA) prior to DNA isolation. Blood samples were stored on ice in the field, and then transferred to –80 oC until DNA isolation. The 21

tip of the first toe on the rear right foot was clipped to prevent resampling of individuals on repeated visits to the same sampling locality. Photographic vouchers were taken, and the lizards were released on site. 22

Figure 1.1 Map of S. jarrovii sampling localities. Map of southern Arizona mountain range outlines represents the lower limit of oak woodland habitat (adapted from Brown & Lowe 1982). Brown shading indicates the Arizona range of S. jarrovii (from Schwalbe in Jones and Lovich 2009). Labels indicate number of lizards sampled per site. 23

Molecular techniques

DNA was isolated from all blood samples by overnight digestion with proteinase

K at 55 oC, followed by phenol/chloroform extraction and an isopropanol/sodium acetate precipitation (Goldberg et al., 2003). Approximately 1.4 Kb of the lizard mitochondrial cytochrome b region was PCR-amplified using primers designed from an alignment of homologous regions from several vertebrate mitochondrial sequences (LizCB3

5’AACCTGTGATATGAAAAACC3’ and LizCB4

5’TTCTCATCTTTGGCTTACAA3’). PCR was performed in 30-µl reaction volumes with 10mM Tris pH 8.3, 50 mM KCl, 3.5 mM MgCl2, 1.5 units of Platinum Taq DNA

Polymerase (Invitrogen), 0.2 mM dNTP mix, and 20 pmol of each primer. The reactions were cycled in an MJ Research Tetrad thermal cycler with an initial 3-minute denaturation step at 94 oC, followed by 35 cycles of 94 oC for 30 seconds, 50 oC for 30 seconds, 72 oC for 90 seconds, with a final 3-minute incubation at 72 oC. The PCR products were then purified and sequenced at the University of Arizona Genetics Core

(UAGC) on an Applied Biosystems 3730 automated DNA sequencer using the amplifying primers as well as internal sequencing primers (LizCB5

5’ACCGTTGTATTCAACTATAA3’ and LizCB6 5’CGCAACCCTTACCCGATT3’).

Two nuclear genes were also examined. Approximately 900 bp of the lizard recombination activating gene-1 (RAG-1) was amplified using primers designed from an alignment of sequences from several related species (LizRAG1-1 24

5’GATAAAGTCACTGAGAAAGAGA3’ and LizRAG1-2

5’AGGAAAGCAAGGATAGCGAC3’). PCR was performed in 30-µl reaction volumes with 10mM Tris pH 8.3, 50 mM KCl, 2.5 mM MgCl2, 1.5 units of Platinum Taq DNA

Polymerase (Invitrogen), 0.2 mM dNTP mix, and 20 pmol of each primer. The reactions were cycled in an MJ Research Tetrad thermal cycler with an initial 3-minute denaturation step at 94 oC, followed by 35 cycles of 94 oC for 30 seconds, 59.2 oC for 30 seconds, 72 oC for 90 seconds, with a final 3-minute incubation at 72 oC. The PCR products were then purified and sequenced at the UAGC on an Applied Biosystems 3730 automated DNA sequencer using the reverse amplifying primer (LizRAG1-2) as well as internal sequencing primers (LizRAG1-1.1

5’GATAAAGTCACTGAGAAAGAGATT3’, LizRAG1-3

5’CTGTGATGTTTGTGGAGCCT3’, and LizRAG1-4

5’AGGCTCCACAAACATCACAG3’). Approximately 700 bp of the lizard brain- derived neurotrophic factor gene (BDNF) was amplified using primers designed from an alignment of sequences from several related species (LizBDNF1

5'GAAAGCTGCCCCAATGAAA3' and LizBDNF2

5'ACAGGAAGTGTCTATTCTTAT3'). PCR was performed in 30-µl reaction volumes with 10mM Tris pH 8.3, 50 mM KCl, 3.0 mM MgCl2, 1.5 units of Platinum Taq DNA

Polymerase (Invitrogen), 0.2 mM dNTP mix, and 20 pmol of each primer. The reactions were cycled in an MJ Research Tetrad thermal cycler with an initial 3-minute denaturation step at 94 oC, followed by 35 cycles of 94 oC for 30 seconds, 53.3 oC for 30 25

seconds, 72 oC for 90 seconds, with a final 3-minute incubation at 72 oC. The PCR products were then purified and sequenced at the UAGC on an Applied Biosystems 3730 automated DNA sequencer using the amplifying primers.

Sequence Analysis

The sequences of each locus were aligned and reviewed using the program CLC

DNA Workbench version 5.7.1 (CLC-Bio, 2009). Prior to analysis of the data, the program PHASE version 2.1.1 (Stephens et al., 2001) was used to resolve the heterozygous sites in the RAG-1 sequences to reconstruct haplotypes. PHASE uses a statistical method to infer linkage phase of polymorphic sites from a population sample of genotypic data. It was unnecessary to use PHASE on the mitochondrial sequences because mitochondrial DNA is haploid and therefore contained no heterozygous sites. It was also unnecessary to use PHASE on the BDNF sequences because they only contained a single heterozygous site that was resolved manually.

Population genetic analysis

The program DnaSP version 5.10 (Librado, 2009) was used to calculate sequence

diversity statistics as well as population pairwise Da. DnaSP calculates Da as the number of net nucleotide substitutions per site where the effect of within-population polymorphism has been subtracted (Nei, 1987). A linear regression was performed on the population sequence diversity (Theta-W) versus the hectares of habitat per mountain range (estimated using the ArcGIS software package) to explore the relationship between the available habitat and population diversity. If the population sampling was sufficient to 26

represent the diversity of the mountain ranges a positive relationship could be expected between the amount of available habitat and population size and thus population sequence diversity. Median joining networks (Bandelt et al., 1999) were created for each locus, using the program Network version 4.6 (fluxus-engineering.com) to visualize relationships among haplotypes and their relative frequencies.

The Arlequin software package version 3.5.1.2 (Excoffier and Lischer, 2010) was used to perform an analysis of molecular variance (AMOVA) to examine the population structure of the Sceloporus jarrovii genetic variation. AMOVA examines the variance in gene frequencies between different groupings while also taking into account the number of mutations between the haplotypes (Excoffier et al., 1992). AMOVA successively groups individuals hierarchically by their region and population and evaluates the proportion of the overall genetic variation that is attributable to that grouping. It examines

the structure of the genetic variation among regions (ΦCT), among-populations within the regions (ΦSC), and among individuals within the populations (ΦST) (Weir and

Cockerham, 1984). It categorizes the distribution of the genetic variation across the geographic space by examining the degree of genetic differentiation within and among the different hierarchical groupings. For the regional comparison, the populations were divided into two regions, those east and west of the San Pedro River (Figure1.1). The San

Pedro River basin is a major geographic barrier that longitudinally divides southern

Arizona, which can be expected to hinder lizard dispersal, both as an aquatic barrier and as a large patch of unsuitable habitat. The river flow will fluctuate over time, but its habitat would remain unsuitable for S. jarrovii (Stebbins, 2003). 27

Arlequin (Excoffier and Lischer, 2010) was also used to estimate the absolute number of migrants exchanged between two populations (M’) (Slatkin, 1991). Slatkin’s

1− ΦST M’ is calculated from ΦST, using the equation M'= Nm × , where N is the 2ΦST effective population size within each population, and m is the fraction of the population that is exchanged as migrants each generation€ (Slatkin, 1991). Population estimates from the mitochondrial DNA data represent the female portion of the population, as mitochondrial DNA (mtDNA) is usually maternally transmitted. Although male lizards do have mtDNA, they do not transmit it on to the next generation, and therefore evidence of their migration events and other demographic processes cannot be detected using mtDNA.

The NTSYSpc software package Version 2.2 (Rohlf, 1997) was used to perform a

Mantel test on the estimated number of migrants among populations (M’) versus the geographic distances among populations. A Mantel test measures the correlation between two dissimilarity matrices and allows one to conduct multiple comparisons using the same data point. This test was used to determine the correlation of a matrix of geographic distances among lizard populations to a matrix of the estimated number of migrants exchanged among the populations (M’). It was performed to determine the extent to which the geographic distance among populations has limited migration events (isolation by distance). Understandably, migration events are rare in species with poor dispersal, the greater the distance between populations and the fewer the migrants. If geographic distance has been a cause of a restriction of migration in Sceloporus jarrovii, there should 28

be a negative correlation between the geographic distance and the number of migrants that two areas will exchange over time. If geographic distance has limited migration then the regression of the log(M’) against the log(geographic distance) should have an inverse relationship.

The geographic relationships and the genetic similarity of the current populations of S. jarrovii can tell us about the ancestral population prior to its subdivision. If the ancestral population was panmictic, the current populations should show a random pattern of genetic differentiation with regard to their relative geographic positions.

Fragmentation of a panmictic population (vicariance) should lead to the formation of genetically identical subpopulations unless new, population-specific mutations arise or new alleles enter the population via migration events and are fixed by drift or selection.

These subpopulations should randomly accumulate genetic differences. If the ancestral population was structured by isolation by distance (IBD), the current populations should show a geographic orientation to their genetic similarity. IBD is a form of population subdivision that occurs in species with limited dispersal abilities (Avise et al., 1987). In species with IBD the mere distance across the geographic range inhibits random mating.

Patches of genetic similarity accumulate across the range of the species over generations.

With time, individuals that are more geographically distant become more genetically distinct due to the reduction in gene flow. If the range of a population experiencing IBD becomes gradually fragmented, the genetic differentiation among the subpopulations should be correlated with the geographic distances among the populations. A second

Mantel test was used to determine the correlation between the among-population genetic 29

distances (Da) with among-population geographic distances.

Phylogenetic analysis

The program PAUP* version 4.0b10 (Swofford, 1998) was used to perform a partition homogeneity incongruence-length difference (ILD) test (Farris et al., 1994) on the data from the three genes. The ILD test evaluates if the level of incongruence between multiple data sets is greater than that expected by chance, given the level of incongruence within each data set. It is necessary to perform this test prior to the concatenation of multiple data sets that may have incongruent evolutionary histories. Subsequent to this test, sequences were concatenated for use in phylogenetic analyses. These were conducted on both the concatenated data and the individual loci. Sequences for all three loci were obtained from GenBank for outgroup comparison (Table 1.2). 30

Table 1.2 GenBank accession numbers for outgroup sequences

31

PAUP* (Swofford, 1998) was used to run the MrModelTest2 block version 2.3

(Nylander, 2004) and the results were interpreted using the program MrModelTest2

(Nylander, 2004) to select the appropriate model of evolution for phylogenetic analysis.

MrModelTest2 evaluates 24 models of evolution on the data set to determine the amount of complexity to add to the model. More complex models will fit the data better, but there must be a significant increase in likelihood to justify the added complexity. The Akaike's information criterion (AIC) (Akaike, 1974) was used to select the appropriate model from the MrModelTest2 output (Posada and Buckley, 2004).

PAUP* (Swofford, 1998) was used to conduct a reconstruct the phylogeny using parsimony as an optimality criterion searches. The analyses were performed with all characters of equal weight, including the Sceloporus species listed in Table 1.2, with Uta stansburiana defined as the sole outgroup in the analysis. The analysis was conducted by performing a heuristic search of 1,000 random addition replicates. Preliminary runs were performed to estimate the length of the most parsimonious trees. The PAUP*

“chuckscore” function was used to enforce a limit of 10 trees per replicate on searches with trees of lengths greater than 781 steps. This function truncates replicates that would otherwise search through vast numbers of equally parsimonious suboptimal trees. A bootstrap analysis with 1,000 replicates of 10 random addition sequences per replicate was performed to determine the support for the inferred relationships in the parsimony analysis.

The Program RAxML-IV-HPC version 2.2.3 (Stamatakis, 2006) was used for a 32

maximum likelihood analysis, using the GTRGAMMA model of nucleotide evolution (as suggested by the MrModelTest2 results). The GTRGAMMA model is a general time- reversible model with an among site rate heterogeneity parameter. Support for inferred relationships was estimated by conducting 1,000 nonparametric bootstrap replicates.

A Bayesian analysis was performed using the program MRBAYES version 3.1.2

(Ronquist and Huelsenbeck, 2003). The GTR+I+G model of nucleotide evolution was selected, using MrModelTest2. This analysis used two parallel runs of four incrementally heated Markov chains (using default heating values), with a 25% burn-in with sampling occurring every 100 generations for 4 x 106 generations, at which point the convergence diagnostic, the average standard deviation of split frequencies, was below 0.01. The burn- in samples were discarded after the run, and the remaining samples produced a 50% majority rule consensus tree, with posterior probabilities as a measure of node support.

The program BEAST version 1.5.3 (Drummond and Rambaut, 2007) was used to conduct a Bayesian phylogenetic analyses with relaxed rates of evolution on the different branches across the phylogeny. BEAST is able to simultaneously implement probabilistic prior distributions on node ages, allow evolutionary rates to vary across the tree and account for uncertainty in the phylogeny. The nuclear loci were not included in this analysis due to their low levels of variation. For this analysis, a 25 million years age of the genus Sceloporus was used as a calibration point for the estimation of the time to most recent common ancestor (TMRCA) for the clades of S. jarrovii. The outgroup taxa

(Table 1.2) encompass the majority of the diversity of the genus (Leache, 2010). Leache and Mulcahy (2007) used the first appearance of Sceloporus in the fossil record in 33

Miocene excavations (Holman, 1995; Holman, 1970; Robinson and Vandeven.Tr, 1973;

Yatkola, 1976) to set the 25 million year age for the genus. The BEAST analysis was performed with a chain length of 2x107 using the GTR+I model of evolution with 6 gamma categories (as suggested by the MrModelTest2 results) with a relaxed clock, and a coalescent Bayesian skyline tree prior.

Results

A total of 1174 bp of the S. jarrovii mitochondrial cytochrome b region were sequenced from a sample of 94 lizards from 8 populations representing 7 mountain ranges (Figure 1.1). The DNA sequences contained 103 variable sites, giving rise to 44 haplotypes. The RAG-1 sequence data contained 838 bp from 86 individuals with 3 variable sites, giving rise to 4 haplotypes. The BDNF sequence data contained 640 bp from 90 individuals with 1 variable site, giving rise to 2 haplotypes. Figure 1.2 displays the median joining networks of each of the three loci. Table 1.3 displays the distributions of the haplotypes for the three loci among populations. The cytochrome b data contain a predominance of private haplotypes, with only haplotype 18 shared between the North

Pinaleños and South Pinaleños (Table 1.3a). The RAG-1 data (Table 1.3b) show no haplotype sharing between the eastern and western regions with the sole exception of the

Chiricahua population containing all observed haplotypes. The BDNF data (Table 1.3c) show only two haplotypes with the second present solely in the Quinlan population. 34

Figure 1.2 Median Joining networks of S. jarrovii haplotypes. Median joining networks of Sceloporus jarrovii sequences for the three loci: a) cytochrome b, b) BDNF, and c) RAG1. Branch lengths are noted on primary branches. Sampling localities are noted by color. Node diameters are proportional to the number of individuals. Population frequencies of shared haplotypes are indicated as pie charts in nodes. 35

Table 1.3 Population haplotype distributions by locus

36

Table 1.3 (continued) Population haplotype distributions by locus 37

The population per-locus sequence diversity is reported in Table 1.4. Cytochrome b has much greater diversity within all populations than the nuclear loci. The number of

net nucleotide substitutions per site among populations (Da) is shown in Table 1.5. There is a strong effect of the per-locus sequence diversity on the among population divergence.

Due to the limited number of variable sites in the two nuclear loci, there is not much of an expectation for among population divergence. 38

Table 1.4 Population sequence diversity by locus

39

Table 1.5 Interpopulation net sequence divergence (Da) by locus

40

The analysis of molecular variance (AMOVA) of the cytochrome b data revealed statistically significant levels of genetic structure of the S. jarrovii populations: 63.97%

(p=0.03) of genetic variation was distributed among regions (ΦCT), 28.91% (p<0.01) among populations within regions (ΦSC), and 7.12% (p<0.01) among individuals within the populations (ΦST). The results of the AMOVA on the RAG-1 data also showed statistically significant levels of genetic structure: the majority of the genetic variation

was distributed among regions, ΦCT 76.62% (p=0.02), the minority of the genetic variation was distributed among populations within regions, ΦSC 5.25% (p<0.01), and

18.13% (p<0.01) was distributed among individuals within the populations, ΦST. The

AMOVA on the BDNF data showed a statistically significant level of genetic structure only among individuals within the populations, ΦST = 85.00% (p<0.01).

The population pairwise ΦST values are reported per locus in Table 1.6. A high degree of population differentiation is evident in the cytochrome b data (Table 1.6a), the

RAG-1 data (Table 1.6b) primarily demonstrates the east-west divide, and the BDNF data

(Table 1.6c) shows the division of the Quinlan population from all others. 41

Table 1.6 Pairwise ΦST values by locus.

Significance at 0.05 level is indicated above the diagonal. Population pairwise ΦST values below diagonal.

42

The Mantel tests were performed only on the cytochrome b data, the other loci lacked sufficient variation for meaningful comparisons. The test of the relationship between log(M’) and the log(geographic distance) showed an inverse and statistically significant correlation (r2= 0.486, p= 0.001) between migration rate and geographic distance (Figure 1.3). The Mantel test of the relationship between genetic distance,

log(Da), and the log(geographic distance) as shown in Figure 1.4 also revealed an inverse and statistically significant correlation (r2= 0.649, p= 0.002). These findings are consistent with a model of isolation by distance (IBD) for the expansion of the lizard populations across southern Arizona. The linear regression of population sequence diversity (Theta-W) versus the hectares of habitat per mountain range displayed a positive trend, but was not statistically significant (r2= 0.188, p= 0.196). 43

Figure 1.3 Mantel test of Log(M’) vs. Log(geographic distance) Graphs of Mantel tests of cytochrome b M’ values (the absolute number of migrants exchanged between two populations). In the top graph the values have been log transformed. 44

Figure 1.4 Mantel test of Log(Da) vs. Log(geographic distance). Graphs of Mantel tests of cytochrome b Da values (Number of net nucleotide substitutions per site between populations where the within population variation has been subtracted). In the top graph the values have been log transformed. 45

The parsimony analysis reconstructed 90,900 equally parsimonious trees with a length of length 784 steps. A strict consensus of the most parsimonious trees (Figure 1.5) shows support for the monophyly of each of the mountain ranges with the exceptions of the Santa Rita, Huachuca, and Patagonia mountains, which form a single clade. The maximum likelihood reconstruction (Figure 1.6) and both of the Bayesian reconstructions

(Figures 1.7 & 1.8) were consistent with the parsimony results for all major clades with only slight variations in the resolution of individuals within clades. The primary difference between the parsimony and BEAST reconstructions and those of the RAxML and MRBAYES analyses is in the placement of the root. In the parsimony and BEAST reconstructions the root is on the branch between the eastern and western populations. By comparison, both the RAxML and MRBAYES reconstructions are rooted within the

Chiricahua population. 46

Figure 1.5 PAUP Parsimony Tree Strict consensus of 90900 most parsimonious trees (tree length 784 steps) from parsimony analysis of concatenated data. Populations labeled by color. Bootstrap percentages are labeled on branches. 47

Figure 1.6 RAxML Maximum Likelihood Tree. Best Tree from maximum likelihood analysis of concatenated data. Populations labeled by color. 48

Figure 1.7 MRBAYES Bayesian Tree. Consensus tree from phylogenetic reconstruction of concatenated data performed with Mr.Bayes. Populations labeled by color. Nodes labeled with Bayesian posterior probability values. 49

Figure 1.8 BEAST Bayesian Tree. Phylogenetic reconstruction of cytochrome b sequences performed with BEAST. Populations labeled by color. Nodes labeled with Bayesian posterior probabilities. Mean time to most recent common ancestor (TMRCA) given below node support. 95% range in parentheses. 50

The BEAST analysis estimated mean TMRCA values ranging from 225 thousand years for the Quinlan population to 334 thousand years for the Santa Rita, Huachuca, and

Patagonia mountains, clade, with eastern and western region mean TMRCA’s at 1,082 and 798 thousand years respectively (Figure 1.8). These findings are highly inconsistent with the Holocene vicariance predicted by the packrat midden data.

Discussion

Population structure of Sceloporus jarrovii

The Arizona populations of Sceloporus jarrovii show a high amount of

isolation in that the majority of the cytochrome b ΦST estimates from the AMOVA are over 0.8 (Table 1.6a). The populations have apparently been subdivided for hundreds of thousands of years (Figure 1.8). In fact, the phylogenetic analysis reveals that the lizards from nearly all of the mountain ranges form monophyletic clades (Figures 1.5 – 1.8). The degree of population structure and the inconsistency of the TMRCA estimates with the

Holocene vicariance opens the question of what could have been the cause of the isolation of the populations of S. jarrovii. A recent review of the climate fluctuations over the last 800,000 years reported that the climate of the Northern Hemisphere has gone through several abrupt cooling cycles within that time span, including one approximately

250,000 years ago (Barker et al., 2011). It is possible that this earlier climate change was responsible for the current distribution of the populations of S. jarrovii and the Holocene cooling and associated woodland expansion did not facilitate gene flow between them.

The exceptions to the monophyly of the lizards from the different mountain 51

ranges are the Santa Rita, Patagonia, and Huachuca Mountains. These three ranges appear to be acting as one “super range” in the phylogenetic reconstructions (Figures 1.5

– 1.8) and in the cytochrome b haplotype network (Figure 1.2a). They are not distinct from each other in the phylogenetic reconstructions, and together they form a monophyletic clade. This is understandable given that the three sampling localities are quite close together – only 36 to 63 km apart (Table 1.7), and the basins that divide them are as high as 1500 meters in elevation and quite possibly contain (or recently contained) suitable habitat bridges. The distance from the Huachuca sampling locality to the

Dragoon sampling locality is only 59 km (only 3 km further than the Santa Rita collection site), but there is a deeper desert basin isolating them. 52

Table 1.7 Population geographic distances in Km

53

Like an inland sea, the desert would have filled in the lowlands, first separating the lizards in the ranges with the lowest basins between them. As the climate continued to warm, more of the lizards would have become isolated as the habitat fragmented, and higher and higher elevations would have turned into inhospitable desert, isolating ranges with successively higher bridges between them. The connection of the populations in the

“super range” may be recent or possibly even incomplete. Alternatively, the connection of these populations may have occurred during the Holocene cooling. This can be addressed in the future with greater sampling from these ranges.

With the grouping of the Santa Ritas, Patagonias, and Huachucas into a “super range”, the cytochrome b haplotype network (Figure 1.2a) demonstrates that the S. jarrovii sequences form monophyletic clades for all of the ranges with numerous fixed differences (synapomorphies uniting) between them. It is clear that the populations have experienced a long-term disruption of gene flow, isolating all of the major mountain ranges. Even the low resolution provided by the RAG-1 data is consistent with a long- term division along the San Pedro river basin, with the potential retention of ancestral polymorphism in the Chiricahuas (the most diverse population as shown in Table 1.4). It is also possible that the shared haplotype between the Chiricahuas and the western ranges is evidence of male dispersal not detected by the mtDNA data. The hypothesis that the populations of S. jarrovii have been isolated long enough to become genetically distinct has been strongly supported by both the findings of the AMOVA and the phylogenetic analysis.

While the present day lizard populations are now restricted to separate sky 54

islands, they likely originated from what was once a continuous range spanning across all of southern Arizona. This ancestral population would very likely have experienced isolation by distance (IBD). This finds support from the significant relationship of the estimated number of migrants log(M’) to the log(geographic distances) among- populations (Figure 1.3) (r2= 0.486, p= 0.001). In a related study, Barber (1999) observed the effects of IBD on the population structure of the canyon tree frog, Hyla arenicolor, in the mountains of southern Arizona. Barber concluded that there had been IBD in a major clade of frogs. He calculated a matrix correlation for this group of r= -0.526, similar to the r= -0.69723 calculated here for S. jarrovii. The consistency of these findings supports the idea that the habitat dividing the ranges was restrictive to the dispersal of multiple montane taxa.

A second finding that lends further support to the IBD hypothesis is the

significant relationship between the genetic distances, log(Da), among populations and the log(geographic distances) among populations (Figure 1.4) (r2= 0.649, p= 0.002). This relationship indicates that populations closer in proximity share more recent common ancestry and thus are separated by a smaller genetic distance. These populations would have diverged more recently than populations separated by greater geographic distances.

This is inconsistent with the expectation of a panmictic ancestral population.

These two correlations – number of migrants vs. geographic distances and genetic distances vs. geographic distances - demonstrate that geography has played an important role in shaping the diversity of these lizards over their history. These lizards have very limited dispersal behavior and have very particular habitat preferences (Ballinger, 1973; 55

Ruby, 1986). This is evident in the high degree of mitochondrial differentiation between

the two Pinaleño mountain populations (ΦST=0.582, P=0.05) despite the fact that they are only 27 km apart and are separated by only a single canyon.

Avise and collaborators (1987) posited that groups of related haplotypes with spatial separation and genetic discontinuities between these groups are most likely the result of long-term barriers to gene flow. It is also likely that the genetic discontinuities are further exaggerated by the possible extinction of intermediate haplotypes in a widely distributed species with limited dispersal. Their description fits the pattern in the mitochondrial haplotype network (Figure 1.2a). The almost complete lack of shared

mitochondrial haplotypes and high ΦST values between populations are indicative of a history of extremely low levels of gene flow. With the degree of habitat specificity exhibited by the lizards, it is possible that there were not continuous corridors of habitat up the mountains connecting to higher elevation refuges.

As the habitat fragmented, the genetic differentiation across the range of the lizards (driven by the IBD) would have been isolated in the populations we observe today. The branching order in the mitochondrial haplotype network (Figure 1.2a) and the phylogenetic reconstructions (Figures 1.5 – 1.8) are consistent with the fragmentation of a population undergoing IBD. If the ancestral population had gene flow across its range there would be no reason for the mountains in closest geographic proximity to have the closest phylogenetic relationships. The fragmentation of a panmictic population would produce a star phylogeny, or one with a branching order without respect to geography by the process of lineage sorting of ancestral polymorphism (Maddison, 1997). This pattern 56

was observed among populations of collared lizards in the Missouri Ozarks after population fragmentation (Templeton et al., 2001). The genetic diversity of the collared lizard populations had no correlation to the geographic distances among them. The branching order of the S. jarrovii mitochondrial haplotype network and the phylogenetic reconstructions show the history of incremental division of the lizard populations. The mountains captured the genetic diversity that had been localized by the IBD.

The idea that the ancestral population was subject to limited dispersal finds further support in the degree of sequence divergence across the San Pedro River drainage in both the mitochondrial and RAG-1 data sets. The TMRCA estimates indicate that the division along this drainage was the first barrier to divide the ancestral range as it extended across southern Arizona. While the San Pedro River itself may not have consistently presented a formidable barrier, it is quite likely that the habitat associated with this drainage would have been a large region unsuitable for colonization and thus a major barrier to dispersal.

Biogeography of Southern Arizona

The evidence from carbon dated packrat middens indicates that the Arizona woodland communities were contiguous as recently as 8,000 to 12,000 years ago (Van

Devender, 1977; Van Devender and Spaulding, 1979). Many studies have reported evidence that this time depth may be too short to explain the observed amounts of morphological variation in several sky island taxa. The packrat midden estimate for the division of the woodland communities is far more recent than the divergences calculated by Masta (2000) for the mountain populations of the jumping spider, Habronattus 57

pugillis. Masta’s estimates ranged from 30,000 years for geographically closely associated ranges (Santa Rita and Huachuca Mountains) to 1.7 million years for the more distant ranges. The degree of divergence between the populations of S. jarrovii (Table

1.4) and the TMRCA estimates (Figure 1.8) are also inconsistent with a vicariance event

8,000 to 12,000 years ago.

The mutation rate of a 2% sequence divergence per million years has been previously applied to mtDNA biogeographic analyses of Sceloporus species (Clark et al.,

1999). The divergence rates calculated from the Sceloporus jarrovii in my analysis are approximately 1%. The use of this faster rate of sequence divergence in this study would alter the vicariance dates by approximately 2 fold, but would not make them comparable to the time scale indicated by the packrat midden data. It is also of consideration that the dating of the population TMRCAs in this study are based on gene coalescence, and should therefore expect to predate the actual population divergences (Maddison, 1997).

The populations would likely have had existing genetic variation at the time of vicariance and so the age estimates calculated would be to the common ancestor of the various gene copies. The 95% confidence range on the TMRCA estimates is over 100,000 years in either direction and given the small population sizes of the lizards the impact on the gene coalescence should not impact the age estimates substantially.

The general concordance of the estimates from the work of Masta (2000) as well as those of Tennessen and Zamudio (2008) with those discussed here suggests that the vicariance events that separated the montane populations of many disparate taxa may have occurred much earlier than has been hypothesized based on the midden data. A 58

more ancient date for the division of these habitat islands is more likely, considering the striking morphological differentiation displayed in some of the “sky island” taxa (Ball,

1966; Boyd, 2002; Elias et al., 2006; Maddison and McMahon, 2000; Slentz et al., 1999;

Weller et al., 2007). It is important to treat proposed dates of vicariance as testable hypotheses. It is clear from the work of Van Devender and collaborators (1977, 1979) that many species of flora did have the opportunity to expand their ranges to lower elevations during the last glacial maximum, and it is also evident from the work of

McCormack and collaborators (2008) that the Mexican Jays also dispersed at that time, but the studies above provide evidence that there is not a consistent pattern across all taxa. Since many isolating mechanisms have the potential to impact diverse taxa in different fashions, it is not appropriate to assume that a single event will have the same effect on all taxa within a region. The evidence of multiple cooling cycles presented by

Barker and collaborators (2011) adds additional complexity to the system. Their work shows evidence of 10 cooling cycles within the last 800,000 years. With the increasing ease of producing larger data sets of sequence data and growing computational power it may become possible to determine which events affected the dispersal of different taxa.

Phylogeography of Sceloporus

The Sceloporus lizards’ high degree of territorial behavior, coupled with their poor dispersal capacity, makes them ideal candidates for phylogeographic studies. In both this current study and in the work of Clark et al. (1999) on lizards of the Florida peninsula and xeric coastal regions, species of Sceloporus have been used to examine the timing of habitat fragmentation. In both studies the mitochondrial gene trees displayed 59

high levels of structure with numerous fixed differences between populations. A study comparing Sceloporus woodi to two skink species in Florida scrub habitat found S. woodi to be the most habitat specific of the species and to have the highest degree of population structure (Branch et al., 2003). In the collection of taxa previously examined across the

Arizona sky islands (Table 1.1) no study to date has displayed the degree of population structure observed here in S. jarrovii.

High levels of habitat specificity have also been reported in two other species of

Arizona Sceloporus (Lowe et al., 1967). These species are not restricted to the tops of sky islands, they may be good systems to test for IBD and population structure. It is possible that many Sceloporus species may be characterized by highly structured populations.

There are ample opportunities to test this hypothesis as Sceloporus is the largest genus of reptiles endemic to North America (Sites et al., 1992), and as it has a well-resolved phylogeny (Leache, 2010; Wiens and Reeder, 1997). The findings of highly structured populations in studies of Sceloporus are echoed in the work of Templeton and collaborators (2001 & 2011) showing high degrees of structure among populations of collared lizards and raises the possibility that this may be a pattern common to numerous iguanid lizards. 60

CHAPTER 2: THE POPULATION STRUCTURE AND ORIGIN OF THE

SQUAMATE HAEMOSPORIDIAN PARASITE, PLASMODIUM CHIRICAHUAE.

Introduction

Host-parasite interactions create coevolutionary conflicts in which both organisms can be selected to maximize their fitness, often at the expense of the other. The population structures of both species are important factors in the evolution of the interaction (Dybdahl and Lively, 1996). Price (1980) hypothesized that the spatial and temporal variability of hosts should drive parasite populations to be highly structured with little gene flow among them. This view certainly applies to many host-parasite systems, especially those in the initial phase of an epidemic or colonization (Blouin et al.,

1999), but it may be less applicable to long-term associations. While spatial and temporal variability of hosts should cause populations of colonizing parasites to be structured via bottlenecking and founder effects, long-term associations will create the potential for gene flow among parasite populations over time.

The amounts of host and parasite gene flow can greatly impact the evolutionary outcome of host-parasite conflicts (Gandon, 2002; Gandon et al., 1996; Kaltz and

Shykoff, 1998; Thompson, 1994). Low levels of gene flow among populations of either host or parasite combine with natural selection can lead to local adaptation. For example, while a host may evolve resistance to a parasite, the evolutionary stability of this will be greatly influenced by the host’s ability to spread this novel adaptation across its range

(Martinez et al., 1999). If the adaptation is restricted to a single host population within a 61

broad range of the parasite, even if that adaptation drives a local extinction of the parasite, the parasite will have opportunities for re-colonization from other host populations lacking this resistance. Selection will favor parasites with a counter adaptation (Thompson, 1994). In cases where the host has greater gene flow across its range, with structured parasite populations, adaptive advantages from any part of the host’s range will be spread across the entire range. This could possibly lead to the complete elimination of the parasite. Estimates of the rates of gene flow for both host and parasite may make it possible to predict the their potential for adaptation and even the outcome of the coevolutionary conflict (Delmotte et al., 1999).

The human malarial parasites (Plasmodium Spp.) are one of the most intensely studied groups of parasites. A multitude of studies have investigated their population structure (Anderson and Day, 2000; Anderson et al., 2000; Babiker and Walliker, 1997;

Bonizzoni et al., 2009; Conway, 2007; Conway et al., 2001; Conway et al., 1999;

Creasey et al., 1990; Li et al., 2001; Lum et al., 2004; Machado et al., 2004; Prugnolle et al., 2008; Razakandrainibe et al., 2005; Rich and Ayala, 2000; Rich et al., 1997; Rich et al., 1998; Zhong et al., 2007). Despite the abundance of work in this area, there is a diversity of opinion of the population structures of these parasites. Several studies hypothesized that P. falciparum may have gone through a recent clonal expansion to propagate the earth (Hartl et al., 2002; Razakandrainibe et al., 2005; Rich and Ayala,

2000; Rich et al., 1997); others have found support for population structure on several different geographic scales. Differentiation has been observed on large geographic scales

(Anderson et al., 2000; Conway, 2007; Conway et al., 2001; Creasey et al., 1990; Ekala 62

et al., 2007; Li et al., 2001), as well as within regions (Babiker et al., 1997; Creasey et al., 1990; Machado et al., 2004; Zhong et al., 2007) and even across relatively short distances (Anthony et al., 2005; Forsyth et al., 1989). There have also been studies that have found no structure among human malarial populations (Bonizzoni et al., 2009;

Prugnolle et al., 2008; Rich and Ayala, 2000; Rich et al., 1997). Some of the inability to differentiate populations may be due to the molecular markers used in the survey

(Anderson et al., 2000), but in some cases even the most diverse loci did not reveal any population subdivision (Bonizzoni et al., 2009; Prugnolle et al., 2008). The factors that maintain and/or disrupt barriers among human malaria populations have proved to be complex and the movement patterns of both human host and mosquito vector have been shown to impact dispersal at different geographic scales (Lum et al., 2004).

Recent work has explored the population subdivision of a lizard malarial parasite,

Plasmodium mexicanum, and its lizard host, Sceloporus occidentalis (Fricke et al., 2010).

This study investigated the degree of population differentiation between both the lizard and parasite using microsatellite markers. The findings showed only minor differentiation among the most distant lizard populations, but it did find a substantial degree of differentiation among the parasite populations even over short geographic distances.

The Study System

Sceloporus jarrovii is a saxicolous lizard occurring in disjunct populations in the mountains of southeastern Arizona, southwestern New Mexico, and Mexico between elevations of 1370-3550 meters (Jones and Lovich, 2009). These discontiguous populations present an ideal system to examine the gene flow in a host-parasite 63

association. Previous work has shown that S. jarrovii has highly subdivided populations

(Kaplan in prep). S. jarrovii is host to the malarial parasite P. chiricahuae (Telford,

1970).

Telford, in his original description of P. chiricahuae, hypothesized that it is closely related to its California and Mexican neighbor P. mexicanum (Telford, 1970).

This has been supported by their placement in subsequent phylogenetic analyses

(Martinsen et al., 2008; Perkins and Schall, 2002). Relatively little work has focused on

P. chiricahuae, as compared to P. mexicanum, which is by far the best-characterized lizard malarial parasite (Schall, 1996). The association of P. mexicanum and its host S. occidentalis in California has been studied for over thirty years (Schall, 1996).

Researchers have examined the effects of the P. mexicanum infection on S. occidentalis’ reproductive success (Schall, 1983), courtship success (Schall and Dearing, 1987), time budgets (Schall and Sarni, 1987), ability to defend territory (Schall and Sarni, 1987), and within-season survivorship (Bromwich and Schall, 1986). These studies have demonstrated that while infection with P. mexicanum does not decrease survivorship

(Bromwich and Schall, 1986), it exerts an evolutionary cost on S. occidentalis. It leads to anemia, limits social activity, reduces the male’s courtship success (Schall and Dearing,

1987), and reduces female’s clutch size (Schall, 1983). Both S. jarrovii and P. chiricahuae are close relatives of S. occidentalis and P. mexicanum, respectively, and it is thus likely that S. jarrovii is impacted in a similar manner by infection with P. chiricahuae. 64

The population differentiation of P. chiricahuae has been previously investigated by performing an analysis of morphological traits (Mahrt, 1987). Mahrt found no differentiation between the populations of P. chiricahuae. He posited that his characters might not have been informative enough to detect a recent population divergence. DNA sequence analysis provides a much more sensitive measure of the population differentiation of P. chiricahuae.

This current research study employs an analysis of P. chiricahuae mitochondrial cytochrome b sequences to compare the population structure of the lizard malarial parasite, P. chiricahuae, to its primary host, S. jarrovii. Unless P. chiricahuae’s vector has provided it with a means to cross from one population to the next, or if it was recently introduced, the population structure of S. jarrovii should have led to structuring of P. chiricahuae’s populations. I tested if P. chiricahuae’s infection of S. jarrovii predates the fragmentation of S. jarrovii’s range and therefore P. chiricahuae’s distribution in Arizona was produced by a process of host-parasite cophylogeny. These hypotheses have been evaluated using comparisons of the gene flow among and phylogenetic reconstructions of the Arizona populations of P. chiricahuae to those of S. jarrovii. This study also provides insight into the potential for local adaptation of either host or parasite. In addition, the P. chiricahuae cytochrome b sequences have also been used in a phylogenetic reconstruction with other New World lizard Plasmodium sequences to explore the origin of the infection. 65

Materials and Methods

Sample collection

Adult lizards were sampled from eight populations representing seven mountain ranges (Figure 2.1). Sampling was conducted within approximately 300-m2 areas between 1600–2500 meters in elevation. Blood samples were collected using a capillary tube to draw 50-200 µl of blood from the post-orbital sinus. A portion of the blood was used to make thin blood smears for microscopy, and the remaining blood was stored with

50 µl of STE buffer (10 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA) for subsequent

DNA isolation. Blood samples were stored on ice and then transferred to –80o C until

DNA could be isolated. The tip of the first toe on the rear right foot was clipped to prevent resampling of individuals on repeated visits to the same sampling locality.

Photographic vouchers where taken, and the lizards were released on site. 66

Figure 2.1 Map of P. chiricahuae sampling localities. Map of southern Arizona mountain ranges outlines represents the lower limit of oak woodland habitat (adapted from Brown & Lowe 1982). Brown shading indicates the Arizona range of S. jarrovii (from Schwalbe in Jones and Lovich 2009). Labels indicate number of lizards sampled per site. Number of lizards infected with P. chiricahuae indicated in parentheses. 67

Molecular techniques

DNA was isolated from all blood samples by overnight digestion with proteinase

K at 55o C, followed by phenol/chloroform extraction and an isopropanol/sodium acetate precipitation (Goldberg et al., 2003). The samples were screened for the presence of malaria via polymerase chain reaction (PCR) assay using the conditions described below for the amplification of the malarial cytochrome b. PCR has been shown to be a highly specific and sensitive method for detection of Plasmodium in lizards (Perkins et al.,

1998).

Approximately 700 bp of the P. chiricahuae cytochrome b gene was amplified using primers 3 and 7 from Creasey et al. (1993). PCR was performed in 40-µl reaction volumes with 1X Gibco PCR Buffer (Invitrogen), 1.6 units of Platinum Taq DNA polymerase (Invitrogen), 10 mM dNTP mix (2.5 mM each nucleotide), and 16 pmol of each primer. The PCR reactions were cycled in an MJ research PTC-100 thermal cycler

(Bio-Rad). The PCR conditions were: 3-minute initial denature at 94o C, followed by 15 cycles of 94o C 30 seconds, 58o C 30 seconds, 72o C 30 seconds, reducing the annealing temperature by 0.5o C per cycle, followed by 20 cycles of 94o C 30 seconds, 51o C 30 seconds, 72o C 30 seconds, with a final 5-minute incubation at 72o C. The PCR products were then purified using the Concert PCR purification kit (Invitrogen). The amplicons were then sequenced at the University of Arizona’s Genetics Core (UAGC) on an

Applied Biosystems 377 automated DNA sequencer. The sequences were aligned using

Sequence Navigator (Applied Biosystems Inc, 1994). 68

Population genetic analysis

The program DnaSP version 5.10 (Librado, 2009) was employed to calculate sequence

diversity statistics as well as population pairwise Da. DnaSP calculates Da as the number of net nucleotide substitutions per site where the effect of within-population polymorphism has been subtracted (Nei, 1987). A median joining network (Bandelt et al.,

1999) was created, using the program Network version 4.6 (fluxus-engineering.com) to visualize relationships among sequences and their relative frequencies in the different populations.

The Arlequin software package version 3.5.1.2 (Excoffier and Lischer, 2010) was used to perform an analysis of molecular variance (AMOVA) to examine the population structure of the P. chiricahuae genetic variation. AMOVA examines the variance in gene frequencies among different groupings while also taking into account the number of mutations between the haplotypes (Excoffier et al., 1992). AMOVA successively groups individuals hierarchically by their region and population and evaluates the proportion of the overall genetic variation that is attributable to that grouping. It examines the structure of the genetic variation among regions (ΦCT), among-populations within the regions

(ΦSC), and among individuals within the populations (ΦST) (Weir and Cockerham,

1984). It categorized the distribution of the genetic variation across the geographic space by examining the degree of genetic differentiation within and among the different hierarchical groupings. For the regional comparison, the populations were divided into two regions, those east and west of the San Pedro River (Figure 2.1). The San Pedro 69

River has been shown to be an important geographic barrier in the population structure of

S. jarrovii in southern Arizona (Kaplan in prep).

Arlequin was also used to estimate the absolute number of migrants exchanged between two populations (M’) (Slatkin, 1991). Slatkin’s M’ is calculated from ΦST, using

1− Φ the equation M'= Nm × ST , where N is the effective population size within each 2ΦST population, and m is the fraction of the population that is exchanged as migrants each generation€ (Slatkin, 1991).

The NTSYSpc software package Version 2.2 (Rohlf, 1997) was used to perform a

Mantel test on the estimated number of migrants among populations (M’) versus the geographic distances among populations. A Mantel test measures the correlation between two dissimilarity matrices. The test of the correlation of a matrix of geographic distances among populations to a matrix of the estimated number of P. chiricahuae migrants, M’, was performed to determine the extent to which the geographic distance among populations has limited migration events. Understandably, migration is difficult in species with limited dispersal, the greater the distance between populations and the fewer the migrants. If geographic distance has been the cause of a restriction of migration in P. chiricahuae, there should be a negative correlation between the geographic distance and the number of migrants that two areas will exchange over time. If geographic distance has limited migration then the regression of the log(M’) against the log(geographic distance) should be significant (Slatkin and Maddison, 1990). 70

The geographic relationships and the genetic similarity of the current populations of P. chiricahuae can tell us about the ancestral population prior to its subdivision. If the ancestral population was panmictic, the current populations should show a random pattern of genetic differentiation with regard to their relative geographic positions.

Fragmentation of a panmictic population should lead to the formation of genetically identical subpopulations. These subpopulations should randomly accumulate genetic differences. If the ancestral population was structured by isolation by distance (IBD), the current populations should show a geographic orientation to their genetic similarity. IBD is a form of population subdivision that occurs in species with limited dispersal abilities

(Avise et al., 1987). In species with IBD the mere distance across the geographic range inhibits random mating. Individuals mate with other individuals within close geographic proximity. Patches of genetic similarity accumulate across the range of the species over generations. With time, individuals that are more geographically distant become more genetically distinct due to the reduction in gene flow. If the range of a population experiencing IBD becomes gradually fragmented, the genetic differentiation among the subpopulations should be correlated with the geographic distances among the populations. A Mantel test was used to determine the correlation between the among-

population genetic distances (Da) with among-population geographic distances.

Additional Mantel tests between the M’, Da, and ΦST values of P. chiricahuae and S. jarrovii were used to explore the relationship between host and parasite dispersal. 71

Phylogenetic analysis

The program PAUP* version 4.0b10 (Swofford, 1998) was used to run the

MrModelTest2 block version 2.3 (Nylander, 2004) and the results were interpreted using the program MrModelTest2 (Nylander, 2004) to select the appropriate model of evolution for phylogenetic analysis. MrModelTest2 evaluates 24 models of evolution on the data set to determine the amount of complexity to add to the model. More complex models will fit the data better, but there must be a significant increase in likelihood to justify the added complexity. The Akaike's information criterion (AIC) (Akaike, 1974) was used to select the appropriate model from the MrModelTest2 output (Posada and Buckley, 2004).

PAUP* was also used to conduct a phylogenetic reconstruction of the P. chiricahuae cytochrome b sequences with parsimony as the optimality criterion. The analysis was performed with all characters of equal weight, and a P. mexicanum sequence as the outgroup. A bootstrap analysis with 10,000 replicates of 10 random addition sequences per replicate was performed to determine the support for the inferred relationships in the parsimony analysis.

The Program RAxML-IV-HPC version 2.2.3 (Stamatakis, 2006) was used for a maximum likelihood analysis, using the GTRGAMMA model of nucleotide evolution.

The GTRGAMMA model is a general time-reversible model with an among-site rate heterogeneity parameter. Support for inferred relationships was estimated by conducting

1,000 nonparametric bootstrap replicates.

A Bayesian analysis was performed with the program MRBAYES version 3.1.2

(Ronquist and Huelsenbeck, 2003). The analysis used the HKY model of nucleotide 72

evolution (Hasegawa et al., 1985), selected, with MrModelTest2. This analysis used two parallel runs of four incrementally heated Markov chains (using default heating values), with a 25% burn-in with sampling occurring every 100 generations for 5 x 106 generations, at which point the convergence diagnostic, the average standard deviation of split frequencies, was below 0.01. The burn-in samples were discarded after the run, and the remaining samples produced a 50% majority rule consensus tree, with posterior probabilities as a measure of node support.

To investigate the possibility of host-parasite cophylogeny, the Program Jane version 3.0 (Conow et al., 2010) was used for comparison of the P. chiricahuae and S. jarrovii phylogenies. For this analysis the individual lizard hosts of the malarial parasites were selected from larger data set of S. jarrovii samples (Kaplan in prep). In the few cases that the specific lizard did not produce suitable sequence data for analysis, a substitute was selected from the same collection date and site. Jane requires fully bifurcating trees for analysis, so PAUP* was used to conduct Neighbor Joining analyses on both the P. chiricahuae and S. jarrovii data sets with P. mexicanum and S. occidentalis as the outgroups, respectively. For the Neighbor Joining analyses the replicate P. chiricahuae sequences were removed leaving only one sequence per haplotype. The two Neighbor Joining phylogenetic reconstructions and a list of host- parasite associations were used in the Jane analysis. The program applies costs for the cospeciation, duplication, host switch, loss/sorting, and failure-to-diverge events. The

Node-Based Cost Model with the event costs of cospeciation = 0, duplication = 1, host switch = 1, loss/sorting =1, and failure to diverge =1 were used to find the optimal 73

reconciliation for the trees in an analysis with 100 solutions considered per iteration for

100 iterations. The same data, cost model, and iteration settings were then run in 500 replicates with randomized paring of host-parasite associations to create a distribution to access the significance of the optimal reconciliation.

To investigate the origins of P. chiricahuae, a data set of cytochrome b sequences was assembled containing: P. chiricahuae samples representing the different Arizona populations (as well as one previously published P. chiricahuae sequence Accession #

AY099061), P. mexicanum samples produced from DNA isolated from microscope slides generously provided by Dr. Sam Telford from Mexican localities, and mammalian, avian, and squamate haemosporidian sequences from GenBank. This data set of 153 taxa was analyzed using Parsimony, Maximum Likelihood, and Bayesian analyses using methodologies similar to those described above for the phylogenetic reconstruction of the

P. chiricahuae data. All three methods showed support for a clade of 22 lizard plasmodia including 14 P. chiricahuae and P. mexicanum samples from North American Sceloporus and eight samples of an undescribed plasmodium species from Bahamian Anolis sagrei.

These samples were then analyzed in a more detailed phylogenetic reconstruction of this clade. With consideration of the current revisions to the polarity of the greater haemosporidian phylogeny (Outlaw and Ricklefs, 2011) a sequence from P. falciparum

(GenBank Accession # AY910013) was used as an outgroup.

74

Results

A total of 104 lizards from eight populations representing seven mountain ranges was sampled (Figure 2.1). Sixty-three of the lizards (61%) were infected with P. chiricahuae. The prevalence of infection in the lizard populations ranged from 17% to

100% with a mean of 64.9% (Table 2.1). A total of 646 bp of the mitochondrial cytochrome b region was sequenced from 52 P. chiricahuae individuals and one P. mexicanum individual (DNA supplied by J. J. Schall). Unfortunately the one sample of P. chiricahuae from the Patagonia Mountains failed to produce a useful DNA sequence, and therefore the population was omitted from the analysis. 75

Table 2.1 Prevalence of P. chiricahuae infection in S. jarrovii populations.

76

The P. chiricahuae sequences contained 4 variable sites that define 5 haplotypes and differ from the P. mexicanum sequence by 8 fixed differences. The median joining network describing the relationships of the P. chiricahuae sequences is shown in Figure

2.2. The distribution of the P. chiricahuae haplotypes within the populations is shown in

Table 2.2. Five of the seven populations are each fixed for single haplotypes. The sequence diversity per population is reported in Table 2.3. The P. chiricahuae cytochrome b sequences show little diversity, but this is not unexpected, as similar surveys of P. mexicanum failed to find any variable sites across its California range

(Schall and St. Denis, 2010). The number of net nucleotide substitutions per site among

populations (Da) is shown in Table 2.4. 77

Figure 2.2 Median joining networks of P. chiricahuae cytochrome b haplotypes. (2.2a) Plasmodium chiricahuae cytochrome b network (2.2b) Sceloporus jarrovii cytochrome b network. Branch lengths are noted on primary branches. Sampling localities are indicated by color. Node diameters are proportional to the number of individuals. Population frequencies of shared haplotypes are indicated as pie charts in nodes. 78

Table 2.2 Distribution of P. chiricahuae haplotypes.

79

Table 2.3 P. chiricahuae sequence diversity.

80

Table 2.4 Pairwise P. chiricahuae population Da values. Da is calculated as the number of net nucleotide substitutions per site between P. chiricahuae populations (where the within population variation has been subtracted).

81

The analysis of molecular variance (AMOVA) revealed statistically significant levels of genetic structure of the P. chiricahuae populations: 47.60% (p=0.02) of the observed genetic variation was among regions (ΦCT), 45.96% (p<0.01) was among populations within regions (ΦSC), and 6.44% (p<0.01) was among individuals within the populations (ΦST). The population pairwise ΦST values display a high degree of population differentiation (Table 2.5). The only exception to this is the lack of differentiation between the Dragoon and the Pinaleños populations. These populations are all fixed for the same P. chiricahuae haplotype, and thus have a ΦST = 0. 82

Table 2.5 P. chiricahuae population pairwise Φ ST values.

Above diagonal: ΦST significance at 0.05 level. Below diagonal: Pairwise ΦST values

83

The results of the Mantel tests did not show any significant associations between the P. chiricahuae sequence divergence or gene flow estimators with the geographic distances or the population structure of S. jarrovii. This is consistent with the fragmentation of an unstructured wide-ranging population rather than a population experiencing IBD.

All three of the phylogenetic analysis methods reconstructed the same tree for the P. chiricahuae cytochrome b sequences (Figure 2.3). The tree has little structure and contains three clades within P. chiricahuae and a basal polytomy. 84

Figure 2.3 Phylogenetic reconstruction of P. chiricahuae cytochrome b sequences. Bayesian phylogenetic reconstruction of Plasmodium chiricahuae cytochrome b sequences. Branches scaled by genetic distance. Interior nodes labeled with posterior probabilities. Branches labeled with Likelihood probabilities above the line and Parsimony bootstrap percentages below. 85

The phylogenetic reconciliation analysis performed using Jane found a group of equally optimal solutions all containing 2 cospeciation events, 0 duplications, 3 host switching events, 12 losses, and 47 failures to diverge with a total cost of 74. This result was well outside of the distribution of solution costs for the randomized host-parasite association that ranged in cost from 280 to 376 (Figure 2.4). 86

Figure 2.4 Results of phylogenetic reconciliation. 2.4a) Cost distribution of reconciliation of random host-parasite associations. 2.4b) Tanglegram of Plasmodium chiricahuae and Sceloporus jarrovii Baysian trees. 87

The phylogenetic reconstruction of the North American lizard malarial parasites was concordant across the different methods of reconstruction (Figure 2.5). The phylogeny roots in the P. mexicanum lizard plasmodia sequences from central Mexico, with weak support for a clade containing the lizard plasmodia of the United States and the Bahamas. In this clade the P. chiricahuae sequences for a polytomy basal to a well- supported clade containing the California P. mexicanum samples and the Anolis plasmodia from the Bahamas. 88

Figure 2.5 Phylogenetic reconstruction of North American lizard Plasmodia. Bayesian phylogenetic reconstruction of North America lizard plasmodia. Internal nodes labeled with posterior probabilities.

89

Discussion

Population structure of Plasmodium chiricahuae

The results of this study show that the P. chiricahuae populations are subdivided and have a strong degree of structure despite a relatively small amount of sequence diversity. This study detected only 4 variable sites in 646 bp of P. chiricahuae cytochrome b sequence. This is extremely low in comparison to the 103 variable sites detected in 1174 bp sequence of the same gene from S. jarrovii (Kaplan in prep). This paucity of sequence diversity makes the detection of population structure more difficult because there is a reduced ability to resolve lineages and differentiate individuals.

The population pairwise ΦST values display a high degree of population differentiation (Table 2.5). The only localities that do not show any differentiation are the samples from the Dragoons and the two from the Pinaleños. These three populations all appear to be fixed for a single haplotype (Table 2.2). All of the haplotypes are geographically restricted, indicating that there is little to no gene flow among populations

(Table 2.2). Haplotype 3 was found only in the Santa Ritas. Haplotype 5 was found only in the Quinlans. Furthermore, Haplotype 4 approaches 90% frequency in both the

Quinlan and Huachuca populations but was not observed in any other populations, including their nearest neighbor, the Santa Ritas. If there were meaningful amounts of gene flow among populations, there would be a more even distribution of haplotypes among populations. In a similar situation, Haplotype 1 was the sole type found in the

Chiricahuas but was not recovered in the samples from the Dragoons nor the Pinaleños. 90

The reciprocal is also true: the Dragoons and the Pinaleños populations are all fixed for

Haplotype 2, which is absent from the Chiricahuas and all other populations. It is possible that if a longer sample of sequence had been used to further resolve the haplotypes, it might have been possible to differentiate these populations like the others in this study.

This is unlikely as recent work (Schall and St. Denis, 2010) has indicated that cytochrome b shows little sequence variation in populations of related lizard plasmodia.

Alternatively, additional insight will likely be gained from future investigation with now available microsatellite loci (Fricke et al., 2010; Schall and Vardo, 2007).

The high degree of population differentiation is also evident on a regional geographic scale. The analysis of S. jarrovii’s population structure demonstrated that the

San Pedro River basin is a major barrier to its dispersal. This river drainage in this study to defines regions of the Arizona populations of P. chiricahuae in the AMOVA analysis.

The AMOVA revealed statistically significant levels of structure across the San Pedro

River basin, with 47.60% of the genetic diversity restricted to among the regions (ΦCT, p=0.02). The majority of the remaining genetic diversity was distributed among the populations (ΦSC=45.96%, p<0.01), with very little diversity among the individuals within the populations (ΦST=6.44%, p<0.01).

The results of the AMOVA are echoed by the data in Table 2.2, which indicate that all of the haplotypes, with the exception of haplotype 1, are endemic to either side of the river. This heterogeneous distribution of genetic diversity between the two regions is what accounts for the AMOVA’s 48% ΦCT estimate. 91

The differentiation of populations on either side of the river, evident from the

46% ΦSC estimate, stems from the patchy fashion in which the haplotypes are distributed among populations. Within each region there are populations with cohesion - the

Dragoons and the Pinaleños in the east, or the Quinlans and the Huachucas in the west, but there are also populations with genetic dissimilarity, i.e., the Chiricahuas in the east, or the Santa Ritas in the west. The ΦSC estimate is being driven by the presence of these dissimilar populations.

Diversity of individuals within populations, the small 6% ΦST estimate reflects the fact that five of seven populations contain only a single haplotype. For these populations there is no variation at this level. The remaining two populations that contain some diversity (the Quinlans and the Huachucas) are greatly skewed in their haplotype frequencies, with almost complete fixation of their primary haplotypes, and thus barely adding to individual diversity.

All of the lizard populations surveyed in this study were found to contain P. chiricahuae (Table 2.1). Although the distribution of P. chiricahuae might be explained in many ways, this study explores two of the more probable scenarios, which I have termed the “single origin hypothesis”, and the “secondary invasion hypothesis”.

The “single origin hypothesis” postulates that the P. chiricahuae infection predates the fragmentation of S. jarrovii’s range and that it was thus transported around

Arizona by S. jarrovii’s population movements. The “secondary invasion hypothesis” postulates that S. jarrovii entered Arizona and dispersed in the region well before P. chiricahuae arrived, with the parasite invading its already fragmented populations at a 92

later time. One of the goals of this research is to determine which of these two hypotheses is the more probable by examining the patterns of genetic diversity of both the lizard and the parasite.

If the “single origin hypothesis” is true, the following conditions should be met:

First, the relationships among the populations of these two species should be roughly identical, with a tight correlation of the phylogenies of these two taxa, as both lizard and parasite would have been dispersing together. The only potential differences in the phylogenies would arise from stochastic processes such as lineage sorting of ancestral polymorphism, although differences of this nature should be the exception rather than the rule (Maddison, 1997). Second, the divergence among the malaria populations should be roughly proportional to the divergence among the lizard populations, as has been shown in avian malarial systems (Ricklefs and Outlaw, 2010). Even if these two species were to have different substitution rates for this gene, divergence would still be accumulating from the same historic population bifurcations and would thus be proportional.

If the “secondary invasion hypothesis” is true, there would not be any expectation of a correlation of divergence or biogeographic relationships among the populations, as the two organisms would have entered Arizona and dispersed entirely independently.

This hypothesis does, however, leave room for the possibility of some correlation of relationships arising from the geographic characteristics of southern Arizona. As these two species may have independently dispersed across the landscape, neither would be expected to have dispersed independently of the geographic features of the region.

Understandably, it is probably easier to migrate from one mountain range to its closest 93

neighbor, rather than to a range on the other side of the state regardless of what type of organism you are. On the other hand, it is possible that these two disparate taxa might have experienced biogeographic boundaries differently, as one must make all of its journeys overland, while the other might have hitched a ride on a gust of wind inside the vector.

This hypothesis does not require any correlation of the divergence between the two taxa, but it does require that the presence of the lizards in Arizona predated the introduction of their parasite. Thus, the malaria populations must, understandably, be younger than their hosts, unless they were previously infecting other species of lizards in the region. The ability to date the age of the malaria populations with respect to the lizard populations would aid in determining which of the above hypotheses is more probable.

Fortunately, the analysis of the population structure of S. jarrovii produced findings with little ambiguity (Kaplan in prep). S. jarrovii has had a long history of population subdivision in Arizona. This provides an excellent model for comparison with

P. chiricahuae. The phylogeny of the S. jarrovii populations is unambiguous, with the lizards from the different mountain ranges forming monophyletic clades (Figure 2.4).

The P. chiricahuae cytochrome b gene tree was reconstructed in order to compare the relationships of the populations of the host and parasite. An important difference between the two hypotheses is in the expectation of correlation of the two phylogenies.

The “single origin hypothesis” requires a high degree of fit between the trees (i.e., biogeographic correlation), while the “secondary invasion hypothesis” allows for the 94

trees to be entirely independent with the potential for similarities stemming from geographic factors.

The results of the phylogenetic reconciliation analysis performed in Jane are shown in Figure 2.4. As this figure illustrates, there is indeed a significant fit of the malaria gene tree to the lizard tree, and this figure also shows that the fit of the lizard tree falls outside the distribution of the randomly paired hosts and parasites.

While the significant fit of the malaria gene tree to the lizard tree supports the “single origin hypothesis”, it is important to consider how this result should be interpreted. The fit does depart from random, but a mere departure from random is not sufficient to resolve which of the two hypotheses’ conditions are being met. The geographic factors directing dispersal within Arizona may bias the fit of the trees towards the left of the distribution. Many of the random trees in the distribution likely suffer from close pairings of ranges that are geographically distant. When hosts and parasites are coupled at random, the geography of the region is not considered, and there are many opportunities for outlandish pairings. The distribution of randomized associations seems like a relevant framework for assessing the fit of the malarial population tree to that of the lizard hosts

(Figure 2.4), but this may be an oversimplified view of the “null hypothesis” for the relationships of these taxa. Clearly, the fact that both organisms exist in highly subdivided populations patterns sets a basis for non-random pairings of hosts and parasites. If the hosts and parasites are paired without consideration for the structure of their populations it is difficult to conceive of good reconciliations to the pairings. While the significant reconciliation of the host-parasite phylogenies supports the “single origin 95

hypothesis”, the impact of the population subdivision on the random distribution, brings the validity of this analysis into question.

While the phylogenetic comparisons provide inconclusive evidence to differentiate the two hypotheses, there is still the sequence divergence data to be considered. There is a distinct difference in the amounts of the sequence divergence observed in P. chiricahuae and S. jarrovii. P. chiricahuae has relatively low divergence among its sequences. Although the haplotypes of both S. jarrovii and P. chiricahuae have a high degree of geographic structure, the P. chiricahuae haplotypes differ by very few mutations (Figure 2.2).

In my work on the population structure of S. jarrovii I sequenced 1174 bp of the cytochrome b region and identified 103 variable sites (Kaplan in prep). In this current study I have sequenced a region of the same gene and have found approximately 7% of the variation observed in S. jarrovii. This reduction in diversity could be the result of many factors, but two seem to be most relevant to the interpretation of the history of this association. First, the lower degree of divergence among the P. chiricahuae haplotypes could be the result of P. chiricahuae having a substitution rate that is much slower than the rate in S. jarrovii. The populations of P. chiricahuae could have undergone population fragmentation at the same time as S. jarrovii but could have accumulated fewer mutations. Second, P. chiricahuae could have colonized the populations of S. jarrovii more recently. If P. chiricahuae has dispersed across southern Arizona more recently, and if the two species have comparable substitution rates, we would expect to observe this relative reduction in the diversity of the P. chiricahuae haplotypes. Recent 96

work comparing the cytochrome b sequence evolution of birds and their associated plasmodia have found the parasite sequences to diverge at a rate of 58% of the host rate

(Ricklefs and Outlaw, 2010).The observed 7% of the lizard sequence divergence is nearly an order of magnitude less than expected for codivergence. The timing of the divergence of the populations of S. jarrovii predates the most recent vicariance of the southern

Arizona woodland communities 8,000 to 10,000 years ago (Van Devender, 1977; Van

Devender and Spaulding, 1979) by hundreds of thousands of years, but it is possible that the vicariance of the P. chiricahuae populations does not.

The vector of P. chiricahuae may have been a more effective source of dispersal across a cooler, wetter environment that existed in southern Arizona 8,000 to 10,000 years ago following the last glacial maximum (Van Devender, 1977; Van Devender and

Spaulding, 1979). At that time the vector could have transmitted P. chiricahuae to all of the populations of S. jarrovii. It is quite possible that a more mesic environment across

Arizona would have allowed for a population expansion of the malarial vector. Although the findings of my previous work (Kaplan in prep) do not support a large range expansion for S. jarrovii at that time, Arizona contains many species of Sceloporus that may have been suitable reservoirs for the expansion of P. chiricahuae, potentially bridging the gaps between the mountains.

In conclusion, the data more strongly support the “secondary invasion hypothesis”. The phylogenetic reconciliation does provide statistical support for the

“single origin hypothesis”, but it also provides substantial qualitative support for the

“secondary invasion hypothesis” given that the results are a possible artifact of the 97

structure of the host and parasite populations. The limited sequence divergence in P. chiricahuae is not consistent with the “single origin hypothesis”.

At this time, the possibility of greater parasite gene flow remains among a subset of the populations (Table 2.5). It has been predicted that a difference in gene flow between host and parasite across structured populations can give an advantage in the coevolutionary conflict to the species with the greater amount of gene flow (Gandon,

2002; Gandon et al., 1996; Kaltz and Shykoff, 1998; Martinez et al., 1999; Thompson,

1994). It is interesting to note that the populations that appear to have the highest amount of gene flow (Dragoons, south Pinaleños, and north Pinaleños) also have the highest prevalence of infection (Table 2.1). This pattern is continued in the two populations with the second most gene flow (Quinlans and Huachucas), which have the next highest prevalences of infection. It is possible that the parasite does have greater gene flow than its host and has been able to spread advantageous mutations across these ranges that allow it to exploit its host more effectively. These data are highly preliminary due to the small sample sizes used for the prevalence estimates, and the low resolution of the haplotypes used to estimate gene flow, but this pattern deserves further investigation.

It is possible that the lower pairwise ΦST estimates between the above populations are due to incomplete lineage sorting of ancestral polymorphisms, and that there is no gene flow between any of the lizard or malarial populations (Maddison, 1997). In this case, it still appears that the malarial parasite might be at a bit of an evolutionary advantage. This investigation of this host-parasite association indicates that the lizard populations have been isolated for a significantly longer time span than the malaria. 98

Thus, over geological time scales, the parasite does have greater gene flow than its host.

It appears that both host and parasite are currently isolated to the mountain ranges and have equal opportunity for localized adaptations, but over geological time the parasite seems to have a much greater probability for spreading adaptations across the range or even recolonization/reinvasion of populations that may have undergone local extinctions.

It is important to view this work, within the context of the literature of parasite population structure, as an analysis of a wild population of a vector-borne parasite.

Criscione and Blouin (2004) demonstrated that the addition of a second mobile host species allowed the parasite to disperse beyond the capacity of a single host. In my current study, although the parasite has two potentially mobile hosts it does not experience much large-scale mobility from either. One of the points of consideration in this discussion that must be addressed is the actual amount of mobility of each of the hosts. If the lizard has the potential to move great distances, but in general will never leave its local habitat (Ruby, 1986), and if the vector is a fly like Lutzomyia vexatrix (the vector of P. mexicanum (Ayala, 1971)) that is also not considered to be a great disperser, then it is of little consequence how many “potentially mobile” hosts the parasite has if none of them actually move about the environment. Having multiple hosts that exhibit little to no overall mobility leads to a parasite with a good deal of population structure. It would be an interesting comparison to perform comparable work on a parasite like

Leishmania that is also vectored by Lutzomyia, but might have a more mobile vertebrate host, or another lizard malarial system with a more mobile vector like Culex or Aedes. 99

Origin of Plasmodium chiricahuae

The phylogenetic reconstruction of the North American lizard plasmodia suggests an origin in south/central Mexico and that P. mexicanum diversified in Mexico in

Sceloporus lizard hosts and worked its way north into Arizona and then into California

(Figure 2.5). The interpretation of this phylogenetic reconstruction is complicated by the placement of the Anolis plasmodia from the Bahamas with the California P. mexicanum samples.

Many authors have posited that the phylogenetic reconstruction of the plasmodia is currently hindered by the limited sampling of this large and diverse group (Martinsen et al., 2008; Rich and Xu, 2011). The use of cytochrome b in combination with additional loci throughout the Plasmodium genome will likely aid in these efforts (Davalos and

Perkins, 2008; Martinsen et al., 2008). To more thoroughly address the relationships of the North-American lizard malarias, it may be necessary to have a larger data set with significantly greater sampling from the region. Recent efforts have shown that even the reconstruction of the human plasmodia suffer from insufficient taxon sampling (Duval et al., 2010; Krief et al., 2010; Prugnolle et al., 2010). It is of note that numerous sequences of lizard plasmodia (including samples from the Caribbean) that were included in the larger haemosporidian analysis grouped into separate clades from the samples used in the

North-American analysis. 100

CHAPTER 3: HOST GENETIC DIVERSITY AND THE PREVALENCE OF THE

LIZARD MALARIAL PARASITE, PLASMODIUM CHIRICAHUAE, IN THE SKY

ISLAND POPULATIONS OF SCELOPORUS JARROVII

Introduction

Advances in technology have made tools to explore genetic diversity in wild populations more widely available. This has made it possible for more research to explore the connection between genetic variation and disease susceptibility (Keller and Waller,

2002). Until the past decade, this field contained a very limited number of rigorous scientific studies (Spielman et al., 2004; Whiteman et al., 2006). Recently, numerous studies have explored this phenomenon both in laboratory/captive systems (Arkush et al.,

2002; Luong et al., 2007; Owen et al., 2008; Spielman et al., 2004; Ugelvig et al., 2010) and wild populations (Acevedo-Whitehouse et al., 2003; Cassinello et al., 2001; Coltman et al., 1999; Gompper et al., 2011; Meagher, 1999; Ortego et al., 2007; Townsend et al.,

2010; Trouve et al., 2003; Whitehorn et al., 2011; Whiteman et al., 2006). The majority of these studies, with limited exception (Trouve et al., 2003), show a negative relationship between genetic diversity and disease resistance. While some of these studies have focused directly on diversity at loci linked to immune function (Arkush et al., 2002;

Owen et al., 2008), most have focused on neutral genetic variation. The findings described here show display a negative correlation between the genetic diversity of the lizard, Sceloporus jarrovii, and the population prevalence of infection of the malarial parasite, Plasmodium chiricahuae. 101

Slatkin (1987) proposed that one of the most important factors for understanding parasite evolution is the transitory population structure of the parasites in the hosts in which they inhabit. This ephemeral population structure is unique in that the number of individual parasites within the host limits potential mating opportunities to a subset of the population as a whole (Blouin et al., 1999; Galvani and Gupta, 1998).

Malarial parasites, Plasmodium, are obligate sexually reproducing protozoa that mate within the gut of their arthropod vector. Plasmodia spend most of their life as haploid stages, dividing mitotically in the vertebrate host, giving rise to both male and female gametes. These gametes are drawn up in a small sample of the peripheral blood from the vertebrate host by the insect vector and then fuse within the gut (Day et al.,

1992). The diversity of the mating pool is dictated by several factors including the number of different individuals (clones) infecting the vertebrate host, the number producing gametic forms at that time, and the stochasticity of the sample of blood drawn by the vector (Read et al., 1995).

There have been many empirical studies in human malarial systems demonstrating a correlation between transmission rates and the frequency of multiclonal infections (Anderson et al., 2000; Anderson et al., 1999; Babiker et al., 2000; Babiker et al., 1999; Ferreira et al., 2002; Imwong et al., 2006; Paul et al., 1998; Paul et al., 1995;

Zakeri et al., 2006). Recent work (Vardo and Schall, 2007) directly measured the association of the prevalence of infection with the frequency of multiclonal infections of the lizard malarial parasite Plasmodium mexicanum. Vardo and Schall used blood samples collected in localities that were characterized as either low or high prevalence. 102

They also used samples from these sites over a time span during which the region experienced an overall drop in parasite prevalence. They employed several microsatellite loci (Schall and Vardo, 2007) to detect multiclonal infections. Their research demonstrated a clear positive association between the prevalence of infection and the frequency of multiclonal infections of P. mexicanum.

By comparison, Anderson and collaborators (2000) used similar protocols to investigate the human malarial parasite P. falciparum, but they did not detect such a clear positive relationship. Their work demonstrated a nonlinear positive association between prevalence of infection and the frequency of multiclonal infections. It is likely that human systems are not ideal candidates for research of this kind due to the constant perturbation of disease control efforts. This question will require additional examples from wild parasite populations to identify the important factors involved in this association.

This study examined the relationship between the genetic diversity of the lizard,

Sceloporus jarrovii, and the population prevalence of infection of the malarial parasite, P. chiricahuae. I have also investigated the correlation between population prevalence of infection and the frequency of multiclonal infections. These relationships have great importance to the population genetics and evolution of the parasite.

103

Methods

Sample collection

Adult lizards were sampled from eight sites representing seven mountain ranges of southern Arizona (Figure 3.1). Sampling was conducted within approximately 300-m2 areas between 1600–2500 meters in elevation. Blood samples were collected using a capillary tube to draw 50-200 µl of blood from the post-orbital sinus. A portion of the blood was used to make thin blood smears for microscopy, and the remaining blood was stored with 50 µl of STE buffer (10 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA) for subsequent DNA isolation. Blood samples were stored on ice and then transferred to –80o

C until DNA could be isolated. The tip of the first toe on the rear right foot was clipped to prevent resampling of individuals on repeated visits to the same sampling locality.

Photographic vouchers where taken, and the lizards were released on site. 104

Figure 3.1 Map of population sampling localities with sample sizes and number of infected lizards. Map of southern Arizona mountain ranges outlines represents the lower limit of oak woodland habitat (adapted from Brown & Lowe 1982). Brown shading indicates the Arizona range of S. jarrovii (from Schwalbe in Jones and Lovich 2009). Labels indicate number of lizards sampled per site. Number of lizards infected with P. chiricahuae indicated in parentheses.

105

Molecular techniques

DNA was isolated from all blood samples by overnight digestion with proteinase

K at 55o C, followed by phenol/chloroform extraction and an isopropanol/sodium acetate precipitation (Goldberg et al., 2003). The samples were screened for the presence of malaria via polymerase chain reaction (PCR) assay using the conditions described below for the amplification of the malarial cytochrome b. PCR has been shown to be a highly specific and sensitive method for detection of Plasmodium in lizards (Perkins et al.,

1998; Vardo et al., 2005).

An approximately 1.7-kb portion of the Plasmodium 18s SSU rRNA region was

PCR amplified from total genomic DNA using the primers AL160 and TM41 (provided by Ananias Escalante). PCR was performed in 40-µl reaction volumes with 1X Gibco

PCR Buffer (Invitrogen), 1.6 units of Platinum Taq (Invitrogen), 1 mM dNTP mix, and

16 pmol of each primer. The PCR reactions were cycled in an MJ research PTC100 thermal cycler with a 2-minute initial denature, followed by 35 cycles of 94o C 30 seconds, 59o C 30 seconds, 72o C 2 minutes, with a final 6-minute incubation at 72o C.

The PCR products were then purified using the Concert PCR purification kit (Invitrogen) and sequenced at the University of Arizona’s Genetics Core (UAGC) on an Applied

Biosystems 377 automated DNA sequencer using AL160 as a sequencing primer. The sequences were aligned using Sequence Navigator (Applied Biosystems Inc 1994) and examined for within sequence polymorphism. Five sites were polymorphic within the first 300 bp of the sequence. These sites were used to identify multiclonal infections. 106

Infections were classified as multiclonal infections if the sequence contained one or more sites at which the secondary peak was at least 30% of the primary peak (Figure 3.2).

Several of the samples were PCR cloned, and the clones were sequenced individually to confirm that the polymorphic sites could be produced as independent sequences. 107

Figure 3.2 Electropherograms of multiclonal and single clone infections. Electropherograms of variable sites for multiple clone infections (above) and single clone infections (below). Informative sites are indicated with arrows.

108

Statistical analysis

The program DnaSP version 5.10 (Librado, 2009) was employed to calculate sequence diversity statistics from Sceloporus jarrovii cytochrome b sequences from the host populations (Kaplan in prep). Linear regressions on the malaria prevalence estimates versus the lizard population nucleotide diversity estimates Pi and Theta-W were used to test for a correlation between the genetic diversity of the lizard populations and the prevalence of the malarial infection. To investigate other aspects of the lizards that have been explored in previous studies of saurian malaria (Schall, 1996), chi square tests were used to compare the prevalence of infection to gender, size (snout-vent length), presence of the original tail, and degree of mite infestation (as categorized subjectively on a scale from 0 to 5). Due to the limited number of samples that were tested to estimate the frequency of multiclonal infections, the three populations with the highest prevalence were grouped as “high-prevalence populations”, and the three with the lowest prevalence were grouped as “low-prevalence populations” in order to examine the relationship of the prevalence of infection to the frequency of multiclonal infections. Fisher’s exact test was performed to determine if the two groups differed significantly in the frequency of multiclonal infections.

A logistic regression was run on all of the factors potentially predictive of individual lizard’s probability of infection with malaria (gender, tail condition, body size, and degree of mite infestation). A logistic regression explores the relative impact of multiple variables simultaneously and enables the identification of the predictive value of each variable. 109

Results

A total of 104 lizards from eight populations representing seven mountain ranges were sampled (Figure 3.1). The Patagonia population was not included in the analysis due to its limited sample size. Sixty-three of the lizards (61%) were infected with P. chiricahuae. The prevalence of infection in the lizard populations ranged from 17% to

100%, with a mean of 64.9% (Table 3.1). These estimates are higher than have been observed in similar studies (Mahrt, 1989; Vardo and Schall, 2007). Mahrt’s 1989 examination of P. chiricahuae, reported mean annual prevalences ranging from 32% to

58% with some estimates as high as 70%. The discrepancy between these two studies may stem from the increased sensitivity of PCR assay over traditional microscopy to detect low-level infections (Perkins et al., 1998; Vardo et al., 2005). 110

Table 3.1 Prevalence of Plasmodium chiricahuae infection in Sceloporus jarrovii populations.

111

Twenty-five18s SSU rRNA sequences were obtained and evaluated for within- sequence polymorphism from infected lizards. Of the 25 sequences, 16 showed evidence of multiclonal infections, while the remaining 9 contained no within-sequence polymorphism. The linear regressions performed to examine the relationship between the prevalence estimates versus the population nucleotide diversity estimates both showed evidence of significant negative relationship between, Pi (R2=0.500, one tailed P= 0.038) and Theta-W (R2=0.488, one tailed P= 0.040) (Figure 3.3). The chi square tests on the lizard correlates of infection found no deviation from a random distribution for lizard gender (P=0.818) (Figure 3.4) or tail condition (P=0.774) (Figure 3.5). The infections were not randomly distributed across lizard size classes (P=0.0006), I found a positive relationship between lizard size and prevalence of infection (Figure 3.6). This association has been previously observed in other lizard malarial studies (Schall, 1996). The distribution of malarial infection across the categories of mite infestation departs from random (P=0.0004). The prevalence of malaria infection is positively associated with the higher levels of mite infestation (classes 3-5), but the lower levels of mite infestation

(classes 0-2) do not show any relationship (Figure 3.7). The results of the logistic regression were consistent with the independent analyses of the correlates of lizard infection. This analysis found significant correlations of infection with lizard size

(p=0.0067) and degree of mite infestation (p=0.0015), and no relationships with gender

(p=0.6098) or tail condition (p=0.1680). 112

Figure 3.3 Graphs of Lizard population genetic diversity vs. population prevalence of malaria infection.

113

Figure 3.4 Graphs of Malaria prevalence by lizard population by lizard sex.

114

Figure 3.5 Graphs of Malaria prevalence by lizard tail condition.

115

Figure 3.6 Graphs of Malaria prevalence by lizard size.

116

Figure 3.7 Graphs of Malaria prevalence by level of lizard mite infestation.

117

The results of Fisher’s exact test showed a significant difference in the frequency of multiclonal infections between the high and low prevalence categories (p=0.0065).

The classification of the populations into groups of “low” and “high” prevalence did show some surprising results. Although all of the high-prevalence populations have a high frequency of multiclonal infections (50% to 100%), the low-prevalence populations were not nearly as uniform, with two of populations having no multiclonal infections, and the third having 75% (Table 3.2). 118

Table 3.2 Frequency of multiclonal infections in populations.

119

Discussion

One of the most interesting results uncovered in this study is the negative association between the prevalence of infection and host genetic diversity. It adds further support to the growing literature of host diversity and disease susceptibility. Studies have found this association in island populations of inbred hosts (Coltman et al., 1999;

Whitehorn et al., 2011; Whiteman et al., 2006). The populations of Sceloporus jarrovii in southern Arizona are another island system of hosts and parasites (Kaplan in prep), and this system provides multiple population replicates for exploration. This finding, combined with the preliminary results that populations with higher prevalence of infection have a higher frequency of multiclonal infections, suggest that populations of hosts with higher diversity might have malarial parasites with reduced opportunity for sexual reproduction.

It is important to view the genetic makeup of a parasite population as a subdivided population. “Genetic neighborhoods” (Slatkin, 1985) are a prominent factor in the genetic makeup of any given parasitic infection. Slatkin defines a genetic neighborhood as “the number of individuals within one dispersal distance”. Neighborhood size determines the amount of genetic differentiation between populations. In parasite populations, transmission methods and frequencies dictate the size of the parasite’s genetic neighborhood. For a vector borne parasite, like Plasmodium, the genetic neighborhood would include all of the parasites infecting a single host.

The probability of selfing vs. outcrossing in a parasitic population is determined by the frequency of hosts that are infected by more than one parasite. If most infections 120

are single clone infections, selfing will be the predominant mode of reproduction within that parasite population. If most are multiclonal infections, then there are ample opportunities to outcross. There has been considerable debate as to how much selfing vs. outcrossing occurs in the human pathogen P. falciparum (Anderson et al., 2000; Conway et al., 1999; Rich and Ayala, 1998; Rich et al., 1997). While some reports have indicated that P. falciparum may be a predominantly clonal organism (Rich et al., 1997), others have shown evidence for high levels of outcrossing (Anderson et al., 2000; Conway et al., 1999). Anderson et al. (2000) showed that the mode of reproduction for P. falciparum varies among populations based on the frequency of multiclonal infections.

The average number of P. falciparum clones within a single human host has been shown to be linked to the infectious bite rate of the vector and the prevalence of infection within host populations (Babiker et al., 1997). If the prevalence of infection is low, then the probability of a single host becoming infected multiple times, while most hosts remain uninfected (barring immunity), is low. On the other hand, if the prevalence of infection is high, the probability of a single host becoming infected multiple times is correspondingly higher (Babiker et al., 1997). This effect is further amplified by multiclonal infections serving as a source to found new multiclonal infections. It has been shown in P. falciparum that multiclonal infections can be established with a single infectious bite (Farnert et al., 2002). The frequency of multiclonal infections should rise as high transmission rates persist, since these infections are the source of future infections. If high transmission frequencies do not persist, the probability of multiply- founded infections would decline, and the genetic diversity within infections would also 121

decline. This diversity within infections in the population would continue to decline as each transmission event would serve as a genetic founder effect. The time that it may take for this spiral of ever-declining diversity to be realized would likely be dependent on the lifespan of the infected hosts. As multiclonal infections persist, they serve as reservoirs of diversity and continue to produce subsequent multiclonal infections even after transmission frequencies have declined. The impact of transmission-driven bottlenecks would likely have to span several generations of vertebrate hosts to reduce the frequency of multiclonal infections.

This study’s high-variance results in the frequency of multiclonal infections in the

“low-prevalence populations” (Table 3.2) raises the prospect that current prevalence estimates may not tell the whole story. The Quinlan population, for example, has the highest prevalence (63.2%) of the “low-prevalence populations” and contains no multiclonal infections. By comparison, the Santa Rita population has about half the prevalence (33.3%) but contains 75% multiclonal infections. This discrepancy could be attributed to the small sample size in the Santa Rita population (only 4 infected lizards), or it could be driven by a reservoir of multiclonal infections established at a time of higher transmission in earlier years. Data from a larger sample size could clarify this discrepancy. This study system might mirror the nonlinear associations observed by

Anderson and collaborators (2000) between prevalence of infection and the frequency of multiclonal infection, or might conversely support the findings of Vardo and Schall

(2007). They observed a linear correlation between prevalence of infection and the frequency of multiclonal infections in their study of P. mexicanum. One of the questions 122

Vardo and Schall raised in their work may also offer an explanation for the variance observed in my research of the “low-prevalence populations”. They observed a “…high proportion of infections that were multiclonal, even for the last year sampled (2005) after

4 years of low prevalence.” This decline in prevalence, with a lag in the decline of the multiclonal infections, and my observations in the Quinlan and Santa Rita populations, could be the result of a longer-than-expected time scale necessary to reduce the frequency of multiclonal infections.

Prevalence of infection may change on a fine-time scale with stochastic events such as shifts in climate or population size of either the vector or the vertebrate host. The impact of a few seasons of low transmission may act to reduce the prevalence for several years. Since it has been demonstrated that lizards do not resolve their malaria infections

(Bromwich and Schall, 1986), multiclonal infections can persist over years of low prevalence. Without novel, multiply-founded infections, sampling variance should eventually result in populations of exclusively single infections, but the time necessary for this process would be dependent on the frequency of multiclonal infections already established and on the life span of the hosts with those infections. The frequency of multiclonal infections may take much longer to decline; with the ability for multiclonal infections to be the direct source of additional multiclonal infections this may require time scales longer than the lifespan of individual lizards.

A positive relationship between the prevalence of infection and the frequency of multiclonal infections has substantial ramifications to the population genetics of the malarial parasites. Since multiclonal infections present the only opportunity for the 123

parasites to outcross, Plasmodium in populations of low prevalence reproduce primarily by selfing. It is therefore possible that the variation of Plasmodium’s effective population size could be extensive between populations of low and high prevalence. In low- prevalence populations not only are fewer hosts infected, but selfing will further reduce the effective population size. In high-prevalence populations the larger number of individuals and the degree of outcrossing will both work to increase effective population size. This has further implications to the relative strengths of genetic drift and natural selection.

Genetic drift is an important force in small populations. Conversely, in larger populations, drift is less important, and natural selection is more effective (Wright, 1931).

This is of particular importance to many of the Plasmodia of wild animals that often occur in low prevalence. In organisms that are considered to be at constant odds with their host, the impact of long periods of time with low prevalence could leave them at a distinct evolutionary disadvantage. In this study the implications of this evolutionary disadvantage put the host and parasite particularly at evolutionary odds – the lizards with the greatest genetic diversity appear to have the parasites with the least and the lizards with the least genetic diversity may have the parasites with the most.

This study system presents many good opportunities for further research. The question of the frequencies of multiclonal infections is a preliminary finding that should be further explored with microsatellite loci (Schall and Vardo, 2007) and greater population sampling. With isolated populations of varied prevalence of infection and varied frequencies of multiclonal infections, the populations of P. chiricahuae will 124

provide an ideal system for further studies of the effects of multiclonal infections on

Plasmodium sex ratios, virulence, and other host-parasite dynamics. 125

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