Evolutionary biology of Australia’s rodents,

the Pseudomys and Conilurus Species Groups

Peter J. Smissen

Submitted in total fulfilment of the requirements of the degree of

DOCTOR OF PHILOSOPHY

May 2017

School of BioSciences

Faculty of Science

University of Melbourne

Produced on archival quality paper

1

Dedicated to my parents: Ian and Joanne Smissen.

2 Abstract

The Australian rodents represent terminal expansions of the most diverse family of mammals in the world, Muridae. They colonised New Guinea from

Asia twice and Australia from New Guinea several times. They have colonised all Australian terrestrial environments including deserts, forests, grasslands, and rivers from tropical to temperate latitudes and from sea level to highest peaks. Despite their ecological and evolutionary success Australian rodents have faced an exceptionally high rates of extinction with >15% of species lost historically and most others currently threatened with extinction.

Approximately 50% of Australian rodents are recognised in the Pseudomys

Division (Musser and Carleton, 2005), including 3 of 10 historically extinct species. The division is not monophyletic with the genera Conilurus,

Mesembriomys, and Leporillus (hereafter Conilurus Species Group, CSG) more closely related to species of the Uromys division to the exclusion of

Zyzomys, Leggadina, Notomys, Pseudomys and Mastacomys (hereafter

Pseudomys Species Group, PSG). In this thesis, I resolved phylogenetic relationships and biome evolution among living species of the PSG, tested species boundaries in a phylogeographically-structured species, and incorporated extinct species into a phylogeny of the CSG.

To resolve phylogenetic relationships within the Pseudomys Species

Group (PSG) I used 10 nuclear loci and one mitochondrial locus from all but one of the 33 living species. The group comprises five genera, which are widely distributed across the continent’s biomes, and represent the most

3 diverse group of rodents to evolve from a single colonization of Australia. With a well-resolved phylogeny I recovered limited support for an early burst in diversification, instead indicating a steady continuous rate of diversification through time. I identify and date at least 14 biome transitions since the group’s origin 5-8 Mya, with early transitions between the monsoon and arid biomes, but with transitions into the temperate mesic biome occurring ~2 MY later. I found that early-evolving genera specialised to individual biomes with few transitions to other biomes, where the phylogenetically-nested genus

Pseudomys transitioned between arid and mesic biomes repeatedly. My results suggest that at the broad environmental scale of biome transitions evolutionarily labile niche divergence can evolve in lineages descended from niche-conservative taxa.

Within the endangered Hastings River mouse, Pseudomys oralis, I tested if geographically-structured mitochondrial lineages reflect distinct species by sequencing nine independent nuclear exons. Pseudomys oralis is distributed along the eastern mesic zone of Australia across the Macleay-Mcpherson

Overlap Zone (MMOZ) in northern New South Wales. This suture zone represents the contact between divergent northern and southern lineages from several taxa driven by the interplay between its biogeographic barriers and Pleistocene climatic fluctuations. Pseudomys oralis is comprised of two mitochondrial lineages that are distributed in the northern and southern parts of its range, and overlap in MMOZ. Using nine nuclear exons I showed the deep divergence between mitochondrial lineages is not reflected in the nuclear genome. I recovered limited differences in allele frequencies of

4 nuclear exons among mitochondrial lineages with a zone of overlap at

Washpool National Park, NSW. However, gene flow between these two populations is most consistent with panmixia, suggesting that the populations are freely interbreeding and do not reflect distinct species. Overall,

Pseudomys oralis exhibit a pattern of shallow divergence with recent secondary contact. This pattern is consistent with isolation and secondary contact resulting from Pleistocene contraction and fragmentation of east coast mesic forests as reported for other taxa in this region.

To resolve phylogenetic relationships in the Conilurus Species Group

(CSG) including placement of the extinct species, Conilurus albipes, I developed a custom in-solution based target enrichment system that targets

1366 nuclear exons and two mitochondrial protein-coding genes. I developed the system to capture and sequence genomic-scale data from both recent tissues and historical skins. The CSG comprises three genera and seven species, three of which have been lost to extinction historically. I sequenced

1368 exonic regions from all 4 living species of the CSG and from one extinct species, the White-footed Rabbit-rat, Conilurus albipes, represented by an historical museum skin collected in the 1850s. I used replicate DNA extractions for the skin specimen to demonstrate high accuracy and limited

DNA damage for 349 of 1368 loci. I placed C. albipes in a phylogeny with its congener C. penicillatus, as well as 11 other species of Australian rodents, and dated their divergence to 2.5 Mya, a comparably deep divergence compared to other sister taxa like Mesembriomys gouldii and M. macrourus, and Pseudomys australis and Mastacomys fuscus.

5 My thesis revealed the rapid diversification, biome transitions, population structure, and loss of genomic diversity through extinction in Australian rodents. I highlight this group as a unique opportunity to investigate questions across numerous areas including: phylogeography, population genetics, conservation, convergence and adaptive evolution, colonisations and biome transitions, extirpation and extinction, and diversification and species delimitation. Australia’s rodent fauna represents an important model system for answering numerous questions across the broad field of evolutionary biology.

6 Declaration

This is to certify that:

i) This thesis comprises only my original work towards the degree of

PhD;

ii) Due acknowledgement has been made in the text to all other

material used; and

iii) The thesis is fewer than 100,000 words in length, exclusive of

tables, maps, bibliographies and appendices.

Peter J. Smissen

May 2017

7 Preface

This thesis comprises a series of independent publications. Chapters 2-4 include co-authored manuscripts that have either been published or will be submitted for publication. Although the publications are co-authored, I performed the majority of laboratory work and analyses. I was also involved in fieldwork and museum visits in order to collect tissue for some specimens.

Specific contributions of each co-author are outlined below.

My supervisor Kevin Rowe assisted with the development of research questions and provided direction regarding methodology and analyses for each chapter. Tissues used in all chapters were collected from Museum

Victoria, South Australia Museum and the Australia Biological Tissue

Collection, and Southern Cross University. Emily Roycroft, Adnan Moussalli and Andrew Hugall aided in analyses in Chapter 4. Kevin Rowe carried out the library preparation for Chapter 4 at the Australian National University in conjunction with the Moritz Laboratory where the libraries for each samples were sequenced. All co-authors commented on previous drafts of the manuscripts.

This thesis includes the following chapters for publication:

Smissen, P. J., Rowe, K. C. (in review, March 2017) Reversible evolution of

biome transitions in the recent evolution of Australian rodents

8

Smissen, P. J., Rowe, K. C. (in prep) Mito-nuclear discordance in the

Hastings River Mouse, Pseudomys oralis, suggests dynamic

population contraction-expansions during glacial cycles

Smissen, P. J., Roycroft, E. J., Moussalli, A., Rowe, K. C. (in prep)

Sequencing historic museum specimens of the extinct White-footed

Rabbit-rat, Conilurus albipes, shows extensive diversity lost to

extinction.

9 Acknowledgements

This thesis was completed through the support of many different people.

First and foremost, I want to thank my supervisor Kevin Rowe. Thank you for your continued support and confidence in my abilities, as well as helping me make sense of what was often confusing to interpret results, and also reading countless drafts of my thesis.

I would like to thank the students and staff at Museum Victoria, particularly

Kirilee Chaplin, Stella Shipway, Kate Trewin, Monique Winterhoff, Heru

Handika, Karen Rowe, Rebecca Laver, Stella Claudius, and Mark Norman. I want to thank Andrew Hugall for his many insightful conversations as well.

I also want to thank Joanna Sumner (Museum Victoria) and Stephen

Donnellan (South Australian Museum) for generously providing tissue samples used in this thesis. Furthermore, I want to thank Craig Moritz (The

Australian National University) and his laboratory for generously providing funding and helping with the library preparation and sequencing of specimens.

Thank you so much Emily Roycroft and Phoebe Burns. Thanks for the endless hours of support and debauch. You guys kept me sane all along this long road to the end. I can’t thank you guys enough and wish you all the best with your PhDs and future careers.

10 I would also like to thank my family. Thank you to Annika Smissen and

Rory Douglas, and to my parents Ian and Joanne Smissen. You both instilled in me an endless amount of curiosity and passion for the natural world from a very young age. Without your constant support and encouragement this would never have been possible. I love you both more than you will ever know.

Thank you!

The work in this thesis was funded through Museum Victoria as well as funding from the Holsworth Wildlife Research Foundation grant.

11 Table of Contents

Abstract ...... 3

Declaration ...... 7

Preface ...... 8

Acknowledgements ...... 10

List of Tables ...... 15

List of Figures ...... 18

Chapter 1 ...... 23

Thesis outline ...... 29

Chapter 2 ...... 32

2.1. Abstract ...... 32

2.2. Introduction ...... 33

2.3. Materials and Methods ...... 39

2.3.1. Specimens and Genetic Sequencing ...... 39

2.3.2. Analysis of Phylogenetic Relationships ...... 43

2.3.3. Molecular Dating and Rate of Diversification ...... 45

2.3.4. Ecological Transitions ...... 48

2.4. Results ...... 50

2.4.1. Phylogenetic Analysis ...... 51

2.4.2. Molecular Dating and Rate of Diversification ...... 54

2.4.3. Ancestral State Reconstruction ...... 55

2.5. Discussion ...... 60

2.5.1. Phylogenetic Constraint and Niche Conservatism ...... 61

2.5.2. Ecological Opportunity and Diversification...... 63

12 2.5.3. Multi-locus phylogeny of the PSG ...... 65

2.6. Acknowledgements ...... 67

2.7. Appendices ...... 69

Chapter 3 ...... 96

3.1. Abstract ...... 96

3.2. Introduction ...... 97

3.3. Methods ...... 102

3.3.1. Specimens and Genetic Sequencing ...... 102

3.3.2. Phylogenetic Analyses ...... 104

3.3.3. Population Genetic Analyses ...... 107

3.4. Results ...... 110

3.4.1. Phylogenetic Analyses ...... 110

3.4.2. Population Genetics ...... 111

3.5. Discussion ...... 113

3.6. Appendices ...... 118

Chapter 4 ...... 129

4.1. Abstract ...... 129

4.2. Introduction ...... 130

4.3. Methods ...... 134

4.3.1 Sample Collection ...... 134

4.3.2. DNA Extraction ...... 135

4.3.3. Exon Capture Library Design ...... 136

4.3.4. Library Preparation And In Solution Exon Capture ...... 137

4.3.5. Sequence Assembly and Alignment ...... 138

4.3.6. Estimates of DNA Damage ...... 140

4.3.7. Phylogenetic Analyses ...... 140

13 4.4. Results ...... 142

4.4.1. hDNA extractions ...... 142

4.4.2. Sequence Assembly and Alignment ...... 143

4.4.3. Estimates of DNA damage ...... 143

4.4.4. Phylogenetic analyses ...... 144

4.5. Discussion ...... 146

4.6. Appendices ...... 150

Supplementary Materials ...... 162

Chapter 5 ...... 166

Literature Cited ...... 176

14 List of Tables

Table 2.1. Primer sets used in the 11-locus dataset.

68

Table 2.2. Twelve partitions of the concatenated sequence data used in

phylogenetic analyses as estimated in PartitionFinder.

69

Table 2.3. Ages and probabilities of biome and binary states estimated for

select nodes discussed in the text and presented on figure 4. Mean

and confidence intervals around age estimates in millions of years.

70

Supplementary Table 2.1. GenBank Accession Numbers for each locus

sequenced in this study or obtained from GenBank. First column is

Taxa ID name and the following 11 columns are the respective loci

used in this study.

71

Supplementary Table 2.2. Sampling used for each of the respective

analyses in this study.

76

Supplementary Table 2.3. Aridity Index means for each species within the

PSG.

80

Supplementary Table 2.4. Estimation of tmrca in millions of years before

present for Pseudomys Species Group based on four fossil

15 calibrations from the rodent subfamily Murinae. ESS = Effective

Sample Size.

81

Supplementary Table 2.5. The seven subgroupings within Pseudomys as

outlined by Breed and Ford (2007).

82

Table 3.1. Sample IDs, mitochondrial lineage as determined by Rowe et al.

(2012), and GPS location.

115

Table 3.2. Best partitioning scheme as estimated in PartitionFinder.

116

Table 3.3. Bayes Factors and log marginal likelihoods as calculated from

MIGRATE-N for the three migration models tested: Panmictic; North

to South (1à2) and South to North (2à1).

117

Table 3.4. Individual locus information as calculated in MEGA v7.

118

Table 4.1. Taxanomic sampling, tissue types, exon sampling for the

‘complete’ and ‘strict’ exon datasets. Nucleic acid concentration

based on Qubit results.

147

Table 4.2. Total number of exons and nucleotides for each sample.

148

Table 4.3. Exon capture mapping output summary. Continues next page.

16

Table 4.3. Continued. Exon capture mapping output summary.

149

Table. 4.4. Calibrations used in the MCMCtree analysis and the resulting node

dates (see Fig. 4.6).

151

17 List of Figures

Figure 2.1. Map of Australia illustrating the sampling of specimens from the

five genera of the Pseudomys Species Group (PSG) used in the

study. The distribution of each of the four biomes is shown using

shading.

83

Figure 2.2. Phylogeny resulting from Bayesian analysis of murid dataset

including PSG and Muridae outgroups. Nodes are labelled with

Posterior Probability (PP; left half of circle) and Maximum Likelihood

Bootstraps (MLBS; right half of circle). Within circles, black fill

indicates PP > 0.95 and MLBS > 90, and white fill indicates PP <

0.95 and MLBS < 90. Breed and Ford (2007) groupings within

Pseudomys indicated by white and black shapes.

84

Figure 2.3. Species tree estimated using BEAST including only the PSG taxa.

Nodes shaded in grey indicate lack of significant support, PP <

0.90. The * represent nodes that are incongruent with the trees from

the phylogenetic analyses.

85

Figure 2.4. Chronogram generated using BEAST, trimmed to show only the

PSG (ingroup) in the analysis. Pie charts on each node represent

the Biome distribution probabilities estimated with BioGeoBEARS

(values and binary traits presented in Table 2.3). Biome states of

terminal taxa are indicated at terminal nodes.

18 86

Figure 2.5. Lineage through time plot of the PSG. Solid line shows median of

1000 sampled species trees. Dashed line represents linear

regression. Shaded area represents 95% CI of estimated from

1000 sampled species trees.

87

Supplementary Figure 2.1. Bayesian phylogeny based on 10 nuclear exons.

88

Supplementary Figure 2.2. Bayesian phylogeny based on mitochondrial

locus Cyt-b.

89

Supplementary Figure 2.3. ML phylogeny based on mitochondrial locus Cyt-

b.

90

Supplementary Figure 2.4. ML phylogeny based on 10 nuclear locus

dataset.

91

Supplementary Figure 2.5. BioGeoBEARS analyses run on the PSG using the

BEAST tree with Sahul, the Philippines and Southeast Asian

outgroups. The key on the bottom left indicates the biome(s) in

which the species is presently distributed (squares), and the pie

graphs indicate the reconstructed node probability that that

ancestor was found in a given biome.

92

19 Figure 3.1. Map of Australia. Pink circles = Northern mitochondrial lineage,

Blue circles = Southern mitochondrial lineage, Pink + Blue circles

= localities where both northern and southern mitochondrial

lineages were found. Black oval = zone of overlap between the

two mitochondrial lineages. GDR = Great Dividing Range, MR =

McPherson Range.

119

Figure 3.2. The multi-locus distance matrix based on allelic variation

generated in POFAD based on the nine-nuclear locus dataset. Pink

= Northern mitochondrial lineage, Blue = Southern mitochondrial

lineage. Red labels = individuals located at the zone of overlap at

Washpool National Park.

120

Figure 3.3. Species tree generated in *BEAST based on the nine-nuclear locus

dataset.

121

Figure 3.4. Bayesian tree based on Cyt-b mitochondrial locus. Pink =

Northern mitochondrial lineage, Blue = Southern mitochondrial

lineage. Red boxes = individuals located at the zone of overlap at

Washpool National Park.

122

Figure 3.5. Phylogenetic tree based on ML and Bayesian analyses of

concatenation of nine nuclear loci. Bootstrap support reported

above branches and Posterior Probability reported below branches.

20 Pink = Northern mitochondrial lineage, Blue = Southern

mitochondrial lineage. Red dots = individuals located at the zone

of overlap at Washpool National Park.

123

Figure 3.6. STRUCTURE analysis results estimated clusters for K = 1, K = 2,

and K = 3.

124

Figure 3.7. Map of posterior probability for cluster 1 and cluster 2 estimated in

GENELAND indicating two population clusters (K = 2) separated at

zone of contact at Washpool National Park between mitochondrial

lineages.

125

Figure 4.1. The distribution of species of Conilurus generated from collection

records in the Atlas of Living Australia (http://www.ala.org

Accessed 18 May 2017). Blue = contemporary distribution of C.

penicillatus. Yellow = location of C. capricornensis fossil discovery.

Red = historical distribution of C. albipes.

152

Figure 4.2. ML trees based on 100bs for the ‘stritct’ dataset on the left and the

‘complete’ dataset on the right.

153

Figure 4.3. Agar gel image of hDNA extraction for C. albipes and R. lutreolus.

154

21 Figure 4.4. Illumina sequencing: Position specific frequencies of nucleotide

misincorporation patterns across reads from the historical samples,

R. lutreolus compared to a contemporary sample of R. fuscipes

(RIGHT) and C. albipes compared to a contemporary sample of C.

penicillatus (LEFT). The graphs indicate the frequencies of different

categories of DNA damage as a function of distance from the 5’-

ends (the first 25 bases, LEFT) and 3’- ends (the final 25 bases,

RIGHT). Coloured lines may be masked as due to the limited

damage in these samples, the frequencies of some damage

converged around 0.00. Red: C → T. Blue: G → A. Purple:

Insertions. Green: Deletions. Orange: Clipped bases. Grey: Other

misincorporations.

155

Figure 4.5. Histogram showing the distribution of loci in the 1368 dataset

(light grey) and 349 dataset (dark grey) with p-distance on the x-

axis and frequency of loci on the y-axis. The slow-evolving HOXC6

labelled and the fast-evolving CO1 and Cyt-b mitochondrial loci are

labelled.

156

Figure 4.6. A time-calibrated ultrametric phylogeny generated in MCMCtree.

Scale is measured in millions of years. Calibrations 1, 2 and 3 are

labelled on their corresponding nodes.

157

22 Chapter 1

General Introduction

Continental Australia (Sahul) supports an extensive endemic rodent fauna that evolved from two colonisations centred on New Guinea (Rowe et al.,

2008, 2011). It comprises >180 species across 38 genera, which are split into two broad groups, the ‘Old Endemic’ and the ‘New Endemic’. The ‘Old

Endemic’ rodents arrived in the Miocene (~7-9 Mya; Rowe et al., 2016), and the second colonists, the endemic Rattus, arrived in the Pleistocene (~1 Mya;

Chapter 2, Fig. 2.2). From New Guinea, rodents colonised Australia at least six times. Their arrival in Australia occurred after the origin of the major modern biomes, including the recent expansion of the arid biome (Byrne et al., 2008). Since their arrival, rodents have adapted to and now occupy all

Australian biomes (Breed and Ford, 2007). However, the order and timing of colonisations into each of these biomes is yet to be resolved (Ford 2006).

Previously, the species examined in my research were classified together within a group called the Conilurini (Lidicker and Brylski, 1987; Ford, 2003,

2006; Baverstock, 1983). However, recent research indicated that the taxa in the Conilurini actually represent two paraphyletic clades (Rowe et al., 2008).

Herein, I split Conilurini into two monophyletic groups of species, the

Pseudomys Species Group (PSG) and the Conilurus Species Group (CSG).

The PSG represents at least 42 species within the five genera Pseudomys,

Mastacomys, Notomys, Leggadina and Zyzomys, and the CSG represents at

23 least seven species within the three genera Conilurus, Leporillus and

Mesembriomys. These two clades are almost wholly restricted to Australia and make up ~80% of native Australian rodent species (Van Dyck and

Strahan, 2008). The PSG is distributed across all major Australian biomes including arid, monsoon and mesic biomes. However, none of its members have colonised the Australia’s tropical rainforest biome, to which many of the

PSG’s closest relatives are restricted (Rowe et al., 2008). Despite numerous morphological and molecular studies, phylogenetic relationships in the PSG remain largely unresolved (Baverstock et al., 1981; 1983; Watts et al., 1992;

Torrance, 1997; Ford, 2006; Crabb, 1977; Lidicker and Brylski, 1987; Breed,

1997). The most recent molecular studies have failed to answer long-standing questions such as the monophyly of genera (e.g. Notomys and Pseudomys), the branching order of early diverging lineages, and the phylogenetic placement of the genus Mastacomys (Ford, 2006). Within the PSG, the genus

Pseudomys accounts for 25 of the 42 species with seven subgroups recognized within the genus (Breed and Ford, 2007). While these subgroups are based on ecological, morphological, and some molecular data they have not been tested with a well-resolved phylogeny.

The contemporary distributions of taxa and the structure of genetic diversity within species are shaped by physiographic and climatic factors (Hewitt, 1996,

2000, 2004; Byrne et al., 2008, 2011; Chapple et al., 2011a). The interplay between these temporally dynamic processes disrupts gene flow within taxa and underpins speciation. In the recent past, global climates have cycled due to glacial cycles, especially during the Plio-Pleistocene. These climate cycles

24 have been widely studied in the Northern Hemisphere, where periodic glaciation underpins the biogeography and diversification of many taxa

(Hewitt, 1996, 2000, 2004). Although the Southern Hemisphere has received less attention in the context of glacial cycles, habitat fluctuations due to climatic cycles also have underpinned the biogeography and diversification of taxa (Byrne et al., 2008, 2011; Chapple et al., 2011a). In addition to climate cycles, long-standing biogeographic barriers whether physiographic or ecological have shaped the contemporary distribution of species (see

Chapple et al., 2011a).

The Eastern Mesic Zone (EMZ) is distributed along the Great Dividing

Range (GDR) running the length of Australia’s eastern coastline, and is arguably the most well-studied biome in Australia. It is bisected by numerous biogeographic barriers, both physical (e.g. McPherson Range) and ecological

(e.g. Hunter Valley), and has experienced substantial fluctuations in habitats that have shaped the structure and distribution of species (reviewed in

Chapple et al., 2011a). The EMZ comprises the majority of Australia’s mesic forests and an extensive portion of the continent’s taxa too (Byrne et al.,

2011). Numerous studies examining the phylogeography of taxa within the

EMZ have indicated that the interaction between barriers and habitat changes in response to historic climatic cycles has disrupted gene flow, shaping the evolutionary history of taxa (Hugall et al., 2002; Hoskin et al., 2005; Moritz et al., 2005; Brown et al., 2006; Moussalli et al., 2009; Chapple et al., 2011b,

2011b; Smissen et al., 2013). Many of these taxa are listed as endangered as

25 a result of land-clearing, introduced pest species, etc. (IUCN, 2016), and require effective management plans for conservation.

One such example is the Hastings River mouse, Pseudomys oralis, an endangered rodent species endemic to temperate sclerophyll forests of the

EMZ. This species is comprised of two divergent mitochondrial lineages, which don’t appear to correspond with any overt biogeographic barrier (Jerry et al., 1998; Rowe et al., 2012). However, the two lineages have a zone of contact in the Macleay-McPherson Overlap Zone (MMOZ), an area where the

Torresian and Bassian biogeographic regions overlap (Burbidge, 1960). The divergence of the mitochondrial lineages is relatively recent, estimated to between 300-900Kya with a genetic divergence of 4.8%, which is comparable to that of other sister-species pairs in Pseudomys (Rowe et al., 2012).

However, the status of these lineages as distinct species remains untested with additional molecular markers (i.e. nuclear loci). Testing whether or not these two mitochondrial lineages represent independent species by using nuclear loci will achieve several things. First, it will lead to a better understanding of how biogeographic barriers and climate cycles underpin species diversification in the EMZ. Secondly, it will improve our understanding of population structure and/or gene flow within the species, as well as allow the design and implementation of more effective conservation measures.

Last, it will lay out a framework that future research can use to test the genetic processes underpinning the evolutionary history of other related taxa.

Australia has the highest rate of mammalian extinctions in recorded history with an estimated 10% of the continent’s native mammal species,

26 representing 40% of worldwide mammal extinctions, recently lost to extinction

(Johnson, 2006; Woinarski et al., 2014). At least 30 species of terrestrial mammals, including at least 10 species of rodents, have become extinct within the 200 years following European colonisation (Woinarski et al., 2015).

The most extreme example is the Conilurus Species Group (CSG), which comprises the genera Conilurus, Mesembriomys and Leporillus. It has lost three of its seven species, accounting for 42% of its diversity, including the species C. albipes, C. capricornensis and Leporillus apicalis. In order to understand the role extinction has played in reducing diversity in groups like the CSG it is important to develop a phylogenetic framework that includes historical museum specimens. Thus, developing methods to successfully extract, sequence, and generate large genetic datasets for extinct species will greatly improve our understanding of genetic variation preceding extinction.

All historically extinct Australian rodent taxa are represented by specimens stored in museums. However, until recently extraction and sequencing genomic-scale datasets from historical/ancient DNA (hDNA/aDNA) specimens was both limited and expensive (see Bi et al., 2013; Gamba et al., 2016;

Rohland et al., 2010; Rowe et al., 2011a). As a result, studies examining the diversity within groups such as the Australian rodents have been unable to include a large and crucial part of the diversity within the group (Baverstock et al., 1981; 1983; Watts et al., 1992; Torrance, 1997; Ford, 2006; Crabb, 1977;

Lidicker and Brylski, 1987; Breed, 1997), thus, giving only a glimpse of the evolutionary history of the group. As tissue sampling and DNA extraction techniques improve and become less invasive (e.g. Mason et al., 2011; Rowe

27 et al., 2011), and with the advent of new sequencing technologies such as

Illumina (Shendure and Ji, 2008; Metzker, 2010), extensive high-quality genetic datasets have become much more readily generated. Such datasets can now be created from fresh DNA of contemporary specimens or from hDNA in historical specimens that may be >100 years old (Bailey et al., 2015;

Burrell et al., 2015; Bragg et al., 2015; Druzhkova et al., 2015; Linderholm,

2016). Thus, historical museum collections remain a largely underutilised source of genetic information. This is of particular note for specimens representing extinct species or populations (Pääbo et al., 2004; Wandeler et al., 2007; Bi et al., 2013).

The motivation for this thesis is to expand knowledge of the evolution of

Australian rodents in the PSG and CSG. Since the PSG represents a rapid radiation of 42 species that remains largely unresolved, whose members are found throughout most of Australia’s biomes, I aimed to first resolve the phylogenetic relationships within the group using 11 independent protein- coding loci. With the resultant phylogeny, I examined the rate of speciation, dated generic divergences within the group and inferred the timing and sequence of biome transitions. Using the nuclear loci developed in Chapter 3,

I estimated gene flow between the two distinct mitochondrial lineages within the endangered Hastings River mouse, Pseudomys oralis. Sister species pairs within Pseudomys have comparable levels of mitochondrial divergence, so testing whether the nuclear genome reflects this pattern is important in order to confirm or reject the presence of two cryptic species within P. oralis.

In Chapter 4, I extracted and sequenced genomic-scale DNA from historical

28 museum specimens representing an extinct taxon. With these data I placed

Conilurus albipes, in a phylogeny and dated its divergence from its congener.

Thesis outline

In the past, there have been numerous attempts to resolve species relationships within the PSG using morphology and more recently using genetic methods (Baverstock et al., 1981; 1983; Watts et al., 1992; Torrance,

1997; Ford, 2006; Crabb, 1977; Lidicker and Brylski, 1987; Breed, 1997).

However, only a limited number of shallow-level relationships have been well resolved, e.g. between sister-species pairs such as P. desertor and P. shortridgei. As a result, not only is it unclear how the species and more broadly their genera are related to one another, but it also remains unclear when and in what order these taxa transitioned into and out of the Australian biomes in which they are presently found. In Chapter 2, I use an 11 locus dataset to resolve phylogenetic relationships among species in the PSG. With this phylogeny, I answered long-standing questions surrounding the PSG including: the order in which the five genera within the PSG diverged; the placement of the genus Mastacomys; the phylogenetic relationships of taxa within each genus; the age of and divergence between the different genera and clades within the PSG; and the number, the direction and the timing of biome transitions within the group.

In Chapter 3, I test if the two distinct mitochondrial lineages within the endangered Hastings River mouse, P. oralis, are divergent at nine nuclear

29 exons. I sequenced these exons from 21 individuals from across the distribution of P. oralis representing the two mitochondrial lineages and including two samples from each mitochondrial lineage where the two lineages come into contact at Washpool National Park. I used coalescent methods to test if the level of divergence uncovered in the mitochondrial genome is reflected in the nuclear genome, and estimated levels of gene flow between mitochondrial lineages.

In Chapter 4, I used a custom exon capture system to sequence 1368 exonic regions from 13 species of Australian rodents, including two historical museum specimens that were collected in the 1850s. The historical specimens included the extinct White-footed Rabbit-rat, Conilurus albipes, and the extant Swamp rat, Rattus lutreolus. For each of the two historical specimens I extracted DNA from two different tissue types (skin and bone).

These two different DNA extractions for each species were treated as replicates and used to test the hDNA extraction methods on these two different tissue types in order to determine whether bone or skin results in higher quality DNA extractions. I used the resulting exon capture dataset to produce a fully-resolved phylogeny that correctly placed the historical specimens phylogenetically. I also estimated phylogenetic divergence of the extinct C. albipes from its extant congener C. penicillatus. With this chapter I demonstrated the reliable application of a custom exon capture system for use across the phylogenetic diversity of Australian rodents, including application to historical museum skins. Thus, this system provides a foundation for comprehensive and systematic genomic-scale sequencing of

30 Australian rodents including historical museum specimens. This system will enable the resolution of a species-level phylogeny for all recently living

Australo-Papuan rodents with robust estimates of topology and branch lengths. It also allows the incorporation of extinct species and extirpated populations into understanding the genetic diversity of Australian rodents, which face major conservation threats.

31 Chapter 2

Reversible evolution of biome transitions in the

recent evolution of Australian rodents

Smissen, P. J. & Rowe, K. C., (In Review April 2017)

2.1. Abstract

Ecological transitions underpin various models of species diversification especially following colonization when a burst in diversification associated with filling of ecological opportunities is predicted. Transitions between biomes, however, should be constrained by the physiological limitations of taxa, while transitions between similar biomes should be more frequent than those between disparate biomes (e.g. arid and mesic). Reverse transitions between disparate biomes are expected to be rare, but few systems provide the taxonomic sampling and geographic scope to test these expectations. The

Pseudomys Species Group comprising 5 genera and 33 species, is the most diverse group of rodents to evolve from a single colonisation of Australia and are widely distributed across the continent’s major biomes. With a well- resolved species-level phylogeny I found limited support for an early burst model, suggesting a continuous rate of diversification through time. I identified

32 and dated 14 biome transitions since the group’s origin 5-8 Mya. I recovered early transitions to the monsoon and arid biomes, but transition to the temperate mesic biome occurred ~2 MY later. Early evolving genera remained largely specialized to a single biome with few transitions to other biomes.

However, I recovered numerous reversals between arid and mesic biomes within the phylogenetically-nested genus Pseudomys. My results suggest at the broad environmental scale of biome transitions that evolutionarily labile niche divergence can evolve in lineages descended from niche-conservative taxa.

2.2. Introduction

Biomes represent environmentally distinct geographic regions, and evolutionary transitions between biomes should be constrained by the physiological limitations of taxa (i.e. niche conservatism; Crisp et al., 2009;

Wiens et al., 2010). Transitions between biomes are expected to take a predictable course whereby transitions between disparate biomes (e.g. arid and mesic) should be less common than between more similar biomes (Crayn et al., 2006; Oliver and Bauer, 2011). The greater the difference between two biomes, the greater the number of genetic changes required to adapt and survive in the novel environment (Holt and Gaines, 1992). Furthermore, as taxa specialize to specific biomes, reversals between disparate biomes are expected to become increasingly rare. This concept of niche conservatism predicts that species retain their ancestral traits, and has been applied

33 particularly to transitions between arid and mesic biomes where the physiological challenges of arid biomes are expected to be extreme (Wiens and Harrison, 2004; Wiens and Graham, 2005; Losos, 2008). However, recent phylogenetic analyses suggest that biome reversals, including between arid and mesic biomes, are more common than expected (Fujita et al., 2010;

Mitchell et al., 2014; Oliver et al., 2014). This suggests that certain taxa are less constrained by niche conservatism and can make large evolutionary jumps, suggesting that reversibility of biome distributions may be more common than previously expected.

Australia comprises four major biomes including the arid (interior Australia), monsoon (northern Australia), tropical rainforest (northeastern Australia) and temperate mesic biomes (southern and southeastern coastal Australia; Fig.

2.1). The arid biome, which occupies ~70% of Australia, is characterised by high temperatures and low annual rainfall and is one of the world’s driest environments (Byrne et al., 2008). Species have evolved highly specialised physiologies to survive in this biome. For example, the Spinifex hopping mouse, Notomys alexis, has evolved the most efficient kidneys of any mammal in order to thrive in this harsh biome (Gordge and Roberts, 2008).

The monsoon biome is characterized by warm temperatures and seasonal cyclonic rainfall with a wet summer season and a dry winter season (Bowman et al., 2010). The temperate mesic biome is characterised by cool temperatures and high winter rainfall (Byrne et al., 2011). The tropical rainforest biome is distinguished by its warm temperatures and aseasonally high rainfall. In Australia, the tropical rainforest biome is represented by the

34 Wet Tropics region in the northeast that is recognised for its ancient origin, and high proportion of endemic species (Schneider and Moritz, 1999; Crisp et al., 2001; Hugall et al., 2002). Unlike other biomes of Australia, the tropical rainforest biome is common across neighbouring landmasses of New Guinea and the Indo-Australian Archipelago (Olson et al., 2001). Many studies demonstrate that the mesic biomes (monsoon, wet tropics and temperate mesic) are responsible for the origin of much of the continent’s biodiversity

(Byrne et al., 2011; Chapple et al., 2011b). For instance, many ancient, long- divergent phylogenetic lineages are restricted to the tropical rainforest biome that has been present in Australia for >80 MY and was the dominant biome until the Mid to Late Miocene (Truswell, 1990; Nix and Switzer, 1991; Nix,

1991; Adam 1992; Williams et al., 1996; Moritz et al., 1997; Yeates et al.,

2002; Pearson et al., 2005; Williams, 2006). In contrast, the arid biome reached its current dominance from 10 to 3 Mya and most arid biome taxa evolved recently from more mesic-adapted ancestors (Byrne et al., 2008).

While many species have diversified after colonizing the arid biome, few taxa have invaded it multiple times, and most lineages that colonised the arid biome are assumed to have remained within it without recolonising the mesic biomes suggesting that they are constrained to the arid biome by niche conservatism (Murphy et al., 2003; Chapple and Keogh 2004; Ariati et al.,

2006; Watanabe et al., 2006; Cooper et al., 2007; Rabosky et al., 2007; Byrne et al., 2008; Hugall et al., 2008; Crisp et al., 2009). The assumption is that the physiological challenges of the arid biome limit invasion by mesic-adapted species and that the adaptations required for survival in the arid biome

35 constrain those species to colonizing other biomes. However, recent phylogenetic studies of geckos and dasyurid marsupials suggest that some mesic lineages have evolved from species adapted to the arid biome suggesting that arid biome adaptations are reversible (Fujita et al., 2010;

Mitchell et al., 2014; Oliver et al., 2014).

The endemic rodents of continental Australia (Sahul) evolved from two colonizations centered on New Guinea (Rowe et al., 2008, 2011b). The ‘Old

Endemic’ rodents arrived in the Late Miocene (~8 Mya; Rowe et al., 2016), and the second colonists, the endemic Rattus, arrived in the Pleistocene (~1

Mya; Fig. 2.2). From New Guinea, rodents colonised Australia at least six times. Their arrival in Australia occurred after the origin of the major modern biomes, including the recent expansion of the arid biome (Byrne et al., 2008).

As such, they provide a unique opportunity to evaluate how taxa have adapted to and diversified within Australia’s biomes following establishment, rather than evolving in situ with the changing environments. Within Australia, rodents adapted to biomes, particularly the arid biome, for which no real analogue exists in neighbouring New Guinea or elsewhere across the Indo-

Australian archipelago where the tropical rainforest biome is dominant (Aplin and Ford, 2014). Thus, within Australia I would expect rodents to colonize the tropical rainforest biome first. Given the physiological challenges of arid environments, especially for tropical rainforest species, I would predict expansion into other mesic biomes before colonising the arid biome. The native Rattus are consistent with this prediction; they first colonized the tropical rainforest biome before expanding into the temperate mesic and

36 monsoon biomes, and colonized the arid biome last (Rowe et al., 2011b). As with Rattus, the origin of the ‘Old Endemic’ rodents is centered on the tropical rainforest biome of New Guinea and the closest relatives of the group are largely restricted to this biome across Southeast Asia and the Philippines

(Rowe et al., 2008; Schenk et al., 2013; Rowe et al., 2016). The order and timing of subsequent colonizations of the other Australian biomes are not yet resolved (Ford, 2006).

The majority of Australian rodent species (60%) are members of the

Pseudomys Species Group (PSG) within the ‘Old Endemics’ (Fig. 2.2), which is almost wholly restricted to Australia and evolved from one of six colonizations from New Guinea (33 extant species in 5 genera; Van Dyck and

Strahan, 2008). The PSG has a distribution across all major Australian biomes except the tropical rainforest biome where most of its closest relatives are restricted (Rowe et al., 2008). Previous morphological or molecular phylogenetic studies within the PSG have resulted in largely unresolved phylogenies (Baverstock et al., 1981; 1983; Watts et al., 1992; Torrance,

1997; Crabb, 1977; Lidicker and Brylski, 1987; Breed, 1997; Ford, 2006). The most recent molecular studies have failed to answer long-standing questions such as the monophyly of genera (e.g. Notomys and Pseudomys), the branching order of early diverging lineages, and the phylogenetic placement of the genus Mastacomys (Ford, 2006). The genus Pseudomys accounts for

23 of the 33 species in the PSG with seven subgroups recognized within the genus (Breed and Ford, 2007). While these subgroups are based on

37 ecological, morphological, and some molecular data they have not been tested with a well-resolved phylogeny.

The extensive ecomorphological disparity among Australian rodents, including arboreal rats, bipedal hoppers, folivores, granivores, omnivores, and carnivores suggests that ecological processes have played a vital role in driving diversification. If ecological processes underlie the diversification of rodents in Australia, I might expect to recover an early burst in species diversification indicative of the filling of ecological niches (Lovette and

Bermingham, 1999; Harmon et al., 2003; Kozak et al., 2006; McPeek, 2008;

Phillimore and Price, 2008). However, early bursts in diversification, evident from phylogenetic analyses can be driven by a number of processes and results can depend on the phylogenetic methods used (Rabosky, 2009;

Burbrink and Pyron, 2011; Moen and Morlon, 2014). Ecological opportunity models also assume that ecological niches are filled early in the history of a colonizing group. As transitions between biomes represent major ecological states for species, I expect a phylogenetic signature of rapid colonization of biomes coupled with an early burst in diversification if consistent with an ecological opportunity model.

Here I reconstruct biome transitions in the evolution of Australia’s rodents to assess the role that constraint has played in the Pseudomys Species

Group by testing whether evolutionary patterns support or contradict models of ecological opportunity and niche conservatism. I sequenced ten independent nuclear exons and one mitochondrial coding region from all but one extant species in the PSG to test the following hypotheses: (1) the PSG

38 evolved from an ancestor from the tropical rainforest biome and expanded into the temperate mesic biome, then the monsoon biome, and colonized the arid biome last as evident in the evolution of the genus Rattus in Australia; (2) transitions to the arid biome were rare and did not result in back transitions to other biomes; (3) the PSG experienced an early burst of rapid diversification following their origin; and (4) biome transitions occurred early in the diversification of the PSG as they quickly filled ecological opportunities. To test these hypotheses, I (1) resolved the phylogenetic relationships among species and dated the timing of diversification; (2) examined the accumulation of lineages through time; and (3) modelled the ancestral states of biomes in the PSG.

2.3. Materials and Methods

2.3.1. Specimens and Genetic Sequencing

I carried out phylogenetic analyses on a final set of taxa comprising 61 specimens from 32 species of the Pseudomys Species Group (PSG), which represent all but one extant species (Zyzomys pendunculatus). I did not sample any of the eight species in the PSG that became extinct historically

(Notomys n = 5; Pseudomys n = 3). Thus, my sampling comprises 32 of 41 historically extant species (78%). We obtained tissue samples from the South

Australian Museum (SAM), Museum Victoria (MV), Southern Cross University

39 (SCU) and Australian National Wildlife Collection (ANWC) (Supplementary

Table 2.2). I confirmed the identification of specimens by examining voucher specimens. I confirmed the monophyly of species and assessed signatures of geographic structure within species by sequencing the mitochondrial locus,

Cytochrome-b (Cyt-b) for 158 samples (data not shown). All Cyt-b sequences were aligned and compared with Cyt-b sequences from Genbank and then used to generate the subsequent Cyt-b trees. From these I selected my set of

61 samples for further sequencing and analysis. I excluded tissues from

Zyzomys pedunculatus, the one extant species missing in my study, because all Cyt-b sequences from these tissues nested them within samples of Z. argurus. Tissues for Z. pedunculatus were derived from a captive population without voucher specimens. The two species are readily distinguishable phenotypically, thus I assumed that tissues purportedly from Z. pedunculatus had been mislabelled. For 28 species, I included at least two individuals with samples selected from geographically disparate areas. Three specimens were included to represent the three distinct lineages of P. delicatulus apparent from my Cyt-b analyses. For the remaining four species (Z. palatalis, P. gracilicaudatus, P. higginsi and P. calabyi) I included a single specimen.

I extracted total genomic DNA from tissue samples on a QIAextractor machine (DX reagents and plasticware) or using a QIAGEN DNeasy blood and tissue kit following the manufacturer’s protocols (QIAGEN Inc, Valencia,

CA, USA). I used previously published primers to amplify Cyt-b and 7 nuclear loci (Table 2.1; A2AB, BRCA1, CB1, GHR, IRBP, RAG1, and vWF; Smith and

Patton, 1993; Jansa and Voss, 2000; Adkins et al., 2001; Steppan et al.,

40 2004; Huchon et al., 2007; Blanga-Kanfi et al., 2009). I designed three new phylogenetic markers for this study including the exon 7 of ARHGAP21, exon

16 of NIN and exon 6 of TLR3. I identified orthologous loci in the Mus musculus and Rattus norvegicus genomes on the OrthoMam database

(Ranwez et al., 2007). I selected exons over 1000 bp and did manual reciprocal blast searches to the Mus musculus and Rattus norvegicus genomes on the GenBank database until I identified a candidate set of single copy loci in both genomes. I designed primers using GENEIOUS v6.1

(Biomatters) from an alignment of the sequences are from both genomes.

Primer sequences listed in Table 2.1. I amplified and sequenced Cytb, A2AB,

ARHGAP21, BRCA1, CB1, GHR, IRBP, NIN, vWF using the following PCR protocol: initial denaturation at 95 °C for 5 min, followed by 95 °C for 30 s, an annealing step of 52 °C for 30 s and 72 °C for 90 s (40 cycles), and a final extension step at 72 °C for 10 min. I used an annealing temperature of 57 °C in the same PCR protocol to amplify the loci RAG1 and TLR3. All PCR reactions included a negative control in order to identify any cases of contamination of reagents, and I visually inspected each product on either an agarose gel or the QIAxcel electrophoresis system (QIAGEN Inc, Valencia,

CA, USA). I prepared successful reactions directly using enzymatic digestion with IllustraTM ExoSTARTM (GE Healthcare Life Sciences). I sequenced both strands of each PCR product via automated DNA Sanger sequencing on the

ABI 3730xl DNA Analyzer at Macrogen Inc (Seoul, Korea). I verified coding regions in GENEIOUS by alignment of mRNA sequences from the R. norvegicus and M. musculus genomes. I observed heterozygous positions,

41 those with two equal sized peaks for the same position, at less than 0.05% of sites in any individual. I treated all heterozygous sites as missing data, replaced with “N”, as haplotypes could not be resolved statistically in PHASE v2.1.1. (Stephens and Scheet, 2005). All new sequences will be submitted to

GenBank on publication.

To provide outgroups, incorporate fossil calibrations, and infer ancestral states, I compiled sequences from an additional 79 species of murid rodents available on Genbank (Supplementary Table 2.1). These samples included three deomyine rodents, two gerbilline rodents, and 74 species of murine rodents representing major lineages and spanning calibration nodes (Musser and Carleton, 2005; LeCompte et al., 2008). Each of these species is represented by at least three of the loci I sequenced for the PSG

(Supplementary Table 2.2). However, none of the species obtained from

GenBank contained all of the sequences I generated for the PSG. Thus, I conducted analyses hierarchically using three datasets with different amounts of taxon sampling, ancestral state information, and completeness of molecular loci. My “murid dataset” is the most taxonomically well-sampled including 111 murid species (n = 140 samples). However, these data represent a patchy supermatrix of loci and are only suitable for analyses using concatenated data. I use this dataset for broad phylogenetic estimates and to date divergence events. In order to estimate ancestral states, I analyzed a subset of the murid dataset that included 75 murid species from Australia and New

Guinea, and which I refer to as the “Sahulian dataset” (n = 104 samples),

Other murid taxa were either insufficiently sampled or lacked information that

42 would be relevant to inferring the ancestral states of the PSG. Finally, my

“PSG dataset”, including 32 species in the PSG (n = 61 samples), is complete for all 11 loci used in this study, and thus is amenable to coalescent-based species tree analyses that provide more robust estimates of my ingroup topology.

2.3.2. Analysis of Phylogenetic Relationships

I estimated phylogenetic relationships using both a concatenated supermatrix approach (murid dataset) and a species tree approach (PSG dataset; see Supplementary Table 2.2). In the concatenated supermatrix approach I carried out phylogenetic analyses using both Bayesian and

Maximum Likelihood (ML) analyses. For the concatenated analyses, I estimated the most appropriate partitioning scheme and models of molecular evolution in PartitionFinder v.1.1.0 (Lanfear et al., 2012), using the ‘greedy’ search algorithm, and I defined ‘Data Blocks’ by genes and codon position, and branch lengths were linked. I set the model parameters appropriate for each of my respective analyses, in BEAST v1.7.5 (Drummond and Rambaut,

2007; Drummond et al., 2012), RAxML v7.4.4 (Stamatakis 2006; Stamatakis et al., 2008) and MrBayes v3.2.1 (Ronquist and Huelsenbeck, 2003; Ronquist et al., 2012). I ran ML analyses in RAxML using XSEDE via the online

CIPRES Science Gateway (Miller et al., 2010), which were allowed to automatically use the extended majority-rule consensus tree criterion (- autoMRE). I estimated phylogenetic trees using the full dataset and the

43 partitioning scheme estimated in PartitionFinder as the partitioning model in

RAxML. I ran Bayesian analyses in MrBayes to estimate phylogenetic trees for the full dataset. I unlinked the parameters for each partition and I allowed branch lengths to vary proportionately across partitions. I set the λ prior to

29.04 using the equation from Brown et al. (2010) and based on a total tree length estimate from RAxML. I ran the MrBayes analysis for 20 million generations with trees and parameters sampled every 1000 generations

(burn-in = 10%). I used the sump and sumt functions in MrBayes to assess convergence and by evaluating the average standard deviation of split frequencies and the average potential scale reduction factor (PSRF) for parameter values. I also used TRACER v1.5 to assess stationarity in the likelihood scores and evaluate the sampling of parameters using estimated sample size (ESS) values (Rambaut and Drummond, 2007). See

Supplementary Figs. 2.1-2.4 for mitochondrial only and nuclear only phylogenies run in RaxML and MrBayes.

I used the program *BEAST to estimate a species tree for the PSG. The program *BEAST estimates the species tree directly from the sequence data, incorporating uncertainty from each gene tree, nucleotide substitution model parameters and coalescent processes (Heled and Drummond, 2010). I included two individuals per species where possible, with 61 individuals in total with complete sampling of all 11 loci for the PSG taxa. I ran separate analyses on the PSG dataset using the hierarchical Bayesian model implemented in *BEAST (Drummond and Rambaut, 2007; Drummond et al.,

2012). Outgroups were not included as they lacked sampling for many of the

44 loci in the PSG dataset, which is a problem for *BEAST as it models the coalescence of each individual locus. For *BEAST, data were partitioned by locus, with substitution model priors estimated for each locus using

MRMODELTEST v2.0 (Nylander, 2004). Each individual was assigned to its respective species, which in this study is unambiguous. I assumed a hierarchical prior for effective population sizes (default settings) as according to Heled and Drummond (2010). I generated posterior samples for all 11 loci using the Bayesian MCMC procedure. I ran two independent runs of 100 million generations with sampling every 1000 generations, and removed the first 20% (20 000 trees) as burn-in. I examined convergence via likelihood plots through time visualized in the program TRACER v.1.4 (Rambaut and

Drummond, 2007) to ensure acceptable convergence to the stationary distribution.

2.3.3. Molecular Dating and Rate of Diversification

To date the diversification of the PSG I estimated a time-calibrated ultrametric phylogeny in BEAST using my murid dataset. I defined data partitions and prior models of molecular evolution based on the results of

PartitionFinder. To incorporate murine fossil calibrations I included an alignment of outgroups from the family Muridae, as detailed above. I used three fossil calibrations within Muridae following the recommendations of

Kimura et al. (2015) as implemented in Rowe et al. (2016). The fossil calibrations used refer to the shared ancestors of (1) Mus and Arvicanthis

45 (11.1-12.3 Ma), (2) Arvicanthis and Otomys (8.7-10.1 Ma), and (3) species of the genus Mus (7.3-8.3). These refined calibrations are similar to but moderately older than previous calibrations used with overlapping datasets

(Rowe et al., 2011b; Schenk et al., 2013). I used CladeAge within BEAST to set fossil priors using the minimum age and maximum age constraints above and as specified in Kimura et al. (2015). I implemented a Yule-process of speciation, and I used an uncorrelated lognormal-relaxed molecular clock model, with all parameters estimated from the data. I unlinked all parameters across partitions except tree topology. I left all remaining parameters as default settings in BEAUTi (Drummond and Rambaut, 2007). I ran the Bayesian

MCMC for 200 million generations with two independent runs of two chains each. The two runs were compared for consistency of results, with only one used in final analysis. Sampling occurred every 20,000 generations, giving a total of 10,000 trees (first 20% excluded as burn-in). I used TRACER v1.5 to assess stationarity in the likelihood scores and evaluate the sampling of parameters using estimated sample size (ESS) values (Rambaut and

Drummond, 2007).

To understand the diversification of the PSG in a comparative context I used the tmrca estimates from the BEAST analysis to calculate net diversification (ND; speciation–extinction) for the PSG and its constituent genera following the methods outlined in Magallón and Sanderson (2001) using GEIGER as implemented in R (Harmon et al., 2008). For the number of species within the PSG I used 41 described species recognized by Breed and

Ford (2007), including the eight species that only recently became extinct

46 following European colonisation of Australia. For the genus Pseudomys I used

24 described species recognized by Breed and Ford (2007) including the monotypic genus Mastacomys, which I show below is nested within the genus

Pseudomys.

To evaluate if the rate of diversification within the PSG has slowed following divergence from their sister group and colonization of Australia’s major biomes I plotted the accumulation of lineages through time (LTT). I calculated LTT on the ultrametric phylogenies resulting from both BEAST

(murid dataset) and *BEAST (PSG dataset). In each case, we randomly sampled 1000 trees over the posterior distribution, and pruned both sets of trees to one individual per species. I plotted LTT using the APE v2.7-2 package implemented in R (Paradis et al., 2004). To test whether lineage accumulation has slowed over time we estimated the γ statistic using the methods implemented in LASER v2.3 package in R (Pybus and Harvey, 2000; Rabosky

2006). I used the γ statistic to test the null hypothesis of a constant lineage accumulation rate through time. Significant P-values for negative γ values indicate that lineage accumulation has slowed towards the present and is consistent with an early burst of diversification. Theoretical studies predict that concatenated supermatrix trees produce earlier divergences than species trees (Pamilo and Nei, 1988), and empirical studies suggest that they are more likely to result in negative γ values (Burbrink and Pyron, 2011).

47 2.3.4. Ecological Transitions

I reconstructed the ancestral biome range for the PSG and the murid relatives using the R package BioGeoBEARS v.0.2.1 (Matzke, 2013). The samples used in this analyses I refer to as the Sahulian dataset, which are listed in Supplementary Table 2.2 BioGeoBEARS uses maximum-likelihood methods to estimate possible ancestral areas by modelling biome transition events onto a phylogeny. I pruned the ultrametric tree from my complete

BEAST analysis to include all representatives of the murine tribe Hydromyini

(i.e. Australian and Philippine old endemics; LeCompte et al., 2008) in which the PSG are nested. I also included their closest relative in the phylogeny, the genus Chiropodomys, as represented by C. gliroides. I did not use my *BEAST species tree because this analysis did not include outgroups to the PSG which set the context for biome origination. I coded all sampled taxa used in the BEAST analyses based on their contemporary distributions in one of four biomes: (1) arid; (2) temperate mesic; (3) monsoon; (4) tropical rainforest. The maximum number of areas per lineage was set to 3 due to two species in the outgroup (Hydromys and Xeromys) and one member of the ingroup

(Pseudomys johnsoni) that have distributions spanning two or three discrete biomes. The remaining species in my analysis are restricted to a single biome.

Analyses were carried out comparing three biogeographic models (1) dispersal-extinction-cladogenesis model DEC (DEC; Ree, 2005); (2) dispersal- vicariance analysis DIVALIKE (DIVA; Ronquist, 1997); and (3) BAYAREA model

48 BAYESLIKE (Landis et al., 2013). Each of these models included two free parameters: d = dispersion or range extension, e = extinction or range contraction, but BioGeoBEARS also includes the free +J parameter for founder events for each model, which was unavailable in DIVA or LAGRANGE programs.

The +J parameter represents the rate of founder event speciation during cladogenesis where one of the daughter species occupies an area that is unoccupied by the other daughter species. Thus, I included and compared the six available models DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYESLIKE and

BAYESLIKE+J. I compared model fit using AIC and used the best-fit model to infer biome ranges at internal nodes on my ultrametric BEAST phylogeny.

Australian biomes are largely defined by aridity and latitude. I reconstructed aridity and latitude as states on my BEAST phylogeny. I defined two binary characters (coded as 0 or 1); mesic/arid and temperate/tropical. I used the

MULTISTATES function in BAYESTRAITS to reconstruct ancestral states on the

BEAST phylogeny resulting from the murid dataset (Pagel, 1994; Pagel et al.,

2004; Pagel and Meade, 2006). For each species in the PSG, I obtained distribution data from the Atlas of Living Australia (Atlas) and obtained climate values for each of these records from the WorldClim AI climate layer (Phillips et al., 2006). I calculated a mean aridity index (AI; see Supplementary Table

2.3 for each species in the PSG using MAXENT v3.3.3 in R. If the AI for a species was < 0.4, I assigned the species as ‘arid’, and if AI was > 0.4 I assigned the species as ‘mesic’, following Byrne et al. (2008). I assigned species with a majority of distribution north of the Tropic of Capricorn as

‘tropical’ and those south of the Tropic of Capricorn as ‘temperate’. I reduced

49 the posterior sample of ultrametric phylogenies from my BEAST analysis to the last 1000 trees. This approach allowed us to account for phylogenetic uncertainty while obtaining the posterior distribution of ancestral character states, with results weighted among alternative models of transition rates between character states, i.e. equal vs unequal.

I ran initial analyses using Maximum Likelihood methods in order to acquire a reasonable first approximation of rate coefficients, which I then used to specify the priors of subsequent Bayesian analysis. In the Bayesian analysis I implemented a reversible-jump Markov chain Monte Carlo (rjMCMC) with the hyperprior set to exponential and sampled from a uniform hyperprior on the interval [0, 30]. I used an unconstrained model, set the prior to uniform [0, 1], and adjusted the RATEDEV function until the rate parameters fell within 20-

40% acceptance ensuring the MCMC chains mixed well. Each analysis received three independent MCMC runs of 100 million generations following a burn-in of 10 million with the posterior sampled every 5000 generations.

Comparable harmonic mean likelihoods across each set of analyses indicated convergence had been reached. I averaged probability values for each ancestral character state at each node across all values from one of the three analyses.

2.4. Results

50 2.4.1. Phylogenetic Analysis

The murid dataset concatenation of the 10 nuclear loci and one mitochondrial locus for the PSG and other Muridae (n = 140) resulted in a dataset consisting of 14,293 aligned base pairs, with 5717 variable sites, and

3603 parsimony informative sites. The PSG dataset (n = 61) was 100% complete and comprised 2052 variable sites and 1410 parsimony informative sites (400 variable sites and 291 parsimony informative sites from the mitochondrial locus alone). PartitionFinder analyses identified eight optimal partitions of the data. Partitions did not correspond to individual gene fragments, but represented combinations of codon positions with similar substitution rates across genes (Table 2.2). In all MrBayes analyses the average standard deviation of split frequencies was <0.01 and the average

PSRF for all parameter values was close to 1.0 suggesting convergence.

Likelihood scores reached a stable plateau and all parameters obtained an

ESS > 200.

All phylogenetic analyses using the murid dataset resulted in a highly resolved phylogeny with concordant topological results from both Bayesian and ML analyses (Fig. 2.2). In these analyses, all nodes within the PSG received strong support (PP > 0.95 and ML bootstraps (MLBS) > 80).

Consistent with previous multi-locus studies my analyses supported a monophyletic PSG sister to a clade comprising the genera Conilurus,

Leporillus, Melomys, Mesembriomys, Paramelomys, Solomys and Uromys

(Fig. 2.2). At the generic level, I recovered an asymmetric phylogeny with

51 early divergence of Zyzomys (node H) followed by Leggadina (node I), and then divergence of the genera Notomys (node J) and Pseudomys with the genus Mastacomys nested within Pseudomys (“Pseudomys clade”; node E).

As with the PSG, the topology of the Pseudomys clade (Pseudomys +

Mastacomys) was largely asymmetric. My results supported the divergence of the four species of pebble-mound mice from all other Pseudomys at the base of the genus (node K). My results supported an early split of P. occidentalis from the remaining Pseudomys excluding the pebble-mound mice. I recovered a clade containing the ‘chestnut mice’, the ‘velvet mice’, and the

‘grizzled mice’ of Breed and Ford (2007) with the exception that my phylogeny included P. oralis within the ‘grizzled mice’ (node L clade). I resolved a fourth clade descending from node N that included the ‘false mice’, ‘delicate mice’ and Mastacomys. I recovered a monophyletic ‘delicate mice’ that, along with

Mastacomys, were nested within a paraphyletic ‘false mice’. In contrast,

Breed and Ford (2007) grouped M. fuscus with the ‘chestnut mice’, and thought the ‘false mice’ were monophyletic.

For the *BEAST species tree analysis using the PSG dataset MRMODELTEST v2.0 selected the HKY substitution model as the prior for each locus. The resulting species tree (Fig. 2.3) was largely concordant with my phylogenetic analyses based on the concatenated murid dataset. However, the PSG dataset only includes species of the Pseudomys Species Group and did not include the 79 species in my outgroup. Within the PSG, the species tree analysis recovered strong support (PP > 0.95) for all nodes except five that were recovered with strong support by the concatenated analyses (nodes L,

52 M, T, X, and one unlabelled node). At the generic level, the species tree resolved the same asymmetric topology with strong support (PP > 0.95) except that the species tree could not resolve the specific placement of

Mastacomys within the clade descending from node N. This resulted in lack of support for node T of the concatenated phylogeny, but both the species tree and concatenated analyses recovered strong support for the monophyly of the node N clade nested within other clades of Pseudomys and for the placement of Mastacomys within this clade. At the species-level, the species tree analysis (Fig. 2.3) could not resolve the placement of the clade comprising the well-supported sister pair P. nanus and P. gracilicaudatus (node R). This resulted in the lack of support for nodes L and M and for the uncertain placement of P. nanus and P. gracilicaudatus with respect to other species in the clades descending from nodes L and N of the concatenated analyses. The species tree recovered strong support for the monophyly of the remaining members of the node L clade. Two additional nodes near the tip of the tree were not well supported by the species tree analysis but were recovered with the same topology as the concatenated analyses. This included the node uniting N. fuscus with N. mitchelli and N. alexis (node unlabelled Fig. 2.3) and the node uniting P. bolami with P. delicatulus and P. novaehollandiae (node X in Fig. 2.3). These minor differences in resolution are not likely to have major impacts on my further inferences in this study, but where relevant they are discussed.

53 2.4.2. Molecular Dating and Rate of Diversification

I estimated a mean date of origin of the PSG in the late Miocene, 6.29 Mya

(node A; CI’s for all mean ages listed below in Table 2.3 and displayed in Fig.

2.4). Mean divergences among genera within the PSG occurred from the late

Miocene to the early Pleistocene. I recovered mean divergence estimates of

Zyzomys from the rest of the PSG at the origin of the crown group 5.69 Mya,

(range: 4.54-7.21 Mya, node B); Leggadina at 5.44 Mya (range: 4.29-6.86

Mya, node C); Notomys and Pseudomys 4.72 Mya (range: 3.65-5.90 Mya, node D); and Mastacomys from Pseudomys at 2.93 Mya (range: 2.22-3.71

Mya, Node T; Fig. 2.4; Table 2.3). Extant species diversity within most genera dates to the mid to late Pliocene with only Leggadina dating to within the

Pleistocene. Extant species within Leggadina have a mean crown age of 0.71

Mya (range: 0.47-0.97 Mya), Notomys species 2.95 Mya (range: 2.10-3.91

Mya), Zyzomys species 3.35 Mya (range: 2.46-4.36 Mya), Pseudomys species 4.44 Mya (range: 3.65-5.90 Mya; Supplementary Table 2.4). Based on these divergence dates, I estimated a net diversification (ND) of the PSG at 0.83 species/lineage/million years. I could not calculate ND for Leggadina and Mastacomys as these genera contain two and one extant species respectively. For the remaining genera I calculated ND rates of 0.52

(Zyzomys), 1.10 (Notomys) and 1.01 (Pseudomys clade). The LTT plot (Fig.

2.5) based on the concatenated BEAST tree supported an early burst in lineage accumulation with a significant departure from a constant linear rate

(γ = -2.789, critical value = -1.461, p < 0.01). In contrast the lineage through

54 time plot based on the *BEAST species tree failed to reject a linear increase in lineage accumulation through time (γ = -1.778, critical value = -2.243, p <

0.15).

2.4.3. Ancestral State Reconstruction

I estimated ancestral biome distributions based on my Sahulian dataset using BioGeoBEARS. Results of the log-likelihood ratio test indicated that all models that assumed founder event speciation (+J) had significantly higher likelihood values than the other models (all p > 0.05). The AIC analysis indicated that the DEC+J model had the highest model fit value (AIC = 185.1) and was > 2 AIC units higher than any other model (DIVALIKE+J AIC = 187.3 and BAYESLIKE+J AIC = 208.4). The parameters estimated for the DEC+J model resulted in d = 0.026, e < 0.000 and j < 0.000. I plotted the most likely ancestral areas for this model on the ultrametric BEAST phylogeny in Fig. 2.4 and reported in Table 2.3. This analysis indicated that the PSG is nested within a larger group of rodents restricted to the tropical rainforest biome (not shown) of Sahul, the Philippines, and Southeast Asia (Fig. 2.4; see

Supplementary Fig. 2.5 for all outgroups). This supports the expectation that the PSG were derived from a tropical rainforest biome ancestor. At the base of the PSG (node B), which represents the divergence of the genus Zyzomys

(node H) from the remainder of the PSG (node C), I inferred a monsoon biome distribution, indicating a transition from the tropical rainforest biome to the monsoon biome prior to the origin of the PSG. At node C, which

55 represents the split between Leggadina (node I) and the remainder of the

PSG (node D) I recovered an ancestral distribution in either the monsoon biome (42% likelihood) or the arid biome (56% likelihood). At node D, which represents the common ancestor of Notomys, Pseudomys, and Mastacomys,

I inferred unambiguous support for an arid biome restricted ancestor, indicating a transition from the monsoon biome to the arid biome sometime between node B and node D. The two species of Leggadina reflect a transition between arid and monsoon biomes but I could not resolve the direction because of uncertainty at node C. Notomys aquilo represents a transition from the arid biome into the monsoon biome. Thus, I inferred a total of four biome transitions in the evolution of PSG excluding the Pseudomys clade (Pseudomys + Mastacomys).

Within the Pseudomys clade I recovered an arid biome distribution for the three most basal nodes (E, F, G; Fig. 2.4). The uncertain placement of P. gracilicaudatus and P. nanus indicated by the species tree could alter my estimate of the biome distribution at node G. However, both species tree and concatenated analyses supported nodes E and F. Thus, my inference of an arid biome distribution for the ancestor of the Pseudomys clade (node E) is not affected by uncertainty in the species tree analysis. Descending from node E at the base of the Pseudomys clade, I identified at least 10 transitions between biomes. One transition to the monsoon biome and one transition to the temperate mesic biome occurred in the pebble-mound mice (node K) leading to P. calabyi + P. johnsoni and P. patrius, respectively. Four transitions occurred in the clade descending from node L as resolved by the

56 concatenated analysis. An early transition from the arid biome to the temperate mesic biome occurred somewhere between node G and node M

(the biome distribution of node L could not be resolved between arid and temperate mesic). I inferred one transition from the temperate mesic biome to the arid biome leading to P. desertor (node S), a transition between arid and temperate mesic biomes leading to the split between P. fumeus and P. albocinereus + P. apodemoides at node O, and one transition from the temperate mesic biome to the monsoon biome leading to P. nanus. The uncertain placement of P. gracilicaudatus and P. nanus indicated by the species tree could alter my estimate of biome distributions at node G and nodes descending from it, but would still require the same number of transitions. In the clade descending from node N, which was supported by both concatenated and species tree analyses I inferred at least four biome transitions. If the ancestor at node G was actually restricted to the temperate mesic biome, a possibility implied by uncertainty in the species tree, I would infer a fifth transition from the temperate mesic to the arid biome between node G and node N. I inferred two transitions from the arid biome to the mesic biome leading to M. fuscus and P. higginsi respectively. The alternative and uncertain placement of M. fuscus suggested by the species tree analysis does not change this inference (Fig. 2.3). I inferred an arid to monsoon biome transition leading to P. delicatulus + P. novaehollandiae and a transition from the monsoon biome to the temperate mesic biome leading to P. novaehollandiae. The lack of strong support at node X in the species tree

57 does not change this inference as node W was supported by both concatenated and species tree analyses.

The results of binary states analysis in BAYESTRAITS (murid dataset) including aridity and latitude were unsurprisingly complementary to my biome analyses (Table 2.3). They indicated an origin in tropical mesic environments followed by transition to tropical arid environments and more recent transition to temperate mesic environments. I estimated at least eight transitions between tropical and temperate zones, and at least 10 transitions between mesic and arid environments. I recovered an initial transition from tropical to temperate zones at the node, which unites Notomys, Pseudomys, and

Mastacomys (node D). The nested position of taxa restricted to the temperate zone is reflected in the branching off of genera within the PSG. The first genus to branch off from the PSG, Zyzomys, is completely restricted to the tropical region as is the second genus Leggadina. In contrast, most species in the nested genera Notomys, Pseudomys and Mastacomys are restricted to the temperate zone but with at least seven transitions between temperate and tropical. Within the genus Notomys (node J) I recovered two transitions from temperate to tropical environments leading to the species N. aquilo and N. alexis. Within the Pseudomys clade I recovered six transitions. A single temperate to tropical transition from node E to node K, the pebble-mound mice, which all have ranges that are restricted to tropical environments. Two independent transitions from temperate to tropical environments within the clade that stems from node L represented by the single species P. desertor and the sister species pair P. gracilicaudatus and P. nanus (node R).

58 However, the uncertain placement of P. gracilicaudatus and P. nanus (node

R) indicated by the species tree could alter my binary state estimates at node

G and the nodes descending from it, but would still include the same number of transitions. If the species tree topology were correct it would likely increase the likelihood of nodes F and G towards tropical, although both nodes F and

G didn’t receive significant support for either state in the concatenated analyses. In the clade descending from node N, supported by both concatenated and species tree analyses, I inferred at least three transitions between tropical and temperate environments. I recovered a single transition from temperate to tropical environments from node T to node W, and two independent transitions from tropical to temperate environments represented by P. bolami and the sister species pair P. delicatulus and P. novaehollandiae

(node Y). The species tree lacks support for node X, which stems from node

W, which if correct might reduce the two tropical to temperate transitions to a single transition.

For aridity indices, I recovered an initial transition from mesic to arid environments at node C with nine additional transitions mostly restricted to the genus Pseudomys. Within the genus Notomys (node J) I recovered a single transition from arid to mesic in the case of N. aquilo. One transition from arid to mesic environments (node Q) and one transition from mesic to arid environments (P. johnsoni) occurred within the pebble-mound mice (node K). I recovered an arid to mesic transition from node G to node L, as well as a single mesic to arid transition from node L to the sister species pair P. albocinereus and P. apodemoides. Given the uncertain placement of node R

59 (P. gracilicaudatus and P. nanus) in the species tree, the support for an arid ancestor at node F and G could be less than estimated from the concatenated tree. If the ancestor of either node were mesic I would infer one additional arid to mesic transition. Within node P I estimated one mesic to arid environment transition leading to P. desertor. In the clade descending from node N most nodes could not be confidently resolved with respect to either mesic or arid environments. Node N was most likely arid but with some probability of a mesic state with the reverse true for the descendent node T. Node T leads to the split between M. fuscus and the remaining Pseudomys and given the uncertainty in the placement of M. fuscus in the species tree I should treat the state of node T as uncertain. Thus, from node T descending I cannot resolve when transitions occurred and in which direction. However, the four mesic species in this clade are each nested within arid species indicating at least three transitions between arid and mesic environments. Transition from arid to mesic, leading to M. fuscus, occurred sometime near the base of clade comprised by node N. The sister relationship between the mesic P. higginsi and arid P. fieldi implies a mesic-arid transition but the direction is uncertain.

The sister species P. delicatulus and P. novaehollandiae are restricted to mesic environments and nested within the arid species P. bolami and P. hermannsburgensis indicating that an arid to mesic transition occurred between node W and Y.

2.5. Discussion

60 2.5.1. Phylogenetic Constraint and Niche Conservatism

The niche conservatism model predicts that the adaptation of species to novel and disparate environments is constrained by physiological limitations that evolve slowly (Crisp et al., 2009; Wiens et al., 2010). Thus, closely related taxa are expected to occupy ecologically similar environments leading to an apparent phylogenetic constraint and taxa that colonize and adapt to novel environments are unlikely to reverse those adaptations to recolonize ancestral environments (Wiens and Harrison, 2004; Wiens and Graham,

2005; Crayn et al., 2006; Losos, 2008; Oliver and Bauer, 2011; Crisp et al.,

2012). Based on this hypothesis, transitions between biomes, which represent geographically isolated and disparate environments, should be especially challenging and infrequent in the evolution of species. This is especially true of extreme environments such as the arid biome of Australia to which specialized taxonomic groups representing multiple species are restricted

(e.g. Chapple and Keogh, 2004; Hugall et al., 2008). Many of these arid biome restricted lineages are descended from mesic biome associated ancestors. In contrast, few mesic-biome-associated lineages are thought to have origins in the arid biome (Mummenhoff et al., 2001; Leys et al., 2003;

Chapple and Keogh, 2004; Shepherd et al., 2004; Crayn et al., 2006;

Watanabe et al., 2006; Cooper et al., 2007; Crisp et al., 2009; Hugall et al.,

2008). However, recent phylogenetic studies suggest that some lineages in mesic biomes evolved from species restricted to arid biomes (Fujita et al.,

2010; Wiens et al., 2013; Guerrero et al., 2013; Mitchell et al., 2014; Oliver et

61 al., 2014). My phylogenetic hypothesis for the Pseudomys Species Group

(PSG) is consistent with these studies, with many mesic lineages within the

PSG derived from arid-restricted ancestors. All genera within the PSG, except for Zyzomys, are descended from an ancestor restricted to the arid biome and from environments with low aridity indices. In addition, seven transitions out of the arid biome are evident in my phylogeny particularly within the phylogenetically-nested genus, Pseudomys, where repeated biome transitions, including between sister taxa, suggest that adaptation to arid and mesic biomes have been reversed over relatively short evolutionary time scales.

Within the PSG the degree of niche conservatism and phylogenetic constraint appears to have changed over time and differs among genera. The earliest diverging genera evolved specialized adaptations allowing them to invade and adapt to their respective biomes. Several phenotypic traits in

Notomys, in particular, such as enlarged ears, bipedal hopping and highly efficient renal systems suggest that transition to the arid biome required a suite of morphological and physiological specializations leading to a niche conservative phylogenetic constraint (Webster and Dawson, 2004; Gordge and Roberts, 2008). In contrast, the sister clade to Notomys, the Pseudomys clade (comprising Pseudomys and the monotypic Mastacomys), is characterized by a lack of niche conservatism among closely related lineages.

Transitions between the monsoon, arid and temperate mesic biomes occurred at least 10 times within this clade as compared to only once in the sister clade, Notomys. However, it should be noted that Pseudomys includes the

62 majority of the PSG taxa sampled, and until complete taxon sampling of the

PSG is accomplished this result may be due to sampling bias. Of these 10 transitions, at least four occurred between recently diverged sister species, including two cogent examples, (1) P. desertor and P. shortridgei that occupy desert and coastal heath environments, respectively; and (2) P. fieldi and P. higginsi that occupy desert and wet forest environments, respectively. The recent divergences of these sister species (~1 Mya and ~2 Mya respectively;

Fig. 2.4) between dramatically different biomes demonstrate the capacity of this group to evolve and diverge rapidly along broad-scale ecological gradients.

2.5.2. Ecological Opportunity and Diversification.

While the niche conservatism model predicts that major ecological transitions should be rare, ecological opportunity theory predicts that colonizations should lead to opportunities for speciation (Lovette and

Bermingham, 1999; Harmon et al., 2003; Kozak et al., 2006; McPeek 2008;

Phillimore and Price, 2008; Price et al., 2016). My phylogenetic hypothesis supports this idea by suggesting that the diversification of Zyzomys and

Notomys occurred following transition into the monsoon and arid biomes respectively. Furthermore, ecological opportunity theory predicts that diversification rates will be rapid as ecological niches are filled in the early history of a colonizing group. Net diversification rates in the PSG are consistent with this prediction with rates two to four times faster than groups

63 including subfamily Murinae (0.42; Rowe et al., 2011b), the family Muridae

(0.36; Stanley, 1998), and the class Mammalia (0.22; Stanley, 1998). Despite being nested within the PSG, the Pseudomys clade and Notomys have relatively high net diversification rates (1.01 and 1.10, respectively), especially compared to the earlier diverging genera Zyzomys (0.52) and Leggadina.

These uneven rates of net diversification within the PSG are reflected in the group’s phylogenetic asymmetry that also suggests that rates of net diversification have varied across lineages within the PSG (Kirkpatrick and

Slatkin, 1993). Thus, given that more nested groups may have higher rates, an early burst in net diversification is not obvious for the PSG.

My estimates of diversification rate shifts based on LTT plots are not consistent with an early burst in diversification and support other recent studies suggesting that support for early burst models are susceptible to methodological assumptions (Moen and Morlon, 2014; Leaché et al., 2014).

Early burst models of diversification are usually reported by phylogenies based on concatenated analyses, whereas species tree analyses often fail to produce the same pattern (Pamilo and Nei, 1988; Burbrink et al., 2012; Reddy et al., 2012; Schenk et al., 2013; but see Fordyce 2010). In my study, the γ estimates from the LTT plot based on the concatenated supermatrix supported a significant slow down in diversification rates, whereas the γ estimate based on a species tree approach failed to reject a continuous rate of diversification. In globally-distributed muroid rodents, phylogenetic tests have detected few signatures of a burst in diversification following continental

64 colonizations (Schenk et al., 2013). Thus, I think it is unlikely that an early burst in diversification occurred in the PSG.

The ecological opportunity model is based on the expectation that ecological niches (opportunities) are realized rapidly and early in a colonizing lineage’s history. As transitions between biomes represent major ecological states for species, based on this hypothesis, I predicted a phylogenetic signature of rapid filling of biomes, following colonization of Australia by the

PSG. Although the PSG exhibits rapid rates of net diversification, it did not rapidly colonize all Australian biomes following its arrival on the continent, taking ~2MY to colonize the temperate mesic biome (node M, Fig. 2.4).

Furthermore, 10 of the 14 biome transitions in the group occurred during the latter half of their history on the continent and within the phylogenetically- nested genus, Pseudomys, demonstrating that ecological niches were not filled early following the group’s colonization of Australia. While the divergence of lineages at the environmental scale of biomes suggests that ecological divergence has been important to the diversification of species in the PSG, the rates of accumulation of lineages and ecological states are not consistent with the early evolution of lineages and states that are predicted by the ecological opportunity model.

2.5.3. Multi-locus phylogeny of the PSG

A well-resolved phylogeny underpins my resolution of biome transitions and diversification of the PSG. The phylogenetic relationships within this

65 group have been notoriously difficult to resolve (Baverstock et al., 1981, 1983;

Torrance, 1997; Ford, 2006). My phylogenetic hypothesis, based on an 11- locus dataset and near complete taxon sampling was well supported by both species tree analysis and concatenated trees, with the exception of five nodes that lacked support in the species tree (Fig. 2.2-2.4). The high resolution of the phylogeny generated from a dataset of 11 loci was surprising given the young age (7.2-4.5 MY) and rapid diversification of this group. This provides an empirical example in support of studies that suggest that under some conditions tens of loci may be sufficient to recover a well-resolved phylogeny even for recent and rapid radiations (e.g. Leaché and Rannala, 2011).

However, the five nodes with low posterior probabilities in the species tree analysis should be considered unresolved even though concatenated analyses recovered strong support for them. Comparisons of empirical and simulated data have shown that concatenated analyses can resolve the wrong tree with strong support, but that species tree analyses do not (e.g.

Giarla and Esselstyn, 2015). In their study, including thousands of loci did not help resolve nodes that were unresolved with tens of loci (Giarla and

Esselstyn, 2015). Whether more loci can help resolve the five unresolved nodes in the PSG is untested, but 11 loci provided much more resolution than in previous studies (Torrance, 1997; Ford, 2006).

In spite of the uncertainty at five nodes within the PSG, I am able to draw several conclusions about the phylogenetic structure of this group. Consistent with previous studies, my findings supported a paraphyletic Pseudomys division with the Uromys division nested within it (Musser and Carleton, 2005;

66 Rowe et al., 2008). In all analyses, I recovered strong support for monophyly for each of the five genera in the PSG except Pseudomys, which was always paraphyletic with respect to a nested Mastacomys. I found strong support for the early divergence of Zyzomys from other genera within the PSG, followed by Leggadina, and finally a split between Notomys and the Pseudomys clade.

My results largely supported the seven subgroupings of Pseudomys by Breed and Ford (2007; see Supplementary Table 2.5) with exception of the placement of P. occidentalis and P. oralis, the placement of which Breed and

Ford identified as uncertain. I also resolved phylogenetic relationships among subgroups leading to a largely asymmetric tree with early divergence of the pebble-mound mice (node E) and P. occidentalis (node F). My well-resolved phylogenetic hypothesis, demonstrating repeated transitions across the broad scale ecological gradients of Australian biomes, provides a foundation for research into the underlying genomic, morphological and physiological processes related to the ecological diversification and evolution of species.

2.6. Acknowledgements

I am grateful to Steve Donnellan (South Australia Museum), Leo Joseph

(Australian National Wildlife Collection), Heather Janetzki (Queensland

Museum), Cathy Nock (Centre for Plant Conservation Genetics), and Fred

Ford (Department of Defence) for loans of tissue samples used in this study, I thank Andrew Hugall and Adnan Mousalli for insightful comments on early drafts of the manuscript. Skipton Woolley provided assistance with MaxEnt

67 analyses. This research was funded in part by the Holsworth Foundation and the Australia and Pacific Science Foundation (12-6); neither funding source had any involvement in conducting the research or the preparation of this article.

68 1 2.7. Appendices

2 Tables: 3 4 Table 2.1. Primer sets used in the 11-locus dataset.

Outgroup + Ingroup Gene Primer Name Direction Sequence (5' to 3') Reference Taxa (n = 140) A2AB n = 72 A2ABFOR Forward AGCCCTACTCGGTGCAGGCCACCG Huchon et al., 2007 A2ABREV Reverse CTGTTGCAGTAGCCRATCCAGAAGAAGAACTG Huchon et al., 2007

ARHGAP21 n = 72 ARHGAP21_F Forward CCACCAGTTTGCTATCCCTGG This study ARHGAP21_R Reverse ATGTGGTGGTCTTAGGTCCG This study

BRCA1 n = 104 PJSBRCA1_F Forward GCAAATGACTGACGCTTTGAAACTTGA This study PJSBRCA1_R Reverse AGAGAAAGGGAAGCCGCCCTCA This study

R_BRCA1_F.1 Forward AAGTAGGTGGGCTCGGATTT Adkins et al., 2001

R_BRCA1_R.2 Reverse TGGTATGTGGTTCCTCGTGA Adkins et al., 2001

CB1 n = 71 CB1-D1 Forward GGCTCRAATGACATTCAGTAYGAA Blanga-Kanfi et al., 2009 CB1-R1 Reverse CAGCCTCTAGAYAACAGCATGGGGGACTC Blanga-Kanfi et al., 2009

CYTB n = 138 MVZ05 Forward CGAAGCTTGATATGAAAAACCATCGTTG Smith and Patton, 1993 MVZ16 Reverse AAATAGGAARTATCAYTCTGGTTTRAT Smith and Patton, 1993

GHR n = 127 S192 Forward GGRAARTTRGAGGAGGTGAACACMATCTT Adkins et al., 2001 S196 Reverse CTACTGCATGATTTTGTTCAGTTGGTCTGTGCTCAC Adkins et al., 2001

IRBP n = 138 S233 Forward GTCCTCTTGGATAACTACTGCTT Jansa and Voss, 2000 S239 Reverse CTCCACTGCCCTCCCATGTCT Jansa and Voss, 2000

NIN n = 65 NIN_F Forward TCTCTCAGATAGAAGCCCAGT This study NIN_R Reverse GGCCTCGTGGTCTCCT This study

RAG1 n = 122 S211 Forward GGGTGMGATCYTTTGAAAA Steppan et al., 2004 S212 Reverse CVGTYCTGTACATCTTRTGRTA Steppan et al., 2004

TLR3 n = 70 TLR3_F Forward TGGGAACTTTCAAACACAAGCA This study

69 TLR3_R Reverse CCTTAGAGATGTCTGGTCATCAA This study vWF n = 71 VD1 Forward GTGTGAACCTTACSTGTGAAGCCTG Huchon et al., 2007 VR4 Reverse AACTTCAATAAGAGCAAGGAGTTC Huchon et al., 2007

70 5 Table 2.2. Twelve partitions of the concatenated sequence data used in phylogenetic analyses as estimated in PartitionFinder. Best Partition Genes and coding positions Model 1 HKY+I+G VWF_pos2, A2AB_pos1, ARHGAP21_pos1, ARHGAP21_pos3, NIN_pos2 2 K80+G A2AB_pos2, CB1_pos1, CB1_pos2 3 HKY+I+G A2AB_pos3, GHR_pos1, IRBP_pos1, RAG1_pos1 4 K80+G TLR3_pos1, ARHGAP21_pos2 5 GTR+G BRCA1_pos1, BRCA1_pos2, CB1_pos3 6 K80+G VWF_pos3, BRCA1_pos3, GHR_pos3 7 TrN+I+G CYTB_pos1 8 GTR+I+G CYTB_pos2 9 SYM+I+G CYTB_pos3 10 GTR+I+G TLR3_pos2, VWF_pos1, GHR_pos2, IRBP_pos2, RAG1_pos2 11 GTR+G IRBP_pos3, NIN_pos3, RAG1_pos3 12 HKY+G TLR3_pos3, NIN_pos1 6

7

8

9

10

11

12

13

71 14 Table 2.3. Ages and probabilities of biome and binary states estimated for select nodes discussed in the text and presented 15 on figure 4. Mean and confidence intervals around age estimates in millions of years. Binary States (%)

Node Ages (Million years) Biomes (%) Aridity Index Latitude

Node Mean CI Lower CI Upper Tropical Monsoo Arid Mesic Mesic Arid Tropical Temperate A 6.29 4.98 7.89 0 85n 5 0 93 7 100 0 B 5.69 4.54 7.21 0 90 8 0 100 0 100 0 C 5.44 4.29 6.86 0 46 52 0 18 82 84 16 D 4.72 3.65 5.9 0 3 95 0 0 100 100 0 E 4.44 3.46 5.6 0 1 98 0 1 99 20 80 F 4.24 3.3 5.36 0 0 99 0 2 98 4 96 G 3.75 2.93 4.77 0 0 93 5 4 96 1 99 H 3.35 2.46 4.36 0 100 0 0 34 66 100 0 I 0.71 0.47 0.97 0 45 47 0 36 64 10 90 J 2.95 2.1 3.92 0 2 96 0 41 59 0 100 K 2.23 1.61 2.91 0 1 90 1 50 50 0 100 L 3.47 2.67 4.42 0 0 48 51 99 1 1 99 M 3.29 2.49 4.18 0 2 3 95 100 0 53 47 N 3.28 2.51 4.16 0 0 95 3 95 5 4 96 O 3.08 2.3 3.94 0 0 49 49 92 8 0 100 P 2.84 2.12 3.67 0 0 3 96 17 83 0 100 Q 1.59 1.12 2.14 0 13 57 15 73 27 2 98 R 1.04 0.67 1.43 0 6 0 90 49 51 0 100 S 0.95 0.64 1.28 0 0 6 90 12 88 28 72 T 2.93 2.22 3.71 0 0 88 10 11 89 98 2 U 2.57 1.97 3.3 0 0 93 5 78 22 63 37 V 1.97 1.39 2.59 0 0 89 8 100 0 74 26 W 1.63 1.19 2.13 0 0 99 0 11 89 98 2 X 1.2 0.86 1.59 0 4 95 0 78 22 63 37

72 Y 0.84 0.56 1.1 0 93 0 7 100 0 74 26 16 17 Supplementary Table 2.1. GeneBank Accession Numbers for each locus sequenced in this study or obtained from 18 GenBank. First column is Taxa ID name and the following 11 columns are the respective loci used in this study. TAXA A2AB ARHGAP21 BRCA1 CB1 CYTB GHR IRBP NIN RAG1 TLR3 vWF

Abeomelomys_sevia - - EU349682.1 - EU349730.1 EU349793.1 EU349832.1 - EU349879.1 - - Anisomys_imitator_ABTC45107 - - - - EU349732.1 DQ019052.1 EU349833.1 - DQ023471.1 - - Apodemus_agrarius_MVZ159220 - - EU349658 - JF318967.1 DQ019054 AB096845.1 - DQ023472.1 - - Apodemus_draco_AB109398 - - - - JQ043423.1 - JQ043402.1 - AB285445 - - Apodemus_flavicollis_AM910943 - - - - AB032853.1 AM910943 AB032860.1 - AB285446.1 - - Apodemus_gurkha - - - - AB032852.1 - AB032859.1 - AB285447.1 - - Apodemus_mystacinus - - - - AF159394.1 DQ019053.1 AB303229.1 - AB285448 - - Apodemus_semotus - - - - EU349734 DQ019055 AB032862.1 - DQ023473.1 - - Apodemus_speciosus - - - - AB675442.1 AB491493 AB264729 - - - - Apodemus_sylvaticus - - - - AB303227 - JX457640 - AB303243.1 - - Apomys_datae_FMNH198581 - - - - HM371074 KJ607288.1 EU349836 - KM099842.1 - - Apomys_gracilirostris_646 - - - - AY324465 - - - KM099852.1 - - Apomys_hylocoetus_FMNH147871 - - AY295000.1 - AY324469 AY294915 - - AY294942.1 - - Apomys_microdon - - - - AY324481 GQ405366 DQ191493 - - - - Archboldomys_luzonensis_EAR1826 AY687858 - EU349675.1 - EU349736.1 EU349794.1 DQ191495.1 - DQ023466.1 - - Batomys_granti - - EU349645.1 - AY324459 AY294917 DQ191496.1 - AY241461.1 - - Chiromyscus_chiropus_ABTC69097 - - EU349665.1 - EU349739.1 EU349796 EU349840.1 - EU349881.1 - - Chiropodomys_gliroides_AMCC10151 - - EU349674 - EU349740 EU349797 EU349841 - EU349882 - - 1 Chiruromys_vates_ABTC43096 - - - - EU349741.1 - EU349842.1 - EU349883.1 - - Chrotomys_gonzalesi_EAR1850 - - - - EU349742 GQ405375.1 DQ191503.1 - AY294943 - - Conilurus_pencillatus_ABTC07411 - - EU349694.1 - EU349743.1 DQ019057.1 EU349844.1 - DQ023467.1 - - Crossomys_moncktoni_ABTC46614 XXXX XXXX - XXXX XXXX XXXX XXXX - XXXX XXXX XXXX Deomys_ferrugineus_FMNH149427 - - AY295007.1 - EU349745.1 AY294922.1 AY326084.1 - AY241460.1 - AJ402716.1 Gerbillurus_vallianus_RA47 - - EU349643.1 - - AF332022.1 - - AY294948.1 - -

73 Gerbillus_gerbillus_CM113822 - - EU349700 - AJ851269 DQ019049 EU349846.1 - DQ023452.1 - - Grammomys_dolichurus - - EU349651.1 - EU275253 EU349800.1 EU349847.1 - EU349887.1 - - Hydromys_chrysogaster - - EU349699 - EU349748 AM910954 EU349849 - EU349699 - - Hyomys_goliath_ABTC42697 - - EU349679.1 - EU349750.1 EU349805.1 XXXX - EU349891.1 - - 19 TAXA A2AB ARHGAP21 BRCA1 CB1 CYTB GHR IRBP NIN RAG1 TLR3 vWF

Leggadina_forresti_ABTC107348 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX - Leggadina_forresti_ABTC36085 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Leggadina_lakedownensis_ABTC07406 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Leggadina_lakedownensis_ABTC32203 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX - Leopoldamys_sabanus - - - - JQ755930.1 DQ019063 JQ755963.1 - - - - Leporillus_conditor_ABTC13335 XXXX XXXX EU349692.1 - EU349752.1 EU349806.1 EU349851.1 XXXX EU349892.1 XXXX - Leptomys_elegans_ABTC45741 - - EU349697.1 - EU349753.1 EU349807.1 EU349852.1 - EU349893.1 - - Lophuromys_flavopunctatus_FMNH14477 - - AY295006.1 - EU349754.1 AY294921 AY326091 - AY294950.1 - - 7 Lorentzimys_nouhuysi_ABTC45265 - - EU349680.1 - EU349755.1 EU349808.1 GQ405362.1 - EU349894.1 - - Macruromys_major_EU349809 - - EU349678.1 - EU349756.1 EU349809.1 EU349853.1 - EU349895.1 - - Mallomys_aroaensis_ABTC45196 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Mallomys_istapantap_ABTC42521 XXXX - - - - XXXX XXXX XXXX XXXX XXXX XXXX EU349896.1 Mallomys_rothschildi_ABTC47402 - - EU349681.1 - EU349758.1 EU349810.1 EU349854.1 - - - GI Mammelomys_lanosus_ABTC47208 - - - - EU349759.1 EU349811.1 EU349855.1 - EU349897.1 - - Mammelomys_rattoides_ABTC47351 - XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Mastacomys_fuscus_ABTC07354 XXXX XXXX EU349687.1 XXXX EU349760.1 EU349812.1 EU349856.1 XXXX EU349898.1 XXXX XXXX Mastacomys_fuscus_ABTC07393 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Maxomys_bartelsii_ABTC48063 - - EU349666 - EU349762.1 XXXX EU349857.1 - DQ023460.1 - - Melomys_bannisteri_M42669 - - XXXX - XXXX XXXX XXXX - XXXX - - Melomys_cooperae_M43822 - - - - XXXX XXXX XXXX - XXXX - - Melomys_rufescens_ABTC43071 - - EU349690.1 - EU349764.1 EU349816.1 EU349860.1 - EU349902.1 - - Mesembiromys_marcrurus_ABTC07337 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Mesembriomys_gouldii_ABTC07412 - - EU349693.1 - - EU349817.1 EU349861.1 - EU349903.1 - -

74 Microhydromys_richardsonii_M35851 - - XXXX - XXXX XXXX XXXX - XXXX - - Mus_cookii_USNM583802 - - - - AB125767.1 - AB125802.1 - AB125826.1 - - Mus_mattheyi - - - - AB125781.1 - AB125815.1 - AB125843 - - Mus_musculus XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Mus_pahari_AMCC110800 - - - - EU349767.1 EU349767.1 EU349864.1 - EU349906.1 - - 20 TAXA A2AB ARHGAP21 BRCA1 CB1 CYTB GHR IRBP NIN RAG1 TLR3 vWF

Mus_platythrix - - - - AJ698880.1 - AB125816.1 - AB125845.2 - - Notomys_alexis_ABTC61767 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_alexis_Z21353 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_aquilo_ABTC18252 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_aquilo_ABTC18253 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_cervinus_ABTC27130 XXXX XXXX XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX Notomys_cervinus_ABTC82963 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_fuscus_ABTC117695 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_fuscus_ABTC80277 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX Notomys_mitchelli_ABTC07351 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Notomys_mitchelli_ABTC27066 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Oenomys_hypoxanthus_CM102548 - - EU349654.1 - EU349769.1 AM910970.1 EU349865.1 - DQ023464.1 - - Parahydromys_asper_ABTC45798 XXXX XXXX EU349698 XXXX EU349771.1 EU349820.1 EU349866.1 XXXX EU349910.1 XXXX XXXX Paramelomys_levipes_160736 - - EU349689.1 - EU349772.1 EU349821.1 EU349867.1 - EU349911.1 - - Paramelomys_platyops_ABTC42567 - - XXXX - XXXX XXXX XXXX - XXXX - - Paramelomys_rubex_ABTC42720 - - XXXX - XXXX XXXX XXXX - XXXX - - Parotomys_brantsii - - EU349646.1 - EU349773 AY294912 - - AY294939 - - Paruromys_dominator_ABTC65763 - - EU349669.1 - EU349774.1 EU349822.1 - - - - - Pogonomelomys_mayeri_ABTC44189 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pogonomelomys_ruemmleri_ABTC4718 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX - 7 Pogonomys_loriae - - EU349683.1 - EU349776.1 EU349823.1 EU349868.1 - EU349912.1 - - Pogonomys_macrourus_ABTC43144 - - EU349684.1 - EU349777.1 EU349824.1 EU349869.1 - EU349913.1 - -

75 Pogonomys_sylvestris_GQ405389 - - - - - GQ405389.1 GQ405365.1 - - - - Praomys_jacksoni - - EU349663.1 - EU349778.1 AM910973.1 AM408326 - DQ023477.1 - - Pseudohydromys_ellermani_ABTC43920 XXXX XXXX EU349695.1 XXXX EU349763.1 XXXX EU349858.1 XXXX EU349900.1 XXXX XXXX Pseudomys_albocinereus_ABTC08044 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_albocinereus_ABTC08091 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_apodemoides_ABTC37663 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX 21 22 TAXA A2AB ARHGAP21 BRCA1 CB1 CYTB GHR IRBP NIN RAG1 TLR3 vWF

Pseudomys_apodemoides_Z7296 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX Pseudomys_australis_ABTC118844 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_australis_ABTC35951 XXXX XXXX EU349688.1 XXXX XXXX AM910975.1 EU349870.1 XXXX DQ023469.1 XXXX XXXX Pseudomys_bolami_ABTC08065 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_bolami_ABTC96553 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_calabyi_ABTC84990 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_chapmani_ABTC62178 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_chapmani_M40577 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_delicatulus_ABTC62035 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_delicatulus_ABTC72733 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_delicatulus_Z18879 XXXX XXXX XXXX XXXX XXXX - XXXX - XXXX XXXX XXXX Pseudomys_desertor_ABTC113870 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_desertor_Z21274 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_fieldi_M56289 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX Pseudomys_fieldi_ABTC08164 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX - XXXX Pseudomys_fumeus_ABTC08168 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_fumeus_Z25963 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX Pseudomys_gracilicaudatus_ABTC08031 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_hermannsburgensis_ABTC9137 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX 5 Pseudomys_hermannsburgensis_Z11008 XXXX XXXX XXXX XXXX XXXX - XXXX - XXXX XXXX XXXX

76 Pseudomys_higginsi_ABTC08139 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX Pseudomys_johnsoni_ABTC08055 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_johnsoni_ABTC30351 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_nanus_ABTC08056 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_nanus_ABTC30578 XXXX XXXX XXXX XXXX XXXX - XXXX XXXX - XXXX XXXX Pseudomys_novaehollandiae_ABTC08140 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_novaehollandiae_ABTC75991 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_occidentalis_ABTC08042 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 23 24 TAXA A2AB ARHGAP21 BRCA1 CB1 CYTB GHR IRBP NIN RAG1 TLR3 vWF Pseudomys_occidentalis_ABTC0814 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 4 Pseudomys_oralis_KR033 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_oralis_KR034 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_patrius_ABTC32205 XXXX XXXX XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX Pseudomys_patrius_ABTC32211 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_shortridgei_ABTC08079 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Pseudomys_shortridgei_Z24891 XXXX XXXX XXXX XXXX XXXX - XXXX XXXX XXXX XXXX XXXX Rattus_leucopus - - EU349672 - EU349781 EU349825 - - EU349914.1 - - Rattus_nitidus - - - - HM217478.1 - HM217715.1 - - - - Rattus_norvegicus XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Rattus_praetor - - HQ334405.1 - DQ191487 GQ405392 HQ334591.1 - HQ334662.1 - - Rattus_rattus - - HQ334389 - JQ823276 AM910976.1 HM217748 - HQ334643.1 - - Rattus_villosissimus - - EU349673 - GU570663 EU349826 HQ334576.1 - EU349915 - - Rhabdomys_pumilio - - XXXX - XXXX XXXX XXXX - XXXX - - Rhynchomys_isarogensis - - XXXX - XXXX XXXX XXXX - XXXX - - Solomys_salebrosus_ABTC64864 - - EU349691.1 - EU349785.1 EU349827.1 EU349872.1 - EU349917.1 - - Sundamys_muelleri_MVZ192234 - - EU349668 - EU349787 AM910979.1 AY326111 - DQ023456 - - Tokudaia_osimensis - - EU349659 - AB033703 EU349828.1 EU349874.1 - EU349918.1 - -

77 Uranomys_ruddi_CM113726 - - EU349642.1 - HM635858.1 DQ019051.1 EU360812.1 - - - AJ402714.1 Uromys_caudimaculatus - XXXX XXXX - EU349789.1 GQ405397.1 EU349875.1 XXXX DQ023470.1 XXXX - Xeromys_myoides_ABTC30709 - - EU349696.1 - EU349790.1 EU349830.1 EU349877.1 - EU349920.1 - - Zyzomys_argurus_ABTC07908 XXXX XXXX EU349685.1 XXXX EU349792.1 EU349831.1 XXXX XXXX EU349921.1 XXXX XXXX Zyzomys_argurus_ABTC61630 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Zyzomys_maini_ABTC08025 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Zyzomys_maini_ABTC08030 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Zyzomys_palatalis_ABTC30744 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Zyzomys_woodwardi_ABTC07092 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX - XXXX Zyzomys_woodwardi_ABTC07937 XXXX XXXX - XXXX XXXX XXXX XXXX XXXX XXXX - XXXX 25 26

78 27 Supplementary Table 2.2. Sampling used for each of the respective 28 analyses in this study. murid PSG Sahulian TAXA dataset dataset dataset Abeomelomys_sevia Yes - Yes Anisomys_imitator_ABTC45107 Yes - Yes Apodemus_agrarius_MVZ159220 Yes - - Apodemus_draco_AB109398 Yes - - Apodemus_flavicollis_AM910943 Yes - - Apodemus_gurkha Yes - - Apodemus_mystacinus Yes - - Apodemus_semotus Yes - - Apodemus_speciosus Yes - - Apodemus_sylvaticus Yes - - Apomys_datae_FMNH198581 Yes - Yes Apomys_gracilirostris_646 Yes - Yes Apomys_hylocoetus_FMNH147871 Yes - Yes Apomys_microdon Yes - Yes Archboldomys_luzonensis_EAR1826 Yes - Yes Batomys_granti Yes - - Chiromyscus_chiropus_ABTC69097 Yes - - Chiropodomys_gliroides_AMCC101511 Yes - Yes Chiruromys_vates_ABTC43096 Yes - Yes Chrotomys_gonzalesi_EAR1850 Yes - Yes Conilurus_pencillatus_ABTC07411 Yes - Yes Crossomys_moncktoni_ABTC46614 Yes - Yes Deomys_ferrugineus_FMNH149427 Yes - - Gerbillurus_vallianus_RA47 Yes - - Gerbillus_gerbillus_CM113822 Yes - - Grammomys_dolichurus Yes - - Hydromys_chrysogaster Yes - Yes Hyomys_goliath_ABTC42697 Yes - Yes Leggadina_forresti_ABTC107348 Yes Yes Yes Leggadina_forresti_ABTC36085 Yes Yes Yes Leggadina_lakedownensis_ABTC07406 Yes Yes Yes Leggadina_lakedownensis_ABTC32203 Yes Yes Yes Leopoldamys_sabanus Yes - - Leporillus_conditor_ABTC13335 Yes - Yes Leptomys_elegans_ABTC45741 Yes - Yes Lophuromys_flavopunctatus_FMNH144777 Yes - - Lorentzimys_nouhuysi_ABTC45265 Yes - Yes Macruromys_major_EU349809 Yes - Yes Mallomys_aroaensis_ABTC45196 Yes - Yes Mallomys_istapantap_ABTC42521 Yes - Yes Mallomys_rothschildi_ABTC47402 Yes - Yes Mammelomys_lanosus_ABTC47208 Yes - Yes

79 29 murid PSG Sahulian TAXA dataset dataset dataset Mammelomys_rattoides_ABTC47351 Yes - Yes Mastacomys_fuscus_ABTC07354 Yes Yes Yes Mastacomys_fuscus_ABTC07393 Yes Yes Yes Maxomys_bartelsii_ABTC48063 Yes - - Melomys_bannisteri_M42669 Yes - Yes Melomys_cooperae_M43822 Yes - Yes Melomys_rufescens_ABTC43071 Yes - Yes Mesembiromys_marcrurus_ABTC07337 Yes - Yes Mesembriomys_gouldii_ABTC07412 Yes - Yes Microhydromys_richardsonii_M35851 Yes - Yes Mus_cookii_USNM583802 Yes - - Mus_mattheyi Yes - - Mus_musculus Yes - - Mus_pahari_AMCC110800 Yes - - Mus_platythrix Yes - - Notomys_alexis_ABTC61767 Yes Yes Yes Notomys_alexis_Z21353 Yes Yes Yes Notomys_aquilo_ABTC18252 Yes Yes Yes Notomys_aquilo_ABTC18253 Yes Yes Yes Notomys_cervinus_ABTC27130 Yes Yes Yes Notomys_cervinus_ABTC82963 Yes Yes Yes Notomys_fuscus_ABTC117695 Yes Yes Yes Notomys_fuscus_ABTC80277 Yes Yes Yes Notomys_mitchelli_ABTC07351 Yes Yes Yes Notomys_mitchelli_ABTC27066 Yes Yes Yes Oenomys_hypoxanthus_CM102548 Yes - - Parahydromys_asper_ABTC45798 Yes - Yes Paramelomys_levipes_160736 Yes - Yes Paramelomys_platyops_ABTC42567 Yes - Yes Paramelomys_rubex_ABTC42720 Yes - Yes Parotomys_brantsii Yes - - Paruromys_dominator_ABTC65763 Yes - - Pogonomelomys_mayeri_ABTC44189 Yes - Yes Pogonomelomys_ruemmleri_ABTC47187 Yes - Yes Pogonomys_loriae Yes - Yes Pogonomys_macrourus_ABTC43144 Yes - Yes Pogonomys_sylvestris_GQ405389 Yes - Yes Praomys_jacksoni Yes - - Pseudohydromys_ellermani_ABTC43920 Yes - Yes Pseudomys_albocinereus_ABTC08044 Yes Yes Yes Pseudomys_albocinereus_ABTC08091 Yes Yes Yes 30 31

80 murid PSG Sahulian TAXA dataset dataset dataset Pseudomys_apodemoides_ABTC37663 Yes Yes Yes Pseudomys_apodemoides_Z7296 Yes Yes Yes Pseudomys_australis_ABTC118844 Yes Yes Yes Pseudomys_australis_ABTC35951 Yes Yes Yes Pseudomys_bolami_ABTC08065 Yes Yes Yes Pseudomys_bolami_ABTC96553 Yes Yes Yes Pseudomys_calabyi_ABTC84990 Yes Yes Yes Pseudomys_chapmani_ABTC62178 Yes Yes Yes Pseudomys_chapmani_M40577 Yes Yes Yes Pseudomys_delicatulus_ABTC62035 Yes Yes Yes Pseudomys_delicatulus_ABTC72733 Yes Yes Yes Pseudomys_delicatulus_Z18879 Yes Yes Yes Pseudomys_desertor_ABTC113870 Yes Yes Yes Pseudomys_desertor_Z21274 Yes Yes Yes Pseudomys_fieldi_M56289 Yes Yes Yes Pseudomys_fieldi_ABTC08164 Yes Yes Yes Pseudomys_fumeus_ABTC08168 Yes Yes Yes Pseudomys_fumeus_Z25963 Yes Yes Yes Pseudomys_gracilicaudatus_ABTC08031 Yes Yes Yes Pseudomys_hermannsburgensis_ABTC91375 Yes Yes Yes Pseudomys_hermannsburgensis_Z11008 Yes Yes Yes Pseudomys_higginsi_ABTC08139 Yes Yes Yes Pseudomys_johnsoni_ABTC08055 Yes Yes Yes Pseudomys_johnsoni_ABTC30351 Yes Yes Yes Pseudomys_nanus_ABTC08056 Yes Yes Yes Pseudomys_nanus_ABTC30578 Yes Yes Yes Pseudomys_novaehollandiae_ABTC08140 Yes Yes Yes Pseudomys_novaehollandiae_ABTC75991 Yes Yes Yes Pseudomys_occidentalis_ABTC08042 Yes Yes Yes Pseudomys_occidentalis_ABTC08144 Yes Yes Yes Pseudomys_oralis_KR033 Yes Yes Yes Pseudomys_oralis_KR034 Yes Yes Yes Pseudomys_patrius_ABTC32205 Yes Yes Yes Pseudomys_patrius_ABTC32211 Yes Yes Yes Pseudomys_shortridgei_ABTC08079 Yes Yes Yes Pseudomys_shortridgei_Z24891 Yes Yes Yes Rattus_leucopus Yes - - Rattus_nitidus Yes - - Rattus_norvegicus Yes - - Rattus_praetor Yes - - Rattus_rattus Yes - - 32 33 34

81 murid PSG Sahulian TAXA dataset dataset dataset Rattus_villosissimus Yes - - Rhabdomys_pumilio Yes - - Rhynchomys_isarogensis Yes - Yes Solomys_salebrosus_ABTC64864 Yes - Yes Sundamys_muelleri_MVZ192234 Yes - - Tokudaia_osimensis Yes - - Uranomys_ruddi_CM113726 Yes - - Uromys_caudimaculatus Yes - Yes Xeromys_myoides_ABTC30709 Yes - Yes Zyzomys_argurus_ABTC07908 Yes Yes Yes Zyzomys_argurus_ABTC61630 Yes Yes Yes Zyzomys_maini_ABTC08025 Yes Yes Yes Zyzomys_maini_ABTC08030 Yes Yes Yes Zyzomys_palatalis_ABTC30744 Yes Yes Yes Zyzomys_woodwardi_ABTC07092 Yes Yes Yes Zyzomys_woodwardi_ABTC07937 Yes Yes Yes 35

82 36 Supplementary Table 2.3. Aridity Index means for each species within the 37 PSG. Taxa Mean AI Leggadina forresti 0.13 Leggadina lakedownensis 0.37 Mastacomys fuscus 1.79 Notomys alexis 0.15 Notomys amplus 0.14 Notomys aquilo 0.77 Notomys cervinus 0.10 Notomys fuscus 0.12 Notomys longicaudatus 0.18 Notomys mitchellii 0.21 Pseudomys albocinereus 0.32 Pseudomys apodemoides 0.37 Pseudomys australis 0.12 Pseudomys bolami 0.16 Pseudomys calabyi 0.71 Pseudomys chapmani 0.17 Pseudomys delicatulus 0.60 Pseudomys desertor 0.16 Pseudomys fumeus 1.08 Pseudomys gouldii 0.18 Pseudomys gracilicaudatus 0.91 Pseudomys 0.14 hermannsburgensis Pseudomys higginsi 1.99 Pseudomys johnsoni 0.34 Pseudomys nanus 0.50 Pseudomys novaehollandiae 0.96 Pseudomys occidentalis 0.31 Pseudomys oralis 1.06 Pseudomys shortridgei 0.68 Zyzomys argurus 0.56 Zyzomys maini 0.74 Zyzomys pedunculatus 0.18 Zyzomys woodwardi 0.73 38 39 40 41 42 43 44 45

83 46 Supplementary Table 2.4. Estimation of tmrca in millions of years before present for Pseudomys Species Group based on 47 four fossil calibrations from the rodent subfamily Murinae. ESS = Effective Sample Size. Calibration 1: Mus- Calibration 2: Calibration 3: Anisomyini Pogonomelomyini Hydromyini Arvicanthis Arvicanthini Mus Mean 15.821 12.063 8.723 8.478 8.804 4.326 Median 15.440 11.822 8.516 8.308 7.570 4.231 95% CI: lower 12.701 9.515 7.319 6.673 6.031 3.277 95% CI: upper 19.610 15.120 10.573 10.514 9.611 5.520 ESS 479 498 638 - - -

Uromyini Conilurini PSG Zyzomys Leggadina Notomys Mean 5.261 3.839 5.690 3.345 0.707 2.950 Median 5.159 3.768 5.568 3.280 0.691 2.890 95% CI: lower 4.027 2.875 4.545 2.460 0.467 2.105 95% CI: upper 6.591 4.897 7.211 4.364 0.972 3.915 ESS ------

Mastacomys Pseudomys clade P. australis + delicate + pebble- velvet + grizzled fuscus + other (incl. Mastacomys) other mice clade other mice mound mice + other mice mice Mean 4.439 3.247 1.630 2.226 3.292 2.927 Median 4.332 3.177 1.591 2.168 3.393 2.865 95% CI: lower 3.649 2.510 1.192 1.612 2.670 2.222 95% CI: upper 5.897 4.162 2.134 2.908 4.420 3.713 ESS ------48

84 49 Supplementary Table 2.5. The seven subgroupings within Pseudomys as 50 outlined by Breed and Ford (2007). Subgroup Species false mice P. higginsi P. fieldi P. auritus (EX)

velvet mice P. fumeus P. apodemoides P. albocinereus P. glaucus (EX)

grizzled mice P. shortridgei P. desertor

chestnut mice + M. fuscus P. nanus P. gracilicaudatus M. fuscus

delicate mice P. hermannsburgensis P. bolami P. delicatulus P. novaehollandiae

pebble-mound mice P. chapmani P. johnsoni P. calabyi P. patrius

other native mice P. oralis P. occidentalis P. gouldii (EX) 51 52

85 53 Figures:

54

55 Figure 2.1. Map of Australia illustrating the sampling of specimens from the 56 five genera of the Pseudomys Species Group (PSG) used in the study. The 57 distribution of each of the four biomes is shown using shading.

86 ‘ N e A w u

s E t n r a d l e i a m n i

c s ’

A H

B I ‘ O A l

J d

False mice u C E s t Velvet mice n r d a e l

Delicate mice i m D a n

Chestnut mice i c K Q s + Mastacomys ’ P

Pebble-mound mice S E G Grizzled mice O Other native mice L F M R PP >95 ML >80 G P S PP >95 ML <80 N V PP <95 ML >80 T U W PP <95 ML <80 X Y 58

59 Figure 2.2. Phylogeny resulting from Bayesian analysis of murid dataset 60 including PSG and Muridae outgroups. Nodes are labelled with Posterior 61 Probability (PP; left half of circle) and Maximum Likelihood Bootstraps (MLBS; 62 right half of circle). Within circles, black fill indicates PP > 0.95 and MLBS > 90, 63 and white fill indicates PP < 0.95 and MLBS < 90. Breed and Ford (2007) 64 groupings within Pseudomys indicated by white and black shapes.

87 65

66 Figure 2.3. Species tree estimated using BEAST including only the PSG taxa. 67 Nodes shaded in grey indicate lack of significant support, PP < 0.90. The * 68 represent nodes that are incongruent with the trees from the phylogenetic 69 analyses.

88 70

71 Figure 2.4. Chronogram generated using BEAST, trimmed to show only the 72 PSG (ingroup) in the analysis. Pie charts on each node represent the Biome 73 distribution probabilities estimated with BioGeoBEARS (values and binary traits 74 presented in Table 2.3). Biome states of terminal taxa are indicated at terminal 75 nodes.

89 s e g a e n i l

f o

. o N

Time (Ma) 76

77 Figure 2.5. Lineage through time plot of the PSG. Solid line shows median of 78 1000 sampled species trees. Dashed line represents linear regression. 79 Shaded area represents 95% CI of estimated from 1000 sampled species 80 trees. 81

82

83

84

85

86

90 87 Gerbillurus_vallianus_RA47 1 Gerbillus_gerbillus_CM113822 1 Uranomys_ruddi_CM113726 1 Lophuromys_flavopunctatus_FMNH144777 1 Deomys_ferrugineus_FMNH149427 Batomys_granti Parotomys_brantsii 1 Oenomys_hypoxanthus_CM102548 1 Grammomys_dolichurus 1 Rhabdomys_pumilio Praomys_jacksoni 0.96 0.79 Mus_pahari Mus_platythrix 0.96 0.76 Mus_mattheyi 0.86 1 Mus_musculus 0.96 0.99 Mus_cookii Tokudaia_osimensis Apodemus_gurkha 0.98 0.94 Apodemus_mystacinus Apodemus_sylvaticus 1 0.98 Apodemus_flavicollis_AM910943 Apodemus_speciosus 0.99 1 Apodemus_agrarius_MVZ159220 0.92 Apodemus_semotus 1 Apodemus_draco_AB109398 Maxomys_bartelsii_ABTC48063 Leopoldamys_sabanus 1 1 Chiromyscus_chiropus_ABTC69097 Sundamys_muelleri_MVZ192234 1 1 Paruromys_dominator_ABTC65763 1 Rattus_rattus 1 Rattus_norvegicus 1 1 Rattus_nitidus Rattus_leucopus 0.91 1 Rattus_praetor 0.86 Rattus_villosissimus Chiropodomys_gliroides_AMCC101511 Archboldomys_luzonensis_EAR1826 1 Chrotomys_gonzalesi_EAR1850 1 0.97 Rhynchomys_isarogensis Apomys_datae 0.93 0.97 Apomys_microdon Apomys_hylocoetus_FMNH147871 Anisomys_imitator_ABTC45107 0.68 Lorentzimys_nouhuysi_ABTC45265 0.99 Macruromys_major_EU349809 0.930.97 Hyomys_goliath_ABTC42697 0.5 Chiruromys_vates_ABTC43096 Pogonomys_loriae 1 Pogonomys_sylvestris 0.98 Pogonomys_macrourus_ABTC43144 Pogonomelomys_ruemmleri_ABTC47187 Mammelomys_rattoides_ABTC47351 0.98 1 0.93 Mammelomys_lanosus_ABTC47208 1 Abeomelomys_sevia 1 Pogonomelomys_mayeri_ABTC44189 1 Mallomys_istapantap_ABTC42521 1 Mallomys_aroaensis_ABTC45196 0.91 Mallomys_rothschildi_ABTC47402 Crossomys_moncktoni_ABTC46614 1 Hydromys_chrysogaster 1 0.92 1 Parahydryomys_asper_ABTC45798 Leptomys_elegans_ABTC45741 0.94 Xeromys_myoides_ABTC30709 1 Microhydromys_richardsonii_M35851 1 Pseudohydromys_ellermani_ABTC43920 Leporillus_conditor_ABTC13335 1 Conilurus_pencillatus_ABTC07411 1 Mesembriomys_gouldii_ABTC07412 1 Mesembriomys_marcrurus_ABTC07337 0.93 1 Uromys_caudimaculatus Paramelomys_rubex_ABTC42720 0.91 1 Paramelomys_levipes_160736 0.62 1 Paramelomys_platyops_ABTC42567 Solomys_salebrosus_ABTC64864 1 Melomys_rufescens_ABTC43071 0.98 Melomys_cooperae_M43822 Melomys_bannisteri_M42669 0.95 Zyzomys_woodwardi_ABTC07937 1 Zyzomys_woodwardi_ABTC07092 1 Zyzomys_maini_ABTC08030 1 Zyzomys_maini_ABTC08025 1 Zyzomys_palatalis_ABTC30744 1 Zyzomys_argurus_ABTC07908 1 Zyzomys_argurus_ABTC61630 Leggadina_forresti_ABTC107348 1 Leggadina_forresti_ABTC36085 0.95 1 Leggadina_lakedownensis_ABTC07406 1 Leggadina_lakedownensis_ABTC32203 Notomys_cervinus_ABTC82963 1 Notomys_cervinus_ABTC27130 Notomys_aquilo_ABTC18253 1 1 Notomys_aquilo_ABTC18252 0.95 Notomys_fuscus_ABTC117695 1 1 Notomys_fuscus_ABTC80277 0.98 Notomys_mitchelli_ABTC27066 1 Notomys_mitchelli_ABTC07351 1 Notomys_alexis_Z21353 1 Notomys_alexis_ABTC61767 Pseudomys_chapmani_ABTC62178 1 1 Pseudomys_chapmani_M40577 1 Pseudomys_patrius_ABTC32205 1 Pseudomys_patrius_ABTC32211 1 Pseudomys_calabyi_ABTC84990 1 Pseudomys_johnsoni_ABTC30351 1 Pseudomys_johnsoni_ABTC08055 Pseudomys_occidentalis_ABTC08144 1 0.98 Pseudomys_occidentalis_ABTC08042 Pseudomys_fumeus_Z25963 1 Pseudomys_fumeus_ABTC08168 1 Pseudomys_apodemoides_Z7296 1 Pseudomys_apodemoides_ABTC37663 1 Pseudomys_albocinereus_ABTC08091 1 0.98 Pseudomys_albocinereus_ABTC08044 1 Pseudomys_gracilicaudatus_ABTC08031 1 Pseudomys_nanus_ABTC30578 1 Pseudomys_nanus_ABTC08056 1 Pseudomys_oralis_KR034 1 Pseudomys_oralis_KR033 1 Pseudomys_desertor_Z21274 1 0.99 Pseudomys_desertor_ABTC113870 1 Pseudomys_shortridgei_Z24891 1 Pseudomys_shortridgei_ABTC08079 Pseudomys_australis_ABTC35951 1 Pseudomys_australis_ABTC118844 Mastacomys_fuscus_ABTC07393 1 1 Mastacomys_fuscus_ABTC07354 Pseudomys_higginsi_ABTC08139 1 1 Pseudomys_fieldi_M56289 1 Pseudomys_fieldi_ABTC08164 1 Pseudomys_hermannsburgensis_ABTC91375 1 Pseudomys_hermannsburgensis_Z11008 Pseudomys_delicatulus_Z18879 1 1 Pseudomys_delicatulus_ABTC62035 1 Pseudomys_novaehollandiae_ABTC75991 1 Pseudomys_novaehollandiae_ABTC08140 0.87Pseudomys_delicatulus_ABTC72733 0.9 Pseudomys_bolami_ABTC96553 1 Pseudomys_bolami_ABTC08065m

88 0.03 89 Supplementary Figure 2.1. Bayesian phylogeny based on 10 nuclear exons

91 1 1 1 Uranomys_ruddi_CM113726 0.8676 1 Deomys_ferrugineus_FMNH149427 1 Lophuromys_flavopunctatus_FMNH144777 0.8775 Tokudaia_osimensis1 0.9624 1 Batomys_granti 1 Grammomys_dolichurus 1 1 Mus_musculus 0.537 Mus_cookii 1 1 0.951 1 Mus_mattheyi 1 Mus_pahari 1 Mus_platythrix 0.9782 1 Gerbillus_gerbillus_CM113822 1 Praomys_jacksoni 0.994 1 Oenomys_hypoxanthus_CM102548 0.5005 0.9978 1 Parotomys_brantsii 1 Rhabdomys_pumilio 0.9913 1 Apodemus_agrarius_MVZ159220 0.8551 1 Apodemus_speciosus 1 0.5131 1 Apodemus_draco_AB109398 1 Apodemus_semotus 0.5093 1 Apodemus_gurkha 0.6019 1 Apodemus_mystacinus 1 1 Apodemus_flavicollis_AM910943 1 Apodemus_sylvaticus 0.9984 1 Maxomys_bartelsii_ABTC48063 1 Chiromyscus_chiropus_ABTC69097 0.8415 1 Leopoldamys_sabanus 0.7413 1 Paruromys_dominator_ABTC65763 1 1 Sundamys_muelleri_MVZ192234 Rattus_rattus1 0.9493 1 1 Rattus_nitidus 0.6601 1 Rattus_norvegicus 1 1 Rattus_villosissimus 0.8497 1 Rattus_leucopus 1 Rattus_praetor 1 Chiropodomys_gliroides_AMCC101511 1 Anisomys_imitator_ABTC45107 1 Hyomys_goliath_ABTC42697 0.7821 1 Macruromys_major_EU349809 1 1 Chiruromys_vates_ABTC43096 1 Lorentzimys_nouhuysi_ABTC45265 1 1 Pogonomys_loriae 0.9793 1 1 Pogonomys_macrourus_ABTC43144 1 Pogonomys_sylvestris 0.9989 1 Chrotomys_gonzalesi_EAR1850 0.7598 1 Archboldomys_luzonensis_EAR1826 1 1 Rhynchomys_isarogensis 0.8998 1 Apomys_datae 1 1 Apomys_gracilostris_646 1 1 Apomys_hylocoetus_FMNH147871 1 Apomys_microdon 1 Pogonomelomys_ruemmleri_ABTC47187 1 Mallomys_aroaensis_ABTC451961 0.5098 1 Mallomys_rothschildi_ABTC47402 1 1 Mammelomys_lanosus_ABTC47208 Mammelomys_rattoides_ABTC473511 1 1 Abeomelomys_sevia Pogonomelomys_mayeri_ABTC44189 0.6955 1 1 Mesembiromys_marcrurus_ABTC07337 1 Xeromys_myoides_ABTC30709 1 1 1 Crossomys_moncktoni_ABTC46614 1 0.7565 1 Hydromys_chrysogaster 0.9793 1 Parahydryomys_asper_ABTC45798 0.6133 Leptomys_elegans_ABTC457411 1 1 Microhydromys_richardsonii_M35851 1 Pseudohydromys_ellermani_ABTC43920 1 Leporillus_conditor_ABTC13335 0.9978 1 Conilurus_pencillatus_ABTC07411 1 Mesembriomys_gouldii_ABTC07412 1 Uromys_caudimaculatus 0.7053 0.9902 1 Paramelomys_rubex_ABTC42720 0.9548 0.8987 0.9967 1 Paramelomys_levipes_160736 1 Paramelomys_platyops_ABTC42567 1 1 Melomys_bannisteri_M42669 0.7135 1 Melomys_rufescens_ABTC43071 0.9248 Melomys_cooperae_M43822 0.5153 1 1 Solomys_salebrosus_ABTC64864 1 1 Pseudomys_gracilicaudatus_ABTC08031 0.9935 1Pseudomys_nanus_ABTC08056 Pseudomys_nanus_ABTC305781 0.9956 1 Leggadina_forresti_ABTC107348 1 1 Leggadina_forresti_ABTC36085 0.5289 Leggadina_lakedownensis_ABTC074061 1 Leggadina_lakedownensis_ABTC32203 1 Zyzomys_maini_ABTC080251 1 Zyzomys_maini_ABTC08030 1 1 1 Zyzomys_woodwardi_ABTC07092 1 Zyzomys_woodwardi_ABTC07937 0.9668 1 Zyzomys_palatalis_ABTC30744 0.9815 1 Zyzomys_argurus_ABTC07908 Zyzomys_argurus_ABTC616301 0.677 1 1 Pseudomys_chapmani_ABTC62178 1 Pseudomys_chapmani_M40577 1 1 1 Pseudomys_patrius_ABTC32205 0.9809 1 Pseudomys_patrius_ABTC32211 1 1 Pseudomys_calabyi_ABTC84990 0.9853 0.7707 1 Pseudomys_johnsoni_ABTC08055 1 Pseudomys_johnsoni_ABTC30351 1 1 Notomys_fuscus_ABTC117695 Notomys_fuscus_ABTC802771 1 1 1 Notomys_aquilo_ABTC18252 1 1 Notomys_aquilo_ABTC18253 0.8922 1 Notomys_mitchelli_ABTC27066 0.5817 1Notomys_mitchelli_ABTC07351 1 1 Notomys_alexis_ABTC61767 1 Notomys_alexis_Z21353 1 Notomys_cervinus_ABTC82963 1 Pseudomys_oralis_KR033 Pseudomys_oralis_KR0341 1 1 Pseudomys_occidentalis_ABTC08042 1 Pseudomys_occidentalis_ABTC08144 0.9897 1 Pseudomys_albocinereus_ABTC08044 1 Pseudomys_albocinereus_ABTC080911 0.506 1 1 Pseudomys_apodemoides_ABTC37663 1 Pseudomys_apodemoides_Z7296 1 1 Pseudomys_australis_ABTC118844 0.7037 Pseudomys_australis_ABTC359511 1 1Pseudomys_fumeus_ABTC08168 1 Pseudomys_fumeus_Z25963 0.6062 1 Pseudomys_shortridgei_Z24891 1 Pseudomys_shortridgei_ABTC080791 1 1 Pseudomys_desertor_ABTC113870 Pseudomys_desertor_Z212741 0.9826 1 Pseudomys_higginsi_ABTC08139 1 1 Pseudomys_fieldi_M56289 0.6776 1 Pseudomys_fieldi_ABTC08164 1 1 Mastacomys_fuscus_ABTC07354 Mastacomys_fuscus_ABTC073931 0.7854 1 1 Pseudomys_bolami_ABTC08065 Pseudomys_bolami_ABTC965531 1 1 1 Pseudomys_hermannsburgensis_ABTC91375 0.6857 1 Pseudomys_hermannsburgensis_Z11008 1 1 Pseudomys_delicatulus_ABTC72733 0.5953 1 Pseudomys_delicatulus_Z18879 1 Pseudomys_delicatulus_ABTC62035 1 1 Pseudomys_novaehollandiae_ABTC08140 Pseudomys_novaehollandiae_ABTC75991

90 0.2 91 Supplementary Figure 2.2. Bayesian phylogeny based on mitochondrial 92 locus Cyt-b

92 Uranomys_ruddi_CM113726 58 Deomys_ferrugineus_FMNH149427 Lophuromys_flavopunctatus_FMNH144777 Maxomys_bartelsii_ABTC48063 56 Chiromyscus_chiropus_ABTC69097 95 Leopoldamys_sabanus Sundamys_muelleri_MVZ192234 43 53 58 Paruromys_dominator_ABTC65763 78 Rattus_nitidus 100 Rattus_norvegicus 56 Rattus_rattus 26 Rattus_villosissimus 99 Rattus_praetor 94 67 Rattus_leucopus Gerbillus_gerbillus_CM113822 51 Praomys_jacksoni Oenomys_hypoxanthus_CM102548 64 Parotomys_brantsii 73 Rhabdomys_pumilio 12 Tokudaia_osimensis 36 Batomys_granti 10 49 Grammomys_dolichurus 33 Mus_musculus 92 Mus_cookii 24 Mus_platythrix 14 62 Mus_mattheyi 61 Mus_pahari Chiropodomys_gliroides_AMCC101511 Pogonomelomys_ruemmleri_ABTC47187 17 Lorentzimys_nouhuysi_ABTC45265 98 Chiruromys_vates_ABTC43096 3 51 Hyomys_goliath_ABTC42697 36 Chrotomys_gonzalesi_EAR1850 93 Archboldomys_luzonensis_EAR1826 43 Rhynchomys_isarogensis 71 Macruromys_major_EU349809 18 Pogonomys_loriae 100 Pogonomys_sylvestris 99 Pogonomys_macrourus_ABTC43144

8 Pogonomelomys_mayeri_ABTC44189 14 Mammelomys_lanosus_ABTC47208 95 Mammelomys_rattoides_ABTC47351 Mallomys_rothschildi_ABTC47402 11 100 Mallomys_aroaensis_ABTC45196 Mesembiromys_marcrurus_ABTC07337

29 Xeromys_myoides_ABTC30709 100 Leptomys_elegans_ABTC45741 Pseudohydromys_ellermani_ABTC43920 49 97 82 Microhydromys_richardsonii_M35851 50 Crossomys_moncktoni_ABTC46614 96 Parahydryomys_asper_ABTC45798 100 Hydromys_chrysogaster 39 Leporillus_conditor_ABTC13335 Conilurus_pencillatus_ABTC07411 76 Mesembriomys_gouldii_ABTC07412 17 Uromys_caudimaculatus Paramelomys_rubex_ABTC42720 24 61 Paramelomys_levipes_160736 69 87 Paramelomys_platyops_ABTC42567 67 Melomys_bannisteri_M42669 97 Melomys_rufescens_ABTC43071 36 Solomys_salebrosus_ABTC64864 44 Melomys_cooperae_M43822 Leggadina_forresti_ABTC36085 44 100 Leggadina_forresti_ABTC107348 100 Leggadina_lakedownensis_ABTC32203 48 Leggadina_lakedownensis_ABTC07406 Zyzomys_maini_ABTC08025 100 Zyzomys_maini_ABTC08030 99 Zyzomys_woodwardi_ABTC07092 97 Zyzomys_woodwardi_ABTC07937 66 Zyzomys_palatalis_ABTC30744 22 71 Zyzomys_argurus_ABTC61630 99 Zyzomys_argurus_ABTC07908 Pseudomys_gracilicaudatus_ABTC08031 100 Pseudomys_nanus_ABTC08056 90 Pseudomys_nanus_ABTC30578 Pseudomys_chapmani_M40577 100 Pseudomys_chapmani_ABTC62178 24 9 99 Pseudomys_patrius_ABTC32211 100 Pseudomys_patrius_ABTC32205 97 Pseudomys_calabyi_ABTC84990 91 Pseudomys_johnsoni_ABTC30351 34 58 Pseudomys_johnsoni_ABTC08055 Notomys_fuscus_ABTC80277 98 Notomys_fuscus_ABTC117695 Notomys_aquilo_ABTC18253 100 100 Notomys_aquilo_ABTC18252 30 85 Notomys_mitchelli_ABTC07351 61 Notomys_mitchelli_ABTC27066 65 Notomys_alexis_Z21353 100 Notomys_alexis_ABTC61767 Pseudomys_oralis_KR033 12 Pseudomys_occidentalis_ABTC08042 100 Pseudomys_occidentalis_ABTC08144 Notomys_cervinus_ABTC82963 Pseudomys_australis_ABTC35951 5 100 Pseudomys_australis_ABTC118844 22 55 Pseudomys_fumeus_Z25963 100 Pseudomys_fumeus_ABTC08168 Pseudomys_apodemoides_ABTC37663 100 Pseudomys_apodemoides_Z7296 96 11 Pseudomys_albocinereus_ABTC08091 84 19 Pseudomys_albocinereus_ABTC08044 Pseudomys_oralis_KR034 37 Pseudomys_shortridgei_Z24891 28 Pseudomys_shortridgei_ABTC08079 97 Pseudomys_desertor_Z21274 3 100 Pseudomys_desertor_ABTC113870 Pseudomys_higginsi_ABTC08139 77 Pseudomys_fieldi_M56289 100 Pseudomys_fieldi_ABTC08164 23 Mastacomys_fuscus_ABTC07354 100 Mastacomys_fuscus_ABTC07393 Pseudomys_bolami_ABTC96553 47 100 Pseudomys_bolami_ABTC08065

98 Pseudomys_hermannsburgensis_Z11008 100 Pseudomys_hermannsburgensis_ABTC91375 83 Pseudomys_delicatulus_ABTC72733 Pseudomys_novaehollandiae_ABTC08140 100 100 Pseudomys_novaehollandiae_ABTC75991 41 Pseudomys_delicatulus_ABTC62035 39 Pseudomys_delicatulus_Z18879

93 0.08 94 Supplementary Figure 2.3. ML phylogeny based on mitochondrial locus Cyt- 95 b 96

93 Gerbillurus_vallianus_RA47 100 Gerbillus_gerbillus_CM113822 100 Uranomys_ruddi_CM113726 100 Deomys_ferrugineus_FMNH149427 100 Lophuromys_flavopunctatus_FMNH144777 Batomys_granti Parotomys_brantsii 100 Oenomys_hypoxanthus_CM102548 100 Rhabdomys_pumilio 94 Grammomys_dolichurus 90 Praomys_jacksoni Mus_pahari 100 Mus_cookii 100 94 Mus_musculus 100 70 Mus_mattheyi 31 38 Mus_platythrix Tokudaia_osimensis Apodemus_speciosus 100 100 Apodemus_agrarius_MVZ159220 78 Apodemus_semotus 100 100 Apodemus_draco_AB109398 Apodemus_flavicollis_AM910943 100 100 Apodemus_sylvaticus 77 Apodemus_mystacinus 58 Apodemus_gurkha Maxomys_bartelsii_ABTC48063 Chiromyscus_chiropus_ABTC69097 100 100 Leopoldamys_sabanus Paruromys_dominator_ABTC65763 100 100 Sundamys_muelleri_MVZ192234 Rattus_leucopus 100 100Rattus_villosissimus 89 100 Rattus_praetor Rattus_rattus 75 99 Rattus_norvegicus 100 Rattus_nitidus Chiropodomys_gliroides_AMCC101511 Apomys_hylocoetus_FMNH147871 100Apomys_microdon 48 100 Apomys_datae Archboldomys_luzonensis_EAR1826 100 99 Rhynchomys_isarogensis 93 Chrotomys_gonzalesi_EAR1850 Anisomys_imitator_ABTC45107 77 Lorentzimys_nouhuysi_ABTC45265 56 Macruromys_major_EU349809 10064 Chiruromys_vates_ABTC43096 44 Hyomys_goliath_ABTC42697 43 Pogonomys_loriae 100 Pogonomys_sylvestris 99 Pogonomys_macrourus_ABTC43144 Pogonomelomys_ruemmleri_ABTC47187 Mammelomys_lanosus_ABTC47208 98 58 100 Mammelomys_rattoides_ABTC47351 100 Abeomelomys_sevia 100 Pogonomelomys_mayeri_ABTC44189 93 Mallomys_istapantap_ABTC42521 100 Mallomys_rothschildi_ABTC47402 78 Mallomys_aroaensis_ABTC45196 Crossomys_moncktoni_ABTC46614 100 Hydromys_chrysogaster 94 100 100 Parahydryomys_asper_ABTC45798 Leptomys_elegans_ABTC45741 72 Xeromys_myoides_ABTC30709 85 Microhydromys_richardsonii_M35851 94 Pseudohydromys_ellermani_ABTC43920 Leporillus_conditor_ABTC13335 100 Conilurus_pencillatus_ABTC07411 100 Mesembriomys_gouldii_ABTC07412 100 Mesembiromys_marcrurus_ABTC07337 100 100 Uromys_caudimaculatus Paramelomys_rubex_ABTC42720 93 100 Paramelomys_levipes_160736 43 45 Paramelomys_platyops_ABTC42567 Solomys_salebrosus_ABTC64864 100 Melomys_bannisteri_M42669 84 Melomys_rufescens_ABTC43071 15 Melomys_cooperae_M43822 100 Zyzomys_woodwardi_ABTC07937 100 Zyzomys_woodwardi_ABTC07092 100 Zyzomys_maini_ABTC08030 100 Zyzomys_maini_ABTC08025 100 Zyzomys_palatalis_ABTC30744 100 Zyzomys_argurus_ABTC07908 100 Zyzomys_argurus_ABTC61630 Leggadina_forresti_ABTC36085 96 Leggadina_forresti_ABTC107348 100 100 Leggadina_lakedownensis_ABTC32203 94 Leggadina_lakedownensis_ABTC07406 Notomys_cervinus_ABTC27130 100 Notomys_cervinus_ABTC82963 Notomys_aquilo_ABTC18252 100 100 Notomys_aquilo_ABTC18253 99 Notomys_fuscus_ABTC117695 100 100 Notomys_fuscus_ABTC80277 62 Notomys_alexis_ABTC61767 98 Notomys_alexis_Z21353 58 Notomys_mitchelli_ABTC27066 100 Notomys_mitchelli_ABTC07351 Pseudomys_chapmani_M40577 100 100 Pseudomys_chapmani_ABTC62178 100 Pseudomys_patrius_ABTC32211 100 Pseudomys_patrius_ABTC32205 99 Pseudomys_calabyi_ABTC84990 100Pseudomys_johnsoni_ABTC08055 100 Pseudomys_johnsoni_ABTC30351 Pseudomys_occidentalis_ABTC08042 100 87 Pseudomys_occidentalis_ABTC08144 Pseudomys_fumeus_ABTC08168 100 Pseudomys_fumeus_Z25963 95 Pseudomys_apodemoides_Z7296 100 Pseudomys_apodemoides_ABTC37663 100 Pseudomys_albocinereus_ABTC08091 99 83 Pseudomys_albocinereus_ABTC08044 96 Pseudomys_gracilicaudatus_ABTC08031 100Pseudomys_nanus_ABTC08056 100 Pseudomys_nanus_ABTC30578 78 Pseudomys_oralis_KR033 100 Pseudomys_oralis_KR034 100 Pseudomys_shortridgei_Z24891 100 100 Pseudomys_shortridgei_ABTC08079 100 Pseudomys_desertor_Z21274 81 Pseudomys_desertor_ABTC113870 Pseudomys_australis_ABTC118844 100 Pseudomys_australis_ABTC35951 Mastacomys_fuscus_ABTC07354 100 100 Mastacomys_fuscus_ABTC07393 Pseudomys_higginsi_ABTC08139 82 96 Pseudomys_fieldi_ABTC08164 100 Pseudomys_fieldi_M56289 97 Pseudomys_hermannsburgensis_Z11008 100 Pseudomys_hermannsburgensis_ABTC91375 Pseudomys_bolami_ABTC08065 100 100 Pseudomys_bolami_ABTC96553 98Pseudomys_novaehollandiae_ABTC08140 100 Pseudomys_novaehollandiae_ABTC75991 30 Pseudomys_delicatulus_ABTC72733 37 Pseudomys_delicatulus_ABTC62035 45 Pseudomys_delicatulus_Z18879

97 0.02 98 Supplementary Figure 2.4. ML phylogeny based on 10 nuclear locus 99 dataset.

94 100

W/T/M A/M Monsoon (M) T/M Temperate Mesic (T) Arid (A) Other (O) Wet Tropics (W)

101 102 Supplementary Figure 2.5. BioGeoBEARS analyses run on the PSG using the 103 BEAST tree with Sahul, the Philippines and Southeast Asian outgroups. The 104 key on the bottom left indicates the biome(s) in which the species is presently 105 distributed (squares), and the pie graphs indicate the reconstructed node 106 probability that that ancestor was found in a given biome. Other = South East 107 Asian tropical rainforest .

95 108 Chapter 3

109

110 Mito-nuclear discordance in the Hastings River

111 Mouse, Pseudomys oralis, suggests dynamic

112 population contraction-expansions during

113 glacial cycles.

114

115 Smissen, P. J., Rowe, K. C. (in prep)

116

117 3.1. Abstract

118

119 The interplay between biogeographic barriers and climatic fluctuations has

120 played a major role in the diversification of taxa distributed across the eastern

121 mesic biome of Australia. The Macleay-McPherson Overlap Zone (MMOZ) of

122 northern New South Wales has been identified as an area where numerous

123 taxa show phylogenetic breaks. One such species is the Hastings River

124 mouse, Pseudomys oralis, which has a broad distribution along the east coast

125 of Australia from southern New South Wales to southern Queensland. The

126 species is comprised of two divergent mitochondrial lineages that are

127 distributed in the northern and southern parts of the species’ range, and which

96 128 overlap in a zone of contact at Washpool National Park in the MMOZ. I

129 extended the sampled distribution of P. oralis to the northernmost reaches of

130 the species’ range and tested the two previously discovered mitochondrial

131 DNA lineages using nine independent nuclear exons. Phylogenetic analyses

132 of nuclear data did not support the deep divergence of northern and southern

133 populations as recovered from mitochondrial sequences. While nuclear DNA-

134 based population-clustering analysis supported the presence of two

135 populations within the species, with a zone of overlap at Washpool NP, gene

136 flow analyses supported the panmictic model of migration. My findings

137 suggest that northern and southern P. oralis are a single species exhibiting a

138 pattern of shallow population divergence with recent overlap. This pattern is

139 consistent with isolation and secondary contact resulting from Pleistocene

140 contraction and fragmentation of east coast mesic forests as reported for

141 other taxa in the region. The fixed differences in mitochondrial and minor

142 allele frequency differences at nuclear loci, indicate that the northern and

143 southern populations of P. oralis should be considered Evolutionary

144 Significant Units.

145

146 3.2. Introduction

147

148 The contemporary distributions of taxa and the structure of genetic diversity

149 within species are shaped by physiographic and climatic factors (Hewitt, 1996,

150 2000, 2004; Byrne et al., 2008, 2011; Chapple et al., 2011a). These

151 processes interact and are temporally dynamic, disrupting gene flow and

97 152 underpinning speciation. In the recent past, global climates have fluctuated

153 with glacial cycles, especially during the Plio-Pleistocene. These historic

154 climate cycles have been widely studied in the Northern Hemisphere, where

155 glaciation has shaped the biogeography and diversification of taxa (Hewitt,

156 1996, 2000, 2004). Although the Southern Hemisphere has received less

157 attention, habitat fluctuations due to fluctuating climates have also been linked

158 to the biogeography and diversification of taxa in eastern Australia (Byrne et

159 al., 2008, 2011).

160 Climate cycles have played a large role in shaping the contemporary

161 biogeography of taxa in Australia’s mesic forests. These cycles ramped up

162 from shorter, less intense 40Ky intervals during the Miocene to longer 100Ky

163 intervals in the Pleistocene (see Byrne et al., 2008, 2011). During these

164 intervals, the climate in eastern Australia fluctuated between extremes of cold

165 and dry periods during glacial maxima to warm and wet periods during glacial

166 minima (Byrne et al., 2008, 2011). In response, both mesic habitats and the

167 taxa restricted within them went through periodic contraction, fragmentation

168 and re-expansion (Kershaw et al., 1991; VanDerWal et al., 2009). The genetic

169 structure within and among taxa in Australia’s mesic zone are consistent with

170 these processes (reviewed in Chapple et al., 2011a; Byrne et al., 2011).

171 In addition to climate cycles, long-standing biogeographic barriers whether

172 physiographic or ecological have shaped the contemporary distribution of

173 species (see Chapple et al., 2011a). The distribution of genetic lineages within

174 species are often attributed to both long-term barriers and fluctuations in the

175 distribution of habitats (Keppel et al., 2012). When habitats contract and

98 176 fragment, gene flow within species distributed within these habitats is

177 disrupted leading to lineage divergence (Bryant and Fuller, 2012; Smissen et

178 al., 2013). Once habitats expand and reconnect, gene flow between lineages

179 can occur if their isolation was sufficiently brief. However, if isolation is

180 prolonged, gene flow may no longer be possible due to reproductive isolation

181 (e.g. Singhal and Moritz, 2013). Thus, the isolation of taxa caused by climatic

182 cycles and the interaction between physiographic or ecological barriers can

183 lead to speciation (Lessa et al., 2003; Tolley et al., 2006, 2008; Willows-

184 Munro and Matthee, 2011).

185 The Eastern Mesic Zone (EMZ) of Australia is distributed along the Great

186 Dividing Range (GDR) running the length of Australia’s eastern coastline (Fig.

187 3.1). It is bisected by numerous biogeographic barriers and has experienced

188 substantial fluctuations in habitats that have shaped the structure and

189 distribution of species (reviewed in Chapple et al., 2011a). It comprises the

190 majority of Australia’s mesic forests and is defined by high mean annual

191 precipitation and an evaporation ratio > 0.4 (Byrne et al., 2011). Well-studied

192 ecological barriers in this region include the Burdekin Gap and the Hunter

193 Valley, and physiographic barriers include the GDR and the McPherson

194 Range (MR). The GDR is Australia’s most substantial mountain range,

195 running 3500 km from north to south along the entire eastern coast of the

196 continent. The MR runs east to west from the GDR to the coast delimiting the

197 border between New South Wales (NSW) and Queensland (Qld). Numerous

198 studies examining the phylogeography of taxa in the EMZ have indicated that

199 the interaction between these barriers and habitat changes in response to

99 200 Plio-Pleistocene climatic cycles has disrupted gene flow leading to the

201 evolutionary divergence of taxa (Hugall et al., 2002; Hoskin et al., 2005;

202 Moritz et al., 2005; Brown et al., 2006; Moussalli et al., 2009; Chapple et al.,

203 2011b, 2011b; Smissen et al., 2013). For example, the GDR is a significant

204 barrier to gene flow in several herpetofauna species (frogs, Symula et al.,

205 2008; and reptiles, Chapple et al., 2011b). While the MR also is a barrier to

206 gene flow in some herpetofauna species (frogs, McGuigan et al., 1998;

207 lizards, Chapple et al., 2011a; snakes, Keogh et al., 2003), a number of other

208 species show no sign of disrupted gene flow across this barrier (frogs,

209 Donnellan et al., 1999; birds, Nicholls and Austin, 2005; mammals, Brown et

210 al., 2006).

211 The Macleay-McPherson Overlap Zone (MMOZ) is located to the south of

212 the MR and east of the GDR between 29 and 32 S latitude (Burbidge, 1960),

213 and is the area of contact between two biogeographic regions, the Torresian

214 and Bassian zones. The Bassian zone is characterised by temperate eucalypt

215 forests inhabited predominantly by old endemic taxa, whereas the Torresian

216 zone is characterised by tropical eucalypt forests and is inhabited by taxa that

217 more recently colonised Australia from the north (Schodde and Calaby, 1972;

218 Heatwole, 1987; Crisp et al., 1999; Keast et al., 2013). Although this region

219 lacks any overt biogeographic barrier, the MMOZ has been repeatedly

220 identified as a region of divergence between taxonomic groups, which include

221 marsupials (Crowther and Blacket, 2003; Crowther et al., 2004), bats (Cooper

222 et al., 1998), birds (Ovenden et al., 1987) and reptiles (Colgan et al., 2010;

223 Smissen et al., 2013). The MMOZ has been much less well studied compared

100 224 with other barriers in the EMZ (e.g. the Burdekin Gap and Hunter Valley,

225 reviewed in Chapple et al., 2011b). Some genetic studies of taxa in this region

226 have indicated that low levels of divergence happened very recently

227 (Crowther and Blacket, 2003; O’Meally and Colgan, 2005).

228 The Hastings River mouse, Pseudomys oralis, is an endangered rodent

229 species endemic to temperate sclerophyll forests of the EMZ. The historical

230 distribution of the species ran along the east side of the GDR from just south

231 of the Hunter Valley in NSW to just north of the MR on the border between

232 NSW and Qld (Qld; Fig. 3.1). However, the species was distributed as far

233 south and west as Victoria within the last 8000 years (Smith and Quin, 1997;

234 Townley, 2000; Meek, 2002; Pyke and Read, 2002). Two divergent

235 mitochondrial lineages have been identified from the species that are

236 distributed north and south of the MMOZ with both lineages recovered from

237 Washpool NP (Jerry et al., 1998; Rowe et al., 2012), which is located within

238 the MMOZ south of the MR. The percent genetic divergence between the two

239 mitochondrial lineages (4.8%) is equivalent to other sister species pairs in

240 Pseudomys suggesting the need for independent management of the two

241 lineages (Rowe et al., 2012). However, the pattern of the mitochondrial

242 divergence between northern and southern populations of P. oralis has not

243 been tested with nuclear markers.

244 In this study, I expanded sampling to include previously unsampled sites to

245 the north of the MR. For representatives of each of the mitochondrial lineages,

246 I sequenced nine independent nuclear exons. I tested (1) if the divergence

247 between northern and southern mitochondrial lineages is supported by

101 248 nuclear exons; (2) if there is gene flow between mitochondrial lineages; and

249 (3) if coalescent-based analyses are consistent with a speciation event

250 between mitochondrial lineages. I discuss these results in light of the history

251 of the forests of the eastern mesic zone of Australia.

252

253 3.3. Methods

254

255 3.3.1. Specimens and Genetic Sequencing

256

257 I selected samples that represent the southern (n = 10) and northern (n =

258 11) mitochondrial lineages identified previously from sequencing a total of 138

259 individuals (Rowe et al., 2012). I also included three samples, from previously

260 unsampled localities at the northern limit of the species’ distribution (Table

261 3.1). The 24 samples in this study span the historical distribution of P. oralis,

262 at its most southern limit in central NSW (32°14'51.41"S, 151°16'30.28"E) to

263 its most northern limit in southern Qld (28° 6'48.40"S, 152°22'4.80"E; Fig. 3.1;

264 Table 3.1). Four samples in this study were from two localities where both

265 northern (n = 2) and southern (n = 2) mitochondrial lineages were detected. I

266 sampled three individuals from P. shortridgei and two individuals from P.

267 desertor as outgroups, as they are the closest living relatives of P. oralis

268 (Chapter 2).

269 I extracted total genomic DNA from tissue samples on a QIAextractor

270 machine (DX reagents and plasticware) or using a QIAGEN DNeasy blood

102 271 and tissue kit following the manufacturer’s protocols (QIAGEN Inc, Valencia,

272 CA, USA). I confirmed the identification of the specimens as P. oralis and their

273 assignment to northern and southern lineages using an additional

274 mitochondrial locus Cyt-b (~1000bp) for the 24 samples. Cyt-b was used

275 instead of D-loop in order to confirm the findings of Rowe et al. (2012), and in

276 order to align mitochondrial sequences to a larger murid dataset. Each

277 individual was sequenced for eight nuclear exons using published primers

278 (Chapter 2; A2AB, ARHGAP21, BRCA1, GHR, IRBP, NIN, RAG1, vWF). I

279 amplified and sequenced A2AB, ARHGAP21, BRCA1, CB1, GHR, IRBP, NIN

280 and vWF using the following PCR protocol: initial denaturation at 95 °C for 5

281 min, followed by 95 °C for 30 s, an annealing step of 52 °C for 30 s and 72 °C

282 for 90 s (40 cycles), and a final extension step at 72 °C for 10 min. I used an

283 annealing temperature of 57 °C in the same PCR protocol to amplify the locus

284 RAG1. All PCR reactions included a negative control in order to identify any

285 cases of contamination of reagents, and I visually inspected each product on

286 either an agarose gel or the QIAxcel electrophoresis system (QIAGEN Inc,

287 Valencia, CA, USA).

288 I prepared successful reactions directly using enzymatic digestion with

289 IllustraTM ExoSTARTM (GE Healthcare Life Sciences). I sequenced both

290 strands of each PCR product via automated DNA Sanger sequencing on the

291 ABI 3730xl DNA Analyzer at Macrogen Inc (Seoul, Korea). Sequences were

292 checked and edited in GENEIOUS v.6.1.8 (Kearse et al., 2012).

293

103 294 3.3.2. Phylogenetic Analyses

295

296 I estimated phylogenetic relationships based on the mitochondrial gene

297 Cyt-b in MRBAYES v3.2.1 (Ronquist and Huelsenbeck, 2003; Ronquist et al.,

298 2012) in order to confirm the presence of the two previously discovered

299 divergent mitochondrial lineages (Jerry et al., 1998; Rowe et al., 2012). I then

300 estimated phylogenetic relationships among individuals of P. oralis based on

301 nine nuclear exons, with the aim to test the pattern of divergence evident in

302 mitochondrial lineages (Jerry et al., 1998; Rowe et al., 2012). My analyses

303 follow three broad phylogenetic approaches, (1) Bayesian and maximum

304 likelihood estimates from a concatenation of all nine loci into one matrix

305 (MRBAYES and RAxML); (2) a multi-locus distance matrix based on allelic

306 variation (POFAD); and (3) the multispecies coalescent approach (*BEAST and

307 BPP). Each of these methods have benefits and limitations relative to my data

308 and the inferences of this study. The assumption of concatenation-based

309 analyses is that all genes share the same topology (i.e. tree), which is not

310 always the case, especially at shallow phylogenetic divergences where

311 incomplete lineage sorting is most likely (Funk and Omland, 2003). However,

312 concatenated data incorporate all the data and can treat individuals as

313 species for testing the divergence among mitochondrial lineages. I ran

314 concatenation analyses using a Bayesian statistical framework in MRBAYES,

315 and a maximum likelihood framework in RAxML v7.4.4 (Stamatakis, 2006;

316 Stamatakis et al., 2008). Both of these analyses were run via the online

104 317 CIPRES Science Gateway using the partitioning scheme listed in Table 3.2,

318 with all other settings left as defaults (Miller et al., 2010).

319 In addition to the concatenated analyses, I used the program POFAD (Fig.

320 3.2 Joly and Bruneau, 2006) to generate a distance-based network

321 incorporating allelic variation among individuals. Unlike concatenation

322 methods, POFAD treats each locus as an independent estimate of the

323 topology, albeit based on distance-based methods as opposed to character-

324 based trees displaying phylogenetic relationships. I converted sequence data

325 files into input and then output files using the online website SEQPHASE

326 (Flot, 2010; http://seqphase.mpg.de/seqphase/) before and after they were

327 phased using the program PHASE v.2.1.1. (Stephens et al., 2001; Stephens and

328 Donnelly, 2003; Stephens and Scheet, 2005). I estimated genetic distance

329 matrices using the biallelic data for each locus using MEGA v7 (Kumar et al.,

330 2016). Genetic distance matrices for the nine nuclear loci were analysed

331 using POFAD, which uses phylogenetic methods to generate a distance matrix

332 tree. I reduced the dataset to include only individuals with complete sampling

333 of 9 loci (n = 16). We visualised the distance matrix tree using SPLITSTREE

334 v.4.13.1. (Huson and Bryant, 2006).

335 I also estimated phylogenetic relationships using a hierarchical coalescent

336 species tree approach in *BEAST v.2.0.3. (Fig. 3.3; Drummond and Rambaut,

337 2007; Drummond et al., 2012). Unlike concatenation, *BEAST estimates the

338 species tree from the distribution of gene trees using the coalescent (Heled

339 and Drummond, 2010). Because the program requires a priori assignment of

340 individuals to species, I treated individuals of P. oralis as different “species”

105 341 with each individual (n=24) represented by two haplotypes. I also included

342 individuals of P. shortridgei (n=3) and P. desertor (n=2) as outgroups. I realise

343 that my treatment of individual P. oralis as “species” violates assumptions of

344 the multi-species coalescent, which assumes that there is no admixture

345 between individuals from different species. However, my objective is not to

346 determine the true relationships among all P. oralis individuals, but to test if

347 individuals from one mitochondrial lineage are more closely related than to

348 individuals from the other mitochondrial lineage taking into account the

349 coalescent history of individual gene trees. I estimated substitution model

350 priors for phased sequence data in MEGA v7 (Kumar et al., 2016). The HKY

351 model was recovered as the most appropriate substitution model for each of

352 the nine loci. All other settings were left as default. I generated posterior

353 samples for all nine loci using the Bayesian MCMC procedure. I ran four

354 independent runs of 5x107 million generations with sampling every 5000

355 generations for a total of 2x108 million generations and 40 000 trees/samples.

356 Log and tree files were combined, with the first 20% of samples removed as

357 burn-in (8 000 trees). I examined convergence via likelihood plots through

358 time visualised in the program TRACER v.1.4. (Rambaut and Drummond, 2007)

359 to ensure acceptable convergence to the stationary distribution.

360 Finally, I implemented a Bayesian modelling approach using the program

361 Bayesian Phylogenetics and Phylogeography v3.1 (BPP; Yang and Rannala, 2010;

362 Rannala and Yang, 2013). BPP calculates posterior probabilities for species

363 boundaries and generates a specific hypothesis for the delimitation of putative

364 species. However, unlike *BEAST, BPP is limited to a Jukes-Cantor model of

106 365 substitution, whereas *BEAST is able to implement more complicated models,

366 which may better reflect the underlying processes of nucleotide substitution in

367 the data. I followed the methods outlined in Leaché and Fujita (2010) using

368 three of the four different combinations of prior distributions for ancestral

5 369 population size (θ) and root age (τ). Each BPP analysis was run for 10 steps

370 after a burn-in of 8,000 was discarded. Both priors were assigned a gamma G

371 (α, β) distribution, with prior mean α/β and prior variance α/β2. The three

372 different combinations of prior distributions are as follows: 1 – large ancestral

373 population with deep divergences θ = 1, 10; τ = 1, 10, both with a prior mean

374 of 0.1 and variance of 0.01. 2 – small ancestral population and shallow

375 divergences θ = 2, 2000; τ = 2, 2000, both with a prior mean of 0.001 and

376 variance of 5 x 107. 3 – a relatively large ancestral pop with shallow

377 divergences θ = 1, 10; τ = 2, 2000. Each analysis was run twice to confirm

378 consistent results. I ran each BPP run combination two times to ensure

379 convergence.

380

381 3.3.3. Population Genetic Analyses

382

383 I used the program STRUCTURE v. 2.3.4. to estimate the number of

384 populations of P. oralis based on allelic data from the nuclear exons (Pritchard

385 et al., 2000). Phased sequence data were analysed using GENALEX v. 6.5.

386 (Peakall and Smouse, 2012) to generate haplotypes for each of the paired

387 sequences for each individual per locus. In STRUCTURE, I used an ancestral

388 admixture model and correlated allele frequencies as mitochondrial lineages

107 389 within P. oralis have only recently diverged and overlap geographically.

390 Number of clusters (K) was set at 1 to 10, with 5 runs of each. The analyses

391 were run for 100 000 iterations and the first 10 000 iterations were removed

392 as burn-in. I used STRUCTURE HARVESTER web v0.6.94 (Earl and Vanholdt,

393 2012) to estimate the most likely number of clusters (i.e. K-value).

394 Because STRUCTURE does not incorporate geographic information, I also

395 estimated the number of populations in GENELAND v. 4.0.2, which uses genetic

396 and spatial information to define genetic structure (Guillot et al., 2005; Coulon

397 et al., 2006). GENELAND assumes Hardy-Weinberg equilibrium within loci and

398 linkage disequilibrium between loci, and that immigrant genes originate from

399 new immigrants (Guillot et al., 2005). Spatial data were entered as geographic

400 coordinates with an uncertainly level left at default (0) based on the

401 assumption that P. oralis is a small rodent species with a limited home range

402 size of <1 hectare, and thus don’t disperse long distances (Meek et al., 2002).

403 Potential K-values were set from 1 to 5 clusters with 1x106 MCMC iterations

404 and thinning every 1000 generations, a maximum rate of Poisson process

405 fixed at 100, and a maximum number of nuclei in the Poisson-Voronoi

406 tessellation fixed at 300 (following Smissen et al., 2013). I assumed an

407 uncorrelated allele frequency model among populations as it is more robust to

408 departures from model assumptions such as the likely presence of an

409 isolation-by-distance pattern. I assumed a spatial model to include GPS

410 location data for each sample. The number of clusters (K) was inferred from

411 the model value of K for the 10 runs and post processing of data was

412 calculated on the run with the highest average log posterior probability.

108 413 Genetic differentiation between the clusters inferred by GENELAND was

414 calculated as population pairwise FST, and FIS was calculated using a Weir and

415 Cockerham model (Weir and Cockerham, 1984).

416 To estimate gene flow between mitochondrial lineages, I used the

417 coalescent-based program MIGRATE-N 3.6.11. (Beerli and Felsenstein, 2001;

418 Beerli, 2006; Beerli and Palczewski, 2010). The accuracy of estimates from

419 MIGRATE-N’s coalescent approach depends upon the number of loci used

420 more so than the number of individuals sampled, and missing data doesn’t

421 influence parameter estimates. I used MIGRATE-N to estimate gene flow

422 between the northern and southern lineages within P. oralis based on the

423 nine-locus nuclear dataset. I estimated marginal likelihoods and Bayes

424 Factors as outlined by Beerli and Palczewski (2010) for the three migration

425 models tested: (1) Panmictic; (2) North to South (1à2); and (3) South to North

426 (2à1; Table 3.3). A single migration model was used: the full model with two

427 population sizes and two migration rates. MIGRATE-N estimates mutation-

428 scaled effective population size Θ = 4Neμ, where Ne = effective population

429 size and μ = mutation rate per generation per locus. The program also

430 estimates mutation-scaled migration rates between the populations, M = m/μ,

431 where m = immigration rate per generation between populations. I used initial

432 test runs to establish the best prior values for mutation-scaled migration (M)

433 and mutation-scaled population size (Θ); maximum Θ value = 0.1, maximum

434 M value = 80,000, sampling occurred every 1000 steps for 10,000 recorded

435 steps (i.e. 10 million generations), burn-in was set to 20,000 trees. I ran four

436 parallel chains under a static heating scheme with the chain temperatures set

109 437 to 1.0, 1.5, 3.0, and 100,000 giving a spacing of 1.0, 0.666, 0.333, and 0.0,

438 and a swapping interval set to 10. Every analysis was run at least two times to

439 ensure consistency.

440

441 3.4. Results

442

443 3.4.1. Phylogenetic Analyses

444

445 I sequenced nine nuclear loci for a total of 9430 bp. MEGA v7 estimated 20

446 parsimony informative sites in my P. oralis only dataset (Table 3.4). My

447 dataset comprised 5% missing data with a maximum of three individuals

448 missing per locus. Average pairwise distance was estimated to be d =

449 0.00006, S.D. = 0.00004 in MEGA. The nine-nuclear locus dataset contained a

450 total of 55 variable sites (0.58%) of which 20 were phylogenetically

451 informative (mean = 2.22 sites per locus). The estimated value for the shape

452 parameter of the discrete Gamma Distribution = 200.00. The total number of

453 parsimony informative sites was 20 (Table 3.4).

454 I sequenced ~1000 bp of the mitochondrial gene Cyt-b for all 24 individuals

455 (Fig. 3.4. My maximum likelihood phylogenetic analysis using MRBAYES

456 confirmed the presence of the two previously discovered mitochondrial

457 lineages (Posterior probability = 1.00, Fig. 3.4; Jerry et al., 1998; Rowe et al.,

458 2012).

110 459 Concatenated phylogenetic analyses of the nine-locus dataset in MRBAYES

460 and RAxML generated phylogenies with significant support for P. oralis as

461 monophyletic (Posterior proababilty = 1.00, Bootstrap = 98; Fig. 3.5).

462 However, within P. oralis I did not recover any significant PP or BS support

463 values for any cluster of more than two individuals (Fig. 3.5).

464 The POFAD analysis generated a network that shows modest separation of

465 southern and northern mitochondrial lineages (Fig. 3.2. Only two individuals

466 (KR033 and KR034) with northern and southern mitochondrial lineages,

467 respectively, grouped together in the POFAD network. These samples are both

468 from Washpool NP where the two mitochondrial lineages are syntopic,

469 indicating nuclear introgression between southern and northern lineages.

470 In the *BEAST analyses, I recovered significant support (PP = 1.00) for the

471 monophyly of P. oralis with respect to sister species P. shortridgei and P.

472 desertor (Fig. 3.3. However, within P. oralis I did not recover any support for

473 relationships among P. oralis individuals (PP 0.02-0.52) with individuals from

474 the two mitochondrial lineages completely intermixed. ESS values for all

475 parameters in analyses were >200 and likelihood scores reached a stable

476 plateau. Likewise, with all replicates and all ranges of priors in the BPP

477 analyses I recovered support for a single species hypothesis for P. oralis (Fig.

478 3.3).

479

480 3.4.2. Population Genetics

481

111 482 Cluster analysis in STRUCTURE gave consistent results over 10 replicated

483 runs that tested cluster values of K = 1 to K = 3. The likelihood score greatly

484 decreased from K = 1 where it reached its maximum (lnL = -772.0; Fig. 3.6).

485 Even with K = 2 or K = 3 STRUCTURE showed no clustering of populations

486 within P. oralis. Thus, based on the nine-locus nuclear dataset, STRUCTURE

487 supports a single genetic population within P. oralis.

488 In contrast to the findings in STRUCTURE, using a spatially explicit model in

489 GENELAND recovered K = 2 as the number of clusters with the highest

490 likelihood across all 10 runs (Fig. 3.7). I carried out post-processing analysis

491 on the run with the highest average log posterior probability of -600.98. For

492 this run, the two clusters were separated at the site of overlap between the

493 two mitochondrial clades reported from Washpool NP (Rowe et al., 2012).

494 Cluster 1 comprised all individuals from the northern mitochondrial lineage

495 recovered in this study. Cluster 2 comprised all individuals from the southern

496 mitochondrial lineage. This is evidence for measurable allele frequency

497 differences between the two mitochondrial lineages as GENELAND assumes

498 Hardy-Weinberg equilibrium within loci and linkage disequilibrium between

499 loci, and that immigrant genes originate from new immigrants (Guillot et al.,

500 2005). GENELAND estimated an FST value (FST = 0.035) that indicates

501 significant nuclear allele frequency differences among mitochondrial lineages,

502 but with most variation residing within populations. For both northern and

503 southern mitochondrial populations I calculated a negative FIS (-0.039 and -

504 0.20 respectively) indicating a lack of significant inbreeding and a modest but

505 significant indication of outbreeding.

112 506 I estimated the level of gene flow between the two populations using

507 MIGRATE-N. The results indicated the panmictic migration model had the

508 highest likelihood (Table 3.3. The panmictic model is a substantial

509 improvement over either of the unidirectional migration models, where the log

510 Bayes Factor for the next best model was -15.39. All three models estimated

511 high rates of gene flow between populations (Table 3.3). MIGRATE-N estimated

512 similar mean rates of migration from NorthàSouth (9666.0) and SouthàNorth

513 (11914.2), suggesting that gene flow between the two populations is

514 effectively equally bidirectional (Table 3.3).

515

516 3.5. Discussion

517

518 In this study, I tested if the two distinct mitochondrial lineages in P. oralis

519 represent independent lineages based on nine independent nuclear exons.

520 Previous studies identified two distinct mitochondrial lineages in P. oralis that

521 are distributed north and south of the MMOZ, respectively (Jerry et al., 1998;

522 Rowe et al., 2012). The zone of overlap between these two lineages falls

523 precisely on the MMOZ, and is suggestive of past isolation followed by recent

524 expansion and contact between the lineages. Analyses based on the nine-

525 locus nuclear dataset in this study generated conflicting results of

526 phylogenetic structure within P. oralis, but predominantly supported P. oralis

527 representing a single species. Both the POFAD and GENELAND analyses

528 supported the presence of two populations, whereas all other analyses

529 supported a single population (Fig. 3.2 and 3.7). The spatially-explicit

113 530 analysis, GENELAND, identified the MMOZ as a break between two populations

531 of P. oralis (Fig. 3.7), indicating a modest geographic structure in allele

532 frequencies between the northern and southern populations. This analysis

533 incorporates explicit geographic localities (GPS data points) for each

534 individual thus taking into account isolation-by-distance. However, my

535 estimate of FST from GENELAND indicated this pattern was supported by very

536 little structure between the northern and southern populations of P. oralis.

537 Furthermore, the FIS values were both negative, which suggests that the level

538 of inbreeding within each respective population was less than expected by

539 chance. Thus, even this analysis indicates extensive gene flow between

540 northern and southern mitochondrial populations, but with some indication

541 that they have come into contact recently. Thus, my results suggest that

542 isolation of P. oralis occurred for a sufficient period of time to fix mitochondrial

543 haplotypes but not to lead to significant divergence in nuclear exons. This

544 confirms with the criterion for considering each lineage an Evolutionary

545 Significant Unit (ESU). The criterion for ESUs is having reciprocally

546 monophyletic mitochondrial alleles as well as significant divergence of allele

547 frequencies at nuclear loci (Moritz, 1994). Such a pattern is consistent with

548 fluctuations in the wet forest habitats of the Eastern Mesic Zone during the

549 Late Pleistocene.

550 The process of isolation and secondary contact that I propose explains why

551 the pattern of genetic structure in P. oralis is common to other taxa in the

552 region. The MMOZ is well known to be associated with phylogeographic

553 breaks in taxa such as frogs (James and Moritz, 2000; Schauble and Moritz,

114 554 2001), lizards (Smissen et al., 2013; Chapple et al., 2011b, 2011b), snakes,

555 (Keogh et al., 2003) and invertebrates (Baker et al., 2008; Heads, 2009).

556 These phylogeographic breaks have been explained by either the interplay

557 between topographical barriers (i.e. the MR, Keogh et al., 2003; Chapple et

558 al., 2011b), by habitat barriers (i.e. the MMOZ, McGuigan et al., 1998;

559 Schauble and Moritz, 2001; Colgan et al., 2010; Chapple et al., 2011a;

560 Smissen et al., 2013), and/or periodic climatic oscillations leading to habitat

561 contraction, fragmentation and expansion (see Byrne et al., 2008, 2011).

562 These patterns have been reported in taxa associated with wet rainforest

563 sclerophyll forests (McGuigan et al., 1998; Keogh et al., 2003; Chapple et al.,

564 2011a), dry sclerophyll forests and heathlands (Schauble and Moritz, 2001;

565 Chapple et al., 2011b), as well as in taxa associated with all these

566 environments (Smissen et al., 2013). The MMOZ is likely characterised by

567 dynamic processes associated with climatic oscillations and subsequent

568 habitat fragmentation, contraction, expansion given that these breaks have

569 occurred across taxa associated with different habitat types, and which have

570 been dated to various geological time periods (Miocene, Schauble and Moritz,

571 2001; late Miocene-Pliocene, McGuigan et al., 1998; Chapple et al., 2011a;

572 Pliocene, Chapple et al., 2011b; Plio-Pleistocene, Smissen et al., 2013; and

573 Pleistocene, Keogh et al., 2003). This process of habitat contracting into

574 refugial pockets where isolated populations of taxa may persist during these

575 periods leading to a repeated pattern of diversification among taxa is well

576 documented in the eastern mesic zone (Hugall et al., 2002; O’Connor and

577 Moritz, 2003; Chapple et al., 2005, 2011a, 2011b; Couper et al., 2008).

115 578 Rowe et al. (2012) dated the mitochondrial lineages split with a broad

579 estimate of between 0.3-0.9 Mya, and in Chapter 2 I dated the crown age of

580 P. oralis to <0.5 My as well as the split between P. oralis and its sister species

581 pair P. desertor and P. shortridgei at ~1.75 Mya. These dates place the origin

582 of the species and all genetic diversity within it well within the Pleistocene,

583 similar to findings in other taxa with phylogenetic structure in this same region

584 (Smissen et al., 2013). The Pleistocene was a period when climatic

585 oscillations reached their most intense, with their periodicity increasing from

586 the 40-60 Ky intervals to more extreme 100 Ky intervals ~800 Kya (Lisiecki

587 and Raymo, 2007; see Byrne et al., 2011). In the last 400 Ky alone there have

588 been four major interglacial periods (Johnson, 2004). I suggest that the

589 MMOZ in combination with periodic climatic oscillations has led to repeated

590 isolation of populations of P. oralis in pockets of refugia, followed by

591 reconnection of these populations as the species followed the distribution of

592 suitable habitats in this region. Following these 100 Ky climatic oscillations,

593 isolation of populations within P. oralis in local refugia may have happened on

594 a long enough timescale for fixed differences to occur in the mitochondrial

595 genome, as found by Rowe et al. (2012), but not for it to occur in the nuclear

596 genome. A similar pattern has been seen in another Australian rodent,

597 Melomys cervinipes, where Pleistocene climatic fluctuations explain the

598 species’ phylogeographic patterning throughout its distribution in the mesic

599 biome (Bryant and Fuller, 2014).

600 Although this study found little evidence for geographic structure in the

601 nuclear genome of P. oralis, this study comprised only nine nuclear exons

116 602 with only 20 informative characters. This lack of informative characters may

603 be reflective of the slow rate at which the nuclear genome accrues these

604 characters; i.e. 4-10 times slower than the mitochondrial genome (e.g., Avise,

605 1994; Hartl and Clark, 1997; Li, 1997). Previous studies demonstrate that nine

606 nuclear loci are generally sufficient to differentiate sister species, (e.g. Phuong

607 et al., 2014). These specific loci have been shown to be relatively fast

608 evolving nuclear genes that are phylogenetically informative (Rowe et al.,

609 2008; 2011b; Schenk et al., 2013). Although, in this study, these loci include a

610 low number of phylogenetically informative characters for P. oralis, in Chapter

611 2 I used the same loci to successfully differentiate closely related sister

612 species within the genus Pseudomys (e.g. P. calabyi and P. johnsoni, and P.

613 desertor and P. shortridgei). Thus the two mitochondrial lineages within P.

614 oralis are not likely to reflect distinct species. However, my population level

615 estimates of gene flow and structure may be biased by the lack of informative

616 polymorphisms, and more data are needed to improve these estimates.

617 Furthermore, my sampling of nine nuclear exons is not adequate to rule out

618 local adaptation between the two mitochondrial lineages (Funk et al., 2012).

619 The presence of reciprocal monophyly of mitochondrial haplotypes from

620 northern and southern populations, centred on Washpool NP and minor allele

621 frequency differences in nuclear loci uncovered in this study, indicate that the

622 northern and southern mitochondrial lineages of P. oralis are consistent with

623 the definition of ESUs but not reflective of distinct species (Moritz et al., 1994).

624

625

117 626 3.6. Appendices

627 Tables:

628 Table 3.1. Sample IDs, mitochondrial lineage as determined by Rowe et al. 629 (2012), and GPS location. Mitochondrial ID Latitude Longitude lineage HRM5 N 28° 6'48.40"S 152°22'4.80"E IG07-001 N 28°12'8.10"S 153° 5'59.80"E KR002 N 28°12'8.10"S 153° 5'59.80"E KR097 N 28°12'8.10"S 153° 5'59.80"E 152°25'36.50" IG06-001 N 28°13'33.00"S E 152°25'36.50" IG07-009 N 28°13'33.00"S E 152°58'18.07" KR149 N 28°20'36.09"S E KR021 N 28°28'18.00"S 152°59'1.50"E 152°19'50.68" KR061 N 29°14'50.64"S E KR152 N 29°15'34.09"S 152° 8'33.59"E KR033 N 29°15'46.15"S 152° 8'18.59"E KR034 S 29°15'46.15"S 152° 8'18.59"E 152°16'51.91" KR013 N 29°24'32.44"S E 152°16'36.20" KR012 N 29°24'34.30"S E KR011 S 29°29'8.76"S 152° 9'38.09"E KR010 N 29°30'12.34"S 152° 9'32.33"E 152°25'33.06" KR110 S 30° 6'3.68"S E 152°26'18.30" KR108 S 30° 9'15.55"S E 152°26'26.25" KR130 S 30°11'10.58"S E 152°13'17.53" 1 S 30°51'55.72"S E KR117 S 31° 0'44.41"S 152°17'7.91"E 151°56'36.02" KR112 S 31°21'27.15"S E 151°18'53.37" KR114 S 32° 9'51.35"S E 151°16'30.28" KR128 S 32°14'51.41"S E 630

118 631

632

633

634

635

636

637

638 Table 3.2. Best partitioning scheme as estimated in PartitionFinder.

Subset Best model Partition Gene1_pos1, Gene2_pos1, Gene2_pos3, Gene4_pos1, 1 HKY+I+G Gene6_pos3, Gene8_pos3, Gene9_pos2, Gene9_pos3 Gene1_pos2, Gene3_pos3, Gene5_pos1, Gene6_pos1, 2 F81 Gene7_pos2, Gene7_pos3, Gene8_pos1 3 JC Gene1_pos3, Gene2_pos2, Gene4_pos2, Gene5_pos3 4 F81 Gene3_pos1, Gene5_pos2, Gene7_pos1 Gene3_pos2, Gene4_pos3, Gene6_pos2, Gene8_pos2, 5 F81 Gene9_pos1 639

640

641

642

643

644

645

646

647

648

649

650

119 651

652

653

654

655

656

657

658 Table 3.3. Bayes Factors and log marginal likelihoods as calculated from 659 MIGRATE-N for the three migration models tested: Panmictic; North to South 660 (1à2) and South to North (2à1). Migration Bezier Harmonic LBF Choice Model Mean model lmL lmL (Bezier) (Bezier) probability Migration Panmictic -13386.68 -13454.31 0 1 (best) 0.99 - 2à1 -13402.07 -13465.03 -15.39 2 2.07E-07 11914.2 1à2 -13411.62 -13467.35 -24.94 3 1.48E-11 9666 661

662

663

664

665

666

667

668

669

670

671

672

673

120 674

675

676

677

678

679

680

681 Table 3.4. Individual locus information as calculated in MEGA v7.

No. of base Parsimony Variable Number of Locus pairs Informative Sites Individuals A2AB 1046 3 10 21 ARHGAP2 1108 5 11 24 1 BRCA1 1098 0 4 22 CB1 1153 3 4 22 GHR 918 0 2 22 IRBP 1063 1 7 24 NIN 964 1 2 24 RAG1 1268 4 6 22 vWF 812 5 9 24 Total 9430 22 55 - 682

683

684

685

686

687

688

689

690

121 691

692

693

694

695

696

697

698

699 Figures:

700

701 Figure 3.1. Map of Australia. Pink circles = Northern mitochondrial lineage, 702 Blue circles = Southern mitochondrial lineage, Pink + Blue circles = localities 703 where both northern and southern mitochondrial lineages were found. Black 704 oval = zone of overlap between the two mitochondrial lineages. GDR = Great 705 Dividing Range, MR = McPherson Range. 706

707

708

122 709

710

711

712

713

714 715

716

717 Figure 3.2. The multi-locus distance matrix based on allelic variation 718 generated in POFAD based on the nine-nuclear locus dataset. Pink = Northern 719 mitochondrial lineage, Blue = Southern mitochondrial lineage. Red labels = 720 individuals located at the zone of overlap at Washpool National Park.

123 721

722 Figure 3.3. Species tree generated in *BEAST based on the nine-nuclear locus 723 dataset. 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748

124 749

750

751 Figure 3.4. Bayesian tree based on Cyt-b mitochondrial locus. Pink = 752 Northern mitochondrial lineage, Blue = Southern mitochondrial lineage. Red 753 boxes = individuals located at the zone of overlap at Washpool National Park. 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777

125 778

779 Figure 3.5. Phylogenetic tree based on ML and Bayesian analyses of 780 concatenation of nine nuclear loci. Bootstrap support reported above 781 branches and Posterior Probability reported below branches. Pink = Northern 782 mitochondrial lineage, Blue = Southern mitochondrial lineage. Red dots = 783 individuals located at the zone of overlap at Washpool National Park. 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798

126 799 800 Figure 3.6. STRUCTURE analysis results estimated clusters for K = 1, K = 2, 801 and K = 3.

127 802

803 Figure 3.7. Map of posterior probability for cluster 1 and cluster 2 estimated in 804 GENELAND indicating two population clusters (K = 2) separated at zone of 805 contact at Washpool National Park between mitochondrial lineages.

128 806 Chapter 4

807

808 Sequencing historic museum specimens of the

809 extinct White-footed Rabbit-rat, Conilurus

810 albipes, shows extensive diversity lost to

811 extinction.

812

813 Smissen, P. J., Roycroft, E., Moussalli, A., Rowe, K. C. (in prep)

814

815 4.1. Abstract

816

817 In this study, I estimated genomic diversity lost by the extinction of the

818 White-footed rabbit-rat, Conilurus albipes. I sequenced 1368 exonic regions

819 from two museum skins collected in the 1850s including the extinct C. albipes

820 and Rattus lutreolus. With replicate extractions for each specimen I

821 demonstrated high accuracy and limited DNA damage for these specimens

822 for at least 349 loci. The loss of other loci was caused almost entirely by one

823 poor extraction for C. albipes suggesting that improvements to extraction

824 protocols could greatly improve sequencing accuracy and efficiency. My

825 phylogenetic placement of C. albipes with its congener C. pencillatus, resulted

129 826 in reasonable branch lengths with a divergence between the two Conilurus

827 dated to 2.5 Ma. This study is the first time an extinct Australian rodent

828 species represented in historical museum specimens has been successfully

829 placed in a phylogeny. My research adds to the growing literature on the

830 genomics of museum specimens that illustrates the profound implications of

831 historical museum collections, in which countless older specimens can now

832 be considered genomic resources.

833

834 4.2. Introduction

835

836 Natural history collections house extensive specimens representing

837 historically extinct species and extirpated populations. Recovery of genetic

838 data from historically preserved specimens has provided great insight into the

839 evolutionary history of species (Bi et al., 2013; McCormack et al., 2015),

840 particularly the amount of diversity lost to extinction (Cooper et al., 2001;

841 Bunce et al., 2005; Orlando et al., 2008, 2009). Until recently, historical DNA

842 (hDNA) studies relied on short sequences from one or very few loci, and

843 conclusions were limited to the topological placement of taxa in a phylogeny.

844 Recently, genomic-scale sequencing from historical specimens has become

845 possible allowing insights into divergence time between taxa, including extinct

846 species, genetic diversity among taxa, and the potential genetic processes

847 underlying extinction (see Bragg et al., 2015; Druzhkova et al., 2015;

848 Linderholm, 2015). For example, Noonan (et al., 2006) was one of the first

849 studies to sequence a large portion of the nuclear genome of the Neanderthal

130 850 and compare it to extant humans. In doing so they discovered human and

851 Neanderthal ancestral populations split ~370Kya predating anatomically

852 modern humans. Mason (et al., 2011) successfully extracted and sequenced

853 DNA from historical museum specimens collected up to 170 years ago, which

854 revealed deep genetic divergence (10-13%) among colugos within and

855 between islands in South East Asia. Menzies (et al., 2012) examined the

856 mitochondrial DNA of 12 historical museum specimens (102-159 years old) of

857 the extinct thylacine, revealing that the species extinction was preceded by a

858 genetic bottleneck. Despite the success of hDNA extraction and sequencing,

859 elevated polymorphism caused by post-mortem DNA mutations still presents

860 a challenge to sequence accuracy (Briggs et al., 2007, 2010; 2012; Molak and

861 Ho, 2011). However, extraction, sequencing and analytical methods are

862 rapidly improving in their ability to correct or filter out such damaged

863 sequences (see Briggs and Heyn, 2012; Ginolhac et al., 2011; Jónsson et al.,

864 2013). As such methods rapidly improve, historical museum collections will

865 begin to answer many long-standing questions regarding species diversity,

866 divergence dates, the processes underlying population and species extinction,

867 and conservation priorities.

868 Australia has the highest rate of mammalian extinctions in recorded history,

869 with extinction of 30 terrestrial mammal species (Johnson, 2006; Woinarski et

870 al., 2014) comprising nearly 40% of historical mammal extinctions globally

871 (n=76, IUCN, 2016). In addition, Australian mammals are highly endemic with

872 phylogenetically divergent lineages preserved nowhere else on earth

873 (Rosauer and Jetz, 2015). For many Australian mammal species the decline

131 874 to extinction was extremely rapid, with some species once widespread and

875 abundant declining to extinction in a matter of decades, such as in numerous

876 rodent species (Breed and Ford, 2007). Thus, genomic-scale studies

877 incorporating historical specimens of Australian mammals are critical to

878 estimate the genetic diversity and structure of species prior to their decline.

879 The white-footed rabbit-rat, Conilurus albipes, is an extinct species of

880 native Australian rodent that is an exemplar of rapid decline from numerical

881 abundance and a widespread distribution. Based on the distribution of

882 historical museum specimens and reports from early Australian explorers, the

883 species was abundant and distributed over a wide range. It was recorded

884 from forests and woodlands in both arid and mesic environments of Australia

885 (Watts and Aslin, 1981; Bilney et al., 2010; Menkhorst, 2011; Woinarski et al.,

886 2014). Within a matter of decades, C. albipes declined from a ubiquitous and

887 widespread species to extinction, with the last specimen collected in 1862

888 (Fig. 4.1; Dixon 2008; McDowell and Medlin, 2009; Cramb and Hocknull,

889 2010; Bilney et al., 2010; Menkhorst, 2011; Woinarski et al., 2014). Although

890 the exact cause of extinction is unknown, it was likely a combination of

891 several factors including: land clearing and habitat fragmentation related to

892 pastoralism, competition with introduced species including rats and rabbits,

893 predation from introduced species including foxes and cats, as well as

894 changed fire regimes (Woinarski et al., 2014). These factors are still affecting

895 many endemic Australian rodent species including all of the closest relatives

896 of C. albipes, as well as many bird, reptile and mammal species (IUCN,

897 2016).

132 898 The White-footed Rabbit-rat has two congeners, the Brush-tailed Rabbit-

899 rat, C. penicillatus, which is the only extant member of the genus Conilurus,

900 and the Capricorn Rabbit-rat, C. capricornensis, which is only known from

901 sub-fossil material that was dated to the historical era after European

902 colonisation (Cramb and Hocknull, 2010). The genus Conilurus is most

903 closely related to the Tree rats, Mesembriomys (2 spp.), and the Stick-nest

904 rats, Leporillus (2 spp. Table 4.1). These three genera were previously placed

905 within the tribe Conilurini (sensu Watts and Aslin, 1981) and thought to be

906 sister to the five genera Pseudomys, Notomys, Leggadina, Mastacomys and

907 Zyzomys within the Pseudomys Species Group (PSG, see Chapter 2).

908 However, more recent studies have shown Conilurini to be paraphyletic,

909 where the clade including Conilurus, Mesembriomys and Leporillus is more

910 closely related to the genera Uromys, Melomys, Paramelomys and Solomys

911 within the Uromys Division, than to the PSG (Chapter 2; Rowe et al., 2008).

912 Herein, I refer to the clade that comprises Conilurus, Mesembriomys and

913 Leporillus as the Conilurus Species Group (CSG). The Conilurus Species

914 Group (CSG) is a clade of ‘Old Endemic’ Australian rodents that colonised the

915 continent >5 Mya (Chapter 2). Despite being a relatively small clade within the

916 ‘Old Endemics’, the CSG has lost three out of the seven species known

917 historically, which accounts for three out of the eleven extinctions in Australian

918 rodent species.

919 In this study, (1) I used hDNA extraction methods to extract DNA from

920 museum specimens of the extinct Conilurus species, C. albipes. (2) I

921 developed a custom exon capture system to target and sequence 1366

133 922 nuclear exons and two mitochondrial loci for 19 species across the

923 phylogenetic diversity of Australian rodents, including the extinct C. albipes.

924 (3) I assessed the level of artifactual substitutions and subsequent

925 misincorporations in the sequence data generated from historical specimen.

926 Finally, (4) I estimated phylogenetic divergence of C. albipes from C.

927 penicillatus by placing it in a robust phylogeny of Australian rodents.

928

929 4.3. Methods

930

931 4.3.1 Sample Collection

932

933 In this study, I included data from 19 specimens representing the extinct

934 Conilurus albipes, its extant congener, C. penicillatus, and 15 other species

935 spanning the phylogenetic breadth of Australian rodents (Table 4.1). I

936 included all extant species of the closest relatives of Conilurus in the genera

937 Mesembriomys (two species) and Leporillus (two species, one extant). I also

938 included closely related species within the Uromys Division (genera Uromys

939 and Melomys) and Pseudomys Species Group (genera Zyzomys, Leggadina,

940 Mastomys, Notomys and Pseudomys), as well as other Australo-Papuan

941 relatives including the genera Hydromys, Mallomys, Anisomys. I also included

942 Rattus norvegicus and Mus musculus using data available from their genome

943 assemblies (Pruitt et al., 2005). All specimens used in this study were

944 obtained from the Museums Victoria or South Australian Museum collections.

134 945 Fifteen specimens used in this study were contemporary tissues preserved in

946 ethanol and/or stored frozen at -20oC. Sequence data for Rattus norvegicus

947 and Mus musculus were obtained from the NCBI genome browser (Pruitt et

948 al., 2005). Two specimens were historical specimens preserved as dried

949 study skins representing the extinct Conilurus albipes (collected by A. W.

950 Howitt in 1862) and the common and extant Rattus lutreolus (accessioned by

951 A. W. Howitt in 1884). For both of these historical specimens I extracted DNA

952 from two independent samples. For the R. lutreolus samples I extracted DNA

953 from the entire phalange (bone and skin of the toe) and from the toe pad

954 alone (skin only). For C. albipes I extracted DNA from a 3x3mm patch of skin

955 from the underside of the specimen near the ventral suture and from a 3x3mm

956 section of the proximal end of the humerus from the inside of the skin which

957 was never stuffed. I used these replicate extractions from each historical

958 specimen to confirm all nucleotide calls in the resulting sequencing accuracy

959 and to assess if DNA quality varied among source material.

960

961 4.3.2. DNA Extraction

962

963 For contemporary samples, total genomic DNA was extracted using a

964 QIAGEN DNeasy blood and tissue kit following the manufacturer’s protocols

965 (QIAGEN Inc, Valencia, CA, USA). For historical specimens, extractions were

966 performed in a separate, dedicated ancient DNA laboratory at Museums

967 Victoria and additional preparation steps were performed. Extraction protocols

968 followed Joseph et al. (2016). Samples were soaked in 300uL PBS for 24

135 969 hours at room temperature, subsequently wrapped in foil and soaked in liquid

970 nitrogen for 10 seconds in order to completely freeze the sample. The

971 samples were then crushed using a hammer in order to ensure thorough

972 pulverization of samples. Crushed samples were soaked in 320uL of Buffer

973 ATL, 40uL proteinase K, and 40uL DTT in 1.5mL tubes, placed on a rotary

974 mixer and incubated at 56oC for 24 hours. Extraction then proceeded using

975 the QIAGEN DNeasy kit, but using QIAQuick PCR purification columns that

976 capture smaller fragments of DNA.

977 Extractions were carried out on both replicates of hDNA from both historical

978 samples. Quantity and quality of all DNA extractions were assessed using

979 both an Invitrogen Qubit 2.0 Fluorometer and BioSpec-nano UV-VIS

980 spectrophotometer. The Qubit is ideal for quantifying low DNA quantities

981 (<40ng/uL) as expected from the historical samples. The BioSpec-Nano was

982 used for checking for contaminants (e.g. salts, ethanol, phenol, proteins, etc.)

983 in extractions by testing the ratio of absorbance between 260nm and 280nm,

984 with a ratio of ~1.8 considered “pure” for DNA samples. I also assessed DNA

985 extraction quality by running both historical and contemporary samples on an

986 agarose gel to compare the difference in smear of fragmentation,

987 representative of potential DNA degradation.

988

989 4.3.3. Exon Capture Library Design

990

991 I designed a custom exon target set using the Roche NimbleGen SeqCap

992 EZ Developer Library. The target set contained 1366 nuclear exonic regions

136 993 and two mitochondrial loci. For all loci, I derived the target sequence from

994 both the Mus musculus and Rattus norvegicus genomes. All probes were

995 designed by Roche using their proprietary software. I selected loci to

996 maximise orthology with other data sets and for use in other mammalian

997 systems. First, I included 385 loci that have been used widely across

998 amniotes (Lemmon et al., 2012). From the online database OrthoMaM, I

999 identified 963 additional exons that are >300 bp in length and orthologous

1000 among rodents, bats, and marsupials (Ranwez et al., 2007; Douzery et al.,

1001 2014). Finally, I included 20 nuclear exons and two mitochondrial protein-

1002 coding regions that have been used in previous phylogenetic studies of

1003 rodents (Rowe et al, 2008; Chapter 2). This combined set contains 1,192,174

1004 bp from 1,368 exons (Table 4.2), each of which is >300bp in length (mean =

1005 879; max = 6,573bp).

1006

1007 4.3.4. Library Preparation And In Solution Exon Capture

1008

1009 For each of the 19 extracted samples in this study I constructed Illumina

1010 libraries following protocols outlined in Potter et al. (2016) using a modified

1011 version of Meyer and Kircher (2010). I barcoded individual libraries using

1012 unique oligonucleotide indexes. I estimated molarity of individual libraries

1013 using a LabChip GX (Caliper, Perkin Elmer) and pooled barcoded libraries in

1014 equimolar ratios before hybridization. My 19 total samples were part of a

1015 larger exon capture experiment that included 56 samples pooled together in a

1016 single sequencing lane. The exon capture hybridization was carried out

137 1017 following the SeqCap EZ Developer Library user’s guide (Roche NimbleGen)

1018 with the following modifications: 1.2 μg amplified pooled barcoded library mix,

1019 including 56 samples, and index-specific blocking oligonucleotides to bind to

1020 custom barcode adapters (see Bragg et al., 2015). The samples were then

1021 heated to 95oC for 10 minutes to denature the DNA, and then combined with

1022 the probe set (Roche NimbleGen) and incubated at a temperate of 47oC for

1023 72 hours. The hybridization mix was washed and then amplified via two

1024 independent enrichment PCRs using Phusion High-Fidelty DNA Polymerase

1025 (Thermo Scientific) (17 cycles; Roche NimbleGen). I used qPCR to check

1026 quality using the DyNAmo Flash SYBR green qPCR kit (Thermo Fisher

1027 Scientific Inc.), following the methods outlined in Bi et al. (2012) and

1028 specialized primers for the amplification of the target and non-target regions of

1029 the genome confirm enrichment of targets and de-enrichment of non-targets. I

1030 used a Bioanalyzer (2100; Agilent Technologies, Inc.) to quantify the

1031 concentration of the pre- and post-capture pooled DNA libraries in order to

1032 quantify the quality and quantity of hybridization. After the libraries had

1033 passed all of these quality checks they were submitted to be sequenced at the

1034 Biomolecular Resource Facility at Australian National University (ANU). Each

1035 of the 56 pooled samples was sequenced (100 bp paired-end) on a single

1036 lane of Illumina HiSeq 2000.

1037 4.3.5. Sequence Assembly and Alignment

1038

1039 Raw paired reads were de-duplicated using FastUniq v1.1 (Xu et al., 2012)

1040 and quality trimmed using Trimmomatic (Bolger et al., 2014). For the latter, I

138 1041 used an average quality score threshold of 20 within a sliding window of 8bp,

1042 with passed reads less than 40bp excluded from further analyses. Clean

1043 paired reads were then assembled de novo using TRINITY v2.0.6 (Grabherr et

1044 al., 2011; Haas et al., 2013), and the best matching contigs homologous to

1045 the reference exons identified using tblastn (i.e. translated reference treated

1046 as the query). Using the blast coordinates I extracted the local matches

1047 (CDS) from the corresponding assembled contigs, creating a sample specific

1048 reference (SSR). Using tblastn improved sensitivity to picking up homologous

1049 sequences for divergent taxa, and ensured that the resulting SSR was in

1050 frame. BBmap (version 35.82, Bushnell, 2015,

1051 sourceforge.net/projects/bbmap/) was then used to map the cleaned paired

1052 reads back onto the SSR, and the resulting SAM file was sorted, indexed and

1053 converted to tabulated pileup format using Samtools v0.1.19 (Li et al., 2009).

1054 Finally, the mpileup2cns command in VarScan v2.3.7 (Koboldt et al., 2012) was

1055 used to call consensus sequences and variants. Resulting consensus

1056 sequences of all exons captured per sample were output in VCF format,

1057 converted to fasta format and indexed. I then used an in-house C++ script to

1058 pool across all samples and produce separate fasta files for each exon

1059 respectively. These were then translated, aligned using MAFFT (Katoh and

1060 Standley, 2013), and back translated using the python wrapper align.py

1061 (Spielman, S.J. - https://github.com/wilkelab/ProteinEvolutionToolbox/). Exons

1062 were sorted by p-distance, and the top 10% of alignments were visually

1063 inspected for evidence of paralogy. I also removed all exons that were

1064 obtained for less than 85% of taxa, those with >3% average heterozygosity

139 1065 across all taxa. I then manually checked the resulting final alignments to

1066 ensure alignment integrity, correcting minor anomalies. Following these

1067 filtering steps, the final dataset was reduced to 1368 exons.

1068

1069 4.3.6. Estimates of DNA Damage

1070

1071 In order to assess any potential DNA damage patterns for the hDNA

1072 samples remaining in the datasets I ran mapDamage (Ginolhac et al., 2011;

1073 Jónsson et al., 2013). mapDamage measures nucleotide misincorporations and

1074 DNA fragmentation signatures in sequencing reads, which would otherwise

1075 remain as erroneous phylogenetic signal in the dataset. I ran mapDamage using

1076 default settings. Misincorporation frequencies were calculated by considering

1077 theta, the difference between the reference sequence and the hDNA

1078 sequence that is not due to DNA damage, delta-D, the probability of observing

1079 a cytosine deamination in a double stranded context, and delta-S, the

1080 probability of observing a cytosine deamination in a single stranded context.

1081

1082 4.3.7. Phylogenetic Analyses

1083

1084 To conduct phylogenetic analyses I concatenated all 1368 loci into a single

1085 super-matrix. I conducted phylogenetic analyses on this complete set of loci

1086 and a strict set of loci that were identical (p-distance=0) between replicate

1087 extractions and library preparations for each of the two historical samples in

140 1088 this study (C. albipes C7586, and R. lutreolus C132). I refer to these analyses

1089 as the ‘complete’ and ‘strict’ datasets in subsequent analyses.

1090 I used BeforePhylo (perl script, v.0.9.0 by Qiyun Zhu) to divide the

1091 concatenated alignment into three separate files representing each codon

1092 position. I used PartitionFinder to estimate the model of evolution for each codon

1093 position using 24 possible models and fixed BIONJ-JC starting tree. I

1094 examined the ranked AICc values for each model and then looked at the delta

1095 values across the AIC model selection. I applied the same model across the

1096 ‘complete’ and ‘strict’ datasets.

1097 I used RAxML v7.4.4 to estimate phylogenetic trees based on the ‘complete’

1098 and ‘strict’ datasets (Fig. 4.2, Stamatakis 2006; Stamatakis et al., 2008). I

1099 partitioned the datasets based into the three-codon positions and used

1100 substitution models determined for each partition. I allowed bootstraps

1101 replicates to halt automatically. To estimate the time of divergence of the

1102 extinct C. albipes from its congener, C. penicillatus and compared to other

1103 taxa, I generated a time-calibrated ultrametric tree using MCMCtree in the PAML

1104 Package (Yang, 1993; 2007). I fixed the topology using the tree generated in

1105 RAxML, but allowed MCMCtree to explore branch lengths. As with the RAxML

1106 analyses, I partitioned the data by codon and applied selected substitution

1107 models to each position with other settings left as default. I used the

1108 approximate likelihood calculation in order to reduce computational time. I set

1109 three secondary calibration points (units in 100’s of millions of years) using

1110 node dates estimated in Chapter 2 (Table 2.3.) including: 1) the root of the

1111 tree (Sahul+Mus+Rattus; Node 1): 16.29 Ma. I set the constraint of the root of

141 1112 this analysis to <0.18, >0.14 (i.e. >14.00 Mya, <18.00 Mya); 2); The node of

1113 all Sahul murids (Node 2): 8.80 Mya. I set the constraint of this node as ‘B

1114 (0.0078,0.098)’ (i.e. >7.80 Mya, <9.80 Mya); 3) the node of all Australian

1115 rodents (Node 3): 6.29Ma. I set the constraint of this node as ‘B (0.0529,

1116 0.0729)’ (i.e. >5.29 Mya, <7.29 Mya). The analysis was first run in Hessian

1117 mode to subsequently use approximation method in order to save

1118 computation time. All other settings were left as default. I set the burnin to

1119 10,000, the sample frequency to 1000, with the number of samples at 10,000

1120 for a total of 107 steps. I ran the analysis twice to assess convergence.

1121

1122 4.4. Results

1123

1124 4.4.1. hDNA extractions

1125

1126 I extracted quantifiable DNA from all 19 samples in this study including the

1127 four historical samples representing two specimens collected in the 1850’s.

1128 On average, DNA yield was lower for hDNA extractions than for “fresh”

1129 contemporary DNA extractions (Table 4.1; Fig. 4.6). For the Rattus lutreolus

1130 hDNA samples, the toe sample yielded a higher concentration of DNA than

1131 the toe pad. For the Conilurus albipes hDNA samples the skin sample yielded

1132 a higher concentration of DNA (67.62ng/uL) than the humerus (bone) sample

1133 (38.82 ng/uL; Table 4.1).

1134

142 1135 4.4.2. Sequence Assembly and Alignment

1136

1137 For the contemporary samples, raw input reads resulting from sequencing

1138 56 pooled samples on an Illumina HiSeq 2500, ranged between 1.2-7.4

1139 million per sample. The proportion of duplicates removed for each sample

1140 was between 15-40%, the proportion of on-target reads was between 41-66%,

1141 and the number of exons captured was between 1354-1368 (99.7-100%;

1142 Table 4.3). For the hDNA samples, raw input reads ranged between 4.3-21.6

1143 million reads. The proportion of duplicates removed for each sample was

1144 between 50-56%, the proportion of on-target reads for each sample was

1145 between 14-29%, and the number of exons captured for each sample was

1146 between 1040-1365 (76-99.8%; Table 4.3). Overall, hDNA samples had

1147 higher values of raw input reads, but with higher proportion of duplicates

1148 removed, and they had a lower proportion of on target reads, and also a lower

1149 number of total exons captured when compared to the contemporary samples

1150 (Table 4.3). The resulting full species by exon matrix was >98.3% data

1151 complete.

1152

1153 4.4.3. Estimates of DNA damage

1154

1155 Based on the results of mapDamage, there is a slightly elevated level of C à

1156 T misincorporations at the 5’ ends of the reads in all four historical samples

1157 (Figure 4.4). Compared to ancient DNA studies, or studies that do not

143 1158 incorporate high fidelity polymerase or DNA repair enzymes (eg.

1159 Schuenemann et al., 2011) the levels of damage are significantly lower. This

1160 is expected, based on my use of the high-fidelity polymerase Phusion.

1161 However, it was surprising to observe an overall elevated level of DNA

1162 damage in R. lutreolus over C. albipes as these samples were collected in the

1163 same period by the same collector and likely prepared the same way. As

1164 mapDamage functions by rescaling base quality scores of the underlying bam

1165 alignment files based on their probability of being damaged, it can be used as

1166 a tool to identify and eliminate misincorporations due to post-mortem DNA

1167 damage before proceeding to variant calling. I did not choose to do this, as

1168 the level of damage appeared to be insignificant and did not warrant

1169 correction.

1170

1171 4.4.4. Phylogenetic analyses

1172

1173 Following automated bioinformatics analysis, problematic loci (as flagged

1174 by >3% heterozygosity) were inspected by eye. Visual inspection made it

1175 clear that a number of loci maintained localised parology issues or were

1176 contaminants either during lab work or due to mis-indexing. Sequences that

1177 had highest p-distance between replicates of the two C. albipes were clear

1178 contaminants as they had p-distance of up to 0.8. The strict dataset

1179 comprised sampling coverage of >99% with no individuals missing more than

1180 two of the 349 loci. The histogram plotting the frequency of exons by p-

144 1181 distance illustrates that both the strict dataset and the complete dataset have

1182 a similar distribution of loci spread across the p-distance axis. Figure 4.5

1183 indicates that the two datasets include a good range of loci evolving at

1184 different rates, showing that a reduction to 349 loci (347 nuclear loci + 2

1185 mitochondrial loci) in the strict dataset did not lead to an overall bias towards

1186 conserved genes. However, the complete dataset is skewed slightly to the

1187 right, towards higher average p-distance. Thus, reducing the dataset down

1188 may have removed a higher relative number of faster-evolving loci. However,

1189 this slight elevation in the p-distance values of the complete dataset is likely a

1190 result of sequencing errors that accounted for the “faster” p-distance values

1191 for those loci for hDNA samples.

1192 The GTR+G+I model was the substitution model that best fit for each

1193 codon position for the data as estimated in PartitionFinder. The two phylogenies

1194 generated in RAxML for the strict dataset and complete dataset were identical

1195 in terms of topology. However, branch length differences between the two C.

1196 albipes and the two R. lutreolus replicates were present in the complete

1197 dataset, which is unsurprising given the elevated average p-distance of loci in

1198 the complete dataset compared to the strict dataset (Fig. 4.5). Unsurprisingly,

1199 after removing all exons where there was a p-distance of >0.00 between

1200 replicates of C. albipes there was no branch length difference between

1201 replicates of C. albipes and R. lutreolus. Within the complete dataset every

1202 node received 100% bootstrap support (bs), whilst in the ‘strict’ dataset a

1203 single node from which Leggadina splits from the clade that includes the

145 1204 genera Pseudomys, Mastacomys and Notomys received reduced support of

1205 78% bs.

1206 The MCMCtree analyses estimated that the extinct species C. albipes

1207 diverged from C. penicillatus 2.2-2.8 Mya (Node A, Table. 4.4, Fig. 4.6). In

1208 comparison, sister species M. gouldii and M. macrourus diverged 1.15-1.64

1209 Mya (Node B.), and the P. australis diverged from M. fuscus 1.74–2.35 Mya

1210 (Node C).

1211

1212 4.5. Discussion

1213

1214 In this study, I demonstrated the use of a custom exon target enrichment

1215 system for rodents, which is orthologous with other rodent (Ranwez et al.,

1216 2007; Rowe et al., 2008; Douzery et al., 2014; Chapter 2) and amniote

1217 datasets (Lemmon et al., 2012), and that it is amenable to historical museum

1218 skin specimens. Even with the most strict criteria of identical sequences

1219 among all replicate extractions of historical specimens, I recovered 349 exons

1220 from an extinct species preserved as dry skin since the 1850s and placed it in

1221 a phylogeny with its congener (Fig. 4.2). I showed that C. albipes and C.

1222 penicillatus diverged earlier than other sister species (e.g. Mesembriomys

1223 gouldi and M. macrourus) and at comparable times to divergence between

1224 genera (e.g. Pseudomys and Mastacomys). Although branch lengths were

1225 inflated in the complete dataset due to the elevated number of erroneous

1226 reads the phylogenies generated from the complete and strict datasets had

1227 identical topologies (Fig. 4.2).

146 1228 There have been numerous studies incorporating historical museum

1229 specimens, including the colugo (Mason et al., 2011), Asian leaf turtles (Stuart

1230 and Fritz, 2008), and the extinct thylacine (Miller et al., 2009; Menzies et al.,

1231 2012). These studies focused on generating datasets for the mitochondrial

1232 genome (~16Kb), which is significantly smaller than the nuclear genome

1233 (>1Gb). Such studies were pivotal to demonstrating the feasibility of

1234 generating genetic datasets from historical museum specimens. However,

1235 answering questions pertaining to divergence between taxa and population

1236 genetics preceding extinction is limited if inference is based solely on the

1237 mitochondrial genome. That said, these studies have led the way for more

1238 recent research, including this study, to access the nuclear genome, as well

1239 as the mitochondrial genome, and generate vast datasets to better address

1240 such questions (e.g. Bi et al., 2013; McCormack et al., 2015). The DNA in

1241 historical specimens (hDNA) is more amenable to contemporary sequencing

1242 methods, as fragmentation of the genome is common with long-term storage

1243 of both tissue and extractions (Knierim et al., 2011; Rowe et al., 2011a). Low

1244 concentrations and subsequent low mapping coverage of hDNA rather than

1245 DNA damage are likely to explain why I only recovered 349 loci that were

1246 identical between replicates of C. albipes and replicates of R. lutreolus skins.

1247 Analyses in mapDamage only indicated a slight degree of post-mortem DNA

1248 damage in historical samples compared to contemporary samples (Fig. 4.4).

1249 Given our use of a high-fidelity polymerase that is incompatible with uracil

1250 during library preparation according to the manufacturer (Thermo Fisher

1251 Scientific), we expected a reduced level of detectable damage. This is

147 1252 because only erroneous 5-methylcytosine to thymine transitions would have

1253 been incorporated, and not spontaneously deaminated unmethylated cytosine

1254 residues.

1255 The concentration of hDNA extractions was on average lower (16.1%) than

1256 the DNA extracted from contemporary samples (Table 4.1). However, some

1257 contemporary samples had DNA concentrations lower than the lowest quality

1258 hDNA extraction. We expected hDNA extractions carried out on bone to yield

1259 higher quality DNA extractions, as research has indicated that aDNA is often

1260 well preserved in bone (Schwarz et al., 2009; Campos et al., 2012). For R.

1261 lutreolus, bone extractions yielded more DNA than skin extractions, but for C.

1262 albipes this pattern was reversed (Table 4.1). Given the small number of

1263 replicates (n = 4) this may be due to random variation in preservation quality.

1264 Further research is required to determine the tissue type that yields the

1265 highest quality hDNA extractions, or if there is no consistent difference.

1266 My study is a first step towards a more comprehensive understanding of

1267 the loss of genetic diversity due to the process of extinction in the CSG, but

1268 also more broadly, in the Australian rodents as a whole. The white-footed

1269 rabbit-rat, Conilurus albipes, is just one of at least ten Australian rodent

1270 species that have gone extinct since European colonisation (Breed and Ford,

1271 2007). Other members of the CSG are either extinct, C. capricornensis and

1272 Leporillus apicalis, or have experienced extensive population extirpation on

1273 the mainland, including C. penicillatus, L. conditor, M. gouldii and M. macrurus

1274 (Woinarski et al., 2014). Museum collections include all extinct Australian

1275 rodent species, as well as specimens representing extirpated populations,

148 1276 including 40 specimens of C. albipes, and 262 specimens of C. penicillatus

1277 (Global Biodiversity Information Facility). Including these taxa will lead to a

1278 more complete understanding of genetic diversity lost through extinction in

1279 this group, and the genetic processes preceding extinction. Considering that a

1280 primary conservation objective for a number of these species is to “restore

1281 subpopulations at selected former sites, through re-introductions” expanding

1282 on this study in order to understand the genetic divergence and population

1283 diversity will be crucial (Woinarski et al., 2014).

149 1284 4.6. Appendices 1285

1286 Tables

1287 Table 4.1. Taxanomic sampling, tissue types, exon sampling for the ‘complete’ and ‘strict’ exon datasets. Nucleic acid 1288 concentration based on Qubit results. Tissue Nucleic acid Taxa Tissue age type concn (ng/uL) Anisomys_imitator_ABTC45107 Tissue Contemporary 56.42 Conilurus_albipes_C7586 H Skin Historical (1800s) 38.82 Conilurus_albipes_C7586 S Skin Historical (1800s) 67.62 Conilurus_penicillatus_ABTC7411 Tissue Contemporary 117.76 Hydromys_chrysogaster_Z5248 Tissue Contemporary 6.78 Leggadina_forresti_ABTC36085 Tissue Contemporary 77.40 Leporillus_conditor_ABTC13335 Tissue Contemporary 29.97 Mallomys_rothschildiABTC47402 Tissue Contemporary 15.93 Mastacomys_fuscus_ABTC07354 Tissue Contemporary 35.83 Melomys_rufescens_ABTC44798 Tissue Contemporary 87.02 Mesembriomys_gouldii_ABTC07412 Tissue Contemporary 56.93 Mesembriomys_macrourus_ABTC07337 Tissue Contemporary 31.33 Notomys_fuscus_ABTC113387 Tissue Contemporary 19.89 Pseudomys_australis_ABTC35951 Tissue Contemporary 15.28 Rattus_fuscipes_GI02R01 Tissue Contemporary 36.40 Rattus_lutreolus_C132 T Skin Historical (1800s) 34.41 Rattus_lutreolus_C132 TP Skin Historical (1800s) 12.33 Uromys_caudimaculatus_ASA09 Tissue Contemporary 23.12 Zyzomys_argurus_ABTC07908 Tissue Contemporary 74.69 1289

150 1290 1291 1292 Table 4.2. Total number of exons and nucleotides for each sample. Taxa Total Exons Total Nucleotides Conilurus_albipes_C7586H 1365 1169674 Conilurus_albipes_C7586S 1040 631122 Rattus_lutreolus_C132T 1261 913397 Rattus_lutreolus_C132TP 1361 1148034 Anisomys_imitator_ABTC45107 1367 1182025 Conilurus_penicillatus_ABTC7411 1366 1189266 Hydromys_chrysogaster_Z5248 1365 1185204 Leggadina_forresti_ABTC36085 1368 1181926 Leporillus_conditor_ABTC13335 1367 1186708 Mallomys_rothschildiABTC47402 1366 1189821 Mastacomys_fuscus_ABTC07354 1366 1192174 Melomys_rufescens_ABTC44798 1367 1188776 Mesembriomys_gouldii_ABTC07412 1365 1186773 Mesembriomys_macrourus_ABTC07337 1364 1186532 Notomys_fuscus_ABTC113387 1364 1178028 Pseudomys_australis_ABTC35951 1366 1190620 Rattus_fuscipes_GI02R01 1367 1190389 Uromys_caudimaculatus_ASA09 1364 1185859 Zyzomys_argurus_ABTC07908 1364 1185956 1293 1294 1295 1296 1297 1298 1299

151 1300 Table 4.3. Exon capture mapping output summary. Continues next page. % post Input Forward Reverse raw input duplicate Both Sample name duplicate Read % Only % Only % reads s Surviving removal Pairs Surviving Surviving removed Anisomys_imitator_ABTC45107 1229082 814291 0.34 814291 699351 85.88 104281 12.81 4427 0.54 Conilurus_albipes_C7586H 12670985 5558015 0.56 5558015 4512241 81.18 943567 16.98 50606 0.91 Conilurus_albipes_C7586S 5824293 2790870 0.52 2790870 644219 23.08 1964758 70.4 13466 0.48 Conilurus_penicillatus_ABTC7411 7028099 4991803 0.29 4991803 4382240 87.79 543896 10.9 27917 0.56 Hydromys_chrysogaster_Z5248 1838888 1202881 0.35 1202881 1098117 91.29 86750 7.21 7582 0.63 Leggadina_forresti_ABTC36085 2424452 1598854 0.34 1598854 1395966 87.31 181460 11.35 7864 0.49 Leporillus_conditor_ABTC13335 1660890 1383705 0.17 1383705 1247644 90.17 120706 8.72 7421 0.54 Mallomys_rothschildiABTC47402 3066516 2608415 0.15 2608415 2386298 91.48 194485 7.46 14185 0.54 Mastacomys_fuscus_ABTC07354 6004095 4284541 0.29 4284541 3779596 88.21 451004 10.53 24853 0.58 Melomys_rufescens_ABTC44798 7131767 4863949 0.32 4863949 4417841 90.83 390133 8.02 29081 0.6 Mesembriomys_gouldii_ABTC074 3377495 2319164 0.31 2319164 2095553 90.36 197913 8.53 11638 0.5 12 Mesembriomys_macrourus_ABTC 4271480 2878311 0.33 2878311 2556134 88.81 283259 9.84 15219 0.53 07337 Notomys_fuscus_ABTC113387 2690397 1605350 0.4 1605350 1447250 90.15 138349 8.62 8143 0.51 Pseudomys_australis_ABTC35951 3101455 2166175 0.3 2166175 1965499 90.74 176377 8.14 12256 0.57 Rattus_fuscipes_GI02R01 3844743 2594292 0.33 2594292 2365448 91.18 201303 7.76 14339 0.55 Rattus_lutreolus_C132T 4334011 2188015 0.5 2188015 1108337 50.65 1025001 46.85 15285 0.7 Rattus_lutreolus_C132TP 21601131 10203920 0.53 10203920 6212730 60.89 3751592 36.77 78652 0.77 Uromys_caudimaculatus_ASA09 7216080 4496813 0.38 4496813 4011039 89.2 419478 9.33 31878 0.71 Zyzomys_argurus_ABTC07908 5709609 3823334 0.33 3823334 3258363 85.22 481943 12.61 21873 0.57 1301 1302 1303 1304

152 1305 1306 1307 1308 1309 1310 Table 4.3. Continued. Exon capture mapping output summary. Reads Reads Reads Reads with at with no with 1 with 2 Droppe Input least Proportion Sample name % ends end ends d reads one end on target mapped mapped mapped mappin . . . g 0.7 Anisomys_imitator_ABTC45107 6232 808059 245888 170285 391886 562171 0.59 7 0.9 358703 129725 191938 Conilurus_albipes_C7586H 51601 5506414 622129 0.29 3 0 5 4 6.0 202827 Conilurus_albipes_C7586S 168427 2622443 438849 155315 594164 0.14 3 9 0.7 167441 244257 327964 Conilurus_penicillatus_ABTC7411 37750 4954053 837068 0.58 6 2 3 1 0.8 Hydromys_chrysogaster_Z5248 10432 1192449 386836 156142 649471 805613 0.61 7 0.8 Leggadina_forresti_ABTC36085 13564 1585290 634547 365219 585524 950743 0.48 5 0.5 103088 Leporillus_conditor_ABTC13335 7934 1375771 344883 239991 790897 0.66 7 8 0.5 145078 187905 Mallomys_rothschildi_ABTC47402 13447 2594968 715912 428269 0.64 2 7 6 0.6 144118 206369 281427 Mastacomys_fuscus_ABTC07354 29088 4255453 750580 0.57 8 3 0 0 Melomys_rufescens_ABTC44798 26894 0.5 4837055 187563 696349 226507 296141 0.54

153 5 6 0 9 0.6 116202 154951 Mesembriomys_gouldii_ABTC07412 14060 2305104 755588 387489 0.59 1 7 6 Mesembriomys_macrourus_ABTC0733 0.8 142843 194381 23699 2854612 910796 515381 0.59 7 2 5 6 0.7 100478 Notomys_fuscus_ABTC113387 11608 1593742 588955 266042 738745 0.55 2 7 0.5 105229 142725 Pseudomys_australis_ABTC35951 12043 2154132 726877 374959 0.58 6 6 5 0.5 114083 158325 Rattus_fuscipes_GI02R01 13202 2581090 997837 442414 0.53 1 9 3 155308 Rattus_lutreolus_C132T 39392 1.8 2148623 323661 271877 595538 0.20 5 1.5 1004297 637419 166978 199899 366878 Rattus_lutreolus_C132TP 160946 0.28 8 4 2 4 8 2 0.7 237571 157844 208667 Uromys_caudimaculatus_ASA09 34418 4462395 508239 0.41 7 6 0 9 141766 166154 234451 Zyzomys_argurus_ABTC07908 61155 1.6 3762179 682972 0.53 5 2 4 1311 1312 1313

154 1314 Table. 4.4. Calibrations used in the MCMCtree analysis and the resulting node 1315 dates (see Fig. 4.6). Calibration Date from Smissen (Chapter 2) Upper limit Lower limit 1) Root 16.29 18.00 14.00 2) Sahul rodents 8.80 9.80 7.80 3) Australian rodents 6.29 7.29 5.29 Node Mean Upper 95% Lower 95% A 2.5 2.2 2.8 B 1.4 1.2 1.6 C 2.0 1.7 2.4 D 3.5 3.2 3.8 E 4.8 4.6 5.1 F 4.8 4.5 5.0 G 5.3 5.1 5.6 1316 1317

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155 1333 Figures

1334

1335 Figure 4.1. The distribution of species of Conilurus generated form collection 1336 records on the Atlas of Living Australia (http://www.ala.org Accessed 18 May 1337 2017). Blue = contemporary distribution of C. penicillatus. Yellow = location 1338 of C. capricornensis fossil discovery. Red = historical distribution of C. 1339 albipes.

156 1340

1341

1342 Figure 4.2. ML trees based on 100bs for the ‘strict’ dataset on the left and the ‘complete’ dataset on the right.

157 (43) (44) (45) (46) (47) (48) (49)

1343

1344 Figure 4.3. Agar gel image of hDNA extraction for C. albipes and R. lutreolus.

1345

1346

1347

158 1348

1349 Figure 4.4. Illumina sequencing: Position specific frequencies of nucleotide misincorporation patterns across reads from the 1350 historical samples, R. lutreolus compared to a contemporary sample of R. fuscipes (RIGHT) and C. albipes compared to a 1351 contemporary sample of C. penicillatus (LEFT). The graphs indicate the frequencies of different categories of DNA damage 1352 as a function of distance from the 5’-ends (the first 25 bases, LEFT) and 3’- ends (the final 25 bases, RIGHT). Coloured lines 1353 may be masked as due to the limited damage in these samples, the frequencies of some damage converged around 0.00. 1354 Red: C → T. Blue: G → A. Purple: Insertions. Green: Deletions. Orange: Clipped bases. Grey: Other misincorporations. 1355 1356 1357

159 1358

1359 Figure 4.5. Histogram showing the distribution of loci in the 1368 dataset (light grey) and 349 dataset (dark grey) with p- 1360 distance on the x-axis and frequency of loci on the y-axis. The slow-evolving HOXC6 labelled and the fast-evolving CO1 and 1361 Cyt-b mitochondrial loci are labelled.

160 1362

1363

1364 Figure 4.6. A time-calibrated ultrametric phylogeny generated in MCMCtree. 1365 Scale is measured in millions of years. Calibrations 1, 2 and 3 are labelled on 1366 their corresponding nodes. 1367

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161 1376 Supplementary Materials 1377 1378 Emap pipeline code: 1379 1380 #!/bin/bash 1381 #Specify full path to BIN, text file listing sample prefix names, path to raw 1382 reads, path for output, and fasta file containg AA reference 1383 BINPATH= 1384 SAMPLELIST= 1385 INPATH= 1386 OUTPATH= 1387 REFPATH= 1388 1389 mkdir -p $OUTPATH/reads/DR && 1390 mkdir -p $OUTPATH/reads/DRTRIM && 1391 mkdir -p $OUTPATH/assemble && 1392 mkdir -p $OUTPATH/blast && 1393 mkdir -p $OUTPATH/blast/out && 1394 mkdir -p $OUTPATH/map && 1395 1396 readarray S < $SAMPLELIST 1397 for i in "${S[@]}" 1398 do 1399 SN=( $i ) 1400 pigz -d $INPATH/$SN*.gz 1401 echo "XXXXX $(date)" >> $OUTPATH/reads/duplication.log 1402 echo $SN >> $OUTPATH/reads/duplication.log 1403 grep '\s1:' $OUTPATH/reads/$SN_R1.fastq | wc -l >> 1404 $OUTPATH/reads/duplication.log 1405 touch $OUTPATH/reads/fastuniqfilelist.txt 1406 echo $OUTPATH/reads/$SN_R1.fastq 1407 >$OUTPATH/reads/fastuniqfilelist.txt 1408 echo $OUTPATH/reads/$SN_R2.fastq 1409 >>$OUTPATH/reads/fastuniqfilelist.txt 1410 1411 # DEDUPLICATE 1412 $BINPATH/fastuniq -i $OUTPATH/reads/fastuniqfilelist.txt -t q \ 1413 -o $OUTPATH/reads/DR/$SN_R1.DR.fastq \ 1414 -p $OUTPATH/reads/DR/$SN_R2.DR.fastq -c 0 1415 grep '\s1:' $OUTPATH/reads/DR/$SN_R1.DR.fastq | wc -l >> 1416 $OUTPATH/reads/duplication.log 1417 1418 # ADAPTER REMOVAL AND TRIMMING 1419 java -jar $BINPATH/Trimmomatic-0.35/trimmomatic-0.35.jar PE - 1420 phred33 -threads 56 \ 1421 $OUTPATH/reads/DR/$SN_R1.DR.fastq \ 1422 $OUTPATH/reads/DR/$SN_R2.DR.fastq \ 1423 $OUTPATH/reads/DRTRIM/$SN_R1.DRTRIMP.fastq \

162 1424 $OUTPATH/reads/DRTRIM/$SN_R1.DRTRIMU.fastq \ 1425 $OUTPATH/reads/DRTRIM/$SN_R2.DRTRIMP.fastq \ 1426 $OUTPATH/reads/DRTRIM/$SN_R2.DRTRIMU.fastq \ 1427 ILLUMINACLIP:$BINPATH/Trimmomatic-0.35/adapters/TruSeq3- 1428 PE-2.fa:1:35:15 LEADING:3 TRAILING:3 SLIDINGWINDOW:8:20 MINLEN:40 1429 pigz $OUTPATH/reads/DR/$SN_R*.DR.fastq 1430 cat $SN_R1.DRTRIMP.fastq $SN_R1.DRTRIMU.fastq 1431 $SN_R2.DRTRIMU.fastq > $SN_R1.DRTRIMPU.fastq 1432 1433 # DENOVO ASSEMBLY 1434 perl $BINPATH/trinity/Trinity --seqType fq --max_memory 40G -- 1435 min_contig_length 100 --no_normalize_reads \ 1436 --left $SN_R1.DRTRIMPU.fastq \ 1437 --right $SN_R2.DRTRIMP.fastq \ 1438 --output $OUTPATH/2_assemble/$SN.trinity --CPU 56 -- 1439 full_cleanup --min_kmer_cov 2 1440 perl $BINPATH/makesomethingNotInterleaved.pl 1441 $OUTPATH/assemble/$SN.trinity.Trinity.fasta > 1442 $OUTPATH/2_assemble/$SN.trinity.fasta 1443 rm -f $OUTPATH/2_assemble/$SN.trinity.Trinity.fasta && 1444 1445 # BLAST ASSEMBLY TO REFERENCE, FIND RECIPROCAL 1446 BEST HIT, CREATE NEW SAMPLE SPECIFIC REFERENCE 1447 $BINPATH/ncbi-blast-2.3.0+/bin/makeblastdb -in 1448 $OUTPATH/assemble/$SN.trinity.fasta \ 1449 -title $SN.trinity -out $OUTPATH/blast/db/$SN.trinity -dbtype nucl 1450 tblastn -query $REFPATH -db $OUTPATH/blast/db/$SN.trinity - 1451 evalue 0.0001 -seg no \ 1452 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart 1453 qend sstart send evalue bitscore qlen slen gaps ppos frames qseq" \ 1454 -num_threads 56 > $OUTPATH/blast/out/$SN.trinity.tblastn.txt 1455 sort -k1,1 -k12,12gr -k11,11g 1456 $OUTPATH/blast/out/$SN.trinity.tblastn.txt \ 1457 | sort -u -k1,1 --merge 1458 >$OUTPATH/blast/out/$SN.trinity.tblastn_top.txt 1459 sort -k2,2 -k12,12gr -k11,11g 1460 $OUTPATH/blast/out/$SN.trinity.tblastn_top.txt >$OUTPATH/blast/out/$SN.tr 1461 inity.tblastn_tops.txt 1462 awk 'BEGIN { FS = OFS = "\t"} {split($1,a,"_"); 1463 $(NF+1)=a[1];$(NF+2)=$2; print}' \ 1464 $OUTPATH/blast/out/$SN.trinity.tblastn_tops.txt 1465 >$OUTPATH/blast/out/$SN.trinity.tblastn_topg.txt 1466 awk -F '\t' 'BEGIN {OFS=FS} {if ($2 == prev2 && $19==prev19) $2 = 1467 $2$1; else prev2 = $2; prev19=$19; print}' \ 1468 $OUTPATH/blast/out/$SN.trinity.tblastn_topg.txt | sort -u -k2,2 -- 1469 merge >$OUTPATH/blast/out/$SN.trinity.tblastn_topf.txt

163 1470 awk '{if ($4/$13>=0.5)print}' 1471 $OUTPATH/blast/out/$SN.trinity.tblastn_topf.txt 1472 >$OUTPATH/blast/out/$SN.trinity.tblastn_top_OHR.txt 1473 awk '{if ($5/$4<=0.3)print}' 1474 $OUTPATH/blast/out/$SN.trinity.tblastn_top_OHR.txt 1475 >$OUTPATH/blast/out/$SN.trinity.tblastn_top_OHR_M30.txt 1476 python $BINPATH/pullexons_EC_AM2.py 1477 $OUTPATH/assemble/$SN.trinity.fasta \ 1478 $OUTPATH/blast/out/$SN.trinity.tblastn_top_OHR_M30.txt 1479 $OUTPATH/map/$SN_trinity_SSR_EO.fasta 1480 cat $OUTPATH/reads/DRTRIM/$SN_R*.DRTRIMU.fastq 1481 >$OUTPATH/reads/DRTRIM/$SN_R12.DRTRIMS.fastq 1482 1483 # MAP READS BACK ONTO SAMPLE SPECIFIC REFERENCE, 1484 VARIANT CALL AND OUTPUT CONSENSUS. 1485 $BINPATH/bbmap/bbwrap.sh tthreads=54 k=12 unpigz=t 1486 minratio=0.25 maxindel=100 local 1487 ref=$OUTPATH/map/$SN_trinity_SSR_EO.fasta \ 1488 in1=$OUTPATH/reads/DRTRIM/$SN_R1.DRTRIMP.fastq,$OUTPA 1489 TH/reads/DRTRIM/$SN_R12.DRTRIMS.fastq \ 1490 in2=$OUTPATH/reads/DRTRIM/$SN_R2.DRTRIMP.fastq,null 1491 out=$OUTPATH/map/$SN_trinity_bbmap.sam append nodisk 1492 samtools view -S $OUTPATH/map/$SN_trinity_bbmap.sam -b -o 1493 $OUTPATH/map/$SN_trinity_bbmap.bam 1494 samtools sort $OUTPATH/map/$SN_trinity_bbmap.bam 1495 $OUTPATH/map/$SN_trinity_bbmap 1496 samtools index $OUTPATH/map/$SN_trinity_bbmap.bam 1497 samtools mpileup -A -f $OUTPATH/map/$SN_trinity_SSR_EO.fasta 1498 $OUTPATH/map/$SN_trinity_bbmap.bam | \ 1499 java -jar $BINPATH/varscan-master/VarScan.v2.4.1.jar 1500 mpileup2cns --min-coverage 4 -output-vcf 1 > 1501 $OUTPATH/map/$SN_trinity_vscns.vcf 1502 pigz $OUTPATH/map/$SN_trinity_vscns.vcf 1503 zcat $OUTPATH/map/$SN_trinity_vscns.vcf.gz | 1504 $BINPATH/vcftools/src/perl/vcf-to-tab > 1505 $OUTPATH/map/$SN_trinity_vscnss.tab 1506 samtools faidx $OUTPATH/map/$SN_trinity_SSR_EO.fasta 1507 perl 1508 $BINPATH/fai2tab.pl $OUTPATH/map/$SN_trinity_SSR_EO.fasta.fai 1509 >$OUTPATH/map/$SN_trinity_SSR_EO.fasta.fai.tab 1510 awk 'NR==FNR{a[$1,$2]=$3OFS$4;next}{$3=a[$1,$2];print}' 1511 OFS='\t' \ 1512 $OUTPATH/map/$SN_trinity_vscnss.tab 1513 $OUTPATH/map/$SN_trinity_SSR_EO.fasta.fai.tab 1514 >$OUTPATH/map/$SN_trinity_vscnsf.tab 1515 echo -e "CHROM\tPOS\tREF\t$SN " | cat - 1516 $OUTPATH/map/$SN_trinity_vscnsf.tab \

164 1517 > /tmp/out && mv -f 1518 /tmp/out $OUTPATH/map/$SN_trinity_vscnsfh.tab 1519 awk 'BEGIN { FS = OFS = "\t" } { for(j=1; j<=NF; j++) if($j ~ /^ *$/) $j 1520 = "n\tn/n" }; 1' \ 1521 $OUTPATH/map/$SN_trinity_vscnsfh.tab > 1522 $OUTPATH/map/$SN_trinity_vscnsfhn.tab 1523 perl 1524 $BINPATH/vcf_tab_to_fasta_alignmentAM2.pl $OUTPATH/map/$SN_trinity_ 1525 vscnsfhn.tab >$OUTPATH/map/$SN_trinity_vscnsfhn.fasta 1526 done 1527

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165 1544 Chapter 5 1545

1546 General Discussion

1547

1548 In this thesis I used the Australian rodents as a model system to investigate

1549 the evolutionary processes of diversification, ecological speciation and

1550 extinction. I examined relationships within this group on multiple scales

1551 including: deep phylogenetic relationships of major lineages (Chapter 2);

1552 species-level relationships between closely related species (Chapter 4); and

1553 intra-specific relationships between divergent populations/lineages (Chapter

1554 3). This research has established several future avenues of research that I

1555 discuss below. In particular, I show that the Australian rodents are a promising

1556 system for future studies examining evolutionary processes such as

1557 ecological and non-ecological diversification, biome transitions and

1558 adaptations, species delimitation, and the use of museum specimens in

1559 genomic studies.

1560 The Australian rodents, particularly the ‘Old Endemics’, are a unique

1561 system to study evolution for many reasons. They represent a terminal

1562 expansion of the most diverse family of mammals in the world, Muridae. They

1563 colonised Australia several times, with one of these colonisation events

1564 leading to the rapid diversification of the most species-rich group of eutherian

1565 mammals to evolve from a single colonisation of Australia (Rowe et al., 2008).

1566 In the process, the group recapitulated much of the global eco-morphological

166 1567 diversity in rodent species occupying equivalent environments. Native rodents

1568 occupy all major Australian biomes from some of the driest deserts on earth to

1569 wet tropical rainforests (Menkhorst and Knight, 2001; Breed and Ford, 2007).

1570 As a result of adaptation to these environments the ‘Old Endemics’ comprise

1571 a diverse range of eco-morphological forms including: large water-rats,

1572 Hydromys; generalist mouse-form genera Pseudomys and Leggadina, with

1573 species’ size range stretching three orders of magnitude, from 8g to >130g;

1574 specialist forms such as the desert-adapted hopping-mice in the genus

1575 Notomys, with the world’s most efficient mammalian kidneys; herbivorous

1576 species such as the vole-like Broad-toothed rat, Mastacomys fuscus; and

1577 arboreal tree-rats, e.g. Mesembriomys. Australia also has murid species taxa

1578 that exhibit unique behavioural traits, for example the stick-nest rats in

1579 Leporillus build large middens from vegetation, and the pebble-mound mice in

1580 Pseudomys build large mounds from small rocks (Menkhorst and Knight,

1581 2001; Breed and Ford, 2007). Despite this wide taxonomic diversity,

1582 specialised traits, and adaptation to dramatically different environments, the

1583 group is remarkably young on an evolutionary timescale (~7 Mya; Chapter 2,

1584 Fig. 2.4.). Unfortunately, this diverse collection of rodents has experienced the

1585 highest rate of contemporary mammalian extinctions in the world (Johnson,

1586 2006; Woinarski et al., 2014). Following European colonisation, which began

1587 in the late 18th century, at least 10 of 64 known species have gone extinct

1588 (Menkhorst and Knight, 2001).

1589 The majority of the Australian rodent diversity, including all known extinct

1590 species, was previously placed in a single group: the Conilurini tribe (8

167 1591 genera; ~52 species; Ford, 2003; 2006). Numerous previous studies based on

1592 morphology and genetics failed to resolve the majority of relationships within

1593 the group deeper than sister species pairs (Baverstock et al., 1981; 1983;

1594 Watts et al., 1992; Torrance 1997; Ford 2006; Crabb 1977; Lidicker and

1595 Brylski 1987; Breed 1997). However, recent research, including work in this

1596 thesis, has shown that the Conilurini is a paraphyletic group (Rowe et al.,

1597 2008; Chapter 2; Fig. 2.2.). In this thesis I referred to these two paraphyletic

1598 clades as the Pseudomys Species Group (PSG; 5 genera, 45 species;

1599 Chapter 2), and the Conilurus Species Group (CSG; 3 genera, 7 species;

1600 Chapter 4), which is nested within the Uromyini tribe, sister to the clade that

1601 includes the Australian genera Melomys and Uromys.

1602 In Chapter 2, I used an 11-locus dataset to resolve the phylogenetic

1603 relationships within the PSG. I used my phylogeny of the group to investigate

1604 the group’s evolution, specifically, its rate of diversification and the roles of

1605 ecological opportunity, phylogenetic constraint, and niche conservatism during

1606 the group’s diversification. I illustrated that the PSG transitioned into the major

1607 Australian biomes in a different order to the Australian Rattus (i.e. the ‘New

1608 Endemics’), which formed a second and more recent colonisation of rodents

1609 into Australia. The ‘New Endemics’ colonised the biomes sequentially from

1610 wet to dry, i.e. wet tropics to monsoon/mesic to arid (Rowe et al., 2011), which

1611 reflects the predictions of phylogenetic constraint and niche conservatism

1612 (Crisp et al., 2009; Wiens et al., 2010). The ancestor of the PSG transitioned

1613 from wet tropical rainforest into the seasonally wet and dry monsoon tropics

1614 (6-5Mya), and then transitioned into the arid biome shortly after (5.5-4.5Mya),

168 1615 and only managed to colonise the mesic biome much more recently (~3Mya;

1616 Chapter 2; Fig. 2.4.). The PSG as a group is unique as it includes examples of

1617 clades that both support and contradict the theory of phylogenetic constraint

1618 and niche conservatism. The genera Notomys and Zyzomys are specialised

1619 genera that are relatively constrained to the arid and monsoon biomes,

1620 respectively. Following colonisation of their respective biomes they appear to

1621 have remained there and diversified in situ. In comparison, despite being one

1622 of the most nested clades in the PSG, the genus Pseudomys is characterised

1623 by its lack of specialisation, and is distributed across all three major biomes of

1624 Australia. Most notably, Pseudomys exhibits unparalleled niche divergence

1625 compared to the rest of the genera within the PSG. Species within

1626 Pseudomys have colonised the mesic biome at least six independent times,

1627 and have back-colonised monsoon biomes at least three times and the arid

1628 biome at least once (Chapter 2; Fig. 2.4.). Thus, the PSG represents an

1629 intriguing mammal radiation where opposing processes of evolutionary

1630 diversification appear to be acting to generate diversity in a single group.

1631 In Chapter 3, I examined the species limits in the endangered Hastings

1632 River mouse, Pseudomys oralis. My findings supported that of previous

1633 research, which uncovered two divergent mitochondrial lineages similar to the

1634 level of divergence seen in other sister species of Pseudomys (Jennings et

1635 al., 1998; Rowe et al., 2012; Chapter 3; Fig. 3.4). However, I found limited

1636 nuclear divergence between these two divergent mitochondrial lineages.

1637 Instead, I uncovered a pattern that reflects a complex history of habitat

1638 contraction, fragmentation and re-expansion, where the north and south

169 1639 populations of P. oralis were likely repeatedly isolated from one another for

1640 short periods of time during glacial periods. The mito-nuclear discordance

1641 uncovered in this chapter provides further evidence that inference based on a

1642 single genomic marker can be misleading. This potential error is of particular

1643 concern when research is aimed at the conservation and management of an

1644 endangered species because it may lead to the under or overestimation of

1645 divergent lineages within a species, and thus the implementation of

1646 inappropriate conservation management plans. Thus, my findings emphasise

1647 the importance of having a complete understanding of a species’ population

1648 genetics in order to implement the most effective conservation management

1649 plans and protect the integrity of divergent populations within an endangered

1650 species. Considering that Australian rodents have experienced

1651 unprecedented rates of population extirpation and extinction following

1652 European colonisation, it will be important to use these same methods to

1653 better understand the underlying phylogeographic and population genetic

1654 patterns of other endangered or threatened rodents in order to conserve them

1655 into the future.

1656 In Chapter 4, I sequenced genomic-level data of an extinct species within

1657 the CSG, the White-footed Rabbit-rat, Conilurus albipes, from museum skin

1658 specimens >150 years old. In doing so, I was able to date the divergence

1659 between the two species, C. albipes and C. penicillatus, and compare it to

1660 other closely related sister-species pairs. I dated the split between the two

1661 species to 2.5Mya, which is almost twice as old as the split between the two

1662 Mesembriomys species in the sister genus to Conilurus (Chapter 4; Fig. 4.6).

170 1663 It is also an older split than that between the genera Pseudomys and

1664 Mastacomys (Chapter 4; Fig. 4.6). These findings show that a substantial

1665 amount of diversity was lost with the extinction of C. albipes, and further

1666 highlights the importance of conserving currently endangered rodent species

1667 into the future. Moreover, the extraction and sequencing methods outlined in

1668 this chapter provide guidance for future research interested in: the population

1669 genetics of C. albipes prior to extinction; the inclusion of the remaining nine

1670 extinct species within the PSG and CSG groups in the Australian rodent

1671 phylogeny; and research focusing on extracting degraded DNA from historical

1672 museum specimens of comparable age and treatment.

1673 The emerging field of phylogenomics is undoubtedly going to uncover a

1674 great deal more about the evolutionary history of the PSG and CSG, as well

1675 as the Australian rodents as a whole. My well-resolved phylogenetic

1676 hypothesis for the Australian rodents is based on an 11-locus dataset, which

1677 is modest compared to the datasets of many 1000s of loci that can now be

1678 generated much more quickly and cost-effectively. As researchers try to

1679 uncover the ‘perfect’ sized dataset for resolving species phylogenies as cost-

1680 effectively as possible, testing my phylogenetic hypothesis against a genomic-

1681 scale dataset of many 1000s of loci is the next step.

1682 Genomic-sized datasets will also be able to be used to answer many other

1683 interesting evolutionary questions beyond the realm of 11-loci datasets. For

1684 instance, examining the molecular evolution of amino acids across the

1685 genomes of rodents within the PSG will allow researchers to examine

1686 evolutionary processes such as convergent evolution, adaptation, and rate of

171 1687 diversification between the numerous closely related lineages. In particular,

1688 exploring how species, such as those in the genus Pseudomys, have so

1689 rapidly transitioned into and out of different biomes is of great value. For

1690 example, what are the genomic-level changes that are occurring to facilitate

1691 adaptation to or from the dry arid biome and the wetter mesic and monsoon

1692 biomes in species such as in the sister-species pairs P. shortridgei and P.

1693 desertor or P. higginsi and P. fieldi? Furthermore, examining the

1694 morphological implications of transitioning between the arid biome and the

1695 mesic and monsoon biomes may uncover novel processes. There appears to

1696 be a significant size difference between sister taxa, where arid-adapted

1697 species are smaller than their mesic or monsoon adapted relatives, e.g. P.

1698 higginsi and P. fieldi, P. shortridgei and P. desertor (see Menkhorst and

1699 Knight, 2001; Breed and Ford 2007). Such questions could also be addressed

1700 at a much broader scale. For instance, are there genomic-level differences

1701 when I compare the genomes of all arid-adapted species to those of all mesic-

1702 or monsoon-adapted species? Are all arid adapted species evolving the same

1703 adaptations to desert environments using the same genomic toolkit, or are

1704 they using a different genomic toolkit each time? This question could also be

1705 asked at a much broader scale comparing other rodents adapted to

1706 equivalent biomes, but which exist on different continents. For example, a

1707 comparative genomic study could be done with the American desert-adapted

1708 kangaroo-rats (genus Dipodomys), the Australian hopping mice (genus

1709 Notomys), and the African/Asian jerboa (family Dipodidae), all of which are

1710 bipedal desert-adapted rodents.

172 1711 Genomic datasets will also allow us to ask more challenging questions

1712 pertaining to species delimitation, phylogeography and population genetics as

1713 in species like P. oralis. There are numerous examples of Australian rodent

1714 species distributed across vast areas of the continent, across numerous

1715 biogeographic barriers, as well as species within which my research

1716 uncovered multiple distinct lineages. For example, Mastacomys fuscus, P.

1717 delicatulus and Z. woodwardi are three species with two or more distinct

1718 lineages that have crown ages similar in age to other sister-species pairs

1719 (Chapter 2; Fig. 2.4). Questions focusing on local adaptation and speciation

1720 could be addressed by examining species with vast distributions that stretch

1721 across more than a single biome. For instance, the species P. johnsoni, P.

1722 delicatulus, P. nanus, L. forresti and Z. argurus are distributed across two or

1723 more biomes, and in some cases stretch the entire width of the continent

1724 (Menkhorst and Knight, 2001; Breed and Ford, 2007). Comparative

1725 phylogeographic studies could be carried out on the numerous taxa that

1726 occupy each biome and are distributed across biogeographic barriers. The

1727 monsoon tropics and mesic biome would be of particular interest due to the

1728 high number of well-known biogeographic barriers (Bowman et al., 2010;

1729 Byrne et al., 2011; Chapple et al., 2011). For instance, closely related species

1730 P. delicatulus, P. gracilicaudatus, and P. patrius are all distributed along the

1731 east coast of Australia spanning biogeographic barriers such as the Burdekin

1732 Gap (see Chapple et al., 2011). Also, each of the genera Conilurus,

1733 Mesembriomys, Zyzomys, Leggadina, and Pseudomys include at least one

1734 species that is distributed across the Kimberleys and Arnhem Land, two

173 1735 biogeographic hotspots of diversity (Doughty, 2011; Pepper and Keogh, 2014;

1736 Rosauer et al., 2016). It would be illuminating to see how the biogeographic

1737 barriers in these biomes have shaped the phylogeography of each one of

1738 these closely related species.

1739 Lastly, all extinct species of Australian rodent are represented in historical

1740 museum collections. Thus, the potential to include the remaining nine extinct

1741 species in future Australian rodent studies in order to get complete taxon

1742 sampling of a continent-wide radiation should be highly enticing to future

1743 researchers. Including such species will allow us to uncover where they lie in

1744 the Australian rodent phylogeny to give us an even deeper understanding of

1745 biome transitions, eco-morphological evolution, rates of diversification, as well

1746 as more accurate crown age estimates in Notomys, Pseudomys, and

1747 Leporillus. The most extreme example is Notomys, which has lost five species

1748 to extinction, representing half the genus’s diversity (Menkhorst and Knight,

1749 2001; Breed and Ford, 2007). Adding these individuals to the phylogeny may

1750 push back the currently young crown age of Notomys (~3Ma; Chapter 2; Fig.

1751 2.4), or may indicate they have only recently diverged showing that Notomys

1752 rapidly diversified. There is also the ability to look at extirpated populations of

1753 extant species to examine population genetics preceding extinction and

1754 further understand the amount of diversity lost to extinction. For example,

1755 species such as P. fieldi and L. conditor have experienced extensive

1756 population extirpation, and are now each restricted to a small island off the

1757 coast of Australia (Menkhorst and Knight; 2001; Breed and Ford, 2007).

1758 Examining available historical museum specimens for such species would

174 1759 give a deeper insight into how much diversity has been lost, and whether or

1760 not species are currently experiencing a bottleneck. Similarly, numerous

1761 historical museum specimens exist for the extinct species L. apicalis and C.

1762 albipes, which can now be included in genetic studies to better understand the

1763 genetic processes underpinning extinction as well as the amount of diversity

1764 lost.

1765 As the burgeoning field of phylogenomics continues to advance, the

1766 Australian rodents will certainly offer a unique opportunity to investigate

1767 questions across numerous areas including: phylogeography, population

1768 genetics, conservation, convergence and adaptive evolution, colonisations

1769 and biome transitions, extirpation and extinction, and diversification and

1770 species delimitation. Overall, my research not only fills in numerous

1771 knowledge gaps in the literature on Australian rodents, but also showcases

1772 Australia’s rodent fauna as a novel system with a great deal to contribute to

1773 the field of evolutionary biology.

1774

1775

1776

1777

1778

1779

1780

1781

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211

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Author/s: Smissen, Peter J.

Title: Evolutionary biology of Australia’s rodents, the Pseudomys and Conilurus Species Groups

Date: 2017

Persistent Link: http://hdl.handle.net/11343/194269

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