What Drives Speciation in Eucalyptus?

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Adaptive variation versus vicariance: what drives speciation in Eucalyptus?

Susan Rutherford

A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy

Evolution and Ecology Research Centre

School of Biological, Earth and Environmental Sciences

Faculty of Science

October 2017

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‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

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AUTHENTICITY STATEMENT

‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’

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Eucalyptus regnans (commonly known as the ‘Mountain ash’) from Mount Field National Park, Tasmania. Eucalyptus regnans is the tallest flowering plant in the world reaching heights of 90–100 m (Photography: S. Rutherford). iv

Statement of contribution of co-authors and declarations of permission to publish

Chapters 2 to 5 of this thesis are stand-alone manuscripts that have been written for publication in peer-reviewed journals. Each chapter is self-contained including tables, figures, references and appendices. The terms, ‘we’ and ‘our’, are frequently used as each chapter has co-authors. Additional assistance is acknowledged at the end of each chapter. All photographs presented in the thesis are by S. Rutherford unless otherwise stated. Permission was obtained from V. Klaphake to use images in Klaphake (2012). The contributions by co-authors of each chapter are listed as follows:

Chapter 2

Phylogenomics of the green ash eucalypts (Myrtaceae): a tale of reticulate evolution and misidentification

Australian Systematic Botany (2016) 28: 326‒354 doi: 10.1071/SB15038

Authors: Rutherford S, Wilson PG, Rossetto M, Bonser SP

This study was conceived by SR, PGW and MR. Leaf material for DNA and voucher specimens were collected by SR and PGW. DNA extractions and phylogenetic analyses were performed by SR with advice from PGW. SR wrote the manuscript and PGW, MR and SPB provided comments and guidance.

All journals published by CSIRO Publishing (e.g. Australian Systematic Botany) allow authors to reproduce the Accepted version of their manuscript in a thesis with no embargo.

Chapter 3

Seedling response to environmental variability: the role of phenotypic plasticity in the evolution of Eucalyptus species

A version of this chapter has been published in American Journal of Botany (2017) 104: 840-857 doi:10.3732/ajb.1600439

Authors: Rutherford S, Bonser SP, Wilson PG, Rossetto M

SR, SPB and PGW developed the research questions. SPB and SR designed the study, SR and PGW collected seed and SR conducted the growth experiment. SR performed statistical and phylogenetic analyses, and wrote the manuscript. SPB, MR and PGW provided editorial advice and guidance on the manuscript drafts.

After publishing an article in American Journal of Botany, authors reserve the right to use all or part of the article in compilations or other publications of the authors' own works.

Chapter 4

Speciation in the presence of gene flow: population genomics of Eucalyptus species along altitudinal and latitudinal gradients in south-eastern Australia

Authors: Rutherford S, Rossetto M, Bragg JG, McPherson H, Benson D, Bonser SP, Wilson PG

This study was conceived and designed by SR and MR. DB provided advice on selection of study species. SR, PGW and DB collected leaf material for DNA analysis and voucher specimens. SR performed the DNA extractions and population genetic analyses with advice from MR, JGB and HM. JGB wrote codes for the analysis of large SNP datasets in R studio. SR wrote the manuscript with guidance from MR and PGW. SPB, JGB, HM and DB provided editorial advice on manuscript drafts.

Chapter 5

From environmental niche modelling to phylogenetics and functional traits: understanding ecological specialisation and species diversification in Eucalyptus

Authors: Rutherford S, Wilson PD, Wilson PG, Bonser SP, Rossetto M

This study was conceived by all authors. PDW provided environmental data for analyses and an R script for calculating geographic range size in R studio. SR performed environmental niche modelling and data analyses with advice from PDW. SR wrote the manuscript and MR, SPB, PGW and PDW provided editorial comments on manuscript drafts.

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Acknowledgments

I have been fortunate to have three very good supervisors during my research: Stephen Bonser (UNSW), Maurizio Rossetto (Royal Botanic Garden Sydney) and Peter G. Wilson (Royal Botanic Garden Sydney). It is due to their patience, generosity, support and guidance that this project has been made possible. Thank you to Stephen for all your advice, for welcoming me into your lab and for ensuring that my PhD at UNSW was a great experience. Maurizio, I thank you for your enthusiasm, ideas, encouragement, and for giving me the opportunity to be a part of your team (Evolutionary Ecology) at the Royal Botanic Garden Sydney. Peter, I am so grateful to you for sharing your knowledge of the Australian flora, taxonomy and phylogenetics.

This thesis presents findings from a joint project between UNSW and the Royal Botanic Garden Sydney. Financial support for this research was provided by ARC Linkage Grant LP110100721. A grant from the Dahl Trust allowed me to complete a large section of my molecular work, for which I am extremely grateful. During this research, I was also in receipt of an Australian Post-graduate Award (APA) from the Australian Government.

Many people from the Royal Botanic Garden Sydney have been very supportive over the last few years. I would like to thank Doug Benson, who often acted as an additional mentor and who was always up for a chat on the green ashes, ecology and the flora of the Blue Mountains. A big thanks to Miguel Garcia from the Daniel Solander Library, who always helped me to hunt down references in the library, and was another person with whom I could discuss all things to do with evolution, history and speciation. I am grateful to Carolyn Connelly from the Molecular Laboratory as it was thanks to her technical knowledge and practical experience that I was able to successfully extract DNA from very difficult eucalypt samples! Thank you Hannah McPherson, who was always available to give advice on genetic analyses. Many thanks to Jason Bragg, who helped me to better understand population genetics and who provided many R scripts that enabled the analysis of DArTseq markers. Also, thank you to Peter D. Wilson who helped me with environmental niche modelling and data analysis. I would like to gratefully acknowledge many others who have given me a helping hand, including

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Marlien van der Merwe, Margaret Heslewood, Joel Cohen, Matt Laurence, Andrew Orme, Trevor Wilson, Bob Coveny, Michael Elgey, Juelian Siow, Richard Johnstone, Abigail Greenfield, Chris Allen, Samantha Yap, Monica Fahey and Marco Duretto. I am also grateful to Louisa Murray, Barbara Briggs, Karen Wilson, Bob Makinson and Barry Conn, who encouraged me to do botanical research in the first place.

I have met many kind people from UNSW, who have been very helpful to me over the last few years. I would like to thank Josh Griffiths, Clara Pang, Ali Namazi, Fatih Fazlioglu and Justin Wan. Thank you Josh, Clara and Justin for assisting me in the glasshouse, and thanks to Fatih for helping me with the plasticity index. Many thanks to Geoff McDonnell (glasshouse manager, UNSW) for providing technical support during my common garden experiment. Thank you to Justin for being so supportive while I was writing up my thesis and for reading over a full draft of the thesis.

Researchers from other institutions have provided me with advice during various aspects of my research. I am grateful to Dorothy Steane (University of Tasmania), Jean- François Flot (University Libres de Bruxelles), Jason Carling (DArT Pty Ltd, Canberra) and Andrzej Kilian (DArT Pty Ltd, Canberra).

Many volunteers have also generously contributed their time to help me with my research, including Lawrence Mou, Stephanie Creer, Emma Oldman, Aaron Smith, Danca Ciric, Christine Smith, Esthel Varma and Brendan Malloy.

Last but not least, I would like to thank my family for all the love and support they have given me: my mother, Christine; my father, David; and my sister, Anne.

Abstract

Speciation is a central process in evolutionary biology and is responsible for the diversity of life on Earth. While there has been much progress in evolutionary research over the last 150 years, understanding the many facets of speciation remains a major challenge. In this thesis, I focus on a group of Eucalyptus species called the green ashes (subgenus Eucalyptus section Eucalyptus). The green ashes comprise tall trees on fertile soils (e.g. the tallest flowering plant in the world, Eucalyptus regnans), as well as medium trees and mallees on low nutrient soils. Although Eucalyptus is Gondwanan in origin, fossil and molecular evidence suggest that many eucalypt groups (including the green ashes) diverged within the last 10 million years. Since the green ashes are highly diverse, occur across a range of habitats and are considered recently radiated, they are an appropriate group for investigating speciation mechanisms.

Phylogenetic and population genetic analyses using genome-wide scans based on Diversity Arrays Technology (DArT) were used to reconstruct the evolutionary history of the species relationships. A common garden experiment was conducted to examine how seedling response to variable resource availability is associated with evolutionary events across species. Environmental modelling was used to investigate differences in the predicted range and climatic niche of species. I found that species boundaries in the green ashes were not always consistent with classifications based on morphology and there was evidence of hybridisation and ongoing gene flow between lineages. The findings suggest that the green ashes are at varying stages of speciation, with some species being highly genetically differentiated and others being at earlier stages on the speciation continuum. Inter-specific differences in seedling traits were significant, with traits such as leaf width and biomass being highly plastic across resource treatments for most species. Predicted environmental niches varied amongst species, and species with lower seedling plasticity tended to be restricted to narrower environmental ranges. Overall, this study demonstrates that an approach incorporating phylogenomics, population genomics, a common garden experiment and environmental modelling can provide insights into the speciation of a group of closely related species, where a number of speciation mechanisms (e.g. reticulate evolution, vicariance and ecological speciation) are operating in concert. x

Table of Contents

Statement of contributions of co-authors and declarations of permission to publish ………………………….…………………………………………….. v

Acknowledgements ...……...…...……………………………….…….……. viii

Abstract ……………………………………………………………………….. x

Chapter 1. General introduction ……………………………...... ………..…… 1

Chapter 2. Phylogenomics of the green ash eucalypts (Myrtaceae): a tale of reticulate evolution and misidentification ……………………...…………… 21

Chapter 3. Seedling response to environmental variability: the role of phenotypic plasticity in the evolution of Eucalyptus species ……………….. 77

Chapter 4. Speciation in the presence of gene flow: population genomics of Eucalyptus species along altitudinal and latitudinal gradients in south-eastern Australia ……………………………………………………………..…..… 131

Chapter 5. From environmental niche modelling to phylogenetics and functional traits: understanding ecological specialisation and species diversification in Eucalyptus ...... 201

Chapter 6. Final discussion and conclusions ………………………...…….. 247

Chapter 1. General introduction

1.1 Speciation

Speciation refers to the process through which new species form (de Queiroz 1998). It can be defined as the division of one reproductive community or genotypic cluster into two (Nosil and Feder 2012), and is responsible for the diversity of life on Earth (Gavrilets 2003). Speciation is a central topic in evolutionary biology and connects a whole range of disciplines, including ecology, genetics, behavioural biology, reproductive biology, physiology and systematics (de Queiroz 1998).

The mechanisms of speciation have been defined in terms of the level of gene flow between diverging (sub) populations (Gavrilets and Losos 2009). According to this model, there are three modes of speciation: (1) allopatric, defined as the origin of new species due to geographical isolation and which involves zero gene flow; (2) sympatric, which is the origin of new species from a single local population, where there is maximum gene flow; and (3) parapatric, which is speciation between adjacent populations where connectivity is intermediate (Gavrilets 2003). Alternatively, speciation mechanisms can be explained in terms of biological mechanisms, such as random drift, ecological selection or sexual selection (Gavrilets and Losos 2009). However, more generally, speciation mechanisms can be categorised under either ecological speciation or mutation-order speciation (Schluter 2009). Ecological speciation is when reproductive isolation arises between populations adapting to different environments or ecological niches, due to the fixation of different alleles that are advantageous in one habitat, but not in the other (Schluter 2000; Rundle and Nosil 2005). Mutation-order speciation is the evolution of reproductive isolation through random events and fixation of different alleles between populations in response to similar selection pressures (Schluter 2009).

Adaptation is an integral part of speciation and involves demographic and geographic factors together with an adaptive response (Carson 1985). Adaptation is as an evolutionary process through which populations become better suited to their specific environment via genetic change (Butlin et al. 2012). It is considered to be driven by natural selection and offset by gene flow between populations (Foster et al. 2007). 1

Local adaptation is the result of strong selective pressures that counter or limit gene flow and can lead to the development of ecotypes to specific habitats within a species (Linhart and Grant 1996; Volis et al. 2002).

In recent years there have been a number of insights into the process of speciation. For example, it is now acknowledged that reproductive barriers can be semipermeable to gene flow, species can diverge while still interbreeding and individual ‘speciation genes’ may be responsible for reproductive isolation (Rieseberg 1997; Wu 2001; Hausdorf 2011; Nosil and Feder 2012). There has also been an increasing interest in the role of phenotypic plasticity in the evolution of organisms. Phenotypic plasticity was traditionally considered to be a barrier for the formation of new species as it was thought to weaken selection for local adaptation (Butlin et al. 2012). However, now it is widely recognised that plasticity itself is under selection and is of ecological and evolutionary significance (Nicotra and Davidson 2010; Nicotra et al. 2010). Furthermore, plasticity may promote novel phenotypes, divergence, colonisation of new environments and ultimately speciation (West-Eberhard 2005; Pfennig et al. 2010).

In spite of the considerable progress in evolutionary research, much debate and uncertainty surrounds many aspects of speciation (Sobel et al. 2009). There are still questions regarding the connection between local adaptation and speciation (Foster et al. 2007); the role of phenotypic plasticity in species diversification (Butlin et al. 2012); the genomic architecture of speciation (Feder et al. 2012); and the development of a unified species concept (Carson 1985; Hausdorf 2011). As Gavrilets (2003) pointed out, while there has been much progress since the publication of Darwin’s (1859) The Origin of Species, understanding the many facets of speciation still remains a major challenge.

1.2 Vicariance and adaptive radiation

Historically, speciation in most groups of organisms has been considered to be allopatric (Mayr 1963; Futuyma and Mayer 1980) and in the classic vicariance scenario, a widespread ancestor undergoes speciation in response to successive subdivisions of its distribution range (Ronquist 1997, Wiens 2004). The results of many studies support vicariance as the primary driver of speciation (Asquith 1993; Chesser and Zink 1994).

For example, the disjunct distribution of a number of tropical plant species, are indicative of vicariant speciation (Xu et al. 2011). In the southern hemisphere, many plant groups that originated in Gondwana have diversified since its break-up from the late Cretaceous into the biogeographic patterns seen today (Wilf and Escapa 2015).

More recently speciation has been found to occur over ecological time-scales and several studies have shown that organisms may undergo adaptive phenotypic evolution in relatively few (less than one hundred) generations (Hendry et al. 2007; Carroll et al. 2007). Adaptive radiation is the process by which an evolutionary group displays a rapid and exceptional extent of diversification into a wide range of ecological niches (Gavrilets and Losos 2009). It involves the evolution of an ancestor into an array of species that inhabit a multitude of environments and which display a range of morphological and physiological traits that are adapted to those habitats (Schluter 2000). Species that undergo adaptive radiation should display an ‘early burst’ signal in their rates of lineage diversification and phenotypic evolution (Moen and Morlon 2014). The genus, Schiedea, is an example of one of the largest plant adaptive radiations in the Hawaiian archipelago, representing 34 endemic perennial herbs, vines and shrubs (Kapralov et al. 2013). Other groups that have undergone adaptive radiation include Darwin’s finches (which evolved 2.3 million years ago, Sato et al. 2001) and the cichlid fishes of Lake Malawi and Lake Victoria (which radiated in the last 2.3 million years, Friedman et al. 2013).

1.3 Eucalyptus as a model for studying speciation

Eucalyptus is one of three genera (the other two are Angophora and Corymbia) commonly referred to as eucalypts (Bayly 2016). Eucalypts are thought to have evolved from a rainforest or rainforest margin ancestor (Potts and Pederick 2000; Bayly et al. 2013). Pollen of Myrtaceidites eucalyptoides, (allied to the Angophora/Corymbia clade) dating back to the Late Eocene was identified from southern Australia (Rozefelds 1996; Thornhill and Macphail 2012). However, recent evidence suggests that eucalypts had a more ancient origin. Gandolfo et al. (2011) described fossils of Eucalyptus from Argentina dating back 51.9 Ma. A more ancient origin of Eucalyptus is supported by the

3 calibrated molecular phylogenies of Crisp et al. (2004, 2011) and Thornhill et al. (2015), and the divergence estimates of Ladiges et al. (2003).

Eucalypts are thought to have occurred in marginal habitats of the tertiary Australian rainforest (Hager and Benson 2010) and molecular studies suggest that diversification of the lineage was relatively steady for approximately 30 million years before Australia separated from Antarctica (Crisp et al. 2004). Frequent and increasing aridity during the Miocene resulted in the expansion of xerophytic components of the Australian flora (Lange 1978; Martin 1978, 1982; Rozefelds et al. 1996) and macrofossil deposits show a vegetational mosaic of eucalypt and rainforest communities in south-eastern Australia during this period (Pole et al. 1993; Macphail 2007). Increases in eucalypt pollen have been correlated with increased levels of charcoal, suggesting that a preadaptation to fire resulted in a radiation in response to a rise in fire frequency (Kershaw 1986).

Pollen evidence indicates that eucalypts only became widespread in the Pleistocene (5 to 1.5 Ma) (Potts and Pederick 2000), while molecular studies suggest that rapid radiation occurred in the Quaternary (during the last 2.6 Ma, McKinnon et al. 2004; Byrne 2007). The poor morphological resolution of many present-day Eucalyptus species complexes suggests that they may be the result of recent and ongoing speciation in conjunction with past and current hybridisation (Griffin et al. 1988). The hypothesis of recent speciation in many eucalypt groups is supported by observations of interspecific hybridisation between extant Eucalyptus species (e.g. Vaillancourt et al. 1994; Rossetto et al. 1997; McKinnon et al. 2001; Field et al. 2011a, 2011b; Steane et al. 2011; Pollock et al. 2013, 2015).

1.3.1 Green ash eucalypts

Species that have undergone recent speciation and which occupy many different ecological niches are considered appropriate for studying speciation mechanisms. I focus on a group of eucalypts known as the ‘green ashes’ (Fig. 1). The green ashes are in subgenus Eucalyptus and are characterised by alternate juvenile leaves, pedicellate buds, reniform anthers and brown to red-brown seeds (Brooker 2000). The group includes Eucalyptus obliqua, which was described as the type species of the genus Eucalyptus by Charles Louis L’Héritier de Brutelle in 1789 (Brooker 2000). They also

4 include the tallest flowering plant in the world (the commercially important E. regnans, which reaches heights up to 100 m, Eldridge et al. 1993), and a small mallee which is generally less than 1 m tall (E. cunninghamii, Hill 2002). Many green ash species are rare, restricted and/or localised, including a medium tree, E. paliformis, which is known from only seven populations in Wadbilliga National Park in southern New South Wales (Prober et al. 1990a, 1990b).

While the green ashes are ecologically and economically important, there is uncertainty concerning the evolutionary history of the group, as well as considerable disagreement regarding the number of recognised species. Although some green ash species (e.g. E. kybeanensis, E. triflora and E. cunninghamii) have distinctive buds, fruits, leaves and bark (Hill 1991, 2002), most of the mallees are very similar morphologically (Lassak and Southwell 1982; Ladiges et al. 1989) and as such are difficult to distinguish (Figs 2 and 3). Defining species boundaries is further complicated by inter-specific hybridisation (Hill 1991, 2002). Integrades of E. stricta (with E. apiculata, E. obstans and E. dendromorpha) have been recorded (Benson and McDougall 1998), while suspected hybrids of E. cunninghamii (with E. dendromorpha and E. stricta) have also been noted (Johnson and Blaxell 1972). Therefore species recognised by some authorities (e.g. Hill 2002) are not recognised by others (e.g. Brooker 2000). Prober et al. (1990a) found low genetic distance among species and populations (using allozyme data), and this was attributed to rapid and recent speciation. Hager and Benson (2010) suggest that reticulate evolution (hybridisation between divergent lineages) is likely to have played a major role in the evolutionary history of the green ashes of the Blue Mountains (based on morphological variation and introgression of species).

A B C

D E F

G H I

Fig. 1. Green ash species. A. Eucalyptus stricta. B. E. apiculata. C. E. burgessiana. D. E. kybeanensis. E. E. codonocarpa. F. E. triflora. G. E. spectatrix. H. E. paliformis. I. E. fastigata.

A B C

D E

F G H

Fig. 2. Flowers, fruits and/or floral buds of green ash species. A. Eucalyptus stricta. B. E. apiculata. C. E. kybeanensis. D‒E. E. langleyi. F. E. obliqua. G. E. codonocarpa. H. E. kybeanensis.

A B C

D E F

G H

Fig. 3. Bark of the study species. A. Eucalyptus kybeanensis. B. E. stricta. C. E. apiculata. D. E. dendromorpha. E. E. fastigata. F. E. paliformis. G. E. regnans. H. E. stricta. 8

1.4 An interdisciplinary approach to studying speciation

According to Wardell-Johnson et al. (1997), a sound taxonomy is required in order to appreciate radiation and speciation in Eucalyptus. However, while taxonomic research forms a basis for speciation studies, it is unlikely that the complexities of speciation will be unravelled by a single scientific discipline (Ackerly et al. 2000). The comparative method using phylogenies (which incorporates trait information) has been increasingly used to better understand character evolution (Garland et al. 2005; Hodson et al. 2005; Goolsby 2015). A number of studies have incorporated ecophysiological datasets into phylogenetic and population genetic analyses to better understand evolution in taxonomic groups (e.g. Bateman et al. 1998; McGowen et al. 2001; Andrew et al. 2010; Shepherd and Raymond 2010; Griffiths et al. 2013; Liu et al. 2015).

In recent years, there has been a shift in genetic studies from techniques focusing on only a few genes to those that target a larger sample of the genome (Nosil and Feder 2012). Next-generation sequencing (NGS) has revolutionised genomics research by discovering, sequencing and genotyping thousands of markers across virtually any genome of interest at unprecedented speed (Mardis 2008; Schuster 2008; Davey et al. 2011; van Dijk et al. 2014). One technique that is gaining increasing attention, particularly in eucalypt research, is Diversity Arrays Technology or DArT (e.g. Sansaloni et al. 2010; Steane et al. 2011, 2014, 2015; Harrison et al. 2014; Larcombe et al. 2015, 2016, Jones et al. 2016). DArT is a high throughput, cost-effective method that simultaneously assays thousands of markers across the genome (Jaccoud et al. 2001). This method, which was developed over a decade ago, has since been used in combination with NGS technology to develop the DArTseq platform (Sansaloni et al. 2011). The advent of NGS and related technologies has facilitated the emergence of phylogenomics (the study of evolutionary relationships based on comparative analysis of genome-scale data, Chan and Ragan 2013) and population genomics (genome-wide analysis of sequence variation within and between closely related taxa, Begun et al. 2007). An interdisciplinary approach that incorporates phylogenomics and population genomics in conjunction with other datasets (such as morphological, ecophysiological and environmental) across a group of closely related species provides a powerful system for addressing long-standing evolutionary questions and is likely to be more successful in understanding the processes involved in speciation.

1.5 Aim of study

The aim of this research is to investigate speciation mechanisms in the green ash eucalypts using an interdisciplinary approach. In order to do this, I address the following questions:

(1) What are the evolutionary relationships between these taxa and what are the patterns of divergence?

(2) What is the role of phenotypic plasticity in the evolution of species across the green ash group?

(3) What are the patterns of genetic differentiation within and between selected green ash species and how is genetic variation associated with geographic factors?

(4) What is the predicted environmental niche of species from across the green ash group and are there associations between seedling functional traits, range size, habitat type and evolutionary relationships?

(5) What constitutes a species in the green ash group and how can a better understanding of evolution in these taxa contribute to theories of speciation?

1.6 Overview of study

This thesis is written as a series of manuscripts, each with a separate Abstract, Introduction, Materials and methods, Results, Discussion, References and Appendices. The thesis comprises data collection from a phylogenomic study of the green ashes and closely related eucalypts, a growth experiment of selected green ash species, a population genomic study of the green ashes of the Sydney region and Greater Blue Mountains World Heritage Area (GBMWHA) and environmental niche modelling of the predicted range and climatic niche of species across the green ash group. Chapters 2 to 5 are written as stand-alone papers that have been produced for publication in peer- reviewed journals. As such, there is some repetition and overlap between the chapters.

In Chapter 2, the evolutionary relationships and patterns of divergence of all green ash taxa and other closely related eucalypts in subgenus Eucalyptus were investigated using DArT presence/absence markers. In Chapter 3, seedlings of 12 green ash species were 10 grown in a common garden under varying nutrient and water regimes to examine the role of phenotypic plasticity in the evolution of the group. In Chapter 4, DArTseq markers were used to investigate the population genomics of six species to better understand the evolutionary origins and speciation mechanisms of green ashes in the Sydney region and GBMWHA. Chapter 5 is a comparative study that uses environmental niche modelling and draws on datasets from the previous chapters, to better understand ecological specialisation in the green ashes.

Each chapter is self-contained and therefore the numbering of tables, figures and Appendices are specific to each chapter. The terms, ‘we’ and ‘our’, are frequently used as Chapters 2 to 5 has co-authors. All co-authors are listed in each chapter, and so are acknowledgements to people or institutions, who contributed to the realisation of the particular chapter.

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Chapter 2. Phylogenomics of the green ash eucalypts (Myrtaceae): a

tale of reticulate evolution and misidentification

Susan Rutherford, Peter G. Wilson, Maurizio Rossetto and Stephen P. Bonser

Published in Australian Systematic Botany

Habitats of the green ashes in south-eastern Australia. From top left to bottom right: Mount Kosciusko National Park (southern New South Wales), Waratah Trig (northern New South Wales), Mount Norman (Queensland), and Mount Seldom Seen (Victoria).

Rutherford S, Wilson PG, Rossetto M, Bonser SP (2016) Phylogenomics of the green ash eucalypts (Myrtaceae): a tale of reticulate evolution and misidentification. Australian Systematic Botany 28: 326–354.

2.1 Abstract

Eucalyptus is a genus that occurs in a range of habitats in Australia, Papua New Guinea, Timor, Sulawesi and the Philippines, with several species being used as sources of timber and fibre. However, despite its ecological and commercial significance, understanding its evolutionary history remains a challenge. The focus of the present study is the green ashes (subgenus Eucalyptus section Eucalyptus). Although previous studies, based primarily on morphology, suggest that the green ashes form a monophyletic group, there has been disagreement concerning the divergence of taxa. The present study aims to estimate the phylogeny of the green ashes and closely related eucalypts (37 taxa from over 50 locations in south-eastern Australia), using genome- wide analyses based on Diversity Arrays Technology (DArT). Results of analyses were similar in topology and consistent with previous phylogenies based on sequence data. Many of the relationships supported those proposed by earlier workers. However, other relationships, particularly of taxa within the Sydney region and Blue Mountains, were not consistent with previous classifications. These findings raise important questions concerning how we define species and discern relationships in Eucalyptus and may have implications for other plant species, particularly those with a complex evolutionary history where hybridisation and recombination have occurred.

2.2 Introduction

Eucalyptus L’Hér. (Myrtaceae) is a highly diverse genus encompassing more than 700 species distributed across Australia, Papua New Guinea, Timor, Sulawesi and the Philippines (Smith et al. 2003; McKinnon et al. 2008; Wilson 2011). Over 98% of species within the genus are endemic to Australia where they are the dominant or co- dominant component of many vegetation types (Potts and Wiltshire 1997; Hager and Benson 2010). Eucalyptus is also commercially important, with many species (such as Eucalyptus grandis W.Hill, E. globulus Labill. and E. tereticornis Sm.) being grown around the world as sources of timber and fibre (Eldridge et al. 1993; Grattapaglia et al. 2012). Eucalypts are considered Gondwanan in origin (Crisp et al. 2011; Gandolfo et al. 2011; Hermsen et al. 2012; Thornhill and Macphail 2012), forming a minor part of Tertiary Australian rainforests (Hill 1994; Hager and Benson 2010). Macrofossil evidence suggests that the distribution of eucalypts expanded in response to increasing

22 aridity during the Miocene, and pollen evidence indicates that they became widespread only in the Pleistocene (5–1.5 million years ago; Pole et al. 1993; Rozefelds 1996; Potts and Pederick 2000; Macphail 2007). Many present-day eucalypt species complexes are thought to be the result of recent and ongoing speciation (McKinnon et al. 2004; Byrne 2007; Yeoh et al. 2013). Morphological differences among species are often narrowly defined (Hill 1991), and clinal variation and morphological convergence between taxa are common (McKinnon et al. 2004). Defining species boundaries is further complicated by interspecific hybridisation, often between distantly related taxa (Griffin et al. 1988; Rossetto et al. 1997; McKinnon et al. 2001; Field et al. 2011a, 2011b; Steane et al. 2011; Pollock et al. 2013, 2015). As a result, understanding evolutionary relationships in Eucalyptus, particularly between closely related species, remains a major challenge.

The focus of the present study is the green ashes in subgenus Eucalyptus section Eucalyptus1 (Brooker 2000). The green ashes are characterised by alternate juvenile leaves, adult leaves with moderate to no reticulation, pedicellate buds, reniform anthers and brown to red–brown seeds (Brooker 2000). They are found in a range of habitats in south-eastern Australia, with some species occurring as trees in tall forests on fertile soils and others as small trees or mallees on shallow soils on sandstone (Ladiges et al. 2010). Thirteen species were recognised by Brooker (2000), including Eucalyptus regnans (the tallest flowering plant in the world, up to 100 m tall), the timber species, E. obliqua and E. fastigata, and the mallee, E. cunninghamii, which is often less than 1 m in height (Fig. 1). Of these, nine are rare, restricted or localised (e.g. E. paliformis is known from only seven populations in Wadbilliga National Park, Prober et al. 1990).

Previous studies, based primarily on morphology, suggest that the green ashes form a monophyletic group (Ladiges et al. 1987, 1989). However, there has been much disagreement concerning the divergence and differentiation of taxa, and, in particular, the number of recognised species (Table 1). A small number of species, namely E. regnans, E. fastigata E. obliqua, E. triflora, E. obtusiflora, E. stricta, E. apiculata, E. kybeanensis and E. approximans, were placed as part of series Obliquae (section Renantheria) by Pryor and Johnson (1971).

1 Authors of plant names are given in Table 2 and authors of both species and higher taxonomic ranks are listed in Appendices 1 and 5. 23

A B

C D

Fig. 1. Taxa from the green ash group (subgenus Eucalyptus section Eucalyptus, sensu Brooker 2000). A. Eucalyptus regnans (series Regnantes) from Mount Field National Park (Tasmania). B. E. codonocarpa (series Strictae) from Washpool National Park (New South Wales). C. E. langleyi (series Strictae) from Nowra (New South Wales). D. E. cunninghamii (series Strictae) from the Greater Blue Mountains World Heritage Area (New South Wales).

Table 1. Classifications of the green ashes Eucalyptus obtusiflora in Pryor and Johnson (1971) is E. stricta subsp. obtusiflora in Ladiges et al. (1989) and E. obstans in Hill (1991, 2002). Eucalyptus obtusiflora var. dendromorpha in Pryor and Johnson (1971) is E. dendromorpha in Ladiges et al. (1989), Hill (1991, 2002) and Brooker (2000), whereas E. rupicola in Ladiges et al. (1989) is E. cunninghamii in Hill (1991, 2002) and Brooker (2000). Eucalyptus spectatrix, E. obstans, E. laophila, E. codonocarpa and E. microcodon, which are recognised by Hill (1991, 2002), are not recognised by Brooker (2000). Brooker and Kleinig (2006) and Slee et al. (2006) considered E. spectatrix to be E. stricta, E. laophila to be E. apiculata, E. obstans to be E. burgessiana and E. codonocarpa and E. microcodon included within E. approximans

Pryor and Johnson (1971) Ladiges et al. (1989) Hill (1991, 2002) Brooker (2000) Subgenus Monocalyptus Subgenus Monocalyptus Subgenus Monocalyptus Subgenus Eucalyptus Section Renantheria Superseries Eucalyptus Section Eucalyptus Series Obliquae Series Regnaninae Green-leaved ashes Series Regnantes Subseries Obliquinae E. regnans E. fastigata E. regnans E. obliqua E. fastigata E. obliqua E. fastigata Subseries Delegatensinae Series Eucalyptus E. triflora Series Eucalyptus E. delegatensis E. obliqua E. dendromorpha E. obliqua Subseries Regnantinae Series Strictinae E. apiculata Series Strictae E. regnans Subseries Dendromorphitae E. laophila Subseries Irregulares E. fastigata E. dendromorpha E. stricta E. triflora Subseries Luehmannianinae Subseries Strictitae E. spectatrix E. dendromorpha E. oreades E. triflora E. burgessiana E. apiculata E. luehmanniana E. stricta E. langleyi E. stricta Subseries Considenianinae subsp. stricta E. obstans E. burgessiana E. consideniana subsp. obtusiflora E. cunninghamii E. langleyi E. remota E. burgessiana E. approximans Subseries Regulares E. sieberi Subseries Approximanitae E. codonocarpa E. approximans E. multicaulis E. kybeanensis E. microcodon E. cunninghamii Subseries Pauciflorinae E. paliformis E. paliformis E. paliformis E. pauciflora E. approximans E. kybeanensis Series Contiguae subsp. pauciflora subsp. approximans E. kybeanensis 25

subsp. niphophila subsp. codonocarpa subsp. debeuzevillei E. rupicola var. nana E. apiculata Subseries Strictinae E. fraxinoides E. triflora E. obtusiflora var. dendromorpha E. obtusiflora E. stricta E. apiculata E. approximans subsp. approximans subsp. codonocarpa Subseries Kybeanensinae E. kybeanensis Subseries Mitchellianinae E. mitchelliana Subseries Stellulatinae E. stellulata E. moorei var. latiscula E. moorei

The green ashes were only later referred to as a ‘group’ (e.g. Ladiges et al. 1987, 1989; Moran et al. 1990; Prober et al. 1990; Hill 1991, 2002). Ladiges et al. (1989) recognised more species (e.g. E. dendromorpha, E. rupicola, E. paliformis and E. burgessiana), although they considered E. obtusiflora (now E. obstans) to be a subspecies of E. stricta. Hill (1991, 2002) treated several taxa as species, e.g. E. codonocarpa (formerly E. approximans subsp. codonocarpa), E. spectatrix, E. laophila and E. microcodon, and recognised E. langleyi, E. obstans (formerly E. obtusiflora) and E. cunninghamii (formerly E. rupicola). However, many of the species recognised by Hill (1991, 2002) were not recognised by Brooker (2000) (Fig. 2). In the green ashes, Brooker and Kleinig (2006) and Slee et al. (2006) considered E. obstans to be a coastal variant of E. burgessiana, E. spectatrix to be a southern outlier of E. stricta, E. laophila to be a synonym of E. apiculata, and include both E. codonocarpa and E. microcodon within E. approximans subsp. codonocarpa. Many of these species are very similar morphologically and are difficult to distinguish in the field (Lassak and Southwell 1982; Ladiges et al. 1989). Consequently, although much effort has gone into the systematics and classification of this group, the ranking of taxa and the nature of the relationships among species remain uncertain.

Fig. 2. Morphological characters (leaf, bud and fruits) of four green ash taxa found in the Sydney region and Greater Blue Mountains World Heritage Area. A. Eucalyptus burgessiana. B. E. obstans. C. E. laophila. D. E. apiculata (Klaphake 2012: 47, 49).

Over the past two decades, molecular methods have become increasingly important in resolving questions concerning evolutionary relationships among taxa. The development of sequence datasets has enhanced our understanding of relationships between eucalypt genera and major subgenera (e.g. Udovicic et al. 1995; Steane et al. 1999, 2002; Udovicic and Ladiges 2000; Whittock et al. 2003; Parra-O. et al. 2006, 2009; Ochieng et al. 2007b). However, although standard DNA markers have successfully been used to resolve relationships at higher taxonomic levels within Eucalyptus, these have generally been unsuccessful in resolving relationships among closely related species (McKinnon et al. 2008). Most phylogenetic studies use sequence data from only a single or few regions of the genome (e.g. chloroplast DNA) and are not sufficiently variable in closely related species (e.g. Steane et al. 1998). There are also problems associated with using sequence data from some regions of nuclear DNA (e.g. ITS and ETS) in eucalypts because of the functional constraints imposed on neutral change of nucleotides during evolution (Bayly and Ladiges 2007; Ochieng et al. 2007a; Bayly et al. 2008). Marker systems theoretically representing the whole genome, such as microsatellites and amplified fragment length polymorphisms (AFLPs), have been used to overcome some of these issues (Steane et al. 2011). However, although microsatellites have moderate levels of throughput, and are highly polymorphic and transferable across populations, their transferability across species is sometimes poor (Rossetto et al. 2000; Semagn et al. 2006). The genotyping density obtained even with AFLPs is only hundreds of markers per sample and because it is a gel-based technique, it is comparatively labour intensive (Sansaloni et al. 2010).

With the advent of next generation sequencing (NGS), analytical approaches that have wider genome coverage have been developed. Bayly et al. (2013) used whole chloroplast genome sequences to construct a phylogeny of 39 eucalypt species, with many branches having 97–100% bootstrap support. Another technique that has recently been used in Eucalyptus is Diversity Arrays Technology (DArT) (Hudson et al. 2012). DArT is a microarray hybridisation-based technique that simultaneously assays hundreds to thousands of markers across the genome (Jaccoud et al. 2001; Sansaloni et al. 2010; Kullan et al. 2012). Steane et al. (2011) used over 8000 DArT markers (primarily nuclear) to construct a phylogeny of Eucalyptus, where relationships among higher taxa were generally concordant with traditional taxonomy and ITS-based

28 phylogenies, with high resolution within major clades (including between some closely related species) relative to previous techniques.

Although several green ash taxa (e.g. Eucalyptus regnans, E. obliqua, E. triflora) have been included in molecular phylogenies over the past 10 years (Bayly and Ladiges 2007; Steane et al. 2011; Bayly et al. 2013), there has been no broader study of the green ash eucalypts using these more advanced techniques. Prober et al. (1990) used allozyme data to investigate diversity in the green ashes, and this revealed low differentiation among taxa and many relationships that were not consistent with those derived from morphological characters. Although the green ashes are widely distributed in south-eastern Australia, they are particularly diverse in the Sydney region and Blue Mountains (the latter was listed as a World Heritage Area partly because of its eucalypt diversity, Hager and Benson 2010). Within this area, the green ashes are distributed across a range of environments and occur sympatrically with other closely related eucalypts, such as blue ashes (including scribbly gums), black sallies, stringybarks and peppermints (sections Cineraceae, Longitudinales, Capillulus and Aromatica respectively; Brooker 2000). The distribution of taxa in this heterogeneous environment, therefore, provides a unique opportunity for using more recent genomic techniques to address specific evolutionary questions concerning the green ashes and closely related taxa. Our objective was to estimate the phylogeny of the green ashes using DArT markers, so as to resolve relationships within the green ash group and between the green ashes and other taxa in subgenus Eucalyptus. Therefore, we aimed to address the following questions: (1) do the green ashes form a monophyletic group, (2) is there evidence of hybridisation among taxa, (3) are phylogenetic relationships of the green ashes and closely related taxa consistent with previous classifications (primarily based on morphological characters), and (4) are phylogenetic relationships correlated with geography and substrate?

2.3 Materials and methods

2.3.1 Sampling of taxa

Leaf material was collected from all taxa assigned to the green ash group by the major authorities (Pryor and Johnson 1971; Ladiges et al. 1989; Hill 2002; Brooker 2000).

Table 2 lists the species sampled, following the species concepts of Hill (2002). For most of these species, more than one individual was sampled from multiple locations. So as to sample across the diversity and geographic range of the group, we collected from 44 locations between southern Queensland and Victoria (Fig. 3). Locations of green ash taxa and habitat details were obtained from the National Herbarium of New South Wales database (Royal Botanic Garden Sydney) and Benson and McDougall (1998) (full accession details are listed in Appendix 1; habitat details, and latitude and longitudes are provided in Appendix 2).

During the sampling, several new populations of green ash taxa (e.g. Eucalyptus stricta) were discovered and included. In addition, closely related co-occurring taxa in subgenus Eucalyptus (sections Aromatica, Capillulus, Cineraceae and Longitudinales) were sampled (often from more than one individual per species from different locations). Eucalyptus cloeziana (subgenus Idiogenes) was included as an out-group to subgenus Eucalyptus on the basis of previous studies (Sale et al. 1993; Hill and Johnson 1995; Ladiges et al. 1995; Steane et al. 1999; Udovicic and Ladiges 2000; Steane et al. 2011). Most taxa were sampled directly in the field and their geographic position (including elevation) was recorded (GPS model: Garmin Rino 650, Garmin Australasia, Sydney, NSW, Australia); vouchers of these were lodged in the National Herbarium of New South Wales. Other species (namely E. approximans, E. regnans, E. deuaensis, E. caliginosa, E. cloeziana and E. apiculata from the Berrima population) were sourced from specimens cultivated at the Currency Creek Arboretum (South Australia), the Royal Botanic Garden Sydney, the Australian Botanic Garden (Mount Annan) and the Blue Mountain Botanic Garden (Mount Tomah). All leaf samples were dried in silica gel and stored at –20°C until used for DNA extraction.

Fig. 3. Study area. A. Distribution of the green ash eucalypts (subgenus Eucalyptus section Eucalyptus) in south-eastern Australia. B. Region from southern Queensland to Victoria where leaf material of green ashes and co-occurring taxa in subgenus Eucalyptus were sourced (see Table 2 for location details). Maps generated using Atlas of Living Australia (2015) and Australia’s Virtual Herbarium (2015).

Table 2. Taxa collected showing taxonomy, provenance and codes used in phylogenetic analyses Columns 2 and 3 follow the classification of Brooker (2000), whereas Column 4 follows the classification of Hill (1991, 2002). Abbreviations: ABG, Australian Botanic Garden, Mount Annan; BMBG, Blue Mountains Botanic Garden, Mount Tomah; CCA, Currency Creek Arboretum; cult., cultivated; GBMWHA, Greater Blue Mountains World Heritage Area; NSW, New South Wales; Qld., Queensland; RBG, Royal Botanic Garden, Sydney; SA, South Australia; SW, south-west; Vic., Victoria

Taxon Subgenus Section Group Provenance Code E. apiculata R.T.Baker & H.G.Sm. Eucalyptus Eucalyptus Green Ash Woodford, GBMWHA, NSW apiculata Wood Hilltop, NSW apiculata Hill Berrima, NSW cult. ABG apiculata Berr E. approximans Maiden Eucalyptus Eucalyptus Green Ash Barren Mountain, NSW cult. BMBG approximans E. burgessiana L.A.S.Johnson & Blaxell Eucalyptus Eucalyptus Green Ash Warrimoo, GBMWHA, NSW burgessiana Warri Linden, GBMWHA, NSW burgessiana Lind Springwood, GBMWHA, NSW burgessiana Sprin Faulconbridge Point, GBMWHA, burgessiana Faul NSW E. codonocarpa Blakely & McKie Eucalyptus Green Ash Waratah Trig, NSW codonocarpa Wara Warra National Park, NSW codonocarpa Warr Girraween National Park, Qld. codonocarpa Girr E. cunninghamii G.Don Eucalyptus Eucalyptus Green Ash Mount Banks, GBMWHA, NSW cunninghamii Bank Kings Tableland, GBMWHA, NSW cunninghamii King E. dendromorpha (Blakely) L.A.S.Johnson & Blaxell Eucalyptus Eucalyptus Green Ash Mount Wilson, GBMWHA, NSW dendromorpha Wils Mount Banks, GBMWHA, NSW dendromorpha Bank Blackheath, GBMWHA, NSW dendromorpha Blac Main falls, Wentworth Falls, dendromorpha Went M GBMWHA, NSW Princes Rock Track, Wentworth Falls, dendromorpha Went P GBMWHA, NSW Fitzroy Falls, NSW dendromorpha Fitz Redhills Road, Fitzroy Falls, NSW dendromorpha Redh E. fastigata H.Deane & Maiden Eucalyptus Eucalyptus Green Ash Mount Tomah, GBMWHA, NSW fastigata E. kybeanensis Maiden & Cambage Eucalyptus Eucalyptus Green Ash Wadbilliga National Park, NSW kybeanensis Wadb 32

Kosciusko National Park, NSW kybeanensis Kosc Snowy River National Park, Vic. kybeanensis Snow E. langleyi L.A.S.Johnson & Blaxell Eucalyptus Eucalyptus Green Ash Braidwood Road, Nowra, NSW langleyi Brai (Nowra) Parma Creek Fire Road, Nowra, NSW langleyi Parm Braidwood Road, 17 km SW Nowra, langleyi Brai (17 km SW Nowra) NSW E. laophila L.A.S.Johnson & Blaxell Eucalyptus Green Ash Wollemi National Park, NSW laophila Woll Garden of Stone National Park, NSW laophila Gard Kings Tableland, GBMWHA, NSW laophila King Lithgow, NSW laophila Lith E. microcodon L.A.S.Johnson & K.D.Hill Eucalyptus Green Ash Woodendong, NSW microcodon E. obliqua L'Hér. Eucalyptus Eucalyptus Green Ash Mount Murray, NSW obliqua E. obstans L.A.S.Johnson & K.D.Hill Eucalyptus Green Ash Beacon Hill, Sydney, NSW obstans Beac Royal National Park, NSW obstans Roya Jervis Bay, NSW obstans Jerv E. paliformis L.A.S.Johnson & Blaxell Eucalyptus Eucalyptus Green Ash Wadbilliga National Park, NSW paliformis E. regnans F.Muell. Eucalyptus Eucalyptus Green Ash Great Ocean Road, Vic. cult. CCA, SA regnans E. spectatrix L.A.S.Johnson & Blaxell Eucalyptus Green Ash Doctor George Mountain, NSW spectatrix Geor Wadbilliga National Park, NSW spectatrix Wadb E. stricta Sieber ex Spreng. Eucalyptus Eucalyptus Green Ash Newnes Plateau, NSW stricta Newn Mount Banks, GBMWHA, NSW stricta Bank Blackheath, GBMWHA, NSW stricta Blac Katoomba, GBMWHA, NSW stricta Kato Little Switzerland Track, Kings stricta King L Tableland, GBMWHA, NSW Tableland Road, Kings Tableland, stricta King T GBMWHA, NSW Stanwell Tops, NSW stricta Stan Sassafras, NSW stricta Sass E. triflora (Maiden) Blakely Eucalyptus Eucalyptus Green Ash Nerriga, NSW cult. ABG triflora E. copulans L.A.S.Johnson & K.D.Hill Eucalyptus Black Jamison Creek, Wentworth Falls, copulans Went J Sallies GBMWHA, NSW cult. RBG Darwins Track, Wentworth Falls, copulans Went D 33

GBMWHA, NSW E. moorei Maiden & Cambage Eucalyptus Longitudinales Black Darwins Track, Wentworth Falls, moorei Went D Sallies GBMWHA, NSW Wentworth Falls Lake, GBMWHA, moorei Went Lake NSW E. luehmanniana F.Muell. Eucalyptus Cineraceae Blue Ash A Sir Bertram Stevens Drive, Royal luehmanniana Roya S National Park, NSW Karloo Track, Royal National Park, luehmanniana Roya K NSW E. oreades R.T.Baker Eucalyptus Cineraceae Blue Ash A Katoomba, GBMWHA, NSW oreades E. piperita Sm. Eucalyptus Cineraceae Blue Ash A Kings Tableland, GBMWHA, NSW piperita King Hilltop, NSW piperita Hill E. consideniana Maiden Eucalyptus Cineraceae Blue Ash B Woodford, GBMWHA, NSW consideniana Wood Braidwood Road, Nowra, NSW consideniana Brai (Nowra) E. haemastoma Sm. Eucalyptus Cineraceae Blue Ash B Beacon Hill, Sydney, NSW haemastoma Beac Royal National Park, NSW haemastoma Roya E. multicaulis Blakely Eucalyptus Cineraceae Blue Ash B Linden, GBMWHA, NSW multicaulis E. rossii R.T.Baker & H.G.Sm. Eucalyptus Cineraceae Blue Ash B Wollemi National Park, NSW rossii E. sclerophylla (Blakely) L.A.S.Johnson & Blaxell Eucalyptus Cineraceae Blue Ash B Linden, GBMWHA, NSW sclerophylla Lind Braidwood Road, Nowra, NSW sclerophylla Brai (Nowra) E. sieberi L.A.S.Johnson Eucalyptus Cineraceae Blue Ash B Wentworth Falls, GBMWHA, NSW sieberi Went Hilltop, NSW sieberi Hill E. stenostoma L.A.S.Johnson & Blaxell Eucalyptus Cineraceae Blue Ash B Wadbilliga National Park, NSW stenostoma E. radiata Sieber ex DC. Eucalyptus Aromatica Peppermint Katoomba, GBMWHA, NSW radiata E. caliginosa Blakely & McKie Eucalyptus Capillulus Stringybark Cult. RBG caliginosa E. deuaensis Boland & Gilmour Eucalyptus Capillulus Stringybark Northeast of Mongamulla Mountain, deuaensis NSW cult. RBG E. oblonga DC. Eucalyptus Stringybark Stanwell Tops, NSW oblonga E. sparsifolia Blakely Eucalyptus Capillulus Stringybark Linden, GBMWHA, NSW sparsifolia E. williamsiana L.A.S.Johnson & K.D.Hill Eucalyptus Capillulus Stringybark Queanbeyan, NSW williamsiana E. cloeziana F.Muell. Idiogenes Cult. RBG cloeziana

2.3.2 DNA isolation

Total genomic DNA was extracted from samples using a CTAB protocol modified from Doyle and Doyle (1990). A total of 1–1.5 g of leaf material per sample was ground under liquid nitrogen and the following modifications were made: (1) 2- mercaptoethanol was replaced by sodium metabisulfite (0.5%); (2) addition of sorbitol (0.35 M), polyvinylpyrollidone (4%) and sarcosyl (5%) to the CTAB isolation buffer; and (3) DNA was purified using a Zymo-Spin I-96 Plate and the ZR-96 Clean and Concentrator Kit (Zymo Research Corporation, CA, USA). DNA quality of each sample was tested by restriction of 2 mL of DNA with 3 mL of the restriction endonuclease, RsaI (New England Biolabs, Irvine, CA, USA), and digests were visualised on a 1.0% agarose gel. DNA concentrations were measured using a Qubit 2.0 Flourometer (Invitrogen, Melbourne, Vic., Australia) and each sample was made up to between 400 and 1000 ng of DNA (targeting a concentration of 50 ng µL –1). Samples were sent to Diversity Arrays Technology Pty Ltd (Canberra, ACT, Australia) for genotyping, using the microarray platform developed by Sansaloni et al. (2010).

2.3.3 Phylogenetic analysis of DArT markers

The DArT microarray genotyping platform produces a binary output showing the marker name, its presence or absence in each sample and statistics regarding the quality and reliability of each marker. The DArT dataset produced for the present study consisted of a total of 2702 presence or absence markers. Phylogenetic trees were constructed using parsimony, Bayesian and distance analyses. To ensure that only the higher quality markers were used, markers with a call rate below 90% and reproducibility less than 100% were removed from the dataset (leaving 1780 markers). Call rate is the proportion of data that is missing (i.e. no data available) for each marker. That is, if a marker has a call rate of 90%, then 10% of data is missing for that marker. Reproducibility is a measure of the consistency of a marker. To assess reproducibility, DArT Pty Ltd performs separate library construction, sequencing and marker data extraction in duplication for approximately 30% of the samples. The consistency of scoring between the technical replicates is compared and expressed as a fraction of scores that are in agreement between the replicates. Therefore, a reproducibility of 100% means that all technical replicates were the same. Maximum parsimony (MP) 35 analyses were conducted in PAUP 4.0 b10 (D. L. Swofford, Sinauer, Sunderland, MA, USA). The MP analysis was performed with a heuristic search using 1000 random addition sequences and tree bisection and reconnection (TBR) branch swapping (characters were equally weighted, gaps were treated as missing and character states were unordered). Bootstrapping for the MP analysis (branch lengths shown) comprised heuristic searches and 1000 replicates. Bayesian analyses were conducted in MrBayes 3.2.4 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003; Ronquist et al. 2012) using a restriction site (binary) model of evolution and default priors. The final analysis was run for 150 million generations sampling every 1000 generations, with two parallel runs each with four chains (three hot and one cold). Convergence was considered reached on the basis of the standard deviation of split frequencies (<0.01) and the first 25% of trees were discarded as burn-in.

DArT datasets are considered to follow a Dollo model of evolution (because it is much easier for a DArT marker to be lost than gained; Woodhams et al. 2013). Although Dollo data have been traditionally analysed using parsimony methods (Le Quesne 1974; Farris 1977), it is well known that parsimony does not take into account branch-length information (Woodhams et al. 2013). Therefore, a distance-based phylogenetic approach, which implements the Dollo model of evolution, was used in the present study. A distance matrix of the DArT data (Partitioned Additive Dollo Distance, or PADD) was calculated following the method outlined by Woodhams et al. (2013) and a tree found by minimum evolution in FastME (Desper and Gascuel 2002; Lefort et al. 2015). Branch support was obtained using a bootstrap analysis in PAUP 4.0 b10 (D. L. Swofford). This comprised a heuristic search and 1000 replicates (under the minimum- evolution criterion). Nexus files containing the raw data and all tree files are available on TreeBase at http://purl.org/phylo/treebase/phylows/study/TB2: S18461 (accessed 9 November 2015).

Relationship networks based on the full DArT dataset (2702 markers) were generated in SplitsTree4 (version 4.13.1) (Huson 1998; Huson and Bryant 2006) using the default settings of the software. Relationship networks are implicit representations of evolutionary history that are used to represent agreement and incompatibilities in the dataset (Huson and Bryant 2006). Therefore, use of the full DArT dataset for these analyses was considered appropriate. In a relationship network, the parallel edges

36 indicate splits in the data and allow samples to be assigned to groups, with the longer lines suggesting more support for that particular split (Huson and Bryant 2006). Relationship networks are an effective way of depicting the character conflicts of DArT markers and allow the complexity of the datasets to be visualised (Steane et al. 2011).

2.3.4 Reconstruction of ancestral states and character evolution

To examine patterns and variation in morphology, ancestral reconstructions were performed on the following diagnostic traits: (1) habit (mallee or tree) and (2) leaf width. These parameters were chosen as they are considered important when identifying species in subgenus Eucalyptus in the classifications of Brooker (2000) and Hill (2002). Leaf width was measured at the widest point (following the method of McGowen et al. 2001) from five random leaves per voucher specimen to the nearest millimetre, with a digital Vernier calliper (Kincrome, Melbourne, Victoria, Australia). For Eucalyptus microcodon and E. williamsiana, vouchers from the same population were used for leaf- width measurements (because of the unavailability of leaves from the samples used for DNA analysis). Categories for leaf length and leaf width have not been standardised in eucalypts. However, in the treatment of Hill (2002), E. stricta is described as narrow- leaved (with leaves <10 mm wide), whereas E. burgessiana is described as broad- leaved (leaf width >15 mm). Therefore, the categories used here for leaf width were based on the descriptions of Hill (2002) and divided into narrow (<10 mm), intermediate (10–15 mm) and broad (>15 mm). The contribution of two environmental variables (altitudinal zone and substrate) to the evolutionary diversification of taxa was also investigated. Altitudinal divisions followed the zones defined by Turak et al. (2011) and were classified as follows: coastal and lowland (0–235 m), upland (235– 1065 m) and highland (>1065 m). The substrate observed (sandstone, granite, basalt or rhyolite) was recorded per sample at each site at the time of collection of leaf material for DNA analysis. Ancestral reconstructions of each morphological and environmental parameter were traced onto the Bayesian phylogenetic tree by using MP reconstructions in the Mesquite software package v. 3.03 (W. P. Maddison and D. R. Maddison, see http://mesquiteproject.org, accessed 1 February 2015). The character data matrix is presented in Appendix 2.

2.4 Results

2.4.1 Phylogenetic analysis of DArT markers

The DArT dataset used to produce phylogenetic trees (comprising 1780 markers) consisted of 76 samples (representing 37 taxa), with the proportion of missing data for most samples being less than 5%. Five samples had 5–10% missing data, whereas Eucalyptus regnans had the highest proportion of missing data (22%). The overall topology and groupings of taxa produced from all analyses were similar. The MP analysis recovered two trees, each with a tree length of 15 909, consistency index (CI) of 0.11, and retention index (RI) of 0.34. Of the 1780 markers in the dataset, 1695 were parsimony informative. The strict consensus tree had 74 nodes, 28 of which had bootstrap support (BS) greater than 50% (Fig. 4). Eucalyptus piperita (section Cineraceae) from two locations (Hilltop and Kings Tableland) was sister to the remainder of the taxa in subgenus Eucalyptus (although E. piperita did not form a monophyletic group). The stringybarks (section Capillulus) formed a monophyletic group (84.9% BS), which was sister to a clade comprising the green ash tall trees, E. regnans, E. obliqua and E. fastigata. Eucalyptus regnans and E. obliqua formed a clade (81.7% BS). The clade comprising the stringybarks and the green ash tall trees was sister to the remainder of the blue ashes (section Cineraceae), black sallies (section Longitudinales), the peppermint (section Aromatica) and the majority of the green ashes. The remainder of the blue ashes (apart from E. consideniana from Nowra) formed a monophyletic group comprising three main clades. The first was of E. oreades and E. luehmanniana (100% BS), the second included E. multicaulis, E. sieberi and E. stenostoma (99.3% BS), and the third comprised E. consideniana from Woodford and the scribbly gums (E. haemastoma, E. sclerophylla and E. rossii). Whereas the samples of E. luehmanniana emerged in a monophyletic group (76.9% BS), the samples of other species (namely E. sieberi, E. haemastoma and E. sclerophylla) did not form a clade.

Blue ashes Green ash (SI)

Stringybarks

Tall green ashes (R+E)

Blue ashes

Scribbly gums

Green ash (SR) Black sallies Peppermint Green ashes (C)

Green ashes (SR)

Green ashes (SI)

Blue ash

Green ashes (SI)

Fig. 4. One of two most parsimonious trees (based on 1780 Diversity Arrays Technology (DArT) markers) of the green ashes (section Eucalyptus) and other taxa in subgenus Eucalyptus: the black sallies (section Longitudinales), blue ashes (including the scribbly gums, section Cineraceae), peppermints (section Aromatica) and stringybarks (section Capillulus). Eucalyptus cloeziana (subgenus Idiogenes) is the out- group. Sample codes correspond to those in Table 2 (Column 6). Series and subseries (Brooker 2000) within the green ashes are shown: series Regnantes (R), Eucalyptus (E), Strictae subseries Regulares (SR), Strictae subseries Irregulares (SI) and Contiguae (C). Node numbers represent bootstrap values greater than 50 %.

With the exception of Eucalyptus apiculata from Hilltop, the remainder of the green ashes, the black sallies, E. radiata and E. consideniana from Nowra formed a clade. Within this group were two main clades. The first comprised E. codonocarpa, E. approximans, E. microcodon, E. cunninghamii, E. kybeanensis, E. paliformis, E. radiata and the black sallies. Eucalyptus codonocarpa from all locations was monophyletic (89.8% BS), as were E. cunninghamii (94.2% BS) and E. kybeanensis (98.9% BS). The black sallies formed a monophyletic clade (99.9% BS); however, within this group, E. copulans from all locations and E. moorei from all locations did not form separate clades.

The second main clade included E. spectatrix, E. consideniana from Nowra and most of the green ash taxa from the Sydney region and Greater Blue Mountains World Heritage Area (GBMWHA). Eucalyptus spectatrix from both locations was monophyletic (89.3% BS), as was E. dendromorpha from Fitzroy Falls and Redhills Road, and E. burgessiana from three locations in the GBMWHA (Linden, Springwood and Faulconbridge). The three E. langleyi samples, E. dendromorpha from the Princes Rock track (Wentworth Falls, GBMWHA) and E. consideniana formed a clade. With the exception of E. stricta from Blackheath and Mount Banks, all other E. stricta populations emerged in a clade that also included E. apiculata from Woodford, E. laophila from Lithgow and Wollemi National Park, and E. dendromorpha from Mount Banks. Eucalyptus apiculata from Hilltop was separate from the other green ash taxa (being sister to all other taxa with the exception of E. piperita).

Bayesian analyses produced a phylogeny with 70 nodes, 49 of which had Bayesian posterior probability (PP) greater than 0.95 (Fig. 5). As in the MP analysis, E. piperita from Hilltop was sister to the remainder of taxa in subgenus Eucalyptus, and the green ash tall trees (E. regnans, E. obliqua and E. fastigata) formed a clade separate from the other green ashes (PP: 0.99). In contrast to the MP analysis, the remainder of the blue ashes were not monophyletic. However, as with the MP analysis, E. luehmanniana and E. oreades formed a monophyletic group (PP: 1), as did samples of E. multicaulis, E. sieberi and E. stenostoma (PP: 1). The remainder of the green ash taxa formed a clade with the black sallies and E. radiata (section Aromatica). This clade was split into the same two main groups as in the MP analysis. However, in contrast to the MP analysis, E. apiculata from Hilltop was grouped with E. spectatrix from southern New South

Wales and the majority of green ash taxa from the Sydney region and GBMWHA (PP: 1). Also, unlike the MP analysis, all the E. burgessiana samples formed a monophyletic group (PP: 0.97); the E. langleyi samples used formed a monophyletic group (PP: 0.99); and the blue ash from Nowra, E. consideniana, was not grouped with E. langleyi, but was in a clade with the rest of the blue ashes.

The minimum evolution tree produced in FastME from the PADD data had 74 nodes (38 of which had BS greater than 50%, Appendix 3). In contrast to the Bayesian and MP analyses, the two E. piperita samples were monophyletic (63.9% BS). Also, unlike the Bayesian and MP analyses, the stringybarks were sister to the remainder of taxa in subgenus Eucalyptus. However, the groupings of most taxa in this tree were similar to those in the Bayesian and MP trees. For example, as with the Bayesian and MP analyses, the green ash tall trees (E. regnans, E. fastigata and E. obliqua) were separate from the remainder of the green ashes (which formed a clade comprising the same two major groups). As in the Bayesian tree (but unlike the MP analysis), E. apiculata from Hilltop was sister to E. spectatrix and the green ashes from the Sydney region and GBMWHA. However, in contrast to the Bayesian tree (but like in the MP analysis), E. consideniana from Nowra was grouped with E. langleyi and E. dendromorpha from Princes Rock track (Wentworth Falls). Like in the Bayesian tree and unlike the MP tree, all samples of E. burgessiana were monophyletic.

Two relationship networks were generated using SplitsTree4, one including all taxa in subgenus Eucalyptus and the other comprising the green ash taxa only. In the relationship network comprising all samples, taxa formed the same broad groups as in the MP, Bayesian and PADD analyses (Appendix 4). The relationship network comprising the green ash taxa only (Fig. 6) was also largely in agreement with the MP, Bayesian and PADD analyses, and allowed geographic differentiation among taxa to be visualised (with the clustering of northern New South Wales and southern Queensland taxa and the clustering of southern New South Wales and northern Victorian taxa). The taxa from the Sydney region and GBMWHA generally clustered together, although Eucalyptus spectatrix from southern New South Wales was nested within this group (as indicated by all phylogenetic analyses). As with the Bayesian and PADD phylogenies, and in contrast to the MP analysis, E. apiculata from Hilltop was grouped with the other green ash taxa from the Sydney region and GBMWHA.

Scribbly gums Blue ashes

Tall green ashes (R+E)

Stringybarks

Green ash (SR) Black sallies Peppermint Green ashes (C)

Green ashes (SR)

Green ashes (SI)

Fig. 5. Bayesian 50 % majority consensus tree (based on 1780 Diversity Arrays Technology (DArT) markers) of the green ashes (section Eucalyptus) and other taxa in subgenus Eucalyptus: black sallies (section Longitudinales), blue ashes (including the scribbly gums, section Cineraceae), peppermints (section Aromatica) and stringybarks (section Capillulus). Eucalyptus cloeziana (subgenus Idiogenes) is the out-group. Sample codes correspond to those in Table 2 (Column 6). Series and subseries (Brooker 2000) within the green ashes are shown: series Regnantes (R), Eucalyptus (E), Strictae subseries Regulares (SR), Strictae subseries Irregulares (SI) and Contiguae (C). Node values are Bayesian posterior probability (PP) values. 42

Southern NSW medium tree Southern NSW to northern Vic. mallees GBMWHA mallee

Tall trees from NSW and Vic. Northern NSW to southern Qld. mallees

Sydney region and GBMWHA GBMWHA medium mallee trees and mallees

Southern NSW mallees

Sydney region and GBMWHA mallees

Fig. 6. Network generated by SplitsTree4 (version 4.13.1) showing relationships among the green ashes (based on 2702 Diversity Arrays Technology (DArT) markers). Sample codes correspond to those in Table 2 (Column 6). Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area; NSW, New South Wales; Qld., Queensland; Vic., Victoria. Scale bar shows uncorrected P genetic distance equivalent to 0.01.

2.4.2 Reconstruction of ancestral states and character evolution

Overall, the Bayesian analysis produced a phylogeny that was most consistent with the current taxonomy of subgenus Eucalyptus (Brooker 2000). In the MP tree, E. apiculata from Hilltop was separate from the other green ashes and closer to the blue ash, E. piperita. Similarly in both the PADD and MP analyses, the blue ash, E. consideniana, from Nowra, was grouped close to the green ash, E. langleyi. Therefore, the Bayesian topology was selected for ancestral reconstructions. The distribution of growth habit showed a marked dichotomy in the Bayesian 50% majority consensus phylogeny (Fig. 7). The deeper diverging clades of the phylogeny (and the ancestral habit of the green ashes) was reconstructed as the tree form. Eucalyptus luehmanniana and E. multicaulis represented the only change to mallee form in the blue ash group and E. deuaensis was the only change to mallee form in the stringybark group. Within the clades comprising the majority of mallees, there were very few reversions to the tree form. For example, E. triflora and E. dendromorpha (from Fitzroy Falls and Redhills Road) represented the only reversions to tree form in the clade comprising most of the Sydney and GBMWHA taxa. Patterns in leaf width were not as marked on the phylogeny as growth form, although narrow leaves appeared only in the clade comprising the green ash mallees, peppermints and black sallies. Nevertheless, there were many reversions in this group to intermediate and broad leaves.

There was some congruence between clades on the Bayesian 50% majority consensus tree and environmental parameters. The majority of taxa studied occurred in upland habitats on sandstone, with some clades occupying lowland and coastal habitats on sandstone (e.g. E. langleyi and E. luehmanniana) and other clades occurring in upland or highland habitats on granite (taxa from northern New South Wales, southern Queensland, southern New South Wales and northern Victoria). The green ash tall trees (E. regnans, E. obliqua and E. fastigata) differed from all other groups being a clade on basalt, as did E. deuaensis, which was the only taxon to occur on rhyolite.

A B

Fig. 7. Ancestral state reconstruction of taxa in subgenus Eucalyptus: A. Habit. B. Leaf width. C. Altitudinal zones. D. Substrate. Leaf width was divided into narrow (<10 mm), intermediate (10–15 mm) and broad (>15 mm). Altitudinal zones are defined as coastal and lowland (0–235 m), upland (235–1065 m) and highland (≥1065 m). Ancestral states were traced on the Bayesian phylogeny (Fig. 5) using maximum parsimony in Mesquite ver. 3.03. Sample codes correspond to those in Table 2 (Column 6).

C D

Fig. 7. (continued)

2.5 Discussion

2.5.1 Phylogenetic relationships and the monophyly of the green ashes

Bayesian, MP and PADD analyses produced phylogenies with similar topologies and groupings of taxa. The phylogenies produced here were more resolved than were previous phylogenies of subgenus Eucalyptus using traditional one-region sequence

46 data (e.g. Steane et al. 1999, 2002; Bayly and Ladiges 2007). These findings demonstrate that phylogenetic analyses based on DArT markers can provide insights into evolutionary relationships among closely related species and groups that are taxonomically challenging. Although only a few taxa from the present study were included in more recent phylogenies, the relationships found here were generally consistent with the findings of Steane et al. (1999, 2002, 2011, for the relationship of Eucalyptus obliqua and E. regnans) and Bayly and Ladiges (2007, for the close relationship of E. triflora, E. spectatrix and E. paliformis). The results from the present study support many of the relationships proposed by Ladiges et al. (1989), Hill (2002) and Brooker (2000). The clade comprising E. williamsiana, E. deuaensis and E. caliginosa is consistent with Brooker’s (2000) section Capillulus and Hill’s (2002) stringybarks, whereas the close relationship of E. regnans and E. fastigata, and the relationship of the E. approximans–codonocarpa–microcodon clade with E. cunninghamii and E. paliformis is congruent with Ladiges et al. (1989) and Brooker (2000).

In the present study, the green ashes (subgenus Eucalyptus section Eucalyptus) as circumscribed by Brooker (2000) did not form a monophyletic group. The separation of the green ash tall trees from the remainder of the green ashes is in contrast to Brooker (2000) and Ladiges et al. (1989). The blue ashes (section Cineraceae) in the present study were polyphyletic, which disagrees with the classifications of Brooker (2000) and Hill (2002). Similarly, the positions of the black sallies (section Longitudinales) and E. radiata were unexpected. The monophyly of E. cunninghamii from both locations, E. kybeanensis from all locations and E. luehmanniana from both locations support the species circumscriptions of Brooker (2000). However, many taxa from different locations (e.g. E. haemastoma and E. sclerophylla and the majority of green ashes from the Sydney region and GBMWHA) do not appear to fit into the species delimitations of Ladiges et al. (1989), Hill (2002) or Brooker (2000). Consequently, these results highlighted the need for a potential revision of the infrageneric ranking of the green ashes, blue ashes, black sallies and peppermints. The implications of the findings from the present study to the taxonomy and classification of subgenus Eucalyptus are discussed in further detail below.

2.5.2 Gene flow and hybridisation

Some of the relationships found in the present study differed from a chloroplast genome study on eucalypts (Bayly et al. 2013), in which E. obliqua and E. radiata formed a monophyletic group, and E. sieberi and E. elata also formed a monophyletic group with E. regnans, all within the ‘Monocalypt’ clade (=subgenus Eucalyptus). Numerous studies have highlighted the issue of incongruence between phylogenies based on chloroplast and nuclear DNA (e.g. Soltis and Kuzoff 1995; Kim and Donoghue 2008; Wang et al. 2011; Yu et al. 2013; Govindarajulu et al. 2015) and, therefore, differences between the findings of Bayly et al. (2013) and the present study (based on DArT markers, which are predominantly nuclear) are not surprising. McKinnon et al. (1999) found extensive sharing of chloroplast DNA haplotypes among sympatric species from subgenus Eucalyptus in Tasmania, which showed a clear correlation with geographic patterns rather than phylogenetic relationships. Consequently, analyses using uni- parentally inherited markers alone may confound phylogenetic reconstruction in groups that frequently hybridise (McKinnon et al. 1999; Bayly and Ladiges 2007). However, McKinnon et al. (2010) found that, although E. globulus and E. cordata maintained strongly differentiated nuclear gene pools, leakage of nuclear DNA did occur between the two species (although cpDNA sharing was much more extensive).

In the present study, some of the relationships found were indicative of hybridisation and introgression between lineages. For example, in the PADD tree, the E. obstans sample from Jervis Bay was in a clade with E. langleyi (from which it is morphologically distinct but geographically proximate). In the MP and PADD analyses, one sample of the blue ash, E. consideniana, appeared in the same clade as E. langleyi from the same location (although it was grouped with the other blue ashes in the Bayesian phylogeny). Similarly, whereas the sample of E. apiculata from Hilltop was grouped with the other green ashes in the Bayesian and PADD trees, in the MP analysis it was separate from the remainder of the green ashes and closer to E. piperita. Comparisons made between this specimen and a specimen at the National Herbarium of New South Wales (Chippendale 1002, NSW327081) recorded as a likely hybrid between E. apiculata and E. piperita (from Berrima, which is geographically close to Hilltop) revealed similarities in leaf colour, shape and size. In addition, the fruit shape from the specimen used in the present study was more spherical, suggesting that it may

48 be the result of hybridisation with E. piperita. Patterns of morphological variation and introgression of eucalypt species in Tasmania indicate that reticulate evolution occurred between divergent lineages during the Quaternary (McKinnon et al. 2004), and Hager and Benson (2010) suggested that such processes are likely to have played a major role in the evolutionary history of the green ashes of the GBMWHA. Future studies should, therefore, use both chloroplast and nuclear genomes to explore the role that reticulate evolution may have played in the evolution of this group. Ecological and phenological studies (e.g. differences in flowering time) focussing on sympatric populations and taxa may also provide insights into patterns of gene flow and hybridisation.

2.5.3 Classification, morphology and the issue of misidentification

Historically, species within subgenus Eucalyptus have been difficult to identify because many of the subgroups have few obvious distinguishing synapomorphic morphological characters (McKinnon et al. 1999). The green ashes exemplify this, with the majority of species being distinguished on the basis of characters such as leaf length, leaf width, fruit size and bud size (characters that can be variable across large geographic areas). In the present study, those taxa that have notably distinctive morphological traits or are geographically isolated tend to form well supported clades. For example, E. cunninghamii is easily identified on the basis of its small, soft-textured, silvery-green leaves, E. kybeanensis is distinguished on the basis of its conical or hemispherical fruits and sessile buds, whereas the E. approximans clade (including E. codonocarpa, and E. microcodon) is geographically disjunct.

However, with the exception of E. cunninghamii, the morphological traits used for species identification in the Sydney region and GBMWHA (such as leaf length and width) often overlap between taxa (the ancestral reconstructions of leaf width in the present study highlighted such overlaps between taxa). Furthermore, previous studies have demonstrated that such morphological traits can be highly plastic. For example, in Nothofagus cunninghamii, it was found that although leaf length and width partially depended on genotype, there was a significant effect of environmental factors on morphology (leaves became smaller and thicker with increasing altitude, Hovenden and Vander Schoor 2004). In the case of the green ashes, many taxa that are difficult to identify on the basis of morphology alone can be assigned to a particular taxon on the

49 basis of geographical location. For example, E. laophila and E. apiculata from the GBMWHA are often distinguished on the basis of the elevation at which they occur (E. laophila is considered to occur at higher altitudes than is E. apiculata). Species definitions that are in large part based on geographical location have likely led to misidentifications, which is an obvious issue in phylogenetic reconstructions. Another problem with such taxa (which are synonymous in Brooker’s (2000) classification) is that there is the possibility that they are the one highly plastic species that has been distinguished on the basis of morphological differences that are not useful in species delimitations. However, although some taxa from the present study (e.g. E. copulans and E. moorei) would become a monophyletic clade if re-labelled according to the classification of Brooker (2000), re-labelling samples used for other taxa (e.g. E. apiculata and E. laophila) does not make them monophyletic in the MP, PADD or Bayesian phylogenies. Many studies have highlighted the importance of comparing physiology and anatomy with phylogenetic information to better understand evolutionary diversification in both plants and animals (e.g. Ackerly et al. 2000; Garland et al. 2005; Hodson et al. 2005). The relationship between genetic variation and physiological and anatomical traits of seedlings, juvenile and adult plants may, therefore, provide insights into the evolution of green ash taxa in the Sydney region and GBMWHA and should be the focus of future studies.

2.5.4 Geography, substrate and evolutionary models

Although the majority of taxa in the present study occupy upland habitats on sandstone, the ancestral reconstructions support the hypothesis of radiation of the green ashes and other taxa in subgenus Eucalyptus into a multitude of habitats, such as lowland and coastal habitats on sandstone, upland and highland habitats on granite, and upland habitats on basalt. There was also a correlation between habit and substrate (e.g. the tall green ash trees, Eucalyptus regnans, E. fastigata and E. obliqua, were found on basalt, whereas the smaller trees and mallees were found on sandstone or granite). The relationship networks in the present study indicated geographic structuring of many taxa and indicated that there is likely to be recombination, hybridisation and introgression. Previous studies have discussed the possibility that evolution in many eucalypts may not necessarily have been divergent (Chappill and Ladiges 1996; McKinnon et al. 2008)

50 and that speciation in both plants and animals can occur during partial reproductive isolation (Wu 2001; Lexer and Widmer 2008; Mallet 2005). Although more traditional evolutionary models assume a tree, it is well known that more complex evolutionary scenarios (such as rapid radiation and reticulate evolution) are poorly described by these models (Huson and Bryant 2006; Morrison 2014). Phylogenetic networks, which allow horizontal reticulation events as well as vertical processes to be visualised, are increasingly being recognised as providing a more comprehensive picture of evolutionary history (Francis and Steel 2015). In the present study, the relationship networks suggested a complex pattern of evolution in the green ashes and closely related eucalypts. The role of environmental parameters (especially substrate and soil type) in the evolutionary diversification of these groups should be investigated. A detailed population-genomic study targeting taxa in the Sydney region and GBMWHA will also be required to better understand the complexity of evolution in the green ashes and to clarify species boundaries.

2.5.5 Consequences for the classification of Eucalyptus subgenus Eucalyptus

The classification of Brooker (2000) and draft scheme of Nicolle (2015) are largely in agreement with regard to the groupings of species considered in the present study (see Appendix 5 for a direct comparison between the two classifications). The major difference is in the ranking; Brooker (2000) recognised several named sections, whereas Nicolle (2015) included the same species in a single section, section Eucalyptus, divided into several series, most of which correspond with Brooker’s groupings. The analyses presented here suggest that some of these groupings should be revised. In the case of E. deuaensis, both Brooker and Nicolle placed this taxon in a series separate from series Pachyphloiae (Appendix 5), the stringybarks, but the MP, Bayesian and PADD trees clearly placed E. deuaensis within the stringybark group as sister to E. caliginosa. The series Psathyroxylon is supported as monophyletic if the monotypic series, series Stenostomae, is included. In all analyses, the sole species in this series, E. stenostoma, is consistently strongly associated (>99% BS, PP: 1) with some species of the subseries Considenianae. The position of E. consideniana itself is problematic, with the different analyses suggesting divergent affinities for the two accessions included, possibly as a result of gene flow from other species in subgenus Eucalyptus. The

51 scribbly gums (E. haemastoma, E. sclerophylla and E. rossii), subseries Haemastomae, are well supported as monophyletic in the Bayesian analysis (PP: 1), with E. rossii indicated as sister to the other species (E. rossii is also sister to the other scribbly gums in the MP analysis, although not in the PADD tree).

All analyses indicated that series Strictae, as recognised by both Brooker (2000) and Nicolle (2015), is not monophyletic and that the rank of the two included subseries should be revised because only subseries Irregulares sensu Brooker is monophyletic, whereas subseries Regulares is paraphyletic and not unambiguously sister to subseries Irregulares. The placement of E. cunninghamii differs between their classifications; both included it within series Strictae (Appendix 5), Nicolle included it in subseries Irregulares with E. stricta and its allies, whereas Brooker placed it in subseries Regulares with E. approximans and allied species. In the present study, all analyses (>70% BS, PP: 1) agreed with Brooker’s placement. A member of Nicolle’s subseries Regulares is E. kybeanensis, which Brooker considered to be a member of the monotypic series Contiguae. Here, also, the phylogeny supports Brooker’s position; the three accessions of E. kybeanensis form a well-supported clade sister to the peppermint, E. radiata, rather than to other taxa from subseries Regulares. A fourth species, E. paliformis, is included by both Brooker and Nicolle in series Strictae subseries Regulares. In this case, the MP and Bayesian analyses suggested that this species is sister to a clade that includes not only other members of subseries Regulares, but also the peppermint, E. radiata, and the black sallies. A fifth species, E. spectatrix, was not recognised by Brooker, but it was included in series Strictae subseries Irregulares by Nicolle. In all of our analyses, E. spectatrix received strong support as a distinct species, even though most other species, with the possible exception of E. langleyi (monophyletic in the Bayesian tree, but not in the MP and PADD analyses) and E. burgessiana (monophyletic in the Bayesian and PADD trees, but not in the MP analysis), did not appear monophyletic. Eucalyptus langleyi and other taxa from the Sydney region and GBMWHA are the focus of ongoing research (S. Rutherford, P.G. Wilson, M. Rossetto and S.P. Bonser, unpubl. data).

2.6 Conclusions

Phylogenetic analysis of DArT markers recovered trees that were consistent with previous phylogenies of subgenus Eucalyptus based on sequence data, with many relationships supporting those from previous classifications. However, some relationships, particularly of taxa in the Sydney region and GBMWHA, were not consistent with previous classifications, highlighting the need for a revision of the green ashes and other taxa in subgenus Eucalyptus. As with many eucalypts, relationships in the green ashes have been defined on the basis of quantitative characters such as leaf length, leaf width, fruit size and bud size, as well as geographic location. However, the results here suggest that some morphological traits may not necessarily be reflective of evolutionary relationships within and among taxa. Defining species boundaries on the basis of geographic location is likely to be equally problematic. A detailed population genomic study focussing on taxa from the Sydney region and GBMWHA is required to better understand patterns of gene flow, species boundaries and the evolutionary history of the group.

Acknowledgements

We are very grateful to our colleagues at the Royal Botanic Garden Sydney (RBG) for assistance with the collections, including Doug Benson, Andrew Orme, Bob Coveny, Michael Elgey and Trevor Wilson; and with the DNA extractions, including Carolyn Connelly, Margaret Heslewood, Juelian Siow, Marlien van der Merwe and Hannah McPherson. We also thank Tracy Armstrong (Australian Botanic Garden, Mount Annan) and Rusty Worsman (formerly Blue Mountains Botanic Garden, Mount Tomah) who facilitated access to cultivated plants; and RBG volunteers, Aaron Smith for assistance in the field and Danca Ciric for providing a specimen of E. luehmanniana. We are grateful to Dean Nicolle for providing leaf material of E. regnans, as well as Jason Carling, Cina Vipin, Vanessa Caig and Andrzej Kilian from Diversity Arrays Technology Pty Ltd who gave advice and technical support for a modified CTAB extraction for DArTs. We also thank Dorothy Steane (University of Tasmania) who provided advice on various aspects of this research, particularly with the analysis of DArT markers. Collecting in New South Wales operated under the Royal Botanic Gardens and Domain Trust (New South Wales). Collections in Snowy River and Alpine

National Park, Victoria, and in Girraween National Park, Queensland, operated under Scientific Licence numbers 10006635 and WITK12361813 respectively. This research was funded by ARC Linkage Grant LP110100721. S. Rutherford is in receipt of an Australian Postgraduate Award. We also thank three anonymous reviewers for providing comments that improved the manuscript.

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Appendix 1. Collection details of taxa sampled showing collector number and name, location, accession number (if cultivated) and voucher number(s) Column 1 follows the taxonomic hierarchy of Brooker (2000), although species names follow those of Hill (1991, 2002). All vouchers were lodged at the National Herbarium of New South Wales unless otherwise stated. Abbreviations: ABG, Australian Botanic Garden (Mount Annan); AD, State Herbarium of South Australia; AO, A. Orme; BMBG, Blue Mountains Botanic Garden (Mount Tomah); CCA, Currency Creek Arboretum; cult., cultivated; DC, D. Ciric; DN, D. Nicolle; GBMWHA, Greater Blue Mountains World Heritage Area; JB, J. Benson; NSW, New South Wales; Qld., Queensland; RBG, Royal Botanic Garden Sydney; SA, South Australia; SR, S. Rutherford; SW, south-west; TW, T. Wilson; Vic., Victoria

Taxon Source Subgenus Eucalyptus Section Aromatica Brooker Series Radiatae Chippendale E. radiata Sieber ex DC. SR100, Katoomba (GBMWHA, NSW), NSW905807, NSW905808

Section Capillulus Brooker Series Pachyphloiae Blakely E. caliginosa Blakely & McKie SR163, cult. (RBG), Accession No. 16211, NSW4138578 E. oblonga DC. SR32, Stanwell Tops (NSW), NSW901027, NSW901028 E. sparsifolia Blakely SR8, Linden (GBMWHA, NSW), NSW900784, NSW900785 E. williamsiana L.A.S.Johnson & K.D.Hill JB2912, Queanbeyan (NSW), NSW931179, JB2909, Queanbeyan (NSW), NSW888063 Series Limitares Brooker E. deuaensis Boland & Gilmour SR162, Northeast of Mongamulla Mountain (NSW), cult. (RBG), Accession No. 841864, NSW4112513

Section Cineraceae Brooker Series Fraxinales Blakely E. luehmanniana F.Muell. SR27, Sir Bertram Stevens Drive (Royal National Park, NSW), NSW900953, NSW900954, NSW900955; DC s.n. (NSW971562), Karloo Track (Royal National Park, NSW) E. oreades R.T.Baker SR101, Katoomba (GBMWHA, NSW), NSW905809

Series Psathyroxylon Blakely Subseries Considenianae Brooker & Slee E. consideniana Maiden SR52, Braidwood Road (Nowra, NSW), NSW901228, NSW901230; SR67, Woodford (GBMWHA, NSW), NSW902351, NSW902361 E. multicaulis Blakely SR3, Linden (GBMWHA, NSW), NSW897519, NSW897520 E. sieberi L.A.S.Johnson SR11, Wentworth Falls (GBMWHA, NSW), NSW900793; SR45, Hilltop (NSW), NSW901213, NSW901214 Subseries Haemastomae Brooker E. haemastoma Sm. SR24, Beacon Hill (Sydney, NSW), NSW900884, NSW900885; SR28, Royal National Park (NSW), NSW900956, NSW900957

E. rossii R.T.Baker & H.G.Sm. SR42, Wollemi National Park (NSW), NSW901061 E. sclerophylla (Blakely) L.A.S.Johnson & Blaxell SR6, Linden (GBMWHA, NSW), NSW897521, NSW897522; SR53, Braidwood Road (Nowra, NSW), NSW901231, NSW901232 Series Stenostomae Brooker E. stenostoma L.A.S.Johnson & Blaxell SR156, Wadbilliga National Park (NSW), NSW971600, NSW971601 Series Piperitales Blakely E. piperita Sm. SR18, Kings Tableland (GBMWHA, NSW), NSW900858; SR44, Hilltop (NSW), NSW901211, NSW901212

Section Eucalyptus Series Regnantes Chippendale E. fastigata H.Deane & Maiden SR33, Mount Tomah (GBMWHA, NSW), NSW901039 E. regnans F.Muell. DN4316, Great Ocean Road (Vic.), cult. (CCA, SA), AD164457

Series Eucalyptus E. obliqua L'Hér. SR50, Mount Murray (NSW), NSW901221, NSW901222

Series Strictae L.A.S.Johnson ex Brooker Subseries Irregulares Brooker E. apiculata R.T.Baker & H.G.Sm. SR46, Hilltop (NSW), NSW901216; SR69, Woodford (GBMWHA, NSW), NSW902380; SR129, Berrima (NSW), cult. (ABG), Accession No. 903465, NSW4120313 E. burgessiana L.A.S.Johnson & Blaxell SR4, Linden (GBMWHA, NSW), NSW897458, NSW897465; SR62, Warrimoo (GBMWHA, NSW), NSW902178, NSW902180; SR106, Springwood (GBMWHA, NSW), NSW905815; SR107, Faulconbridge Point (GBMWHA, NSW), NSW905816 E. dendromorpha (Blakely) L.A.S.Johnson & Blaxell SR12, Princes Rock Track (Wentworth Falls, GBMWHA, NSW), NSW900795, NSW900797; SR13, Main falls (Wentworth Falls, GBMWHA, NSW), NSW900798, NSW900799; SR36, Mount Wilson (GBMWHA, NSW), NSW901045; SR47, Redhills Road (Fitzroy Falls, NSW), NSW901217; SR49, Fitzroy Falls (NSW), NSW901220; SR73, Blackheath (GBMWHA, NSW), NSW902476, NSW902477; SR121, Mount Banks (GBMWHA, NSW), NSW970903 E. langleyi L.A.S.Johnson & Blaxell SR51, Braidwood Road (Nowra, NSW), NSW901226; SR91, Parma Creek Fire Road (Nowra, NSW), NSW904641; SR94, Braidwood Road (17 km SW Nowra, NSW), NSW904644 E. laophila L.A.S.Johnson & Blaxell SR39, Wollemi National Park (NSW), NSW901056, NSW901057; SR83, Kings Tableland (GBMWHA, NSW), NSW904368; SR125a, Lithgow (NSW), NSW984456; TW429, Garden of Stone National Park (NSW), NSW906163 E. obstans L.A.S.Johnson & K.D.Hill SR21, Beacon Hill (Sydney, NSW), NSW900872,

NSW900873, NSW900874; SR25, Royal National Park (NSW), NSW900947, NSW900949, NSW900950; SR85a, Jervis Bay (NSW), NSW904635 E. spectatrix L.A.S.Johnson & Blaxell SR136, Doctor George Mountain (NSW), NSW971575; SR160, Wadbilliga National Park (NSW), NSW971605 E. stricta Sieber ex Spreng. SR9, Little Switzerland Track (Kings Tableland, GBMWHA, NSW), NSW900786, NSW900787; SR16, Tableland Road (Kings Tableland, GBMWHA, NSW), NSW900847, NSW900853; SR30, Stanwell Tops (NSW), NSW901022; SR37, Newnes Plateau (NSW), NSW901047, NSW901049; SR55, Sassafras (NSW), NSW901235; SR75, Blackheath (GBMWHA, NSW), NSW902556; SR98, Katoomba (GBMWHA, NSW), NSW905718; SR123a, Mount Banks (GBMWHA, NSW), NSW984458 E. triflora (Maiden) Blakely SR127, Nerriga (NSW), cult. (ABG), Accession No. 861018, NSW4124002

Subseries Regulares Brooker E. approximans Maiden SR114, Barren Mountain (NSW), cult. (BMBG), Accession No. 872906, NSW707119 E. codonocarpa Blakely & McKie SR109, Waratah Trig (NSW), NSW906600, NSW906601; SR112, Warra National Park (NSW), NSW906606; SR131, Girraween National Park (Qld.), NSW970972 E. cunninghamii G.Don SR104, Kings Tableland (GBMWHA, NSW), NSW905813; SR 118a, Mount Banks (GBMWHA, NSW), NSW984419 E. microcodon L.A.S.Johnson & K.D.Hill AO1054, Woodendong (NSW), NSW931184; AO1052, Woodendong (NSW), NSW848075 E. paliformis L.A.S.Johnson & Blaxell SR139, Wadbilliga National Park (NSW), NSW971580

Series Contiguae Brooker & Slee E. kybeanensis Maiden & Cambage SR143, Wadbilliga National Park (NSW), NSW971584; SR147, Kosciusko National Park (NSW), NSW971591; SR151, Snowy River National Park (Vic.), NSW971595

Section Longitudinales (Blakely) Brooker E. copulans L.A.S.Johnson & K.D.Hill SR164, Jamison Creek, Wentworth Falls (GBMWHA, NSW), cult. (RBG), Accession No. 961650, NSW4211706; AO1049, Darwins Track (Wentworth Falls, GBMWHA, NSW), NSW973317 E. moorei Maiden & Cambage AO1048, Darwins Track (Wentworth Falls, GBMWHA, NSW), NSW973316; AO1051, Wentworth Falls Lake (GBMWHA, NSW), NSW973329

Subgenus Idiogenes L.D.Pryor & L.A.S.Johnson ex Brooker E. cloeziana F.Muell. SR181, cult. (RBG), Accession No. 811164, NSW4138580

Appendix 2. Character matrix used for ancestral reconstructions Sample codes correspond to those in Table 2 (Column 6). Location details unavailable for E. cloeziana and latitude and longitude values unavailable for E. caliginosa

Mean leaf Latitude (S) Longitude (E) Code Habit width Altitude Substrate (mm) (m) apiculata Hill Mallee 8.50 600 Sandstone 34° 19' 28.8" 150° 28' 23.5" apiculata Wood Mallee 8.16 589 Sandstone 33° 45' 19.3" 150° 29' 8.3" apiculata Berr Mallee 5.26 660 Sandstone 34° 29’ 15” 150° 15’ 55” approximans Mallee 6.25 1430 Granite 30° 24’ 0” 152° 29’ 45” burgessiana Lind Mallee 17.20 584 Sandstone 33° 41' 44.7" 150° 29' 27" burgessiana Warri Mallee 18.34 295 Sandstone 33° 44' 23.9" 150° 35' 2.2" burgessiana Sprin Mallee 19.46 433 Sandstone 33° 39' 20.6" 150° 33' 25.2" burgessiana Faul Mallee 17.93 445 Sandstone 33° 37' 1.7" 150° 33' 55.3" codonocarpa Wara Mallee 12.00 1180 Granite 29° 29' 45.2'' 152° 18' 24.9" codonocarpa Warr Mallee 13.27 1263 Granite 29° 59' 3.8" 151° 55' 30.3" codonocarpa Girr Mallee 12.85 1232 Granite 28° 51' 47.9" 151° 57' 34.8" cunninghamii Bank Mallee 4.87 960 Sandstone 33° 35' 4.9" 150° 22' 4.5" cunninghamii King Mallee 4.63 794 Sandstone 33° 46' 15.1" 150° 22' 33.7" dendromorpha Went P Mallee 11.81 833 Sandstone 33° 43' 33.9" 150° 32' 13.6" dendromorpha Went M Mallee 16.95 846 Sandstone 33° 43' 39.6" 150° 22' 29.5" dendromorpha Wils Mallee 17.38 1004 Sandstone 33° 31' 15.7" 150° 22' 14.8" dendromorpha Redh Tree 17.30 635 Sandstone 34° 38' 47.0" 150° 26' 10.3" dendromorpha Fitz Tree 21.33 658 Sandstone 34° 38' 54.1" 150° 28' 47.1" dendromorpha Blac Mallee 20.68 960 Sandstone 33° 37' 40.4" 150° 18' 42" dendromorpha Bank Mallee 17.38 963 Sandstone 33° 35' 5.8" 150° 22' 6.1" fastigata Tree 11.83 992 Basalt 33° 32' 57.9" 150° 25' 26.4" kybeanensis Wadb Mallee 10.84 1338 Granite 36° 20' 31.3" 149° 36' 7.3" kybeanensis Kosc Mallee 11.39 1465 Granite 36° 21' 23.7" 148° 24' 35.6" kybeanensis Snow Mallee 9.05 1198 Granite 37° 6' 27.8" 148° 11' 46.6" langleyi Brai (Nowra) Mallee 25.46 224 Sandstone 34° 58' 25.3" 150° 29' 40.2" langleyi Parm Mallee 24.67 220 Sandstone 34° 59' 30.6" 150° 29' 13.3" langleyi Brai (17km SW Mallee 23.79 235 Sandstone 35° 0' 22.4" 150° 28' 35.8" Nowra) laophila Lith Mallee 7.83 1114 Sandstone 33° 29' 55.3" 150° 9' 59.2" laophila Woll Mallee 7.51 939 Sandstone 33° 15' 23.7" 150° 13' 7.3" laophila King Mallee 6.96 866 Sandstone 33° 43' 57.8" 150° 22' 22.3" laophila Gard Mallee 8.45 c. 1000 Sandstone 33° 16’ 16” 150° 5’ 58” microcodon Mallee 10.56 960‒970 Granite 28° 22’ 2” 152° 45’ 47” obliqua Tree 24.9 625 Basalt 34° 33' 33.5" 150° 38' 22' obstans Beac Mallee 10.82 135 Sandstone 33° 44' 34.8" 151° 15' 35.6" obstans Roya Mallee 19.92 120 Sandstone 34° 7' 16.4" 151° 4' 31.7" obstans Jerv Mallee 17.79 50 Sandstone 35° 0' 30.5" 150° 49' 51.9" paliformis Tree 9.8 1305 Sandstone 36° 20' 30.8" 149° 35' 47.1" regnans Tree 30.99 330‒500 Basalt 38° 45' 32" 143° 35' 51" spectatrix Geor Mallee 12.7 323 Granite 36° 39' 34.4" 149° 54' 19.5" spectatrix Wadb Mallee 14.31 277 Granite 36° 35' 17.6" 149° 41' 21.6" stricta King L Mallee 10.36 853 Sandstone 33° 44' 15.6" 150° 22' 21.9" stricta King T Mallee 9.37 843 Sandstone 33° 45' 17.6" 150° 22' 32.7" stricta Stan Mallee 9.06 328 Sandstone 34° 12' 39.7" 150° 57' 20.4" stricta Newnes Mallee 8.44 1183 Sandstone 33° 27' 10.4" 150° 13' 53.1" stricta Sass Mallee 14.41 738 Sandstone 35° 04' 22" 150° 12' 24.2" stricta Blac Mallee 9.79 906 Sandstone 33° 37' 57" 150° 18' 48"

67 stricta Kato Mallee 6.98 965 Sandstone 33° 44' 3.7" 150° 16' 56.2" stricta Bank Mallee 9.54 940 Sandstone 33° 34' 59.8" 150° 22' 1.5" triflora Tree 18.04 750 Sandstone 35° 5’ 150° 9’ caliginosa Tree 18.36 800‒950 Probably granite consideniana Brai Tree 16.56 224 Sandstone 34° 58' 25.7" 150° 29' 37.2" (Nowra) consideniana Wood Tree 17.08 587 Sandstone 33° 45' 20.3" 150° 29' 7.4" copulans Went J Tree 10.38 850 Sandstone 33° 42’ 32” 150° 22’ 26” copulans Went D Tree 12.45 850 Sandstone 33° 43' 0.2" 150° 22' 31.5" cloeziana Tree 13.38 deuaensis Mallee 11.28 660 Rhyolite 35° 49' 149° 49' haemastoma Beac Tree 26.81 139 Sandstone 33° 44' 35.1" 151° 15' 35.7" haemastoma Roya Tree 30.32 118 Sandstone 34° 7' 16.3" 151° 4' 31.5" luehmanniana Roya K Mallee 25.75 115 Sandstone 34° 6’ 16” 151° 2’ 5” luehmanniana Roya S Mallee 22.81 116 Sandstone 34° 7' 16.6" 151° 4' 31" moorei Went D Mallee 8.71 850 Sandstone 33° 42' 60" 150° 22' 31.5" moorei Went Lake Mallee 7.46 890 Sandstone 33° 42' 3.4" 150° 22' 16.8" multicaulis Mallee 14.71 583 Sandstone 33° 41' 54.1" 150° 29' 27.6" oblonga Tree 14.69 330 Sandstone 34° 12' 38.9" 150° 57' 21.6" oreades Tree 20.29 969 Sandstone 33° 44' 2.4" 150° 16' 58" piperita King Tree 19.49 842 Sandstone 33° 45' 19.2" 150° 22' 34" piperita Hill Tree 21.44 598 Sandstone 34° 19' 29.4" 150° 28' 25.7" radiata Tree 9.09 967 Sandstone 33° 44' 2.3" 150° 16' 58" rossii Tree 10.58 930 Sandstone 33° 15' 22.6" 150° 13' 6.4" sclerophylla Lind Tree 27.15 583 Sandstone 33° 41' 44.8" 150° 29' 26.3" sclerophylla Brai Tree 24.72 221 Sandstone 34° 58' 26.2" 150° 29' 36.4" (Nowra) sieberi Hill Tree 17.50 600 Sandstone 34° 19' 29.7" 150° 28' 25.6" sieberi Went Tree 18.26 850 Sandstone 33° 43' 33.8" 150° 22' 19.3" sparsifolia Tree 12.46 583 Sandstone 33° 41' 44.8" 150° 29' 26.3" stenostoma Tree 12.87 712 Sandstone 36° 32' 12.8" 149° 38' 57.6" williamsiana Tree 40.52 716 Granite 35° 25’ 2” 149° 14’ 51”

Appendix 3. Phylogeny derived from 1780 Diversity Arrays Technology (DArT) markers analysed with Partitioned Additive Dollo Distance (PADD) and minimum-evolution tree estimation of the green ashes (section Eucalyptus) and other taxa in subgenus Eucalyptus Includes the black sallies (section Longitudinales), blue ashes (including the scribbly gums, section Cineraceae), peppermints (section Aromatica) and stringybarks (section Capillulus). Eucalyptus cloeziana (subgenus Idiogenes) is the out-group. Sample codes correspond to those in Table 2 (Column 6). Series and subseries (Brooker 2000) within the green ashes are shown: Regnantes (R), Eucalyptus (E), Strictae subseries Irregulares (SI), Strictae subseries Regulares (SR) and Contiguae (C). Node numbers represent bootstrap values greater than 50%

Stringybarks

Blue ashes

Scribbly gums

Tall green ashes (R+E) Peppermint Black sallies

Green ashes (C)

Green ashes (SR)

Green ashes (SI)

Blue ash

Green ashes (SI)

Appendix 4. Network generated in SplitsTree4 (version 4.13.1) based on 2702 Diversity Arrays Technology (DArT) markers Relationships among the green ashes (section Eucalyptus) and other taxa in subgenus Eucalyptus (sections Longitudinales, Cineraceae, Aromatica and Capillulus). Sample codes correspond to those in Table 2 (Column 6). Scale bar shows uncorrected P genetic distance equivalent to 0.01

Appendix 5. Comparison of the published classification of subgenus Eucalyptus by Brooker (2000) with the online draft scheme of subgenus Eucalyptus section Eucalyptus by Nicolle (2015) Asterisks indicate dubious species; hashes indicate possible or probable hybrid. Authors of plant names are given in Table 2 and authors of both species and higher taxonomic ranks are listed in Appendices 1 and 5

Brooker (2000) Nicolle (2015) Subgenus Eucalyptus Subgenus Eucalyptus Section Eucalyptus Section Amentum Brooker Series White-mahoganies Blakely (white mahoganies) E. acmenoides Schauer E. acmenoides Schauer E. apothalassica L.A.S.Johnson & K.D.Hill E. apothalassica L.A.S.Johnson & K.D.Hill E. psammitica L.A.S.Johnson & K.D.Hill E. psammitica L.A.S.Johnson & K.D.Hill E. carnea R.T.Baker E. carnea R.T.Baker E. umbra R.T.Baker E. umbra R.T.Baker E. mediocris L.A.S.Johnson & K.D.Hill E. irritans L.A.S.Johnson & K.D.Hill E. helidonica K.D.Hill E. portuensis K.D.Hill E. latisinensis K.D.Hill Section Pseudophloius Brooker Series Pseudostringybarks Blakely E. pyrocarpa L.A.S.Johnson & Blaxell E. pyrocarpa L.A.S.Johnson & Blaxell E. pilularis Sm. E. pilularis Sm. Section Aromatica Brooker Series Radiatae Chippendale Series Radiatae Chippendale (peppermints) E. elata Dehnh. E. elata Dehnh. E. radiata Sieber ex DC. E. radiata Sieber ex DC. E. croajingolensis L.A.S.Johnson & K.D.Hill E. croajingolensis L.A.S.Johnson & K.D.Hill E. willisii Ladiges, Humphries & Brooker E. willisii Ladiges, Humphries & Brooker E. dives Schauer E. dives Schauer Series Insulanae Brooker E. amygdalina Labill. E. amygdalina Labill. E. pulchella Desf. E. pulchella Desf. E. tenuiramis Miq. E. tenuiramis Miq. E. risdonii Hook.f. 71

E. risdonii Hook.f. E. nitida Hook.f. E. nitida Hook.f. E. coccifera Hook.f. E. coccifera Hook.f. E. robertsonii Blakely E. nebulosa A.M.Gray E. arenicola Rule E. falciformis (Newnham, Ladiges & Whiffin) Rule E. molyneuxii Rule* (possibly E. falciformis (Newnham, Ladiges & Whiffin) Rule) Section Capillulus Brooker Series Pachyphloiae Blakely Series Pachyphloiae Blakely (stringybarks) E. muelleriana A.W.Howitt E. muelleriana A.W.Howitt E. laevopinea R.T.Baker E. laevopinea R.T.Baker E. macrorrhyncha F.Muell. E. macrorhyncha F.Muell. E. cannonii R.T.Baker E. expressa S.A.J.Bell & D.Nicolle E. mackintii Kottek E. mackintii Kottek E. williamsiana L.A.S.Johnson & K.D.Hill E. williamsiana L.A.S.Johnson & K.D.Hill E. youmanii Blakely & McKie E. youmanii Blakely & McKie E. capitellata Sm. E. capitellata Sm. E. cameronii Blakely & McKie E. cameronii Blakely & McKie E. globoidea Blakely E. globoidea Blakely E. caliginosa Blakely & McKie E. caliginosa Blakely & McKie E. eugenioides Sieber ex Spreng. E. eugenioides Sieber ex Spreng. E. sparsifolia Blakely E. sparsifolia Blakely E. tenella L.A.S.Johnson & K.D.Hill E. tenella L.A.S.Johnson & K.D.Hill E. mckieana Blakely E. mckieana Blakely E. ligustrina DC. E. ligustrina DC. E. conglomerata Maiden & Blakely E. conglomerata Maiden & Blakely E. tindaliae Blakely E. tindaliae Blakely E. agglomerata Maiden E. agglomerata Maiden E. blaxlandii Maiden & Cambage E. blaxlandii Maiden & Cambage E. camfieldii Maiden E. camfieldii Maiden E. bensonii L.A.S.Johnson & K.D.Hill E. bensonii L.A.S.Johnson & K.D.Hill E. arenacea Marginson & Ladiges E. arenacea Marginson & Ladiges E. imitans L.A.S.Johnson & K.D.Hill E. imitans L.A.S.Johnson & K.D.Hill 72

E. serraensis Ladiges & Whiffin E. serraensis Ladiges & Whiffin E. verrucata Ladiges & Whiffin E. verrucata Ladiges & Whiffin E. curta Brooker E. oblonga DC.* (possibly E. globoidea Blakely) E. baxteri (Benth.) Maiden & Blakely ex J.M.Black E. yangoura Blakely* (possibly E. globoidea Blakely) Series Limitares Brooker E. erosa A.R.Bean* (possibly E. eugenioides Sieber ex Spreng.) E. deuaensis Boland & Gilmour E. aurifodina Rule E. alpina Maiden E. victoriana Ladiges & Whiffin E. reducta L.A.S.Johnson & K.D.Hill* (possibly E. tindaliae Blakely) E. prominula L.A.S.Johnson & K.D.Hill* (possibly E. youmanii Blakely & McKie) Section Nebulosa Brooker Series Olsenianae Chippendale E. olsenii L.A.S.Johnson & Blaxell E. olsenii L.A.S.Johnson & Blaxell E. boliviana J.B.Williams & K.D.Hill E. deuaensis Boland & Gilmour Section Eucalyptus Series Regnantes Chippendale Series Regnantes Chippendale E. fastigata H.Deane & Maiden E. fastigata H.Deane & Maiden E. regnans F.Muell. E. regnans F.Muell. Series Eucalyptus Series Eucalyptus E. obliqua L'Hér. E. obliqua L'Hér. Series Strictae L.A.S.Johnson ex Brooker Series Strictae L.A.S.Johnson ex Brooker (mallee ashes) Subseries Irregulares Brooker Subseries Irregulares Brooker E. triflora (Maiden) Blakely E. triflora (Maiden) Blakely E. dendromorpha (Blakely) L.A.S.Johnson & Blaxell E. dendromorpha (Blakely) L.A.S.Johnson & Blaxell E. apiculata R.T.Baker & H.G.Sm. (includes E. laophila L.A.S.Johnson & Blaxell) E. apiculata R.T.Baker & H.G.Sm (includes E. laophila L.A.S.Johnson & Blaxell) E. stricta Sieber ex Spreng. (includes E. spectatrix L.A.S.Johnson & Blaxell) E. stricta Sieber ex Spreng. E. burgessiana L.A.S.Johnson & Blaxell (includes E. obstans L.A.S.Johnson & E. burgessiana L.A.S.Johnson & Blaxell (includes E. obstans L.A.S.Johnson & K.D.Hill) K.D.Hill) E. langleyi L.A.S.Johnson & Blaxell E. langleyi L.A.S.Johnson & Blaxell E. spectatrix L.A.S.Johnson & Blaxell* (possibly E. stricta Sieber ex Spreng.) E. cunninghamii G.Don Subseries Regulares Brooker Subseries Regulares Brooker E. approximans Maiden (includes E. E. approximans Maiden 73

codonocarpa Blakely & McKie and E. microcodon L.A.S.Johnson & K.D.Hill) E. microcodon L.A.S.Johnson & K.D.Hill E. cunninghamii G.Don E. codonocarpa Blakely & McKie E. paliformis L.A.S.Johnson & Blaxell E. paliformis L.A.S.Johnson & Blaxell Series Contiguae Brooker & Slee E. kybeanensis Maiden & Cambage E. kybeanensis Maiden & Cambage Section Longitudinales (Blakely) Brooker Series Longitudinales (Blakely) Brooker (sallees) E. mitchelliana Cambage E. mitchelliana Cambage E. stellulata Sieber ex DC. E. stellulata Sieber ex DC. E. moorei Maiden & Cambage E. moorei Maiden & Cambage E. forresterae Molyneux & Rule * (possibly E. stellulata Sieber ex DC.) Eucalyptus × copulans L.A.S.Johnson & K.D.Hill # (E. moorei Maiden & Cambage subsp. moorei × E. stellulata Sieber ex DC. hybrid) Section Cineraceae Brooker Series Fraxinales Blakely Series Fraxinales Blakely E. fraxinoides H.Deane & Maiden E. fraxinoides H.Deane & Maiden E. luehmanniana F.Muell. E. luehmanniana F.Muell. E. oreades R.T.Baker E. oreades R.T.Baker E. delegatensis R.T.Baker E. delegatensis R.T.Baker Series Sphaerocarpae Brooker E. sphaerocarpa L.A.S.Johnson & Blaxell E. sphaerocarpa L.A.S.Johnson & Blaxell Series Pauciflorae L.A.S.Johnson ex Brooker & Slee Series Pauciflorae L.A.S.Johnson ex Brooker & Slee (snow gums) E. lacrimans L.A.S.Johnson & K.D.Hill E. lacrimans L.A.S.Johnson & K.D.Hill E. gregsoniana L.A.S.Johnson & Blaxell E. gregsoniana L.A.S.Johnson & Blaxell E. pauciflora Sieber ex Spreng. E. pauciflora Sieber ex Spreng. Series Psathyroxylon Blakely Series Psathyroxyla Blakely Subseries Considenianae Brooker & Slee Subseries Considenianae Brooker & Slee E. sieberi L.A.S.Johnson E. sieberi L.A.S.Johnson E. multicaulis Blakely E. multicaulis Blakely E. remota Blakely E. remota Blakely E. consideniana Maiden E. consideniana Maiden E. andrewsii Maiden E. andrewsii Maiden E. olida L.A.S.Johnson & K.D.Hill E. olida L.A.S.Johnson & K.D.Hill E. campanulata R.T.Baker & H.G.Sm. 74

Subseries Haemastomae Brooker Subseries Haemastomae Brooker (scribbly gums) E. racemosa Cav. E. racemose Cav. E. haemastoma Sm. E. haemastoma Sm. E. rossii R.T.Baker & H.G.Sm. E. rossii R.T.Baker & H.G.Sm. Series Stenostomae Brooker Series Stenostomae Brooker E. stenostoma L.A.S.Johnson & Blaxell E. stenostoma L.A.S.Johnson & Blaxell Series Piperitales Blakely Series Piperitales Blakely E. piperita Sm. E. piperita Sm. Section Insolitae Brooker Series Planchonianae Chippendale E. planchoniana F.Muell. E. planchoniana F.Muell. Section Pedaria L.A.S.Johnson ex Brooker E. brevistylis Brooker Section Longistylus Brooker Subsection Arboreae Brooker Series Jacksoniae Brooker E. jacksonii Maiden Series Occidentales Blakely E. marginata Donn ex Sm. E. staeri Kessell & C.A.Gardner Series Patentes L.A.S.Johnson ex Brooker E. patens Benth. Subsection Frutices Brooker Series Diversiformae Blakely Subseries Neuropterae (Maiden) Brooker E. diversifolia Bonpl. E. pachyloma Benth. E. erectifolia Brooker & Hopper E. lateritica Brooker & Hopper E. todtiana F.Muell. E. johnsoniana Brooker & Blaxell Subseries Cochleatae (Maiden) Brooker E. buprestium F.Muell. Subseries Finales Brooker 75

E. dolorosa Brooker & Hopper Series Angulares Brooker E. angularis Brooker & Hopper Series Muricatae Maiden E. exilis Brooker E. pendens Brooker E. sepulcralis F.Muell. Series Calcicolae Brooker E. calcicola Brooker E. ligulata Brooker Series Preissianae L.D.Pryor & L.A.S.Johnson ex Brooker & Slee Subseries Glandulares Blakely E. megacarpa F.Muell. E. aquiline Brooker E. coronata C.A.Gardner Subseries Pluriloculares Blakely E. preissiana Schauer Series Proximae Brooker E. acies Brooker Series Subereae Chippendale E. suberea Brooker & Hopper Subsection Unicae Brooker E. insularis Brooker

Chapter 3. Seedling response to environmental variability: the role of phenotypic plasticity in the evolution of Eucalyptus species

Susan Rutherford, Stephen P. Bonser, Peter G. Wilson and Maurizio Rossetto

A version of this chapter has been published in American Journal of Botany

Seedlings of Eucalyptus dendromorpha from the Kangaroo Valley in the Southern Highlands, New South Wales.

Rutherford S, Bonser SP, Wilson PG, Rossetto M (2017) Seedling response to environmental variability: the relationship between phenotypic plasticity and evolutionary history in closely related Eucalyptus species. American Journal of Botany 104, 840-857.

3.1 Abstract

Phenotypic plasticity is an important means through which organisms cope with environmental variability. However, it can be problematic when describing species. We investigated seedling plasticity in the green ash eucalypts, a diverse group that occur across a range of habitats, and which include the tallest flowering plant in the world (Eucalyptus regnans) and a rare mallee less than 1 m in height (E. cunninghamii). Seedlings of twelve species were exposed to high and low nutrient and water availability in a factorial experiment. Leaf trait and total plant plasticity were evaluated using the phenotypic plasticity index. A phylogeny of the species was estimated using genome scans based on Diversity Arrays Technology. We found significant differences in functional traits across species, growth forms and substrates in response to changes in resource availability. Many traits (e.g. leaf width) were highly plastic for most species. Other traits (e.g. specific leaf area) were not as variable between treatments for many species. Overall, plasticity was not correlated with phylogeny and more widespread species had higher leaf-level plasticity than species with narrower environmental ranges. Our results provided insights for species delimitation in Eucalyptus, which have implications for the management of the green ashes. Due to the high number of rare species and the fact that other species within the group are commercially important, a more comprehensive understanding of plasticity is essential for predicting their responses to changing climates.

3.2 Introduction

Understanding how species adapt and respond to new and heterogeneous environments is a central goal in evolutionary biology. Phenotypic plasticity is the ability of an organism (or genotype) to express a range of phenotypes as a function of the environment (Bradshaw 1965; Schlichting and Levin 1986; Scheiner 1993) and is an important mechanism through which many species cope with environmental variability (Gratani 2014). The expression of phenotypic plasticity can allow individuals to have a wider tolerance to environmental conditions, thereby enabling higher fitness in a range of habitats, and buffering populations against novel stresses (e.g. Bradshaw 1965; Ghalambor et al. 2007; Nicotra et al. 2010). Phenotypic plasticity may promote

78 adaptive responses and can influence lineage diversification (Sultan 2000; Sultan and Spencer 2002). For example, plasticity may allow individuals that are subjected to new environmental conditions to persist, until natural selection further enhances the fitness of a population (Price et al. 2003; Pigliucci 2005). Furthermore, plasticity may rapidly change the phenotypes in a population that is targeted by natural selection (Ghalambor et al. 2007; Lande 2009). The ability of species to respond to environmental variability is likely to be constrained to some extent by their evolutionary history (Kellermann et al. 2012). Because phenotypic plasticity is a quantitative trait that is itself under selection and which has evolutionary consequences, research of plasticity in a phylogenetic framework is important for predicting the response of species to changing climates (Pigliucci 2005; Matesanz et al. 2010).

A number of species within the plant genus Eucalyptus display a high degree of phenotypic plasticity (Eldridge et al. 1993; Slee et al. 2006). Eucalyptus is a highly diverse genus (comprising more than 700 species) and is found in all climatic regions of Australia (Eldridge et al. 1993; Potts and Wiltshire 1997). We focus on a group of eucalypts commonly known as the green ashes (subgenus Eucalyptus section Eucalyptus, sensu Brooker 2000). The green ashes are found in a range of habitats in south-eastern Australia, with some species occurring as trees in tall forests on fertile soils and others as smaller trees or mallees on sandstone substrates (Ladiges et al. 2010). They include the tallest flowering plant in the world (Eucalyptus regnans, up to 100 m tall), widespread and commercially important trees (e.g. E. obliqua), and a rare mallee that is often less than 1 m in height (E. cunninghamii). Mallees are long-lived multi-stemmed trees with an underground lignotuber from which they are able to resprout after disturbances, such as fire, frost and herbivory (Mullette 1978). While much effort has gone into understanding the evolutionary history of the green ashes, there has been considerable disagreement concerning the circumscription and ranking of taxa (e.g. Ladiges et al. 1989; Brooker 2000; Hill 2002). Some green ash species (e.g. E. obliqua and E. dendromorpha) naturally occur as both trees and mallees (Hill 2002), while other species are mallees in the wild (e.g. E. burgessiana and E. apiculata) but grow as trees when cultivated. Species boundaries within the group are further complicated by inter-specific hybridisation (Benson and McDougall 1998). As a consequence, many species are difficult to distinguish either chemically (Lassak and Southwell 1982) or morphologically (Ladiges et al. 1989) and are identified on the basis

79 of narrow differences in morphological characters (e.g. leaf length, leaf width and capsule size), or on the basis of geographic location, or a combination of these. Because the green ashes are a group of closely related species representing a range of growth forms and habitats, they are an appropriate system for investigating the relationship between phenotypic plasticity and lineage diversification.

We examined phenotypic plasticity in response to nutrient and water variability in seedlings of species within the green ash group. Comparisons of eucalypt seedlings from xeric or oligotrophic habitats with those from mesic environments have been useful in identifying physiological and morphological traits that have evolved in response to stressful environments (e.g. Anderson et al. 1996; Merchant et al. 2007). Seedling trials have also been used to better understand the genetic basis and plasticity of physiological traits in populations of E. tricarpa (Andrew et al. 2010; McLean et al. 2014). Adaptive responses to environmental variability in seedlings are important in the establishment of these relatively long-lived species. The seedling stage comprises rapid developmental changes in morphology and biomass allocation patterns (Kitajima 1996) and seedling morphological and physiological traits are associated with plant establishment and regeneration (Zheng et al. 2009; Bonito et al. 2011). The degree of plasticity in seedling functional traits (e.g. plant biomass, plant height, leaf size, leaf thickness) is therefore likely to have implications for the ability of plants to acclimatize to changes in habitat and environmental conditions. For example, a high degree of plasticity in such traits could facilitate the survival and persistence of seedlings when dispersing to, and establishing in, new environments (Robinson and Dukas 1999).

Comparative ecophysiological studies that incorporate phylogenetic data have the potential to address important evolutionary questions, including those concerning phenotypic plasticity (Ackerly et al. 2000; Donoghue 2008). Traits of closely related species are expected to resemble each other more closely than expected by chance (Davies et al. 2013). If plasticity is a phylogenetically conserved trait, then differences in plasticity across species should reflect their relatedness (Valladares et al. 2000), and the capacity to respond to heterogeneous environments may be limited (or promoted) by evolutionary history. However, previous studies on the phylogenetic constraints of plasticity have yielded inconsistent results (Herben et al. 2014). For example, root phenotypic plasticity was found to be phylogenetically conserved in a range of plant

80 species (Kembel and Cahill 2005). In contrast, phenotypic plasticity in other plant species was not correlated with either phylogenetic relationships or growth form (Godoy et al. 2011). Rather, plasticity may be associated with habitat affiliation (Valladares et al. 2000). Phenotypic plasticity is predicted to be favoured in heterogeneous environments (Van Buskirk 2002; Murren et al. 2015) and to have evolved in response to variable selection pressures that occur in such environments (Bradshaw 1965). Plasticity mechanisms may be related to habitat stability and productivity (Grime et al. 1986), with morphological plasticity predicted to be pre- eminent in plants from resource rich environments, and not sustainable in slow-growing plants from unproductive habitats (Grime and Mackey 2002). In the case of Eucalyptus, nutrient and water availability are considered to be major factors in the evolution of the genus (Wardell-Johnson et al. 1997). Distribution patterns of eucalypts have been correlated with changes in (and interactions between) soil fertility and moisture (e.g. Parsons and Specht 1967; Parsons 1968, 1969; Kirkpatrick et al. 1987), while the distribution of specific mallee species has been attributed to only slight differences in water and nutrient availability (Parsons 1969).

In a previous study (Rutherford et al. 2016) we used genomic analyses based on Diversity Arrays Technology (DArT) to estimate the phylogeny of the green ashes. We found that ‘species’ concepts were not always consistent with previous classifications, which were based primarily on morphology (particularly for species from the Sydney region and the Greater Blue Mountains World Heritage Area). Our results indicated that some of the parameters that have been used to identify species in this group (e.g. leaf width) were problematic and not as useful in species delimitation as previously thought. Consequently a follow-up question emerging from that study was: are these characters poor indicators because we are dealing with broader plastic taxonomic units? For example, have populations of some species been incorrectly assigned specific rank due to plastic morphological characters? Furthermore, what role has phenotypic plasticity played in the ability of the green ashes to respond to environmental variability?

In the present study, we investigated the morphological and physiological plasticity of green ash species in response to changes in nutrient and water availability in a common garden experiment. Common garden experiments can allow genetic and environmental factors to be differentiated, and thereby enhancing our understanding of evolutionary

81 patterns and adaptation (Warren et al. 2005; Lewis et al. 2011). In this study, we addressed the following questions: (1) Are plant performance and leaf functional traits plastic across nutrient and water treatments? (2) Is the degree of phenotypic plasticity correlated with phylogenetic relationships, growth form and substrate (soil type)? (3) Has plasticity impacted species delimitation in the green ashes and what are the implications of this for the conservation and management of species within the group? We tested the hypotheses that functional traits will be plastic across resource treatments and inter-specific differences in plasticity will be correlated with phylogeny.

3.3 Materials and methods

3.3.1 Selection of species and experimental design

We selected twelve species from subgenus Eucalyptus section Eucalyptus (Table 1). To ensure that all recognised species in the Sydney region and the Greater Blue Mountains World Heritage Area (GBMWHA) were included in the study, we followed the species descriptions of Hill (2002). The species descriptions of Hill (2002) are narrowly defined (and do not define species according to the definition of the biological species concept). Location and habitat details of each species were obtained from the National Herbarium of New South Wales database (Royal Botanic Garden Sydney) and Benson and McDougall (1998), and are summarised herein. With the exception of E. regnans (which occurs on deep fertile soils in Victoria and Tasmania), the species that were selected are found across a range of altitudes and latitudes in the Sydney region and GBMWHA. This included E. obliqua and E. fastigata, which occur on high fertility soils; as well as E. triflora and E. dendromorpha, both of which have a scattered but locally frequent distribution on low fertility slopes and escarpments. The remainder of species that were included were mallees on low nutrient ridges, escarpments, plateaus and hillsides. Eucalyptus cunninghamii and E. laophila are found at elevations of 650‒ 1100 m. Eucalyptus burgessiana and E. apiculata occur at altitudes of 300‒750 m, while E. obstans and E. langleyi are found only in coastal areas. Eucalyptus stricta is widespread being found in both coastal and inland habitats (0‒1200 m).

We collected fruits from all species found in the Sydney region and GBMWHA. One population was sampled from each of ten selected species. Two populations were

82 sampled of E. dendromorpha, one from Mount Wilson in the GBMWHA and the other from Fitzroy Falls, located 100 km south of Sydney (since the mallee form in the GBMWHA and the tree form from south of Sydney have previously been considered separate taxa; Klaphake 2012). At least ten randomly selected plants per population (approximately 10 m apart) were sampled. The growth form of each species and the substrate observed at each site were recorded at the time of collection. Substrates were categorised as either deep, fertile soils (generally derived from basalt) or skeletal and sandy soils (predominantly derived from sandstone).

Fruits were returned to UNSW and air-dried in a cool, low humidity environment until seeds were released. Due to the logistical constraints of field collections, seed for E. regnans was ordered from the Australian Tree Seed Centre (CSIRO, Canberra, Australia) and represented 12 parents from a wild population at Rubicon, Victoria. Habitat details (e.g. altitude, soil type) for this population were provided by the Australian Tree Seed Centre.

In December (early summer) 2013, seeds from all species were germinated on filter paper moistened with deionised water. Twenty-eight pots (15 cm deep and 20 cm diameter) per species were set up in the glasshouse at UNSW, 14 of which contained a low nutrient soil treatment and 14 containing a high nutrient soil treatment. The soil mix used in the high nutrient treatment was an Australian Native potting mix designed for optimal growth of native plants, which comprised 33% Organic Garden Mix (Australian Native Landscapes, Sydney, Australia), 33% washed river sand, 33% coco peat and 200 mL of a slow release low phosphate fertilizer (Osmocote, Sydney, Australia). In the low nutrient treatment, the fertilizer was diluted to 20% of that applied to the high nutrient treatment. Once germinated (the emergence of the cotyledons), seedlings for each species were transplanted into pots and assigned to high and low nutrient treatments. Over the following few months, a total of three seedlings were planted per pot. All plants were well-watered (to maximum soil water holding capacity) weekly, for three months until established. Seedlings were randomly thinned (by uprooting) during this period until a single plant per pot remained. After this period, pots were randomly assigned to high and low water treatments. Plants in the high water treatment received ample water as needed (enough to ensure the potting mix remained moist throughout the experiment). The low water treatment was established by gradually reducing water over

83 the course of 14 days until they received a water allocation of 30‒40% of the high water treatments. Therefore there were four treatments per species in a full factorial design: low nutrient and low water (LNLW), low nutrient and high water (LNHW), high nutrient and low water (HNLW), and high nutrient and high water (HNHW). Pot positions were randomised once per month within blocks. The temperature of the glasshouse ranged from 18‒30°C during the period of growth, with daily high temperatures generally ranging between 25 and 27°C.

3.3.2 Plant performance, leaf morphology and leaf functional traits

Once per month, we measured leaf length (including the petiole when present) and leaf width (at the widest point) from five to ten randomly selected true leaves using a digital Vernier calliper (Kincrome, Scoresby, Victoria, Australia). The number of leaves selected depended on the number present at the time of growth. The position of leaves on the stem was also recorded (node and leaf number) for as long as possible (until the cotyledons fell off the plant) in order to note any ontogenetic changes.

After eight months of growth, 14 variables representing whole plant and leaf-level traits were measured. We measured the shoot height of each plant (from the soil surface to the top of the plant) to the nearest mm. The stem diameter to the nearest 0.01 mm was measured 2 cm above the soil surface with a digital Vernier calliper and the total number of leaves per plant was recorded. Plants were then harvested and immediately placed into a sealed plastic bag and stored in a cooler box. Within an hour after harvesting, four leaves were randomly selected per plant and their fresh mass determined. The length (including the petiole), width (at the widest point), petiole length and thickness (at four randomly selected points on the leaf) of each leaf were measured with a digital Vernier calliper. Each leaf was scanned and the leaf area (LA, to the nearest mm²) determined using the Leaf Area Measurement version 1.3 software (University of Sheffield, Sheffield, UK). Each leaf and all other aboveground parts were oven dried for 24 hours at 70°C and their dry mass determined. Fresh mass versus dry mass (FW/DW) of each plant and each leaf was determined and the specific leaf area (SLA) per leaf was calculated as the total leaf area (mm²) divided by the dry mass of the leaf (mg) using the formula of Wright and Westoby (1999). 84

Table 1. Provenance, growth form, distribution and habitat of the study species Two populations of E. dendromorpha were sampled. Altitude was measured directly in the field at each site (GPS model: Garmin Rino 650) for all species except for E. regnans (which was provided by the Australian Tree Seed Centre, CSIRO). Column 1 follows the taxonomy of Hill (2002) and data in column 6 was compiled from Hill (2002), Benson and McDougall (1998), Slee et al. (2006) and Mills (2010). Growth form categories are: mallees (< 15 m tall), medium trees (15‒30 m tall) and tall trees (> 30 m tall). Abbreviations: GBMWHA (Greater Blue Mountains World Heritage Area), NSW (New South Wales), Qld. (Queensland), SA (South Australia), Tas. (Tasmania) and Vic. (Victoria). Species Provenance Growth form Substrate Altitude (m) Distribution and conservation E. apiculata Woodford Mallee Skeletal sandy soil 550‒600 Rocky outcrops, GBMWHA to Berrima, NSW. Rare and localised E. burgessiana Springwood Mallee Sandy soil 400‒450 Ridges with rocky outcrops, GBMWHA, NSW. Locally frequent but restricted E. cunninghamii Kings Tableland Mallee Skeletal sandy soil 785‒805 Slopes and escarpments, GBMWHA, NSW. Locally frequent but restricted E. dendromorpha Mount Wilson Mallee Sandy soil 990‒1010 Escarpment and hillsides above cliffs from GBMWHA to Monga, Fitzroy Falls Medium tree Sandy soil 290‒310 NSW. Locally abundant but restricted E. fastigata Mount Tomah Tall tree Deep, fertile soils 990‒1000 Wet sclerophyll forest in cold wet areas on fertile soils in NSW and Vic. Widespread E. langleyi South Nowra Mallee Sandy soil 195‒265 Confined to Nowra district, South Coast, NSW. Restricted and localised E. laophila Wollemi National Mallee Skeletal sandy soil 900‒935 Pagoda rocky outcrops, from Coricudgy to Newnes Plateau, NSW. Park Locally frequent but restricted E. obliqua Mount Murray Tall tree Deep, fertile soil 600‒650 Wet sclerophyll or grassy forest in Qld., NSW, Vic., SA and Tas. Widespread and locally dominant E. obstans Royal National Mallee Shallow sandy soil 110‒125 Plateaus and upper slopes in coastal areas from Ku-ring-gai Chase Park National Park to Jervis Bay, NSW. Locally abundant E. regnans Rubicon, Vic. Tall tree Deep, fertile soil 550‒600 Wet sclerophyll forests, mostly in mountainous regions in Vic. and Tas. Widespread and locally abundant E. stricta Newnes Plateau Mallee Shallow sandy soil 1170‒1180 Ridges and plateaus from Newnes Plateau to Budawang Ranges, NSW. Widespread and locally abundant E. triflora Nerriga Medium tree Skeletal sandy soil 700‒800 Steep sandstone slopes, confined to Budawang Ranges, NSW. Scattered but locally frequent 85

3.3.3 Phenotypic plasticity index

We evaluated leaf trait and total plant plasticity for the two extreme treatments: low nutrient and low water (LNLW), and high nutrient and high water (HNHW). These treatments were chosen as they represented the poorest and most favourable environments. Phenotypic plasticity was calculated with the plasticity index (PI) using the formula created by Valladares et al. (2000):

푀푒푎푛(푒푛푣1) − 푀푒푎푛(푒푛푣2) 푃퐼 = 푀푎푥(푀푒푎푛(푒푛푣1), 푀푒푎푛(푒푛푣2)) where Mean(env1) and Mean(env2) are the mean values of a specific trait for each species in environment 1 and 2; and Max(Mean(env1), Mean(env2)) is the maximum mean value (from environment 1 or 2). PI values range from a value of zero to one (no plasticity to maximum plasticity; Valladares et al. 2000). In the present study, PI was calculated for each trait and species. The mean plant-level PI, mean leaf-level PI and overall mean PI were calculated.

3.3.4 Statistical analysis

We used ANOVA to assess the main effects of water, nutrients, and species, and interactions between these main effects, on variability in seedling growth. A mixed model ANOVA was used to investigate the effect of nutrients, water, and species on changes in plant-level and leaf-level traits. Nutrient and water treatments were fixed effects and species was a random effect in this analysis. Species was used as a random factor in this analysis because we were only using a random selection of green ash species and we wanted to make inferences about all possible groups from our sample selection (Quinn and Keough 2002). We used a three factor ANOVA to assess the effect of water, nutrients, and growth form on plant-level and leaf-level traits. A three-factor ANOVA was also used to examine the effect of water, nutrients, and substrate on plant- level and leaf-level traits. To investigate the effect of the block design of the experiment on our analyses, we performed ANOVAs with and without ‘block’ (i.e. ‘replicate number’) as a fixed effect. We found that the blocking effect did not change the significance of the other effects or interactions (apart from two factors out of all analyses performed), and therefore the ‘block’ effect was removed from the analysis. 86

For each species, we examined differences in all plant-level and all leaf-level traits across treatments using a multivariate analysis of variance (MANOVA). Because we had multiple response variables, many of which are likely to be highly correlated, we considered a MANOVA to be appropriate for investigating treatment differences on all response variables (MANOVA allows all response variables in an experiment to be considered simultaneously, Quinn and Keough 2002). We performed all these analyses in SPSS version 24.0 (IBM, Armonk, New York, USA).

We analysed individual traits further in R version 3.1.3 (R Development Core Team 2015). We assessed the effect of water and nutrient treatments on variation in each trait using a two factor ANOVA, focusing on differences between treatments for each species, growth form and substrate. To examine the effect of water and nutrient treatments on leaf length and leaf width over the period of growth for each species, we used a two-factor ANOVA focusing on differences between treatments at each monthly measurement. Significant differences (at P < 0.05) between pairwise mean values within each ANOVA were assessed using Tukey’s HSD test. Prior to each analysis, homogeneity of variances among groups was examined with Levene’s test and traits were log10 transformed where necessary to meet the variance assumptions of ANOVA.

While phylogenetic comparative methods (e.g. phylogenetic independent contrasts) are considered the most appropriate way to analyse trait data in closely related species, these methods are most effective using a completely resolved phylogeny (this is because incompletely resolved phylogenies can inflate estimates of phylogenetic conservatism, Davies et al. 2012). In cases where a completely resolved phylogeny is not available, a mixed model ANOVA (with species included as a random factor) has been found to perform well (Funk et al. 2015). Although we estimated the phylogeny of our study species (see below for full details of analysis), it should be noted that some clades had a posterior probability (PP) of less than 0.95 (and therefore, all clades were not entirely resolved). However, because most of the clades in our phylogeny had a PP of 1, we were able to use our phylogenetic analysis to address our questions concerning the relationship between phenotypic plasticity and evolutionary relationships (across species, growth forms, and substrates).

3.3.5 Phylogenetic analysis of plasticity across species, growth forms and substrate

In order to investigate phylogenetic patterns in phenotypic plasticity (plant-level, leaf- level and overall mean PI) across the green ash group, we constructed a phylogeny of the study species. In Rutherford et al. (2016) phylogenetic trees were constructed from DArT presence/absence markers, with the Bayesian phylogeny showing a strong correlation with the current taxonomy of the green ashes and closely related eucalypts. Using the molecular dataset from Rutherford et al. (2016), a Bayesian phylogenetic tree was constructed of the species in the present study (using genetic data for only those populations that were sampled for seed), with Eucalyptus piperita selected as the out- group. Bayesian analyses were conducted in MrBayes 3.2.4 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003; Ronquist et al. 2012) using a restriction site (binary) model of evolution and default priors. We ran the final analysis for 40 million generations sampling every 500 generations with two parallel runs each with four chains (three hot and one cold). Convergence was considered reached based on the standard deviation of split frequencies (< 0.01) and the first 25% of trees discarded as burn-in. Ancestral reconstructions of growth form and substrate were traced onto the Bayesian tree using maximum parsimony reconstructions in the Mesquite software package version 3.03 (Maddison and Maddison 2015). The data matrix used for ancestral reconstructions are presented in Appendix 1.

We tested the effect of phylogenetic history on the evolution of phenotypic plasticity in the green ashes. A range of approaches are available that quantify phylogenetic signal in comparative data sets (Diniz-Filho et al. 2012). These include approaches based on statistical methods (i.e. spatial autocorrelation indices), which quantify the level of phylogenetic autocorrelation for a specific trait (e.g. Moran’s I and Abouheif’s C mean, Moran 1950; Gittleman and Kot 1990; Abouheif 1999). However, autocorrelation indices, such as Moran’s I, were not originally designed to provide a quantitative interpretation, and the earlier literature suggests that such interpretations are inappropriate (Ord 1975; Li et al. 2007). Other approaches are based on a theoretical model of trait evolution (e.g. Brownian motion) where phylogenetic signal is estimated using indices such as Pagel’s lambda (λ) and Blomberg’s K (Pagel 1997, 1999; Blomberg et al. 2003). Under Brownian motion, trait evolution follows a random walk along the branches of a phylogeny, with phenotypic variance being proportional with

88 branch lengths (Felsenstein 1985; Martins 1996). In a comparison of approaches and indices, it was found that Pagel’s λ performed better than Blomberg’s K on simulated data sets when the underlying evolutionary process was based on a Brownian motion model of trait evolution (Münkemüller et al. 2012). Although computationally more intensive, Pagel’s λ provided a reliable effect size measure and performed better at discriminating between complex models of trait evolution (Münkemüller et al. 2012). Therefore, in the current study, Pagel’s λ was chosen as the most appropriate parameter to quantify phylogenetic signal.

We estimated phylogenetic signal of plant-level, leaf-level, and overall mean PI using the ‘Continuous’ module in BayesTraits V2 (Pagel 1997; Pagel and Meade 2014). The Continuous option implements the generalised least-squares model, which accounts for nonindependence among species (Pagel and Meade 2014) and is analytically equivalent to independent contrasts (Garland et al. 2005). We selected 1000 random trees (from the MrBayes output excluding the 25% of trees discarded as burn-in) to analyse in BayesTraits. We estimated phylogenetic signal of plant-level, leaf-level and overall mean PI using λ in Continuous (using a random-walk model and MCMC analysis). The λ parameter models the variance of the trait attributable to phylogenetic conservatism or ‘heritability’ (Lee et al. 2013). We then compared the model where the value of λ is estimated to models where λ is forced to be either be 0 or 1. A λ value of 0 suggests that trait evolution is independent of the phylogeny, whereas a λ value of 1 indicates that the pattern of trait evolution is consistent with phylogenetic relationships (Kang et al. 2014). Analyses were run for 140 million generations, with a sampling frequency of 100 generations and a burn-in of 35 million generations. To assess convergence, each analysis was repeated three times. Bayes factors (BF) were used to infer whether the λ parameter improved model fit (using the criterion of twice the marginal log-likelihood differences, Newton and Raftery 1994; Kass and Raftery 1995). We calculated log BF following the formula used in the BayesTraits manual (Pagel and Meade 2014): Log BF = 2 × (log marginal likelihood complex model – log marginal likelihood simple model) BF compares the marginal log likelihoods of two models to assess which model better fits the data. A log BF value of less than 2 suggests the simpler model should be favoured (in this case, the model where λ is estimated), whereas, a log BF greater than 2

89 indicates that the more complex model (where λ is 0 or 1) is preferred (Pagel and Meade 2014).

3.4 Results

3.4.1 Plasticity in plant performance and plant-level traits

We found that all plant-level traits varied significantly in response to changes in nutrient treatments across species, growth forms and substrates (P < 0.05, Table 2). With the exception of stem diameter, all plant-level traits were significantly different across growth forms (P < 0.05, Table 2). Only stem diameter, total leaf number and total leaf dry mass were significantly different amongst species; while total leaf number, total aboveground dry mass, total leaf dry mass and total FW/DW were significantly different across substrates (P < 0.05, Table 2).

We found that plants generally grew significantly larger under higher nutrients (differences in growth of E. dendromorpha from Mount Wilson between high and low nutrient treatments are demonstrated in Fig. 1). For the majority of species, all plant- level traits varied significantly in response to nutrients (P < 0.05, Appendix 2). Aboveground dry mass, plant height and stem diameter were all significantly higher in the high nutrient treatments compared with the low nutrient treatments for most species, each growth form and each substrate (P < 0.05, Fig. 2, Appendix 3). In E. fastigata, E. triflora, E. dendromorpha (from Fitzroy Falls), E. dendromorpha (from Mount Wilson), E. obstans and E. stricta, the aboveground dry mass in the high nutrient and low water (HNLW) and high nutrient and high water (HNHW) treatments were significantly higher than that in the low nutrient and low water (LNLW) and the low nutrient and high water (LNHW) treatments (P < 0.05, Fig. 2). For each growth form and plants from each substrate, the aboveground dry mass in the HNLW and HNHW treatments were significantly higher than in the LNLW and LNHW treatments (P < 0.05, Fig. 2).

Table 2. Significance levels from analysis of variance (ANOVA) of plant-level traits Differences across species, growth form, substrate, nutrient (N) treatment, water (W) treatment are shown, as well as interactions between factors. Abbreviations: d.f., degrees of freedom; FW/DW, fresh mass versus dry mass.

Stem Total leaf Total Stem dry Total leaf Total d.f. Height diameter number aboveground mass dry mass FW/DW

dry mass Species 12 0.051 0.023 0.042 0.054 0.151 0.028 0.055 N 1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 W 1 0.885 0.002 0.036 0.071 0.073 0.035 0.042 N*W 1 0.027 0.002 0.001 0.053 0.025 0.010 0.037 Species* N 12 0.022 0.079 <0.001 0.003 0.012 <0.001 0.597 Species *W 12 0.583 0.638 0.404 0.527 0.278 0.282 0.492 Species *N*W 12 0.458 0.670 0.931 0.832 0.760 0.976 0.571

Growth form 2 <0.001 0.162 <0.001 0.003 0.008 0.003 0.020 N 1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 W 1 0.798 0.052 0.293 0.453 0.184 0.365 0.109 N*W 1 0.080 0.098 0.132 0.430 0.164 0.350 0.023 Growth form *N 2 0.222 0.699 0.008 0.046 0.047 0.090 0.071 Growth form *W 2 0.982 0.752 0.895 0.642 0.691 0.856 0.344 Growth form* 2 0.690 0.326 0.685 0.726 0.820 0.801 0.889 N*W

Substrate 1 0.110 0.076 <0.001 0.015 0.764 0.007 0.028 N 1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 W 1 0.767 0.032 0.217 0.541 0.311 0.296 0.261 N*W 1 0.041 0.027 0.063 0.407 0.202 0.216 0.042 Substrate* N 1 0.515 0.407 0.002 0.092 0.681 0.111 0.342 Substrate* W 1 0.852 0.778 0.958 0.387 0.396 0.815 0.146 Substrate *N*W 1 0.429 0.784 0.949 0.558 0.565 0.966 0.992

7 weeks of LNHW 7 weeks of HNHW

16 weeks of LNHW 16 weeks of HNHW

25 weeks of LNHW 25 weeks of HNHW

Fig. 1. Changes in growth and vegetative morphology (at ages 7, 16 and 25 weeks) of Eucalyptus dendromorpha seedlings from Mount Wilson over a 6 month period in response to the following treatments: low nutrient high water (LNHW) and high nutrient high water (HNHW) treatments. (Photography: A. Orme, L. Mou and S. Rutherford). 92

B C

Fig. 2. Total aboveground dry mass of green ash seedlings (subgenus Eucalyptus section Eucalyptus) after 8 months of the following treatments: low nutrients and low water (LNLW), low nutrients and high water (LNHW), high nutrients and low water (HNLW), and high nutrients and high water (HNHW). Results are shown for: A. Species. B. Growth forms. C. Substrates. Values are the mean ± s.e. (n = 4–66). Significant differences between treatments for each species, growth form and substrate are denoted with different letters above columns (P < 0.05, Tukey’s HSD post-hoc comparison). 93

We found many plant-level traits did not vary significantly in response to changes in water. However, stem diameter, total leaf number, total leaf dry mass and total FW/DW varied significantly in response to water across species; only stem diameter varied significantly in response to water across substrates (P < 0.05, Table 2). For individual species, there was no significant effect of water on most plant-level traits (Appendix 2). There were no significant differences in total aboveground dry mass between the LNLW and LNHW treatments or between the HNLW and HNHW treatments for any species, growth form or substrate (Fig. 2). Similar results were found for height and stem diameter (Appendix 3).

We found the interaction of water treatment × nutrient treatment was significant for most plant-level traits (with the exception of total aboveground dry mass) across species pooled (P < 0.05, Table 2). However, the interaction of water treatment × nutrient treatment was only significant for total FW/DW for growth form, and was only significant for height, stem diameter and total FW/DW for substrates (P < 0.05, Table 2). For individual species, interactions of water treatment × nutrient treatment for most plant-level traits were not significant (Appendix 2). Most of the other interactions were not significant for most plant-level traits across species, growth form or substrate (P > 0.05, Table 2).

3.4.2 Plasticity in leaf morphology and functional traits

We found significant differences in leaf length and leaf width between treatments at each monthly measurement for E. fastigata, E. regnans, E. dendromorpha (Fitzroy Falls), E. dendromorpha (Mount Wilson), E. burgessiana, E. obstans, E. triflora and E. stricta (P < 0.05, Fig. 3). For these species, leaf length and width were significantly higher in the high nutrient treatments than the low nutrient treatments. In the case of E. cunninghamii, while differences in leaf length amongst treatments were significant at each monthly measurement, no significant difference in leaf width was found between treatments at 11, 20 and 25 weeks of age.

E. regnans E. fastigata

E. burgessiana E. obstans

E. stricta E. cunninghamii

Fig. 3. Leaf length and leaf width over a 6 month period of selected species in response to the following treatments: low nutrient and low water (LNLW), low nutrient and high water (LNHW), high nutrient and low water (HNLW) and high nutrient and high water (HNHW). Symbols for leaf length under the different treatments are: LNLW (■), LNHW (■), HNLW (■) and HNHW (□); and for leaf width are: LNLW (▲), LNHW (▲), HNLW (▲) and HNHW (∆).Values are the mean ± s.e. (n = 17–58). Differences in leaf length and leaf width across treatments at each time period were significant for Eucalyptus fastigata, E. regnans, E. burgessiana, E. obstans and E. stricta (P < 0.05, two-way ANOVA).

As with plant-level traits, we found all leaf-level traits varied significantly in response to nutrient treatments for species, growth form and substrate at the end of the study period (P < 0.05, Table 3). With the exception of SLA and leaf FW/DW, leaf-level traits were significantly different amongst species in response to nutrients (P < 0.05, Table 3). All leaf-level traits varied significantly amongst growth forms, while the majority of leaf-level traits (except for leaf width) varied significantly between substrates (P < 0.05, Table 3). For most species, leaf-level traits varied significantly in response to nutrients (P < 0.05, Appendix 4). Differences in leaf length and leaf width at the end of the eight month study period were significant across nutrient treatments for all species except for E. regnans, E. obliqua, E. laophila, E. apiculata and E. cunninghamii (P < 0.05, Appendix 5). Although significant differences in SLA across nutrient treatments was found for most species (P < 0.05, Appendix 4), differences in SLA between treatments (LNLW, LNHW, HNLW and HNHW) for many species (namely E. fastigata, E. dendromorpha from Mount Wilson, E. burgessiana, E. obstans, E. langleyi, E. laophila, E. apiculata and E. cunninghamii) were not significant (Fig. 4). Similarly, between- treatment differences (LNLW, LNHW, HNLW and HNHW) in leaf thickness were not significant for E. regnans, E. obliqua, E. triflora, E. dendromorpha (Mount Wilson), E. burgessiana, E. langleyi, E. stricta and E. laophila (Appendix 6).

We found that leaf-level traits did not vary significantly in response to changes in water across species, growth forms and substrates (Table 3). For individual species, there was only a significant effect of water on leaf FW/DW in E. regnans and E. obliqua, leaf length in E. stricta and petiole length in E. obstans and E. apiculata (P < 0.05, Appendix 4). Differences in all other leaf-level traits across water treatments were not significant for any species. No significant differences were found between LNLW and LNHW or between HNLW and HNHW for leaf length, leaf width, SLA and leaf thickness in any growth form or substrate.

Nutrient × water treatment interactions across species, growth forms and substrate were not significant for most leaf-level traits (Table 3). Similarly, all other interactions were not significant for most leaf-level traits across species, growth form and substrate (Table 3). For individual species, nutrient × water treatment interaction had a significant effect on leaf length in E. regnans, leaf width in E. regnans and E. fastigata; leaf thickness in E. apiculata, E. cunninghamii and E. dendromorpha (Fitzroy Falls); leaf FW/DW in E.

96 dendromorpha (Fitzroy Falls); leaf area in E. regnans and E. fastigata; and SLA in E. stricta (P < 0.05, Appendix 4).

Table 3. Significance levels from analysis of variance (ANOVA) of leaf-level traits Differences across species, growth form, substrate, nutrient (N) treatment, water (W) treatment are shown, as well as interactions between factors. Abbreviations: d.f., degrees of freedom; FW/DW, fresh mass versus dry mass; SLA, specific leaf area.

Leaf Leaf Petiole Leaf Leaf Leaf d.f. SLA length width length thickness area FW/DW Species 12 0.004 <0.001 0.003 0.029 <0.001 0.223 0.059 N 1 <0.001 <0.001 <0.001 <0.001 0.002 <0.001 <0.001 W 1 0.670 0.967 0.321 0.615 0.931 0.296 0.079 N*W 1 0.052 0.040 0.113 0.473 0.071 0.160 0.060 Species* N 12 0.162 0.032 0.021 0.849 0.003 0.519 0.510 Species *W 12 0.525 0.396 0.640 0.510 0.251 0.960 0.351 Species *N*W 12 0.434 0.372 0.607 0.185 0.611 0.309 0.381

Growth form 2 <0.001 <0.001 0.016 <0.001 <0.001 <0.001 0.002 N 1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 W 1 0.339 0.739 0.603 0.929 0.521 0.410 0.103 N*W 1 0.126 0.188 0.402 0.272 0.264 0.033 0.047 Growth form *N 2 0.704 0.836 0.539 0.753 0.419 0.028 0.020 Growth form *W 2 0.637 0.903 0.837 0.818 0.682 0.766 0.900 Growth form* N*W 2 0.667 0.683 0.981 0.630 0.833 0.623 0.993

Substrate 1 <0.001 0.615 0.039 <0.001 0.001 <0.001 0.003 N 1 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 W 1 0.467 0.908 0.412 0.660 0.774 0.687 0.089 N*W 1 0.072 0.137 0.425 0.315 0.234 0.052 0.044 Substrate* N 1 0.480 0.708 0.311 0.449 0.212 0.033 0.264 Substrate* W 1 0.630 0.986 0.905 0.633 0.883 0.609 0.811 Substrate *N*W 1 0.376 0.404 0.850 0.496 0.578 0.542 0.963

B C

Fig. 4. Specific leaf area (SLA) after 8 months of the following treatments: low nutrients and low water (LNLW), low nutrients and high water (LNHW), high nutrients and low water (HNLW), and high nutrients and high water (HNHW). Results are shown for: A. Species. B. Growth forms. C. Substrates. Values are the mean ± s.e. (n = 4–66). Significant differences between treatments for each species, growth form and substrate are denoted with different letters above columns (P < 0.05, Tukey’s HSD post-hoc comparison).

3.4.3 Phylogenetic analysis of plasticity

We found values for whole plant phenotypic plasticity (plant-level PI) were greater than those of leaf functional traits (leaf-level PI) for the study species (Fig. 5). Eucalyptus fastigata had the highest overall mean PI, followed by E. burgessiana, E. dendromorpha (Fitzroy Falls) and E. stricta. Species with lower overall mean PI values were E. regnans, E. laophila, E. apiculata, E. triflora and E. cunninghamii.

Bayesian analyses produced a phylogeny with 12 nodes, eight of which had a Bayesian Posterior Probability greater than 0.95 (Fig. 5). The phylogeny produced here was consistent with Rutherford et al. (2016) in terms of the same overall topology and similar groupings of species. In the present study, the tall trees on deep, fertile soils formed a clade that was sister to the medium trees and mallees on skeletal and sandy soils. However, there was no apparent pattern between plant-level, leaf-level or overall mean plasticity and phylogenetic relationships. For example, although the tall trees, E. regnans and E. fastigata, were in the same clade, they had the lowest and highest overall mean plasticity indices (PI) respectively. Similarly, a mallee from skeletal and sandy soils, E. stricta, had the second highest plant-level PI, yet was in the same clade as another mallee on skeletal and sandy soils, E. apiculata, which had the lowest plant- level PI. This was supported by the BayesTraits analysis, with λ values for plant-level PI, leaf level PI and overall mean PI being less than 0.5, indicating that plasticity was not strongly correlated with phylogeny (Table 4). Log BF indicated that the models with the estimated λ values for plant-level, leaf-level and overall mean PI better described the data than the models when λ was fixed at either 1 (where trait evolution is correlated with phylogenetic relationships) or 0 (trait evolution is independent of the phylogeny). Log BF values were all less than 2 indicating that the estimated λ model was more appropriate than when λ was fixed at either 1 or 0 (Table 4).

Fig. 5. Comparison between the Bayesian phylogeny and the overall mean, plant-level and leaf-level phenotypic plasticity index (PI) of the study species. Character states were traced onto the phylogeny using Mesquite version 3.03 and show: trees on deep, fertile soils (■); medium trees on skeletal and sandy soils (■); and mallees on skeletal and sandy soils (□). PI values are the mean (n = 7–14).

100

Table 4. Phylogenetic signal (Pagel’s lambda or λ) for plant-level, leaf-level and overall mean phenotypic plasticity index (PI) The mean log-likelihood and harmonic mean from each analysis is shown. Also shown are two times the log marginal likelihood difference (log BF) between the estimated model and a model in which λ is fixed at either 1 or 0. Abbreviations: BF, Bayes Factor; HPD, highest posterior density. λ fixed λ fixed at Log BF Log BF λ estimated at 0 1 for λ= 0 for λ= 1 Plant-level PI

Harmonic mean 7.981 7.131 7.001 -1.7 -1.96 Mean log-likelihood 9.364 9.564 9.112

Mean λ (95 % HPD) 0.497 (0.00009, 0.947) 0 1

Leaf-level PI

Harmonic Mean 13.28 12.874 14.031 -0.812 1.502 Mean log-likelihood 14.725 15.075 14.095

Mean λ (95 % HPD) 0.458 (0.000006, 0.928) 0 1

Overall mean PI

Harmonic mean 12.249 12.399 11.593 0.3 -1.312 Mean log-likelihood 13.835 14.214 13.171

Mean λ (95 % HPD) 0.453 (0.0000006, 0.926) 0 1

3.5 Discussion

3.5.1 Plasticity in seedling functional traits of the green ash eucalypts

We found high levels of phenotypic plasticity in many plant- and leaf-level traits across nutrient treatments for most species. This is consistent with our predictions, and the findings of previous studies demonstrating phenotypic plasticity in plant morphology and physiology (e.g. Cordell et al. 1998; Hovenden and Vander Schoor 2004; Herben et al. 2014). A number of leaf functional traits (e.g. leaf FW/DW and SLA) did not vary significantly between resource treatments across most of the study species. This was consistent with the findings of Schulze et al. (2006) who found that Eucalyptus species growing in different rainfall environments maintained their SLA, and with the results of Steane et al. (2017) who found SLA in E. loxophleba to be stable across environments. Similarly, Hovenden and Vander Schoor (2004) found that SLA in Nothofagus

101 cunninghamii was not as plastic as leaf length and width. They suggested that traits such as SLA are under stronger genetic control than other leaf morphological characters.

Unexpectedly, many plant- and leaf-level traits did not vary significantly across water treatments. In fact, when water availability was lowered below a certain threshold during the period of growth (less than 30% of the high water treatment) plants would start to die. These findings are likely to be associated with the current distribution of the study species, which all occur in higher rainfall environments (700‒1800 mm annual rainfall; Benson and McDougall 1998) and are therefore probably not well adapted to prolonged periods of drought. A number of studies have demonstrated that eucalypts may be sensitive to water stress. For example, Stoneman et al. (1994) found that rates of leaf growth and photosynthesis in seedlings of E. marginata were sensitive to water deficits. Similarly, eucalypt species from drier habitats were found to maintain lower osmotic potentials under both well-watered and water deficit conditions than eucalypt species from higher rainfall environments (this included E. obliqua, Merchant et al. 2007). Leaf traits in many Eucalyptus species may show low overall variation to water availability within and between populations. For example, leaf traits were not correlated with water availability between populations across rainfall gradients in E. sideroxylon (Warren et al. 2005). Similarly, in a study of 28 eucalypt species, differences in hydraulic traits were mainly genotypic in origin rather than environmentally plastic (with no indication of plasticity in these traits across an aridity gradient; Pfautsch et al. 2016). Furthermore, Pita et al. (2003) found that there were no significant differences in xylem vulnerability to cavitation between clones of E. globulus. However, in E. tricarpa, the degree of plasticity of leaf size and leaf density differed between provenances from high rainfall areas compared to provenances from drier environments (McLean et al. 2014). In growth experiments, it can be difficult to subject plants to the appropriate levels of water stress without causing plant death. Therefore, future research focusing on the water relations and hydraulic traits of the study species is required before conclusions are drawn.

102

3.5.2 Role of plasticity in the evolution and diversification of the green ashes

We found phenotypic plasticity (plant-level, leaf-level and overall mean PI) was not strongly correlated with phylogeny, nor was it associated with growth form. This suggests that there is low phylogenetic conservatism in the degree of plasticity in section Eucalyptus and that plasticity perhaps reflects a convergent evolutionary strategy (e.g. Valladares et al. 2000; Godoy et al. 2011; Herben et al. 2014). Unexpectedly, phenotypic plasticity (plant-level, leaf-level and overall mean PI) was not associated with substrate. Although species in section Eucalyptus occupy high and low fertility soils, they occur along an altitudinal gradient (0‒1200 m; Benson and McDougall 1998), with many other environmental factors (e.g. temperature and light) being highly variable between habitats. In the present study, SLA was higher in plants from deep, fertile soils than those from skeletal and sandy soils. This finding was consistent with that found in a range of species. For example, higher SLA was found for plants from higher fertility soils in a study of 79 perennial species from high and low nutrient habitats (Wright et al. 2001). A similar pattern in SLA was found for temperate rainforest tree species from high and low fertility sites (Lusk et al. 1997). However, SLA of each growth form and of plants from each substrate in the present study was significantly higher in the low nutrient treatments than in the high nutrient treatments. Once again, this was somewhat surprising as high SLA is often correlated with higher nutrient availability (e.g. Wright et al. 2001, 2004). SLA is also positively correlated with relative growth rate (Lusk et al. 1997; Wright and Westoby 1999, 2000). Negative relationships between relative growth rate and leaf nitrogen concentration have been found for other species and it has been suggested this may be because under high nutrient supply, slower growing species store essential nutrients to support growth after soil reserves are exhausted (‘luxury consumption’, Chapin 1980; Tateno and Chapin 1997). This type of mechanism may explain the higher SLA in the lower nutrient treatments found for many of the species here.

Phenotypic plasticity, which was once considered to be ‘noise’ masking the true appearance of organisms, is increasingly being recognised to be genetically controlled, heritable and of significance to the evolution of species (Bradshaw 2006; Nicotra et al. 2010). For example, phenotypic plasticity can enhance the ability of a species to occur across diverse and variable habitats (Sultan 2000) and more widespread species are

103 thought to be better able to capitalise on increased availability of resources (Dawson et al. 2012). In the present study, the rare species, Eucalyptus cunninghamii, was not as plastic as other species in leaf-level PI and this may explain why this species is restricted to higher altitude escarpments and cliff edges, generally in the upper Blue Mountains. Eucalyptus obliqua and E. fastigata had higher leaf-level PI and this could account for why they are more widespread (E. obliqua has ecotypes, which are able to cope with a wide range of environmental conditions; Eldridge et al. 1993). Eucalyptus regnans, which is also a widespread species, had the lowest leaf-level and overall mean PI. However, although E. regnans is widespread, it has a very narrow environmental range, occurring on high fertility, well-drained soils with high and reliable rainfall (Tng et al. 2012), where fire is the primary form of natural disturbance (Burns et al. 2015). Forests dominated by E. regnans can have a high fire intensity (McCarthy et al. 1999). Eucalyptus regnans is a fire-sensitive eucalypt (Waters et al. 2010), and is an obligate seeder and therefore not capable of resprouting following fire (Nicolle 2006).

3.5.3 Implications for species delimitation and consequences for conservation and management

Much of the work on species delimitation in Eucalyptus has focused on seedling or juvenile morphology (e.g. Ladiges et al. 1989, 2010; Brooker 2000; Slee et al. 2006). The size and shape of juvenile leaves are important taxonomic characters in eucalypts, with juvenile leaf traits frequently being used to discriminate between species with similar adult foliage (King 1999). Because species are one of the fundamental units of analysis in biology, improving our knowledge of species boundaries is essential when attempting to address many evolutionary, biogeographic, and ecological questions (Rissler and Apodaca 2007; Wiens 2007). The success of conservation efforts also requires species to be clearly defined for their effective management (Allendorf and Luikart 2007; Flot et al. 2010).

In the present study, there were significant differences in leaf width for many species in response to changes in nutrients. Many species that had lower leaf-level PI, such as E. cunninghamii and E. triflora, were relatively easy to identify in the field (E. cunninghamii is distinguished on the basis of its thin, silvery-green leaves, while E.

104 triflora usually has a three-flowered inflorescence). The high degree of phenotypic plasticity in juvenile leaves for many taxa in the present study is consistent with Rutherford et al. (2016), where adult leaf morphology for many green ash species was found to be highly variable. These findings suggest that phenotypic plasticity in leaf morphology may have confounded species delimitations in section Eucalyptus and that some traits that are regarded as key diagnostic features (e.g. leaf width) are not so useful in understanding evolutionary relationships and patterns of divergence. In the present study, petioles were present on the first true (opposite) leaves of E. regnans, E. fastigata, E. obliqua, and E. cunninghamii. In contrast, the first true opposite leaves were sessile in all the other study species. In the Bayesian phylogeny, the tall trees (E. regnans, E. fastigata and E. obliqua) were monophyletic and occupied a position as sister to the remainder of the group, while E. cunninghamii was sister to a clade comprising all other green ash mallees and medium trees. Therefore, petiole presence or absence on the first true opposite leaves is associated with phylogenetic relationships and should be investigated as a potentially genetically fixed trait in section Eucalyptus.

In groups like the eucalypts, where reproductive isolation is weak and hybridisation occurs between closely related species, deciding how species and other entities should be delineated for management can be difficult and problematic (Shepherd and Raymond 2010). If trait differences in natural populations of some species are highly responsive to changes in environment, then incorrect species boundaries in the green ashes may have been drawn. There has been considerable disagreement regarding the number of recognised species in the green ash group. While Brooker (2000) considers there to be only 13 species in the group, other authorities recognise more (e.g. Hill 2002). However, where species boundaries are drawn in the green ashes has significant implications for their management and conservation. For example, many populations of E. burgessiana occur in protected areas (in the GBMWHA). In contrast, E. obstans, which was once widespread in the Sydney region is much less common (Benson and McDougall 1998), with some populations occurring outside protected areas (e.g. the Beacon Hill population). If E. burgessiana and E. obstans are considered to be the one species (as defined by Brooker 2000), then they may be regarded as sufficiently conserved under the current protected areas. However, in Rutherford et al. (2016), E. obstans and E. burgessiana did not form a clade. Therefore if considered to be two separate taxa (as defined by Hill 2002), then E. obstans may not be sufficiently

105 protected. A limitation of the current study is that with the exception of E. dendromorpha, trait variation was investigated in only one population per species. It is well known that there can be a high degree of intraspecific variation in functional traits in natural populations (Dalla-Salda et al. 2009; Bolnick et al. 2011). A future study investigating population-level differences in functional traits and morphology of the study species would therefore provide further insights into their plasticity, especially in species where there are taxonomic issues (e.g. E. stricta, E. burgessiana, E. obstans, E. apiculata and E. laophila).

The ecological impacts of climate change will be largely dependent on the ability of species to respond to changing conditions (McLean et al. 2014). The survival and persistence of plant species under climate change will be influenced by the extent of plasticity in functional traits, as well as a number of other factors, such as reproduction, recruitment, dispersal, population size, and competition (Lavergne et al. 2010). It has been suggested that species and populations that display higher plasticity will be able to adjust more rapidly and therefore, may be in a better position to respond to changing climates (West-Eberhard 2005; Lande 2009). The high degree of phenotypic plasticity of some species observed here (e.g. E. fastigata and E. stricta) could therefore be advantageous under climate change because it could allow those species to respond to new and variable environments. Conversely, species that are less plastic and are restricted in distribution (e.g. E. cunninghamii and E. triflora) could be particularly vulnerable. For example, leaf size has been found to be highly plastic in some other species of Eucalyptus (e.g. E. tricarpa, McLean et al. 2014). It has been suggested that high plasticity in leaf size may enable leaf hydraulic, stomatal and boundary layer conductance to be adjusted in response to changes in evaporative demand and light availability (Yates et al. 2010). Other traits where high plasticity may be advantageous in response to altered environmental conditions are seedling height and plant biomass (which are both associated with plant performance, Poorter et al. 2008). It should be noted that cases have been reported where high plasticity in a particular trait has been found to be maladaptive. For example, seedlings of woody Mediterranean species that were plastic in response to light availability (i.e. had greater stem elongation in the shade), were found to be severely affected by cold temperatures (Sánchez-Gómez et al. 2006; Valladares et al. 2007). Therefore, plasticity in a trait can become detrimental when the organism is exposed to different environmental stresses (Valladares et al.

106

2007). In regards to the present study, given the large number of rare taxa in this group and that it includes many commercially important species, future investigations into plastic responses to changing environments is warranted. For example, investigations into both seedling and adult plant responses to changes in temperature and atmospheric carbon would further enhance our understanding of the ability of rare and common green ash species to cope with climate change.

3.6 Conclusions

We found significant plasticity in plant and leaf functional traits in response to changes in resource treatments (particularly nutrients). Some traits (e.g. SLA) were not significantly variable across treatments in many species and should be investigated as potentially fixed adaptive traits. There was low phylogenetic conservatism in plant and leaf-level plasticity in the green ashes. Our results suggest that plasticity in leaf morphology (especially leaf width) of many of the study species may have confounded species delimitations, and this has significant implications for their management due to the many rare and restricted taxa, as well as the commercially important species within the group. We found more widespread species to have higher leaf-level plasticity than species with more restricted distributions. A better understanding of plasticity of the green ashes is likely to be important in predicting their ability to adapt under future climate change.

Acknowledgements

We thank Geoff McDonnell, Josh Griffiths, Clara Pang, Justin Wan, Richard Johnstone, Andrew Orme, Joel Cohen, Aaron Smith and Lawrence Mou for technical support. We thank Maureen Phelan for advice on experimental design, Chris Allen for advice on substrates, Fatih Fazlioglu for advice on using the plasticity index, Peter D. Wilson and Jason Bragg for advice on data analysis, and Miguel Garcia for facilitating access to resources in the Daniel Solander Library (RBG Sydney). Collection of seeds operated under the Royal Botanic Gardens and Domain Trust (New South Wales). S. Rutherford was in receipt of an Australian Postgraduate Award at the time this research was

107 undertaken. The authors thank Jean-François Flot for comments that improved the manuscript.

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Appendix 1. Table of characters used for Mesquite analysis

Species Growth form Substrate E. regnans Tree Basalt E. fastigata Tree Basalt E. obliqua Tree Basalt E. triflora Medium tree Sandstone E. dendromorpha Fitzroy Falls Medium tree Sandstone E. dendromorpha GBMWHA Mallee Sandstone E. burgessiana Mallee Sandstone E. obstans Mallee Sandstone E. langleyi Mallee Sandstone E. stricta Mallee Sandstone E. laophila Mallee Sandstone E. apiculata Mallee Sandstone E. cunninghamii Mallee Sandstone

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Appendix 2. Multivariate analysis of variance (MANOVA) of plant-level traits for each species showing differences across nutrient (N) and water (W) treatments, and interactions between the two treatments Abbreviations: d.f., degrees of freedom; FW/DW, fresh mass versus dry mass; GBMWHA, Greater Blue Mountains World Heritage Area; MS, mean square; P, significance. Height Stem diameter Total leaf number Aboveground dry mass Stem dry mass Leaf dry mass Total FW/DW

N W N*W N W N*W N W N*W N W N*W N W N*W N W N*W N W N*W d.f. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Species E. regnans MS 50504 19612 78730 21.47 0.00 2.21 5081 21.28 726.6 65.19 1.77 6.50 8.24 0.49 0.31 27.07 0.40 3.98 0.83 0.47 0.06 F 3.36 1.31 5.24 16.45 0.00 1.69 11.18 0.05 1.60 14.34 0.39 1.43 14.10 0.84 0.53 14.08 0.21 2.07 12.96 7.28 0.94 P 0.080 0.266 0.032 0.001 0.986 0.207 0.003 0.831 0.219 0.001 0.539 0.245 0.001 0.369 0.477 0.001 0.654 0.164 0.002 0.013 0.343

E. fastigata

MS 446294 926 27094 24.65 0.08 0.63 17751 10.32 22.32 76.83 0.27 0.92 13.18 0.14 0.20 26.37 0.02 0.26 3.34 0.02 0.02 F 21.15 0.04 1.28 25.49 0.08 0.65 15.29 0.01 0.02 16.72 0.06 0.20 13.82 0.15 0.21 18.95 0.02 0.19 10.46 0.06 0.07 P <0.001 0.836 0.268 <0.001 0.774 0.429 0.001 0.926 0.891 <0.001 0.811 0.659 0.001 0.705 0.649 <0.001 0.903 0.670 0.004 0.811 0.791

E. obliqua MS 69601 2840 4526 15.51 4.94 0.03 6151 996.0 211.8 21.66 0.01 8.31 7.04 0.24 0.18 19.13 3.94 0.01 2.33 0.14 0.34 F 7.31 0.30 0.48 10.39 3.31 0.02 19.36 3.14 0.67 2.90 0.00 1.11 9.03 0.30 0.23 7.54 1.55 0.00 18.33 1.07 2.66 P 0.012 0.590 0.497 0.004 0.081 0.890 <0.001 0.089 0.422 0.102 0.972 0.302 0.006 0.588 0.635 0.011 0.225 0.957 <0.001 0.311 0.116

E. triflora MS 520684 42.0 3756 21.37 0.50 0.02 1553 69.94 120.0 246.8 4.10 1.87 29.30 1.12 0.65 106.0 0.93 0.32 3.36 0.29 0.02 F 47.76 0.00 0.35 32.72 0.77 0.03 59.34 2.67 4.59 51.52 0.86 0.39 49.71 1.91 1.10 48.50 0.43 0.15 22.48 1.93 0.10

P <0.001 0.951 0.563 <0.001 0.389 0.871 <0.001 0.116 0.043 <0.001 0.365 0.538 <0.001 0.181 0.306 <0.001 0.521 0.706 <0.001 0.178 0.751 121

E. dendromorpha Fitzroy Falls MS 201451 1119 0.32 32.36 0.05 0.18 2721 3.57 36.57 119.5 0.68 0.81 13.83 0.01 0.01 52.03 0.53 0.65 2.74 0.08 0.73 F 15.70 0.09 0.00 33.04 0.05 0.18 23.64 0.03 0.32 33.23 0.19 0.23 31.44 0.02 0.02 31.79 0.32 0.40 15.77 0.46 4.21 P 0.001 0.770 0.996 <0.001 0.830 0.675 <0.001 0.862 0.578 <0.001 0.668 0.639 <0.001 0.887 0.888 <0.001 0.575 0.534 0.001 0.504 0.051

E. dendromorpha GBMWHA MS 280200 612.9 8058 37.49 1.60 0.04 3344 343.0 782.3 122.7 3.22 4.26 17.43 0.81 0.79 47.65 0.80 1.39 0.60 0.01 0.30 F 19.49 0.04 0.56 33.51 1.43 0.03 27.61 2.83 6.46 24.21 0.64 0.84 30.16 1.41 1.36 20.35 0.34 0.59 3.80 0.08 1.90 P <0.001 0.838 0.461 <0.001 0.243 0.857 <0.001 0.105 0.018 <0.001 0.433 0.368 <0.001 0.247 0.255 <0.001 0.565 0.449 0.063 0.780 0.181

E. burgessiana MS 505720 68310 59340 47.22 4.10 2.91 302.3 3.57 104.1 177.3 29.33 19.00 18.56 3.61 3.13 81.12 12.36 6.70 1.87 0.95 0.13 F 20.71 2.80 2.43 28.01 2.44 1.72 11.63 0.14 4.01 19.02 3.15 2.04 21.22 4.13 3.58 17.42 2.65 1.44 5.95 3.03 0.41 P <0.001 0.107 0.132 <0.001 0.132 0.202 0.002 0.714 0.057 <0.001 0.089 0.166 <0.001 0.053 0.071 <0.001 0.116 0.242 0.022 0.095 0.528

E. obstans MS 322081 2088 4819 38.51 1.66 1.88 1447 107.7 167.0 204.9 1.32 0.28 14.92 0.52 0.39 109.2 0.18 0.01 0.94 0.32 0.83 F 30.48 0.20 0.46 15.56 0.70 0.76 15.88 1.18 1.83 23.22 0.15 0.03 27.28 0.95 0.71 20.80 0.04 0.00 2.33 0.78 2.04 P <0.001 0.661 0.506 0.001 0.422 0.393 0.001 0.289 0.190 <0.001 0.703 0.861 <0.001 0.339 0.410 <0.001 0.854 0.967 0.141 0.386 0.167

E. langleyi MS 263284 23280 345.2 25.53 0.00 0.26 129.5 20.06 0.06 171.5 2.08 0.84 11.00 1.07 0.60 95.65 0.17 0.02 0.54 0.63 0.76 F 11.95 1.06 0.02 13.02 0.00 0.13 6.50 1.01 0.00 11.99 0.15 0.06 11.54 1.12 0.63 11.74 0.02 0.00 1.24 1.43 1.75

P 0.002 0.315 0.902 0.002 0.980 0.719 0.018 0.327 0.957 0.002 0.707 0.810 0.003 0.301 0.435 0.002 0.888 0.961 0.277 0.244 0.200

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E. stricta MS 183507 10466 160.9 65.93 6.63 8.66 8999 1140 688.1 107.3 6.30 4.35 11.65 0.55 0.41 48.26 3.07 2.09 1.66 0.34 0.06 F 27.02 1.54 0.02 36.84 3.71 4.84 47.18 6.00 3.61 42.25 2.45 1.71 39.29 1.86 1.37 42.24 2.69 1.83 8.31 1.71 0.29 P <0.001 0.227 0.879 <0.001 0.067 0.038 <0.001 0.023 0.070 <0.001 0.131 0.204 <0.001 0.185 0.253 <0.001 0.115 0.189 0.008 0.204 0.595

E. laophila MS 34321 3384 24019 9.17 0.09 2.77 2221 68.01 408.0 25.94 1.27 3.40 3.08 0.13 0.31 11.15 0.58 1.67 0.34 0.08 0.00 F 2.47 0.24 1.73 5.74 0.05 1.74 9.24 0.28 1.70 7.86 0.38 1.03 9.01 0.39 0.89 7.12 0.37 1.06 2.80 0.64 0.02 P 0.132 0.627 0.204 0.027 0.820 0.203 0.007 0.601 0.208 0.011 0.543 0.323 0.007 0.540 0.356 0.015 0.550 0.315 0.111 0.432 0.890

E. apiculata

MS 473.5 43.8 24819 1.63 1.86 5.28 78.82 0.27 504.3 1.50 0.63 3.26 0.09 0.03 0.14 0.87 0.39 2.06 0.70 0.18 0.27 F 0.08 0.01 4.39 1.99 2.27 6.45 0.53 0.00 3.41 1.86 0.78 4.06 2.45 0.83 3.97 1.71 0.77 4.06 2.01 0.50 0.78 P 0.776 0.931 0.055 0.181 0.154 0.024 0.477 0.966 0.086 0.194 0.392 0.064 0.140 0.379 0.066 0.212 0.397 0.063 0.178 0.490 0.392

E. cunninghamii

MS 16650 11077 3202 11.59 2.57 1.65 18322 145.8 90.87 8.52 1.57 1.30 1.36 0.26 0.21 3.07 0.54 0.43 0.62 0.09 0.13 F 2.37 1.58 0.46 14.22 3.16 2.03 20.40 0.16 0.10 13.20 2.43 1.90 14.09 2.74 2.16 12.42 2.20 1.72 6.24 0.90 1.35 P 0.138 0.222 0.507 0.001 0.089 0.169 <0.001 0.691 0.753 0.001 0.134 0.182 0.001 0.112 0.156 0.002 0.152 0.204 0.020 0.352 0.258

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Appendix 3. Height and stem diameter of species, growth forms and substrate after 8 months of the following treatments: low nutrient low water (LNLW), low nutrient high water (LNHW), high nutrient low water (HNLW) and high nutrient high water (HNHW) Values are the mean ± se. (n = 4–66). Differences in diameter and height amongst species are significant, while differences in height amongst growth forms are significant (P < 0.001). Significant differences between treatments for each species, growth form and habitat are denoted with different letters after values (P < 0.05, Tukey’s HSD post-hoc comparison). Height Stem diameter LNLW LNHW HNLW HNHW LNLW LNHW HNLW HNHW Species E. regnans 377.14±49.88a 210.4±52.86a 355±54.61a 410.71±32.76a 2.87±0.44a 2.27±0.42a 4.12±0.5ab 4.7±0.39b E. obliqua 195.14±18.79a 240.71±45.89a 320.29±44.38a 315±31.85a 2.14±0.26a 2.91±0.37ab 3.56±0.61ab 4.47±0.52b E. fastigata 202±37.43ab 151.29±44.22a 392.29±63.05bc 466±68.73c 1.57±0.26a 1.38±0.18a 3.15±0.52b 3.56±0.43b E. triflora 330.29±24.71a 309.14±46.18a 585±33.09b 611.14±49.45b 2.82±0.19a 3.05±0.35a 4.56±0.36b 4.88±0.33b E. dendromorpha Fitzroy Falls 286.86±55.85ab 274.43±35.08a 456.71±25.35c 443.86±48.39bc 1.9±0.29a 2.14±0.33a 4.21±0.33b 4.14±0.51b E. dendromorpha Mount Wilson 264.14±45.62a 307.43±42.9ab 498.14±31.97bc 473.57±57.21b 2.37±0.43a 2.92±0.25a 4.76±0.28b 5.16±0.56b E. burgessiana 223±36.22a 229.71±34.04a 399.71±51.24ab 590.57±94.11b 2.24±0.23a 2.37±0.32ab 4.2±0.51bc 5.61±0.74c E. obstans 275.71±38.28a 230.43±50.93a 471.67±30.81b 481±33.4b 2.95±0.48a 2.92±0.78a 4.86±0.6ab 5.9±0.53b E. langleyi 229±32.33ab 176.29±27.31a 438.17±89.94b 370.83±74.59ab 2.63±0.32a 2.44±0.32a 4.42±0.53a 4.63±0.93a E. stricta 243.5±15.22ab 199.14±34.49a 403.86±43.49c 369.29±21.75bc 2.06±0.22ab 1.92±0.35a 4.06±0.67bc 6.19±0.61c E. laophila 319.33±27.24a 230.17±54.89a 332±63.03a 372.5±48.86a 2.94±0.24a 2.12±0.45a 3.51±0.65a 4.09±0.68a E. apiculata 191.75±39.09a 119.5±43.22a 126.75±36.4a 205.33±27.34a 1.98±0.11ab 1.53±0.38ab 1.49±0.36a 3.24±0.52b E. cunninghamii 191±32.43a 210.14±23.62a 219.5±39.87a 283.17±36.2a 1.47±0.13a 1.6±0.25a 2.31±0.33ab 3.45±0.62b

Growth form

Tall tree 258.1±27.91ab 199.79±27.32a 355.86±30.59bc 397.24±29.59c 2.19±0.22a 2.18±0.23a 3.61±0.31b 4.24±0.27b Medium tree 308.57±29.95a 291.79±28.27a 515.92±26.92b 527.5±40.53b 2.36±0.21a 2.6±0.26a 4.37±0.24b 4.51±0.31b Mallee 243.61±12.82a 217.9±14.6a 376.15±23.48b 398.25±24.41b 2.35±0.12a 2.27±0.16a 3.83±0.23b 4.83±0.26c

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Substrate

Deep, fertile soils 258.1±27.91ab 199.79±27.32a 355.86±30.59bc 397.24±29.59c 2.19±0.22a 2.18±0.23a 3.61±0.31b 4.24±0.27b Skeletal and sandy soils 257.6±12.29a 233.58±13.41a 405.93±20.62b 426.09±21.94b 2.35±0.11a 2.34±0.14a 3.95±0.19b 4.76±0.22b

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Appendix 4. Multivariate analysis of variance (MANOVA) of leaf-level traits for each species showing differences across nutrient (N) and water (W) treatments, and interactions between the treatments Abbreviations: d.f., degrees of freedom; FW/DW, fresh mass versus dry mass; GBMWHA, Greater Blue Mountains World Heritage Area; MS, mean square; P, significance; SLA specific leaf area.

Leaf FW/DW Leaf Length Leaf width Petiole length Leaf thickness Leaf area SLA

N W N*W N W N*W N W N*W N W N*W N W N*W N W N*W N W N*W d.f. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Species E. regnans MS 1.39 0.54 0.01 2211 2095 4708 567.3 162.8 838.7 35.12 18.67 13.99 0.00 0.00 0.00 4009777 2887851 8554666 206.1 18.23 0.20 F 11.97 4.66 0.04 2.23 2.11 4.75 3.86 1.11 5.70 3.16 1.68 1.26 4.63 0.09 0.61 2.64 1.90 5.63 15.30 1.35 0.02 P 0.002 0.042 0.842 0.149 0.160 0.040 0.062 0.304 0.026 0.089 0.209 0.274 0.043 0.765 0.444 0.118 0.182 0.027 0.001 0.257 0.905

E. fastigata MS 2.66 0.07 0.37 7214 80.48 1278 504.4 0.53 154.4 48.83 1.17 4.10 0.01 0.00 0.00 3246604 15584 899276 239.0 0.09 14.31 F 6.11 0.16 0.85 18.25 0.20 3.23 25.97 0.03 7.95 12.07 0.29 1.01 19.00 0.75 3.00 26.59 0.13 7.36 3.97 0.00 0.24

P 0.021 0.695 0.367 <0.001 0.656 0.085 <0.001 0.870 0.009 0.002 0.596 0.324 <0.001 0.395 0.096 <0.001 0.724 0.012 0.058 0.969 0.630

E. obliqua MS 3.46 1.71 0.24 256.4 337.3 415.6 91.51 124.4 31.88 9.61 5.74 15.36 0.00 0.00 0.00 554793 818783 959321 105.1 6.86 35.24 F 9.59 4.74 0.66 0.37 0.49 0.60 0.97 1.31 0.34 1.31 0.78 2.09 3.10 1.64 1.99 0.77 1.13 1.33 11.81 0.77 3.96 P 0.005 0.040 0.424 0.548 0.492 0.446 0.336 0.263 0.567 0.264 0.386 0.161 0.091 0.213 0.171 0.390 0.298 0.261 0.002 0.389 0.058

E. triflora MS 4.31 0.24 0.07 4290 1554 117.4 786.0 95.36 18.05 91.80 0.45 1.73 0.01 0.00 0.00 14695935 3397938 135687 92.54 9.76 1.55 F 18.54 1.01 0.28 9.91 3.60 0.27 9.57 1.16 0.22 18.86 0.10 0.36 3.61 1.11 0.03 9.97 2.31 0.09 11.47 1.21 0.19

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P <0.001 0.325 0.599 0.004 0.071 0.607 0.005 0.292 0.644 <0.001 0.764 0.557 0.070 0.303 0.868 0.004 0.143 0.764 0.003 0.283 0.665

E. dendromorpha

Fitzroy Falls MS 4.03 0.00 0.96 7341 1.11 35.95 814.9 5.78 12.94 70.06 1.82 0.18 0.02 0.00 0.01 9689647 148311 261828 154.8 4.31 42.74 F 23.67 0.00 5.63 11.57 0.00 0.06 13.77 0.10 0.22 18.01 0.47 0.05 13.33 0.02 5.38 13.67 0.21 0.37 13.69 0.38 3.78 P <0.001 0.988 0.026 0.002 0.967 0.814 0.001 0.757 0.644 <0.001 0.500 0.830 0.001 0.883 0.029 0.001 0.651 0.549 0.001 0.543 0.064

E. dendromorpha GBMWHA MS 0.41 0.53 0.02 3575 33.61 1335 812.8 0.83 57.21 60.78 0.05 12.00 0.01 0.00 0.00 7257678 22718 1147993 35.66 6.38 11.20 F 2.75 3.53 0.13 6.83 0.06 2.55 16.88 0.02 1.19 19.68 0.02 3.88 7.47 0.12 0.77 11.69 0.04 1.85 5.28 0.94 1.66 P 0.110 0.073 0.724 0.015 0.802 0.123 <0.001 0.896 0.287 <0.001 0.899 0.060 0.012 0.737 0.389 0.002 0.850 0.187 0.031 0.341 0.210

E. burgessiana MS 1.92 1.09 0.80 8946 1373 16.81 1225 472.7 83.40 220.7 0.01 8.53 0.01 0.00 0.00 23596443 8193032 1231235 28.57 0.05 14.43 F 3.81 2.16 1.59 7.20 1.11 0.01 8.59 3.32 0.59 10.57 0.00 0.41 5.32 0.34 1.07 8.70 3.02 0.45 3.40 0.01 1.72 P 0.063 0.154 0.220 0.013 0.304 0.908 0.007 0.081 0.452 0.003 0.980 0.529 0.030 0.566 0.312 0.007 0.095 0.507 0.077 0.939 0.202

E. obstans MS 1.11 0.28 0.79 7887 6.00 311.1 1910 158.3 1.40 114.2 0.40 3.73 0.02 0.01 0.00 24386246 1038132 186333 49.75 0.60 14.48 F 4.31 1.10 3.07 10.47 0.01 0.41 19.79 1.64 0.02 15.51 0.05 0.51 9.23 5.47 0.02 19.51 0.83 0.15 5.66 0.07 1.65 P 0.050 0.306 0.094 0.004 0.930 0.527 <0.001 0.214 0.905 0.001 0.818 0.484 0.006 0.029 0.904 <0.001 0.372 0.703 0.026 0.796 0.213

E. langleyi MS 0.64 1.65 1.22 17459 40.25 219.9 2552 48.19 78.07 247.4 25.47 19.45 0.02 0.00 0.00 68401812 642972 1400556 19.64 9.34 13.70 127

F 1.09 2.82 2.09 10.53 0.02 0.13 11.90 0.23 0.36 11.20 1.15 0.88 3.64 0.00 0.42 12.68 0.12 0.26 1.00 0.47 0.70

P 0.308 0.107 0.163 0.004 0.878 0.719 0.002 0.640 0.552 0.003 0.295 0.358 0.070 0.969 0.522 0.002 0.733 0.615 0.329 0.498 0.414

E. stricta MS 1.86 0.72 0.75 4030 2022 169.3 67.44 8.36 59.49 31.28 0.01 0.01 0.01 0.00 0.00 1177993 187541 270220 111.9 22.86 41.86 F 7.58 2.92 3.05 9.30 4.67 0.39 4.76 0.59 4.19 12.39 0.00 0.00 4.76 1.25 0.08 8.32 1.32 1.91 11.91 2.43 4.46 P 0.011 0.101 0.094 0.006 0.041 0.538 0.040 0.450 0.052 0.002 0.954 0.962 0.040 0.275 0.785 0.008 0.262 0.181 0.002 0.132 0.046

E. laophila MS 0.04 0.03 0.00 68.35 171.3 363.83 0.96 37.90 8.00 0.84 9.16 4.86 0.01 0.00 0.00 15406 113196 35847 2.06 1.35 3.06 F 0.11 0.08 0.01 0.08 0.20 0.43 0.05 1.78 0.38 0.30 3.25 1.72 2.77 0.04 0.13 0.07 0.50 0.16 0.18 0.12 0.27 P 0.741 0.784 0.927 0.781 0.660 0.522 0.834 0.198 0.547 0.591 0.087 0.205 0.112 0.849 0.719 0.797 0.488 0.695 0.677 0.736 0.612

E. apiculata MS 0.08 0.02 0.82 38.74 499.4 2633 1.52 46.46 62.98 1.19 1.87 5.58 0.01 0.00 0.01 29742 416058 550128 25.46 1.67 79.68 F 0.11 0.03 1.08 0.04 0.53 2.80 0.05 1.47 2.00 0.41 0.65 1.93 5.36 5.36 6.97 0.10 1.44 1.90 1.35 0.09 4.21 P 0.750 0.868 0.315 0.842 0.478 0.116 0.829 0.245 0.179 0.531 0.435 0.186 0.036 0.036 0.019 0.753 0.251 0.190 0.265 0.771 0.059

E. cunninghamii MS 0.41 0.00 0.04 328.0 755.0 325.0 6.23 0.04 0.56 2.47 0.99 0.52 0.00 0.00 0.00 34507 14199 18388 13.84 0.07 2.58 F 2.53 0.00 0.24 1.08 2.48 1.07 3.80 0.03 0.34 2.14 0.86 0.46 2.83 0.62 7.10 2.84 1.17 1.51 1.22 0.01 0.23 P 0.126 0.996 0.629 0.310 0.129 0.313 0.064 0.871 0.564 0.157 0.365 0.507 0.107 0.440 0.014 0.106 0.291 0.231 0.282 0.937 0.639

128

Appendix 5. Leaf length (□) and width (■) after 8 months of the following treatments: low nutrients and low water (LNLW), low nutrients and high water (LNHW), high nutrients and low water (HNLW), and high nutrients and high water (HNHW) Results shown for: A. Species. B. Growth forms. C. Substrates. Values are the mean ± s.e. (n = 4–66). Significant differences between treatments for each species, growth form and substrate are denoted with different letters above columns (P < 0.05, Tukey’s HSD post-hoc comparison).

B C

129

Appendix 6. Leaf thickness after 8 months of the following treatments: low nutrients and low water (LNLW), low nutrients and high water (LNHW), high nutrients and low water (HNLW), and high nutrients and high water (HNHW) Results shown for: A. Species. B. Growth forms. C. Substrates. Values are the mean ± s.e. (n = 4–66). Significant differences between treatments for each species, growth form and substrate are denoted with different letters above columns (P < 0.05, Tukey’s HSD post-hoc comparison).

B C

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Chapter 4. Speciation in the presence of gene flow: population genomics of Eucalyptus species along altitudinal and latitudinal gradients in south-eastern Australia

Susan Rutherford, Maurizio Rossetto, Jason G. Bragg, Hannah McPherson, Doug

Benson, Stephen P. Bonser and Peter G. Wilson

Habitats of the green ashes in the Sydney region and Blue Mountains. From top left to bottom right: Pulpit Rock (where Eucalyptus cunninghamii is found), Govetts Leap (where a mallee form of E. dendromorpha occurs), Mount Banks (where E. stricta is found), Wollemi National Park (E. laophila occurs here), Fitzroy Falls (where a tree form of E. dendromorpha occurs) and Beacon Hill (where E. obstans is found).

131

4.1 Abstract

Speciation is a complex process that is fundamental to the origin of biological diversity. While there has been considerable progress in our understanding of speciation, there are still many unanswered questions, especially regarding barriers to gene flow in diverging populations. We examine patterns of genetic variation within and among six closely related Eucalyptus species in subgenus Eucalyptus section Eucalyptus (commonly known as the green ashes) in south-eastern Australia. The green ashes are an ideal group for investigating speciation mechanisms since they are highly diverse, occur across a range of habitats and are considered to be relatively recently radiated. We used reduced representation sequencing to genotype samples from populations across altitudinal and latitudinal gradients in the Sydney region and Greater Blue Mountains World Heritage Area. We found one species, Eucalyptus cunninghamii, to be highly genetically differentiated from the others, and a population of mallees from Mount Banks to also be genetically distinct, and therefore likely to be an undescribed species. Only modest levels of differentiation were found between all the other study species, suggesting ongoing gene flow. There was population structure within some species (e.g. E. obstans and E. dendromorpha) corresponding to altitude and latitude, indicating that vicariance may have played a role in the genetic differentiation and hence the evolution of the group. Our results suggest that the green ashes fit into the framework of the genic view of speciation, with some species being completely diverged and others at earlier stages on the speciation continuum.

4.2 Introduction

Speciation is responsible for the origin of biological diversity (Gavrilets 2003), and typically begins with a barrier to gene flow, which promotes further genetic and phenotypic divergence (Nosil and Feder 2012). Species are traditionally defined as a group of interbreeding populations that are reproductively isolated from other such groups (i.e. the biological species concept, Mayr 1942, 1963). While it was known that many plant species could hybridise (Carson 1985), the biological species concept prevailed amongst evolutionary biologists in the 20th century (Mallet 2005). However, in recent years there has been a significant change in our understanding of the role of barriers to gene flow between diverging populations (Coyne and Orr 2004). It is now 132 widely recognised that hybridisation between lineages is more common than previously thought (Mallet 2008; Abbott et al. 2013) and that genealogies may differ among loci within the genome (Lexer and Widmer 2008). In the genic view of speciation, species divergence occurs along a continuum of genetic differentiation and reproductive isolation (Wu 2001), with incipient species passing through a phase where they are only partly reproductively isolated (Kopp and Frank 2005). Evidence of partial speciation has been documented in many organisms, such as Drosophila (Legrand et al. 2011) and in the plant genus, Nuphar (Shiga and Kadono 2007). Lineages that are in the early stages of speciation offer unique opportunities for understanding the mechanisms that drive species divergence (Schield et al. 2015). In this study we examine patterns of diversification and speciation mechanisms within Eucalyptus.

Eucalyptus is one of the larger genera in the family Myrtaceae, comprising more than 700 species, and forms a significant component of the Australian biota (Smith et al. 2003). Although Eucalyptus is Gondwanan in origin (with fossils dated 51.9 Ma, Gandolfo et al. 2011; Hermsen et al. 2012), the present-day dominance of this genus across the Australian landscape is considered to be relatively recent (Pole et al. 1993; Rozefelds 1996; Crisp et al. 2004). Pollen evidence indicates that Eucalyptus only became widespread in the Pleistocene (5 to 1.5 Ma, Potts and Pederick 2000), and molecular studies suggest the occurrence of rapid radiations during the Quaternary (McKinnon et al. 2004a; Byrne 2007).

In south-eastern Australia, eucalypts are particularly diverse, especially in the sub- coastal region of central New South Wales, including Sydney and the Blue Mountains (Wardell-Johnson et al. 1997), with the latter region being declared a World Heritage Area partly due to its high eucalypt diversity (c. 100 species, Hager and Benson 2010). One diverse group of eucalypts from this area is the green ashes (subgenus Eucalyptus section Eucalyptus, sensu Brooker 2000), which includes tall trees on fertile soils, as well as smaller trees and mallees on shallow soils (Ladiges et al. 2010). While larger trees can be fire sensitive (Nicolle 2006), mallees are multi-stemmed plants usually less than 10 m tall that grow from an underground lignotuber (from which they can resprout following fire, Mullette 1978). Many green ash species are morphologically similar, and cases of hybridisation have previously been reported (Johnson and Blaxell 1972). As

133 such, the green ashes represent an appropriate group to study active evolutionary processes.

A comprehensive understanding of population genetic differentiation along environmental gradients is important when investigating the origins of taxa and the causes of lineage diversification (Storfer 1999; Gaudeul et al. 2012). Genetic variation within a species often has a geographic basis since the evolutionary processes of adaptation, gene flow and genetic drift act differentially across landscapes and may be strongly influenced by the demography and spatial distributions of populations (Eckert et al. 2008; Shepherd et al. 2010). For example, McGowen et al. (2001), investigated genetic differentiation (using microsatellite analysis) in closely related eucalypts along steep mountains in Tasmania, and found that Eucalyptus vernicosa at higher altitudes had evolved in allopatry to E. subcrenulata (which occurs at lower altitudes). Alternatively, Foster et al. (2007) found, using nuclear microsatellite and chloroplast (cp) DNA markers, that distinct tree and dwarf ecotypes of E. globulus along a latitudinal gradient were maintained despite being geographically close, indicating that ecotypes, as well as species, could evolve in parapatry. More recently, cpDNA variation in closely related eucalypts from mid and high elevations in Victoria was found to be more correlated with geography than species identity suggesting past or current interspecific hybridisation (Pollock et al. 2013).

With the advent of next-generation sequencing (NGS) and associated technologies, there has been a significant transition in DNA sequencing techniques (McCormack et al. 2013). These approaches enable a much higher genomic resolution since the number of markers sequenced increases by orders of magnitude compared with traditional molecular methods (Keller et al. 2013). One such technique that is increasingly being used in studies of Eucalyptus is Diversity Arrays Technology (DArT, e.g. Steane et al. 2011, Rutherford et al. 2016; Jones et al. 2016). DArT is based on genome complexity reduction using restriction enzymes, followed by hybridisation to microarrays in order to simultaneously assay thousands of markers across the genome (Jaccoud et al. 2001). This method has recently been used in combination with NGS to develop DArTseq, which provides at least three times as many markers as the microarray DArT method, as well as an additional set of co-dominant single nucleotide polymorphisms (SNPs, Sansaloni et al. 2011). With technological advances such as DArTseq, evolutionary

134 processes at a genomic scale can now be investigated and genetic variation across landscapes can be examined in greater detail (Begun et al. 2007; Hohenlohe et al. 2010; Bragg et al. 2015).

The phylogeny of the green ashes and closely related eucalypts was previously estimated using the DArT microarray method (Rutherford et al. 2016). It was found that while some of the lineage relationships were consistent with taxonomic classifications primarily based on morphology, other relationships were not. In addition, there was evidence of hybridisation between many species from the Sydney region and Greater Blue Mountains World Heritage Area (GBMWHA). Recent speciation events may result in incongruence between gene trees, which can lead to the non-monophyly of well adapted taxa (Jones et al. 2016). Poor molecular resolution of taxa can also be due to hybridisation and gene flow between species, or incomplete lineage sorting (Seehausen et al. 2014). As low levels of genetic divergence within eucalypt species complexes have been interpreted as recent speciation, hybridisation and/or incomplete lineage sorting (Jones et al. 2013), the green ashes represent a relevant system for investigating mechanisms of species divergence. Reticulate evolution (hybridisation between divergent lineages) was suggested to have played a role in the evolution of the green ashes (Hager and Benson 2010). However, the concept of reticulate evolution is poorly described by traditional evolutionary models, that is, those based on the assumption that evolution is successfully captured by a bifurcating tree (Mindell 2013). As a preliminary allozyme study by Prober et al. (1990) found low genetic differentiation between the green ashes, a population genomic study is likely to provide novel insights into the evolutionary history of these closely related lineages.

In the current study, we investigated the evolutionary origins and population dynamics of six green ash species across altitudinal and latitudinal gradients in the Sydney region and GBMWHA using DArTseq markers. Our objective was to improve our understanding of speciation mechanisms, the speciation process and species boundaries within the green ash group. In particular, based on previous phylogenetic analyses (Rutherford et al. 2016), we aimed to: (1) investigate patterns of genetic differentiation amongst populations and taxa, (2) better understand the role of inter-specific hybridisation, and (3) investigate associations between genetic differentiation and geographic factors.

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4.3 Materials and methods

4.3.1 Study species and sampling strategy

The green ashes have long been taxonomically challenging, with much disagreement over the number of recognised species. Many of the species recognised by Hill (2002) were not recognised by Brooker (2000). For example, E. obstans is considered to be a coastal variant of E. burgessiana, and E. laophila to be a synonym of E. apiculata (Brooker and Kleinig 2006). In Rutherford et al. (2016), it was found that E. obstans and E. burgessiana were not monophyletic, nor were E. apiculata and E. laophila. Therefore, in order to consider species concepts by all the major authorities, we follow the classification of Hill (2002).

We selected six green ash species (Fig. 1), five of which were very closely related (i.e. in the same clade based on previous phylogenetic analyses, Rutherford et al. 2016). The sixth species, E. cunninghamii, was chosen as it is highly morphologically distinctive and was previously found to be in a separate clade to the other study species (Rutherford et al. 2016). We aimed to cover the distribution of each species by selecting populations across a range of latitudes and altitudes in the Sydney region and GBMWHA (Table 1). Locations of populations were obtained from the National Herbarium of New South Wales database (Royal Botanic Garden Sydney), Benson and McDougall (1998) and Mills (2010); and are summarised as follows. The Sydney region and GBMWHA is environmentally heterogeneous (0 to 1200 m a.s.l., 700 to 1800 mm annual rainfall). Eucalyptus stricta is widespread throughout this region, occurring in coastal, upland and highland habitats. The other green ash species are rare, restricted or localised. Both the mallee and tree forms of E. dendromorpha occur on sandstone substrates, generally in moist habitats (near waterfalls or creeks). Eucalyptus obstans occurs only in coastal habitats primarily from Sydney to Jervis Bay (200 km south of Sydney). Eucalyptus laophila is confined to higher elevations on sandstone ridges and pagodas (650‒1100 m a.s.l.), while E. cunninghamii is restricted to escarpments (700‒ 1000 m a.s.l.). Many species are geographically proximate or have overlapping distributions (e.g. E. stricta and E. cunninghammii at Mount Banks). Other species are geographically disjunct and isolated. For example, E. langleyi is endemic to Nowra (160 km south of Sydney). These distribution patterns enabled us to test for admixture and isolation.

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Fig. 1. Distribution of the study species showing: Eucalyptus cunninghamii (●), E. laophila (●), E. stricta (●), E. langleyi (●), E. obstans (●) and E. dendromorpha (●). Locations sampled in the present study (∆) are shown. Leaf morphology and variations in bud and capsule size of each species are also shown (Klaphake 2012: 45‒49). Location details of species and populations sampled are presented in Table 1. Maps were generated using Australia’s Virtual Herbarium (2015). 137

Table 1. Location details and growth form of green ash species and populations sampled for genomic DNA Latitude, longitude and altitude of each population were measured directly in the field at each site (GPS model: Garmin Rino 650). Distribution and conservation details were compiled from Hill (2002) and Royal Botanic Garden and Domain Trust (2016). Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area; NSW, New South Wales; ROTAP, Rare or Threatened Australian Plants list. ROTAP codes: 2RCa (range less than 100 m, rare in the wild, occurs within reserves, population exceeds 1000 plants), 2V (range less than 100 m, vulnerable and at risk over a 20‒50 year period).

Species Location Growth Latitude Longitude Altitude (m) Distribution and conservation form (S) (E)

E. cunninghamii Mount Banks, GBMWHA Mallee –33.58 150.37 955‒960 Restricted but locally frequent, confined to Pulpit Rock, GBMWHA Mallee ‒33.62 150.33 920‒935 GBMWHA, NSW. Threatened species Wentworth Falls, GBMWHA Mallee ‒33.73 150.37 810‒820 (ROTAP: 2RCa) Kings Tableland, GBMWHA Mallee ‒33.78 150.38 785‒805 E. dendromorpha Mount Wilson, GBMWHA Mallee ‒33.52 150.37 990‒1010 Locally frequent but restricted, from Mount Banks, GBMWHA Mallee ‒33.59 150.37 940‒960 GBMWHA to Monga, NSW Blackheath, GBMWHA Mallee ‒33.63 150.31 920‒960 Wentworth Falls, GBMWHA Mallee ‒33.73 150.37 790‒870 Redhills Road, Fitzroy Falls Tree ‒34.65 150.44 310‒330 Jersey Lookout, Fitzroy Falls Tree ‒34.65 150.48 290‒310 E. laophila Glow Worm Tunnel Road, GBMWHA Mallee ‒33.25 150.22 1115‒1130 Restricted but locally frequent, from Coricudgy Lithgow Mallee ‒33.50 150.17 900‒935 to Newnes Plateau, GBMWHA, NSW E. langleyi Braidwood Road, Nowra Mallee ‒34.97 150.50 195‒265 Restricted and localised, confined to Nowra, Parma Creek Firetrail, Nowra Mallee ‒34.99 150.49 215‒220 NSW. Threatened species (ROTAP: 2V) E. obstans Beacon Hill, Sydney Mallee ‒33.74 151.26 135‒145 Locally abundant from Kuringai Chase to Royal National Park, Sydney Mallee ‒34.12 151.08 110‒125 Jervis Bay, NSW Jervis Bay Mallee ‒35.01 150.83 20‒30 E. stricta Newnes Plateau Mallee ‒33.45 150.23 1170‒1180 Widespread and locally abundant from Newnes Mount Banks, GBMWHA Mallee ‒33.58 150.37 865‒940 Plateau to Budawang Ranges, NSW Blackheath, GBMWHA Mallee ‒33.63 150.31 910‒925 Kings Tableland, GBMWHA Mallee ‒33.75 150.38 840‒860 Stanwell Tops, Sydney Mallee ‒34.21 150.96 325‒335 Sassafras Mallee ‒35.07 150.20 725‒735

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Leaf material was collected from up to eight individuals (at least 10 m apart) per population. Mallees occur as stands (or clumps) that appear to grow from the same lignotuber and as such, it can be difficult to discern a genetically distinct individual in the field (Rossetto et al. 1999). Therefore a mallee stand was conservatively regarded as a genet and leaf material was collected from a single stem per stand. For some of the target populations, mallee stands were very close together and could not be sampled 10 m apart. Other populations (e.g. E. cunninghamii from Mount Banks) were small and it was not possible to sample eight individuals. In such cases, each mallee stand in the population was sampled. Eucalyptus laophila on the Glow Worm Tunnel Road (Wollemi National Park) was a scattered population, with some individuals occurring to the north and over the tunnel itself, while others were approximately 1.5 km south of the tunnel. For this population eight individuals at the tunnel itself and five individuals south of the tunnel were sampled. The geographic position (including elevation) of each sample was recorded (GPS model: Garmin Rino 650, Garmin Australasia, Sydney, NSW, Australia). Vouchers for each population were lodged at the National Herbarium of New South Wales. Leaf samples were freeze-dried and stored at ‒20°C prior to DNA extraction.

4.3.2 DNA isolation

Total genomic DNA was extracted from leaf samples following the protocol outlined in Rutherford et al. (2016). This was a CTAB protocol modified from Doyle and Doyle (1990). DNA concentrations were measured using a Qubit 2.0 Flourometer (Invitrogen, Melbourne, Vic., Australia) and each sample was made up to between 400 and 1000 ng of DNA (targeting a concentration of 50 ng mL–1). Samples were sent to Diversity Arrays Technology Pty Ltd for genotyping using the DArTseq platform (Sansaloni et al. 2011).

Sequences were generated using proprietary DArT analytical pipelines. In the primary pipeline, ‘fastq files’ were processed to filter away poor quality sequences. Approximately 2 500 000 sequences per sample were identified and used in marker calling. Identical sequences were collapsed into ‘fastqcoll files’, which were cleaned up using DArT Pty Ltd’s proprietary algorithm. DArT Pty Ltd’s proprietary algorithm

139 corrects a low quality base from a singleton tag into a correct base using collapsed tags with multiple members as a template. The cleaned fastqcoll files were used in the secondary pipeline for DArT Pty Ltd’s proprietary SNP and SilicoDArT (presence/absence of restriction fragments in representation) calling algorithms (i.e. DArTsoft14). For SNP calling, all tags from all libraries included in the DArTsoft14 analysis were clustered using DArT Pty Ltd’s C++ algorithm. Additional selection criteria were added to the algorithm based on analysis of approximately 1000 controlled cross populations. Mendelian distribution of alleles was tested in these populations and this facilitated the selection of technical parameters discriminating true allelic variants from paralogous sequences. In addition, multiple samples were processed from DNA to allelic calls, as technical replicates and scoring consistency was used as the main selection criteria for high quality and low error rate markers. Calling quality was assured by high average read depth per locus (average across all markers was over 30 reads/locus).

4.3.3 Relationships of green ash species and populations

DArTseq analysis produced a dataset comprising a total of 54 303 SNPs. A screen of the dataset was conducted in SplitsTree4 (Huson 1998; Huson and Bryant 2006). SplitsTree4 infers the genetic relationships among a set of samples as a network, based on information from multiple loci. The network can represent evolutionary histories with substantial reticulation, arising from incomplete lineage sorting and hybridisation (Huson and Bryant 2006). In the present study, a relationship network of the total dataset was generated in SplitsTree4 using the default settings of the software.

To ensure that only the higher quality DArTseq markers were used for subsequent analyses, all SNPs with a reproducibility (proportion of replicate assay pairs for which the marker score is consistent) of less than 100% and which had more than 5% missing data were excluded from the dataset. So that the results were not influenced by linkage, one SNP was randomly selected from each of the clones that contained more than one SNP. This left 11 739 SNPs for analyses. We used the package adegenet 2.0.1 (Jombart 2008) in R (version 3.3.0, R Core Development Team) to perform a principle coordinate analysis (PCoA) to better understand relationships between species and populations. In

140 addition, a discriminant analysis of principle components (DAPC, Jombart et al. 2010) was performed using adegenet to explore genetic structure within and between the study species. DAPC takes into account the multilocus genotype of each individual and identifies and describes clusters on the basis of genetic similarity without considering a model of evolution (Jombart et al. 2010).

To further investigate relationships and gene flow among individuals and populations, analyses were performed using STRUCTURE version 2.3.4 (Pritchard et al. 2000, Falush et al. 2003). A subset of the highest quality markers from the dataset was used, since a smaller dataset would reduce the run-time of the final analysis. All markers with a reproducibility of less than 100%, an average count of reference alleles less than 10, and an average count of SNP alleles less than 10 were removed from the dataset. In addition, all markers with missing data at any locus were excluded. This left 4783 markers for analyses. To ensure this subset was representative of the larger dataset, a PCoA was conducted based on Nei’s unbiased genetic distances in the PCoA module of GenAlEx v6.501 (Peakall and Smouse 2006, 2012).

STRUCTURE uses a Bayesian model-based approach to group individuals by multilocus genotypes, however does not impose taxonomic or geographic groups a priori (Pritchard et al. 2000). A STRUCTURE analysis was performed using default parameters, K values ranging from 1 to 25 (with 5 iterations for each K), 100 000 MCMC steps, and assuming a burn-in period of 100 000 steps. The optimal value of genetically distinct clusters (K) was calculated based on the maximal mean posterior probability across replicates as well as the second rate of change (ΔK) (Evanno et al. 2005). We used STRUCTURE HARVESTER v0.6.94 (http://taylor0.biology.ucla.edu/structureHarvester, accessed 30 June 2015) to inspect each run and to calculate the value of K. Once the major sources of structure were identified, a hierarchical approach was used to examine the data further, with additional STRUCTURE analyses iterated for each subpopulation identified.

Selecting the optimum value of K using STRUCTURE can be problematic as there is some degree of uncertainty as to what value of K best fits the data (Janes et al. 2017). As such, estimating values of K that represent biological groups may not be straightforward and this issue should be treated with care (Pritchard et al. 2000). In the STRUCTURE manual, a number of steps are outlined to ensure that a biological

141 sensible value of K is selected, including running the MCMC scheme for different values of K, running several independent runs for each K, and plotting an average across all iterations of the estimated natural logarithm of the probability of the data (Ln Pr(X/K) (Pritchard et al. 2010). However, it is acknowledged in the manual that this is an ad-hoc estimation of K (Pritchard et al. 2010). Similarly, using ΔK (via the method of Evanno et al. 2005) is also an ad-hoc approximation. Another issue with the method of Evanno et al. (2005) is that it does not allow K to be 1 (Janes et al. 2017). As such, it is advised that users of STRUCTURE should not rely exclusively on ΔK, and to follow some recommendations when selecting the optimum value of K (Janes et al. 2017). Therefore in addition to using ΔK, we followed the recommendations of Janes et al. (2017), which involved inspecting all barplots produced by each STRUCTURE analysis at different values of K (from multiple runs and iterations), as well as including the Ln Pr(X/K) plots and STRUCTURE barplots for multiple values of K in the supplementary material when reporting results.

4.3.4 Genetic diversity of species and populations

We calculated genetic diversity parameters from the highest quality markers. The expected heterozygosity (He), observed heterozygosity (Ho), number of observed alleles

(Na), genetic differentiation between regions (FST) and degree of inbreeding (FIS) were determined using the Frequency Module of GenAlEx v6.501 (Peakall and Smouse 2006, 2012). Genetic diversity parameters were calculated for both the ‘taxonomic’ species according to the species concepts of Hill (2002) and for the groups found in the present study using the PCoA, DAPC and STRUCTURE analyses described above. We calculated pairwise FST values across all species and location combinations using the Frequency Module of GenAlEx v6.501 (Peakall and Smouse 2006, 2012).

To better understand the contribution of species identity and geographical region to genetic differentiation, a hierarchical analysis of molecular variance (AMOVA) was used. AMOVA was performed using the PhiPT (Excoffier et al. 1992) analogue of FST in the AMOVA module of GenAlEx v6.501. The extent of genetic differentiation was determined between and within populations of each ‘taxonomic’ species and for the groups found in the present study using the PCoA, DAPC and STRUCTURE analyses. The extent of genetic differentiation was also determined between and within 142 geographic regions (Blue Mountains, Sydney coastal, and Southern Highlands and South Coast). Statistical significance was determined by comparison to 999 random permutations of the data.

4.3.5 Network analysis of connectivity between species, populations and individuals

Patterns of connectivity were further examined using network analysis implemented in EDENetworks version 2.18 (Kivelä et al. 2015). Network analysis can illustrate gene evolution by taking into account uncertainties in mutational pathways or reticulate events such as recombination, lateral transfer and hybridisation (Posada and Crandall 2001). Network-based methods of data exploration are free of many of the ‘a priori’ assumptions that usually underlie other methods of interpreting population molecular datasets (e.g. geographic clustering) and provide a graphical approach of viewing multidimensional data (Kivelä et al. 2015). In EDENetworks, networks are constructed from genetic distances that are calculated using the FST based distance of Reynolds (Reynolds et al. 1983). These networks can be analysed at various thresholds (Aketarawong et al. 2015). In the present study, population-level and individual centred networks were produced from the dataset comprising the highest quality markers (4783 SNPs). Scanning at a range of thresholds was performed to analyse possible substructures and clusters (in the final analysis, the threshold value for the network was set at a value of 0.16). In order to investigate connectivity across the landscape, geographic locations of each sample were used to overlay the network onto a map of the Sydney region and GBMWHA. The geographic coordinates of each sample are presented in Appendix 1.

4.3.6 Chloroplast haplotype diversity across species and geographic regions

Chloroplast diversity was investigated to further explore hybridisation and reticulate evolution in the study species. To investigate patterns of chloroplast diversity, complete chloroplast genome sequences of 22 species of Eucalyptus (Appendix 2) were downloaded from the NCBI (National Center for Biotechnology Information) database (https://www.ncbi.nlm.nih.gov/nuccore/?term=Eucalyptus, accessed 23 August 2016). To identify chloroplast data within the DArTseq dataset, a BLAST search of the full

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DArTseq SNP dataset against the downloaded chloroplast genomes was performed in CLC Genomics Workbench using the default settings (version 8, www.clcbio.com). Eight DArTseq markers were identified as potential chloroplast sequences and checked to be homozygous before being submitted to a BLAST search of the whole genome of E. grandis on the NCBI database (https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed 24 August 2016). Results of the BLAST search were used to confirm hits to the chloroplast genome and five DArTseq markers were confirmed to be of chloroplast origin. Chloroplast haplotype networks were constructed from these five chloroplast markers. Relationships among chloroplast haplotypes were determined using the median-joining method in NETWORK 5.0.0.0 (Bandelt et al. 1999) and haplotype networks were coded by species identity and geographic region (Blue Mountains, Sydney coastal and Southern Highlands/South Coast).

4.3.7 Between-species gene flow and admixture

We used TreeMix (version 1.13, Pickrell and Pritchard 2012) to test for and visualise gene flow and admixture between species. TreeMix uses allele frequency data and a Gaussian approximation for genetic drift among taxa and populations to estimate a maximum likelihood tree (Pickrell and Pritchard 2012). Admixture between branches of the tree is then determined in a stepwise likelihood procedure, where the tree is searched for the optimal placement of each migration event (Pickrell and Pritchard 2012). The proportion and directionality of gene flow is displayed on the tree (this is estimated from the asymmetries in the relationships inferred by the tree, Martin et al. 2015). For our analysis, we used the filtered DArTseq dataset (11 739 SNPs) that was used to generate the PCoA and DAPC (described above). We estimated a maximum likelihood tree with E. cunninghamii (all populations) selected as the outgroup taxon (based on the phylogenetic analysis of Rutherford et al. 2016). We inferred a topology without admixture, as well as allowing for between one and six migration events.

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4.4. Results

DArTseq analysis produced a large dataset with a relatively high proportion of high quality markers. Of the 54 303 markers, 77.6% of markers had a reproducibility of 100% (with 91.3% of markers having a reproducibility of ≥98%) and 50.2% of markers had a call rate of ≥90%. The proportion of missing data for samples ranged from 13.2 to 43.9%.

4.4.1 Relationships of green ash species and populations

The genetic relationships of the green ashes of the Sydney region and GBMWHA were determined using relationship networks generated in SplitsTree 4 (a relationship network derived from all SNP markers is illustrated in Fig. 2). In a relationship network, the parallel lines indicate splits in the data and allow samples to be assigned to groups, with the longer lines suggesting more support for that particular split (Huson and Bryant 2006). Eucalyptus cunninghamii formed a distinct cluster that was strongly differentiated from the other species. Similarly, one population of E. dendromorpha (from Mount Banks) also formed a distinct group. One sample from the Stanwell Tops site (NSW908486) did not cluster with any group in the relationship network and was on a longer branch and isolated from all other species.

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Trees from the Southern Highlands

Blue Mountains mallees Blue Mountains mallees

E. sp. Mount Banks

NSW908486 E. cunninghamii Upper Blue Mountains mallee Mallee from Southern Highlands South Coast mallees

Sydney coastal mallees

Fig. 2. Relationship network generated in SplitsTree4 of all green ash taxa and populations (based on 54 303 DArTseq SNPs). Abbreviations: Bank, Mount Banks; Beac, Beacon Hill (Sydney); Blac, Blackheath; Brai, Braidwood Road (Nowra); Glow, Glow Worm Tunnel Road (Wollemi National Park); Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; King, Kings Tableland; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Pulp, Pulpit Rock; Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Sass, Sassafras; Stan, Stanwell Tops (Sydney); Went, Wentworth Falls; Wils, Mount Wilson.

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These relationships were supported by a PCoA based on 11 739 DArTseq SNPs (Fig. 3). Four main groups were identified: E. cunninghamii, E. dendromorpha from Mount Banks (referred to as E. sp. Mount Banks hereafter), a sample from Stanwell Tops (NSW908486) and the remainder of the green ashes. A PCoA of the taxa excluding E. cunninghamii, E. sp. Mount Banks and the sample from Stanwell Tops (NSW908486) allowed the resolution of further divisions among the remainder of the green ashes (Fig. 3). The two E. langleyi populations (both from Nowra) clustered together, while the two E. obstans populations from Sydney (Beacon Hill and Royal National Park) also formed a group. The E. obstans population from Jervis Bay was positioned between E. langleyi and the Sydney populations of E. obstans (although was positioned closer to E. obstans from Sydney). The GBMWHA populations of E. dendromorpha (all mallees) clustered together, while the tree form of E. dendromorpha from Jersey Lookout, Fitzroy Falls (Southern Highlands) formed a separate group. All populations of E. stricta and E. laophila clustered together. A DAPC derived from the same dataset identified 14 clusters (Fig. 4). As in the PCoA, all E. stricta and E. laophila populations were grouped together and the two populations of E. langleyi clustered together. Also as in the PCoA, the mallee form of E. dendromorpha (GBMWHA) and the tree form of E. dendromorpha from Jersey Lookout (Southern Highlands) formed two separate groups. Eucalyptus dendromorpha from Redhills Road, Fitzroy Falls (another tree form from the Southern Highlands) was positioned between E. dendromorpha from the GBMWHA and E. dendromorpha from Jersey Lookout.

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E. sp. Mount Banks

Focal group

E. cunninghamii NSW908486 (Stanwell Tops)

E. langleyi

E. dendromorpha Jersey Lookout E. obstans

E. dendromorpha Redhills Road

E. dendromorpha Blue Mountains

E. stricta & E. laophila

Fig. 3. Principle coordinate analysis (PCoA) derived from the DArTseq SNP dataset. A. All study taxa (based on 11 739 DArTseq markers). B. The focal group (based on 10 868 DArTseq markers and including all taxa except for Eucalyptus cunninghamii, NSW908486 from Stanwell Tops and E. sp. Mount Banks).

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E. stricta & E. laophila

E. obstans

E. dendromorpha Blue Mountains

E. dendromorpha E. langleyi Redhills Road

E. dendromorpha Jersey Lookout

Fig. 4. Discriminant analysis of principal components (DAPC) derived from 10 868 DArTseq SNPs of the focal group: Eucalyptus stricta, E. laophila, E. dendromorpha, E. obstans and E. langleyi. The scatterplot shows the first two principal components of the DAPC. Groups are indicated by inertia ellipses (which are colour coded and numbered). The dots, triangles, squares and diamonds represent individual samples.

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A PCoA of the high quality dataset (4783 loci, Appendix 3) used for STRUCTURE analyses displayed similar groupings of species and populations as the PCoA of the larger dataset (Fig. 3). STRUCTURE analyses of the high quality markers revealed genetic differentiation among species. For example, coefficient of membership values indicated that all populations of E. cunninghamii were assigned to one group (Q>0.9), while the remainder of species, with the exception of NSW908486 from Stanwell Tops, were assigned to a second group (Q>0.9, Appendix 4). However, when all populations of E. cunninghammii were excluded from the dataset, only E. sp. Mount Banks registered a high coefficient of membership to one group (Q>0.9, Appendix 4). All populations of other species had lower coefficients of membership to a second group (Q<0.8), with small coefficients of membership assigned to one or more of another five groups (Appendix 4). STRUCTURE analyses also revealed genetic differentiation between populations of many species (Fig. 5). In E. cunninghamii, the Pulpit Rock and Mount Banks population were differentiated from the other two populations which were geographically closer together (Wentworth Falls and Kings Tableland).

The mallee form (from the GBMWHA) and the tree form (from the Southern Highlands) of E. dendromorpha formed two groups. STRUCTURE analyses of the mallee form of E. dendromorpha from the GBMWHA revealed that there was little genetic differentiation between the population at Mount Wilson (which was geographically further away) and the populations from Wentworth Falls and Blackheath. The two populations of the tree form of E. dendromorpha from the Southern Highlands (Redhills Road and Jersey Lookout) were genetically differentiated. The E. obstans population from Jervis Bay was differentiated from the two Sydney populations (Beacon Hill and Royal National Park). No genetic differentiation was detected between E. stricta and E. laophila despite the geographic location of the populations. A low degree of genetic variation was detected between the two populations of E. langleyi.

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E. cunninghamii, K=3 E. dendromorpha, K=2

E. dendromorpha (Blue Mountains), K=2 E. dendromorpha (Southern Highlands), K=2

E. obstans, K=2 E. langleyi, K=2

E. stricta and E. laophila, K=1

Fig. 5. Population structure (STRUCTURE analysis based on 4783 DArTseq SNP markers) of Eucalyptus species from the Sydney region and Greater Blue Mountains World Heritage Area (GBMWHA). The optimum value of K was calculated using the method of Evanno et al. (2005) (the ∆K plots from STRUCTURE HARVESTER for each STRUCTURE analysis are presented in Appendix 5). Each graph produced from STRUCTURE was also inspected to ensure the optimum value of K for each species was calculated. STRUCTURE barplots with different values of K are shown in Appendix 6. Abbreviations: Bank, Mount Banks; Beac, Beacon Hill (Sydney); Blac, Blackheath; Brai, Braidwood Road (Nowra); Glow, Glow Worm Tunnel Road (Wollemi National Park); Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; King, Kings Tableland; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Pulp, Pulpit Rock; Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Sass, Sassafras; Stan, Stanwell Tops (Sydney); Went, Wentworth Falls; Wils, Mount Wilson.

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4.4.2 Genetic diversity of populations and species

Eucalyptus cunninghamii had the highest FST and the lowest FIS (0.127 and –0.101 respectively, Table 2). Eucalyptus laophila had the highest FIS (–0.027), followed by E. stricta and E. laophila together (–0.042). Eucalyptus langleyi had the lowest FST (0.054) and the second lowest FIS (–0.090). Eucalyptus langleyi had the highest NA, He and Ho

(1.767, 0.246 and 0.220), while E. stricta and E. laophila together had the lowest NA, He and Ho (1.458, 0.133 and 0.125). Pairwise FST values revealed that there was relatively strong genetic differentiation between E. cunninghamii and the other study species, as the magnitude of pairwise values exceeded the intra-specific (between-population) values (Table 3). Similarly, there was relatively strong genetic variation between E. sp.

Mount Banks and all the other study species (indicated by relatively high pairwise FST values, Table 3). However, the pairwise FST values did not support strong genetic variation between E. stricta, E. laophila, E. obstans, E. langleyi and E. dendromorpha, since the magnitude of inter-specific pairwise values was not greater than the intraspecific (between-population) pairwise values (Table 3).

We found most of the genetic variation to be within populations rather than between populations for each species (AMOVA results revealed that for each species, > 80% of the variation could be attributed to within population variation, Table 4). Eucalyptus obstans had the highest between-population variation (19%), followed by E. cunninghamii (18%). Eucalyptus stricta and E. laophila had the lowest between- population variation, as did the populations of E. dendromorpha only from the GBMWHA (8%). We found that variation was better explained by species identity rather than by geographic region. The variation among species was more than four times greater than variation amongst geographic regions (28% and 6% respectively). The majority of the genetic variation was within species and within geographic regions (as indicated by the AMOVA results, Table 4).

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Table 2. Summary of genetic diversity parameters for Eucalyptus species from the Sydney region and the Greater Blue Mountains World Heritage Area (GBMWHA)

Values are means and standard errors (SE) across loci. Abbreviations: NA, observed number of alleles; FIS, degree of inbreeding in each individual relative to its local population; FST, inbreeding in local populations relative to the total sample; He, expected heterozygosity; Ho, observed heterozygosity; N, sample size.

Species N NA (SE) Ho (SE) He (SE) FIS (SE) FST (SE) E. cunninghamii 23 1.621 (0.006) 0.234 (0.003) 0.206 (0.002) –0.101 (0.008) 0.127 (0.003) E. langleyi 16 1.767 (0.008) 0.246 (0.004) 0.220 (0.003) –0.090 (0.007) 0.054 (0.001) E. obstans 22 1.631 (0.006) 0.202 (0.003) 0.183 (0.002) –0.082 (0.006) 0.094 (0.002) E. dendromorpha (excluding E. sp. Mount Banks) 40 1.521 (0.004) 0.153 (0.002) 0.143 (0.001) –0.045 (0.004) 0.087 (0.001) E. dendromorpha (Blue Mountains only) 24 1.642 (0.006) 0.185 (0.002) 0.174 (0.002) –0.050 (0.005) 0.063 (0.001) E. stricta 45 1.507 (0.004) 0.148 (0.002) 0.138 (0.001) –0.052 (0.004) 0.079 (0.001) E. laophila 18 1.727 (0.007) 0.204 (0.003) 0.197 (0.003) –0.027 (0.007) 0.055 (0.001) E. stricta and E. laophila 63 1.458 (0.003) 0.133 (0.001) 0.125 (0.001) –0.042 (0.004) 0.086 (0.001)

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Table 3. Pairwise FST among species/location combinations for the green ash eucalypts Abbreviations: Bank, Mount Banks; Beac, Beacon Hill (Sydney); Blac, Blackheath; Brai, Braidwood Road (Nowra); Glow, Glow Worm Tunnel Road; Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; King, Kings Tableland; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Pulp, Pulpit Rock; Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Stan, Stanwell Tops (Sydney); Went, Wentworth Falls; Wils, Mount Wilson.

E. sp. E. cunninghamii E. stricta E. laophila E. obstans E. langleyi E. dendromorpha Mt Banks Pulp Went Bank King Stan King Blac Newn Brai Bank Glow Lith Beac Roya Jerv Brai Parm Black Went Wils Redh Jers E. cunninghamii

Pulp 0.00 Went 0.09 0.00 Bank 0.10 0.11 0.00 King 0.07 0.07 0.09 0.00 E. stricta

Stan 0.15 0.17 0.17 0.14 0.00 King 0.15 0.16 0.17 0.14 0.05 0.00 Blac 0.15 0.16 0.17 0.14 0.05 0.05 0.00 Newn 0.14 0.16 0.16 0.14 0.05 0.04 0.05 0.00 Brai 0.15 0.17 0.17 0.14 0.05 0.05 0.06 0.05 0.00 Bank 0.15 0.16 0.17 0.14 0.05 0.04 0.05 0.04 0.05 0.00 E. laophila

Glow 0.13 0.15 0.15 0.13 0.05 0.04 0.05 0.04 0.05 0.04 0.00 Lith 0.17 0.19 0.20 0.16 0.07 0.06 0.07 0.06 0.07 0.06 0.06 0.00 E. obstans

Beac 0.17 0.19 0.21 0.16 0.07 0.07 0.08 0.07 0.07 0.07 0.07 0.09 0.00 Roya 0.15 0.16 0.17 0.14 0.05 0.05 0.06 0.05 0.06 0.06 0.05 0.07 0.06 0.00 Jerv 0.17 0.19 0.21 0.17 0.07 0.07 0.08 0.07 0.07 0.07 0.07 0.10 0.09 0.07 0.00

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E. langleyi

Brai 0.16 0.18 0.19 0.15 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.09 0.08 0.06 0.08 0.00 Parm 0.17 0.19 0.20 0.16 0.08 0.08 0.08 0.08 0.07 0.08 0.07 0.10 0.09 0.07 0.09 0.05 0.00 E. dendromorpha

Blac 0.14 0.15 0.16 0.13 0.06 0.06 0.05 0.06 0.06 0.06 0.05 0.07 0.08 0.06 0.08 0.07 0.08 0.00 Went 0.14 0.15 0.16 0.13 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.07 0.07 0.06 0.07 0.06 0.07 0.04 0.00 Wils 0.15 0.16 0.17 0.14 0.07 0.06 0.06 0.06 0.07 0.06 0.06 0.08 0.08 0.07 0.08 0.07 0.08 0.05 0.05 0.00 Redh 0.15 0.16 0.17 0.14 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.08 0.08 0.06 0.08 0.08 0.08 0.06 0.05 0.06 0.00 Jers 0.15 0.16 0.17 0.14 0.08 0.08 0.07 0.08 0.08 0.08 0.07 0.10 0.10 0.08 0.10 0.09 0.10 0.07 0.06 0.07 0.05 0.00 E. sp. Mt Banks 0.25 0.29 0.32 0.25 0.14 0.13 0.14 0.13 0.14 0.14 0.12 0.17 0.17 0.14 0.18 0.16 0.17 0.13 0.12 0.14 0.14 0.15 0.00

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Table 4. Partitioning of molecular variance (AMOVA) for 4783 DArTseq SNPs of the green ash eucalypts in the Sydney region and GBMWHA (Greater Blue Mountains World Heritage Area) Shows degrees of freedom (d.f.), sum of squares (SS), mean square (MS) and the percentage of variance attributed to the following groupings: (a) between and within populations for each species, (b) between and within species, and (c) between and within geographic regions (Sydney coastal, Blue Mountains and Southern Highlands/South Coast) for all species.

Source d.f. SS MS % (a) E. cunninghamii Between populations 3 2196.829 732.276 18% Within populations 19 6277.519 330.396 82% E. langleyi Between populations 1 683.188 683.188 11% Within populations 14 4841.625 345.830 89% E. obstans Between populations 2 1966.063 983.032 19% Within populations 19 6787.482 357.236 81% E. dendromorpha (excluding E. sp. Mount Banks) Between populations 4 3636.300 909.075 13% Within populations 35 14793.000 422.657 87% E. dendromorpha (Blue Mountains only) Between populations 2 1435.167 717.583 8% Within populations 21 9008.250 428.964 92% E. stricta Between populations 5 3164.007 632.801 7% Within populations 39 15443.482 395.987 93% E. laophila Between populations 1 713.931 713.931 10% Within populations 16 6377.569 398.598 90% E. stricta and E. laophila Between populations 7 4757.885 679.698 8% Within populations 55 21821.051 396.746 92%

(b) All species Between species 7 23896.849 3413.836 28% Within species 164 65653.046 400.323 72% (c) All species Between regions 2 4282.717 2141.359 6% Within regions 170 85816.543 504.803 94%

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4.4.3 Network analysis of connectivity between species, populations and individuals

In networks produced by EDENetworks, network nodes (or vertices) represent populations as defined by sampling sites, and links (or edges) represent their relationships and interactions (Kivelä et al. 2015). In the population network analyses produced in the present study, most populations were grouped together according to species. In the minimum-spanning tree, populations within a species that were geographically close were grouped together (Appendix 7a). In the threshold network, two distinct groups could be identified (Appendix 7b). The first group comprised E. cunninghamii, where there was a high degree of connectivity between all populations. The second group consisted of the remainder of the study species, where there were varying levels of connectivity between species and populations. A high degree of connectivity between many of the study species was found across latitudes and along altitudinal gradients (Fig. 6 shows the genetic network of species and sampling sites overlain on a topographic map of the study region). There was connectivity between populations from the coastal areas, GBMWHA and the Southern Highlands.

obstans Beac N

obstans Roya

stricta Stan

obstans Jerv

dendromorpha Jers langleyi Brai & Mount Banks Parm populations dendromorpha Redh dendromorpha Wils Wentworth Falls and Kings Tablelands populations Blackheath and Pulpit Rock populations stricta Newn laophila Glow laophila Lith stricta Sass

Fig 6. Minimum spanning tree (based on 4783 SNPs) overlain on a map of the study area. Blue and green lines show network linkages. Line thickness is proportional to linkage strength and node size is proportional to the degree of connectivity to other populations. The map was generated in Google Maps (http://www.google.com.au/maps, accessed September 2016). Abbreviations: Beac, Beacon Hill; Brai, Braidwood Road (Nowra); Glow, Glow Worm Tunnel Road; Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Sass, Sassafras; Stan, Stanwell Tops; Wils, Mount Wilson. 157

4.4.4 Chloroplast haplotype diversity across species and geographic regions

We found a high level of sharing of cpDNA haplotypes amongst species (Appendix 8). Only the E. obstans population from Royal National Park, the E. cunninghamii population from Pulpit Rock and an E. dendromorpha individual from Mount Wilson had unique haplotypes. All other species and populations shared haplotypes. The cpDNA haplotypes were also extensively shared between regions (Appendix 8). There were two haplotypes only occurring in the Blue Mountains and one haplotype only found in the Sydney coastal region.

4.4.5 Between-species gene flow and admixture To test whether the low levels of genetic differentiation between some of the study species (found in our other analyses) were due to introgression, we used TreeMix to construct a maximum likelihood tree featuring varying numbers of admixture events (Appendix 9 presents analyses allowing 0–3 migration events, and Fig. 7 shows an analysis with 4 migration events). Our TreeMix analysis without admixture of the filtered dataset (11 739 SNPs, Appendix 9a) recovered a maximum likelihood tree with similar groupings of populations and taxa to those found in the PCoA and DAPC (Figs 3 and 4). The populations of E. dendromorpha from the Blue Mountains (Mount Wilson, Wentworth Falls and Blackheath) were grouped together, as were the two populations of E. dendromorpha from Fitzroy Falls (Jersey Lookout and Redhills Road, Appendix 9a). The Sydney populations of E. obstans (Beacon Hill and Royal National Park) formed one clade, as did the two populations of E. langleyi (Appendix 9a). All populations of E. stricta and E. laophila were grouped together, while E. sp. Mount Banks and NSW908486 each occupied isolated positions in the phylogeny (Appendix 9a). When one migration event was added to the analysis, weak admixture (<15%) was detected between E. cunninghamii from Wentworth Falls and E. dendromorpha from Jersey Lookout (Appendix 9b). When two migration events were added, relatively strong admixture (>20%) was found between E. stricta from Stanwell Tops and E. obstans from Beacon Hill (in addition to the weak admixture detected between E. cunninghamii from Wentworth Falls and E. dendromorpha from Jersey Lookout, Appendix 9c).

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When four migration events were added to the analysis, comparatively strong admixture (>20%) was detected between E. stricta from Stanwell Tops and E. obstans from Beacon Hill, between E. stricta from Stanwell Tops and NSW908486, and between E. stricta from Sassafras and E. dendromorpha from Redhills Road (Fig. 7). Weaker admixture (<15%) was found between E. cunninghamii from Wentworth Falls and the clade comprising the populations of E. dendromorpha from Fitzroy Falls (Fig. 7). With the addition of each migration event, the groupings of populations and taxa were similar (although there were slight changes, e.g. the position of E. dendromorpha from Mount Wilson and E. obstans from Jervis Bay). After each migration event was added, the log- likelihood (LL) of the analysis increased by a value of 36–132 until the addition of the sixth migration event (where the LL decreased by a value of 6). Since our TreeMix analyses with the first four migration events were generally consistent with our other analyses, and analyses with greater than four admixture events only showed minor improvements in LL, the outputs of the trees featuring up to four migration events are presented here.

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Fig. 7. TreeMix analysis of the filtered dataset (11 739 SNPs). A. Inferred maximum likelihood phylogeny showing four migration events (directionality of gene flow is indicated by the arrows and coloured according to their weight). B. Residual fit plotted from the maximum likelihood tree in A (colour bar to the right of the matrix indicates degree of relatedness between populations and species). Residuals above zero indicate populations that are more closely related to each other in the data than in the best-fit tree (e.g. bluer shades indicate population pairs that are more closely related to each other and are therefore candidates for admixture events). 160

4.5 Discussion

DArTseq markers provided useful insights into the evolutionary origins of a group of closely related species within the green ash group. Patterns of gene flow across the study species and associations between genetic variation and geographic factors suggest that a range of speciation mechanisms (e.g. ecological speciation, reticulate evolution and geographic isolation) are or have been operating in the green ash eucalypts. DArTseq markers were also informative for delineating species boundaries within a group where there has been much uncertainty regarding the divergence and differentiation of taxa.

4.5.1 Patterns of genetic variation amongst populations and species

We found E. cunninghamii to be highly genetically differentiated compared to all the other study species. This was consistent with the phylogenetic analysis of Rutherford et al. (2016), where E. cunninghamii formed a clade with species found in northern New South Wales and southern Queensland (E. approximans, E. codonocarpa and E. microcodon) that was sister to a clade that included the green ashes from the Sydney region and GBMWHA. Eucalyptus cunninghamii is morphologically distinct from all other green ash species in that it is usually less than 1 m tall with thin, soft, silvery green leaves (Brooker 2000; Hill 2002). While the TreeMix analysis detected some introgression between one population of E. cunninghamii (Wentworth Falls) and E. dendromorpha from Fitzroy Falls, this admixture was relatively weak (<15%, Fig. 7). Furthermore, although E. cunninghamii is sympatric with E. stricta and E. dendromorpha from the Blue Mountains, it has remained genetically differentiated

(with high inter-specific pairwise FST values relative to intra-specific pairwise values, Table 3) while maintaining high levels of outcrossing (its populations have the lowest

FIS, Table 2). The comparatively high genetic differentiation of E. cunninghamii may be due to differences in phenology between species, as E. cunninghamii is thought to be an autumn-winter flowering species, whereas E. stricta and E. dendromorpha flower during the summer months (based on voucher specimens from the National Herbarium of New South Wales, Benson and McDougall 1998). Differences in flowering times may limit interbreeding opportunities among closely related sympatric species (Mohler

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1990; Cavender-Bares and Pahlich 2009) and additional research on the phenology of green ash species would further clarify the role of temporal (asynchronous) isolation in facilitating species co-occurrence without admixture.

Eucalyptus cunninghamii occupies a narrow environmental niche on exposed cliff edges and escarpments (Benson and McDougall 1998; Hill 2002). In contrast, E. stricta is not usually found at the cliff edge and E. dendromorpha in the GBMWHA occurs both near and far from cliffs. Eucalyptus cunninghamii might be restricted to these sites due to its small stature, making it likely to be shaded out by other species in less extreme habitats. However, being restricted to cliff edges has probably left E. cunninghamii highly exposed to fire disturbance (since the GBMWHA is highly prone to fire disturbance). The ability of a species to resprout following fire is an important life-history trait that can have profound impacts on community composition, population dynamics and hence the evolution of species (Bond and Midgley 2003; Clarke et al. 2015). The combination of shade and fire exposure may therefore explain why E. cunninghamii currently occurs in isolated populations on cliff edges.

Specimens of E. sp. Mount Banks Records in the National Herbarium of New South Wales were previously identified as E. dendromorpha or, in one case, as a possible hybrid between E. dendromorpha and E. cunninghamii. However, the results from our study strongly suggest that this population is differentiated from other populations of E. dendromorpha or between sympatric or nearby species. In Rutherford et al. (2016), E. sp. Mount Banks (referred to as E. dendromorpha from Mount Banks) was in a clade with E. stricta and E. laophila. On closer inspection, E. sp. Mount Banks has morphologically distinctive characters, in that its leaves have a bluish tinge and are shorter than E. dendromorpha and wider than E. stricta. Eucalyptus sp. Mount Banks is potentially an undescribed species existing in sympatry with related species.

With the exception of E. cunninghamii and E. sp. Mount Banks, there was relatively low genetic differentiation across all other species. Low between-species genetic divergence was consistent with the findings of previous studies (Prober et al. 1990; Rutherford et al. 2016) and could be interpreted as a signature of reticulate evolution and recent radiation. Recent molecular dating of eucalypts places the radiation of subgenus Eucalyptus (including E. regnans) in the last 10 Ma (Crisp et al. 2011); while the earliest known fossils of subgenus Eucalyptus are from Late Miocene deposits (5‒

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10 Ma, Blazey 1994). However, despite the low genetic differentiation between many species in the present study, they maintain their distinct morphological characters. For example, E. dendromorpha and E. stricta in the GBMWHA were often sympatric but could be distinguished by habit (E. dendromorpha is taller than E. stricta, Brooker and Kleinig 2006) and leaf size (leaves are significantly narrower in E. stricta, Hill 2002). These observations of gene flow between sympatric species that can be distinguished morphologically are consistent with previous studies of Eucalyptus (e.g. McKinnon et al. 2004b; Shepherd and Raymond 2010; Pollock et al. 2013), and other plant genera, such as Quercus (e.g. Whittemore and Schaal 1991; Petit et al. 1997) and the Australian and South American genus Lomatia (McIntosh et al. 2014).

4.5.2 The role of inter-specific hybridisation

Between-species gene flow and hybridisation in the present study was confirmed by cpDNA haplotype sharing (Appendix 8) and our TreeMix analyses, which showed strong admixture (>20%) between sympatric populations of many species (Fig. 7). This was consistent with analyses of cpDNA in other eucalypt species (e.g. Steane et al. 1998; McKinnon et al. 1999, 2001; Nevill et al. 2014), as well as species of Lomatia (Milner et al. 2012), Quercus (e.g. Petit et al. 1997; Belahbib et al. 2001) and Pinus (e.g. Matos and Schaal 2000). Such cpDNA haplotype sharing between species can be due to the following reasons: (1) convergent mutations, (2) shared retention of ancestral polymorphisms resulting from incomplete lineage sorting, and (3) DNA transfer through hybridisation and introgression (McKinnon et al. 2004b). The sharing of cpDNA haplotypes and the absence of strong genetic isolation found for many species in the present study, is consistent with historic and/or present hybridisation, as well as incomplete lineage sorting. While, E. cunninghamii was found to have a unique cpDNA haplotype, this species also shared a haplotype with E. laophila, E. stricta and E. obstans (Appendix 8). Given that the nuclear data suggest that E. cunninghamii is genetically distinct from the other green ash species, this shared haplotype may be due to either modest sharing of cpDNA with a small number of species, or more likely incomplete lineage sorting from a shared ancestor.

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Natural interspecific hybridisation and introgression is likely to have played a significant role in plant evolution and speciation (Mallet 2005; Rieseberg and Willis 2007). While gene flow has traditionally been considered to be a countervailing process to speciation, more recently hybridisation is thought to have resulted in the evolution of new and stable evolutionary lineages (Rieseberg 1997; Abbott et al. 2013). Hybridisation can be a source of adaptive variation, functional novelty, and new species (Seehausen 2004). For example, incomplete barriers to gene flow can result in the introgression of selectively favoured alleles from one population into another (Abbott et al. 2013). In Darwin’s finches, it has been estimated that the genetic variation introduced into populations by hybridisation is 2–3 times greater than that introduced by mutation (Grant and Grant 1994). The promotion of adaptive genetic variation as a consequence of introgression may lead to increased reproductive isolation among populations (Abbott et al. 2013). Hybridisation can also act as a gene dispersal mechanism by extending a population’s gene pool (and hence ecological range) through introgression (Potts and Reid 1988). Gene flow via hybridisation and introgression could be very important in species with small, fragmented and isolated populations (Pollock et al. 2015), such as many of the species within the green ashes, and could contribute to the high outcrossing rates measured for most populations (Table 2).

Although many of the species in the present study have overlapping distributions, gene flow via seeds is likely to be limited because seeds in eucalypts generally lack dispersal appendages (and are therefore dispersed by wind or gravity; Cremer 1966, 1977). In the case of mallees, seed dispersal distances are shorter, with seeds falling only a few metres from the parent plant (Potts and Reid 1988). Pollen may be dispersed more widely than seed in eucalypts (Potts and Reid 1988) and individuals are highly outcrossing with generalist pollinators (Potts and Wiltshire 1997). A detailed field study focusing on seed and pollen dispersal is needed to better understand hybridisation between green ash species. Such a study could include progeny trials and genetic analysis of seeds from more isolated populations, as well as from stands of potential hybrid zones.

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4.5.3 Association between genetic variation and geographic factors

Genetic differentiation between E. langleyi and the other study species was higher than between E. stricta, E. laophila and E. obstans (as indicated by the coefficient membership values, Appendix 4). Eucalyptus langleyi is one of the most morphologically distinct species within the green ashes (with angular buds, Klaphake 2012). It is also geographically restricted (occurring within a 17x7 km area in Nowra, Mills 2010) and isolated. The nearest populations examined here were more than 20 km away (E. obstans from Jervis Bay and E. stricta from Sassafras). Allopatric divergence is therefore a likely evolutionary pathway for E. langleyi.

With the exception of E. stricta and E. laophila, we also found genetic structuring between populations within each species, possibly caused by geographic disjunction (as shown by the STRUCTURE analysis). For example, in E. cunninghamii the Mount Banks and Pulpit Rock populations were genetically differentiated from the others (these populations are higher in altitude and are on the other side of the valley from the Wentworth Falls and Kedumba Valley populations). Eucalyptus dendromorpha from Mount Wilson (which is higher in altitude) was also found to be more genetically distinct from the other GBMWHA populations. A similar pattern was found along a latitudinal gradient for E. obstans, where the Jervis Bay population was genetically more distinct than the Sydney populations. These findings suggest that there can be strong associations between genetic variation and geographic factors, which is consistent with previous studies (Eckert et al. 2008; Ohsawa and Ide 2008), and indicate that vicariance and/or local adaptation may have had a role in the genetic differentiation and broader evolution of the green ashes. Although dispersal could offer an explanation for the patterns observed here, it should be noted that the majority of studies suggest that eucalypts have very limited seed dispersal capabilities (Booth 2017). For example, earlier studies found that the bulk of seeds in most eucalypt species fall within a distance approximately equal to the height of the tree (e.g. Cremer 1966). More recent studies (e.g. Ruthrof et al. 2003) suggest that seed dispersal rates in many eucalypt species is 1–2 m per year over a 70 year period. Dispersal of seed by ants is also limited (Wellington and Noble 1985), and while some bird species are known to be attracted to the fruits of Corymbia species, there is very little published data on how far the seeds are transported (Booth 2017). While pollen in many eucalypt species may travel as far

165 as 1 km, most of the pollen has been found to be distributed within 200 m of the parent plant (Byrne et al. 2008; Broadhurst 2013).

4.5.4 Species boundaries within the green ash eucalypts

Based on our findings, E. cunninghamii and E. sp. Mount Banks were the only species to be highly genetically differentiated from the other green ash species. In contrast, higher levels of gene flow and connectivity were found between all of the other species. However, combining all taxa (with the exception of E. cunninghamii and E. sp. Mount Banks) into one species would ignore observed morphological differences and may underestimate the diversity of the green ashes. We found no genetic differentiation between E. stricta and E. laophila in the STRUCTURE analysis or the PCoA (Figs 3 and 5), while our TreeMix analyses consistently grouped all populations of these two taxa together (Fig. 7, Appendix 9). While more than one cluster was detected within E. stricta and E. laophila in the DAPC, all samples were grouped together (Fig. 4). Our results therefore suggest that E. laophila has been incorrectly assigned specific rank and is, rather, likely to be an ecotype of E. stricta. A sample from Stanwell Tops (NSW908486) should be investigated as a possible new species. It was genetically distinct from the other species in all of our analyses and was morphologically distinct from the E. stricta population found at Stanwell Tops with which it co-occurred (in that it had broader leaves). However, genomic DNA from more individuals from this site will be needed to investigate this further. A detailed morphometric analysis of the study species would complement this molecular dataset and could be used to resolve these taxonomic issues.

4.6 Implications for the speciation process and concluding remarks

Speciation in the presence of gene flow was historically considered to be problematic because gene flow constrains population differentiation, thereby preventing the evolution of reproductive isolation (Mayr 1963). However, examples of speciation in the presence of gene flow are now emerging (e.g. Nosil 2008; Pinho and Hey 2010), and in such cases, concepts that recognise speciation with gene flow have been regarded

166 as more appropriate in understanding species divergence (Feder et al. 2012; Schield et al. 2015).

In the present study, the green ashes fit into the framework of the genic view of speciation. In the genic view of speciation, four stages are identified: stage I, population differentiation has occurred at a small number of loci, with no reproductive isolation; stage II, populations have undergone further differentiation, although a large proportion of their genomic regions remain shared and the populations could fuse together; stage III, diverging populations are not able to fuse and are differentiated in at least some aspects of reproductive biology, sexual behaviour, and/or morphology; and stage IV, reproductive isolation has evolved and the two gene pools cease sharing alleles at any part of the genome by breeding (Wu 2001). Many closely related ‘good species’ can be identified to be at stage III (Wu 2001). Our results suggest that E. cunninghamii and E. sp. Mount Banks are likely to be at stage IV. The other green ash species are at an earlier stage along the speciation continuum (perhaps at stage II or III, where populations are morphologically differentiated, but are still exchanging genes). Overall, our findings indicate that many mechanisms of speciation (e.g. vicariance, ecological speciation and reticulate evolution) can or have been operating in tandem within the one species complex.

Acknowledgements

We thank Carolyn Connelly, Margaret Heslewood, Andrew Orme, Matt Laurence, Bob Coveny, Michael Elgey, Juelian Siow, Joel Cohen, Samantha Yap, Stephanie Creer, Aaron Smith, Christine Smith, Esthel Verma, Lawrence Mou, Andrzej Kilian, Cina Vipin, Vanessa Caig and Jason Carling for technical support. We thank Peter D. Wilson for assisting with data analyses and Miguel Garcia for assisting us in accessing resources from the Daniel Solander Library (RBG). This research was made possible by a grant from the Dahl Trust. All collecting of leaf material for DNA analyses operated under the Royal Botanic Gardens and Domain Trust (New South Wales). S. Rutherford was in receipt of an Australian Post-graduate Award when this research was undertaken.

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Appendix 1. Geographic location of each sample in the study Latitude and longitude of each sample was measured directly in the field at each site (GPS model: Garmin Rino 650). Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area. Species Location Sample Latitude (S) Longitude (E) E. cunninghamii Pulpit Rock, GBMWHA s1 –33.62087 150.3279 E. cunninghamii Pulpit Rock, GBMWHA s2 –33.6209 150.3279 E. cunninghamii Pulpit Rock, GBMWHA s3 –33.62094 150.3279 E. cunninghamii Pulpit Rock, GBMWHA s4 –33.62104 150.328 E. cunninghamii Pulpit Rock, GBMWHA s5 –33.62096 150.328 E. cunninghamii Pulpit Rock, GBMWHA s6 –33.62109 150.328 E. cunninghamii Pulpit Rock, GBMWHA s7 –33.62111 150.3281 E. cunninghamii Pulpit Rock, GBMWHA s8 –33.62134 150.3281 E. cunninghamii Wentworth Falls, GBMWHA s9 –33.72705 150.3725 E. cunninghamii Wentworth Falls, GBMWHA s10 –33.72691 150.3741 E. cunninghamii Wentworth Falls, GBMWHA s11 –33.72715 150.3743 E. cunninghamii Mount Banks, GBMWHA s12 –33.58471 150.3679 E. cunninghamii Mount Banks, GBMWHA s13 –33.58479 150.3679 E. cunninghamii Mount Banks, GBMWHA s14 –33.5847 150.3679 E. cunninghamii Mount Banks, GBMWHA s15 –33.58469 150.3679 E. cunninghamii Mount Banks, GBMWHA s16 –33.5847 150.3679 E. cunninghamii Kings Tableland, GBMWHA s17 –33.77544 150.3795 E. cunninghamii Kings Tableland, GBMWHA s18 –33.77559 150.3794 E. cunninghamii Kings Tableland, GBMWHA s19 –33.77123 150.3761 E. cunninghamii Kings Tableland, GBMWHA s20 –33.77066 150.3759 E. cunninghamii Kings Tableland, GBMWHA s21 –33.77119 150.3761 E. cunninghamii Kings Tableland, GBMWHA s22 –33.77334 150.3785 E. cunninghamii Kings Tableland, GBMWHA s23 –33.77344 150.3784 E. stricta Stanwell Tops s24 –34.21102 150.9556 E. stricta Stanwell Tops s25 –34.21098 150.9549 E. stricta Stanwell Tops s26 –34.21072 150.9554 E. stricta Stanwell Tops s27 –34.21034 150.9556 E. stricta Stanwell Tops s28 –34.21056 150.9559 E. stricta Stanwell Tops s29 –34.21051 150.9582 E. stricta Stanwell Tops s30 –34.2108 150.9575 E. stricta Stanwell Tops s31 –34.21243 150.9568 E. stricta Kings Tableland, GBMWHA s32 –33.75472 150.3756 E. stricta Kings Tableland, GBMWHA s33 –33.75414 150.3755 E. stricta Kings Tableland, GBMWHA s34 –33.75409 150.375 E. stricta Kings Tableland, GBMWHA s35 –33.75454 150.3753 E. stricta Kings Tableland, GBMWHA s36 –33.75499 150.3751 E. stricta Kings Tableland, GBMWHA s37 –33.7548 150.3763 E. stricta Kings Tableland, GBMWHA s38 –33.75573 150.3777 E. stricta Kings Tableland, GBMWHA s39 –33.75505 150.3768 E. stricta Blackheath, GBMWHA s40 –33.63224 150.314

179

E. stricta Blackheath, GBMWHA s41 –33.63253 150.3138 E. stricta Blackheath, GBMWHA s42 –33.63273 150.3136 E. stricta Blackheath, GBMWHA s43 –33.63351 150.3133 E. stricta Blackheath, GBMWHA s44 –33.63335 150.3131 E. stricta Blackheath, GBMWHA s45 –33.63302 150.3133 E. stricta Blackheath, GBMWHA s46 –33.63263 150.3134 E. stricta Newnes, GBMWHA s47 –33.45552 150.2303 E. stricta Newnes, GBMWHA s48 –33.45498 150.2305 E. stricta Newnes, GBMWHA s49 –33.45457 150.2315 E. stricta Newnes, GBMWHA s50 –33.45483 150.2315 E. stricta Newnes, GBMWHA s51 –33.45443 150.2319 E. stricta Newnes, GBMWHA s52 –33.4539 150.2318 E. stricta Newnes, GBMWHA s53 –33.45366 150.2317 E. stricta Newnes, GBMWHA s54 –33.45369 150.2313 E. stricta Sassafras s55 –35.07227 150.2044 E. stricta Sassafras s56 –35.07194 150.2042 E. stricta Sassafras s57 –35.0715 150.2034 E. stricta Sassafras s58 –35.07091 150.2041 E. stricta Sassafras s59 –35.07192 150.2049 E. stricta Sassafras s60 –35.07254 150.206 E. stricta Sassafras s61 –35.07283 150.2074 E. stricta Mount Banks, GBMWHA s62 –33.58335 150.3673 E. stricta Mount Banks, GBMWHA s63 –33.58304 150.3671 E. stricta Mount Banks, GBMWHA s64 –33.58267 150.3669 E. stricta Mount Banks, GBMWHA s65 –33.58153 150.366 E. stricta Mount Banks, GBMWHA s66 –33.58057 150.3657 E. stricta Mount Banks, GBMWHA s67 –33.5802 150.3651 E. stricta Mount Banks, GBMWHA s68 –33.57964 150.364 E. laophila Glow Worm Tunnel Road, GBMWHA s69 –33.249361 150.2213 E. laophila Glow Worm Tunnel Road, GBMWHA s70 –33.248972 150.221 E. laophila Glow Worm Tunnel Road, GBMWHA s71 –33.248778 150.2212 E. laophila Glow Worm Tunnel Road, GBMWHA s72 –33.249111 150.2206 E. laophila Glow Worm Tunnel Road, GBMWHA s73 –33.249806 150.2199 E. laophila Glow Worm Tunnel Road, GBMWHA s74 –33.249972 150.2214 E. laophila Glow Worm Tunnel Road, GBMWHA s75 –33.256556 150.2186 E. laophila Glow Worm Tunnel Road, GBMWHA s76 –33.249194 150.2211 E. laophila Glow Worm Tunnel Road, GBMWHA s77 –33.249 150.2201 E. laophila Glow Worm Tunnel Road, GBMWHA s78 –33.256639 150.2186 E. laophila Glow Worm Tunnel Road, GBMWHA s79 –33.256667 150.2187 E. laophila Glow Worm Tunnel Road, GBMWHA s80 –33.256444 150.2187 E. laophila Glow Worm Tunnel Road, GBMWHA s81 –33.256639 150.2187 E. laophila Lithgow s82 –33.49826 150.166 E. laophila Lithgow s83 –33.49881 150.1664 E. laophila Lithgow s84 –33.49918 150.1663 E. laophila Lithgow s85 –33.49905 150.1659 E. laophila Lithgow s86 –33.49925 150.1657 180

E. obstans Beacon Hill, Sydney s87 –33.74287 151.2598 E. obstans Beacon Hill, Sydney s88 –33.74269 151.26 E. obstans Beacon Hill, Sydney s89 –33.74269 151.2603 E. obstans Beacon Hill, Sydney s90 –33.74238 151.2605 E. obstans Beacon Hill, Sydney s91 –33.74224 151.2605 E. obstans Beacon Hill, Sydney s92 –33.74271 151.2595 E. obstans Beacon Hill, Sydney s93 –33.74285 151.2605 E. obstans Royal National Park, Sydney s94 –34.12135 151.0753 E. obstans Royal National Park, Sydney s95 –34.12179 151.0752 E. obstans Royal National Park, Sydney s96 –34.122 151.0758 E. obstans Royal National Park, Sydney s97 –34.12169 151.076 E. obstans Royal National Park, Sydney s98 –34.12067 151.0758 E. obstans Royal National Park, Sydney s99 –34.12029 151.0759 E. obstans Royal National Park, Sydney s100 –34.12062 151.0761 E. obstans Royal National Park, Sydney s101 –34.12135 151.0764 E. obstans Jervis Bay s102 –35.00846 150.8311 E. obstans Jervis Bay s103 –35.00839 150.8312 E. obstans Jervis Bay s104 –35.00832 150.8314 E. obstans Jervis Bay s105 –35.00821 150.8319 E. obstans Jervis Bay s106 –35.00797 150.832 E. obstans Jervis Bay s107 –35.00776 150.8326 E. obstans Jervis Bay s108 –35.00738 150.833 E. langleyi Braidwood Road, Nowra s109 –34.97373 150.4947 E. langleyi Braidwood Road, Nowra s110 –34.97345 150.4952 E. langleyi Braidwood Road, Nowra s111 –34.9737 150.4956 E. langleyi Braidwood Road, Nowra s112 –34.97382 150.4966 E. langleyi Braidwood Road, Nowra s113 –34.97354 150.4972 E. langleyi Braidwood Road, Nowra s114 –34.97356 150.4966 E. langleyi Braidwood Road, Nowra s115 –34.9735 150.4962 E. langleyi Braidwood Road, Nowra s116 –34.97325 150.496 E. langleyi Parma Creek Firetrail, Nowra s117 –34.99198 150.4869 E. langleyi Parma Creek Firetrail, Nowra s118 –34.99191 150.4871 E. langleyi Parma Creek Firetrail, Nowra s119 –34.99184 150.487 E. langleyi Parma Creek Firetrail, Nowra s120 –34.99184 150.487 E. langleyi Parma Creek Firetrail, Nowra s121 –34.99188 150.487 E. langleyi Parma Creek Firetrail, Nowra s122 –34.99169 150.4871 E. langleyi Parma Creek Firetrail, Nowra s123 –34.99169 150.4869 E. langleyi Parma Creek Firetrail, Nowra s124 –34.99176 150.4867 NSW908486 Stanwell Tops s125 –34.2107 150.9553 E. dendromorpha Blackheath, GBMWHA s126 –33.62889 150.3118 E. dendromorpha Blackheath, GBMWHA s127 –33.62862 150.3118 E. dendromorpha Blackheath, GBMWHA s128 –33.62828 150.312 E. dendromorpha Blackheath, GBMWHA s129 –33.62801 150.3119 E. dendromorpha Blackheath, GBMWHA s130 –33.6279 150.3118 E. dendromorpha Blackheath, GBMWHA s131 –33.62789 150.3117 E. dendromorpha Blackheath, GBMWHA s132 –33.62708 150.3117 181

E. dendromorpha Blackheath, GBMWHA s133 –33.62696 150.3116 E. dendromorpha Wentworth Falls, GBMWHA s134 –33.7272 150.3713 E. dendromorpha Wentworth Falls, GBMWHA s135 –33.72755 150.3709 E. dendromorpha Wentworth Falls, GBMWHA s136 –33.7269 150.3741 E. dendromorpha Wentworth Falls, GBMWHA s137 –33.72754 150.3744 E. dendromorpha Wentworth Falls, GBMWHA s138 –33.72781 150.3747 E. dendromorpha Wentworth Falls, GBMWHA s139 –33.72815 150.3743 E. dendromorpha Wentworth Falls, GBMWHA s140 –33.7274 150.3747 E. dendromorpha Wentworth Falls, GBMWHA s141 –33.7261 150.3711 E. dendromorpha Mount Wilson, GBMWHA s142 –33.521472 150.3708 E. dendromorpha Mount Wilson, GBMWHA s143 –33.521389 150.3707 E. dendromorpha Mount Wilson, GBMWHA s144 –33.521278 150.3706 E. dendromorpha Mount Wilson, GBMWHA s145 –33.521194 150.3704 E. dendromorpha Mount Wilson, GBMWHA s146 –33.521139 150.3703 E. dendromorpha Mount Wilson, GBMWHA s147 –33.520917 150.3701 E. dendromorpha Mount Wilson, GBMWHA s148 –33.520889 150.3704 E. dendromorpha Mount Wilson, GBMWHA s149 –33.521139 150.3708 E. dendromorpha Redhills Road, Fitzroy Falls s150 –34.64626 150.4371 E. dendromorpha Redhills Road, Fitzroy Falls s151 –34.64621 150.4371 E. dendromorpha Redhills Road, Fitzroy Falls s152 –34.64611 150.4374 E. dendromorpha Redhills Road, Fitzroy Falls s153 –34.6464 150.4374 E. dendromorpha Redhills Road, Fitzroy Falls s154 –34.64657 150.4373 E. dendromorpha Redhills Road, Fitzroy Falls s155 –34.64626 150.4366 E. dendromorpha Redhills Road, Fitzroy Falls s156 –34.64618 150.4365 E. dendromorpha Redhills Road, Fitzroy Falls s157 –34.64637 150.436 E. dendromorpha Jersey Lookout, Fitzroy Falls s158 –34.64833 150.4797 E. dendromorpha Jersey Lookout, Fitzroy Falls s159 –34.64851 150.4793 E. dendromorpha Jersey Lookout, Fitzroy Falls s160 –34.64808 150.4763 E. dendromorpha Jersey Lookout, Fitzroy Falls s161 –34.64848 150.4751 E. dendromorpha Jersey Lookout, Fitzroy Falls s162 –34.64844 150.475 E. dendromorpha Jersey Lookout, Fitzroy Falls s163 –34.64862 150.4742 E. dendromorpha Jersey Lookout, Fitzroy Falls s164 –34.64842 150.4742 E. dendromorpha Jersey Lookout, Fitzroy Falls s165 –34.64836 150.4746 E. sp. Mount Banks Mount Banks, GBMWHA s166 –33.58497 150.3684 E. sp. Mount Banks Mount Banks, GBMWHA s167 –33.58504 150.3684 E. sp. Mount Banks Mount Banks, GBMWHA s168 –33.58498 150.3684 E. sp. Mount Banks Mount Banks, GBMWHA s169 –33.585 150.3684 E. sp. Mount Banks Mount Banks, GBMWHA s170 –33.58502 150.3684 E. sp. Mount Banks Mount Banks, GBMWHA s171 –33.58501 150.3683 E. sp. Mount Banks Mount Banks, GBMWHA s172 –33.58502 150.3683 E. sp. Mount Banks Mount Banks, GBMWHA s173 –33.58501 150.3683

182

Appendix 2. Sources of genome sequences used in this study

Species Version Data source E. baxteri NC_022382.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022382.1 E. camaldulensis NC_022398.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022398.1 E. deglupta KC180792 https://www.ncbi.nlm.nih.gov/nuccore/KC180792.1 E. delegatensis NC_022380.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022380.1 E. diversifolia NC_022383.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022383.1 E. elata NC_022385.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022385.1 E. globulus KC180787.1 https://www.ncbi.nlm.nih.gov/nuccore/KC180787.1 E. globulus subsp. globulus AY780259.1 https://www.ncbi.nlm.nih.gov/nuccore/AY780259.1 E. grandis HM347959.1 https://www.ncbi.nlm.nih.gov/nuccore/HM347959.1 E. marginate KC180781.1 https://www.ncbi.nlm.nih.gov/nuccore/KC180781.1 E. melliodora NC_022392.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022392.1 E. nitens KC180788.1 https://www.ncbi.nlm.nih.gov/nuccore/KC180788.1 E. obliqua NC_022378.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022378.1 E. patens KC180780.1 https://www.ncbi.nlm.nih.gov/nuccore/KC180780.1 E. radiate NC_022379.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022379.1 E. regnans NC_022386.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022386.1 E. saligna NC_022397.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022397.1 E. salmonophloia NC_022403.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022403.1 E. sieberi NC_022384.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022384.1 E. torquate KC180794.1 https://www.ncbi.nlm.nih.gov/nuccore/KC180794.1 E. umbra NC_022387.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022387.1 E. verrucata NC_022381.1 https://www.ncbi.nlm.nih.gov/nuccore/NC_022381.1

183

Appendix 3. Ordination derived from principle coordinate analysis of Nei’s genetic distance Calculated from 4783 DArTseq loci. A. All study taxa. B. Taxa excluding Eucalyptus cunninghamii, NSW908486 and E. sp. Mount Banks (axis 1 and axis 2). C. Taxa excluding Eucalyptus cunninghamii, NSW908486 and E. sp. Mount Banks (axis 1 and axis 3).

Abbreviations: Bank, Mount Banks; Beac, Beacon Hill (Sydney); Blac, Blackheath; Brai, Braidwood Road (Nowra); Glow, Glow Worm Tunnel Road (Wollemi National Park); Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; King, Kings Tableland; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Pulp, Pulpit Rock; Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Sass, Sassafras; Stan, Stanwell Tops (Sydney); Went, Wentworth Falls; Wils, Mount Wilson.

A B

184

Appendix 4. Average assignments values (Q) to different groups in STRUCTURE analyses Calculated from 4783 SNPs showing: (a) all taxa, and (b) taxa excluding Eucalyptus cunninghamii. Values where Q > 0.90 are highlighted in bold. (a) K=2 Group 1 Group 2 E. cunninghamii Pulpit Rock 0.0004 0.9996 E. cunninghamii Wentworth Falls 0.0062 0.9938 E. cunninghamii Mount Banks 0.0048 0.9952 E. cunninghamii Kings Tableland 0.0054 0.9946 E. stricta Stanwell Tops 0.999 0.001 E. stricta Kings Tableland 0.9994 0.0006 E. stricta Blackheath 0.9978 0.0022 E. stricta Newnes 0.9932 0.0068 E. stricta Mount Banks 0.9966 0.0034 E. stricta Sassafras 0.9988 0.0012 E. laophila Glow Worm Tunnel Road 0.9986 0.0014 E. laophila Lithgow 0.9978 0.0022 E. obstans Beacon Hill 0.9994 0.0006 E. obstans Royal National Park 0.9988 0.0012 E. obstans Jervis Bay 0.9988 0.0012 E. langleyi Braidwood Road 0.9994 0.0006 E. langleyi Parma Creek Firetrail 0.9994 0.0006 E. dendromorpha Blackheath 0.9972 0.0028 E. dendromorpha Wentworth Falls 0.9988 0.0012 E. dendromorpha Mount Wilson 0.9988 0.0012 E. dendromorpha Redhills Road 0.9988 0.0012 E. dendromorpha Jersey Lookout 0.992 0.008 NSW908486 0.8168 0.1832 E. sp. Mount Banks 0.9994 0.0006

(b) K=7 Group Group Group Group Group Group Group 1 2 3 4 5 6 7 E. stricta Stanwell Tops 0.768 0.0014 0.1316 0.0012 0.0008 0.001 0.0962 E. stricta, Kings Tableland 0.7642 0.0036 0.0104 0.0012 0.0014 0.0006 0.2186 E. stricta Blackheath 0.7254 0.0018 0.0016 0.0014 0.1264 0.0018 0.1422 E. stricta Newnes 0.7662 0.002 0.0016 0.001 0.0254 0.0022 0.2018 E. stricta Mount Banks 0.7652 0.0066 0.004 0.0082 0.0476 0.001 0.168 E. stricta Sassafras 0.7636 0.0014 0.046 0.0084 0.0032 0.0514 0.1262 E. laophila Glow Worm Tunnel Road 0.7414 0.0014 0.0006 0.0004 0.0008 0.0002 0.2542 E. laophila Lithgow 0.7578 0.002 0.0018 0.0004 0.0026 0.0006 0.235 E. dendromorpha Blackheath 0.6492 0.0052 0.0012 0.0012 0.3198 0.0206 0.003 E. dendromorpha Wentworth Falls 0.6976 0.0078 0.0028 0.0014 0.237 0.0314 0.0226 E. dendromorpha Mount Wilson 0.6392 0.0024 0.0008 0.0006 0.3406 0.0144 0.0022 E. dendromorpha Redhills Road 0.6454 0.0014 0.0042 0.0016 0.0016 0.3416 0.0048 E. dendromorpha Jersey Lookout 0.5168 0.0012 0.0008 0.0006 0.0056 0.4744 0.0002 E. obstans Beacon Hill 0.7334 0.0008 0.2616 0.001 0.001 0.0004 0.002 185

E. obstans Royal National Park 0.7294 0.001 0.2488 0.0076 0.0024 0.0012 0.0098 E. obstans Jervis Bay 0.7372 0.001 0.2488 0.0112 0.0002 0.0008 0.0006 E. langleyi Braidwood Road 0.6376 0.0016 0.0062 0.3526 0.0006 0.0006 0.0006 E. langleyi Parma Creek Firetrail 0.58 0.0004 0.0004 0.4178 0 0.0004 0.0002 NSW908486 0.5792 0.0008 0.1232 0.0048 0.0994 0.019 0.1734 E. sp. Mount Banks 0.0014 0.9982 0 0 0 0 0

186

Appendix 5. ∆K (Delta K) plots produced by STRUCTURE HARVESTER showing the optimum value of K for each STRUCTURE analysis

All taxa

All taxa excluding Eucalyptus cunninghamii

187

Eucalyptus cunninghamii

Eucalyptus dendromorpha (all populations)

188

Eucalyptus obstans

Eucalyptus langleyi

189

Eucalyptus stricta and E. laophila

190

Appendix 6. Different barplots produced by STRUCTURE for each species at varying values of K

Eucalyptus cunninghamii (population 1: Pulpit Rock, population 2: Wentworth Falls, population 3: Mount Banks, population 4: Kings Tableland)

K=2

K=4

191

Eucalyptus dendromorpha (population 1: Blackheath, population 2: Wentworth Falls, population 3: Mount Wilson, population 4: Redhills Road, population 5: Jersey Lookout)

K=3

K=4

K=5

192

Eucalyptus obstans (population 1: Beacon Hill, population 2: Royal National Park, population 3: Jervis Bay)

K=3

193

Eucalyptus stricta (population 1: Stanwell Tops, population 2: Kings Tablelands, population 3: Blackheath, population 4: Newnes, population 5: Sassafras, population 6: Mount Banks) and Eucalyptus laophila (population 7: Glow Worm Tunnel Road, population 8: Lithgow)

K=2

K=3

K=4

194

K=5

K=6

195

K=7

K=8

196

Appendix 7. Population-level network analyses of the highest quality DArTseq markers (4783 SNPs) A. Minimum-spanning tree. B. Threshold network. Blue and green lines show network linkages. Size of nodes and edges are scaled to the degree of connectedness to other populations. Abbreviations: Bank, Mount Banks; Beac, Beacon Hill (Sydney); Blac, Blackheath; Brai, Braidwood Road (Nowra); Glow_N, north of the tunnel on Glow Worm Tunnel Road; Glow_S, south of the tunnel on Glow Worm Tunnel Road; Jers, Jersey Lookout (Fitzroy Falls); Jerv, Jervis Bay; King, Kings Tableland; Lith, Lithgow; Newn, Newnes; Parm, Parma Creek Firetrail (Nowra); Pulp, Pulpit Rock; Redh, Redhills Road (Fitzroy Falls); Roya, Royal National Park; Stan, Stanwell Tops (Sydney); Went, Wentworth Falls; Wils, Mount Wilson.

sp. Mt Banks

197

Appendix 8. Haplotype network of chloroplast DArTseq markers for Eucalyptus species from the Sydney region and Greater Blue Mountains World Heritage Area Each circle represents a haplotype. Circle size indicates the relative haplotype frequency among populations and species. The size of the sections in each circle corresponds to the number of individuals that possess that particular haplotype. Branch lengths indicate the relative distance between haplotypes. The writing on each branch is the haplotype name. Haplotype networks are colour coded by: A. Species identity. B. Geographic region.

198

Appendix 9. Maximum likelihood tree and corresponding residual fit matrix inferred by TreeMix (based on 11 739 SNPs) under varying migration events A. Analysis without admixture. B. Analysis with one admixture event. C. Analysis with two admixture events. D. Analysis with three admixture events. A

199

200

Chapter 5. From environmental niche modelling to phylogenetics and functional traits: understanding ecological specialisation and species diversification in Eucalyptus

Susan Rutherford, Peter D. Wilson, Peter G. Wilson, Stephen P. Bonser and Maurizio

Rossetto

Eucalyptus seedlings (from top left to bottom right): Eucalyptus burgessiana, E. obstans, E. langleyi, E. triflora, E. fastigata, E. regnans, E. dendromorpha (Southern Highlands), E. laophila and E. cunninghamii.

201

5.1 Abstract

We investigated the role of ecological specialisation in the divergence of Eucalyptus species from south-eastern Australia. The green ashes (subgenus Eucalyptus section Eucalyptus) are a diverse group from this region and include both widespread and narrowly distributed species. We used genomic data, trait measurements from a common garden experiment and environmental niche modelling to examine the predicted environmental range and phylogenetic niche conservatism of 12 green ash species. We found two tall trees (Eucalyptus obliqua and E. fastigata) had the highest predicted environmental ranges, whereas the tall tree, E. regnans, and all of the mallees and medium trees had much narrower predicted environmental ranges. Our findings suggest that the green ashes are a group with multiple evolutionary shifts, with a clade of trees in tall open forests, and another clade of mallees and medium trees in low open shrublands, woodlands, open woodlands, low open woodlands, and open forests. Species from tall open forests and low open shrublands tended to have juvenile leaves with higher specific leaf area (SLA), lower thickness, lower leaf area (LA) and lower leaf length, while species from woodlands and open woodlands had juvenile leaves with lower SLA, higher thickness, higher LA and higher leaf length. A strong phylogenetic signal was found in some traits (e.g. seedling height and LA). Traits with a relatively low phylogenetic signal include leaf fresh mass versus dry mass (FW/DW) and leaf width. Our results demonstrate important trends in the diversification of an iconic Australian group, and how major evolutionary shifts impact the capacity for seedlings to adapt to their respective environments.

5.2 Introduction

Ecological specialisation is a process that underlies many evolutionary patterns (Poisot et al. 2011) and ecological factors are known to have an important role in speciation (Schluter 2000). All organisms have a subset of abiotic conditions (e.g. nutrients, water availability and topography) and biotic (e.g. pollinators, seed dispersers) resources in which they thrive and are to some extent ecologically specialised (Futuyma and Moreno 1988; Forister et al. 2012). When populations of a species inhabit a range of environments (and experience different ecological conditions), divergent natural

202 selection acts on ecologically important traits leading to different adaptations (Wiens 2004). Population adaptation to varying habitats can result in cumulative changes in traits that confer high fitness to their respective environments (Chase and Leibold 2003; Kawecki and Ebert 2004). Specialisation to different environments can be a factor resulting in evolutionary divergence leading to reduced gene flow between populations, and ultimately reproductive isolation (Lee and Mitchell-Olds 2013). While there have been numerous studies on the processes through which species form (e.g. Darwin 1859; Mayr 1942, 1963; Coyne and Orr 2004), there are many unanswered questions concerning the relationship between ecological specialisation and species divergence (Wiens and Graham 2005). For example, what are the similarities and differences in ecological factors and attributes (e.g. traits) across closely related species (Pearman et al. 2008)? Furthermore, what is the role of ecological specialisation in the diversification of species (Poisot et al. 2011)?

This study presents recent research on the evolution and ecological specialisation of a group of Eucalyptus species, commonly known as the green ashes, in subgenus Eucalyptus section Eucalyptus from south-eastern Australia (sensu Brooker 2000). The green ashes as circumscribed by Brooker (2000) include tall trees on deep, fertile soils (up to 100 m tall, e.g. E. regnans), as well as medium trees and mallees (multi-stemmed plants less than 5 m in height) on skeletal and sandy soils (Mullette 1978; Ladiges et al. 2010). Although some green ash species are widespread in south-eastern Australia (e.g. E. obliqua and E. fastigata, Eldridge et al. 1993), others are restricted to narrow environmental niches (e.g. E. regnans, Tng et al. 2012), with many species being classified as rare or localised (e.g. E. triflora, E. burgessiana and E. cunninghamii, Benson and McDougall 1998). Previous studies suggest that many groups in subgenus Eucalyptus (including the green ashes) have recently radiated. The earliest known fossils of subgenus Eucalyptus are from Late Miocene deposits (5–10 Ma, Blazey 1994). This is concordant with recent molecular estimates (which places the radiation of subgenus Eucalyptus in the last 5–10 Ma, Crisp et al. 2011; Thornhill et al. 2015). In the case of the green ashes, there is poor morphological resolution between many species and hybridisation is common, indicating recent speciation (Ladiges et al. 1989; Prober et al. 1990; Hill 2002) such that, resolving evolutionary relationships in this group has historically been problematic. However, recent advances in genomic research (e.g. genome-wide scans) are enabling better resolution of species boundaries in closely 203 related lineages (Wagner et al. 2013). We previously used a genome complexity reduction technique (Diversity Arrays Technology or DArT), to better understand evolutionary relationships in the green ashes (Rutherford et al. 2016; Rutherford et al. in prep.) and found that improved resolution of species boundaries could be achieved using these newer techniques. Our results also suggested that these species are likely to have recently radiated with a number of speciation mechanisms involved (e.g. vicariance, ecological speciation and reticulate evolution, Rutherford et al. in prep., see Chapter 4). Since the green ashes are likely to have recently radiated and include both widespread and narrowly distributed species, with different degrees of range overlap, this group is an ideal system for investigating the relationship between ecological specialisation and the diversification of lineages.

Studies that integrate phylogenetic, geographic and ecological data have provided novel insights into the factors that influence the distribution and diversification of species (Graham et al. 2004). Many techniques have been developed for modelling environmental niches of species, and environmental niche models (ENMs), which are generated by combining species occurrence data with environmental GIS data layers, are now widely used in ecology, evolutionary biology and conservation (Warren et al. 2008; Merow et al. 2014). The niche of a species can be defined as the total environmental conditions and resources required for that species to sustain a stable population (Hutchinson 1957). The degree to which environmental niches are conserved across phylogenetic clades (phylogenetic niche conservatism) has implications for both ecological and evolutionary phenomena (Wiens et al. 2010). For example, Peterson et al. (1999) found that many climatic niches are conserved (i.e. similar) between sister clades of mammals, birds and butterflies in Mexico. In contrast, niches were found to not overlap in Anolis lizards (Losos et al. 2003) and in dendrobatid frogs (Graham et al. 2004). More recently low support for phylogenetic niche conservatism (in habitat and climate) was found for 405 species of European birds (Pearman et al. 2014), while in a Brazilian rainforest more closely related tree species occupied different habitats, whereas species that were geographically close were more distantly related (de Oliveira et al. 2014). If niches of related species remain unchanged or only slowly change over hundreds to millions of years (i.e. niches are phylogenetically conserved), then it is predicted that those species will have limited ability to respond to environmental change (Pearman et al. 2008). Conversely, if phylogenetic niche conservatism of species is low, 204 then niches have not remained static over time and it is predicted that those species can expand, contract or shift in response to ecological change (Wiens et al. 2010). Niche conservatism may also provide insights into species delimitation (Wiens and Graham 2005). For example, niche modelling was used to predict where chameleon species might be expected to occur and as a result seven undescribed species were identified (Raxworthy et al. 2003).

Functional traits are also widely used to better understand adaptive evolution of species (Geber and Griffen 2003). Functional traits are attributes that influence the ability of an organism to establish, survive, grow and reproduce (Ackerly 2003). A number of functional traits are considered to be important for plant performance, including specific leaf area (leaf area per unit leaf mass, SLA) and plant height (Westoby 1998; Poorter et al. 2008). Studies of interspecific variation in plant functional traits has led to insights into trade-offs between traits, the identification of plant functional groups, and an understanding of the role of trait trade-offs and functional groups in ecosystem functioning (Grime et al. 1997; Reich et al. 2003). An understanding of seedling functional traits is of particular importance, since seedling emergence and establishment are critical stages in the life history of plants (Ibarra-Manríquez et al. 2001). Attributes of seedlings (e.g. morphological and physiological characters) are important for resource acquisition and are likely to be crucial to the ability of a plant to cope with mortality agents (Fenner 1987). Common garden experiments provide an invaluable tool for investigating adaptive traits of seedlings. Growing species in a common garden limits the effects of phenotypic plasticity and to some extent genotype-by-environment interactions, thereby enabling the study of the genetic basis of functional traits (de Villemereuil et al. 2016). A functional trait may be regarded as adaptive if the phenotype enhances an organism’s performance and fitness to their environment (Ackerly 2003). The adaptive value of a functional trait can be tested using patterns of trait variation across a phylogeny of closely related species. A pattern of no phylogenetic signal or weak phylogenetic signal in a functional trait suggests that the trait varies randomly across the phylogeny or that it remains unchanged over time (Wiens et al. 2010). Therefore, if phylogenetic conservatism of functional traits is low, then it may be inferred that species have adapted to their present environmental conditions (Khaliq et al. 2015).

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In a previous study, we grew 12 green ash species in a common garden and found that differences in functional traits amongst species were significant (Rutherford et al. 2017). We also found a number of seedling traits for many species (e.g. plant size and leaf width) were highly plastic in response to changes in nutrients, while others (such as SLA and leaf thickness) were less plastic between resource treatments (Rutherford et al. 2017). Varying levels of overall phenotypic plasticity between species were found, with the more widespread species tending to display higher plasticity, and the more restricted species having lower plasticity (Rutherford et al. 2017). Life history and physiological traits have previously been found to be related to environmental range size in Eucalyptus (Matthews and Bonser 2005). Here, we examine associations between seedling functional traits, environmental factors and evolutionary history to investigate the role of ecological specialisation in the evolution of the green ashes.

Species presence data in conjunction with genomic and functional trait data are likely to be informative for testing hypotheses linking environmental parameters, species ecology and clade evolution (Case et al. 2005; McLeish et al. 2011). Furthermore, comparative studies of ecophysiological traits of closely related species can enable hypotheses to be tested regarding adaptive significance of functional traits (Ackerly et al. 2000). This study is a comparative analysis using environmental niche modelling, the genomic dataset of Rutherford et al. (2016) and the functional trait dataset of Rutherford et al. (2017) to investigate ecological specialisation and phylogenetic niche conservatism of the 12 green ash species aforementioned. Therefore we asked the following questions: (1) what combination of climate and habitat variables are projected to be important in limiting the distribution and range of the study species? (2) What are the patterns of habitat diversification across clades in the green ashes and is the predicted environmental range of a species influenced by evolutionary (phylogenetic) relationships? (3) Are inter-specific differences in seedling functional traits associated with habitat types and are functional traits influenced by evolutionary history?

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5.3 Materials and methods

5.3.1 Study system

The 12 species selected for this study represent a range of growth forms, habitats and clades across the green ash group. Although distributed throughout south-eastern Australia with two species also occurring on the island of Tasmania (E. regnans and E. obliqua), one of which extends into South Australia (E. obliqua), the majority of green ash species in this study occur in the Sydney region and Greater Blue Mountains World Heritage Area (GBMWHA, located 70 km to the west of Sydney, Fig. 1). The tall trees, E. regnans, E. obliqua and E. fastigata have been previously reported to occur on higher nutrient soils, while the mallees and medium trees are generally found on lower nutrient substrates (Benson and McDougall 1998, Hill 2002, Rutherford et al. 2016). So that all recognised species within the Sydney region and GBMWHA were included, we followed the narrower species concepts of Hill (2002). Previous phylogenetic (Rutherford et al. 2016) and population genetic (Rutherford et al. in prep., Chapter 4) analyses suggest that the mallee form of E. dendromorpha from GBMWHA is a separate taxon to the tree form of E. dendromorpha found south of Sydney. Therefore, for analyses in the present study, these two were firstly treated as one taxon and then as separate taxa. Similarly, population genetic analyses (Rutherford et al. in prep., Chapter 4), suggest that E. stricta and E. laophila are likely to be the same taxon. Therefore for the analyses here, we firstly treated them as two separate taxa and then treated them as the one taxon.

5.3.2 Climate niche modelling and range size

We used environmental niche models (ENMs) to estimate the climatic niche of each study species. Locality data of all species were obtained from data from the Atlas of Living Australia (ALA), which includes records from all herbaria in Australia (available at http://www.ala.org.au, accessed 19 December 2016). Records for each species were downloaded and filtered so that only data for preserved specimens from 1960 to the present were included. This time period was selected so that only more accurate location details were used for ENMs. The use of data from preserved specimens ensured that no records for which the species identity was unknown were used in analyses.

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Species and populations in B the GBMWHA

Fig. 1. Current distribution of the 12 Eucalyptus species. A. All species in south-eastern Australia. B. The species found in the Sydney region, Greater Blue Mountains World Heritage Area (GBMWHA), and to the south of Sydney. Maps generated using Australia’s Virtual Herbarium (2017). The species concept used in Australia’s Virtual Herbarium follows the broader classification of Brooker (2000). In the narrower classification of Hill (2002), coastal populations of Eucalyptus burgessiana are E. obstans, and all populations of E. apiculata to the north of Katoomba are E. laophila.

Distribution maps for each species were generated using QGIS version 2.18.1 (QGIS Development Team, 2016) from the filtered dataset and any obvious outliers were removed (i.e. implausible locations and cultivated individuals). Maps were also checked for areas of low sampling effort. Since our study species are in well sampled areas, we found that sampling was well distributed.

We estimated niche models for each species with the maximum entropy method as implemented in Maxent version 3.3.3 (Phillips et al. 2006; Phillips and Dudik 2008). Maxent implements a Bayesian approach through which a species’ probability

208 distribution is calculated under the maximum entropy criterion and is dependent on environmental constraints (Phillips et al. 2004, 2006). Maxent is considered to be a reliable algorithm for modelling with different sample sizes and works effectively with presence data (Pearson et al. 2007). In the present study, niche modelling was performed using a raster database of climatic, topographic and soil variables. We used 30 variables for the spatial area of the continent of Australia (Appendix 1). The climate data for the ‘current’ or observed climate were downloaded from the eMAST data set (available at http://www.emast.org.au, accessed 1 November 2016). This included monthly minimum and maximum temperatures and rainfall data that were extracted for the period of 1983–2012 (which were averaged to give a 30-year climatology). From these average monthly values, we calculated the 19 basic bioclimatic variables. The 19 bioclimatic variables comprise a range of measurements of temperature and precipitation (see the WorldClim database, available at http://www.worldclim.org/bioclim, accessed 23 February 2017). The eMAST monthly evaporation data were also used for this 30-year period to determine mean monthly evaporation, minimum and maximum monthly evaporation, and evaporation seasonality. In addition, topographic data were obtained from the CSIRO Data Portal (https://data.csiro.au/dap/home?execution=e2s1, accessed 23 February 2017). This included topographic position index (TPI), a measure of a grid cell’s position in the sequence from the valley floor to the ridge top; as well as topographic wetness index (TWI), a measure of the degree to which rainfall will flow into a grid cell from upslope locations. The data for TPI and TWI were provided at a grid cell resolution of 3 arc- seconds and were resampled to the same grid geometry as the eMAST climate data (0.01 degree grid cells). We also downloaded soil structural data (percent sand, silt and clay) from the CSIRO Data Portal and these were resampled to the eMAST grid geometry. ENMs were produced for each species, by switching off hinge features and threshold features and the analysis was run for 10 replicates. We used the area under the receiver operating characteristic curve (AUC) of each ENM to determine the general accuracy of the models.

The predicted geographical extent of maximally suitable environmental conditions for each species was estimated in R version 3.1.3 (R Development Core Team 2015) using the ENMs from Maxent. We used functions in the R package ‘raster’ (Hijmans and van Etten 2012) to apply maximum sum of specificity plus sensitivity threshold computed 209 by Maxent to the output raster converting it to a binary form. The count of grid cells greater than or equal to the threshold was used as an index of the geographical extent of suitable environments.

We used climate data for a 30-year period as this was consistent with the time-span of the location data that were used. Individuals of the study species are known to be long- lived and are therefore likely to have been in the area for long periods of time. For example, mature trees of E. regnans have a life-span of approximately 100–300 years, with some individuals living longer than 400 years (Ashton 1975). Mallees are thought to be longer lived, with age estimates ranging from 500 to 900 years old (Head and Lacey 1988; Tyson et al. 1998). Although the climate data used in the present study is not consistent with the maximum ages of the study species, it should be noted that we only have adequate temperature and rainfall data for approximately the last 75 years (which is also significantly shorter than the life-span of the study species). A mismatch between climate data and the age of the study species is therefore unavoidable and can impact the utility of the ENMs (see Roubicek et al. 2010). However, the 30-year climate dataset that is used here is informative regarding the tolerances of the study species under recent environmental conditions and can be useful in a comparative study since it is indicative of the current degree of differentiation in environmental preferences between taxa.

5.3.3 Phylogenetic analysis and ancestral reconstruction of habitats

To investigate patterns of habitat diversification and phylogenetic niche conservatism, we used the molecular dataset from Rutherford et al. (2016) and the phylogeny estimated in Rutherford et al. (2017) to investigate patterns of habitat diversification across the green ash group. The molecular dataset in Rutherford et al. (2016) comprised 1780 presence/absence DArT markers. DArT is a high throughput method based on genome complexity reduction using restriction enzymes followed by hybridisation to microarrays which simultaneously assays hundreds to thousands of markers across the genome (Jaccoud et al. 2001). A phylogeny of the 12 study species was constructed using MrBayes version 3.2.4 (Huelsenbeck and Ronquist 2001; Ronquist and

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Huelsenbeck 2003; Ronquist et al. 2012) with the blue ash, E. piperita used as the out- group (for details of analysis see Rutherford et al. 2017).

We used the classification of vegetation types of Specht (1970) for ancestral reconstructions. Specht (1970) classified habitats on the basis of the height of the tallest stratum and percentage of canopy cover (Appendix 2). Details on the stratum and canopy cover of each population that was sampled for the phylogeny were noted in the field and vegetation types determined. Ancestral habitat states were inferred using parsimony-based reconstructions using Mesquite version 3.03 (Maddison and Maddison 2015). The character matrix for ancestral reconstructions is presented in Appendix 3. We used the ‘Continuous’ module in BayesTraits V2 (Pagel 1999; Pagel and Meade 2014) to investigate phylogenetic signal in geographical range size across the green ashes. BayesTraits implements the generalized least-squares model (Pagel 1997, 1999; Pagel and Meade 2014). We used 1000 trees from our MrBayes phylogenetic analysis for analyses in BayesTraits. Phylogenetic signal of range size was calculated using Pagel’s lambda (λ) in Continuous Mode (using a random-walk model and MCMC analysis). Pagel’s λ provides an estimate of the variance of the trait that is due to phylogenetic conservatism (Lee et al. 2013). The model where λ is estimated was compared to models where λ is forced to be either 0 or 1. In the model when λ is equal to 1, trait similarity is inferred to be directly proportional to the evolutionary history of the species (i.e. consistent with the phylogeny). However, in the model when λ is approaching 0, the pattern of trait evolution is regarded as independent of the phylogeny (Rasmann and Agrawal 2011). In our BayesTraits analysis, we used the parameters that were used in Rutherford et al. (2017), which included a burn-in period of 35 million generations, a sampling frequency of 100 generations and a running time of 140 million generations. All outputs from BayesTraits were viewed in Tracer version 1.6 (Rambaut et al. 2014) where the mean value and 95% highest posterior density (HPD) of λ were calculated. Bayes factors (BF) were used to compare the model with the estimated value of λ, to models where λ was fixed at 0 or 1. BF compares the marginal log likelihoods of two models to determine which model better fits the data. In the present study, BF were calculated using the criterion of twice the marginal log-likelihood differences of the models (Kass and Raftery 1995). The log BF was calculated using the formula provided in the BayesTraits manual (Pagel and Meade 2014): Log BF = 2 × (log marginal likelihood complex model – log marginal 211

likelihood simple model) When a log BF is less than 2, it is inferred that there is weak support for one model over the other (Pagel and Meade 2014). That is, the simpler model should be favoured (in this case, the model where λ is estimated, Pagel and Meade 2014). A log BF greater than 2 suggests one model is strongly supported over another (Pagel and Meade 2014). In this case, the more complex model (where λ is 0 or 1) is preferred (Pagel and Meade 2014).

5.3.4 Associations between phylogeny, habitat and seedling functional traits

We investigated associations between seedling traits, habitat and phylogeny using the functional trait dataset from Rutherford et al. (2017). This dataset included trait measurements from seedlings of all species from the present study grown in a common garden experiment for an 8-month period under varying nutrient and water regimes (Rutherford et al. 2017). Seedlings were grown from seed sourced from the same populations of each species that were previously sampled for the DArT phylogenetic analysis. Fourteen plant-level and leaf-level traits were measured at the end of the 8 month period. To effectively compare traits across species and investigate phylogenetic signal, only data collected from the optimal treatment (high nutrient and high water) were used in this study.

To better understand leaf functional trait differences across habitats, we tested for a relationship between specific leaf area (SLA) and other leaf traits, namely leaf fresh mass versus dry mass, leaf length, leaf thickness and leaf area (LA) via regression analysis. SLA was used in each regression analysis as it represents the light capturing foliar area per unit of leaf biomass invested (Poorter et al. 2008) and is positively correlated with relative growth rate and is therefore considered to be a key plant functional trait (Lusk et al. 1997). We also performed regression analyses of SLA and two plant performance traits (seedling height and total aboveground dry mass). Samples were coded by habitat type to visualise differences in these parameters across habitats. All regression analyses were conducted in R version 3.1.3 (R Development Core Team 2015) and relationships were reported as significant where P < 0.05.

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We also investigated the effect of phylogenetic history on the evolution of all seedling functional traits using the ‘Continuous’ module in BayesTraits V2 (Pagel 1999; Pagel and Meade 2014). The phylogenetic signal of each trait was estimated using Pagel’s λ. BayesTraits analyses were conducted for each functional trait using the procedure and parameters outlined above.

5.4 Results

5.4.1 Environmental niche modelling

The mean AUC values for replicate runs of our models ranged from 0.962 to 0.999 with a standard ranging from 0.003 to 0.001 (Appendix 4), suggesting high accuracy of the ENMs generated. We found that E. obliqua had the widest projected geographic range of all the study species (Fig. 2), with a potential current distribution in both south- eastern and south-western Australia (although this species is not found in the south-west of the continent). The estimated range size for E. obliqua was the highest of all species, followed by E. fastigata (Table 1). Eucalyptus fastigata had a projected geographic range in Tasmania where it is not known to occur. Eucalyptus regnans had a much narrower projected environmental range relative to the other tall trees (E. fastigata and E. obliqua, Fig. 2) and had a much lower estimated range size (which was almost half of that found for E. fastigata, Table 1).

The mallees and medium trees generally had narrower projected environmental ranges than the tall trees. For example, the medium tree, E. triflora and the mallees, E. cunninghamii, E. burgessiana and E. langleyi, all had very restricted projected geographic ranges (Figs 3 and 4) and relatively low estimated range sizes (Table 1). Eucalyptus stricta had the widest projected environmental range of all the mallee species, being predicted to be suitable to both inland and coastal environments (Fig. 4). Eucalyptus apiculata had a similar (and narrower) projected environmental range to E. stricta. Although E. laophila had a narrower projected environmental range, it was similar to the inland projected distribution of E. stricta.

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A B

C D

Fig. 2. Predicted habitat distribution (■) and current known distribution (●) in Australia of the study species. A. Eucalyptus regnans. B. E. fastigata. C. E. obliqua. D. E. cunninghamii.

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A B

C D

Fig. 3. Predicted habitat distribution (■) and current known distribution (●) in Australia of the study species. A. Eucalyptus triflora. B. E. dendromorpha (all populations). C. E. dendromorpha (GBMWHA populations only). D. E dendromorpha (southern populations only). Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area.

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Table 1. Estimated predicted range size (grid cell index) calculated from environmental niche models (ENMs) produced in Maxent Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area.

Species Area Index (Number of grid cells) E. regnans 70 579 E. fastigata 120 534 E. obliqua 314 986 E. cunninghamii 1 669 E. triflora 3 604 E. dendromorpha (all populations) 10 604 E. dendromorpha (GBMWHA populations only) 873 E. dendromorpha (southern populations only) 5 625 E. burgessiana 1 176 E. langleyi 1 239 E. obstans 4 517 E. apiculata 19 828 E. stricta 25 508 E. laophila 5 009 E. stricta and E. laophila combined 50 326

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A B

C D

E F

Fig. 4. Predicted habitat distribution (■) and current known distribution (●) in Australia of green ash species. A. Eucalyptus burgessiana. B. E. langleyi. C. E. obstans. D. E. apiculata. E. E. stricta. F. E. laophila. G. E. stricta and E. laophila combined. 217

When the locality data for the populations of the mallee form of E. dendromorpha from the GBMWHA were combined with that of the tree form of E. dendromorpha from south of Sydney, the projected geographic range was all along the coastal and inland environments in New South Wales (Fig. 3). However, the projected environmental range of populations only from the GBMWHA was very restricted, with the lowest estimated range size of all study species (and the AUC of this model was closer to 1.000). Similarly, the projected geographic range of only those E. dendromorpha populations from south of Sydney was also smaller (and the AUC of this model was also closer to 1.000). When the locality data of E. stricta and E. laophila were combined, a wider environmental range was predicted in Maxent than when only data for individual species were used (Fig. 4). When E. stricta and E. laophila were combined, the estimated range size was considerably larger (almost twice the estimated range size when only the E. stricta populations were used, Table 1).

We found that many of the climate parameters that contributed the highest to the predicted environmental range of each species were temperature, evaporation and precipitation variables (Table 2 presents the top five variables that contribute to the projected geographic range of each species). For example, precipitation of the driest month made a relative high contribution (11.3–33.1%) to the ENMs of most species, including E. fastigata, E. cunninghamii, E. dendromorpha (all populations), E. dendromorpha (populations south of Sydney), E. langleyi, E. obstans, E. apiculata, E. stricta, as well as E. stricta and E. laophila combined. Similarly, mean temperature of the driest quarter also made a relative high contribution (8.5–15.5%) to the projected geographic range of many species: E. fastigata, E. cunninghamii, E. triflora, E. dendromorpha (all populations), E. dendromorpha (GBMWHA populations), E. dendromorpha (populations south of Sydney), E. apiculata, E. stricta, E. laophila, and E. stricta and E. laophila combined. Mean temperature of the wettest quarter made a high contribution to the ENMs of E. regnans and E. obliqua (30.5% and 47.9% respectively), and made a relative high contribution to the projected geographic range of E. dendromorpha (all populations), E. burgessiana and E. laophila (3.9–13.5%). Maximum monthly evaporation made a relative high contribution (5.9–20.9%) to the projected geographic range of most species: E. regnans, E. fastigata, E. obliqua, E. langleyi, E. obstans, E. apiculata, E. stricta, E. laophila, and E. stricta and E. laophila combined. 218

Table 2. Percent contribution of variables to the environmental niche models (ENMs) produced in Maxent (predicted distribution) of each green ash species Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area; max, maximum; min, minimum; TPI, topographic position index; TWI, topographic wetness index.

Species Variable Contribution (%) E. regnans Mean temperature of wettest quarter 30.5 Max monthly evaporation 11.5 Isothermality (mean diurnal range / temperature annual range) (* 100) 10 Mean diurnal range (mean of monthly (max temp – min temp)) 8.2 Max temperature of warmest month 6.7 E. fastigata Max monthly evaporation 15.1 Precipitation of driest month 14.1 Temperature seasonality (standard deviation *100) 13 Mean temperature of driest quarter 9.6 Mean monthly evaporation 9.6 E. obliqua Mean temperature of wettest quarter 47.9 Mean monthly evaporation 8.6 Max monthly evaporation 8 Mean diurnal range (mean of monthly (max temp ‒ min temp)) 7 Temperature annual range (max temp of warmest month ‒ min temp of coldest 4.8 month) E. cunninghamii Mean temperature of driest quarter 13.5 Slope 12.6 Precipitation of driest month 11.3 Temperature seasonality (standard deviation *100) 8.5 Evaporation seasonality 7.5 E. triflora Precipitation of driest month 22.3

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Mean temperature of driest quarter 15.1 Temperature seasonality (standard deviation *100) 12.5 Temperature annual range (max temp of warmest month ‒ min temp of coldest 12 month) Evaporation seasonality 6 E. dendromorpha (all populations) Precipitation of driest month 30.6 Temperature seasonality (standard deviation *100) 24.2 Mean temperature of driest quarter 8.8 Precipitation of driest quarter 4.2 Mean temperature of wettest quarter 3.9 E. dendromorpha (GBMWHA populations) Temperature seasonality (standard deviation *100) 14.6 Mean temperature of driest quarter 13.5 Precipitation of driest month 13.4 Evaporation seasonality 10.5 Precipitation of warmest quarter 8.8 E. dendromorpha (southern populations) Precipitation of driest month 32.9 Temperature seasonality (standard deviation *100) 25.7 Mean temperature of driest quarter 8.5 Evaporation seasonality 4.9 TPI 3.3 E. burgessiana Slope 19.5 Mean temperature of wettest quarter 12.1 Evaporation seasonality 9.6 TPI 7.2 Precipitation of warmest quarter 6.4 E. langleyi Precipitation of driest month 33.1 Max monthly evaporation 20.9 Percentage of clay in soil 5.7 Evaporation seasonality 4.1 TWI 3.2 220

E. obstans Precipitation of driest month 25.9 Max monthly evaporation 20.2 Mean diurnal range (mean of monthly (max temp ‒ min temp)) 9.8 Temperature annual range (max temp of warmest month ‒ min temp of coldest 5.6 month) Precipitation of driest quarter 4.6 E. apiculata Mean temperature of driest quarter 15.5 Precipitation of driest month 13.3 Temperature seasonality (standard deviation *100) 13 Max monthly evaporation 10.3 Mean monthly evaporation 7 E. stricta Precipitation of driest month 27.6 Temperature seasonality (standard deviation *100) 22.3 Mean temperature of driest quarter 11.7 Max monthly evaporation 6 Mean diurnal range (mean of monthly (max temp ‒ min temp)) 4.2 E. laophila Mean temperature of driest quarter 14.8 Evaporation seasonality 14.7 Slope 13.4 Max monthly evaporation 11 Mean temperature of wettest quarter 6.3 E. stricta and E. laophila combined Precipitation of driest month 26.4 Temperature Seasonality (standard deviation *100) 21.8 Mean temperature of driest quarter 10.5 Temperature annual range (max temp of warmest month ‒ min temp of coldest 6.1 month) Max monthly evaporation 5.9

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Other variables that were found to make comparatively high contributions to the ENMs of many of the study species were soil and topography parameters. For example, the contribution of slope to the projected geographic range of E. cunninghamii, E. burgessiana and E. laophila ranged from 12.6 to 19.5%. The percentage of clay in the soil contributed 5.7% to the projected geographic range of E. langleyi. TPI made a contribution (< 10%) to the predicted geographic range of E. burgessiana and E. dendromorpha (populations south of Sydney), while TWI provided 3.2% contribution to the projected geographic range of E. langleyi.

5.4.2 Phylogenetic analysis and ancestral reconstruction of habitat

Each species could be assigned to one of six structural vegetation types as defined by Specht (1970): tall open forest, open forest, woodland, open woodland, low open woodland and low open shrubland (Fig. 5). The tall trees (E. regnans, E. obliqua and E. fastigata) formed a clade separate to the green ash mallees and medium trees, and which was reconstructed as the tall open forest habitat. Eucalyptus cunninghamii was classified as occurring in low open shrubland and this was sister to the remainder of the green ash mallees and medium trees. The remainder of the green ash species formed a clade of plants from open forest, woodland, open woodland and low open woodland habitats. All plants in open forests and woodlands were in one clade that was sister to another clade comprising species from open woodlands and low open woodlands. The out-group species, E. piperita, is a tree that is generally found in open forests and woodlands (Hill 2002) on low nutrient soils and the sample we used in the present study was from a woodland habitat (at an altitude of 598 m, Rutherford et al. 2016).

We found that geographical range size was not strongly associated with phylogeny. For example, while the two tall trees, E. obliqua and E. fastigata, both had the highest estimated range sizes, E. regnans had a much lower estimated range size (although it was in the same clade). Phylogenetic signal (Pagel’s λ) for range size was 0.55 and the log BF values were < 2 when λ was fixed at 0 and when λ was fixed at 1 (Table 3). This suggests that the model with the estimated value for λ better described the data than the model when λ was fixed at either 1 (where the parameter is influenced by evolutionary history) or 0 (where the parameter is independent of evolutionary relationships).

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Fig. 5. Ancestral character state reconstructions of habitat type using parsimony analysis on a phylogeny of the green ashes estimated from DArT presence/absence markers.

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Table 3. Phylogenetic signal (Pagel’s lambda or λ) in range size across the green ash eucalypts The mean log-likelihood and harmonic mean for each analysis are presented. Bayes Factors (log BF) were calculated as two times the log marginal likelihood differences between the estimated model and a model where λ is fixed at either 0 or 1. The mean value of λ is highlighted in bold. Abbreviations: HPD, highest posterior density.

Log BF Log BF λ estimated λ fixed at 0 λ fixed at 1 when λ=0 when λ=1 Harmonic mean ‒162.82 ‒163.69 ‒163.01 Mean log-likelihood ‒162.73 ‒163.65 ‒162.94 ‒1.75 ‒0.39 Mean λ (95% HPD) 0.55 (0.10, 1) 0 1

5.4.3 Associations between functional traits, phylogeny and habitat

We found significant negative relationships (P < 0.05, Fig. 6) between SLA and three of the other leaf functional traits (leaf thickness, leaf length and leaf area). In these analyses there was a clustering of habitats: the species from tall open forests and low open shrubland (E. regnans, E. obliqua, E. fastigata and E. cunninghamii) generally had juvenile leaves with higher SLA, thinner leaves, lower leaf length, and lower LA; while the species from woodlands and open woodlands (namely E. stricta, E. burgessiana, E. laophila, E. langleyi, E. obstans and E. triflora) had higher SLA, thicker leaves, longer leaves and higher LA. We also found significant negative relationships between SLA and plant performance traits (seedling height and total aboveground dry mass, Fig. 6). Here again there was a clustering of habitats: seedlings from tall open forests and low open shrublands were shorter and had lower aboveground dry mass, while those from woodlands and open woodlands were taller and had higher aboveground dry mass.

A significant positive relationship (P<0.05, Fig. 6) was found between SLA and leaf FW/DW. Plants from tall open forests and open forests (E. dendromorpha from both the GBMWHA and south of Sydney) generally had higher leaf FW/DW, and seedlings from woodlands and low open woodlands had lower leaf FW/DW.

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A B

C D

E F

Fig. 6. Relationships between specific leaf area (SLA) and other seedling functional traits including: A. Leaf thickness. B. Leaf length. C. Leaf fresh mass versus dry mass (leaf FW/DW). D. Leaf area (LA). E. Seedling height. F. Total aboveground dry mass. Data points are coded by habitat: tall open forest (■), open forest (■), woodland (▲), open woodland (∆), low open woodland (▲) and low open shrubland (□). All relationships are significant (P<0.05).

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The BayesTraits analysis of juvenile functional traits revealed that values for Pagel’s λ were highest for seedling height and LA (0.76 and 0.74 respectively, Table 4). The log BF value for these traits was greater than 2 indicating that the model where λ was fixed at 1 was strongly supported. This suggests that seedling height and LA are strongly associated with the phylogeny. The lowest value of λ was found for leaf FW/DW and total FW/DW (0.43 and 0.44 respectively). Other traits with relatively low values of λ were stem diameter, stem dry mass, leaf dry mass, total aboveground dry mass and leaf width (0.54–0.58). The log BF values for all traits (with the exception of seedling height and LA) were less than 2, suggesting that the model with the estimated value of λ was better supported than the model when λ was fixed at either 0 or 1 (and therefore were not associated with the phylogeny).

5.5 Discussion

We found that representative species from the major clades of the green ash eucalypts differ widely in terms of vegetation types, with one clade (E. regnans, E. obliqua and E. fastigata) comprising tall open forests on high fertility soils, and another clade including coastal, upland and highland habitats (e.g. open woodlands, low open woodlands and low open shrublands) on skeletal and sandy soils. The findings here demonstrate a complex interplay between environmental variables, evolutionary relationships and seedling functional traits. This interplay provided a number of insights into ecological specialisation and species diversification across the green ashes.

Most of the variables that made relatively large contributions to the ENMs of all species were precipitation, evaporation and temperature variables. The finding that precipitation and evaporation parameters are highly associated with predicted environmental range is consistent with previous studies that have found a strong relationship between plant species distribution patterns and water availability (rainfall and seasonality, e.g.

McKenzie et al. 2003; Engelbrecht et al. 2007; Toledo et al. 2012). Our results are also consistent with the findings of Rutherford et al. (2017), where we observed that green ash seedlings were highly sensitive to changes in water availability (i.e. if water availability was lowered below a certain threshold seedlings would die).

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Table 4. Phylogenetic signal (Pagel’s lambda or λ) of seedling functional traits

The mean log-likelihood and harmonic mean for each analysis are presented. Bayes Factors (log BF) were calculated as two times the log marginal likelihood differences between the estimated model and a model where λ is fixed at either 0 or 1. The mean value for λ and log BF values greater than 2 are highlighted in bold. Abbreviations: FW/DW, fresh mass versus dry mass; HPD, highest posterior density; LA, leaf area; SLA, specific leaf area.

Mean log- Harmonic Trait Model λ (95% HPD) log BF likelihood mean Height Estimated λ ‒91.57 ‒93.06 0.76 (0.36, 1)

λ fixed at 0 ‒95.52 ‒96.39 0 ‒6.67

λ fixed at 1 ‒90.66 ‒91.70 1 2.73

Diameter Estimated λ ‒18.99 ‒20.59 0.54 (0.06, 1)

λ fixed at 0 ‒19.04 ‒20.62 0 ‒0.05

λ fixed at 1 ‒18.84 ‒20.30 1 0.58

Total leaf number Estimated λ ‒57.54 ‒59.01 0.63 (0.11, 1)

λ fixed at 0 ‒58.39 ‒59.96 0 ‒1.89

λ fixed at 1 ‒56.99 ‒58.33 1 1.36

Total aboveground dry mass Estimated λ ‒30.66 ‒32.48 0.56 (0.07, 1)

λ fixed at 0 ‒30.90 ‒32.65 0 ‒0.33

λ fixed at 1 ‒30.44 ‒32.17 1 0.63

Stem dry mass Estimated λ ‒15.66 ‒17.05 0.55 (0.07, 1)

λ fixed at 0 ‒15.81 ‒17.42 0 ‒0.73

λ fixed at 1 ‒15.51 ‒17.01 1 0.09

Leaf dry mass Estimated λ ‒25.85 ‒27.45 0.56 (0.07, 1)

λ fixed at 0 ‒26.08 ‒28.14 0 ‒1.37

λ fixed at 1 ‒25.58 ‒27.15 1 0.60

Total FW/DW Estimated λ ‒0.87 ‒2.57 0.44 (5.82×10-7,0.93)

λ fixed at 0 ‒0.43 ‒2.39 0 0.36

λ fixed at 1 ‒1.56 ‒3.08 1 ‒1.01

LA Estimated λ ‒121.12 ‒121.76 0.74 (0.30, 1)

λ fixed at 0 ‒124.46 ‒124.51 0 ‒5.49

λ fixed at 1 ‒120.24 ‒120.55 1 2.43

Leaf length Estimated λ ‒61.74 ‒64.03 0.67 (0.17, 1)

λ fixed at 0 ‒63.31 ‒65.61 0 ‒3.15

λ fixed at 1 ‒61.19 ‒63.29 1 1.49

Leaf width Estimated λ ‒55.72 ‒57.11 0.58 (0.08, 1)

λ fixed at 0 ‒56.09 ‒62.58 0 ‒10.93

λ fixed at 1 ‒55.39 ‒57.17 1 ‒0.13

Petiole length Estimated λ ‒34.20 ‒35.59 0.59 (0.08, 1)

λ fixed at 0 ‒34.64 ‒36.12 0 ‒1.06

λ fixed at 1 ‒38.85 ‒35.36 1 0.47

Leaf thickness Estimated λ 18.64 17.02 0.65 (0.14, 1)

λ fixed at 0 17.41 15.92 0 ‒2.19

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λ fixed at 1 19.27 17.65 1 1.28

SLA Estimated λ ‒31.51 ‒33.21 0.66 (0.17, 1)

λ fixed at 0 ‒33.15 ‒34.91 0 ‒3.39

λ fixed at 1 ‒31.01 ‒32.67 1 1.08

Leaf FW/DW Estimated λ ‒4.30 ‒5.85 0.43 (1.12×10-6, 0.91)

λ fixed at 0 ‒3.80 ‒5.09 0 1.53

λ fixed at 1 ‒5.19 ‒6.78 1 ‒1.85

Plants face major challenges when water is limiting (Alpert and Oliver 2002). Water deficits can inhibit plant growth, reduce fertility and enhance leaf senescence (Jones 1983), and can have severe structural and metabolic implications (e.g. induce xylem cavitation, Larcher 2003). Furthermore, the availability of water is important for the germination, establishment and survival of plant seedlings (Evans and Etherington 1990).

Temperature has also been found to play a significant role in plant survival and distribution patterns (Scherrer and Körner 2011). Our study species occur along an altitudinal gradient (0–1400 m), with many species being restricted to particular elevations. For example, E. obstans is only found in coastal habitats (0–200 m altitude) with temperatures ranging from a monthly mean minimum of 7.6–18.4°C, to a monthly mean maximum of 16–26.7°C (Benson and McDougall 1998; Bureau of Meteorology 2017). In contrast, E. burgessiana is restricted to upland habitats (400‒750 m) with temperatures ranging from a monthly mean minimum of 6.4–17.2°C, to a monthly mean maximum of 15.9–28.6°C, while E. cunninghamii is confined to higher altitudes (700‒ 1000 m) with temperatures ranging from a monthly mean minimum of 2.5–13.3°C to a monthly mean maximum of 9.4–24°C (Benson and McDougall 1998; Bureau of Meteorology 2017). Temperature is considered to be a driver of species limits (Scherrer and Körner 2011). In addition, flowering phenology, as well as pollinator and plant interactions can be affected by changes in temperature (Hegland et al. 2009). Future research is needed focusing on the tolerance limits of green ash species to variations in temperature using a common garden environment or via reciprocal transplant experiments. Field experiments investigating flowering phenology of the study species (and their pollinators) along altitudinal gradients would also provide insights into responses to temperature.

228

We found a number of soil and topographic parameters to make high contributions to the ENMs of some species. For example, percentage of clay in the soil made a comparatively large contribution to the predicted distribution of E. langleyi. Fine- grained soils, such as loam and clay, have a higher water-holding capacity than coarse- grained soils (e.g. sand) (Larcher 2003). Eucalyptus langleyi is a highly restricted species, occurring within a 17x7 km radius on Nowra Sandstone (160 km south of Sydney), and is often found near creeks (Mills 2010). Our finding of the proportion of clay being an important factor to the predicted range of this species may therefore be associated with the water-holding capacity of the soil. We also found slope to make a relatively high contribution to the predicted distributions of a number of species (e.g. E. burgessiana, E. cunninghamii, and E. laophila). Slope is a significant factor in relation to exposure to sun, wind, rainfall and frost, and is also important for the availability of soil moisture and waterlogging, as well as the depth of soil profile available to roots (Eldridge et al. 1993). Soil composition and characteristics also affect nutrient availability to plants (Hopkins 1999) and this has likely significantly impacted the distribution of the green ash eucalypts.

In an earlier study (Rutherford et al. 2016), we found that the green ash tall trees were in a clade that was sister to the stringy barks (subgenus Eucalyptus section Capillulus, sensu Brooker 2000) and which was separate from the remainder of the green ash mallees and medium trees. Furthermore, the green ash mallees and medium trees were found to form a monophyletic group (Rutherford et al. 2016). The finding here of a marked pattern in vegetation type across clades between the green ash tall trees occurring on fertile soils in tall open forests and the medium trees and mallees being found on low nutrients in open forests, woodlands, open woodlands, low open woodlands and low open shrublands is therefore consistent with the notion that we are dealing with two separate groups. However, while there was a pattern in vegetation type across the phylogeny, we found that geographical range size was not phylogenetically conserved (λ=0.55, Table 4). This suggests that range size in is influenced by factors other than shared evolutionary history. The boundaries of a species range is likely to be influenced by a multitude of factors, such as all aspects of their ecology (including dispersal and competition), physiological tolerances, and past and present climatic geographical barriers (Geber 2011). Future research focusing on the physiology of the study species, abiotic and biotic factors affecting species distributions, and the effect of 229 past climates (especially during the Last Glacial Maximum) will be required to better understand the results found here.

We found significant relationships between SLA and a number of other seedling functional traits and that many seedling traits could be associated with particular habitat types (Fig. 6). Tall open forests are characterised by lower light, higher moisture and higher nutrient soils compared with woodlands and open woodlands (Ashton and Attiwill 1994). Therefore the finding here that seedlings of species from tall open forests generally had higher SLA, higher leaf FW/DW and thinner leaves is consistent with adaptations to these environments. Thinner leaves is thought to facilitate higher light capture per unit of resource expenditure in lower light environments (Ryser and Eek 2000), while higher SLA (and higher growth rate) is associated with higher nutrient soils (Lusk et al. 1997). In contrast, woodlands and open woodlands have comparatively higher light, lower soil nutrients and lower moisture (Wardell-Johnson et al. 1997). In the present study, species from such habitats had thicker leaves, lower SLA and lower leaf FW/DW. This is consistent with the prediction that plants from such environments display higher scleromorphy (tougher leaves) and slower growth rates (Barlow 1994; Wright et al. 2001). Overall, our findings suggest that there is a trade-off in seedling functional traits associated with habitat type. It has been suggested that ecophysiological trade-offs that protect vital functions from environmental extremes can lead to a reduction in the heritable niche (Crisp and Cook 2012). A trade-off in seedling functional traits to adapt to different environments would explain the phylogenetic patterns of habitat and associations between vegetation type and seedling functional traits observed here.

Our results suggest that while some seedling functional traits displayed a high degree of phylogenetic conservatism (e.g. height and LA), many traits (particularly leaf FW/DW, total FW/DW, total aboveground dry mass and leaf width) did not have a strong phylogenetic signal. Since a weak phylogenetic signal of traits can be indicative that species have adapted to the contemporary environmental conditions (Khaliq et al. 2015), the low phylogenetic signal of seedling traits found here may suggest that these functional traits have been strongly influenced by environmental factors (and could potentially be adaptive to these environments). However, it should be noted that there are many underlying processes which influence patterns of niche conservatism,

230 including genetic variation, gene flow and natural selection (Wiens et al. 2010). For example, the evolution of niche related traits may be limited by lower genetic variation (Bradshaw 1991). Also, high levels of interspecific gene flow may impede adaptation to particular environmental niches (Wiens et al. 2010). Therefore, future research should focus on the influence of processes such as genetic variation, gene flow and natural selection, as well as phylogenetic signal of traits for a more comprehensive understanding of patterns of niche conservatism. Since higher levels of phylogenetic conservatism in traits are thought to imply a lower capability to adapt successfully to changing climates (Wiens and Graham 2005; Losos 2008), our findings have significant implications for the ability of the green ashes to respond to climate change. However, assuming that all traits which have a weak phylogenetic signal will be better able to cope with climate change may be misleading, if the degree of plasticity of those traits is not considered. In Rutherford et al. (2017) we found that seedling height (reported to be a phylogenetically conserved trait here) was plastic between resource treatments for many species. Conversely, we found other traits that have low phylogenetic signal here (e.g. total FW/DW and leaf FW/DW) to display low plasticity between resource treatments for many species (Rutherford et al. 2017). High levels of plasticity may enable plants to respond to rapid changes in climate (Nicotra et al. 2010). Therefore, traits that have low plasticity may not be able to adapt quickly enough to rapid climate change (even if they have low phylogenetic conservatism).

The findings from the present study have implications for species delimitation in the green ashes, which ultimately has consequences for their conservation and management. Although species distribution is not directly used in species delimitation, discontinuities in environmental range may have incorrectly resulted in the recognition of segregated taxa. This may be the case with E. stricta and E. laophila, which are morphologically very similar (with overlapping leaf widths and fruit sizes), and that have been distinguished primarily on the basis of geographic location (Hill 2002). Our ENMs suggest that when regarded as one species (i.e. when populations of E. stricta and E. laophila are combined) they cover a much larger projected range than if considered separate taxa. On the other hand, an ENM of disjunct populations that should be recognised as separate species could lead to the overestimation of their range size. In the present study, when all populations of E. dendromorpha were regarded as one species, they covered a much larger predicted range than when this species was divided into two 231 taxa (based on previous population genetic analyses, Rutherford et al. in prep., Chapter 4).

5.6 Conclusions

We found the projected geographic range of all species in the green ashes to be most influenced by precipitation, evaporation and temperature variables. Soil and topographic variables also were found to make a relatively high contribution to the predicted range of many species. While a pattern in vegetation type was found across clades on a phylogeny of the green ashes, geographical range size was not phylogenetically conserved. We also found only two of the 14 seedling functional traits measured to be strongly associated with the phylogeny, suggesting that most functional traits may be either adaptive or strongly influenced by environmental factors. Many functional traits could be associated with vegetation type and we found evidence of a trade-off in traits between tall open forests, and woodlands and open woodlands. Our findings therefore have significant implications for ecological specialisation and species diversification across major clades of the green ash eucalypts. Future research investigating the effect of past climates on the distribution of the green ashes is required, as well as studies on their ecology (particularly flowering phenology, pollinators, competition and dispersal) and physiological tolerances (especially in response to temperature) to better understand the findings presented here.

Acknowledgements

We thank staff from the Royal Botanic Garden Sydney for assisting us with our research, especially Doug Benson for providing advice on classification of habitat types, Monica Fahey for advice on environmental niche modelling analyses and Jason Bragg for assistance with the BayesTraits analysis. S. Rutherford was in receipt of an Australian Post-graduate Award when this research was undertaken.

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Appendix 1. Climatic, topographic and soil variables used for maximum entropy modelling Abbreviations: max, maximum; min, minimum; TPI, topographic position index; TWI, topographic wetness index.

Variable Source Annual mean temperature eMAST Mean diurnal range (mean of monthly (max temp ‒ min temp)) eMAST Isothermality (mean diurnal range/ temperature annual range) (* 100) eMAST Temperature seasonality (standard deviation *100) eMAST Max temperature of warmest month eMAST Min temperature of coldest month eMAST Temperature annual range (max temperature of warmest month ‒ min eMAST temperature of coldest month) Mean temperature of wettest quarter eMAST Mean temperature of driest quarter eMAST Mean temperature of warmest quarter eMAST Mean temperature of coldest quarter eMAST Annual precipitation eMAST Precipitation of wettest month eMAST Precipitation of driest month eMAST Precipitation seasonality (coefficient of variation) eMAST Precipitation of wettest quarter eMAST Precipitation of driest quarter eMAST Precipitation of warmest quarter eMAST Precipitation of coldest quarter eMAST Mean monthly evaporation eMAST Max monthly evaporation eMAST Min monthly evaporation eMAST Evaporation seasonality eMAST Aspect CSIRO Data Portal Slope CSIRO Data Portal TPI CSIRO Data Portal TWI CSIRO Data Portal Percentage clay in the soil CSIRO Data Portal Percentage sand in the soil CSIRO Data Portal Percentage silt in the soil CSIRO Data Portal

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Appendix 2. Structural forms of vegetation in Australia (based on Specht 1970) Based on life-form and height of tallest stratum and percentage foliage cover of tallest plant layer

Percentage foliage cover of tallest stratum Height of Dense (70‒100%) Mid-dense (30‒70%) Sparse (10‒30%) Very sparse (< 10%) tallest stratum Trees > 30 m Tall closed forest Tall open forest Tall woodland Tall open woodland Trees 10‒30 m Closed forest Open forest Woodland Open woodland Trees 5‒10 m Low closed forest Low open forest Low woodland Low open woodland Shrubs 2‒8 m Closed scrub Open scrub Tall shrubland Tall open shrubland Shrubs 0‒2 m Closed heath Open heath Low shrubland Low open shrubland

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Appendix 3. Character matrix for ancestral reconstructions Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area.

Species Habitat Green ashes E. regnans Tall open forest E. obliqua Tall open forest E. fastigata Tall open forest E. triflora Woodland E. dendromorpha (GBMWHA populations) Open forest E. dendromorpha (populations from south of Sydney) Open forest E. obstans Open woodland E. burgessiana Open woodland E. langleyi Open woodland E. cunninghamii Low open shrubland E. apiculata Low open woodland E. stricta Open woodland E. laophila Open woodland

Blue ash E. piperita Woodland

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Appendix 4. Mean test AUC for the replicate runs standard deviation of models of each species Abbreviations: GBMWHA, Greater Blue Mountains World Heritage Area.

Species AUC Standard deviation E. regnans 0.992 0.003 E. obliqua 0.962 0.002 E. fastigata 0.988 0.003 E. cunninghamii 0.998 0.001 E. triflora 0.997 0.001 E. dendromorpha (all populations) 0.995 0.001 E. dendromorpha (GBMWHA populations) 0.999 0.001 E. dendromorpha (southern populations) 0.996 0.001 E. burgessiana 0.998 0.001 E. langleyi 0.998 0.001 E. obstans 0.997 0.003 E. apiculata 0.995 0.002 E. stricta 0.989 0.001 E. laophila 0.997 0.001 E. stricta and E. laophila combined 0.986 0.001

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Chapter 6. Final discussion and conclusions

6.1 Key findings from this research

In this thesis, I demonstrate that an interdisciplinary approach incorporating phylogenomics, population genomics, a common garden experiment and environmental niche modelling can provide valuable insights into speciation mechanisms in a group of closely related species. The use of a phylogenomic and population genomic approach allowed a significant improvement in the resolution of relationships between species in the green ash group (that have long been ambiguous) and provided a better understanding of species boundaries (Chapters 2 and 4). As with many other groups in the genus Eucalyptus, resolving relationships among the green ashes has historically been particularly challenging (e.g. Lassak and Southwell 1982; Ladiges et al. 1989; Prober et al. 1990a, 1990b; Bayly and Ladiges 2007). I found a phylogenomic and population genomic approach used in combination allowed relationships at both a higher taxonomic level and at the species level to be examined. This approach could be effective for other species of Eucalyptus, as well as other genera where there have been difficulties in resolving relationships among taxa. The use of recently developed genomic techniques also enabled the identification of undescribed species (one at Mount Banks and a possible second one at Stanwell Tops). These techniques also allowed mechanisms of speciation to be investigated and hypotheses regarding these mechanisms to be tested (e.g. vicariance, reticulate evolution and ecological speciation) which have contributed to the present-day distribution patterns of the green ashes (Chapter 4).

I found significant differences in plant- and leaf-level functional traits of seedlings across the green ash group in a common garden experiment, and this had implications for their ability to respond to environmental variability (Chapter 3). Plant performance and leaf functional traits were found to differ considerably in the degree of plasticity. High levels of plasticity in seedling leaf traits were found for some species, which supported findings for adult leaf morphology of representative species sampled across subgenus Eucalyptus (Chapter 2) and suggests that plasticity in leaf morphology may have confounded species delimitations in the green ashes. The common garden

247 experiment also allowed potential adaptive traits to be identified. For example, petioles were present on the first few leaves of seedlings of E. regnans, E. obliqua, E. fastigata and E. cunninghamii regardless of resource treatment. In contrast, the first few leaves were always sessile in all other green ash species. Similarly, there was much lower plasticity in leaf thickness and specific leaf area (SLA) between resource treatments, and these traits should therefore be investigated in the future as potential adaptive traits. Plant-level, leaf-level and overall plasticity were not strongly correlated with phylogeny (Chapter 3), suggesting that plasticity in the green ashes is probably a converged trait.

Environmental models were used to predict the environmental niche and range size of species from across the major clades of the green ash group, which enabled a better understanding of ecological specialisation of species and allowed hypotheses regarding habitat diversification and seedling trait evolution to be tested (Chapter 5). A marked pattern in vegetation type was found across clades of the phylogeny. However, range size was not phylogenetically conserved, nor was the majority of the seedling functional traits. Only two traits were found to be phylogenetically conserved (height and leaf area), indicating that the other traits were either adaptive or strongly influenced by environmental factors.

6.2 DArT microarray and DArTseq

I found both the DArT microarray and DArTseq methods were very useful in resolving species relationships and investigating the evolutionary history of the green ashes. However DArTseq was more informative when investigating species boundaries of very closely related species in the Sydney region and GBMWHA. The DArT microarray method was first reported by Jaccoud et al. (2001) and was developed as a hybridisation-based alternative to existing genotyping technologies (Grzebelus 2015). DArT genotyping does not require any prior knowledge of the genome sequence and produces thousands of dominant (presence/absence) markers (Jaccoud et al. 2001). It has been widely utilised in many agricultural plant species (e.g. Arabidopsis thaliana and Sorghum bicolor, Wittenberg et al. 2005; Mace et al. 2008) and has also been found to perform well in non-model organisms, such as species of Asplenium and Garovaglia (James et al. 2008). However, the dominant nature of DArT

248 presence/absence markers may limit their usefulness in detecting diversity in highly heterozygous obligatory outcrossing species (Bolibok-Brągoszewska et al. 2009). In regards to my research, the dominant nature of DArT presence/absence could be one of the reasons why relationships among most of the closely related green ash species of the Sydney region and GBMWHA (where there was evidence of inter-specific hybridisation) were not well resolved in the phylogenetic analysis.

The DArTseq platform, which was developed a decade after the original DArT methodology, uses the complexity reduction protocol of DArT in combination with high throughput NGS technology to sequence the first 60 bp of DNA fragments in each genomic representation (Sansaloni et al. 2011; Byrne et al. 2013). This produces two large datasets: the first comprising a set of presence/absence markers, and the second consisting of a set of co-dominant SNPs for the 60 bp of sequence data that are provided for each fragment in a sample (Byrne et al. 2013). DArTseq markers have many of the same qualities as traditional DArT presence/absence markers, in that they are randomly dispersed across the genome and largely come from coding regions (Petroli et al. 2012). Since DArTseq provides up to three fold as many markers as the DArT microarray (Byrne et al. 2013) and provides the additional SNP dataset, it was much more informative at the species level for the green ashes in this study. Recent studies using the DArTseq approach in phylogenetic analyses have found higher resolution of many clades especially in closely related lineages (e.g. Jones et al. 2016; Rosser et al. 2017). In the case of the green ashes, a phylogenetic analysis using DArTseq would probably have provided further evolutionary insights and should be used in the future to better resolve relationships of other species in subgenus Eucalyptus.

One limitation that I found with the DArT methodology concerned the preparation of the samples for analysis. DArT requires 400–1000 ng of DNA (at a concentration of 50–100 ng µL –1) per sample. This is a high concentration relative to some other NGS methods. For example, Nextera DNA (Illumina shotgun sequencing platform) requires 400 ng at 20 ng µL –1 (https://www.ramaciotti.unsw.edu.au/sequencing/genome- sequencing, accessed 19 January 2016). Furthermore, it can be notoriously difficult to obtain both high quantity and high quality DNA from Eucalyptus due to the presence of high levels of impurities in the foliage such as phenols, terpenes and polysaccharides, which are co-extracted with DNA (Shepherd et al. 2002). When I began my research, there was only one published study where DArTs had been successfully used on 249

Eucalyptus from natural populations (Steane et al. 2011). While some of the species used in Steane et al. (2011) were common to those in my study (e.g. E. regnans, E. piperita), the majority of the species were different. Therefore, much experimentation was required to obtain sufficient quantity of high quality DNA from all green ash species and other eucalypts in this study (approximately 6 months of trialling different DNA extraction methods). However, following experimentation and discussions with other people who had had similar difficulties (including J. Carling and A. Kilian from Diversity Arrays Technology Pty Ltd, as well as D. Steane from the University of Tasmania), I was able to develop an appropriate extraction method for these species. I am now preparing a manuscript documenting this method and the results of my experimentation in the hope that it will be able to assist other people who are using DArTs and NGS technologies on species from which it can be difficult to extract high quality and high quantity DNA.

Another limitation of the DArT methodology in terms of evolutionary plant research is that it provides very few chloroplast (cp) markers for analysis (DArT markers are predominantly nuclear). Previous studies have shown discordance between cpDNA and nuclear markers in eucalypts (e.g. Steane et al. 1998; McKinnon et al. 1999; McKinnon et al. 2010; Bayly et al. 2013) suggesting different evolutionary dynamics for the chloroplast and nuclear genomes (Byrne et al. 1998). The chloroplast is maternally inherited (Byrne et al. 1993) and has proven to be very informative in understanding population structure, phylogeographic patterns and hybridisation within Eucalyptus (e.g. Vaillancourt and Jackson 2000; McKinnon et al. 2001; Byrne 2007; Pollock et al. 2013). In my DArTseq analysis, I was able to identify and confirm only five chloroplast markers out of over 50 000 markers. Given the low number of potential chloroplast markers in a DArT analysis, I would suggest that researchers use chloroplast genome sequencing in conjunction with DArTs for evolutionary plant research, especially when probing deeply into inter-specific hybridisation and incomplete lineage sorting.

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6.3 Adaptive variation versus vicariance

Vicariance has traditionally been viewed as a primary driver of biological diversification (Smith et al. 2014). However, it is increasingly being recognised that adaptive radiation can play a major role in speciation (Meier et al. 2017). The evidence presented in this thesis suggests that adaptive radiation is an important mechanism in the evolution of the green ash eucalypts. While there are no known fossils of green ash species, fossils of subgenus Eucalyptus have been found from Late Miocene deposits (dated 5–10 Ma, Blazey 1994). Furthermore, molecular dating suggests that subgenus Eucalyptus diverged within the last 10 Ma (Crisp et al. 2011; Thornhill et al. 2015). Considering the diversity we currently see in subgenus Eucalyptus (>100 species covering a range of habitats across Australia, Brooker 2000; Hill 2002), there has been a high degree of radiation within this group in a relatively short period of time compared to some other long-lived tree species, as well as other angiosperms. For example, molecular and fossil evidence indicate that the genus Pinus (approximately 111 extant species) diverged within the last 92–128 Ma (Eckert and Hall 2006; Willyard et al. 2007), while molecular dating suggests that the genus Lomatia (12 species) diverged between 35 and 64 Ma (Milner et al. 2015). Radiation in subgenus Eucalyptus is comparable to other groups that have undergone relatively rapid diversification, such as clades in the genus Quercus (Manos et al. 1999; Kremer et al. 2012; Cavender-Bares et al. 2015).

Adaptive radiation has two components: (1) the formation of new species (speciation), and (2) the adaptation of those species to a range of ecological niches (Gavrilets and Losos 2009). The phylogenetic analyses presented here suggest that the green ash mallees and medium trees are a rapidly diversifying lineage (Chapter 2), and the distribution maps and predicted environmental ranges show that they occur across a multitude of environments (with the highest number of species occupying a diverse array of habitats in the Sydney region and GBMWHA, Chapter 5). Phylogenetic analyses and the SplitsTree 4 analyses (derived from both the phylogenetic and population genetic datasets, Chapters 2 and 4) indicated a high degree of radiation and reticulation between many of those lineages. Ecological hybrid speciation may occur very rapidly in plants (Hendry et al. 2007; Abbott et al. 2013). For example, Senecio squalidus is a hybrid between two species of Senecio from Mount Etna which was

251 introduced to the British Iles in the 18th century (James and Abbott 2005). It is now widespread and well adapted to ecological conditions in the UK, while both the parental species (S. aethnensis and S. chrysanthemifolius) are not, even though they had similar opportunities to become invasive (James and Abbott 2005). Evolutionary divergence due to hybridisation has also been documented in Helianthus (e.g. Rieseberg et al. 2003). At the outset of my research, the population of mallee individuals referred to as E. sp. Mount Banks (Chapter 4) was thought to be either a population of E. dendromorpha or a hybrid between E. dendromorpha and E. cunninghamii. While the analyses presented here suggest that this population is reproductively isolated from all other species (and should therefore be formally described as a new species), its origins are as yet unknown. Whether E. sp. Mount Banks is a hybrid that has become reproductively isolated or a population of E. dendromorpha that has undergone rapid speciation are questions that warrant further investigation.

The evidence from my research indicates that vicariance has also played a role in the evolution of the green ashes. For example, phylogenetic analyses revealed that the green ash species from far northern New South Wales and southern Queensland (E. approximans, E. codonocarpa and E. microcodon) were in a clade with E. cunninghamii from the Sydney region and green ashes from southern New South Wales and northern Victoria (E. kybeanensis and E. paliformis, Chapter 2). Given that the southern species, northern species and E. cunninghamii occur in isolated patches so far apart geographically (the closest population of E. kybeanensis is approximately 300 km from E. cunninghamii, while the closest population of E. approximans is >400 km from E. cunninghamii), it is probable that following the divergence of this lineage, populations migrated to a wide range of habitats in south-eastern Australia and vicariant processes followed by subsequent diversification occurred, resulting in the species we see today. In the Sydney region itself, population genetic analyses suggested that some species may also be evolving in allopatry (e.g. E. langleyi, Chapter 4). This was supported by the ancestral reconstructions from Chapter 5, which revealed that sister species from similar habitats in the Sydney region and GBMWHA were often not sympatric. For example, although E. burgessiana and E. langleyi were sister species and were classified as occurring in the same vegetation type, they are not sympatric (these species are more than 100 km apart). Similarly, while E. obstans appeared in the same clade as E. stricta and E. laophila, it is found more than 100 km from these species. 252

These findings would suggest that allopatric speciation may have played a role in the diversification of these lineages. The isolating mechanisms that led to these divergences should be investigated further.

6.4 What’s in a name? Implications for species delimitation in the green ashes

Species are fundamental units of biodiversity and yet the practice of delineating species can be difficult, especially in groups with uninformative and plastic morphological characters, or where there is ongoing gene flow between lineages (Mayr 1982; Hebert et al. 2003; Sangster 2014; Fontaneto et al. 2015). In The Origin of Species, Darwin (1859) commented on differences in opinion between naturalists of the day in regards to species delimitation of specific groups. For example, Darwin (1859) wrote that for the flora of the British Isles ‘Mr Babington gives 251 species, whereas Mr Bentham gives only 112’ (p. 104). Darwin (1859) also commented on species definitions and stated that ‘no one definition has as yet satisfied all naturalists, yet every naturalist knows vaguely what he means when he speaks of a species’ (p. 101). Since the publication of The Origin of Species, over 20 species concepts have been proposed (e.g. Mayr 1942, 1963; Simpson 1961; Hennig 1966; Blackwelder 1967; Sokal and Crovello 1970; Ghiselin 1974; Sneath 1976; Van Valen 1976; Rosen 1978; Wiley 1978; Cracraft 1983; Donoghue 1985; Paterson 1985; McKitrick and Zink 1988; Ridley 1989; Templeton 1989; Waples 1991; Kornet 1993; Baum and Shaw 1995; Mallet 1995). The majority of these are based on the notion that ‘reproductive isolation’ is the primary driver of species divergence, and are also centred on the ‘monophyly of lineages’ (for a discussion of modern species concepts see de Queiroz 1998; Mayden 1999; de Queiroz 2007). In fact, de Queiroz (1998) argued that all modern species concepts fall within a general species concept called the ‘General Lineage Concept’. However, recent evidence from studies into speciation (e.g. that reproductive barriers can be semipermeable to gene flow and species can diverge despite ongoing gene flow, see Rieseberg 2001; Rieseberg et al. 2003; Mallet 2005, 2008; Lexer and Widmer 2008) calls for a reconsideration of how we think of and define species (Hausdorf 2011).

The formulation of a species concept and delineating species are two different endeavours, and until relatively recently these tasks were often confused (de Queiroz

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2007). Species delimitation provides the criteria for determining whether populations are distinct species, whereas a species concept deals with ideas concerning what a species actually is (Paul 2002). While it is important that species delimitation does not become confused with the controversy surrounding the species concept (Hey 2006), the process of describing species inevitably depends on the criteria through which species are defined, which is dependent on the species concept being used (Balakrishnan 2005). Very few studies have investigated how species are delineated in practice (Sangster 2014), however such research could potentially inform and direct theoretical work on species concepts (McDade 1995).

In the case of the green ashes, there has been much disagreement regarding where species boundaries should be drawn (e.g. Pryor and Johnson 1971; Ladiges et al. 1989; Brooker 2000; Hill 2002; Nicolle 2015). The data presented here provide some explanation as to why this is the case by highlighting the low degree of genetic differentiation across many species and the high degrees of phenotypic plasticity in morphological characters (e.g. leaf width) frequently treated as key diagnostic features have affected species delimitation (Chapters 2, 3 and 4). This research highlights the importance of identifying traits that are less plastic when delineating species.

One of the most intriguing findings in this thesis was that many of the sympatric and geographically proximate species of the Sydney region and GBMWHA were able to maintain morphological differences despite low between-species genetic differentiation (Chapter 4). The mechanism through which this happens is as yet unclear. Hybridisation is now known to occur in many groups of organisms (Mallet 2005, 2008; Abbott et al. 2013), with many species of plants and animals displaying evidence of historic and/or present inter-specific gene flow (Carson 1985; Fong and Chen 2010). Therefore, recognising that gene flow can or may have occurred across species boundaries and that divergent lineages can fuse is essential when describing species in such groups.

Overall, the data presented here provide support for the genic view of speciation (Wu 2001), which recognises a continuum of genetic differentiation and reproductive isolation. While some of the species studied were found to be reproductively isolated (e.g. E. cunninghamii), other species are likely to be at earlier stages on the speciation continuum (e.g. E. langleyi, E. obstans, E. stricta, Chapter 4). Wu (2001) suggested that genes of differential adaptation (i.e. ‘speciation genes’) may only account for a small

254 proportion of the genome. Barriers to gene flow due to speciation genes take place at both prezygotic and postzygotic life history stages (Rieseberg and Blackman 2010). For example, speciation genes involved in hybrid sterility and inviability have been identified in Drosophila (Orr et al. 2004) and in a number of plant species (including rice, Oryza sativa, Rieseberg and Blackman 2010). However, more recently it has been widely recognised that speciation is probably not controlled by a small number of genes and that gene interactions are likely to be critical to reproductive isolation and speciation (Wolf et al. 2010). Epistatic interactions between genes have been found to play a role in hybrid infertility (Presgraves 2007). For example, Brideau et al. (2006) found that genes of a pair of epistatically interacting loci caused hybrid incompatibility in Drosophila. Similarly, Costa e Silva et al. (2012) found that epistatic effects contributed to outbreeding depression in hybrid populations of Eucalyptus. Intrinsic postzygotic isolation and reduced hybrid fitness may also result from chromosomal rearrangements (Rieseberg 2001; Rieseberg and Willis 2007). The identification of loci and gene interactions that are potentially involved in the speciation process should therefore be the focus of future research (see Nosil and Schluter 2011 for a full discussion). Furthermore, the identification of loci that may be associated with adaptive traits could also facilitate our understanding of the speciation process (Nosil and Schluter 2011). Since the phenotype of an organism is not only affected by genes and gene interactions, but also by interactions between genes and the environment (Wade 2002), a more comprehensive understanding of the relationship between loci and the environment is likely to be an important avenue of research in future speciation studies.

During my research I used phylogenetic and population genetic analyses as a means of delineating species boundaries in the green ash eucalypts. These analyses are the most common approaches of delineating species using molecular data (Flot et al. 2010). However, the disadvantage of these methods is that a high degree of subjectivity can be involved when drawing boundaries between groups and lineages. One way of delimiting species that was recently proposed and which uses a different methodology, is an approach known as ‘haplowebs’ (Flot et al. 2010). Haplowebs are increasingly being used to delineate species in groups that are taxonomically challenging, such as corals and crustaceans (e.g. Flot et al. 2011; Adjeroud et al. 2014; Flot et al. 2014). This approach delineates species on the basis of the criterion of mutual allelic diversity (Doyle 1995; Flot et al. 2010). Haplowebs implement a graphical approach that start 255 from a network or tree (based on nuclear haplotypes) and then adds connections between the haplotypes that are found to occur between individuals (Fontaneto et al. 2015). Once this process is finished, the graph reveals pools of interconnected alleles corresponding to groups of individuals that are reproductively isolated from other groups (Dellicour and Flot 2015). The advantage of this approach is that it provides a ‘yes’ or ‘no’ answer to whether a group should be regarded as a species (J-F Flot, personal communication). A disadvantage of the approach is that it is based on the biological species concept and may pool species that are known to occasionally hybridise (Fontaneto et al. 2015), which may be a limitation in groups like the green ashes, where hybridisation is common. However, species delimitation requires a wide range of methods to effectively and appropriately delineate species boundaries (Carstens et al. 2013). Therefore, although the software for implementing haplowebs is yet to be published (J-F Flot, personal communication), in the future I would like to use haplowebs and compare the results with the analyses presented here.

6.5 Implications of findings for the systematics of the green ash eucalypts

The phylogenetic analyses in this thesis suggest that the green ashes as currently circumscribed are not monophyletic and that the tall trees (Eucalyptus regnans, E. obliqua and E. fastigata) and the remainder of the green ashes do not form a monophyletic group. Therefore two groups should be recognised, one comprising the tall trees, E. regnans, E. obliqua and E. fastigata, and the other the remainder of the green ashes. The second group may need to also include the peppermint, E. radiata, and the black sallies, E. copulans and E. moorei, however, further genomic work on the peppermints and black sallies will be required to investigate this further. The two samples of the blue ash, E. consideniana, were not shown to be sister taxa. This will also need to be investigated as the population of E. consideniana from Nowra may be an undescribed species (V. Klaphake, personal communication).

Population genetic analyses suggested that E. dendromorpha as it is currently circumscribed does not form a single group. Klaphake (2012) recognises two species: E. dendromorpha (the tree form from the Southern Highlands) and E. sp. Blackheath (the mallee form the Blue Mountains). While analyses in this thesis support Klaphake’s

256 view, they also suggest that the populations of E. dendromorpha from Fitzroy Falls in the Southern Highlands (Redhills Road and Jersey Lookout) may also be separate species. A population of mallees from Mount Banks formerly identified as either E. dendromorpha, or a putative hybrid between E. dendromorpha and E. cunninghamii (referred to in this thesis as E. sp. Mount Banks), is strongly supported as an undescribed species. A revision and detailed morphometric analysis of what is currently considered E. dendromorpha is therefore urgently needed.

All population genetic analyses indicated that E. stricta and E. laophila are likely to be the one species. This result was not surprising, considering the species are very similar morphologically. The green ash species, E. langleyi and E. obstans, formed separate groups corresponding to currently circumscribed species. Unfortunately E. apiculata was not included in the population genomic study. In the phylogenetic analysis, samples from this species did not form a single clade, with all three accessions being in three separate clades (Chapter 2). The sample of E. apiculata from the GBMWHA was in a clade with samples of E. stricta and E. laophila from the GBMWHA in all phylogenetic analyses. It has been suggested that populations of E. apiculata from the GBMWHA may be a narrow-leaved ecotype of E. stricta (D. Benson, personal communication). While the data presented in Chapter 2 support this hypothesis, a population genomic study of E. apiculata is required to investigate this further.

6.6 Overall summary and future directions

The green ashes are a group with a complex evolutionary history involving many speciation mechanisms (e.g. reticulate evolution, ecological speciation and vicariance). My research has enhanced our understanding of the speciation mechanisms of the green ash eucalypts and closely related species in subgenus Eucalyptus by utilising an interdisciplinary approach (incorporating phylogenomics, population genomics, a common garden experiment and environmental niche modelling). Such an approach is likely to be effective in understanding the evolutionary history of other species within Eucalyptus, as well as other groups of organisms.

There are now more questions to be answered concerning the evolutionary history of the green ash eucalypts, as well as other species within subgenus Eucalyptus. A taxonomic

257 revision of subgenus Eucalyptus is required, which should include a detailed morphometric analysis of adult and juvenile characters of all species recognised by various authorities. Genome-wide analyses at the population level are now needed for other groups in subgenus Eucalyptus from this study (i.e. the blue ashes, peppermints, stringy barks, scribbly gums and black sallies). Both phylogenetic and population genomic analyses are also needed on groups of species in subgenus Eucalyptus that were not included in this study (e.g. white mahoganies and snow gums). More work on the ecological factors that affect species divergence in the green ashes should be undertaken (e.g. detailed studies on flowering phenology of each species, pollinators and the role of competition with other plant species). Chloroplast genome sequencing should also be used in combination with DArT analyses to better understand hybridisation in the green ashes, as well as other species in subgenus Eucalyptus.

Future work should aim to advance speciation theory and provide further insights into species delimitation. For example, additional species delineation tools (e.g. haplowebs) could be used in combination with population genomics and phylogenomics to better define species boundaries in the green ashes, as well as other taxonomically challenging groups. It is also necessary to investigate the extent to which phenotypic plasticity in adult characters may affect species delimitation. Ideally this could be done by conducting a longer term (e.g. 5 year) study in a common garden complemented by field studies of population differences in morphological traits within each species. A study measuring morphological and physiological traits across populations within each species and investigating relationships with outlier loci should be undertaken to identify potential adaptive traits. The identification of loci that are likely to be associated with adaptation and speciation should be the focus of future research.

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