I Is the Sunda-Sahul Floristic Exchange Ongoing?

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Is the Sunda-Sahul floristic exchange ongoing? A study of distributions, functional traits, climate and landscape genomics to investigate the invasion in Australian rainforests

By Jia-Yee Samantha Yap

Bachelor of Biotechnology

Hons.

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2018

Queensland Alliance for Agriculture and Food Innovation

Abstract

Australian rainforests are of mixed biogeographical histories, resulting from the collision between Sahul (Australia) and Sunda shelves that led to extensive immigration of rainforest lineages with Sunda ancestry to Australia. Although comprehensive fossil records and molecular phylogenies distinguish between the Sunda and Sahul floristic elements, species distributions, functional traits or landscape dynamics have not been used to distinguish between the two elements in the Australian rainforest flora. The overall aim of this study was to investigate both Sunda and Sahul components in the Australian rainforest flora by (1) exploring their continental-wide distributional patterns and observing how functional characteristics and environmental preferences determine these patterns, (2) investigating continental-wide genomic diversities and distances of multiple species and measuring local species accumulation rates across multiple sites to observe whether past biotic exchange left detectable and consistent patterns in the rainforest flora, (3) coupling genomic data and species distribution models of lineages of known Sunda and Sahul ancestry to examine landscape-level dynamics and habitat preferences to relate to the impact of historical processes.

First, the continental distributions of rainforest woody representatives that could be ascribed to Sahul (795 species) and Sunda origins (604 species) and their dispersal and persistence characteristics and key functional characteristics (leaf size, fruit size, wood density and maximum height at maturity) of were compared. Sunda species richness decreased with increasing latitude but maintained high levels of endemism, including in the south, and comparative functional analyses suggest that Sunda-derived lineages are on average more skewed towards more efficient dispersal and faster growth than Sahul- derived lineages. Studying distributional patterns at finer scale also revealed the influence of two highly correlated environmental factors on both ancestries: temperature and altitude, and the local distribution of invading lineages was shown to be resisted in stable, saturated communities of Sahul lineages.

Next, whole-chloroplast genome sequencing was used to measure diversities and distances across multiple, common rainforest species of differing ancestry in the Tropics and Subtropics, and from within, a smaller sample of species was investigated across both regions to study landscape connectivity. A recent framework that explores the timing and rate of accumulation of co-occurring species at each study site was applied to the genetic data to study how species of distinct biogeographic histories accumulate locally. Also, floristic composition within Tropics and Subtropics plots were included in the study to investigate the role of biogeographical and ecological processes ii

and landscape characteristics in determining Sunda and Sahul species distributions. Species of Sunda ancestry displayed consistently lower chloroplast genomic diversity than Sahul ancestry, with recent accumulation rates for Sunda species being measured across all sites, confirming recent arrival and expansion across eastern Australia. Sunda-derived species with continuous distributions exhibited the highest diversity at the most northerly sampled site, suggesting a north to south colonisation process. The same species however, differed in the levels of genomic divergence between the Tropics and Subtropics, suggesting that continental expansion occurs at different temporal scales, with some species experiencing a northern time lag before a southern expansion along the east coast of Australia.

Genome-wide nuclear markers wer used to study the landscape-level genetic patterns of Doryphora sassafras of Sahul ancestry and Toona ciliata of Sunda ancestry across their shared distributional range in New South Wales. Population genomic patterns were supported by predicting availability of habitat during the Last Glacial Maximum (LGM), mid-Holocene (MH) and current periods using environmental niche modelling (ENM). Future (2070) habitat models were also developed to make predictions on the impact of anthropogenic climate change. D. sassafras exhibited high levels of north / south genomic divergence and higher genomic diversity at higher latitudes, whereas T. ciliata showed landscape-level homogeneity of genomic diversity, indicating differential responses to past climate involving long-term persistence vs. recent invasion. Habitat suitability models provided independent support for species differences across landscape, and further predicted T. ciliata had a recent rapid expansion that took place in the mid-Holocene, 6,000 ybp suggesting the species may not have adjusted its distributional range to current climatic conditions yet.

These findings show biogeographic history, together with functional attributes and local habitat are important determinants of Australian rainforest species’ distribution and assembly at local as well as continental scales. Additionally, the genomic patterns confirm the recent and rapid expansion of Sunda-derived lineages across the Australian landscape, revealing insights into the timing, mode and pace of the expansion. This study demonstrates how the study of species distributions, functional characteristics and genetic variation can provide insights into the current distribution and assembly of flora at a specific biogeographic region of floristic exchange. It is anticipated that the outcomes of this study will lead to (1) reciprocal studies across Southeast Asia to investigate floristic origins and drivers of intercontinental exchange, (2) distribution-wide investigations to better understand the factors (e.g. environmental factors, biotic interactions, species attributes) that drive invasion, range expansion and long-term persistence, (3) a new set of conservation priorities for Australian rainforests that considers species’ biogeographic history. iii

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material.

Publications included in this thesis

Yap, J. S., Rossetto, M., Costion, C., Crayn, D., Kooyman, R. M., Richardson, J., & Henry, R. J. (2018). Filters of floristic exchange: How traits and climate shape the rain forest invasion of Sahul from Sunda. Journal of Biogeography, 45, 838-847. – Incorporated as Chapter 2

Contributor Statement of contribution Conception and design (75%) Data collection (20%) Yap, J. S. (Candidate) Analysis and interpretation (75%) Wrote the paper (90%) Edited the paper (40%) Conception and design (20%) Rossetto, M. (Supervisor) Analysis and interpretation (20%) Wrote the paper (10%) Edited the paper (10%) Costion, C. Data collection (20%) Edited the paper (10%) Crayn, D. Data collection (20%) Edited the paper (10%) Conception and design (5%) Kooyman, R. M. Data collection (20%) Analysis and interpretation (5%) Edited the paper (10%) Richardson, J. Data collection (20%) Edited the paper (10%) Henry, R. J. Edited the paper (10%)

Submitted manuscripts included in this thesis

Yap, J. S., van der Merwe, M., Ford, A., Rossetto, M., & Henry, R. J. (Under review) Biotic exchange leaves detectable patterns in the Australian rainforest flora. Biotropica. – Incorporated as Chapter 3

Contributor Statement of contribution Conception and design (65%) Field work (50%) Yap, J. S. (Candidate) Lab work (100%) Analysis and interpretation (80%) Wrote the paper (90%) Edited the paper (75%) Van der Merwe, M. Edited the paper (5%) Analysis and interpretation (10%) Ford, A. Field work (50%) Edited the paper (5%) Henry, R. J. Edited the paper (5%) Conception and design (35%) Analysis and interpretation (10%) Rossetto, M. (Supervisor) Wrote the paper (10%)

Edited the paper (10%)

Manuscripts included in this thesis

Yap, J. S., Rossetto, M., Sourav, D., Wilson, P. D., & Henry, R. J. Landscape genomics and habitat modelling reveal contrasting histories in co-occurring rainforest species across New South Wales, Australia. – Incorporated as Chapter 4

Contributor Statement of contribution Conception and design (75%) Field work (100%) Yap, J. S. (Candidate) Data collection (90%) Analysis and interpretation (90%) Wrote the paper (90%) Edited the paper (50%) Conception and design (25%) Rossetto, M. (Supervisor) Analysis and interpretation (10%) Wrote the paper (10%) Edited the paper (35%) Sourav, D. Data collection (10%) Edited the paper (5%) Wilson, P. D. Edited the paper (5%) Henry, R. J. Edited the paper (5%)

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Other publications during candidature

Peer-reviewed papers

Rossetto, M., Kooyman, R. M., Yap, J. S., & Laffan, S. W. (2015) From ratites to rats: the size of fleshy fruits shapes species' distributions and continental rainforest assembly. Proceedings of the Royal Society B, 282, 20151998.

Rossetto, M., Ens, E. J., Honings, T., Wilson, P. D., Yap, J. S., Costello, O., ... & Bowern, C. (2017). From Songlines to genomes: Prehistoric assisted migration of a rain forest tree by Australian Aboriginal people. PloS ONE, 12, e0186663.

Greenfield, A., McPherson, H., Auld, T., Delaney, S., Offord, C. A., van der Merwe, M., Yap, J. S., & Rossetto, M. (2017). Whole-chloroplast analysis as an approach for fine-tuning the preservation of a highly charismatic but critically endangered species, Wollemia nobilis (Araucariaceae). Australian Journal of Botany, 64, 654-658.

Van der Merwe, M., Yap, J. S., Bragg, J. G., Cristofolini, C., Foster, C. S. P., Ho, S. Y. W., & Rossetto, M. (2019). Assemblage Accumulation Curves: a framework for resolving species accumulation in biological communities using chloroplast genome sequences. Methods in Ecology and Evolution, doi: 10.1111/2041‐210X.13181

Conference presentations

Yap, J. S. (2017). Exploring the evolutionary history of the Australian rainforest flora. XIX International Botanical Congress (IBC), Shenzhen, China, July, 29th.

Yap, J. S. (2018). Tracking the expansion of a Sunda invader: comparative case study on rainforests of New South Wales. 55th Annual meeting of the Association for Tropical Biology and Conservation (ATBC), Kuching, Malaysia, July 2nd.

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Contributions by others to the thesis

In addition to those cited in previous section of this thesis, the following have contributed:

- Dr. Caroline Cristofolini: field work assistance and help with DNA extractions - Joel Cohen, Monica Fahey and Dr. Susan Rutherford: field work assistance - Dr. Hannah McPherson, Brendan Wilde and Dr. Jason Bragg: NGS data analysis

Statement of parts of the thesis submitted to qualify for the award of another degree

No works submitted towards another degree have been included in this thesis.

Research involving Huamn or Animal Subjects

No animal or human subjects were involved in this research.

Acknowledgements

Embarking on a PhD has led to a most hectic but fruitful 3 years and 7 months of my life!

I would first like to thank my supervisor, Dr. Maurizio Rossetto for introducing me to this super interesting project on the Australian rainforests, and for providing all the support needed throughout my PhD. Thank you for giving me the freedom to explore new ways to research, helping me to improve my writing and presentation skills, making time for the annual trips to Brisbane, prepping me for conferences, introducing me to potential colloborators, and funding my projects. Simply put, your mentorship has helped shape me into a better researcher and I am deeply appreciative of everything you have done for me.

I would also like to thank my co-supervisor, Prof. Robert Henry for your support. I am grateful for for the useful advice you gave throughout my candidature. Thank you for sharing your knowledge and wisdom; I always learned a lot when we meet.

I would like to thank the University of Queensland for awarding me the Australian Postgraduate Award (APA) which made undertaking this PhD program possible. I would also like to thank Queensland Alliance for Agriculture and Food Innovation (QAAFI), especially Annie, for helping me with my APA application, as well as providing assistance with the administration and financial aspects of this project. I am also grateful for their help with my conference travel applications, as I got to present at both the XIX International Botanical Congress (IBC) in Shenzhen, China and the 55th Annual meeting of the Association for Tropical Biology and Conservation (ATBC) in Kuching, Malaysia. I would like to thank my supervisors for encouraging me to attend these conferences as both conferences provided opportunities for me to meet members of the wider scientific community.

I would also like to thank the advisory committee, Dr. Agnelo Furtado, Dr. Paul Forster and Dr. Margaret Mayfield for providing encouragement and feedback throughout various stages of my PhD.

A thank you to my colleagues, particularly from the National Herbarium of New South Wales: Dr. Hannah McPherson and Dr. Marlien van der Merwe, for your valuable advice and support on many aspects of my PhD; Andrew Ford, Joel Cohen, Monica Fahey and Dr. Susan Rutherford for your assistance in the field;, Dr. Jason Bragg and Dr. Peter D. Wilson, for your help with computing scripts for data analyses; Brendan Wilde and Dr. Caroline Cristofolini for your company and for helping out with field work/lab work/data analyses; Dr. Robert Kooyman, for sharing your wisdom and helping x

in the field, and also thank you for sharing your beautiful lodge in Myocum while field collecting; Carolyn Connelly and Dr. Trevor Wilson, for your excellent cooperation and wonderful company in the lab; Rony, for answering all my questions about environmental modelling; everyone at the herbarium (e.g. Peter, Andre, Mel, Guy, Alison, Kathi, Marg, Kristina, Ifeana, Marco, Matt, Herve, Lisa, Jude, Peta, etc..) for your joyful company and delicious homemade goodies.

I would also like to thank my family, in particular my parents, for your unconditional support, love and encouragement. I am also thankful to my friends, for always being there when needed. Special thanks to Richard for your love and support, and your company is much appreciated as it has helped me to settle into writing mode and to complete my thesis.

Financial support

No financial support was provided to fund this research.

Keywords

Australian rainforests, Sunda-Sahul floristic exchange, species distributions, community dynamics, genomics.

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060302, Biogeography and Phylogeography

ANZSRC code: 060411, Population, Ecological and Evolutionary Genetics

ANZSRC code: 060408, Genomics

Fields of Research (FoR) Classification

FoR code: 0602, Ecology

FoR code: 0603, Evolutionary Biology

FoR code: 0604, Genetics

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Table of Contents Chapter 1 General Introduction ...... 1 1.1 Brief background on the Sunda-Sahul floristic exchange ...... 2 1.2 Brief introduction to Australian rainforest assemblages ...... 3 1.3 Evidence of floristic exchange in Australia ...... 6 1.4 Evidence of the floristic exchange between Australia and Sunda ...... 7 1.5 Central questions addressed in this thesis...... 10 1.6 Methods: a modern set of tools for exploring floristic exchange ...... 11 1.7 Thesis outline ...... 13 Chapter 2 Filters of floristic exchange: how traits and climate shape the rainforest invasion of Sahul from Sunda ...... 16 2.1 Abstract ...... 16 2.2 Introduction ...... 17 2.3 Materials and Methods ...... 19 2.3.1 Comparing continental species distributions and phylogenetic endemism ...... 19 2.3.2 Functional trait dataset ...... 20 2.3.3 Regional plot-based dataset ...... 21 2.4 Results ...... 22 2.4.1 Do latitudinal measures of species richness and phylogenetic endemism differ between Sunda and Sahul species? ...... 22 2.4.2 Do the Sunda ‘invaders’ display different or distinct functional characteristics? .. 26 2.4.3 Do latitude, elevation and temperature act as selective filters to expansion? ...... 29 2.5 Discussion ...... 31 2.5.1 When two worlds collide: a continuing flow of Sunda migrants into Australian rainforests...... 31 2.5.2 Is floristic exchange favoured by specific functional characteristics? ...... 32 2.5.3 The importance of local conditions: can elevation and cooler temperatures limit the expansion of northern invaders? ...... 32 2.6 Conclusion ...... 33 Chapter 3 Biotic exchange leaves detectable patterns in the Australian rainforest flora ...... 36 3.1 Abstract ...... 36 3.2 Introduction ...... 37 xiii

3.3 Materials and Methods ...... 38 3.3.1 Study Areas and taxon sampling ...... 38 3.3.2 DNA extraction and chloroplast genome sequencing ...... 43 3.3.3 Chloroplast assembly and genomic variation detection ...... 43 3.3.4 Estimation of species accumulation in rainforests ...... 44 3.3.5 Local species distributions at study sites ...... 45 3.4 Results ...... 45 3.5 Discussion ...... 49 3.5.1 Recent incursions into the Australian rainforest flora ...... 49 3.5.2 Environmental disturbances facilitated immigration and integration...... 50 3.5.3 Same mode but different tempo of expansion ...... 50 3.6 Conclusion ...... 51 Chapter 4 Landscape genomics and habitat modelling reveal contrasting histories in co-occurring rainforest species across New South Wales, Australia ...... 53 4.1 Abstract ...... 53 4.2 Introduction ...... 54 4.3 Materials and Methods ...... 56 4.3.1 Study species ...... 56 4.3.2 Sampling and sequencing ...... 57 4.3.3 DArTseq analysis ...... 60 4.3.4 Environmental niche modelling ...... 61 4.4 Results ...... 63 4.4.1 Summary of DArTseq data ...... 63 4.4.2 Nuclear genome diversity patterns ...... 63 4.4.3 Environmental niche modelling outputs ...... 66 4.5 Discussion ...... 69 4.5.1 Learning from the past: a persistent tree tracking habitat ...... 69 4.5.2 Learning from the past: exploratory expansion of an invading tree ...... 70 4.5.3 Learning from the past to predict the future ...... 72 Chapter 5 General discussion, conclusion and future directions ...... 73 5.1 Functional traits and habitat preferences facilitate floristic exchange ...... 73 5.2 Floristic exchange occurred recently, and continental expansion follows different tempo ...... 74 xiv

5.3 Recent, explosive invasions across the landscape ...... 75 5.4 Conclusions & Future directions ...... 76 References ...... 79 Appendices ...... 94

List of Figures

Figure 1.1 The extent of the Sunda and Sahul continental landmasses with the Wallacean archipelago in between...... 1

Figure 1.2 Australian rainforest species distributions by region...... 4

Figure 1.3 Proportions of rainforest species of Sunda (black) and Sahul (white) ancestry at various locations ...... 5

Figure 2.1 Proportion of Sunda and Sahul rainforest species by region across the Australian continent...... 23

Figure 2.2 Spatial and phylogenetic patterns across the Australian continent for Sunda and Sahul rainforest species...... 24

Figure 2.3 Sunda and Sahul rainforest plot data in Australia...... 25

Figure 2.4 Functional Trait comparison for Australian woody rainforest species of Sunda and Sahul ancestry: ...... 28

Figure 2.5 Sunda (black dots) and Sahul (white dots) plot data for Australian rainforests...... 30

Figure 3.1 The general location of the four study sites, with two in each of the two rainforest regions (Tropics and Subtropics) along the east coast of Australia...... 42

Figure 3.2 Comparative analyses between Sunda and Sahul ancestry based on genomic data: Genomic diversity and genomic distance in the Tropics (A, C) and the Subtropics (B, D)...... 46

Figure 4.1 Study sites of Doryphora sassafras (green triangles) and Toona ciliata red dots) on an elevation map of New South Wales (NSW), Australia...... 58

Figure 4.3 Environmental niche modelling outputs showing the change in area of suitable habitat across New South Wales (NSW) for Doryphora sassafras (top six maps) and Toona ciliata...... 67

List of Tables

Table 1.1 Landscape-level genetic studies conducted on rainforest flora in Sunda and Sahul.8

Table 2.1 Tropical and subtropical Australian rainforest plot data summary...... 26

Table 2.2 Qualitative traits data summary for Australian woody rainforest species of Sunda and Sahul ancestry...... 27

Table 3.1 List of the Australian rainforest species in this study, with relevant information about family, ancestry (Sunda or Sahul) and the region in which species were sampled included. . 39

Table 3.2 Summary of genomic diversity and genomic distance measures for species of Sunda and Sahul ancestry in the Tropics and the Subtropics...... 46

Table 3.3 Genomic diversity and genomic distance measures for four Sunda-derived species and two Sahul-derived species...... 49

Table 4.1 Genetic diversity in populations of Doryphora sassafras and Toona ciliata arranged in latitudinal order...... 59

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Chapter 1 General Introduction

Figure 1.1 The extent of the Sunda and Sahul continental landmasses with the Wallacean archipelago in between. Map was adopted with permission from Harrison et al. (2006).

Processes that contribute to changes in biodiversity have long been of great interest to the scientific community. One such process is biotic exchange (Pennington et al., 2006), which takes place when a barrier that separated biotas with distinct evolutionary histories breaks down, thus enabling species to move between biotic assemblages and increase local biodiversity (Vermeji, 1991). Biotic exchanges have occurred between continents (e.g. between South America and Africa / North America, Pennington & Dick, 2004; between India and Asia, Ali & Aitchison, 2008; between East Asia and North America, Wen et al., 2010), and can date back as far as the Paleocene epoch (63-55 million years ago or Mya; e.g. India-Asia faunal exchange occurred from late Paleocene onwards, Ali & Aitchison, 2008). Although such exchanges can have a long history, they often are still ongoing thus adding to the complexity in the temporal evolution and assembly of present-day biota. Unlike other biotic exchanges, the one between the Sunda continental plate (the Malay Archipelago; Fig. 1.1) and the Sahul continental plate (including Australia and New Guinea; Fig. 1.1) has been particularly active in more recent times (described further in this general introduction), increasing in intensity during the Quaternary (2.5 Mya to present). Consequently, rainforest floristic assemblages within each region consist of a mix of lineages with distinct Sunda and Sahul ancestries (van Welzen & Slik, 2009; Richardson et al., 2012).

1.1 Brief background on the Sunda-Sahul floristic exchange

The exchange of rainforest flora between these two continental plates became possible when Sahul, once part of the southern supercontinent Gondwana, approached and collided with Sunda in the late Oligocene (starting from 25 Mya, Morley, 2000; Hall, 2009; 2012).

Although the initial continental plate collision triggered some exchange as early as 20 Mya (Sniderman & Jordan, 2011; Crayn et al., 2015), dispersal was still limited by a vast ocean barrier (Hall, 2009; Lohman et al., 2011). It was only when the landmasses approached their present location (around 12 Mya; e.g. Hall, 2009; 2012; Lohman et al., 2011), the frequency of the exchange increased (Sniderman & Jordan, 2011; Crayn et al., 2015). By 5 Mya New Guinea started to uplift, and additional land formation and boundary shifts of both plates began to form the present-day Wallacean Archipelago. The latter geological changes also coincided with climatic and sea-level fluctuations, and the interplay between these events led to increasing exchanges between Sunda and Sahul (Sniderman & Jordan, 2011; Crayn et al., 2015). During the glacial maxima of the Quaternary, the Sunda Shelf formed a continuous land mass with present-day Southeast Asian mainland, while present-day New Guinea was linked to Australia across the Torres Strait (Fig. 1.1). Sunda and Sahul

were never directly connected because deep water barriers remained in the Wallacean Archipelago (Fig. 1.1; Voris, 2000). Only plants capable of long-distance dispersal events and / or capable of using the archipelago as stepping stones were likely to move between the two landmasses (e.g. Bänfer et al., 2006; Muellner et al., 2008; 2009; Su & Saunders, 2009; Nauheimer et al., 2012; Thomas et al., 2012; Grudinski et al., 2014). Consequently, both regions still have distinct floristic compositions, but also share closely related rainforest lineages (van Welzen & Slik, 2009; Richardson et al., 2012; Kooyman et al., 2013).

1.2 Brief introduction to Australian rainforest assemblages

On mainland Australia, rainforests are one of the oldest types of vegetation (Hill, 1994; 2004; Morley, 2000; Crisp et al., 2013). Many present-day plant families (e.g. Cunoniaceae, Proteaceae and Myrtaceae) date back to the early Tertiary Period when the continent was part of Gondwana (Hill 1994; 2004; Greenwood & Christophel, 2005; Kooyman et al., 2011; 2014). The ancestors of these families were historically widespread, until Australia broke away from Gondwana and separated from Antarctica during the late Eocene (Greenwood & Christophel, 2005; Martin, 2006). This continental breakup established the circumpolar current that induced globally cooler and drier climates and drove increasing aridification resulting in the range-wide contraction of Australian rainforest and the loss of many lineages (Hill, 2004; Greenwood & Christophel, 2005; Martin, 2006). Rainforests were further subjected to climatic instability during the Quaternary, as the cooler and drier conditions of the glacial periods led to the contraction of available habitat, and only during the brief interglacial periods were warmer wetter habitats able to re-expand. It was also during this period that the Sunda- Sahul floristic exchange intensified, with a notable increase in the invasion of Sunda lineages into Australia (Sniderman & Jordan, 2011; Crayn et al., 2015). To date, these immigrant lineages represented by Sunda plant families (e.g. Annonaceae, Meliaceae and Rubiaceae) are found mixing with families of Gondwanan origins (Kooyman et al., 2011).

Presently, Australian rainforests are distributed along the eastern and northern coastlines (Fig. 1.2; Kooyman et al., 2013 and references therein), with the main diversity occurring in the Australian Wet Tropics (AWT or WT) in Northern Australia, followed by the subtropical areas of South-eastern Queensland and northern New South Wales (BR).

Cape York at the northern tip of Australia shares the most species with neighbouring landmasses such as Indo-Malesia and New Guinea relative to other regions of Australia (Fig. 1.2) (Kooyman et al., 2013), followed by the AWT. Whether these shared species are of Sunda or Sahul origin was not investigated by Kooyman et al. (2013), but another study that attempted to assign all AWT rainforest species to biogeographic origins estimated 47.2% (or 477 out of 1010 species) are of Sunda-derived lineages (Richardson et al., 2012; Fig. 1.3). The same study observed a higher proportion (63.2% or 842 out of 1502 species) of species of Sunda origin in New Guinea, which is in closer proximity to Sunda than the AWT, suggesting that the proportion of species of Sunda origin decreases with increasing distance from Sunda and increasing latitude within Australia.

Figure 1.2 Australian rainforest species distributions by region. Adopted with permission from Kooyman et al. (2013). Each black dot indicates the occurrence of rainforest species (five or more species). Regions from northwest to southeast are Western Australia and Northern Territory (WA- NT); Cape York (CY); the Australian Wet Tropics (WT); central Queensland (CQ); South-eastern Queensland and northern New South Wales (BR); southern New South Wales (SNSW); Victoria (Vic); and Tasmania (Tas).

Figure 1.3 Proportions of rainforest species of Sunda (black) and Sahul (white) ancestry at various locations. Pie charts in Australia are drawn based on data generated in Chapter 2, and outside of Australia are drawn based on data generated by Richardson, Costion & Muellner (2012). In Australia, proportions of Sunda/Sahul species were measured for the following regions: Western Australia (WA); Northern Territory (NT); Cape York (CY); the Australian Wet Tropics (FNQ); central Queensland (CQ); South-eastern Queensland and northern New South Wales (BR); southern New South Wales (SNSW); Victoria (VIC); and Tasmania (TAS).

1.3 Evidence of floristic exchange in Australia

Fossil and phylogenetic evidence suggest that large-scale invasions of Sunda lineages into Australia occurred during the Quaternary when climatic fluctuations subjected rainforests to repeated expansion / contraction events (Sniderman & Jordan, 2011; Crayn et al., 2015; Costion et al., 2015). It is likely that these lineages took advantage of habitat instability, and rapidly invaded Australia during the interglacial forest expansion periods when unoccupied habitat emerged.

If opportunistic invasion took place, we would expect that successfully invading species would tend to have certain functional traits that facilitate dispersal, establishment (Crayn et al., 2015), growth and competition (Kunstler et al., 2016). These species may also have retained ancestral physiological traits that relate to photosynthetic rate, water balance and growth temperature (e.g. large leaf area, Wright et al., 2017). Limited evidence is available to show how trait selection influenced Sunda species distribution in Australia aside from the suggestion that 90% of 49 Sunda clades are animal dispersed, highlighting the importance of this dispersal mode for movement across vegetation mosaics and water barriers (Crayn et al., 2015).

The suggested pattern of recent continental expansion of Sunda-derived species can be expected to have left very different genetic signatures from those found across resident Sahul-derived lineages. DNA-based analyses across multiple species in subtropical Northern New South Wales identified different local histories for lineages of Sunda vs. Sahul origins across areas with different disturbance histories (Rossetto et al., 2015a). Sunda species consistently showed recent local histories, suggesting that these immigrants are potentially still expanding into the Subtropics. It can be expected that colonisation generally originated from Northern Australia as the distances involved are too large for direct invasion from Sunda.

1.4 Evidence of the floristic exchange between Australia and Sunda

Molecular data are increasingly used for studying past distributional changes of species (Table 1.1). This is because landscape-level patterns of genetic variation can identify sites where species persist (i.e. source or refugia) and track associated expansion patterns (Hewitt, 2004). Identifying similar genetic patterns among co-distributed species can be useful as it can identify commonalities among a range of species’ attributes such as functional traits, biogeographic history, and responses to past climatic events (Manel, 2003; van der Merwe et al., 2014; Rossetto et al., 2015a; Worth et al., 2017).

There have been few landscape-level genetic studies on the rainforest floras of Sunda and Sahul (Table 1.1), and reviewing these studies provides insights into how past climatic conditions facilitated plant movements within both Sunda and Sahul, and how floristic exchange occurred between regions.

Based on the available evidence (Table 1.1), strong genetic differentiation between populations was commonly observed in both regions. Sunda studies estimated population divergence was around the Plio-Pleistocene (e.g. Kamiya et al., 2012; Iwanaga et al. 2012; Otahni et al., 2013). This is consistent with the divergence times generated from a review on Australian biota exploring common biogeographic barriers across eastern Australia (Bryant & Krosch, 2016). Most studies suggest that climatic fluctuations impacted on species distributions by subjecting species to repeated contractions into isolated refugia, with the ensuing interruption of gene flow resulting in genetic differentiation between populations. Admixture of diverged genetic groups was observed in some populations of Shorea leprosula in Sunda (Ohtani et al., 2013) and Podocarpus elatus in Sahul (Mellick et al., 2011; 2012), suggesting that species still experience repeated expansion events despite the progressively increasing divergence driven by climatic fluctuations.

Genetic signatures suggesting that species recently expanded across Sunda and Sahul were also observed (Guickling et al., 2011; McPherson et al., 2013). In Sunda, Macaranga gigantea shared closely related haplotypes in Malay Peninsula and East Kalimantan (Guicking et al., 2011), where marked lineage divergence between both areas has been observed for other species (Table 1.1). Sea level fluctuations caused by the Pleistocene climate drove the formation of land connections between landmasses, and a corridor of open, dry vegetation was suggested to have divided rainforests. However, this likely favoured the expansion of M. gigantea across Sunda, as it is a light-demanding pioneer rainforest species that colonises open, disturbed habitats. Toona ciliata, also a pioneer in Sahul, displayed extremely low within and between population genetic variation across its Australian distributional range, suggesting recent colonisation in Australia through a founder event followed by rapid expansion (McPherson et al., 2013).

Table 1.1 Landscape-level genetic studies conducted on rainforest flora in Sunda and Sahul.

Species Study site Study DNA marker type Reference General conclusions location Shorea curtisii Peninsula Malaysia, Sunda Chloroplast Kamiya et al., Divergence between Peninsula (Dipterocarpaceae) Borneo 2012 Malaysia and Borneo occurred between 1.29–0.43 Mya Shorea leprosula Peninsula Malaysia, Sunda Nuclear, Chloroplast Ohtani et al., 2013 Divergence between Peninsula (Dipterocarpaceae) Borneo, Sumatra Malaysia/Sumatra and Borneo occurred between 0.28–0.09 Mya, genetic admixture detected Shorea parvifolia Peninsula Malaysia, Sunda Nuclear Iwanaga et al., Divergence between Peninsula (Dipterocarpaceae) Borneo 2012 Malaysia and Borneo occurred between 2.6-0.7 Mya, genetic admixture detected Trigonobalanus verticillata Peninsula Malaysia, Sunda Nuclear, Chloroplast Kamiya et al., Strong divergence between Peninsula (Fagaceae) Borneo, Indonesia 2002 Malaysia/Indonesia and Borneo occurred 16.7-8.3 Mya, low diversity within populations Shorea leprosula Peninsula Malaysia, Sunda Nuclear Lee et al., 2000 Low differentiation between (Dipterocarpaceae) Borneo populations (7 populations from Peninsula Malaysia, only a population from Borneo) Lithocarpus Borneo, parts of Sunda Nuclear, Chloroplast Cannon & Manos, Closely related haplotypes in parts of (Fagaceae) mainland Asia 2003 mainland Asia and east Borneo, expansion probably during the Quaternary period 41 Macaranga sp. Borneo, Sumatra, Sunda Chloroplast Bänfer et al., 2006 Closely related haplotypes in Malay (Euphorbiaceae) Peninsula Malaysia peninsula and Borneo, expansion probably during the Quaternary period 2 Macaranga sp. Borneo, Peninsula Sunda Chloroplast Guicking et al., Multiple species groups have closely (Euphorbiaceae) Malaysia 2011 related haplotypes in East Kalimantan and Peninsula Malaysia 11 Elaeocarpus sp. AWT Sahul Nuclear Rossetto et al., Pre-Pleistocene differentiation was (Elaeocarpaceae) 2009 suggested, 8 out of 11 species did not show strong differentiation explained by species functional attributes

Podocarpus elatus Eastern Australia Sahul Nuclear Mellick et al., Separation of two lineages by a (Podocarpaceae) 2011; 2012 biogeographic barrier, secondary contact was detected Toona ciliata Eastern Australia Sahul Chloroplast McPherson et al., Haplotypes were shared between the (Meliaceae) 2013 AWT and Northern New South Wales (NNSW) Ceratopetalum apetalum Eastern Australia Sahul Nuclear, Chloroplast Heslewood et al., Divergent patterns differed using (Cunoniaceae) 2014 different DNA markers 9 species NNSW, Royal Sahul Chloroplast Van der Merwe et Level of population differentiation (various families) National Park al., 2014 varied among species 71 species NNSW Sahul Chloroplast Rossetto et al., Level of population differentiation (various families) 2015a varied among species, explained by biogeographic history and functional traits

1.5 Central questions addressed in this thesis

Understanding processes that drive diversity is important as exemplified by the floristic exchange between Sunda and Sahul that enriched the Australian rainforests. Preliminary studies of the floristic exchange in the rainforests suggest that the distribution of Sunda species in Australia is driven by a suite of factors such as functional attribute, habitat preference and climate (Sniderman & Jordan, 2011; Crayn et al., 2015; Costion et al., 2015). In order to further investigate the factors that contribute to floristic exchanges, I combined previous Australian flora datasets assigned to Sunda or Sahul origins to provide baseline data to describe how biogeographic processes influence species distributions. This baseline dataset is generated in Chapter 2 and is used to address the following questions: Do current species distributions reflect biogeographic origins? How do species functional characteristics and environmental preferences explain species distributions?

The colonisation of Australia by Sunda lineages is suggested to have increased in recent geological times (as recent as the late Quaternary; Sniderman & Jordan, 2011; Crayn et al., 2015). Different landscape-level genetic patterns in multiple co-distributed Sunda and Sahul species in the Subtropics reflect their contrasting histories and provide support for the recent expansion history of Sunda species (Rossetto et al., 2015a). However, tropical Northern Australia in close proximity to Sunda is likely to have enabled the initial invasion of species during the earlier phase (Pliocene or earlier) of the floristic exchange. A targeted, multispecies genomic study across the Tropics and Subtropics can estimate the timing of arrival of species with Sunda ancestry into Australia. In Chapter 3, I assess the following questions: Have the invading species recently expanded within the Tropics and Subtropics, and do fine-scale landscape features impact on species expansion? Do the landscape patterns of species with continental distributions indicate common repeated colonisation patterns?

A previous study of expansion of a rainforest tree across Australia showed extremely weak differentiation across a small number of distantly spaced sites across the species’ distributional range (McPherson et al., 2013). This was suggested to either be the result of a recent expansion or an extreme bottleneck followed by recent expansion. To investigate the expansion of Sunda species in fine detail, landscape-level genetics data is required and a comparative study between Sunda and Sahul species to reveal whether genetic patterns across the landscape are consistent with species history. In Chapter 4, I assess the following questions: Do Sunda and Sahul species show contrasting genetic patterns consistent with their history? What influence does climate (both past and future) have on species distributions, and can historical habitat availability validate species distributions?

Taken together, the chapters examine questions about floristic exchange between Sunda and Sahul by studying both the Sunda and Sahul components in the Australian rainflorest flora, and provide insights into the determinants of Australian rainforest species’ distribution and assembly at local as well as continental scales.

1.6 Methods: a modern set of tools for exploring floristic exchange

The study of contact between floras is timely. Advances in the generation and analyses of genetic, spatial, and plant trait data mean it is now possible to study of the current distribution and assembly of flora at a specific biogeographic region of floristic exchange. This section describes how species distributions can be used as evidence of the exchange and shows how the distributions can be explained by species’ functional characteristics and environmental preferences. This is followed by a brief description of how genetic data can provide fine-scale information about past species distribution and reveal the impact of historical processes. This can be supplemented by environmental data, to show whether historical habitat availability drove changes in species distributions.

Contact between formerly isolated landmasses provides opportunities for plants to disperse across new terranes. Often the complex histories of land formation and high concentration of diversity (especially in rainforests) make it challenging to study floristic exchanges. Previous studies have traditionally determined movement of flora between regions by tracking distribution patterns of selected plants (e.g. van Steenis, 1950). Increasingly available phylogenetic information and species distributional data distinguishing between migrant and resident species, has enabled the quantification of floristic exchanges (e.g. van Welzen & Slik, 2009; van Welzen et al., 2011; Richardson et al., 2012). For instance, the concentration (proportion or richness) of species with different origins can detect distinct biogeographic histories with those that are invading tend to decrease in concentration away from the source.

To understand why species’ distribution vary, a number of methods can be employed. Integration of phylogenetic information with species richness measures can provide insights into the observed distributional patterns. For example, an informative phylogenetic metric that can supplement the richness measure is phylogenetic endemism, as it determines the amount of the evolutionarily distinct species that became range restricted (Rosauer et al., 2009; Kooyman et al., 2013). Where species occurrence records are comprehensive (e.g. Australia), absence records of migrant species can also provide insights, as the lack of migrant species can be interpreted as the result of unsuitable habitat, or that such habitat may be beyond their current invasion front (Elith et al., 2010). 11

Species-level trait data can also provide insights into migrant species’ distribution patterns. It is expected that for these migrant species to colonise suitable habitats, they need traits suited to dispersal, establishment, growth and competition to promote colonisation success (Rossetto et al., 2015b; Kunstler et al., 2016). These include leaf area, plant height, wood density and seed mass, traits that have been identified as most informative in determining whether species are pioneers (Westoby et al., 2002; Wright et al., 2004; Falster et al., 2011). Comparative trait analyses between resident and migrant species can detect if there is convergence of traits towards larger leaves (reflecting faster growth; Wright et al., 2004), lighter wood and shorter stature (reflecting faster growth; Moles et al., 2009), smaller seeds (enabling wider dispersal; Rossetto et al., 2015b), as all are representative of the pioneer functional mode.

Additionally, migrant species distributions can potentially be explained by their habitat preferences, through tree inventory data from forest plots available across broad geographic regions (Slik et al., 2015). Within these plots, migrant and resident species’ proportion or richness per plot can be examined and related to landscape features. The relationship between species richness and environmental conditions can be used to address the factors contributing to the relative occurrence of migrant species. For example, high frequencies could be related to environmental conditions that are similar to the source and / or the accessibility to suitable habitat.

Molecular data can infer past distributions of migrant and non-migrant species. Particularly, the study of genome-wide variation or single nucleotide polymorphisms (SNPs) provides higher resolution of fine-scale differences between individuals and populations than those using traditional markers of fewer than a hundred loci (Bragg et al., 2015). Genetic variation across a species’ range can reflect differences in population histories (Hewitt, 2004), and this is particularly useful for studying recent expansion history and determining the directionality of expansion. For instance, if genetic homogeneity remains, it suggests that expansion was achieved through an explosive wave from a small initial source (Petit, 1997; Hewitt, 2000).

The decreasing costs associated with next generation sequencing (NGS) allow for more genomic resources to be generated especially in non-model organisms (Rossetto & Henry, 2014). Landscape genomic studies of species without a reference genome can be conducted using genotype-by- sequencing (GBS; Elshire et al., 2011) methods. GBS typically targets a fraction of the genome to be sequenced by NGS rather than the entire genome, and still provides high density, genome-wide markers. Sampling across a species’ range can provide estimates of population-level genetic diversity and structure and detect signatures of expansion in migrant species (see review by Cristescu, 2015) or local persistence in non-migrant species (see review by Bragg et al., 2015). Alternatively,

chloroplast genome data can be acquired through NGS platforms to identify variation within species. Genome skimming (low coverage whole genome sequencing) is a NGS technique that generates long conserved DNA sequences such as the chloroplast genome (120,000-150,000 base pairs or bp; Coissac et al., 2016). Assembly of the chloroplast genome only requires as little as 1 Gigabyte (GB) of genomic sequences, and the resulting DNA markers can capture fine-scale population genetic information (Li et al., 2015).

The reconstruction of species histories can be supplemented by environmental niche modelling (ENM) data to improve predictions of species distributional patterns (Knowles, 2009). ENM data can predict suitability of habitats across the landscape based on current species occurrences, and can estimate past, current and future distribution of suitable habitat (Scoble & Lowe, 2010). In terms of tracking floristic exchanges, it can be useful to determine whether species colonisation patterns inferred from genetic data correspond with shifts in suitable habitat driven by climate.

The applicability of these datasets is demonstrated in this thesis investigating the invasion of Sunda species into the Australian rainforest flora.

1.7 Thesis outline

This chapter (Chapter 1) provides a general introduction to the Sunda-Sahul floristic exchange and its influence on the history and current distributions of Australian rainforests, followed by a review of the drivers of the exchange through species distribution, functional and genetic evidence, and a brief overview of the general biogeographical patterns associated with the exchange. Project objectives, along with a general description of the tools available to explore the floristic exchange are included at the end of the chapter.

In Chapter 2 continent-wide distribution patterns for Sunda and Sahul species were explored, and their differences in functional characteristics were determined with particular focus on the patterns identifying Sunda lineages as migrants. To further investigate the recent biogeographic history of these lineages, the influence of temperature and elevational gradients on the local distributions of Sunda-derived species were also evaluated.

Chapter 3 investigates continental-wide genomic diversities and distances, and local patterns of species accumulation to observe whether the influence of biogeographical processes is consistent throughout the Tropics and Subtropics. A main goal of this study was to understand the colonisation

process of Sunda lineages into and across Australia. The impact of ecological processes and landscape variation were included in this study.

Chapter 4 examines whether landscape-level genetic signatures of Doryphora sassafras, of Sahul ancestry, and Toona ciliata, of Sunda ancestry, conform to expectations of how species with different biogeographic origins respond to past climatic changes. The resulting population expansion / contraction patterns were validated by predicted changes in the availability of suitable habitat from the Last Glacial Maximum to the current period through Environmental Niche Modelling. Future (2070) habitat models were used in making predictions of the expected impact of anthropogenic climate change.

Chapter 5 recapitulates and discusses the main findings of the research, and the applicability of the approaches and results of this study.

Preface to Chapter 2

The following chapter was published in the Journal of Biogeography as Yap, J. S., Rossetto, M., Costion, C., Crayn, D., Kooyman, R. M., Richardson, J., & Henry, R. J. (2018). Filters of floristic exchange: How traits and climate shape the rain forest invasion of Sahul from Sunda. Journal of Biogeography, 45, 838-847.

My contribution to this study was as follows, designing the experiments (75%), data collection (20%), data analysis and interpretation (75%), writing (90%) and editing the manuscript (40%).

The list of species of Sahul and Sunda ancestry was collected by the co-authors of this study, Dr. Robert Kooyman, Dr. Craig Costion, Dr. Darren Crayn and Dr. James Richardson.

Chapter 2 Filters of floristic exchange: how traits and climate shape the rainforest invasion of Sahul from Sunda

2.1 Abstract

The Australian rainforest flora is of mixed biogeographic origins. Here we evaluate how biogeographic and ecological processes influenced species distributions and community assembly of the continental rainforest flora. We identified 795 species with Sahul ancestry (Australian rainforest flora of Gondwanan origin) and 604 species with Sunda ancestry (rainforest plant lineages of Indo- Malesian origin) from a total of 1872 free-standing Australian woody rainforest taxa. We then compared the distribution of Sunda to Sahul species in relation to variation in species richness and phylogenetic endemism at continental scale, and local species distributions in available plot data from the Tropics (Cape York and the Australian Wet Tropics in northern Queensland) and Subtropics (Nightcap-Border Ranges, Washpool and Dorrigo in northern New South Wales). We compared the dispersal and persistence characteristics, and key functional traits (leaf size, fruit size, wood density and maximum height at maturity) of the Sunda and Sahul components of the continental rainforest flora. The influence of climate (temperature) and local environmental (altitude) factors in driving fine-scale distributional patterns were evaluated. Sunda rainforest species richness decreased with increasing latitude but maintained high levels of endemism, including in the south. Sunda species traits suggest more efficient dispersal and faster growth than Sahul lineages. Resprouting (persistence) was less evident in species with Sunda than Sahul ancestry. We show that Sunda lineage distributions were influenced by interacting environmental and climatic factors, as well as historical contingencies. Efficient dispersal and relatively fast growth likely facilitated the establishment and spread of Sunda lineages in Australia. However, the Sunda invasion was resisted in stable, saturated communities of Sahul lineages, and in the temperate south where climate acted as a strong filter. The results highlight the importance of integrating historical biogeography and contemporary ecological processes to study continental-scale rainforest distribution and assembly.

2.2 Introduction

Combining insights from historical biogeography with those of contemporary ecological processes can refine our understanding and interpretation of how current and historical plant communities assemble (e.g. Wiens & Donoghue, 2004; Gallien et al., 2016). As Australia separated from Antarctica during the Eocene (c. 38 million years ago or Mya), the continent moved northward. The available habitat for rainforest vegetation contracted in response to significant shifts in climate caused by the formation of the Southern Ocean and the commencement of a circum-polar current. The Australian continental plate (Sahul) rafted northward and eventually collided with the Southeast Asian plate (Sunda; Hall, 2002; 2012). This collision caused major uplift and mountain building in Papua New Guinea starting in the Oligocene (c. 30 Mya), and set the scene for further orogeny and extensive volcanism along the junction with Southeast Asia starting in the Miocene (c. 23 Mya) (Hall, 2002). These events coincided with the onset of increasing rainfall and rainforest expansion in Southeast Asia (Sunda) and is characterised by a major period of biotic exchange (Hall, 2009; Sniderman & Jordan, 2011; Crayn et al., 2015). The resulting interactions between the two previously separate floras of these continental plates shaped the Australian tropical and sub-tropical rainforests we see today and resulted in the mixing of Sunda (Indo-Malesian) rainforest plant lineages (e.g. Crayn et al., 2015) with the ‘original’ Australian rainforest flora of Gondwanan origin (from here on referred to as Sahul lineages; e.g. Webb et al., 1984; Kooyman et al., 2014). The decline in area and extent of suitable habitat for rainforest continued as the Australian continent aridified further during the more extreme climatic fluctuations of the Quaternary (VanDerWal et al., 2009). While some lineages of Sunda ancestry arrived in Australia well before the Quaternary (Sniderman & Jordan, 2011; Crayn et al., 2015), many more continued to migrate and establish during the more recent climatic fluctuations and recurring cycles of rainforest expansion and contraction (Richardson et al., 2012; Costion et al., 2015).

Within this scenario it could be expected that current lineage distributions reflect the influence of the distinct biogeographic and evolutionary histories of the Sahul and Sunda floras, and the filtering of functionally similar species along environmental gradients into assemblages. The expectation then is for some convergence in functional and ecological characteristics between rainforest species of Sunda and Sahul ancestry, but retention of some detectable differentiation in general functional and ecological characteristics. Segregating characteristics could be expected in the capacity of Sunda lineages to invade, versus the capacity of Sahul lineages to persist within refugia. While preliminary phylogenetic analyses have explored distributional patterns explained by evolutionary and ecological processes in the Wet Tropics (Costion et al., 2015), our study includes continent-wide distributions of Australian rainforest species and incorporates additional functional and environmental 17

information. Here, we take advantage of continental and regional datasets to identify the functional and ecological characteristics that likely assisted the migration of species with Sunda ancestry into the Australian continent.

As Sunda immigrant lineages mostly originate from tropical climates, it is generally assumed that they will prefer warmer lowland rainforest habitats that correspond more closely to the conditions at source (Richardson et al., 2012). Functional traits shape the distribution of rainforest species by influencing dispersal, establishment (Crayn et al., 2015), growth and competition (Kunstler et al., 2016). In combination, these same factors shape community assembly (Kooyman et al., 2011; 2012; 2013). In light of those factors, and in relation to traits that enhance migration, we expected Sunda species to have larger leaves (reflecting tropical origins, Wright et al., 2017), lighter wood and shorter stature (reflecting faster growth; Moles et al., 2009), and smaller seeds particularly in fleshy, palatable fruits (enabling wider dispersal) (Rossetto et al., 2008; 2009; 2015b). Here, we investigate relative dispersal potential, as well as differences in growth characteristics such as wood density, maximum height and leaf area that are associated with growth rates and relative competitiveness in different environments (Kunstler et al., 2016).

We expect that cooler temperatures and frost acted as limiting factors for the expansion of Sunda species along both the elevational and latitudinal gradients of eastern Australia. An exception might be for cool, high altitude Sunda immigrants such as those in Rhododendron (Section Schistanthe, Ericaceae; Goetsch et al., 2011). To test the relationship to temperature, we compared fine-scale distributional patterns of Sunda and Sahul lineages using plot data from the Australian Tropics and Subtropics (Kooyman et al., 2012), and investigated climatic and environmental factors that may play a role in determining current species distributions.

Using continent-wide species distribution data, representative plot data, extensive trait databases, and available phylogenetic information we addressed the following questions:

1) How have biogeographic processes influenced the current distribution of rainforest species relative to their functional characteristics and environmental preferences? 2) Are there functional characteristics that identify Sunda (Indo-Malesian) rainforest lineages as migrants? If so, on average, do these characteristics differ between the Sunda and Sahul components of the flora? 3) Has the distribution of Sunda rainforest lineages in the Tropics and Subtropics been influenced by temperature and / or elevational gradients?

2.3 Materials and Methods

To address our questions, we compared Australian rainforest woody species recognized as of Sunda (Indo-Malesian) ancestry to those recognised as of Sahul ancestry (Australian continental plate derived) using continent-wide species distribution data, plot-level data and species-level functional trait data. For our comparative analyses, we allocated 795 species to Sahul ancestry and 604 species to Sunda ancestry from a starting total of 1872 free-standing (vines excluded) Australian woody rainforest taxa (Table S1.1). The determinations were derived from various sources, including floristic origins data from Richardson et al. (2012), phylogenetic allocations from published studies (e.g. Sniderman & Jordan, 2011; Richardson et al., 2012; Crayn et al., 2015) and investigations of Australian and Gondwanan rainforest fossil floras (e.g. Kooyman et al., 2014). While we acknowledge that non-rainforest lineages form an important component of the Sunda-Sahul floristic exchange, the focus here is on rainforest lineages. This is in part because of data availability, but also reflects our ongoing research into Gondwanan rainforests.

We further conducted an exclusion trial where any species with ambiguous origin were completely removed by excluding: species from lineages (genera) associated with an ancestry in which some taxa (from the genus) might have returned to Sahul; species with some doubt about ancestry; and species from genera for which we have little information (Table S1.1). The exclusion trial resulted in the retention of 453 Sunda species and 410 Sahul species for which we had high confidence about ancestry based on available published data. With this species list, we reanalysed the datasets for continent-wide and plot-level species distributions, and species-level functional traits, and showed that species with ambiguous origins did not significantly influence the outcomes of this study when compared to that of the full dataset (see Appendix 2).

2.3.1 Comparing continental species distributions and phylogenetic endemism

In our comparative analysis of continent-wide species distributions, we investigated patterns of taxonomic (species) richness and phylogenetic endemism (PE) for Sunda and Sahul species. We used the dataset from Kooyman et al. (2013), composed of 50 x 50 km grid cells that was generated from species distribution records aggregated into presences in 10 x 10 km grid cells. To compare between the Sunda and Sahul components, we produced two subsets consisting of: 1) species allocated to Sunda ancestry (N = 604); 2) species allocated to Sahul ancestry (N = 795).

We derived species richness from raw data consisting of species presence within the grid cells. The diversity measures were generated using BIODIVERSE 1.1 (http://purl.org/biodiverse; Laffan et al., 2010) to obtain the proportional measures of species richness and PE (Rosauer et al., 2009) for each of the 50 x 50 km grid cells. We used taxonomic (species) diversity in Australian rainforests to show latitudinal changes in species richness.

To include branch lengths in our analysis of phylogenetic endemism, a rainforest superphylogeny generated by Kooyman et al. (2013) was imported into BIODIVERSE 1.1. We used PE to detect concentrations of endemism that included range-restricted migrants, and to provide a signature of how restricted the species within each cell were (i.e. it measures range-restricted Phylogenetic Diversity; Rosauer et al., 2009). PE is particularly effective for this comparative study as Sunda and Sahul species are mostly from unrelated lineages. The exception is for some widespread genera such as Cryptocarya, Endiandra and Elaeocarpus that have lineages both in and outside Australia and which may have left and re-entered Australia more than once. Overall, the small number of these lineages that are shared between Sunda and Sahul did not noticeably influence PE values (i.e. they were never the main cause of high PE; refer to Rosauer et al., 2009).

2.3.2 Functional trait dataset

For a comparative analysis of functional traits, we investigated if there were any characteristics common to the Australian woody rainforest species with Sunda ancestry, and any differences between species with Sunda versus Sahul ancestry. We used continuous trait-measures for leaf size (surface area), fruit size, wood density and maximum height at maturity, as well as categorical data from dispersal (mode) and persistence (resprouting) traits, all previously used in Australian rainforest studies (Rossetto & Kooyman, 2005; Rossetto et al., 2009; 2015a, b; Kooyman et al., 2011; 2012; 2013). Both the continuous and categorical traits were taken from published floras and other sources for all species (Appendix 3). The continuous measures represent a single value by trait for each species and data were log10 transformed to reduce skew with the original units of measurement being leaf area (in cm2), wood density (in kg/m3), maximum height (in metres or “m”), fruit size (fruit width, in millimetres or “mm”). Statistical differences between Sunda and Sahul species were based on an analysis of variance model (ANOVA) and Tukey HSD post-hoc test in R using the functions, aov() and TukeyHSD().

Of the categorical data (Table 2.2), dispersal mode was first partitioned into fleshy-fruited (animal dispersed) versus wind-dispersed species. Fleshy fruits were then further partitioned into size classes 20

(less than or equal to 30 mm, and greater than 30 mm in width) consistent with Rossetto et al. (2015b). Resprouting was the trait selected to describe a species’ ability to self-replace (representing in situ persistence of individuals) and survive disturbance (Rossetto & Kooyman, 2005). Differences between Sunda and Sahul species based on categorical traits were statistically tested using a two- sample test for equality of proportions in R with the function, prop.test().

2.3.3 Regional plot-based dataset

For a local species analysis, we tested whether the distribution of Sunda rainforest lineages in the Tropics and Subtropics has been influenced by temperature and / or elevational gradients. This was achieved by using available plot data from five representative regions (Kooyman et al., 2011; 2012): Cape York, Wet Tropics (from tropical Queensland, Australia), Nightcap-Border Ranges, Dorrigo and Washpool (from subtropical northern NSW, Australia; Fig. S4.1). The number of Sunda- and Sahul- derived species per plot was calculated to characterise the proportion of Sunda species in these regional representations of Australian rainforests. Proportional variation was also used for a preliminary test on the effect of local environmental conditions on Sunda vs. Sahul lineages. The proportions in each plot (referred to as proportion per plot) were calculated as follows, the proportion of the Sunda component for a plot site being the number of Sunda species over the total number of species (i.e. both Sunda and Sahul) present in the plot, and the relative proportion of the Sunda component for a region being the average of all Sunda proportions from plots in the region.

As a preliminary test of the effect of local conditions on lineages of Sunda and Sahul ancestry, we assigned environmental and climatic variables to each regional plot. The main climatic variable presented is bio06 (minimum temperature for the coldest month; http://www.worldclim.org/bioclim) to represent cool climatic conditions that could restrict the elevational (and / or latitudinal) expansion of Sunda lineages. We then fitted a least squares regression to each scatterplot with the y-axis as the relative proportion per plot, and the x-axis as either elevation or minimum temperature for the coldest month. We used a least square regression line method for line fitting in each scatterplot to determine whether a relationship exists between the axes (i.e. significant P-value, high R2) based on the function, lm() in R. We also implemented AICc (the second order Akaike information criterion corrected for small or finite samples) to observe whether the environmental and climatic variables are important explanatory variables that influence the proportion of Sunda species per plot (See Appendix 4 for details about the modelling).

2.4 Results

The resolved dataset included 33 Sunda (Southeast Asian plate derived) and 21 Sahul (Australian continental plate derived) families, but more Sahul (795) than Sunda (604) species (see Table S1.1). Sixty-four of 168 Sahul genera were monotypic and 71 of 168 Sunda genera were represented by a single species in Australia. Only eight families were shared between Sunda and Sahul.

2.4.1 Do latitudinal measures of species richness and phylogenetic endemism differ between Sunda and Sahul species?

The analysis of continental species-distribution showed that species richness differs latitudinally for Sunda and Sahul species (Fig. 2.1-2.2). The proportion of Sunda species was higher in northern parts of Australia (i.e. Western Australia, Northern Territory, Cape York, Far North and Central Queensland), but lower along most of South-eastern Australia including New South Wales and Victoria, with no Sunda species recorded for Tasmania (Fig. 2.1). Richness of Sunda species was concentrated in Cape York and the Wet Tropics, diminishing towards the west into Western Australia and Northern Territory, and along the southern coast in New South Wales and Victoria (Fig. 2.2). In comparison, Sahul species richness was highest in the Wet Tropics but remained high in northern New South Wales (NNSW; Fig. 2.2a).

Figure 2.1 Proportion of Sunda and Sahul rainforest species by region across the Australian continent. The regions labelled follows Kooyman et al. (2013) study. (“WA”= Western Australia, “NT”= Northern Territory, “CY”= Cape York, “FNQ”= Far North Queensland, “CQ”= Central Queensland, “BR”= Border Ranges, “SNSW”= Southern New South Wales, “Vic”= Victoria and “Tas”= Tasmania). Plots obtained from CY, FNQ (i.e. the Australian Wet Tropics), and BR (i.e. Nightcap-Border Ranges, Washpool and Dorrigo; refer to Fig. S4.1 for these locations) were used in this study.

Figure 2.2 Spatial and phylogenetic patterns across the Australian continent for Sunda and Sahul rainforest species. (a) Species richness, based on the number of species in each grid cell, (b) Phylogenetic endemism (“PE”), represents millions of years (“My”) of evolutionary history partitioned across species range. For instance, PE of 8 represent branches summing to 8 My now restricted to a single 5 km cell, hence 40 My across five cells with PE of 8. Grid cell (50 x 50 km) view on continental species pools of Sunda and Sahul ancestry. Lighter colours (orange to light yellow) = lower values; darker colours = greater concentrations (black to red).

Regional plots confirmed the latitudinal decline in Sunda species (proportion and richness) from tropical Queensland to subtropical NNSW (Fig. 2.3; Fig. S4.2; Table 2.1). Generally, regional (plot- based) proportions of Sahul species were higher in the mountainous Australian Wet Tropics (AWT) and NNSW regions, Nightcap-Border Ranges, Washpool and Dorrigo. Cape York (as the only representative of the monsoon tropics in the plot-based data) differed significantly from the other plot-based samples (P-value < 0.001, based on Tukey HSD post hoc test; Fig. 2.3) in having less relief (few tall mountains) and more Sunda than Sahul species.

Figure 2.3 Sunda and Sahul rainforest plot data in Australia. Proportions of Sunda (black boxplots) and Sahul (grey boxplots) species in plot samples for each regional area (“CY” = Cape York, “AWT” = Australian Wet Tropics, “Night” = Nightcap-Border Ranges, “Dorri” = Dorrigo, “Wash” = Washpool). Significance in the comparisons was identified using ANOVA and Tukey HSD post-hoc tests, and the resulting significant comparisons (all had P-value < 0.001) were labelled with “***”.

Table 2.1 Tropical and subtropical Australian rainforest plot data summary. Table shows the region where the plots are located, the number of plots studied (“N plots”), the number of Sunda (“N Sunda sp.”), and Sahul (“N Sahul sp.”) woody rainforest species observed and the total number of species (“N sp.”) in each regional area (refer to Fig. S4.1 for these locations).

Regional area N plots N Sunda sp. N Sahul sp. N sp. CY 140 211 168 526 AWT 146 222 297 662 Nightcap 140 79 158 321 Washpool 43 29 64 134 Dorrigo 127 58 116 236

Although Phylogenetic Endemism (PE) patterns were somewhat similar for both the Sunda and Sahul rainforest components across Australia, there were some regional differences (Fig. 2.2b). PE values for Sunda species were highest in the north of the continent including Northern Territory, Cape York and the Wet Tropics, remained relatively high in NNSW, and then diminished further south. PE values for Sahul species were highest in the Wet Tropics and the Nightcap-Border Ranges region of NNSW, with both Cape York and Central Queensland retaining high values, and noticeably less decline (than for Sunda species) further south.

Within the subtropical plots (Nightcap-Border Ranges, Dorrigo and Washpool; Fig. 2.3), the proportion and richness of Sunda species was substantially lower than within tropical plots. A significant per-plot reduction of Sunda species proportion and richness (P-value < 0.001 using Tukey HSD post-hoc test in Fig. 2.3; Table 2.1) was also observed between Nightcap-Border Ranges (north) and Dorrigo (to the south). These two areas are separated by the Clarence River Corridor, a previously recognised biogeographic barrier (e.g. Rossetto et al., 2015a).

2.4.2 Do the Sunda ‘invaders’ display different or distinct functional characteristics?

Although trait values differed between regions and in relation to ancestry, shifts in trait values across the broader rainforest biome were not large. Across the continental dataset, the number and percentage of taxa with fleshy fruits was higher among species of Sunda ancestry (Table 2.2). In comparison, the percentage of wind-dispersed lineages was significantly higher in the Sahul (21.91%)

than the Sunda (7.65%) species pool. Sunda species with fleshy fruits had significantly smaller fruits compared to those of Sahul ancestry (Fig. 2.4; Table 2.2). The percentage of resprouting species with Sahul ancestry was significantly higher (52.83%) than for Sunda (30.79%; Table 2.2).

Table 2.2 Qualitative traits data summary for Australian woody rainforest species of Sunda and Sahul ancestry. The qualitative traits studied are listed in the first column with the number of species of each ancestry listed in the subsequent columns under “N Sunda sp.” and “N Sahul sp.”. For dispersal, species that are fleshy fruited are further categorised into either those with fruits that are less than or equal to 30 mm (“≤ 30mm Fleshy”), or greater than 30 mm in width (“>30mm Fleshy”). Each trait is tested for statistical significance to observe whether the proportion of each trait for Sunda and Sahul is significantly different from each other based on a two-sample test for equality of proportions.

Qualitative N Sunda sp. N Sahul sp. X2 P-value Dispersal traits Fleshy fruited 543 (92.35%) 606 (78.09%) 43.729 < 0.001 ≤ 30mm Fleshy 445 (81.95%) 458 (75.58%) 6.916 < 0.01 > 30mm Fleshy 98 (18.05%) 148 (24.42%) 6.916 < 0.01 Non-fleshy 45 (7.65%) 170 (21.91%) 51.232 < 0.001 Persistence trait Resprouting 186 (30.79%) 420 (52.83%) 67.877 < 0.001 Non-sprouting 418 (69.21%) 375 (47.17%) 66.982 < 0.001

Figure 2.4 Functional Trait comparison for Australian woody rainforest species of Sunda and Sahul ancestry: The traits studied are fruit size (“FruitSize***”), height (“Height***”), leaf area (“LeafArea**”) and wood density (“WoodDensity***”) for species of Sunda (black boxplots) and Sahul (white boxplots) ancestry, as well as for all Australian free-standing rainforest species (grey boxplots). ANOVA test for significance between Sunda and Sahul was performed for each trait (x- axis), asterisk next to each trait shows significance when compared between Sunda and Sahul components using ANOVA and Tukey HSD post hoc test. High significance or P-value < 0.001 is indicated by “***”, and P-value < 0.01 is indicated by “**”.

Fig. 2.4 shows that on average Sunda-derived species have shorter stature, lower wood density and bigger leaves compared to Sahul-derived species (wood density measures were only available for a subsample of 205 Sunda- and 287 Sahul-derived species). Differences in trait values between Sunda and Sahul were statistically significant (P-value < 0.001), however, significance for leaf area variation was lower (P-value < 0.01) and this trait included the largest number of outliers (i.e. species with very large or very small leaves; Table S3.1). For all traits, there was considerable overlap in values between the Sunda and Sahul groups (Fig. 2.4; Table S3.1). A correlation analysis of the traits did not show any significant correlation in pairs of traits for both Sunda and Sahul groups (Fig. S3.3).

2.4.3 Do latitude, elevation and temperature act as selective filters to expansion?

Among the tropical and subtropical regional plots, Cape York had the narrowest elevational range (1-500 m) and the narrowest range of minimum temperature for the coldest month (15-21°C). The NNSW plots had a steeper decline in minimum temperatures with increasing elevation compared to the Australian Wet Tropics plots (AWT; Fig. S4.3).

For each of the environmental and climatic factors considered, only AWT showed a significant decline in the proportion of Sunda species at higher elevation and at lower minimum temperature for the coldest month (Fig. 2.5; see Appendix 4 for statistical outputs related to plot and climate data). The other regions did not show similar effects of elevation and minimum temperature (bio06) on the relative proportions of species.

Figure 2.5 Sunda (black dots) and Sahul (white dots) plot data for Australian rainforests in relation to: (a) Elevation vs. Sunda / Sahul plot proportions. (b) Minimum temperature for the coldest month (Bio06) vs. Sunda / Sahul plot proportions.

Plots are grouped by the following regions, Cape York (abbreviated as “CY”), the Australian Wet Tropics (abbreviated as “AWT”), Nightcap-Border Ranges, Washpool and Dorrigo (Correlation between Sunda / Sahul proportion and climatic / environmental factor is shown using Least Square Regression lines (black line for the Sunda component and grey line for the Sahul component). Only

AWT showed a significant decline in the proportion of Sunda species at higher elevation and at lower minimum temperature for the coldest month

2.5 Discussion

2.5.1 When two worlds collide: a continuing flow of Sunda migrants into Australian rainforests.

Our study shows that Sunda (Indo-Malesian) and Sahul (Australian rainforest flora of Gondwanan origin) species have different continental-scale distributions in Australia (Fig. 2.1-2.2). Sunda lineages decline in diversity with increasing latitude, with the highest species diversity in Northern Australia, particularly in far northern Queensland (i.e. Cape York and the Wet Tropics). These immigrant lineages are also in higher proportions within the monsoon tropics of Cape York as well as westwards into Northern Territory and Western Australia. In contrast, the proportion of Sunda species is lower in subtropical NNSW, with a smaller number of Sunda-derived species extending further south (Table S6.1).

Phylogenetic endemism patterns provide additional insight into the distribution of Sunda lineages. The high endemism and diversity observed in the monsoonal tropics (i.e. Cape York, Northern Territory, Western Australia) indicates that this area had suitable environmental and climatic conditions, promoting dispersal and expansion from Southeast Asia into Australia. In contrast, high levels of Sunda-derived endemism but not diversity were maintained further south. These southern Sunda-derived endemics are likely to represent lineages that have migrated to Australia during the earlier phases of the floristic exchange (Appendix 6) and either lost northern populations to localised extinction events or diversified through time. Interestingly, many of these endemics are part of species rich clades within Australia such as Acronychia and Litsea. A better understanding of the history of each of these lineages in Australia will have to await in-depth phylogenetic treatment.

No Sunda species was present in Tasmania. Given the intermittent land-bridge connections to the mainland that would have provided opportunities to colonize (Worth et al., 2017), their absence suggests that Sunda species expansion could be limited by the climatic conditions at higher latitudes. This is consistent with the fossil data examined by Sniderman & Jordan (2011) showing that Asian lineages are rare among microthermal floras exposed to extreme temperatures (mean annual temperatures < 12 °C). The absence of these northern immigrants of tropical ancestry suggests it may be beyond their physiological tolerance to colonise the cooler regions of Australia.

2.5.2 Is floristic exchange favoured by specific functional characteristics?

We confirmed that despite broad-scale functional commonalities with Sahul lineages, on average, Sunda species display characteristics that suggest more effective dispersal and more rapid growth (Fig. 2.4). The Sunda element of the flora has significantly more animal-dispersed (zoochorous) lineages than the Sahul element, with the majority (81.95%) of Sunda species having small fruits that allow for easier and less specialised dispersal across water barriers (Crayn et al., 2015; Rossetto et al., 2015b) and the vegetation mosaics (e.g. dryland to wetland; sclerophyll to rainforest) characteristic of Australian landscapes (Wilson et al., 1989).

Resprouting is an important mechanism that enables rainforest (and other) species to persist in a location and resist disturbance (Rossetto & Kooyman, 2005). The significantly higher proportion of resprouting lineages with Sahul ancestry highlights the importance of this trait. In particular, resprouting potential coupled with a longer life span can enable tree species to persist by surviving some level of disturbance and by not having to compete via seed-based regeneration for space. As a result, the capacity to persist locally represents an important advantage against expanding species competing for similar habitats, especially since many taxa of Sahul ancestry have small populations, can be highly localised due to the lack of dispersal vectors, and can be at high risk of extinction from exceptional events (Rossetto & Kooyman, 2005; Rossetto et al., 2009).

On average, the lower wood density, shorter stature, smaller fruits (and seeds) and larger leaves of Sunda species (Fig. 2.4) reflect life history attributes that favour faster growth and fecundity over longevity and persistence (Adler et al., 2013). Overall, fast growth is most often associated with higher colonisation potential and greater potential to invade (i.e. efficient dispersal and establishment ability; Kunstler et al., 2016). The Sahul component, characterized by species with taller stature, higher wood density, smaller leaf area, and the ability to resprout were more effective at surviving within continuously forested refugia areas.

2.5.3 The importance of local conditions: can elevation and cooler temperatures limit the expansion of northern invaders?

Low temperatures are known to determine the latitudinal and elevational limits of woody plants and have played a key role in plant evolution (Zanne et al., 2014). In addition, cool temperatures can inhibit growth and productivity, which in turn limit a species’ competitive ability (Woodward & Williams, 1987). From our plot analysis, the latitudinal decrease of Sunda species richness and

proportion could be explained, at least in part, by a lack of suitable warm habitat. However, we observed the proportional decrease of Sunda lineages at higher (and cooler) elevations expected from a scenario of better adaptation to warmer habitats only in the Australian Wet Tropics (AWT; Fig. 2.5). Also, using only endemic Sunda (N = 71) and endemic Sahul (N = 141) species in the AWT plot analysis did not yield a different species distribution pattern (Fig. S6.1). This could be explained by fine scale ecological filtering of species able to establish in the Tropics (AWT) versus coarser scale filtering of species able to expand south. Subsequently, only the species not affected by cooler temperatures were able to establish further south. The latitudinal reduction in both Sunda species richness and proportion supports this scenario. Furthermore, recent population genetic studies found that unlike Sahul lineages, Sunda lineages generally display latitudinally decreasing diversity and structure. This suggests that at least some lineages have only recently expanded in a southerly direction (McPherson et al., 2013; Rossetto et al., 2015a).

We also suggest that historical contingencies may explain local distributions of Sunda lineages. In our study, we show that important refugial areas in AWT and NNSW (Nightcap-Border Ranges and Dorrigo) may have resisted Sunda invasion. We observed the expected lower proportions of Sunda lineages in NNSW including the Nightcap-Border Ranges and Dorrigo regions (compared to AWT), and note a similar pattern in Washpool, a primarily recolonized area (Kooyman et al., 2011; Rossetto et al., 2015a). This suggests that while Sahul lineages were able to recolonise Washpool from neighbouring refugia (within Nightcap-Border Ranges and Dorrigo), the number of Sunda lineages able to colonise from source populations (assumed to be further north) was limited.

2.6 Conclusion

Our analysis has provided an exploration of Australia's tropical and sub-tropical rainforest diversity based on biogeographic ancestry. It highlighted the interactive processes that define the relative proportion and the distribution of autochthonous and invading lineages in a continent-scale floristic exchange. Biogeographic history, functional attributes and local habitats interact to determine species’ distribution and assembly at local as well as continental scales.

We have confirmed the substantial contribution of Sunda lineages to Australian rainforests, and further support the concept that Australian rainforests are not just ‘ancient stable assemblages’ in refugia but are part of dynamic, landscape scale regional biogeographic processes that include current dynamics and assembly processes (Kooyman et al., 2013). We anticipate that our study will lead to:

(i) reinterpretation of observed continental-scale ecological patterns that are linked to species composition, including variation in functional traits, genetic diversity, forest biomass, community dynamics, phenology and structure (based on ancestry groups);

(ii) the development of new testable hypotheses in biogeographic studies;

(iii) the definition and delimitation of new interpretations for setting conservation priorities for tropical and subtropical forests.

Preface to Chapter 3

The following chapter has been submitted to Biotropica as Yap, J. S., van der Merwe, M., Ford, A., Henry R. J., Rossetto, M. Biotic exchange leaves detectable patterns in the Australian rainforest flora under the manuscript ID: BITR-19-082.

My contribution to this study was as follows: designing the experiments (65%), field work (50%), lab work (100%), data analysis (80%), writing (90%) and editing the manuscript (75%).

Next Generation Sequencing libraries were sequenced using the Illumina NextSeq 500 platform by Ramaciotti Centre of Genomics (Sydney, Australia).

Chapter 3 Biotic exchange leaves detectable patterns in the Australian rainforest flora

3.1 Abstract

Invasion of plant lineages from Sunda (the Malay Archipelago) in Sahul (mainland Australia) resulted in present-day Australian rainforest flora consisting of a combination of Sunda- and Sahul- derived species. Integration of the floras increased during the Quaternary when rainforest vegetation was subjected to recurrent expansion/contraction cycles. Recent expansion history has been investigated through landscape-level genetic analyses within the Subtropics, but not in tropical Northern Australia, although this is presumably the point of contact for Sunda lineages. Here, we investigate multiple co-distributed species of Sunda and Sahul ancestry in the Australian Tropics and Subtropics to characterise the arrival and dispersal of Sunda-derived species across the continent. We use whole-chloroplast genomic datasets to obtain measures of diversity and to estimate community dynamics through time across multiple rainforest sites. Sunda-derived species show consistently low genomic diversities and recent site-specific species accumulation rates across both regions, confirming their recent arrival and expansion across Australia. Comparison between multispecies genomic dataset across multiple locations differentiated stable refugia from areas that were recently recolonised. A subset of Sunda-derived species with continental distributions consistently exhibited highest diversity at the most northerly site sampled, suggesting a north to south colonisation process. The same species however, differed in the levels of genomic differentiation between the Tropics and Subtropics, suggesting that continental expansion occurred at different temporal scales, with some lineages experiencing a lag time before expanding to south along the east coast of Australia.

3.2 Introduction

Biological communities are made up of species with different expansion dynamics and migration histories. Species composition in a community changes through time and space due to a combination of biotic and abiotic factors, as well as species-specific biogeographic histories (Webb et al., 2002, Kraft & Ackerly, 2014, Yap et al., 2018). Disentangling the histories of species currently assembled within a community enable us to understand the process involved in community turnover and help us determine the ability of species to respond to change (Carvalho et al., 2011, Rosauer et al., 2015). With increasing threats posed by fragmentation of natural landscapes and climate change, this information is critical for developing long-term biodiversity management strategies (Crandall et al., 2000, Diniz-Filho et al., 2012).

Broadleaf vegetation dominated Sahul (mainland Australia) during the mid-late Eocene (c. 49-35 million years ago or My) when the continental plate was still attached to the southern supercontinent Gondwana (Morley, 2000; Crisp & Cook, 2013). Following the breakup of Sahul from Gondwana, circumpolar currents resulted in the drying and cooling of global climates and this climatic shift significantly impacted on Australia’s vegetation, with a progressive loss of habitat suitable to rainforest vegetation and, consequently, of rainforest lineages (Greenwood & Christophel, 2005; Martin, 2006). However, from 12 Mya onwards, Sunda rainforest lineages began to migrate into Australia as the Sahul continental plate rafted northward and approached the Sunda continental plate (Malay Archipelago) (Sniderman & Jordan, 2011; Crayn et al., 2015). The ensuing collision between Sunda and Sahul facilitated an elevated rate of biotic exchange resulting in present-day Australian rainforest assemblages containing lineages with contrasting expansion histories (Yap et al., 2018).

The invasion of Australia by Sunda lineages is likely to have accelerated during the Quaternary, when the rainforests were subjected to recurrent contraction / expansion cycles in response to climatic fluctuations (Sniderman & Jordan, 2011; Crayn et al., 2015). The ensuing habitat instability was suggested to have increased the availability of new habitat during the interglacial periods and favoured the invasion by Sunda immigrants (Sniderman & Jordan, 2011; Richardson et al., 2012; Crayn et al., 2015; Costion et al., 2015; Yap et al., 2018). The concept of this opportunistic invasion of Northern Australia, is supported by the higher concentrations of Sunda lineages in the Australian Wet Tropics (AWT) lowlands, which are more impacted by recurrent climatic fluctuations (Richardson et al., 2012; Costion et al., 2015; Yap et al., 2018). The current distribution of Sunda species at higher latitudes suggests that the lack of tolerance for cooler conditions was an important selective filter preventing further southern expansion (Yap et al., 2018).

Genetic data in general has been regularly used to investigate range expansions (Nei et al., 1975, Ibrahim et al., 1996, also see Hewitt, 2004). The expansion history of the Sunda floristic element has been investigated in the Subtropics through a chloroplast DNA-based study across multiple co- distributed rainforest species (Rossetto et al., 2015a). The study showed contrasting local histories for lineages of Sunda vs. Sahul origins, suggesting Sunda-derived species recently expanded to the Subtropics and confirmed that expansion followed a north to south pattern (Rossetto et al., 2015a). Our understanding of expansion processes at a continental scale of rainforest plants is currently constrained by the paucity of suitable data. A previous study on the Sunda-derived Toona ciliata M.Roem. (red cedar) shows very little genetic variation across its distributional range, suggesting either recent colonisation or rapid continental expansion following an initial northern bottleneck phase (McPherson et al., 2013). As the distribution and richness of Sunda species varies across Australia, a range of contrasting expansion scenarios can be expected along the latitudinal distribution gradient (Yap et al., 2018). Although almost half of the lineages found in the AWT (a main centre of Australian rainforest diversity; Stork et al., 2009, Kooyman et al., 2013) are of Sunda origins (Yap et al., 2018), it is unknown if the local histories of the Sunda and Sahul derived lineages show contrasting patterns.

The present study is the first to rely on chloroplast genome data from multiple species to investigate how ancestry influences regional and continental dynamics. We combine community dynamics, plot- level species data and continent-wide genomic datasets, to address the following questions:

1. Do species with Sunda ancestry have a more recent history in Australia than Sahul derived taxa, and what are the factors impacting on their expansion dynamics?

2. Do Sunda-derived species show consistent continental colonisation patterns?

3.3 Materials and Methods

3.3.1 Study Areas and taxon sampling

To explore the history of species with different ancestry across the Australian landscape (Table 3.1), we sampled ten common species of Sunda ancestry and ten common species of Sahul ancestry from each of two sites in the Tropics, and integrated and re-analysed data for 13 species of Sunda ancestry and 26 species of Sahul ancestry from each of two sites in the Subtropics (derived from Rossetto et

al., 2015a). Four species of Sunda ancestry and two of Sahul ancestry were present in both Tropics and Subtropics. Species ancestry was previously determined in Yap et al. (2018), derived from various sources, including floristic origins data from Richardson et al. (2012), phylogenetic allocations from published studies (e.g. Crayn et al., 2015, Richardson et al., 2012, Sniderman & Jordan, 2011) and investigations of Australian and Gondwanan rain forest fossil floras (e.g. Kooyman et al., 2014).

Table 3.1 List of the Australian rainforest species in this study, with relevant information about family, ancestry (Sunda or Sahul) and the region in which species were sampled included.

Species Family Ancestry Tropicsa Subtropicsb Alangium villosum (Blume) Cornaceae Sunda ✓ Wangerin Archirhodomyrtus beckleri Myrtaceae Sahul (F.Muell.) A.J.Scott ✓ Argyrodendron trifoliolatum Malvaceae Sunda F.Muell. ✓ ✓ Atractocarpus benthamianus Rubiaceae Sunda ✓ (F.Muell.) Puttock Austrobuxus swainii (Beuzev. & Picrodendraceae Sahul ✓ C.T.White) Airy Shaw Beilschmiedia bancroftii Lauraceae Sahul (F.M.Bailey) C.T.White ✓ Brachychiton acerifolius (A.Cunn. Malvaceae Sahul ✓ ex G.Don) F.Muell. Breynia cernua (Poir.) Müll.Arg. Phyllanthaceae Sunda ✓ Callicoma serratifolia Andrews Cunoniaceae Sahul ✓ Cardwellia sublimis F.Muell. Proteaceae Sahul ✓ Ceratopetalum apetalum D.Don Cunoniaceae Sahul ✓ Cinnamomum laubatii F.Muell. Lauraceae Sunda ✓ Cinnamomum oliveri F.M.Bailey Lauraceae Sunda ✓ Claoxylon australe Baill. Euphorbiaceae Sunda ✓ Cryptocarya meissneriana Lauraceae Sahul ✓ B.Hyland Cryptocarya obovata R.Br. Lauraceae Sahul ✓ Cryptocarya rigida Meisn. Lauraceae Sahul ✓ Darlingia darlingiana (F.Muell.) Proteaceae Sahul L.A.S.Johnson ✓ Diploglottis australis (G.Don) Sapindaceae Sahul ✓ Radlk. Doryphora aromatica (F.M.Bailey) Atherospermata Sahul L.S.Sm ceae ✓ Atherospermata Doryphora sassafras Endl. Sahul ✓ ceae 39

Duboisia myoporoides R.Br. Solanaceae Sahul ✓ Ehretia acuminata R.Br. Boraginaceae Sunda ✓ Elaeocarpus reticulatus Sm. Elaeocarpaceae Sahul ✓ Elaeocarpus sericopetalus F.Muell. Elaeocarpaceae Sahul ✓ Endiandra bessaphila B.Hyland Lauraceae Sahul ✓ Endiandra crassiflora C.T.White & Lauraceae Sahul ✓ W.D.Francis Endiandra muelleri Meisn. Lauraceae Sahul ✓ Eupomatia laurina R.Br. Eupomatiaceae Sahul ✓ ✓ Ficus coronata Spin Moraceae Sunda ✓ Homalanthus populifolius Graham Euphorbiaceae Sunda ✓ Litsea leefeana (F.Muell.) Merr. Lauracae Sunda ✓ Neolitsea dealbata (R.Br.) Merr. Lauraceae Sunda ✓ ✓ Nothofagus moorei (F.Muell.) Nothofagaceae Sahul ✓ Krasser Pennantia cunninghamii Miers Pennantiaceae Sahul ✓ Pittosporum revolutum W.T.Aiton Pittosporaceae Sunda ✓ Planchonella australis (R.Br.) Sapotaceae Sahul ✓ Pierre Polyscias murrayi (F.Muell.) Araliaceae Sunda Harms ✓ ✓ Prunus turneriana (F.M.Bailey) Ka Rosaceae Sunda ✓ lkman Quintinia sieberi A.DC. Paracryphiaceae Sahul ✓ Rhodamnia blairiana F.Muell. Myrtaceae Sahul ✓ Rhodamnia rubescens (Benth.) Myrtaceae Sahul ✓ Miq. Sarcopteryx stipata (F. Muell.) Sapindaceae Sahul ✓ Radlk. Schizomeria ovata D.Don Cunoniaceae Sahul ✓ Scolopia braunii (Klotzsch) Salicaceae Sunda Sleumer ✓ Sloanea australis (Benth.) F.Muell. Elaeocarpaceae Sahul ✓ ✓ Stenocarpus salignus R.Br. Proteaceae Sahul ✓ Synoum glandulosum (Sm.) A.Juss. Meliaceae Sunda ✓ Tabernaemontana pandacaqui Apocynaceae Sunda ✓ Lam. Toona ciliata M.Roem. Meliaceae Sunda ✓ ✓ Tristaniopsis collina Peter Myrtaceae Sahul ✓ G.Wilson & J.T.Waterh. Trochocarpa laurina (Rudge) R.Br. Ericaceae Sahul ✓ Wilkiea huegeliana S.W.L.Jacobs Monimiaceae Sahul & J.Pickard ✓

a Sampled from Mt Lewis (16° 34' 46.4556" S, 145° 18' 58.572" E) and Mt Baldy-Tinaroo (Mt Baldy: 17° 16' 36.48" S, 145° 25' 43.1106" E; Tinaroo: 17° 10' 38.8626" S, 145° 39' 32.9112" E). b Sampled from Nightcap-Border Ranges (Nightcap: 28° 38' 25.3464" S, 153° 20' 4.47" E; Border Ranges: 28° 29' 21.825" S, 153° 9' 20.073" E) and Dorrigo (30° 20' 21.2598" S, 152° 48' 51.5628" E).

Within the Tropics, samples were collected from Mt Lewis and Mt Baldy-Tinaroo (MB-Tinaroo) located on either side of (BMC; Fig. 3.1) a well-recognised biogeographic barrier, the Black Mountain Corridor (e.g. Moritz et al., 2009; Rossetto et al., 2009; Mellick et al., 2014). In the Subtropics we obtained data from Nightcap-Border Ranges (Nightcap-BR) and Dorrigo National Park, located on either side of (CRC; Fig. 3.1), another well-recognised biogeographic barrier, the Clarence River Corridor (e.g. Milner et al., 2012; Heslewood et al., 2014; Rossetto et al., 2015). Sampling across the BMC and the CRC enabled us to investigate the potential impact of the biogeographic barriers on the dynamics of species with contrasting ancestry.

The sampling strategy for the Tropics replicated the method previously used in van der Merwe et al. (2014), Rossetto et al. (2015a) and Worth et al. (2017), which involves low-intensity sampling that captures broad and even representation of genetic diversity across common, representative species. Six individuals located a minimum of 10 m apart were collected from each species at each Tropics study site.

Figure 3.1 The general location of the four study sites, with two in each of the two rainforest regions (Tropics and Subtropics) along the east coast of Australia. Rainforest vegetated areas in both regions are shaded in grey, and the black bars indicate the location of previously recognised geographic barriers (the Black Mountain Corridor or “BMC”, e.g. Moritz et al., 2009; Rossetto et al., 2009; Mellick et al., 2014; the Clarence River Corridor or “CRC”, e.g. Milner et al., 2012; Heslewood et al., 2014; Rossetto et al., 2015). The scale bar in the Subtropics box also applies to the Tropics map.

3.3.2 DNA extraction and chloroplast genome sequencing

Fresh leaf tissue was stored at -80 °C, lyophilised and stored at -20 °C in the presence of silica prior to total genomic DNA extraction using a modified cetyl trimethylammonium bromide (CTAB) method (Doyle & Doyle, 1990; Appendix 8). DNA purification was performed using silica-based columns and washing solutions based on Alexander et al. (2007). Site-specific NGS libraries were prepared by pooling normalised DNA from each of the six individuals collected per site per species following the protocol from previous studies (van der Merwe et al., 2014; Rossetto et al., 2015a; Worth et al., 2017; see Fig. S7.1 for a graphical representation of the work flow). DNA was quantified using a Qubit Fluorometer, and DNA quality (260/280 ratio) was checked using a Nanodrop. The libraries were sequenced using the Illumina NextSeq 500 platform by Ramaciotti Centre of Genomics (Sydney, Australia).

3.3.3 Chloroplast assembly and genomic variation detection

The advent of next generation sequencing technology (NGS) has enabled the identification of landscape-level patterns across large numbers of species using standardised sampling, sequencing and analytical methods (Rossetto & Henry, 2014). This DNA marker was favoured as its maternal inheritance and conserved nature can reveal seed-mediated expansion and contraction patterns within studied species (Li et al., 2015). To generate a comparable genomic dataset that includes all species, consistent bioinformatic analyses were performed across all pooled NextSeq libraries. We followed the approach of McPherson et al. (2013) and van der Merwe et al. (2014) with slight modifications. To obtain a reference chloroplast genomic sequence for each species, Organelle Assembler (http://metabarcoding.org/org.asm) was used for de novo assemblies. This assembler utilises a seed- and-extend algorithm to assemble small genomes without requiring a large amount of time and computational effort (Coissac et al., 2016).

For each library we created a consensus sequence containing annotations of the SNPs detected from the pooled library using CLC Bio Genomics Workbench 8.0 (CLC; http://www.clcbio.com).The raw reads were first trimmed using the Quality Trimming Tool and then mapped to the relevant reference chloroplast genomic sequence created with the Organelle assembler using the default settings. A “library specific” consensus sequence was generated and remapped with the quality trimmed reads using the more stringent mapping parameters of 0.8 for the similarity and 0.9 for the length fraction. Refer to Table S9.1 for additional information about the species-specific libraries. 43

The Basic Variant Detection tool, with a minimum coverage of 40 times and a minimum variant frequency (mvf) based on the assumption that variants come from at least one individual per library (i.e. in a pooled library of six individuals, a 16.67% mvf) was implemented for SNP detection. The SNPs were visually verified in each mapping, counted and annotated on the consensus sequence. After creating these SNP annotated consensus sequences for each library, chloroplast alignments for each species were generated using MAUVE (Darling et al., 2004) (with default parameters) in Geneious Pro 9.1.8. Areas of low coverage and poor alignment were removed and variable sites in the alignment were counted to obtain the number of fixed SNPs between sampling localities for each species.

For each species at a site, genomic diversity was calculated as “modified Nei’s” as per van der Merwe et al. (2014) based on SNP counts, chloroplast sequence length and sample size per site using the modified calculation of Nei & Li (1979). Genomic distance within each region was calculated as the proportion of nucleotide sites at which the consensus sequence for each site was different, following Nei & Kumar (2000). Genomic distance between regions uses a similar calculation, but the within region SNPs were labelled as ambiguities, and the SNPs obtained from the re-aligned consensus sequences were used to obtain the proportion of nucleotide sites at which the consensus sequence for each region was different.

Non-parametric tests were implemented to examine relationships between ancestries based on genomic diversities and distances, following Rossetto et al., (2015a). The tests were implemented using the wilcox_test function (Wilcoxon–Mann–Whitney test) in the R package coin (Hothorn et al., 2008) applied within R version 3.1.2 (R Development Core Team, 2016).

3.3.4 Estimation of species accumulation in rainforests

To investigate how species of Sunda and Sahul ancestry accumulate in the Tropics and Subtropics, we used assemblage accumulation curves (AACs; van der Merwe et al., 2019). These curves illustrate how the sampled species accumulated within each of the study sites as based on the time to coalescence of each population at each site. An estimate of the effective population size (Ne), using Watterson’s estimator (Watterson, 1975) was used as a proxy of the time to coalescence. The species- specific branch rates reported in the supportive information of van der Merwe et al. (2019) were used.

In each site, the estimated times to coalescence were partitioned by ancestry (Sunda and Sahul) and ranked from oldest to youngest. The percentage of species present at a given time that the

accumulation of the present-day species (in this dataset) initiated. AACs (percentage of present-day species vs. time in the past) was generated for each site.

3.3.5 Local species distributions at study sites

To study local species distributions of Sunda and Sahul ancestry, plot-level data at the study sites in both regions obtained from Kooyman et al. (2011) and Metcalfe & Ford (2008) were analysed. The plot analyses of Sunda- and Sahul- derived species follow Yap et al. (2018), by first assigning each species within each plot with an ancestry based on the study’s species ancestry allocations. The number of Sunda- and Sahul- derived species per plot was used for calculating the proportion of Sunda- and Sahul- derived species in each plot. The proportions per plot were then averaged to generate the proportion of the Sunda and Sahul components for a given study site.

3.4 Results

To investigate the local history of species with diverse biogeographic histories we measured patterns of genomic diversity among and between ancestral groups. On average, species of Sunda ancestry exhibit lower genomic diversities than those of Sahul ancestry within the Tropics and Subtropics (Table 3.1; Fig. 3.2), with the exception of MB-Tinaroo. Average genomic diversity for species with Sunda ancestry in MB-Tinaroo (3.69 x 10-5) was similar to that measured at Mt Lewis (5.37 x 10-5), but the average for species with Sahul ancestry in MB-Tinaroo (3.38 x 10-5) was considerably lower than that at Mt Lewis (1.05 x 10-4). In the Subtropics, average genomic diversity for species with Sunda ancestry was highly similar for Nightcap-BR and Dorrigo, but the genomic diversity in Nightcap-BR was lower than that measured for species with Sahul ancestry (P-value = 0.09, Fig. 3.2).

Table 3.2 Summary of genomic diversity and genomic distance measures for species of Sunda and Sahul ancestry in the Tropics and the Subtropics.

N species Sunda ancestry Sahul ancestry Genomic diversity Overall Tropics 20 5.45 x 10-5 7.58 x 10-5 Mt Lewis 5.37 x 10-5 1.05 x 10-4 MB-Tinaroo 3.69 x 10-5 3.38 x 10-5 Overall Subtropics 39 2.19 x 10-5 5.45 x 10-5 Nightcap-BR 1.93 x 10-5* 6.39 x 10-5* Dorrigo 2.44 x 10-5 4.44 x 10-5 Genomic distance Within the Tropics 20 4.54 x 10-4 4.28 x 10-4 Within the Subtropics 39 4.13 x 10-4 9.28 x 10-4

*Comparison between Sunda and Sahul ancestry yielded significant differences (P-value <0.05)

Figure 3.2 Comparative analyses between Sunda and Sahul ancestry based on genomic data: Genomic diversity and genomic distance in the Tropics (A, C) and the Subtropics (B, D). In each boxplot, the yellow dot represents the average, the bar within the boxplots represents the median, and the top and bottom of the box represent the ﬁrst and third quartiles. Non-parametric tests between Sunda and Sahul ancestries only resulted in significant differences (significant P-value labelled with asterisk) in genomic diversities within Nightcap-BR (B), and P-value of 0.09 within the Subtropics (D).

We used Assemblage Accumulation Curves (AACs; van der Merwe et al., 2019) to assess the local history of each site and compare trends between the two different ancestral groups (Fig. 3.2). Difference in the shape of AACs for Sunda and Sahul ancestry suggests a consistently steep (i.e. recent) accumulation of Sunda-derived species across the Tropics and Subtropics. The least homogeneous accumulation of Sunda-derived lineages was in Mt Lewis, the most northerly location sampled and the one in closest proximity to the likely source for Sunda species. Across most sites, Sahul-derived component of the assemblage began to accumulate and expand to current effective population sizes long before the Sunda-derived component. Interestingly, the exception to this repeated pattern was in MB-Tinaroo, located south of Mt Lewis, where the AAC suggest that the species began to accumulate at around 250 thousand years ago (Ka), suggesting that post-disturbance recolonisation processes have impacted on this area differently than in other study sites.

Lastly, we used plot data to assess the effect of historical contingencies on local species distributions of Sunda and Sahul ancestry. A higher proportion (0.41 vs. 0.3) and number of species of Sunda ancestry (109 vs. 93) was observed in MB-Tinaroo than Mt Lewis (Table S10.1). In the Subtropics, the proportion of species of Sunda ancestry in Dorrigo and Nightcap-BR were lower (0.27 vs. 0.3), but with less than half the number of Sunda species in Dorrigo than Nightcap-BR (24 vs. 52).

Figure 3.3 Assemblage accumulation curves for Sunda- and Sahul- derived species in the Tropics sites, (A) Mt Lewis and (B) MB-Tinaroo, and in the Subtropics sites, (C) Nightcap-BR and (D) Dorrigo. To better assess the recent local histories across sites, all lineages except a Sahul-derived lineage in Dorrigo with a relatively older age (> 1,500 Ka) were studied.

To further investigate the local history of both sets of species and the potential for multiple invasion events, we estimated average between-site genomic differentiation across biogeographic barriers. Sunda-derived species exhibited low average genomic distances across both biogeographic barriers (Fig. 3.2), consistent with Edwards et al. (2017) suggesting that the BMC is not a strong barrier for plants compared to other recognized biogeographic barriers in the Australian Tropics. In contrast, Sahul-derived species exhibited a significantly higher average divergence across the CRC in the subtropics (P-value < 0.01) than across the BMC in the Tropics.

We also investigated continent-wide genomic diversities and distances for the subset of species distributed across both Tropics and Subtropics to assess if expansion dynamics were consistent (Table 3.3). All Sunda-derived species displayed highest diversity in the most northerly site (Mt Lewis), and decreasing to the south with each species having a subtropical site displaying no chloroplast DNA variation across all six individuals sampled. However, while two species (Neolitsea dealbata and Toona ciliata) also displayed little continental divergence, the other two (Argyrodendron trifoliolatum and Polyscias murrayi) displayed levels of genomic differentiation between the Tropics and Subtropics that are similar to those observed in Sahul-derived species (Table 3.3). The two Sahul- derived species showed no particular pattern in the geographic distribution of genomic diversity.

Table 3.3 Genomic diversity and genomic distance measures for four Sunda-derived species and two Sahul-derived species.

Neolitsea Toona Polyscias Argyrodendron Eupomatia Sloanea Species dealbata ciliata murrayi trifoliolatum laurina australis Sunda Sunda Sunda Sunda Sahul Sahul North Qld North Qld to North Qld North Qld North Qld North Qld Distribution to north eastern to southern to Illawarra to Victoria to Victoria Illawarra NSW NSW Genomic diversity Mt Lewis 3.35 x 10-5 1.19 x 10-5 1.51 x 10-4 8.70 x 10-5 2.39 x 10-4 1.36 x 10-5 MB-Tinaroo 1.22 x 10-5 0 1.54 x 10-5 4.87 x 10-5 7.75 x 10-5 4.34 x 10-5 Nightcap-BR 1.22 x 10-5 0 0 0 0 3.62 x 10-5 Dorrigo 0 0 9.26 x 10-6 2.78 x 10-5 1.45 x 10-4 0 Genomic distance Between 7.61 x 10-6 8.94 x 10-6 1.77 x 10-4 1.89 x 10-3 4.37 x 10-4 8.14 x 10-4 region

3.5 Discussion

3.5.1 Recent incursions into the Australian rainforest flora

We identified contrasting landscape genomic patterns between Sahul and Sunda derived lineages in the Tropics and Subtropics of Australian rainforests. Lower genomic diversity in species of Sunda ancestry in both Tropical and Subtropical sites in Australia (Table 3.2; Fig. 3.2) is consistent with expectations of recent expansion events. Steep species accumulation curves of Sunda-derived species (Fig. 3.3), indicate the advancement of Sunda lineages into Australian rainforest communities were rapid and largely simultaneous for multiple species.

The weak between-population differentiation among Sunda derived species across recognized biogeographic barriers, the BMC and CRC (Fig. 3.2), corresponds to expectations from rapid expansions of genetically depauperate founder populations. This consistent genetic signature from multiple Sunda species across multiple rainforest assemblages confirms previous suggestions based on distributional, phylogenetic and functional studies that migrations into Australia intensified in more recent geological times (Sniderman & Jordan, 2011, Richardson et al., 2012, Crayn et al., 2015, Yap et al., 2018).

3.5.2 Environmental disturbances facilitated immigration and integration

In the Tropics, both Sunda- and Sahul- derived species showed lower genomic diversities, and more recent species accumulation rates in MB-Tinaroo than Mt Lewis (Fig. 3.2-3.3). The recent local species histories detected are consistent with interpretations based on fossil and habitat suitability modelling suggesting that the western edge of the Wet Tropics Bioregion, where the MB-Tinaroo site is located, experienced less stable climatic conditions during the Quaternary (Kershaw, 1994; Haberle, 2005; Hilbert et al., 2007; VanDerWal et al., 2009). While the presence of locally endemic species highlights persistence of species diversity in small upland refugial pockets (Mokany et al., 2014), we show that common species such as those targeted in this study retain the signal of recent histories of expansion into less stable habitats. The proximity to the Sunda shelf also made these recurrently disturbed areas easier to access to invading lineages (as well as Sahul-derived lineages) that have been selectively filtered to favour opportunistic invasions of newly available habitat (Costion et al., 2015; Yap et al., 2018).

More stable, higher altitude refugial sites in the Tropics such as Mt Lewis have a lower proportion of species of Sunda ancestry than MB-Tinaroo (Table S10.1), higher genetic diversity, and less-steep AAC for Sahul-derived species (Fig. 3.2-3.3). This genomic signature is consistent with floristic patterns and environmental modelling suggesting that Mt Lewis site is located within a climatically stable, refugial area (Hilbert et al., 2007; VanDerWal et al., 2009), that favoured the persistence of high concentrations of Sahul lineages (Costion et al., 2015).

3.5.3 Same mode but different tempo of expansion

The study species with continuous distributions across eastern Australia, exhibited the highest diversity at the most northerly site sampled (Table 3.3), confirming that Sunda-derived species generally establish in the tropical north before expanding in a southerly direction. Continental expansions originating from founder events generate genetically homogenous populations along the expansion front (Nei et al., 1975; Ibrahim et al., 1996; also see Hewitt, 2004), and the lower diversity measures for the Sunda species in the Subtropics (Table 3.3) are in agreement with these southern regions being the current edge of a rapid expansion front. Different levels of genomic differentiation observed among the species (Fig. 3.2), suggest however, that continental expansion occurred at different temporal scales and continental measures of landscape level richness supports this (Table S10.1; Yap et al., 2018). For example, weak genomic differentiation between the Tropics and Subtropics in Neolitsea dealbata (Lauraceae) and Toona ciliata (Meliaceae) suggests that for these 50

species, colonisation in the north was soon followed by rapid expansion to the south. Whereas, the strong genomic differentiation between the Tropical and Subtropical distributions of Argyrodendron trifoliolatum (Malvaceae) and Polyscias murrayi (Araliaceae) suggests a longer and more complex history of arrival within the Tropics and expansion into the Subtropics.

3.6 Conclusion

We confirm the recent arrival of Sunda lineages into the Australian continent and their establishment was facilitated by opportunities created during the climatic fluctuations of the Quaternary. In contrast to Sahul-derived species, a southward expansion process was observed for Sunda-derived species. This expansion process occurred at different tempos with some species, but not others, requiring a lag time to expand. These time lags could have been imposed by the limited number of dispersible individuals available, and/or the need of invasive species to adapt to novel conditions before being capable of range-wide colonisation success (Ibrahim et al., 1996, Le Corre et al., 1997, Theoharides & Dukes, 2007). The distribution of several of the Sunda and Sahul rainforest lineages along the east coast of Australia covers more than 3000 km along a latitudinal gradient, and future fine-scale landscape genetic studies will provide additional insights into the expansion dynamics of the geographic and ecological extremes of species with contrasting phylogeographic histories.

Preface to Chapter 4

The following chapter has been prepared for submission to Molecular Ecology as Yap, J. S., Rossetto, M., Sourav, D., Wilson, P. D., & Henry, R. J. Landscape genomics and habitat modelling reveal contrasting histories in co-occurring rainforest species across New South Wales, Australia

My contribution to this study was as follows: designing the experiments (75%), field work (100%), data analysis (90%), writing (90%) and editing the manuscript (50%).

Genome-wide SNP markers were generated by Diversity Arrays Technology Pty Ltd (DArT), by extracting DNA and providing high-throughput genotyping using the DArTseq platform.

The Environmental Niche Modelling data analysed in this chapter was provided by Das Sourav from Macquarie University, NSW, Australia.

Chapter 4 Landscape genomics and habitat modelling reveal contrasting histories in co- occurring rainforest species across New South Wales, Australia

4.1 Abstract

Present-day Australian rainforests are the product of intercontinental floristic exchange. The recurring cycles of expansion and contraction experienced by rainforest vegetation during the Quaternary were particularly influential in driving the invasion of lineages from Sunda (the Malay Archipelago) into Sahul (mainland Australia). While distributional changes in response to past climatic change have been investigated in a range of Sahul lineages through genetic and environmental analyses, the same is not true for Sunda lineages. Here we compare two Australian rainforest species of distinct ancestries but with distributional overlaps to investigate their temporal dynamics and evolutionary responses to changing climatic conditions. We explored the landscape genomics and temporal variation in habitat availability for Doryphora sassafras and Toona ciliata across New South Wales (Australia). Genome-wide SNP analysis revealed high levels of north to south divergence and markedly higher within-population diversity at higher latitudes in D. sassafras, whereas T. ciliata displayed landscape-level homogeneity of diversity and structure. This suggests D. sassafras prefers cooler climates and progressively contracted to higher latitudes as habitat availability decreased during the interglacial, whereas T. ciliata has a history of recent rapid expansion influenced by habitat availability (the most recent expansion taking place in the mid- Holocene, 6,000 years before present). Based on genome-wide evidence and Environmental Niche Modelling (ENM) data, we predict that Sahul lineages such as D. sassafras will continue to persist in situ and track local suitable habitats through expansion / contraction events, while Sunda lineages such as T. ciliata although currently widespread as a result of arising opportunities, is likely to adjust to local conditions and / or competition that might eventually lead to some distributional contractions.

4.2 Introduction

Until 25 million years ago (Mya), the Sahul continental plate (mainland Australia) was geographically distinct from the Sunda continental plate (the Malay Archipelago) (Hall, 2011). Long-term separation resulted in the evolution of distinct floras within the two regions (Morley, 2000). In Sahul, broad-leaf vegetation dominated until the mid-late Eocene (c. 49-38 Mya) when the continental plate was still attached to the southern supercontinent Gondwana. During the late Eocene, the establishment of the circumpolar current triggered the drying and cooling of global climates, the progressive aridification of Sahul and consequently, the range-wide distributional contractions and extinctions of rainforest lineages (Greenwood & Christophel, 2005; Martin, 2006). In contrast, the history of Sunda’s flora is linked to its complex geological history (Holloway & Hall, 1998; Morley, 2000). From the Miocene (23 Mya) onwards, major components (Malay Peninsula, Sumatra and Borneo) of the present-day Sunda continental plate became periodically connected by dry land, enabling extensive biodiversity exchange and diversification of the flora through time (Morley, 2000; Hall, 2012; de Bruyn et al., 2014). The collision between the Sahul and Sunda continental plates increased the opportunities for movement between these evolutionarily differentiated floras. Consequently, present-day Australian rainforests are a product of a floristic exchange resulting in a mix of species with Sunda and Sahul ancestries (Yap et al., 2018).

It has been previously suggested that the rapid and intense climatic fluctuations of the Quaternary (2.5 Mya to present) were most influential in driving the expansion of Sunda lineages to Australia (Sniderman & Jordan, 2011; Richardson et al., 2012). During the Pliocene (5.3 to 2.5 Mya), rainforest habitats within Australia were relatively stable along the east coast, but during the Quaternary they were subjected to repeated cycles of contraction in response to the cool-dry glacial periods, and expansion in response to the warm-wet interglacial periods. The Last Glacial Maximum (LGM, 22,000 years before present or ybp) was particularly intense, leading to extreme contractions and localized, distribution-wide extinctions (Kershaw et al., 2007).

The habitat instability of the Quaternary increased vulnerability to invasion by Sunda immigrants, particularly as new habitat became available during the interglacial periods (Sniderman & Jordan, 2011; Richardson et al., 2012; Crayn et al., 2015; Costion et al., 2015; Yap et al., 2018). As could be expected, the majority of immigrant lineages display the characteristics of pioneers with a functional capacity to rapidly invade newly-available habitats (Yap et al. 2018). The ensuing opportunistic invasion of the Australian rainforests is demonstrated by a higher concentration of Sunda lineages at lower altitudes in the Australian Wet Tropics (AWT), where habitat stability was more intensively impacted by glacial events (Costion et al., 2015; Yap et al., 2018).

Distributional changes of Sahul lineages in response to the climatic fluctuations of the Quaternary have been previously investigated through DNA-based analyses (Rossetto et al., 2004; Rossetto et al., 2007; Worth et al., 2009; 2010; 2011; Mellick et al., 2012; 2013; Mellick et al., 2014; Heslewood et al., 2014). The investigation of landscape-level patterns of genetic variation identified long-term refugial areas where Sahul species persisted during unfavorable climatic conditions and associated signatures of range expansions / contractions. Applying the landscape level-approach to multiple, co- distributed Sahul species uncovered that fine-scale persistence and expansion patterns varied according to species’ functional characteristics (Rossetto et al., 2009). Comparative, multispecies genetic investigations were also used to infer different histories of disturbance among selected rainforest areas, as well as to identify contrasting local histories for lineages of Sunda vs. Sahul origins (Rossetto et al., 2015a). The impact of changing climatic conditions on the distribution and temporal dynamics of rainforest species of Sahul origins is highlighted by Environmental Niche Modelling (ENM) estimating past changes in species’ habitat suitability (Mellick et al., 2012; 2013; Mellick et al., 2014).

Currently, no study has combined genetic and environmental data to investigate in detail the recent expansion of Sunda-derived species and contrast it to the long-term persistence of the Sahul-derived flora. A comparative study of two co-distributed Australian rainforest species of distinct ancestries across broad landscapes can provide insights into different temporal signatures of expansion / contraction dynamics and evolutionary responses to changing climatic conditions, and this allows species vulnerability to climate change to be assessed. From a conservation planning perspective, the combination of genetic and environmental information can also provide invaluable resources to capture important refugial locations and formulate strategies for managing plants groups with selected attributes (Broadhurst et al., 2017).

This study compares landscape genomics and temporal variation in habitat suitability between a Sahul-derived species, Doryphora sassafras Endl. (Atherospermataceae) an Australian endemic genus with a continental history dating back to over 50 Mya (Renner, 2004), and a Sunda-derived species, Toona ciliata M.Roem. (Meliaceae) with recent continental history dating back to the mid- late Pleistocene (Muellner et al., 2008). Although the distributional range of T. ciliata occurs extensively outside (India, southern China, Southeast Asia and New Guinea) of and within (from Cape York at the tip of Northern Australia to Milton in South-eastern Australia) Australia, the focus is on the area of distributional overlap stretching approximately 800 km along the coast of New South Wales (NSW), which also corresponds to the southern limit of T. cilita. Landscape-level dynamics were explored using genome-wide Single Nucleotide Polymorphism (SNP) data. Habitat preferences and temporal changes in habitat suitability were also investigated to evaluate the expansion / 55

contraction dynamics that took place during the LGM, mid-Holocene (MH, 6,000 ybp), and the current period. Finally, species response to future climate was modelled through 2070 in order to predict the possible impact on species distributions.

Using genome-wide SNP and ENM data, we asked these questions:

(1) Do T. ciliata and D. sassafras show contrasting patterns of genetic diversity consistent with their history? (2) Do maps of modelled habitat availability under past climate support species histories inferred by genetic data? (3) What can we learn from the past to inform the future using environmental niche modelling?

4.3 Materials and Methods

4.3.1 Study species

Doryphora sassafras (canary or yellow sassafras, Atherospermataceae) belongs to a small genus endemic to Australia, with only two species. D. sassafras is distributed across subtropical to warm temperate rainforests from southeast Queensland to southern NSW (SNSW) (Fig. S11.1), and D. aromatica is restricted to the uplands of the AWT in northeast Queensland. D. sassafras typically occurs at higher elevations in the Subtropics, and in gullies and along creeks in warm temperate rainforests (Floyd, 1990). This late-successional tree can be dominant and grow to 35 m, as well as coppice, forming shade-tolerant stands in the understorey (Floyd, 1990). Atherospermataceae are generally insect-pollinated (Renner, 2004), as typified by pollen and flower structure observed in D. sassafras and its close relative, Atherosperma moscatum (Sampson & Foreman, 1988; Worth et al., 2011). Both species are also wind-dispersed with hairy achenes and plumose styles, but D. sassafras display a wider distributional range across South-eastern Australia than A. moscatum.

Toona ciliata (red cedar or toon) belongs to the mahogany pantropical family Meliaceae (Muellner et al., 2006). The genus is known for its remarkable morphological variation (e.g. Bahadur, 1998), but a recent revision by Mabberley et al. (1995) suggests that Toona consists of four (or possibly even five) species. Toona ciliata var. australis occurs in eastern Australia (from Cape York to SNSW, Fig. S11.1), whereas variety ciliata is widespread in tropical and subtropical Asia (Boland et al., 2006). In Australia, the species occurs in warm temperate, subtropical and tropical rainforests, and

along large rivers (Floyd, 1989). This tree was extensively logged throughout its distribution for its high-quality timber (Bygrave & Bygrave, 2005). T. ciliata favours high light conditions in nutrient- rich soils, often along moderately disturbed areas (e.g. Herwitz, 1993; Thompson et al., 1992; Webb et al., 1997). This deciduous tree can adjust to dry seasonal conditions and tolerate moderate frost although it prefers a mean annual rainfall of 1,200-3,800 mm and warmer temperatures (Herwitz, 1993). It is wind-dispersed, monoecious and capable of self-pollinating (Liu et al., 2014).

4.3.2 Sampling and sequencing

To determine landscape-level genomic variation, and corroborate the patterns associated with the long-term presence of D. sassafras vs. the recent arrival of T. ciliata (McPherson et al., 2013; van der Merwe et al., 2014; Rossetto et al., 2015a), we sampled the two species across their NSW distributions (Fig. S11.1) using the sampling approach of Rossetto et al. (2018). We sampled core rainforest areas across NSW (by consulting experts in the field) and tried to maximise site overlap across the two species, targeting 24 sites within the known distribution of D. sassafras and 22 sites across the distribution of T. ciliata (Fig. 4.1; Table 4.1). Leaf material from six individuals that were at least 10 m apart were collected at each site. Collected samples were freeze-dried and stored with silica beads at room temperature prior to DNA extraction.

Figure 4.1 Study sites of Doryphora sassafras (green triangles) and Toona ciliata red dots) on an elevation map of New South Wales (NSW), Australia. Refer to Table S12.2 for population details.

Table 4.1 Genetic diversity in populations of Doryphora sassafras and Toona ciliata arranged in latitudinal order. a

Doryphora sassafras Toona ciliata Clusters Population names ar He Ho Population names ar He Ho AirdropRd 1.406 0.187 0.204 Sheepstationcreek 1.636 0.263 0.282 Nightcap 1.434 0.198 0.225 BorderRanges 1.614 0.254 0.272 BigScrub 1.431 0.194 0.231 RummeryRd 1.594 0.248 0.278 Washpool 1.393 0.178 0.214 BigScrub 1.609 0.253 0.29 NNSW NymboiBinderay 1.412 0.187 0.22 Washpool 1.63 0.261 0.275 DorrigoNP 1.405 0.19 0.205 NymboiBinderay 1.61 0.257 0.255 Darkwood 1.64 0.267 0.278

WilliWilliNP 1.583 0.24 0.289 Werrikimbe 1.483 0.213 0.244 Knodingbull 1.583 0.247 0.255 OxleyHwy 1.466 0.21 0.244 UpperAllynfootofhill 1.603 0.251 0.271 Knodingbull 1.487 0.217 0.247 SkimmingGapRd 1.602 0.253 0.255 Werrikimbe Plateau Boorganna 1.475 0.212 0.244 CarawiryCreek 1.574 0.235 0.281 to Barrington Tops StarrsBigNellie 1.488 0.217 0.251

GloucesterTops 1.428 0.191 0.251

JerusalemTrack 1.425 0.195 0.228 WalkersRidgeOlneySF 1.518 0.228 0.29 WatagansNP 1.533 0.224 0.239 Central Coast PalmGrove 1.534 0.238 0.307 KatandraReserve 1.556 0.239 0.23 KatandraReserve 1.534 0.237 0.295 Wahronga 1.615 0.257 0.263 StanwellPark 1.507 0.229 0.272 Wombarra 1.587 0.243 0.275 MtKeira 1.512 0.231 0.269 SublimePtBulli 1.609 0.252 0.287 MacquariePass 1.52 0.235 0.262 MtKeira 1.595 0.249 0.264 Illawarra FitzroyFalls 1.505 0.229 0.257 MtKembla 1.615 0.258 0.258 escarpment and Broughton 1.463 0.212 0.239 MacquariePass 1.604 0.255 0.268 SNSW Cambewarra 1.479 0.218 0.249 MinnamurraNP 1.602 0.252 0.276 McDonaldSF 1.431 0.21 0.221 Nowra 1.578 0.242 0.265 ClydeMountain 1.428 0.196 0.257

a Populations are clustered according to the geographically structured groups observed in D. sassafras

(based on results from Fig. S12.3-12.4). “ar” = allelic richness, “He” = expected heterozygosity and

“Ho” = observed heterozygosity. The amount of genomic diversity was represented by a colour gradient, with high genomic diversity indicated in red and low genomic diversity indicated in green, and this colour scheme was only partitioned within each genomic diversity measure in each species.

Genome-wide SNP markers were generated by Diversity Arrays Technology Pty Ltd (DArT) using the DArTseq platform. This is a cost-effective restriction-based, reduced-representation genome sequencing method that simultaneously assays thousands of markers, as well as co-dominant single nucleotide polymorphisms (SNPs) across the genome (Sansaloni et al., 2011; Killian et al., 2012; Cruz et al., 2013). DArTseq markers can be used for landscape-scale examination of genetic variation and for understanding relationships among populations and species (Yang et al., 2016; Melville et al., 2017; Rutherford et al., 2018). Briefly, the generation of DArTseq markers involves genome reduction and library construction that is described by Killian et al. (2012) and Cruz et al. (2013), followed by processing of the sequencing libraries using proprietary DArT analytical pipelines. Poor- quality sequences were first removed from Illumina fastq files, and then identical sequences were collapsed (creating “fastqcoll” files) followed by marker calling from the DArTsoft14 software package. This involved a BLAST search of all loci to remove putative microbe contaminants to generate DArTseq markers ready for genome-wide analysis of nuclear variation.

4.3.3 DArTseq analysis

The analysis of DArTseq data was performed using the workflow (Fig. S11.2) of Rossetto et al. (2018) implemented in R version 3.1.2 (R Development Core Team, 2016). A range of descriptive statistics were first calculated for each species-specific dataset, to detect and remove poor-quality SNP loci (reproducibility average < 0.96, genotypes missing in > 5% of samples) and poor-quality samples (samples missing data in a large proportion of loci).

We then conducted population analyses using the multivariate cluster-based approach, Discriminant Analysis of Principal Components (DAPC) in the R package, Adegenet (Jombart & Ahmed, 2011) and a model-based approach involving sparse Non-negative Matrix Factorization (snmf) in the R package, LEA (Frichot et al., 2014; Frichot & Fancois, 2015). DAPC was used as it has been shown to efficiently separate subpopulations by maximizing the separation between groups while minimizing variation within groups (Jombart et al., 2010). The most likely number of clusters was determined by comparing Bayesian information criterion (BIC) values for 1 to 20 subpopulations. 60

The first two principal components of the DAPC were then plotted to evaluate relationships among clusters. The latter generates an estimation of ancestry coefficients for each sample using the snmf function to select the best K from a test of 10 runs of K from 1 to 20.

We also assessed genetic differentiation (i.e. gene flow between populations) based on FST (Weir &

Cockerham, 1984), GST (Nei & Chesser, 1983) and G’ST (Hedrick, 2005) with 95% bootstrap confidence interval (999 replicates) in the R package, diveRsity (Keenan et al., 2013). Significance in the correlation between each of the genetic differentiation estimates and geographic distance (i.e. the presence of isolation-by-distance (IBD)) was tested using a simple Mantel test with 999 random permutations in the R package, Vegan (Mantel, 1967; Oksanen et al., 2017).

Genetic diversity measures, allelic richness (ar), expected heterozygosity (He) and observed heterozygosity (Ho) were estimated for D. sassafras and T. ciliata populations in diveRsity (Keenan et al., 2013), with the allelic richness calculated using the built-in repeated random sampling technique (999 bootstrap replications) to correct for different sample sized populations. Genetic diversity measures were tested against latitude to see if there is a north–south pattern of genetic diversity. The relationships between latitude and genetic parameters were tested using a linear regression analysis and the non-parametric Spearman rank-order correlation coefficient test (Siegel & Castellan, 1989).

4.3.4 Environmental niche modelling

To assess the effect of climate on rainforest distributions in NSW, we studied the distribution of suitable habitat across time for both species. Species-specific suitability measures to generate maps of past, current and future habitat suitability across eastern Australia were calculated using a machine learning algorithm, Maxent (Philips et al., 2006; Elith et al., 2011). We briefly describe here the modelling approach which was detailed in Elith et al. (2011). The predictive modelling used 15 bioclimatic variables and two topographic variables - Topographic Position Index (TPI) and the Topographic Wetness Index (TWI) (Table S13.1) obtained from the Ecosystem Modelling and Scaling Infrastructure Facility (eMAST; http://www.emast.org.au). Climate modelling data for the Last Glacial Maximum (LGM; ~22,000 ybp), mid-Holocene (MH; ~6,000 ybp), current period and 2070 were downloaded from the CMIP5 data portal through the Earth Survey Grid Federation (https://esgf-node.llnl.gov/projects/esgf-llnl). Both the LGM and MH represent climatic extremes of the last Quaternary glacial cycle (i.e. LGM had a cool-dry glacial climate, whereas the MH had a warm-wet interglacial climate). Two future climatic scenarios based on different Representative 61

Concentration Pathways (RCPs) were also employed to project future (2070) habitat suitability, with RCP 4.5 being more conservative than RCP 8.5 for projecting a lower temperature increase hence lesser impact from climate change. Projections of the eastern Australian climate were refined using Global Climate Models (GCMs; Buisson et al. 2010; Table S13.2) based on the approach of Whetton et al. (2015). Model performance was judged using area under the receiver operating characteristic curve (AUC; Swets, 1988) and and the maximum true skill statistic (TSS = sensitivity + specificity - 1; Allouche et al., 2006; Shabani et al., 2016).

Species occurrence data for D. sassafras and T. ciliata was obtained from the Atlas of Living Australia (ALA; http://ala.org.au), and data-cleaning procedures to the occurrences were applied to remove records that were: i) non-georeferenced; ii) located beyond the terrestrial zone of Australia; iii) located in botanic gardens or classified as cultivated material; iv) collected prior to 1950; and / or v) categorized by ALA as environmental or spatial outliers. Additionally, duplicate records for a given species were removed to reduce sampling bias, by retaining only one record within a 1 × 1 km area. Data analyses including ENM, spatial data layer clipping and overlaying of the species occurrence data were performed using the R packages, packages dismo (Hijmans et al., 2017), rmaxent (Baumgartner et al., 2018), sp (Pebesma & Bivand, 2005), raster (Hijmans & van Ettern, 2014) and maptools (Bivand & Lewin-Koh, 2016).

To examine the relationship between changes in environmental suitability between LGM, MH and current climate, and patterns of genetic diversity, we computed the percent of overlap in species occurrence data and the area of optimal habitat predicted for each climatic period. To identify the area of optimal habitat, a threshold specific to each species was generated using a sensitivity- specificity combined approach and applied to the climate models (Liu et al., 2005). We interpreted high levels of overlap between current species occurrence and the spatial distribution of optimal environmental suitability to mean little change between times steps (e.g. if species occurrences overlap strongly overlap with the area of optimal habitat predicted for the MH, it suggests species’ distributional range most likely remained unchanged since that period).

4.4 Results

4.4.1 Summary of DArTseq data

DArTseq yielded a total of 16,443 SNPs for 136 D. sassafras individuals from 24 sites, and 46,785 SNPs for 124 T. ciliata individuals from 22 sites (Table 4.1). After quality filtering, we obtained a total of 3,031 SNPs for D. sassafras, and 9,198 SNPs for T. ciliata. The lower number of SNPs / higher levels of missing data in D. sassafras was caused by high levels of secondary metabolites (e.g. safrole; Brophy et al., 1993), resulting in a greater portion of poor quality data that was stringently removed in our quality filtering step.

4.4.2 Nuclear genome diversity patterns

Nuclear genome diversity patterns were different across D. sassafras and T. ciliata populations in NSW (Table 4.1). D. sassafras displayed higher genomic diversities in the south (i.e. populations in Central Coast, the Illawarra escarpment and Southern NSW (SNSW)), whereas T. ciliata displayed higher genomic diversity in Northern NSW (NNSW). There was a significant negative correlation 2 between latitude and genomic diversity measures for D. sassafras (e.g. for He: R = 0.38, slope = - 0.005, P-value < 0.001, Fig. S12.1), but no significant correlation was observed for T. ciliata.

Overall genomic diversity was higher for T. ciliata than D. sassafras (e.g. the average He were respectively 0.250 and 0.211). The genomic results for T. ciliata (average He = 0.250, average Ho = 0.269) were consistent with the signature of a recent invader / migrant showing higher observed heterozygosities than expected (Frankham, 2005). While coppicing can lead to the loss of genetic diversity resulting in higher expected heterozygosity than observed (Frankham, 2005), the range of expected heterozygosity values (minimum He = 0.178, maximum He = 0.238, average He = 0.211) is smaller and the average is lower than observed (mininum Ho = 0.204, maximum Ho = 0.307, average

Ho = 0.247) for D. sassafras demonstrating the species retains random mating.

High levels of population genomic differentiation were observed among populations of D. sassafras but not among populations of T. ciliata (Fig. 4.2; Fig S12.5). D. sassafras displayed higher pairwise

FST values (average FST = 0.27, maximum FST = 0.51) than T. ciliata (average FST = 0.069, maximum

FST = 0.16). DAPC and snmf results also support the substantial differentiation among populations of D. sassafras contrasting with the weak differentiation among populations of T. ciliata (Fig. 4.2). In D. sassafras, the number of major genetic clusters inferred by DAPC as derived from the BIC graph 63

(Fig. S12.3) identified K = 2 as optimal, followed by K = 4. In contrast, K = 1 alone was deemed as optimal for T. ciliata. To further compare the results from two species, K = 2 and K = 4 were also tested on T. ciliata. While K = 2 in D. sassafras displays two distinct, sharper and non-overlapping peaks (discriminant function 1 of both peaks approximately center at -9 and 20) with stronger clustering in northern group (peak density = 0.63) than the southern group (peak density = 0.30), T. ciliata does not (discriminant function 1 of both peaks center at -3 and 3; Fig. 4.2). In D. sassafras these peaks represent distinctive northern (populations from the Border Ranges to Central Coast) and southern (populations from Illawarra escarpment to Clyde Mountain in SNSW) groups, whereas such latitudinal distinction was not identified in T. ciliata. Model-based ancestry estimation via snmf also estimated the same number of genetic clusters as DAPC for both species (Fig. S12.3-12.4).

Figure 4.2 Population differentiation and structure inferred from pairwise FST plot, Discriminant Analysis of Principal Components (DAPC) and snmf using K = 2 for Doryphora sassafras (A, C, E) and Toona ciliata (B, D, F) based on DArTseq data. Within each pairwise FST scatterplot (A, B), each dot represents genetic distance between paired populations, and the Mantel statistical results (correlation test for significant isolation by distance) were indicated in each scatterplot. Within each DAPC density plot (C, D), densities of individuals on the first discriminant function were shown, with the “N” and “S” labels respectively indicating the northern and southern groups. Each snmf barplot (E, F) shows estimated ancestry proportions of sampled individuals sorted by increasing latitude. Labels below the barplots show where each cluster begins (clusters are based on results from Fig. S12.3-12.4): “NNSW” represents populations from Border Ranges to Dorrigo in Northern New South Wales (NNSW), “W-BT” represents populations from Werrikimbe to Barrington Tops, “CC” represents populations from the Central Coast and “RNP” represents populations from the Illawarra escarpment and SNSW.

For D. sassafras, the snmf analysis found admixed geographically intermediate populations in the Central Coast share almost equal ancestries from northern and southern groups (Fig. 4.2). Although a significant correlation between species-wide pairwise FST measures and geographical distance was observed in D. sassafras (R = 0.828, P-value <0.001; Fig. 4.2), strong north / south differentiation with a narrow intervening band of admixture suggests that population differentiation appears to follow an IBD patterns within groups (Fig. S12.6) rather than across the whole species. No relationship between geographic and genetic distances was observed in T. ciliata and the pairwise FST results (Fig. 4.2; Fig. S12.5) is typical of a recent invasion (e.g. Rossetto et al., 2004; Rossetto et al., 2007).

Fine-scale geographic structure was observed in the northern group of D. sassafras based on both DAPC and snmf results (Fig. S12.3-12.4). This group contained a cluster of populations from NNSW and a cluster of populations from Werrikimbe to Barrington Tops that correspond to the mountain systems along the Great Dividing Range, and the southernmost cluster contains the admixed populations in the Central Coast. No clustering of populations was observed in T. ciliata (Fig. S12.3- 12.4).

4.4.3 Environmental niche modelling outputs

To support the expansion / contraction patterns suggested by the genomic datasets, we investigated changes in availability of suitable habitat across the LGM, MH and current periods using ENM data.

Favourable habitat conditions for both species are mostly explained by the seasonal change in temperature and precipitation (Table S13.1). T. ciliata generally displays broader and more continuous areas of suitable habitat than D. sassafras (Fig. 4.3). While both showed increasing availability of suitable habitat from the LGM to MH (D. sassafras: from 15,474 to 24,877 km2; T. ciliata: from 26,117 to 97,140 km2; Table S13.1). For D. sassafras this involved a shift towards higher altitudes and higher latitudes, whereas for T. ciliata this involved a considerable expansion throughout its distributional range (Fig. 4.3). While present conditions generated a further increase in habitat availability for D. sassafras (to 42,663 km2), they resulted in a slight decrease for T. ciliata (to 82,475 km2).

By overlaying current species occurrences on habitat modelling data, we observed that the current area of suitable habitat explained the current distribution of D. sassafras, as the highest overlap of the occurrence data was with the current area of suitable habitat (74.6%). In contrast, the area of suitable habitat during the MH was better at explaining the current distribution of T. ciliata with a 76.3% overlap of the occurrence data with the area of suitable habitat during the MH than the current period (Fig. S13.1)

Future models were also developed to predict relative vulnerabilities to climate change. Area of suitable habitat in 2070 was predicted to contract for both species according to the two different scenarios tested (Fig. 4.3). Using the more conservative model (F4.5), area of suitable habitat will contract approximately a quarter (F4.5: to 10,142 km2) for the more range-restricted D. sassafras, whereas the area of suitable habitat for the more widely distributed T. ciliata will contract to approximately half of its present distribution (F4.5: 45,093 km2).

Figure 4.3 Environmental niche modelling outputs showing the change in area of suitable habitat across New South Wales (NSW) for Doryphora sassafras (top six maps) and Toona ciliata (bottom six maps). For each species, the left most map of NSW shows the current occurrences as black circles and sampled populations as red circles. The subsequent maps (left to right) represent the area of suitable habitat (green – most favourable, orange – unfavourable) during the Last Glacial Maximum (LGM, 22,000 ybp), mid-Holocene (MH, 6,000 ybp), the current period (Current), and future projections

for 2070 under two representative concentration pathways RCP 4.5 (“F4.5”) and RCP 8.5 (“F8.5”). Refer to Fig. S13.1 for further analysis of habitat suitability throughout different periods.

4.5 Discussion

4.5.1 Learning from the past: a persistent tree tracking habitat

The current distribution of the Australian rainforest flora is the product of long-term geological and short-term climatic processes (Kershaw et al., 2007; Kooyman et al., 2014). While Sahul-derived rainforest species still maintain relatively wide distributions across available habitat, their landscape- level genetic signatures highlight the impact of historical processes (Rossetto et al., 2007, 2009; Mellick et al., 2012; Heslewood et al., 2014). The evidence derived from the genome-wide SNP data of D. sassafras suggests this species tracked suitable habitat via contraction and expansion events that left distinct genetic signatures across the landscape. The long-term persistence of this cool- climate species was validated by high levels of north / south genomic divergence and markedly higher diversity at higher latitudes. Overall, the combination of local measures of genomic diversity and environmental models support a hypothesis of progressive range contraction at higher latitudes.

Cool-temperate Sahul-derived rainforest lineages in southern Australia have survived in multiple refugial areas (Taylor et al., 2005; Worth et al., 2009; 2010; 2011; Rossetto et al., 2015a). Stable refugia can maintain large effective population sizes, potentially enabling long-term survival in isolation and increasing the chances of between-population allopatric divergence (Taberlet, 1998). The distribution of diversity in D. sassafras is consistent with such expectations, as we observed a significantly negative relationship between latitude and genomic diversity (Fig. S12.1; Table S12.1), as well as a major north / south disjunction across the Hunter River Corridor (Fig. S12.2), a previously recognized latitudinal biogeographic barrier (e.g. Playford, 1993; Milner et al., 2012; Heslewood et al., 2014).

Landscape-level diversity and connectivity patterns also suggest that D. sassafras responded to the climatic fluctuations of the Quaternary differently across its distribution. Populations from the Central Coast (Fig. 4.3) suggest that the northern and southern groups recently made contact and became admixed, presumably as northern populations expanded to the south in response to the increased availability of suitable habitat in the Central Coast during the mid-Holocene period (Fig. 4.3). The expectation for greater impact of post-glacial warming on cool temperate species, was substantiated by fine-scale geographic structuring among the northern group (Fig. S12.3-12.4) where D. sassafras is mostly found in mountainous systems rather than closer to the coast as is the case in the south (Table S12.2). Differences in topography and how it impacts on gene flow and dispersal might partly

contribute to the measured structure, but the ENM data suggests that while during the LGM habitat availability in the north extended from the mountains to the coast, it has since contracted to more upland areas in the north (Fig. 4.3). The lower measures of population-level genomic diversity and measurable levels of regional substructure (Table 4.1; Fig. S12.3-12.4), support a scenario of localized bottlenecks across the northern distribution of this rainforest tree.

D. sassafras has undergone bottleneck events, most likely due to susceptibility to warming climates and the associated changes in the competitive landscape. Nevertheless, this did not prevent it from surviving within substantial, albeit genetically depauperate populations in the NNSW rainforests. In fact, a study by van der Merwe et al. (2019) suggests that populations of D. sassafras found in some of the major northern refugia have persisted locally in sizeable effective population sizes dating back to 850 thousand years ago (Ka). The ability to persist locally even when conditions are not ideal is partly a consequence of the species’ capacity to promptly coppice in response to some levels of disturbance (such as the increased frequency of fire events that would have characterized the drier LGM). The capacity for post-disturbance coppicing has been previously identified as an important trait imparting resilience to some disturbances in Sahul-derived rainforest species and enabling localized persistence and sometimes even structural dominance (Rossetto & Kooyman, 2005; Taylor et al., 2005; Rossetto et al., 2008; 2015; van der Merwe et al., 2019).

4.5.2 Learning from the past: exploratory expansion of an invading tree

While the expansion and contraction events of the Quaternary regulated rainforests habitat fragmentation, they also created opportunities for the spread of Sunda invaders into and across Australia (Sniderman & Jordan, 2011; Crayn et al., 2015; Costion et al., 2015; Yap et al., 2018). Within this context, the landscape-level homogeneity of nuclear genomic diversity in T. ciliata indicates that this species had a recent and rapid expansion across NSW. This is consistent with chloroplast genome-based population study on T. ciliata that found extremely weak differentiation across a small number of distantly spaced sites across the species’ distributional range (McPherson et al., 2013). The genomic patterns here are considerably different from those obtained from D. sassafras, despite the two species being sampled uniformly. In T. ciliata, population structure and latitudinal directionality were absent (Fig. 4.2). This is consistent with the expectations from a landscape-level expansion being achieved through an explosive wave from a small initial source (Petit et al., 1997; Hewitt, 2000). 70

A recent study suggests that the current effective population sizes of T. ciliata populations have only been sustained for a very short period (between 6 and 7 Ka) in NNSW (van der Merwe et al., 2019). ENM analyses support this recent expansion pattern by suggesting that suitable habitat conditions have been expanding since the LGM, occupying maximum extent in the MH (Fig. 4.3).

Interestingly, the considerable amount of nuclear genomic diversity across the sampled T. ciliata populations (Table 4.1) suggest that the overall population size of this Sunda-derived rainforest tree might have been considerably larger before extensive logging started in the late 1800s (Baur, 1957; Frith, 1977; Stubbs, 1996).

Establishment of an invader in a new area can be influenced by habitat availability and species interactions (Stohlgreen et al., 1999; Davies et al., 2005). Sunda-derived pioneer species are expected to quickly establish in habitat newly made available during disturbance, and by doing so potentially outcompete some of the resident taxa (Yap et al., 2018). In four to six years it can reach reproductive stage and typically seed production is prolific in order to prevent other pioneer species from taking substantial hold (Stanley & Ross, 1989).

Following preliminary invasion success, changing habitat conditions (e.g. resource depletion, unsuitable environmental conditions) can drive a distributional contraction for pioneer species (see review by Wingfield et al., 2015). Additionally, the current distribution and local decline of T. ciliata can also be explained by extensive logging and habitat clearing. Interestingly the current distribution of T. ciliata is better explained by the conditions of the mid-Holocene than those of the current period (Fig. 4.3). This suggests that while the conditions of the MH may have driven range expansion, the species might not be particularly suited to parts of its current range that are presently experiencing drier conditions (Murphy & Timbal, 2008; Ashcroft et al., 2014) and where it might be expected to locally decline in numbers. Concurrently, T. ciliata in the south is likely to be constrained by cool conditions. A glasshouse study has shown that this species has significantly lower growth rates in cool conditions mirroring those in temperate areas (22 / 10 °C) compared to warm conditions mirroring those in subtropical area (29 / 22 °C; Herwitz, 1993). T. ciliata seedlings are also susceptible to frost and drought (Dordel et al., 2011). Although climatic conditions are expected to become warmer in the south, precipitation rates are also expected to decline and consequently contractions might also be expected in the southern margins of the range (Fig. 4.3).

4.5.3 Learning from the past to predict the future

Our study of D. sassafras and T. ciliata integrating genome-wide evidence and ENM data provides insights into species histories across broad landscapes, facilitating predictions of how species will respond to future climate change.

Sahul lineages such as D. sassafras track suitable habitat to the extent of their capacity to efficiently disperse, thus potentially leaving unoccupied habitat following repeated expansion-contraction events. At the same time their capacity to persist and compete for space after disturbance suggest that they might be able to maintain an historical ‘distributional legacy’ even in increasingly unsuitable habitat, as suggested for other Sahul-derived lineages (e.g. Rossetto & Kooyman, 2005; Rossetto et al., 2008; Mellick et al., 2013). As present rainforest habitats will be increasingly vulnerable to changes in fire regime, rising temperatures, changing rainfall patterns and other extreme events (Laurance et al., 2011), overall available habitat for the cool-adapted D. sassafras will decline (with a predicted reduction of up to one quarter of current area; Fig. 4.3). However, based on historical survival in challenging, unsuitable conditions it can be postulated that small, localised extensively coppicing populations should be able to persist within the northern range of this species’ distribution.

Early-stage invading Sunda lineages such as T. ciliata undergo ‘distributional experimentation’ by being able to shift distributions in response to arising opportunities (often driven by disturbance history) and become locally dominant for a short period of time. Although these species can be currently widespread (Fig. 4.3; Fig. S11.1), it is likely that adjustments to local conditions and / or competition might eventually involve some distributional contractions.

Chapter 5 General discussion, conclusion and future directions

Studying the floristic exchange between Sunda and Sahul can help understand the processes that drive the differential assembly of diversity across the landscape. Preliminary studies on the floristic exchange across the Australian rainforests suggest that the substantial contribution of invading Sunda-deriving species into the flora is impacted by a suite of factors such as functional attribute, habitat preference and climate (Sniderman & Jordan, 2011; Crayn et al., 2015). The research within this thesis identifies significant contrasts between the Sunda and Sahul components of the Australian rainforest flora, revealing functional characteristics of the invading flora that are skewed towards the pioneer spectrum and some tolerance to the cooler regions of Australia for what are tropical-derived lineages (Chapter 2). The genetic differences between multiple species of Sunda and Sahul ancestry across multiple sites confirmed that disturbance events facilitated the invasion from northern contact points, with the subsequent expansion across Australia occurring at different temporal scales (Chapter 3). Landscape-level genetic homogeneity at the southern distributional limit for Sunda species reveals an extremely recent expansion in response to climate, one that might characterise an invading species that is yet to adapt fully to local conditions (Chapter 4).

5.1 Functional traits and habitat preferences facilitate floristic exchange

Plant species occurring as disjunct populations separated by unsuitable habitat, are likely to rely on specific functional and environmental characteristics. Limited evidence is available to show how trait selection influenced Sunda species distributions in Australia, except that animal dispersal was suggested to facilitate large-scale migration into Australia (Crayn et al., 2015). Chapter 2 describes comparative analyses between the geographic distribution and the functional characteristics of the Sunda and Sahul elements of the Australian rainforest flora using continental and regional distributional datasets and key functional characteristics (leaf size, fruit size, wood density and maximum height at maturity). High concentrations of Sunda-derived species were observed at low latitudes, but species richness remains substantial along South-eastern Australia (Fig. 2.2-2.3), to reflect the recent arrival of these invaders and their subsequent continental expansion. Sunda species traits were skewed towards efficient dispersal and fast growth (Fig. 2.4), consistent with the expectation that the observed species distribution pattern in Australia is largely driven by opportunistic pioneer species capable of long-distance dispersal, rapid establishment and spread.

However, continental species distributional patterns also show that not all species are widespread, but are mostly concentrated in the Northern parts of Australia (Fig. 2.2-2.3), suggesting that other factors are likely to influence their advance. Based on the study of plot data, interacting environmental and climatic factors were observed to influence local distributions of invading species (Fig. 2.5), as these species may have specific habitat preferences derived from their original distribution at the source. The data presented suggests that the invasion of Sunda lineages could have been resisted within stable, saturated communities of Sahul lineages revealing that historical contingencies may play important roles in colonisation success.

Overall, Chapter 2 has revealed that there is a large component of flora within the Australian rainforests with the functional ability to shift geographic range. These species that can implement climate-driven range shifts are of high research value as they are predicted to have the capacity to move away from unfavourable habitat conditions under future climate. Also, Chapter 2 found species range shifts can be influenced by interacting environmental and climatic factors, as well as biotic interactions, providing insights into factors that are important for predicting future distributions. Hence, it is imperative to further examine the expansion process of the Sunda-derived lineages, and thus Chapter 3 and 4 studied fine-scale genetic patterns to compare the landscape-level dynamics of migrant and resident floras.

5.2 Floristic exchange occurred recently, and continental expansion follows different tempo

It has been previously suggested that the Sunda-Sahul floristic exchange has been particularly active in more recent times, increasing in intensity during the Quaternary (Sniderman & Jordan, 2011; Crayn et al., 2015). The recent expansion histories of migrant lineages from Sunda into Australia have been investigated through landscape-level genetic analyses within the Subtropics (Rossetto et al., 2015a), but not in tropical Australia, although this is presumably the point of contact for Sunda lineages. In Chapter 3, multiple co-distributed species of Sunda and Sahul ancestry were compared to characterise the timing and tempo of invasion by Sunda lineages into the Australian Tropics and Subtropics.

Whole chloroplast sequencing revealed that Sunda-derived species consistently have low genomic diversity and steep site-specific species accumulation curves across both regions (Table 3.2; Fig. 3.2- 3.3), providing evidence of recent invasion and expansion. Genomic datasets from multiple species sampled at multiple locations differentiated stable refugia from recently recolonised areas. The 74

repeated disturbance events of the Quaternary favoured a greater concentration of opportunistic invading lineages from Sunda in recolonised areas, as highlighted by the empirical evidence generated when compared between the tropical study sites, Mt Lewis and Mt Baldy-Tinaroo.

The ability to respond to disturbance is however not the only factor that drives colonization, as the proximity to the source is also a major contributing factor. Landscape-level patterns of a subset of Sunda-derived species with continental distributions consistently showed the highest diversity at the most northerly site sampled (Table 3.3), suggesting that invasion originated from a similar point of contact that is closest to Sunda (the source). However, different levels of species-specific genomic differentiation between the Tropics and Subtropics (Table 3.3), suggest that continental expansion by these invading lineages follows different temporal scales.

As the expansion dynamics between southern and northern distributional limits of rainforest species in Australia can involve areas that are 3,000 km apart, much can be learned from fine-scale studies on their tempo and mode of continental invasion. In Chapter 4, the landscape-level dynamics of representative species were compared to investigate their expansion in response to recent climatic events (i.e. within the last few glacial periods).

5.3 Recent, explosive invasions across the landscape

A landscape-level genetic analysis at the distributional limit of an invader can potentially locate the recent expansion front, and show if current distributional patterns are the result of multiple expansion events. Chapter 4 explores the landscape genomics and temporal variation in habitat availability of two co-distributed Australian rainforest species of distinct ancestries across the southern distributional limit of the Sunda-derived species.

Genome-wide SNP analyses revealed high levels of north / south divergence (Fig. 4.2) with higher diversity at higher latitudes (Table 4.1) in the Sahul-derived Doryphora sassafras. The Sunda-derived Toona ciliata however, displayed landscape-level genetic homogeneity (Fig. 4.2). The combination of genomic (Table 4.1; Fig. 4.2) and environmental data (Fig. 4.3) suggests that D. sassafras prefers cooler climates and progressively contracted to higher latitudes as habitat availability decreased during the interglacial. Conversely, T. ciliata displays the signature of recent rapid expansion influenced by habitat availability, with the most recent expansion taking place in the mid-Holocene (6,000 years before present) (Table 4.1; Fig. 4.2). 75

Although Sunda species are expanding through available habitat, their current distribution is unlikely to be stabilized, and it may adjust as local environmental and / or competitive filters change, leading to localized extinctions or even range-wide contractions. In contrast, Sahul species will continue to persist in situ even when conditions are not ideal and track only local suitable habitats through expansion / contraction events.

5.4 Conclusions & Future directions

This thesis provides insights into the invasion of Sunda lineages into Australia through distributional, functional, genomic and climatic datasets, with the following key findings:

 Sunda lineages tend to have pioneer functional attributes and habitat preferences associated with their tropical ancestry  Lineages from Sunda first colonised tropical Australia and then expanded at different tempos to subtropical Australia  Sunda-derived invaders are capable of extremely rapid distributional expansion in response to how climate impacts on habitat availability

The applicability of these datasets demonstrates how (functional and environmental filtering), when (during the most recent glacial disturbance events) and where (highest concentration closest to the source) floristic exchanges occur.

It is anticipated that the outcomes of this study will lead to:

(1) Reciprocal studies across Southeast Asia to investigate floristic origins and the drivers of the intercontinental exchange:

Various studies suggest that floristic exchange is strongly asymmetrical (Sniderman & Jordan, 2011; Richardson et al., 2012; Crayn et al., 2015), as there is a higher concentration of Sunda lineages in Australia than Sahul lineages in Asia (Figure 1.3). The migration of Sunda lineages into Australia is driven by habitat instability and by the functional capacity to rapidly invade newly-available habitats (Chapter 2). These same factors are likely to have had an impact on the invasion of Sunda by Sahul lineages, if not, what are the limitations to successful invasions?

Fluctuations in distribution and extent of rainforests across Sunda during the Quaternary glacial cycles remain unclear. Various lines of evidence from the Last Glacial period suggest two possible scenarios: a savanna corridor across the Sunda Shelf separating major rainforest areas (Bird et al., 2005); and full expansion of rainforests across the Shelf (Cannon et al., 2009). In both cases, Sahul lineages would require potentially highly specialized trait combinations facilitating dispersal, establishment, growth and competition to either expand across savannah and open vegetation, or to compete for resources within hyper-diverse assemblages of Sunda (Slik et al., 2015).

(2) Distribution-wide genetic investigations (including the sources) to better understand the factors (e.g. environmental factors, biotic interactions, species attributes) that drive invasion, range expansion and long-term persistence:

Chapter 4 has shown the feasibility of studying the genetic patterns of co-distributed species of distinct ancestries across broad landscapes to observe the different temporal signatures of expansion / contraction dynamics and evolutionary responses to changing climatic conditions. The study of additional co-distributed rainforest species would allow for a better assessment of how landscape- level dynamics, biogeographic history and functional factors can shape the distribution of genetic diversities and divergence patterns in rainforest trees (e.g. Rossetto et al., 2015a; Worth et al., 2017). For instance, the diversity patterns can locate refugial areas where rainforests typically persist during glacial periods, as well as determine whether invading species commonly expand explosively across broad landscapes driven by recent climate.

Studying invading lineages at their source (Sunda) can provide insights into the expansion process. Patterns of genetic differentiation and diversity between Sunda and Sahul populations can reveal directionality and identify the original source/s into Australia. Coalescent simulations and estimations of demographic parameters (i.e. Ne, effective population sizes, 2 Ne,m, rates of dispersal, and T, divergence time) can provide additional temporal insights, as previously demonstrated within Sunda (Iwanaga et al., 2012; Kamiya et al., 2012; Ohtani et al., 2013).

(3) A new set of conservation priorities for Australian rainforests that takes into account species’ biogeographic history:

Chapter 3 showed that multiple Sunda species are naturally genetically depauperate within Australian rainforests. The low levels of genetic diversity resulting from recent expansion history can lead to reduced viability of populations, and this could affect species’ ability to persist long-term (Reed & Frankham, 2003). Alternatively, modern weed species (i.e. the result of accidental or deliberate introduction by human) can respond to novel environmental changes through the plasticity of their functional traits (i.e. phenotypic plasticity; Baker, 1965; Parker et al., 2003). Various evidence from this thesis show that Sunda species resemble modern weed species, in that they are both widespread, tend to have pioneer functional characteristics, broad environmental preferences and the ability to opportunistically shift distributions in response to changing / novel conditions (Rejmánek & Richardson, 1996, Sakai et al., 2001, Colautii & MacIsacc, 2004, Pyšek & Richardson, 2007). It is expected that phenotypic plasticity would be greater in the invading species than resident species, similar to that observed in invasive studies (see review by Davidson et al., 2011). Testing the plasticity of Australian rainforest species is important, given that the present rainforest habitats will be increasingly vulnerable to changes in fire regime, rising temperatures, changing rainfall patterns and other extreme events (Laurance et al., 2011).

High competitive ability of modern invasive species promotes successful invasive potential (Barker, 1965), but also competitive exclusion by native plant species resists these invasions (Keane & Crawley, 2002). Chapter 2 has shown that both of these events take place within Australian rainforests, as invading Sunda lineages have traits (relating to efficient dispersal and establishment) that were skewed towards high competitive ability, and their invasion appears to be resisted in stable, saturated communities of locally persistent Sahul lineages. Detailed studies across species distributions focusing on where both lineages appear to co-exist will reveal the role of competition during invasion. For instance, this can be through the study of population sizes / abundance of the invader and resident species along an altitudinal gradient, in that an increase in number of propagules of Sunda species with decreasing altitude would provide evidence of the competitive behavior of these invaders, and competitive exclusion by the resident lineages.

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Appendices

Appendix 1 List of Sunda and Sahul species

Table S1.1 List of species of Sahul (N = 795) and Sunda (N = 604) ancestry (species are arranged according to ancestry and then by family in alphabetical order). The certainty in the allocation of species ancestry is also listed, and was used in an exclusion trial to exclude species with ambiguous origins. Species with a certainty of “1” are those species that we are confident about ancestry, “2” are species species from lineages (genera) we are confident about but for which some taxa might have returned to (say) Sahul more recently, “3” are species with some doubt about ancestry and “4” belong to genus for which we have little information. Only those with a certainty of “1” were used in the exclusion trial (see next section: Appendix 2).

Species Family Ancestry Certainty Pleiogynium timorense Anacardiaceae Sahul 1 Euroschinus falcatus Anacardiaceae Sahul 1 Alyxia spicata Apocynaceae Sahul 2 Alyxia sharpei Apocynaceae Sahul 2 Alyxia ruscifolia Apocynaceae Sahul 2 Alyxia orophila Apocynaceae Sahul 2 Alyxia oblongata Apocynaceae Sahul 2 Alyxia magnifolia Apocynaceae Sahul 2 Alyxia ilicifolia Apocynaceae Sahul 2 Alyxia buxifolia Apocynaceae Sahul 2 Mackinlaya macrosciadea Araliaceae Sahul 2 Mackinlaya confusa Araliaceae Sahul 2 Delarbrea michieana Araliaceae Sahul 2 Wollemia nobilis Araucariaceae Sahul 1 Araucaria cunninghamii Araucariaceae Sahul 1 Araucaria bidwillii Araucariaceae Sahul 1 Agathis robusta Araucariaceae Sahul 1 Agathis microstachya Araucariaceae Sahul 1 Agathis atropurpurea Araucariaceae Sahul 1 Dryadodaphne MtLewis Atherospermataceae Sahul 1 Doryphora sassafras Atherospermataceae Sahul 1 Doryphora aromatica Atherospermataceae Sahul 1 Daphnandra tenuipes Atherospermataceae Sahul 1 Daphnandra repandula Atherospermataceae Sahul 1 94

Daphnandra micrantha Atherospermataceae Sahul 1 Daphnandra melasmena Atherospermataceae Sahul 1 Daphnandra johnsonii Atherospermataceae Sahul 1 Daphnandra apatela Atherospermataceae Sahul 1 Atherosperma moschatum Atherospermataceae Sahul 1 Gymnostoma australianum Casuarinaceae Sahul 1 Siphonodon membranaceus Celastraceae Sahul 3 Siphonodon australis Celastraceae Sahul 3 Maytenus WindsorTableland Celastraceae Sahul 3 Maytenus silvestris Celastraceae Sahul 3 Maytenus fasciculiflora Celastraceae Sahul 3 Maytenus disperma Celastraceae Sahul 3 Maytenus cunninghamii Celastraceae Sahul 3 Maytenus bilocularis Celastraceae Sahul 3 Corynocarpus rupestrisrupestris Corynocarpaceae Sahul 3 Corynocarpus rupestrisarborescens Corynocarpaceae Sahul 3 Corynocarpus cribbianus Corynocarpaceae Sahul 3 Vesselowskya venusta Cunoniaceae Sahul 1 Vesselowskya rubifolia Cunoniaceae Sahul 1 Schizomeria whitei Cunoniaceae Sahul 1 Schizomeria ovata Cunoniaceae Sahul 1 Pullea stutzeri Cunoniaceae Sahul 1 Pseudoweinmannia lachnocarpa Cunoniaceae Sahul 1 Gillbeea whypallana Cunoniaceae Sahul 1 Gillbeea adenopetala Cunoniaceae Sahul 1 Geissois biagiana Cunoniaceae Sahul 1 Geissois benthamii Cunoniaceae Sahul 1 Eucryphia wilkiei Cunoniaceae Sahul 1 Eucryphia sp1 Cunoniaceae Sahul 1 Eucryphia moorei Cunoniaceae Sahul 1 Eucryphia lucida Cunoniaceae Sahul 1 Eucryphia jinksii Cunoniaceae Sahul 1 Davidsonia pruriens Cunoniaceae Sahul 1 Davidsonia johnsonii Cunoniaceae Sahul 1 Davidsonia jerseyana Cunoniaceae Sahul 1 Ceratopetalum virchowii Cunoniaceae Sahul 1 Ceratopetalum succirubrum Cunoniaceae Sahul 1 Ceratopetalum macrophyllum Cunoniaceae Sahul 1 Ceratopetalum iugumensis Cunoniaceae Sahul 1

Ceratopetalum hylandii Cunoniaceae Sahul 1 Ceratopetalum corymbosum Cunoniaceae Sahul 1 Ceratopetalum apetalum Cunoniaceae Sahul 1 Callicoma serratifolia Cunoniaceae Sahul 1 Caldcluvia australiensis Cunoniaceae Sahul 1 Anodopetalum biglandulosum Cunoniaceae Sahul 1 Acsmithia davidsonii Cunoniaceae Sahul 1 Ackama paniculata Cunoniaceae Sahul 1 Hibbertia melhanioides Dilleniaceae Sahul 4 Hibbertia hexandra Dilleniaceae Sahul 4 Hibbertia banksii Dilleniaceae Sahul 4 Sloanea woollsii Elaeocarpaceae Sahul 2 Sloanea macbrydei Elaeocarpaceae Sahul 2 Sloanea langii Elaeocarpaceae Sahul 2 Sloanea australisparviflora Elaeocarpaceae Sahul 2 Sloanea australis Elaeocarpaceae Sahul 2 Peripentadenia phelpsii Elaeocarpaceae Sahul 1 Peripentadenia mearsii Elaeocarpaceae Sahul 1 Elaeocarpus WindsorTableland Elaeocarpaceae Sahul 2 Elaeocarpus williamsianus Elaeocarpaceae Sahul 2 Elaeocarpus thelmae Elaeocarpaceae Sahul 2 Elaeocarpus stellaris Elaeocarpaceae Sahul 2 Elaeocarpus sericopetalus Elaeocarpaceae Sahul 2 Elaeocarpus sedentarius Elaeocarpaceae Sahul 2 Elaeocarpus ruminatus Elaeocarpaceae Sahul 2 Elaeocarpus reticulatus Elaeocarpaceae Sahul 2 Elaeocarpus obovatus Elaeocarpaceae Sahul 2 Elaeocarpus MtSpurgeon Elaeocarpaceae Sahul 2 Elaeocarpus MtBellendenKer Elaeocarpaceae Sahul 2 Elaeocarpus MossmanBluff Elaeocarpaceae Sahul 2 Elaeocarpus linsmithii Elaeocarpaceae Sahul 2 Elaeocarpus largiflorensretinervis Elaeocarpaceae Sahul 2 Elaeocarpus largiflorenslargiflorens Elaeocarpaceae Sahul 2 Elaeocarpus kirtonii Elaeocarpaceae Sahul 2 Elaeocarpus johnsonii Elaeocarpaceae Sahul 2 Elaeocarpus holopetalus Elaeocarpaceae Sahul 2 Elaeocarpus grandis Elaeocarpaceae Sahul 2 Elaeocarpus grahamii Elaeocarpaceae Sahul 2 Elaeocarpus foveolatus Elaeocarpaceae Sahul 2

Elaeocarpus ferruginiflorus Elaeocarpaceae Sahul 2 Elaeocarpus eumundi Elaeocarpaceae Sahul 2 Elaeocarpus elliffii Elaeocarpaceae Sahul 2 Elaeocarpus culminicola Elaeocarpaceae Sahul 2 Elaeocarpus coorangooloo Elaeocarpaceae Sahul 2 Elaeocarpus carolinae Elaeocarpaceae Sahul 2 Elaeocarpus bancroftii Elaeocarpaceae Sahul 2 Elaeocarpus arnhemicus Elaeocarpaceae Sahul 2 Aristotelia peduncularis Elaeocarpaceae Sahul 1 Aristotelia australasica Elaeocarpaceae Sahul 1 Aceratium sericoleopsis Elaeocarpaceae Sahul 4 Aceratium megalospermum Elaeocarpaceae Sahul 4 Aceratium ferrugineum Elaeocarpaceae Sahul 4 Aceratium doggrellii Elaeocarpaceae Sahul 4 Aceratium concinnum Elaeocarpaceae Sahul 4 Macaranga tanarius Euphorbiaceae Sahul 1 Eupomatia laurina Eupomatiaceae Sahul 1 Eupomatia bennettii Eupomatiaceae Sahul 1 Eupomatia barbata Eupomatiaceae Sahul 1 Acacia polystachya Fabaceae Sahul 4 Acacia orites Fabaceae Sahul 4 Acacia melanoxylon Fabaceae Sahul 4 Acacia mangium Fabaceae Sahul 4 Acacia maidenii Fabaceae Sahul 4 Acacia fasciculifera Fabaceae Sahul 4 Acacia disparrima Fabaceae Sahul 4 Acacia bakeri Fabaceae Sahul 4 Acacia aulacocarpa Fabaceae Sahul 4 Hernandia nymphaeifolia Hernandiaceae Sahul 4 Hernandia bivalvis Hernandiaceae Sahul 4 Hernandia albiflora Hernandiaceae Sahul 4 Gyrocarpus americanus Hernandiaceae Sahul 4 Galbulimima baccata Himantandraceae Sahul 1 Endiandra xanthocarpa Lauraceae Sahul 2 Endiandra wolfei Lauraceae Sahul 2 Endiandra virens Lauraceae Sahul 2 Endiandra sieberi Lauraceae Sahul 2 Endiandra sideroxylon Lauraceae Sahul 2 Endiandra sankeyana Lauraceae Sahul 2

Endiandra pubens Lauraceae Sahul 2 Endiandra phaeocarpa Lauraceae Sahul 2 Endiandra palmerstonii Lauraceae Sahul 2 Endiandra muelleribracteata Lauraceae Sahul 2 Endiandra muelleri Lauraceae Sahul 2 Endiandra montana Lauraceae Sahul 2 Endiandra monothyratrichophylla Lauraceae Sahul 2 Endiandra monothyramonothyra Lauraceae Sahul 2 Endiandra microneura Lauraceae Sahul 2 Endiandra longipedicellata Lauraceae Sahul 2 Endiandra limnophila Lauraceae Sahul 2 Endiandra leptodendron Lauraceae Sahul 2 Endiandra jonesii Lauraceae Sahul 2 Endiandra introrsa Lauraceae Sahul 2 Endiandra insignis Lauraceae Sahul 2 Endiandra impressicosta Lauraceae Sahul 2 Endiandra hypotephra Lauraceae Sahul 2 Endiandra hayesii Lauraceae Sahul 2 Endiandra grayi Lauraceae Sahul 2 Endiandra globosa Lauraceae Sahul 2 Endiandra glauca Lauraceae Sahul 2 Endiandra floydii Lauraceae Sahul 2 Endiandra discolor Lauraceae Sahul 2 Endiandra dielsiana Lauraceae Sahul 2 Endiandra dichrophylla Lauraceae Sahul 2 Endiandra crassiflora Lauraceae Sahul 2 Endiandra cowleyana Lauraceae Sahul 2 Endiandra cooperana Lauraceae Sahul 2 Endiandra compressa Lauraceae Sahul 2 Endiandra collinsii Lauraceae Sahul 2 Endiandra bessaphila Lauraceae Sahul 2 Endiandra bellendenkerana Lauraceae Sahul 2 Endiandra anthropophagorum Lauraceae Sahul 2 Endiandra acuminata Lauraceae Sahul 2 Cryptocarya WorldsEndPocket Lauraceae Sahul 2 Cryptocarya williwilliana Lauraceae Sahul 2 Cryptocarya vulgaris Lauraceae Sahul 2 Cryptocarya triplinervistriplinervis Lauraceae Sahul 2 Cryptocarya triplinervisriparia Lauraceae Sahul 2

Cryptocarya triplinervispubens Lauraceae Sahul 2 Cryptocarya smaragdina Lauraceae Sahul 2 Cryptocarya sclerophylla Lauraceae Sahul 2 Cryptocarya saccharata Lauraceae Sahul 2 Cryptocarya rigida Lauraceae Sahul 2 Cryptocarya rhodosperma Lauraceae Sahul 2 Cryptocarya putida Lauraceae Sahul 2 Cryptocarya pleurosperma Lauraceae Sahul 2 Cryptocarya onoprienkoana Lauraceae Sahul 2 Cryptocarya obovata Lauraceae Sahul 2 Cryptocarya oblata Lauraceae Sahul 2 Cryptocarya novaanglica Lauraceae Sahul 2 Cryptocarya murrayi Lauraceae Sahul 2 Cryptocarya microneura Lauraceae Sahul 2 Cryptocarya melanocarpa Lauraceae Sahul 2 Cryptocarya meissneriana Lauraceae Sahul 2 Cryptocarya mackinnoniana Lauraceae Sahul 2 Cryptocarya macdonaldii Lauraceae Sahul 2 Cryptocarya lividula Lauraceae Sahul 2 Cryptocarya leucophylla Lauraceae Sahul 2 Cryptocarya laevigata Lauraceae Sahul 2 Cryptocarya hypospodia Lauraceae Sahul 2 Cryptocarya grandis Lauraceae Sahul 2 Cryptocarya glaucocarpa Lauraceae Sahul 2 Cryptocarya glaucescens Lauraceae Sahul 2 Cryptocarya Gadgarra Lauraceae Sahul 2 Cryptocarya foveolata Lauraceae Sahul 2 Cryptocarya foetida Lauraceae Sahul 2 Cryptocarya floydii Lauraceae Sahul 2 Cryptocarya exfoliata Lauraceae Sahul 2 Cryptocarya erythroxylon Lauraceae Sahul 2 Cryptocarya endiandrifolia Lauraceae Sahul 2 Cryptocarya dorrigoensis Lauraceae Sahul 2 Cryptocarya densiflora Lauraceae Sahul 2 Cryptocarya cunninghamii Lauraceae Sahul 2 Cryptocarya corrugata Lauraceae Sahul 2 Cryptocarya cocosoides Lauraceae Sahul 2 Cryptocarya claudiana Lauraceae Sahul 2 Cryptocarya clarksoniana Lauraceae Sahul 2

Cryptocarya burckiana Lauraceae Sahul 2 Cryptocarya brassii Lauraceae Sahul 2 Cryptocarya Boonjee Lauraceae Sahul 2 Cryptocarya bidwillii Lauraceae Sahul 2 Cryptocarya bellendenkerana Lauraceae Sahul 2 Cryptocarya bamagana Lauraceae Sahul 2 Cryptocarya angulata Lauraceae Sahul 2 Beilschmiedia volckii Lauraceae Sahul 2 Beilschmiedia tooram Lauraceae Sahul 2 Beilschmiedia recurva Lauraceae Sahul 2 Beilschmiedia peninsularis Lauraceae Sahul 2 Beilschmiedia oligandra Lauraceae Sahul 2 Beilschmiedia obtusifolia Lauraceae Sahul 2 Beilschmiedia elliptica Lauraceae Sahul 2 Beilschmiedia collina Lauraceae Sahul 2 Beilschmiedia castrisinensis Lauraceae Sahul 2 Beilschmiedia brunnea Lauraceae Sahul 2 Beilschmiedia bancroftii Lauraceae Sahul 2 Brachychiton xanthophyllus Malvaceae Sahul 1 Brachychiton viridiflorus Malvaceae Sahul 1 Brachychiton velutinosus Malvaceae Sahul 1 Brachychiton rupestris Malvaceae Sahul 1 Brachychiton populneus Malvaceae Sahul 1 Brachychiton paradoxus Malvaceae Sahul 1 Brachychiton Ormeau Malvaceae Sahul 1 Brachychiton grandiflorus Malvaceae Sahul 1 Brachychiton garrawayae Malvaceae Sahul 1 Brachychiton diversifolius Malvaceae Sahul 1 Brachychiton discolor Malvaceae Sahul 1 Brachychiton compactus Malvaceae Sahul 1 Brachychiton chillagoensis Malvaceae Sahul 1 Brachychiton bidwillii Malvaceae Sahul 1 Brachychiton australis Malvaceae Sahul 1 Brachychiton albidus Malvaceae Sahul 1 Brachychiton acerifolius Malvaceae Sahul 1 Wilkiea wardellii Monimiaceae Sahul 1 Wilkiea RussellGorge Monimiaceae Sahul 1 Wilkiea Palmerston Monimiaceae Sahul 1 Wilkiea MtMolloy Monimiaceae Sahul 1

100

Wilkiea MtLewis Monimiaceae Sahul 1 Wilkiea MtHemmant Monimiaceae Sahul 1 Wilkiea McIlwraith Monimiaceae Sahul 1 Wilkiea McDowallRange Monimiaceae Sahul 1 Wilkiea macrophylla Monimiaceae Sahul 1 Wilkiea huegeliana Monimiaceae Sahul 1 Wilkiea Barong Monimiaceae Sahul 1 Wilkiea austroqueenslandica Monimiaceae Sahul 1 Wilkiea angustifolia Monimiaceae Sahul 1 Tetrasynandra pubescens Monimiaceae Sahul 1 Tetrasynandra longipes Monimiaceae Sahul 1 Steganthera macooraia Monimiaceae Sahul 1 Steganthera laxifloralewisensis Monimiaceae Sahul 1 Steganthera laxifloralaxiflora Monimiaceae Sahul 1 Steganthera hirsuta Monimiaceae Sahul 1 Steganthera australiana Monimiaceae Sahul 1 Monimiaceae DaviesCreek Monimiaceae Sahul 1 Levieria acuminata Monimiaceae Sahul 1 Kibara rigidifolia Monimiaceae Sahul 1 Hedycarya loxocarya Monimiaceae Sahul 1 Hedycarya angustifolia Monimiaceae Sahul 1 Austromatthaea elegans Monimiaceae Sahul 1 Xanthostemon youngii Myrtaceae Sahul 2 Xanthostemon xerophilus Myrtaceae Sahul 2 Xanthostemon whitei Myrtaceae Sahul 2 Xanthostemon verticillatus Myrtaceae Sahul 2 Xanthostemon umbrosus Myrtaceae Sahul 2 Xanthostemon oppositifolius Myrtaceae Sahul 2 Xanthostemon graniticus Myrtaceae Sahul 2 Xanthostemon formosus Myrtaceae Sahul 2 Xanthostemon eucalyptoides Myrtaceae Sahul 2 Xanthostemon crenulatus Myrtaceae Sahul 2 Xanthostemon chrysanthus Myrtaceae Sahul 2 Xanthostemon arenarius Myrtaceae Sahul 2 Welchiodendron longivalve Myrtaceae Sahul 1 Waterhousea unipunctata Myrtaceae Sahul 1 Waterhousea mulgraveana Myrtaceae Sahul 1 Waterhousea hedraiophylla Myrtaceae Sahul 1 Waterhousea floribunda Myrtaceae Sahul 1

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Uromyrtus tenella Myrtaceae Sahul 1 Uromyrtus metrosideros Myrtaceae Sahul 1 Uromyrtus lamingtonensis Myrtaceae Sahul 1 Uromyrtus australis Myrtaceae Sahul 1 Tristaniopsis laurina Myrtaceae Sahul 1 Tristaniopsis exiliflora Myrtaceae Sahul 1 Tristaniopsis collina Myrtaceae Sahul 1 Thryptomene oligandra Myrtaceae Sahul 4 Thaleropia queenslandica Myrtaceae Sahul 4 Syzygium xerampelinum Myrtaceae Sahul 2 Syzygium wilsoniiwilsonii Myrtaceae Sahul 2 Syzygium wilsoniicryptophlebium Myrtaceae Sahul 2 Syzygium wesa Myrtaceae Sahul 2 Syzygium velarum Myrtaceae Sahul 2 Syzygium trachyphloium Myrtaceae Sahul 2 Syzygium tierneyanum Myrtaceae Sahul 2 Syzygium suborbiculare Myrtaceae Sahul 2 Syzygium sharoniae Myrtaceae Sahul 2 Syzygium sayeri Myrtaceae Sahul 2 Syzygium rubrimolle Myrtaceae Sahul 2 Syzygium puberulum Myrtaceae Sahul 2 Syzygium pseudofastigiatum Myrtaceae Sahul 2 Syzygium papyraceum Myrtaceae Sahul 2 Syzygium paniculatum Myrtaceae Sahul 2 Syzygium oleosum Myrtaceae Sahul 2 Syzygium NoahCreek Myrtaceae Sahul 2 Syzygium nervosum Myrtaceae Sahul 2 Syzygium moorei Myrtaceae Sahul 2 Syzygium monospermum Myrtaceae Sahul 2 Syzygium monimioides Myrtaceae Sahul 2 Syzygium minutuliflorum Myrtaceae Sahul 2 Syzygium maraca Myrtaceae Sahul 2 Syzygium malaccense Myrtaceae Sahul 2 Syzygium macilwraithianum Myrtaceae Sahul 2 Syzygium luehmannii Myrtaceae Sahul 2 Syzygium kuranda Myrtaceae Sahul 2 Syzygium johnsonii Myrtaceae Sahul 2 Syzygium hodgkinsoniae Myrtaceae Sahul 2 Syzygium hemilamprumorophilum Myrtaceae Sahul 2

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Syzygium hemilamprumhemilamprum Myrtaceae Sahul 2 Syzygium gustavioides Myrtaceae Sahul 2 Syzygium glenum Myrtaceae Sahul 2 Syzygium fratris Myrtaceae Sahul 2 Syzygium francisii Myrtaceae Sahul 2 Syzygium fortepotamophilum Myrtaceae Sahul 2 Syzygium forteforte Myrtaceae Sahul 2 Syzygium fibrosum Myrtaceae Sahul 2 Syzygium erythrodoxum Myrtaceae Sahul 2 Syzygium erythrocalyx Myrtaceae Sahul 2 Syzygium endophloium Myrtaceae Sahul 2 Syzygium dansiei Myrtaceae Sahul 2 Syzygium crebrinerve Myrtaceae Sahul 2 Syzygium corynanthum Myrtaceae Sahul 2 Syzygium cormiflorum Myrtaceae Sahul 2 Syzygium canicortex Myrtaceae Sahul 2 Syzygium bungadinnia Myrtaceae Sahul 2 Syzygium buettnerianum Myrtaceae Sahul 2 Syzygium branderhorstii Myrtaceae Sahul 2 Syzygium Bora Myrtaceae Sahul 2 Syzygium boonjee Myrtaceae Sahul 2 Syzygium banksii Myrtaceae Sahul 2 Syzygium bamagense Myrtaceae Sahul 2 Syzygium australe Myrtaceae Sahul 2 Syzygium armstrongii Myrtaceae Sahul 2 Syzygium argyropedicum Myrtaceae Sahul 2 Syzygium aqueum Myrtaceae Sahul 2 Syzygium apodophyllum Myrtaceae Sahul 2 Syzygium anisatum Myrtaceae Sahul 2 Syzygium angophoroides Myrtaceae Sahul 2 Syzygium alliiligneum Myrtaceae Sahul 2 Syzygium alatoramulum Myrtaceae Sahul 2 Stockwellia quadrifida Myrtaceae Sahul 1 Sphaerantia discolor Myrtaceae Sahul 1 Sphaerantia chartacea Myrtaceae Sahul 1 Ristantia waterhousei Myrtaceae Sahul 1 Ristantia pachysperma Myrtaceae Sahul 1 Ristantia gouldii Myrtaceae Sahul 1 Rhodomyrtus trineuratrineura Myrtaceae Sahul 4

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Rhodomyrtus trineuracapensis Myrtaceae Sahul 4 Rhodomyrtus sericea Myrtaceae Sahul 4 Rhodomyrtus psidioides Myrtaceae Sahul 4 Rhodomyrtus pervagata Myrtaceae Sahul 4 Rhodomyrtus macrocarpa Myrtaceae Sahul 4 Rhodomyrtus effusa Myrtaceae Sahul 4 Rhodomyrtus canescens Myrtaceae Sahul 4 Rhodamnia whiteana Myrtaceae Sahul 1 Rhodamnia UpperMassyCrk Myrtaceae Sahul 1 Rhodamnia spongiosa Myrtaceae Sahul 1 Rhodamnia sessiliflora Myrtaceae Sahul 1 Rhodamnia rubescens Myrtaceae Sahul 1 Rhodamnia pauciovulata Myrtaceae Sahul 1 Rhodamnia McIlwraithRange Myrtaceae Sahul 1 Rhodamnia maideniana Myrtaceae Sahul 1 Rhodamnia longisepala Myrtaceae Sahul 1 Rhodamnia glabrescens Myrtaceae Sahul 1 Rhodamnia dumicola Myrtaceae Sahul 1 Rhodamnia costata Myrtaceae Sahul 1 Rhodamnia CapeYork Myrtaceae Sahul 1 Rhodamnia blairiana Myrtaceae Sahul 1 Rhodamnia australis Myrtaceae Sahul 1 Rhodamnia argentea Myrtaceae Sahul 1 Rhodamnia angustifolia Myrtaceae Sahul 1 Rhodamnia acuminata Myrtaceae Sahul 1 Pilidiostigma tropicum Myrtaceae Sahul 1 Pilidiostigma tetramerum Myrtaceae Sahul 1 Pilidiostigma rhytispermum Myrtaceae Sahul 1 Pilidiostigma recurvum Myrtaceae Sahul 1 Pilidiostigma MtLewis Myrtaceae Sahul 1 Pilidiostigma glabrum Myrtaceae Sahul 1 Neofabricia myrtifolia Myrtaceae Sahul 1 Mitrantia bilocularis Myrtaceae Sahul 1 Lophostemon suaveolens Myrtaceae Sahul 1 Lophostemon lactifluus Myrtaceae Sahul 1 Lophostemon grandiflorus Myrtaceae Sahul 1 Lophostemon confertus Myrtaceae Sahul 1 Lithomyrtus obtusa Myrtaceae Sahul 1 Lindsayomyrtus racemoides Myrtaceae Sahul 1

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Leptospermum wooroonooran Myrtaceae Sahul 1 Lenwebbia prominens Myrtaceae Sahul 1 Lenwebbia MainRange Myrtaceae Sahul 1 Lenwebbia lasioclada Myrtaceae Sahul 1 Lenwebbia BlackallRange Myrtaceae Sahul 1 Gossia shepherdii Myrtaceae Sahul 1 Gossia sankowskiorum Myrtaceae Sahul 1 Gossia retusa Myrtaceae Sahul 1 Gossia punctata Myrtaceae Sahul 1 Gossia pubiflora Myrtaceae Sahul 1 Gossia myrsinocarpa Myrtaceae Sahul 1 Gossia lucida Myrtaceae Sahul 1 Gossia lewisensis Myrtaceae Sahul 1 Gossia inophloia Myrtaceae Sahul 1 Gossia hillii Myrtaceae Sahul 1 Gossia GreenBark Myrtaceae Sahul 1 Gossia grayii Myrtaceae Sahul 1 Gossia gonoclada Myrtaceae Sahul 1 Gossia fragrantissima Myrtaceae Sahul 1 Gossia floribunda Myrtaceae Sahul 1 Gossia dallachiana Myrtaceae Sahul 1 Gossia bidwillii Myrtaceae Sahul 1 Gossia bamagensis Myrtaceae Sahul 1 Gossia acmenoides Myrtaceae Sahul 1 Eugenia reinwardtiana Myrtaceae Sahul 2 Decaspermum struckoilicum Myrtaceae Sahul 2 Decaspermum humile Myrtaceae Sahul 2 Choricarpia subargentea Myrtaceae Sahul 1 Choricarpia leptopetala Myrtaceae Sahul 1 Barongia lophandra Myrtaceae Sahul 1 Baeckea gunniana Myrtaceae Sahul 1 Backhousia TullyRiver Myrtaceae Sahul 1 Backhousia sciadophora Myrtaceae Sahul 1 Backhousia oligantha Myrtaceae Sahul 1 Backhousia myrtifolia Myrtaceae Sahul 1 Backhousia kingii Myrtaceae Sahul 1 Backhousia hughesii Myrtaceae Sahul 1 Backhousia citriodora Myrtaceae Sahul 1 Backhousia Belgamba Myrtaceae Sahul 1

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Backhousia bancroftii Myrtaceae Sahul 1 Backhousia angustifolia Myrtaceae Sahul 1 Austromyrtus dulcis Myrtaceae Sahul 1 Asteromyrtus brassii Myrtaceae Sahul 2 Asteromyrtus angustifolia Myrtaceae Sahul 2 Archirhodomyrtus beckleri Myrtaceae Sahul 2 Allosyncarpia ternata Myrtaceae Sahul 2 Acmenosperma pringlei Myrtaceae Sahul 2 Acmenosperma claviflorum Myrtaceae Sahul 2 Acmena smithii Myrtaceae Sahul 2 Acmena resa Myrtaceae Sahul 2 Acmena mackinnoniana Myrtaceae Sahul 2 Acmena ingens Myrtaceae Sahul 2 Acmena graveolens Myrtaceae Sahul 2 Acmena divaricata Myrtaceae Sahul 2 Nothofagus moorei Nothofagaceae Sahul 1 Nothofagus gunnii Nothofagaceae Sahul 1 Nothofagus cunninghamii Nothofagaceae Sahul 1 Sundacarpus amara Podocarpaceae Sahul 2 Prumnopitys ladei Podocarpaceae Sahul 2 Podocarpus smithii Podocarpaceae Sahul 2 Podocarpus lawrencei Podocarpaceae Sahul 2 Podocarpus grayae Podocarpaceae Sahul 2 Podocarpus elatus Podocarpaceae Sahul 2 Podocarpus dispermus Podocarpaceae Sahul 2 Phyllocladus aspleniifolius Podocarpaceae Sahul 1 Pherosphaera hookeriana Podocarpaceae Sahul 1 Lagarostrobus franklinii Podocarpaceae Sahul 3 Tapeinosperma repandulum Primulaceae Sahul 3 Tapeinosperma pseudojambosa Primulaceae Sahul 3 Tapeinosperma CedarBay Primulaceae Sahul 3 Agastachys odorata Primulaceae Sahul 1 Triunia youngiana Proteaceae Sahul 1 Triunia robusta Proteaceae Sahul 1 Triunia montana Proteaceae Sahul 1 Triunia erythrocarpa Proteaceae Sahul 1 Telopea truncata Proteaceae Sahul 1 Telopea oreades Proteaceae Sahul 1 Telopea mongaensis Proteaceae Sahul 1

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Stenocarpus verticis Proteaceae Sahul 1 Stenocarpus sinuatus Proteaceae Sahul 1 Stenocarpus salignus Proteaceae Sahul 1 Stenocarpus reticulatus Proteaceae Sahul 1 Stenocarpus Hinchinbrook Proteaceae Sahul 1 Stenocarpus davallioides Proteaceae Sahul 1 Stenocarpus cunninghamii Proteaceae Sahul 1 Stenocarpus cryptocarpus Proteaceae Sahul 1 Sphalmium racemosum Proteaceae Sahul 1 Placospermum coriaceum Proteaceae Sahul 1 Persoonia volcanica Proteaceae Sahul 1 Persoonia silvatica Proteaceae Sahul 1 Persoonia media Proteaceae Sahul 1 Persoonia arborea Proteaceae Sahul 1 Persoonia amaliae Proteaceae Sahul 1 Persoonia adenantha Proteaceae Sahul 1 Orites megacarpa Proteaceae Sahul 1 Orites excelsus Proteaceae Sahul 1 Opisthiolepis heterophylla Proteaceae Sahul 1 Neorites kevediana Proteaceae Sahul 1 Musgravea stenostachya Proteaceae Sahul 1 Musgravea heterophylla Proteaceae Sahul 1 Megahertzia amplexicaulis Proteaceae Sahul 1 Macadamia whelanii Proteaceae Sahul 1 Macadamia tetraphylla Proteaceae Sahul 1 Macadamia ternifolia Proteaceae Sahul 1 Macadamia jansenii Proteaceae Sahul 1 Macadamia integrifolia Proteaceae Sahul 1 Macadamia grandis Proteaceae Sahul 1 Macadamia claudiensis Proteaceae Sahul 1 Lomatia myricoides Proteaceae Sahul 1 Lomatia fraxinifolia Proteaceae Sahul 1 Lomatia fraseri Proteaceae Sahul 1 Lomatia arborescens Proteaceae Sahul 1 Hollandaea sayeriana Proteaceae Sahul 1 Hollandaea riparia Proteaceae Sahul 1 Hollandaea PinnacleRock Proteaceae Sahul 1 Hollandaea DevilsThumb Proteaceae Sahul 1 Hicksbeachia pinnatifolia Proteaceae Sahul 1

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Hicksbeachia pilosa Proteaceae Sahul 1 Helicia recurva Proteaceae Sahul 1 Helicia nortoniana Proteaceae Sahul 1 Helicia lewisensis Proteaceae Sahul 1 Helicia lamingtoniana Proteaceae Sahul 1 Helicia grayi Proteaceae Sahul 1 Helicia glabriflora Proteaceae Sahul 1 Helicia ferruginea Proteaceae Sahul 1 Helicia blakei Proteaceae Sahul 1 Helicia australasica Proteaceae Sahul 1 Hakea salicifolia Proteaceae Sahul 1 Hakea ochroptera Proteaceae Sahul 1 Hakea eriantha Proteaceae Sahul 1 Grevillea robusta Proteaceae Sahul 1 Grevillea pteridifolia Proteaceae Sahul 1 Grevillea hilliana Proteaceae Sahul 1 Grevillea helmsiae Proteaceae Sahul 1 Grevillea heliosperma Proteaceae Sahul 1 Grevillea baileyana Proteaceae Sahul 1 Grevillea agrifolia Proteaceae Sahul 1 Gevuina bleasdalei Proteaceae Sahul 1 Floydia praealta Proteaceae Sahul 1 Eidothea zoexylocarya Proteaceae Sahul 1 Eidothea hardeniana Proteaceae Sahul 1 Darlingia ferruginea Proteaceae Sahul 1 Darlingia darlingiana Proteaceae Sahul 1 Cenarrhenes nitida Proteaceae Sahul 1 Catalepidia heyana Proteaceae Sahul 1 Carnarvonia araliifoliamontana Proteaceae Sahul 1 Carnarvonia araliifoliaaraliifolia Proteaceae Sahul 1 Cardwellia sublimis Proteaceae Sahul 1 Buckinghamia ferruginiflora Proteaceae Sahul 1 Buckinghamia celsissima Proteaceae Sahul 1 Banksia integrifolia Proteaceae Sahul 1 Banksia aquilonia Proteaceae Sahul 1 Austromuellera valida Proteaceae Sahul 1 Austromuellera trinervia Proteaceae Sahul 1 Athertonia diversifolia Proteaceae Sahul 1 Alloxylon wickhamii Proteaceae Sahul 1

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Alloxylon pinnatum Proteaceae Sahul 1 Alloxylon flammeum Proteaceae Sahul 1 Schistocarpaea johnsonii Rhamnaceae Sahul 4 Pomaderris notata Rhamnaceae Sahul 1 Pomaderris clivicola Rhamnaceae Sahul 1 Pomaderris cinerea Rhamnaceae Sahul 1 Pomaderris aspera Rhamnaceae Sahul 1 Pomaderris argyrophylla Rhamnaceae Sahul 1 Emmenosperma cunninghamii Rhamnaceae Sahul 2 Emmenosperma alphitonioides Rhamnaceae Sahul 2 Alphitonia whitei Rhamnaceae Sahul 3 Alphitonia sp. excelsa (Forty Mile Rhamnaceae Sahul 3 Scrub BH 25763RFK) Alphitonia petriei Rhamnaceae Sahul 3 Alphitonia Kimberley Rhamnaceae Sahul 3 Alphitonia incana Rhamnaceae Sahul 3 Alphitonia excelsa Rhamnaceae Sahul 3 Halfordia scleroxyla Rutaceae Sahul 1 Halfordia kendack Rutaceae Sahul 1 Geijera salicifoliasalicifolia Rutaceae Sahul 1 Geijera salicifolialatifolia Rutaceae Sahul 1 Geijera parviflora Rutaceae Sahul 1 Flindersia xanthoxyla Rutaceae Sahul 1 Flindersia schottiana Rutaceae Sahul 1 Flindersia pimenteliana Rutaceae Sahul 1 Flindersia oppositifolia Rutaceae Sahul 1 Flindersia laevicarpalaevicarpa Rutaceae Sahul 1 Flindersia ifflaiana Rutaceae Sahul 1 Flindersia collina Rutaceae Sahul 1 Flindersia brayleyana Rutaceae Sahul 1 Flindersia brassii Rutaceae Sahul 1 Flindersia bourjotiana Rutaceae Sahul 1 Flindersia bennettiana Rutaceae Sahul 1 Flindersia australis Rutaceae Sahul 1 Flindersia acuminata Rutaceae Sahul 1 Tristiropsis acutangula Sapindaceae Sahul 4 Toechima tenax Sapindaceae Sahul 1 Toechima pterocarpum Sapindaceae Sahul 1 Toechima monticola Sapindaceae Sahul 1 Toechima erythrocarpum Sapindaceae Sahul 1 109

Toechima dasyrrhache Sapindaceae Sahul 1 Toechima daemelianum Sapindaceae Sahul 1 Synima macrophylla Sapindaceae Sahul 1 Synima cordierorum Sapindaceae Sahul 1 Sarcotoechia villosa Sapindaceae Sahul 1 Sarcotoechia serrata Sapindaceae Sahul 1 Sarcotoechia protracta Sapindaceae Sahul 1 Sarcotoechia MtCarbine Sapindaceae Sahul 1 Sarcotoechia lanceolata Sapindaceae Sahul 1 Sarcotoechia heterophylla Sapindaceae Sahul 1 Sarcotoechia cuneata Sapindaceae Sahul 1 Sarcopteryx stipata Sapindaceae Sahul 1 Sarcopteryx reticulata Sapindaceae Sahul 1 Sarcopteryx montana Sapindaceae Sahul 1 Sarcopteryx McIlwraithRange Sapindaceae Sahul 1 Sarcopteryx martyana Sapindaceae Sahul 1 Sarcopteryx acuminata Sapindaceae Sahul 1 Sapindaceae NoahCreek Sapindaceae Sahul 4 Rhysotoechia robertsonii Sapindaceae Sahul 1 Rhysotoechia mortoniana Sapindaceae Sahul 1 Rhysotoechia florulenta Sapindaceae Sahul 1 Rhysotoechia flavescens Sapindaceae Sahul 1 Rhysotoechia bifoliolatanitida Sapindaceae Sahul 1 Rhysotoechia bifoliolatabifoliolata Sapindaceae Sahul 1 Mischocarpus stipitatus Sapindaceae Sahul 2 Mischocarpus pyriformisretusus Sapindaceae Sahul 2 Mischocarpus pyriformispyriformis Sapindaceae Sahul 2 Mischocarpus macrocarpus Sapindaceae Sahul 2 Mischocarpus lachnocarpus Sapindaceae Sahul 2 Mischocarpus grandissimus Sapindaceae Sahul 2 Mischocarpus exangulatus Sapindaceae Sahul 2 Mischocarpus australis Sapindaceae Sahul 2 Mischocarpus anodontus Sapindaceae Sahul 2 Mischocarpus albescens Sapindaceae Sahul 2 Mischarytera macrobotrys Sapindaceae Sahul 4 Mischarytera lautereriana Sapindaceae Sahul 4 Lepisanthes senegalensis Sapindaceae Sahul 4 Lepisanthes rubiginosa Sapindaceae Sahul 4 Lepidopetalum fructoglabrum Sapindaceae Sahul 4

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Lepiderema sericolignis Sapindaceae Sahul 1 Lepiderema punctulata Sapindaceae Sahul 1 Lepiderema pulchella Sapindaceae Sahul 1 Lepiderema largiflorens Sapindaceae Sahul 1 Lepiderema ixiocarpa Sapindaceae Sahul 1 Lepiderema ImpulseCreek Sapindaceae Sahul 1 Lepiderema hirsuta Sapindaceae Sahul 1 Jagera pseudorhuspseudorhus Sapindaceae Sahul 1 Jagera pseudorhusintegerrima Sapindaceae Sahul 1 Jagera javanica Sapindaceae Sahul 1 Harpullia rhyticarpa Sapindaceae Sahul 1 Harpullia ramiflora Sapindaceae Sahul 1 Harpullia pendula Sapindaceae Sahul 1 Harpullia hillii Sapindaceae Sahul 1 Harpullia frutescens Sapindaceae Sahul 1 Harpullia arborea Sapindaceae Sahul 1 Harpullia alata Sapindaceae Sahul 1 Guioa semiglauca Sapindaceae Sahul 1 Guioa sarcopterifructa Sapindaceae Sahul 1 Guioa montana Sapindaceae Sahul 1 Guioa lasioneura Sapindaceae Sahul 1 Guioa acutifolia Sapindaceae Sahul 1 Ganophyllum falcatum Sapindaceae Sahul 1 Elattostachys xylocarpa Sapindaceae Sahul 1 Elattostachys nervosa Sapindaceae Sahul 1 Elattostachys microcarpa Sapindaceae Sahul 1 Elattostachys megalantha Sapindaceae Sahul 1 Dodonaea viscosaviscosa Sapindaceae Sahul 2 Dodonaea triquetra Sapindaceae Sahul 2 Dodonaea polyandra Sapindaceae Sahul 2 Dodonaea platyptera Sapindaceae Sahul 2 Dodonaea lanceolatasubsessilifolia Sapindaceae Sahul 2 Dodonaea lanceolatalanceolata Sapindaceae Sahul 2 Diploglottis smithii Sapindaceae Sahul 1 Diploglottis pedleyi Sapindaceae Sahul 1 Diploglottis obovata Sapindaceae Sahul 1 Diploglottis macrantha Sapindaceae Sahul 1 Diploglottis harpullioides Sapindaceae Sahul 1 Diploglottis diphyllostegia Sapindaceae Sahul 1

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Diploglottis campbellii Sapindaceae Sahul 1 Diploglottis bracteata Sapindaceae Sahul 1 Diploglottis bernieana Sapindaceae Sahul 1 Diploglottis australis Sapindaceae Sahul 1 Dimocarpus australianus Sapindaceae Sahul 1 Dictyoneura obtusa Sapindaceae Sahul 1 Cupaniopsis wadsworthii Sapindaceae Sahul 2 Cupaniopsis TullyFalls Sapindaceae Sahul 2 Cupaniopsis simulatus Sapindaceae Sahul 2 Cupaniopsis shirleyana Sapindaceae Sahul 2 Cupaniopsis serrata Sapindaceae Sahul 2 Cupaniopsis parvifolia Sapindaceae Sahul 2 Cupaniopsis newmanii Sapindaceae Sahul 2 Cupaniopsis foveolata Sapindaceae Sahul 2 Cupaniopsis fleckeri Sapindaceae Sahul 2 Cupaniopsis flagelliformisaustralis Sapindaceae Sahul 2 Cupaniopsis flagelliformis Sapindaceae Sahul 2 Cupaniopsis diploglottoides Sapindaceae Sahul 2 Cupaniopsis dallachyi Sapindaceae Sahul 2 Cupaniopsis cooperorum Sapindaceae Sahul 2 Cupaniopsis baileyana Sapindaceae Sahul 2 Cupaniopsis anacardioides Sapindaceae Sahul 2 Cossinia australiana Sapindaceae Sahul 1 Cnesmocarpon dasyantha Sapindaceae Sahul 1 Castanospora alphandii Sapindaceae Sahul 1 Atalaya variifolia Sapindaceae Sahul 4 Atalaya sericopetala Sapindaceae Sahul 4 Atalaya salicifolia Sapindaceae Sahul 4 Atalaya rigida Sapindaceae Sahul 4 Atalaya oligoclada Sapindaceae Sahul 4 Atalaya multiflora Sapindaceae Sahul 4 Atalaya hemiglauca Sapindaceae Sahul 4 Atalaya collina Sapindaceae Sahul 4 Atalaya calcicola Sapindaceae Sahul 4 Atalaya australiana Sapindaceae Sahul 4 Atalaya angustifolia Sapindaceae Sahul 4 Arytera pseudofoveolata Sapindaceae Sahul 2 Arytera pauciflora Sapindaceae Sahul 2 Arytera microphylla Sapindaceae Sahul 2

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Arytera foveolata Sapindaceae Sahul 2 Arytera Dryander Sapindaceae Sahul 2 Arytera divaricata Sapindaceae Sahul 2 Arytera distylis Sapindaceae Sahul 2 Arytera dictyoneura Sapindaceae Sahul 2 Arytera bifoliolata Sapindaceae Sahul 2 Allophylus cobbe Sapindaceae Sahul 1 Alectryon tropicus Sapindaceae Sahul 1 Alectryon tomentosus Sapindaceae Sahul 1 Alectryon subdentatus Sapindaceae Sahul 1 Alectryon subcinereus Sapindaceae Sahul 1 Alectryon semicinereus Sapindaceae Sahul 1 Alectryon reticulatus Sapindaceae Sahul 1 Alectryon repandodentatus Sapindaceae Sahul 1 Alectryon ramiflorus Sapindaceae Sahul 1 Alectryon pubescens Sapindaceae Sahul 1 Alectryon oleifolius Sapindaceae Sahul 1 Alectryon kimberleyanus Sapindaceae Sahul 1 Alectryon forsythii Sapindaceae Sahul 1 Alectryon diversifolius Sapindaceae Sahul 1 Alectryon coriaceus Sapindaceae Sahul 1 Alectryon connatus Sapindaceae Sahul 1 Pouteria xylocarpa Sapotaceae Sahul 2 Pouteria xerocarpa Sapotaceae Sahul 2 Pouteria unmackiana Sapotaceae Sahul 2 Pouteria singuliflora Sapotaceae Sahul 2 Pouteria sericea Sapotaceae Sahul 2 Pouteria queenslandica Sapotaceae Sahul 2 Pouteria pearsoniorum Sapotaceae Sahul 2 Pouteria papyracea Sapotaceae Sahul 2 Pouteria obovata Sapotaceae Sahul 2 Pouteria myrsinodendron Sapotaceae Sahul 2 Pouteria myrsinifolia Sapotaceae Sahul 2 Pouteria Mt Lewis Sapotaceae Sahul 2 Pouteria euphlebia Sapotaceae Sahul 2 Pouteria eerwah Sapotaceae Sahul 2 Pouteria cotinifolia Sapotaceae Sahul 2 Pouteria chartacea Sapotaceae Sahul 2 Pouteria castanosperma Sapotaceae Sahul 2

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Pouteria brownlessiana Sapotaceae Sahul 2 Pouteria Barong Sapotaceae Sahul 2 Pouteria australis Sapotaceae Sahul 2 Pouteria asterocarpon Sapotaceae Sahul 2 Planchonella pohlmanianapohlmaniana Sapotaceae Sahul 2 Niemeyera whitei Sapotaceae Sahul 4 Niemeyera prunifera Sapotaceae Sahul 4 Niemeyera MtLewis Sapotaceae Sahul 4 Niemeyera ChurchillCreek Sapotaceae Sahul 4 Niemeyera chartacea Sapotaceae Sahul 4 Niemeyera antiloga Sapotaceae Sahul 4 Pimelea sericostachya Thymelaeaeceae Sahul 1 Pimelea linifolia Thymelaeaeceae Sahul 1 Pimelea ligustrina Thymelaeaeceae Sahul 1 Pimelea latifolia Thymelaeaeceae Sahul 1 Pimelea drupacea Thymelaeaeceae Sahul 1 Pimelea axiflora Thymelaeaeceae Sahul 1 Pimelea aquilonia Thymelaeaeceae Sahul 1 Tasmannia xerophilarobusta Winteraceae Sahul 1 Tasmannia stipitata Winteraceae Sahul 1 Tasmannia purpurascens Winteraceae Sahul 1 Tasmannia MtBellendenKer Winteraceae Sahul 1 Tasmannia membranea Winteraceae Sahul 1 Tasmannia lanceolata Winteraceae Sahul 1 Tasmannia insipida Winteraceae Sahul 1 Tasmannia glaucifolia Winteraceae Sahul 1 Bubbia whiteana Winteraceae Sahul 1 Bubbia semecarpoides Winteraceae Sahul 1 Bubbia queenslandianaqueenslandiana Winteraceae Sahul 1 Bubbia queenslandianaaustralis Winteraceae Sahul 1 Saurauia andreana Actinidiaceae Sunda 1 Semecarpus australiensis Anacardiaceae Sunda 1 Rhus taitensis Anacardiaceae Sunda 1 Buchanania obovata Anacardiaceae Sunda 1 Buchanania mangoides Anacardiaceae Sunda 1 Buchanania arborescens Anacardiaceae Sunda 1 Blepharocarya involucrigera Anacardiaceae Sunda 1 Xylopia maccreae Annonaceae Sunda 1 Xylopia BertiehaughHomestead Annonaceae Sunda 1

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Pseuduvaria villosa Annonaceae Sunda 1 Pseuduvaria mulgraveanamulgraveana Annonaceae Sunda 1 Pseuduvaria mulgraveanaglabrescens Annonaceae Sunda 1 Pseuduvaria hylandii Annonaceae Sunda 1 Pseuduvaria froggattii Annonaceae Sunda 1 Polyalthia Wyvuri Annonaceae Sunda 1 Polyalthia nitidissima Annonaceae Sunda 1 Polyalthia michaelii Annonaceae Sunda 1 Polyalthia australis Annonaceae Sunda 1 Mitrephora diversifolia Annonaceae Sunda 1 Miliusa traceyi Annonaceae Sunda 1 Miliusa horsfieldii Annonaceae Sunda 1 Miliusa brahei Annonaceae Sunda 1 Meiogyne stenopetala Annonaceae Sunda 1 Meiogyne MtLewis Annonaceae Sunda 1 Meiogyne HenriettaCreek Annonaceae Sunda 1 Meiogyne cylindrocarpatrichocarpa Annonaceae Sunda 1 Meiogyne cylindrocarpacylindrocarpa Annonaceae Sunda 1 Haplostichanthus Annonaceae Sunda 1 submontanussubmontanus Haplostichanthus Annonaceae Sunda 1 submontanussessiliflorus Haplostichanthus RockyRiver Annonaceae Sunda 1 Haplostichanthus JohnstoneRiver Annonaceae Sunda 1 Haplostichanthus johnsonii Annonaceae Sunda 1 Haplostichanthus CooperCreek Annonaceae Sunda 1 Goniothalamus australis Annonaceae Sunda 1 Fitzalania heteropetala Annonaceae Sunda 1 Fitzalania GregoryRiver Annonaceae Sunda 1 Cananga odorata Annonaceae Sunda 1 Ilex Gadgarra Aquifoliaceae Sunda 3 Ilex arnhemensisferdinandi Aquifoliaceae Sunda 3 Ilex arnhemensisarnhemensis Aquifoliaceae Sunda 3 Dolichandrone spathacea Bignoniaceae Sunda 4 Deplanchea tetraphylla Bignoniaceae Sunda 1 Garuga floribunda Burseraceae Sunda 1 Canarium vitiense Burseraceae Sunda 1 Canarium muelleri Burseraceae Sunda 1 Canarium australianumaustralianum Burseraceae Sunda 1 Canarium australasicum Burseraceae Sunda 1 115

Canarium acutifolium Burseraceae Sunda 1 Trema tomentosaviridis Cannabaceae Sunda 2 Trema tomentosatomentosa Cannabaceae Sunda 2 Trema orientalis Cannabaceae Sunda 2 Celtis timorensis Cannabaceae Sunda 2 Celtis philippensis Cannabaceae Sunda 2 Celtis paniculata Cannabaceae Sunda 2 Celtis hildebrandii Cannabaceae Sunda 2 Aphananthe philippinensis Cannabaceae Sunda 2 Citronella smythii Cardiopteridaceae Sunda 1 Citronella moorei Cardiopteridaceae Sunda 1 Euonymus globularis Celastraceae Sunda 1 Euonymus australiana Celastraceae Sunda 1 Mesua larnachiana Clusiaceae Sunda 1 Mesua Boonjee Clusiaceae Sunda 1 Mammea touriga Clusiaceae Sunda 1 Garcinia warrenii Clusiaceae Sunda 1 Garcinia riparia Clusiaceae Sunda 1 Garcinia Mossman Clusiaceae Sunda 1 Garcinia mestonii Clusiaceae Sunda 1 Garcinia gibbsiae Clusiaceae Sunda 1 Garcinia dulcis Clusiaceae Sunda 1 Garcinia DaviesCreek Clusiaceae Sunda 1 Garcinia ClaudieRiver Clusiaceae Sunda 1 Garcinia brassii Clusiaceae Sunda 1 Calophyllum soulattri Clusiaceae Sunda 1 Calophyllum sil Clusiaceae Sunda 1 Calophyllum inophyllum Clusiaceae Sunda 1 Calophyllum costatum Clusiaceae Sunda 1 Calophyllum bicolor Clusiaceae Sunda 1 Calophyllum australianum Clusiaceae Sunda 1 Terminalia subacroptera Combretaceae Sunda 2 Terminalia sericocarpa Combretaceae Sunda 2 Terminalia porphyrocarpa Combretaceae Sunda 2 Terminalia platyptera Combretaceae Sunda 2 Terminalia platyphylla Combretaceae Sunda 2 Terminalia petiolaris Combretaceae Sunda 2 Terminalia oblongatavolucris Combretaceae Sunda 2 Terminalia oblongataoblongata Combretaceae Sunda 2

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Terminalia muelleri Combretaceae Sunda 2 Terminalia melanocarpa Combretaceae Sunda 2 Terminalia erythrocarpa Combretaceae Sunda 2 Terminalia complanata Combretaceae Sunda 2 Terminalia catappa Combretaceae Sunda 2 Terminalia aridicolachillagoensis Combretaceae Sunda 2 Terminalia arenicola Combretaceae Sunda 2 Macropteranthes montana Combretaceae Sunda 2 Macropteranthes leiocaulis Combretaceae Sunda 2 Macropteranthes fitzalanii Combretaceae Sunda 2 Dansiea grandiflora Combretaceae Sunda 2 Dansiea elliptica Combretaceae Sunda 2 Alangium villosumtomentosum Cornaceae Sunda 1 Alangium villosumpolyosmoides Cornaceae Sunda 1 Alangium ClaudieRiver Cornaceae Sunda 1 Callitris rhomboidea Cupressaceae Sunda 1 Callitris macleayana Cupressaceae Sunda 1 Callitris intratropica Cupressaceae Sunda 1 Callitris glaucophylla Cupressaceae Sunda 1 Callitris baileyi Cupressaceae Sunda 1 Tetrameles nudiflora Datiscaceae Sunda 4 Dillenia alata Dilleniaceae Sunda 1 Diospyros SwipersFlat Ebenaceae Sunda 1 Diospyros hebecarpa Ebenaceae Sunda 1 Margaritaria indica Euphorbiaceae Sunda 2 Margaritaria dubiumtraceyi Euphorbiaceae Sunda 2 Mallotus resinosus Euphorbiaceae Sunda 1 Mallotus polyadenos Euphorbiaceae Sunda 1 Mallotus philippensis Euphorbiaceae Sunda 1 Mallotus paniculatus Euphorbiaceae Sunda 1 Mallotus nesophilus Euphorbiaceae Sunda 1 Mallotus mollissimus Euphorbiaceae Sunda 1 Mallotus megadontus Euphorbiaceae Sunda 1 Mallotus dispersus Euphorbiaceae Sunda 1 Mallotus discolor Euphorbiaceae Sunda 1 Mallotus claoxyloides Euphorbiaceae Sunda 1 Bischofia javanica Euphorbiaceae Sunda 1 Antidesma parvifolium Euphorbiaceae Sunda 1 Antidesma hylandii Euphorbiaceae Sunda 1

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Antidesma ghesaembilla Euphorbiaceae Sunda 1 Antidesma erostre Euphorbiaceae Sunda 1 Antidesma bunius Euphorbiaceae Sunda 1 Fagraea racemosa Gentianaceae Sunda 1 Fagraea fagraeacea Gentianaceae Sunda 1 Fagraea cambagei Gentianaceae Sunda 1 Fagraea berteroana Gentianaceae Sunda 1 Cyrtandra baileyi Gesneriaceae Sunda 1 Ostrearia australiana Hamamelidaceae Sunda 1 Noahdendron nicholasii Hamamelidaceae Sunda 1 Neostrearia fleckeri Hamamelidaceae Sunda 1 Ryticaryum longifolium Icacinaceae Sunda 4 Irvingbaileya australis Icacinaceae Sunda 1 Gomphandra australiana Icacinaceae Sunda 1 Apodytes brachystylis Icacinaceae Sunda 1 Neolitsea dealbata Lauraceae Sunda 1 Neolitsea brassii Lauraceae Sunda 1 Neolitsea australiensis Lauraceae Sunda 1 Litsea reticulata Lauraceae Sunda 1 Litsea macrophylla Lauraceae Sunda 1 Litsea leefeana Lauraceae Sunda 1 Litsea granitica Lauraceae Sunda 1 Litsea glutinosa Lauraceae Sunda 1 Litsea fawcettiana Lauraceae Sunda 1 Litsea connorsii Lauraceae Sunda 1 Litsea breviumbellata Lauraceae Sunda 1 Litsea bindoniana Lauraceae Sunda 1 Litsea bennettii Lauraceae Sunda 1 Litsea australis Lauraceae Sunda 1 Lindera queenslandica Lauraceae Sunda 1 Cinnamomum virens Lauraceae Sunda 1 Cinnamomum propinquum Lauraceae Sunda 1 Cinnamomum oliveri Lauraceae Sunda 1 Cinnamomum laubatii Lauraceae Sunda 1 Cinnamomum baileyanum Lauraceae Sunda 1 Planchonia rupestris Lecythidaceae Sunda 1 Planchonia careya Lecythidaceae Sunda 1 Barringtonia racemosa Lecythidaceae Sunda 1 Barringtonia calyptrata Lecythidaceae Sunda 1

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Barringtonia asiatica Lecythidaceae Sunda 1 Barringtonia acutangulaacutangula Lecythidaceae Sunda 1 Pemphis acidula Lythraceae Sunda 1 Lagerstroemia archeriana Lythraceae Sunda 1 Sterculia shillinglawii Malvaceae Sunda 1 Sterculia quadrifida Malvaceae Sunda 1 Sterculia holtzei Malvaceae Sunda 1 Kleinhovia hospita Malvaceae Sunda 1 Heritiera littoralis Malvaceae Sunda 1 Helicteres semiglabra Malvaceae Sunda 1 Helicteres isora Malvaceae Sunda 1 Helicteres hirsuta Malvaceae Sunda 1 Franciscodendron laurifolium Malvaceae Sunda 1 Firmiana papuana Malvaceae Sunda 1 Commersonia rossii Malvaceae Sunda 1 Commersonia macrostipulata Malvaceae Sunda 1 Commersonia Klondike Malvaceae Sunda 1 Commersonia fraseri Malvaceae Sunda 1 Commersonia bartramia Malvaceae Sunda 1 Argyrodendron Whyanbeel Malvaceae Sunda 1 Argyrodendron Whitsundays Malvaceae Sunda 1 Argyrodendron trifoliolatum Malvaceae Sunda 1 Argyrodendron polyandrum Malvaceae Sunda 1 Argyrodendron peralatum Malvaceae Sunda 1 Argyrodendron MtHaig Malvaceae Sunda 1 Argyrodendron KinKin Malvaceae Sunda 1 Argyrodendron Karnak Malvaceae Sunda 1 Argyrodendron Boonjee Malvaceae Sunda 1 Argyrodendron Malvaceae Sunda 1 actinophyllumdiversifolium Argyrodendron Malvaceae Sunda 1 actinophyllumactinophyllum Abroma fastuosa Malvaceae Sunda 1 Pternandra coerulescens Melastomataceae Sunda 4 Osbeckia australiana Melastomataceae Sunda 4 Memecylon pauciflorum Melastomataceae Sunda 1 Memecylon hylandii Melastomataceae Sunda 1 Melastoma malabathricum Melastomataceae Sunda 1 Melastoma cyanoides Melastomataceae Sunda 1 Medinilla ballsheadleyi Melastomataceae Sunda 1 119

Xylocarpus rumphii Meliaceae Sunda 1 Xylocarpus moluccensis Meliaceae Sunda 1 Xylocarpus granatum Meliaceae Sunda 1 Vavaea australiana Meliaceae Sunda 1 Vavaea amicorum Meliaceae Sunda 1 Turraea pubescens Meliaceae Sunda 1 Toona ciliata Meliaceae Sunda 1 Synoum glandulosumpaniculosum Meliaceae Sunda 1 Synoum glandulosumglandulosum Meliaceae Sunda 1 Owenia Xreliqua Meliaceae Sunda 4 Owenia venosa Meliaceae Sunda 4 Owenia cepiodora Meliaceae Sunda 4 Owenia acidula Meliaceae Sunda 4 Melia azedarach Meliaceae Sunda 1 Dysoxylum setosum Meliaceae Sunda 3 Dysoxylum rufum Meliaceae Sunda 3 Dysoxylum pumilum Meliaceae Sunda 3 Dysoxylum pettigrewianum Meliaceae Sunda 3 Dysoxylum parasiticum Meliaceae Sunda 3 Dysoxylum papuanum Meliaceae Sunda 3 Dysoxylum oppositifolium Meliaceae Sunda 3 Dysoxylum mollissimum Meliaceae Sunda 3 Dysoxylum latifolium Meliaceae Sunda 3 Dysoxylum klanderi Meliaceae Sunda 3 Dysoxylum gaudichaudianum Meliaceae Sunda 3 Dysoxylum fraserianum Meliaceae Sunda 3 Dysoxylum Deepwater Meliaceae Sunda 3 Dysoxylum arborescens Meliaceae Sunda 3 Dysoxylum alliaceum Meliaceae Sunda 3 Dysoxylum actutangulatum Meliaceae Sunda 3 Chisocheton longistipitatus Meliaceae Sunda 1 Anthocarapa nitidula Meliaceae Sunda 1 Aglaia tomentosa Meliaceae Sunda 1 Aglaia spectabilis Meliaceae Sunda 1 Aglaia silvestris Meliaceae Sunda 1 Aglaia SilverPlains Meliaceae Sunda 1 Aglaia sapindina Meliaceae Sunda 1 Aglaia meridionalis Meliaceae Sunda 1 Aglaia euryanthera Meliaceae Sunda 1

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Aglaia elaeagnoidea Meliaceae Sunda 1 Aglaia brownii Meliaceae Sunda 1 Aglaia brassii Meliaceae Sunda 1 Aglaia australiensis Meliaceae Sunda 1 Aglaia argentea Meliaceae Sunda 1 Streblus glaber Moraceae Sunda 1 Streblus brunonianus Moraceae Sunda 1 Maclura cochinchinensis Moraceae Sunda 1 Ficus watkinsiana Moraceae Sunda 1 Ficus virgata Moraceae Sunda 1 Ficus virensvirens Moraceae Sunda 1 Ficus virenssublanceolata Moraceae Sunda 1 Ficus variegata Moraceae Sunda 1 Ficus triradiata Moraceae Sunda 1 Ficus tinctoria Moraceae Sunda 1 Ficus superba var henneana Moraceae Sunda 1 Ficus subpuberula Moraceae Sunda 1 Ficus subnervosa Moraceae Sunda 1 Ficus septica Moraceae Sunda 1 Ficus scobina Moraceae Sunda 1 Ficus rubiginosarubiginosa Moraceae Sunda 1 Ficus rubiginosaglabrescens Moraceae Sunda 1 Ficus racemosa Moraceae Sunda 1 Ficus podocarpifolia Moraceae Sunda 1 Ficus pleurocarpa Moraceae Sunda 1 Ficus platypoda Moraceae Sunda 1 Ficus opposita Moraceae Sunda 1 Ficus obliqua Moraceae Sunda 1 Ficus nodosa Moraceae Sunda 1 Ficus mollior Moraceae Sunda 1 Ficus microcarpa Moraceae Sunda 1 Ficus melinocarpahololampra Moraceae Sunda 1 Ficus macrophylla Moraceae Sunda 1 Ficus leptoclada Moraceae Sunda 1 Ficus hispida Moraceae Sunda 1 Ficus fraseri Moraceae Sunda 1 Ficus drupacea Moraceae Sunda 1 Ficus destruens Moraceae Sunda 1 Ficus cumingiiandrobrota Moraceae Sunda 1

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Ficus crassipes Moraceae Sunda 1 Ficus coronulata Moraceae Sunda 1 Ficus coronata Moraceae Sunda 1 Ficus copiosa Moraceae Sunda 1 Ficus congesta Moraceae Sunda 1 Ficus brachypoda Moraceae Sunda 1 Ficus benjamina Moraceae Sunda 1 Ficus atricha Moraceae Sunda 1 Ficus albipila Moraceae Sunda 1 Ficus adenosperma Moraceae Sunda 1 Ficus aculeataindecora Moraceae Sunda 1 Artocarpus glaucus Moraceae Sunda 1 Antiaris toxicaria Moraceae Sunda 1 Myristica lancifoliaaustraliana Myristicaceae Sunda 1 Myristica insipida Myristicaceae Sunda 1 Myristica globosa Myristicaceae Sunda 1 Horsfieldia australiana Myristicaceae Sunda 1 Sphenostemon lobosporus Paracryphiaceae Sunda 3 Pennantia cunninghamii Pennantiaceae Sunda 4 Sauropus macranthus Phyllanthaceae Sunda 1 Sauropus albiflorus Phyllanthaceae Sunda 1 Phyllanthus subcrenulatus Phyllanthaceae Sunda 1 Phyllanthus sauropodoides Phyllanthaceae Sunda 1 Phyllanthus reticulatus Phyllanthaceae Sunda 1 Phyllanthus praelongipes Phyllanthaceae Sunda 1 Phyllanthus novaehollandiae Phyllanthaceae Sunda 1 Phyllanthus microcladus Phyllanthaceae Sunda 1 Phyllanthus lamprophyllus Phyllanthaceae Sunda 1 Phyllanthus hypospodius Phyllanthaceae Sunda 1 Phyllanthus cuscutiflorus Phyllanthaceae Sunda 1 Phyllanthus clamboides Phyllanthaceae Sunda 1 Phyllanthus brassii Phyllanthaceae Sunda 1 Glochidion xerocarpum Phyllanthaceae Sunda 1 Glochidion sumatranum Phyllanthaceae Sunda 1 Glochidion sessiliflorumstylosum Phyllanthaceae Sunda 1 Glochidion sessiliflorumsessiliflorum Phyllanthaceae Sunda 1 Glochidion sessiliflorumpedicellatum Phyllanthaceae Sunda 1 Glochidion pungens Phyllanthaceae Sunda 1 Glochidion pruinosum Phyllanthaceae Sunda 1

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Glochidion philippicum Phyllanthaceae Sunda 1 Glochidion lobocarpum Phyllanthaceae Sunda 1 Glochidion hylandii Phyllanthaceae Sunda 1 Glochidion harveyanumpubescens Phyllanthaceae Sunda 1 Glochidion harveyanumharveyanum Phyllanthaceae Sunda 1 Glochidion ferdinandi Phyllanthaceae Sunda 1 Glochidion disparipes Phyllanthaceae Sunda 1 Glochidion benthamianum Phyllanthaceae Sunda 1 Glochidion apodogynum Phyllanthaceae Sunda 1 Cleistanthus xerophilus Phyllanthaceae Sunda 1 Cleistanthus semiopacus Phyllanthaceae Sunda 1 Cleistanthus peninsularis Phyllanthaceae Sunda 1 Cleistanthus myrianthus Phyllanthaceae Sunda 1 Cleistanthus hylandii Phyllanthaceae Sunda 1 Cleistanthus discolor Phyllanthaceae Sunda 1 Cleistanthus dallachyanus Phyllanthaceae Sunda 1 Cleistanthus cunninghamii Phyllanthaceae Sunda 1 Cleistanthus apodus Phyllanthaceae Sunda 1 Bridelia tomentosa Phyllanthaceae Sunda 1 Bridelia leichhardtii Phyllanthaceae Sunda 1 Bridelia insulana Phyllanthaceae Sunda 1 Bridelia finalis Phyllanthaceae Sunda 1 Bridelia exaltata Phyllanthaceae Sunda 1 Breynia stipitata Phyllanthaceae Sunda 1 Breynia sp. (Black Mountain) Phyllanthaceae Sunda 1 Breynia oblongifolia Phyllanthaceae Sunda 1 Breynia cernua Phyllanthaceae Sunda 1 Actephila Wooroonooran Phyllanthaceae Sunda 1 Actephila sp Lockerbie Phyllanthaceae Sunda 1 Actephila sessilifolia Phyllanthaceae Sunda 1 Actephila RockyRiver Phyllanthaceae Sunda 1 Actephila PossumScrub Phyllanthaceae Sunda 1 Actephila petiolarispetiolaris Phyllanthaceae Sunda 1 Actephila petiolarisjagonis Phyllanthaceae Sunda 1 Actephila lindleyi Phyllanthaceae Sunda 1 Actephila latifolia Phyllanthaceae Sunda 1 Actephila grandifolia Phyllanthaceae Sunda 1 Actephila foetida Phyllanthaceae Sunda 1 Actephila ClaudieRiver Phyllanthaceae Sunda 1

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Actephila bella Phyllanthaceae Sunda 1 Maesa haplobotrys Primulaceae Sunda 2 Prunus turneriana Rosaceae Sunda 1 Prunus brachystachya Rosaceae Sunda 1 Wendlandia urceolata Rubiaceae Sunda 1 Wendlandia ThorntonPeak Rubiaceae Sunda 1 Wendlandia inclusa Rubiaceae Sunda 1 Wendlandia connata Rubiaceae Sunda 1 Wendlandia basistaminea Rubiaceae Sunda 1 Triflorensia ixoroides Rubiaceae Sunda 1 Triflorensia cameronii Rubiaceae Sunda 1 Timonius timon Rubiaceae Sunda 1 Timonius singularis Rubiaceae Sunda 1 Tarenna MtLewis Rubiaceae Sunda 1 Tarenna dallachianaexpandens Rubiaceae Sunda 1 Tarenna dallachiana Rubiaceae Sunda 1 Tarenna australis Rubiaceae Sunda 1 Rubiaceae ShuteHarbour Rubiaceae Sunda 4 Randia tuberculosa Rubiaceae Sunda 1 Randia Peninsula Rubiaceae Sunda 1 Randia moorei Rubiaceae Sunda 1 Randia Boonjee Rubiaceae Sunda 1 Randia audasii Rubiaceae Sunda 1 Psydrax odorataaustraliana Rubiaceae Sunda 1 Psydrax odorataarnhemica Rubiaceae Sunda 1 Psydrax lamprophylla Rubiaceae Sunda 1 Psydrax johnsonii Rubiaceae Sunda 1 Psychotria UtcheeCreek Rubiaceae Sunda 1 Psychotria submontana Rubiaceae Sunda 1 Psychotria simmondsiana Rubiaceae Sunda 1 Psychotria ShuteHarbour Rubiaceae Sunda 1 Psychotria poliostemma Rubiaceae Sunda 1 Psychotria Pajinka Rubiaceae Sunda 1 Psychotria nesophila Rubiaceae Sunda 1 Psychotria nematopoda Rubiaceae Sunda 1 Psychotria MtLewis Rubiaceae Sunda 1 Psychotria loniceroides Rubiaceae Sunda 1 Psychotria fitzalanii Rubiaceae Sunda 1 Psychotria daphnoides Rubiaceae Sunda 1

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Psychotria Danbulla Rubiaceae Sunda 1 Psychotria dallachiana Rubiaceae Sunda 1 Psychotria Daintree Rubiaceae Sunda 1 Psychotria coelospermum Rubiaceae Sunda 1 Psilanthus brassii Rubiaceae Sunda 4 Pavetta platyclada Rubiaceae Sunda 1 Pavetta kimberleyana Rubiaceae Sunda 1 Pavetta granitica Rubiaceae Sunda 1 Pavetta brownii Rubiaceae Sunda 1 Pavetta australiensis Rubiaceae Sunda 1 Ophiorrhiza australianaheterostyla Rubiaceae Sunda 4 Ophiorrhiza australiana Rubiaceae Sunda 4 Oldenlandia gibsonii Rubiaceae Sunda 4 Neonauclea gordoniana Rubiaceae Sunda 1 Neolamarckia cadamba Rubiaceae Sunda 1 Nauclea orientalis Rubiaceae Sunda 1 Morinda reticulata Rubiaceae Sunda 1 Morinda citrifoliacitrifolia Rubiaceae Sunda 1 Morinda citrifoliabracteata Rubiaceae Sunda 1 Lasianthus strigosus Rubiaceae Sunda 1 Lasianthus cyanocarpus Rubiaceae Sunda 1 Larsenaikia ochreata Rubiaceae Sunda 1 Larsenaikia jardinei Rubiaceae Sunda 1 Ixora timorensis Rubiaceae Sunda 1 Ixora queenslandica Rubiaceae Sunda 1 Ixora NorthMary Rubiaceae Sunda 1 Ixora biflora Rubiaceae Sunda 1 Ixora beckleri Rubiaceae Sunda 1 Ixora baileyana Rubiaceae Sunda 1 Hodgkinsonia ovatiflora Rubiaceae Sunda 1 Hodgkinsonia frutescens Rubiaceae Sunda 1 Guettarda speciosa Rubiaceae Sunda 1 Gardenia vilhelmii Rubiaceae Sunda 1 Gardenia scabrella Rubiaceae Sunda 1 Gardenia psidioides Rubiaceae Sunda 1 Gardenia ovularis Rubiaceae Sunda 1 Gardenia actinocarpa Rubiaceae Sunda 1 Everistia vacciniifoliavacciniifolia Rubiaceae Sunda 4 Everistia vacciniifolianervosa Rubiaceae Sunda 4

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Cyclophyllum rostellatum Rubiaceae Sunda 1 Cyclophyllum protractum Rubiaceae Sunda 1 Cyclophyllum multiflorum Rubiaceae Sunda 1 Cyclophyllum maritimum Rubiaceae Sunda 1 Cyclophyllum longipetalum Rubiaceae Sunda 1 Cyclophyllum costatum Rubiaceae Sunda 1 Cyclophyllum coprosmoides Rubiaceae Sunda 1 Cyclophyllum brevipes Rubiaceae Sunda 1 Coprosma quadrifida Rubiaceae Sunda 1 Coprosma nitida Rubiaceae Sunda 1 Canthium WhitfieldRange Rubiaceae Sunda 3 Canthium Weipa Rubiaceae Sunda 3 Canthium ThursdayIsland Rubiaceae Sunda 3 Canthium ThorntonPeak Rubiaceae Sunda 3 Canthium Thornton Peak Rubiaceae Sunda 3 Canthium HerbertonRange Rubiaceae Sunda 3 Canthium graciliflorum Rubiaceae Sunda 3 Canthium FridayIsland Rubiaceae Sunda 3 Canthium BerrigurraStation Rubiaceae Sunda 3 Canthium attenuatum Rubiaceae Sunda 3 Bobea myrtoides Rubiaceae Sunda 1 Atractocarpus sessilis Rubiaceae Sunda 1 Atractocarpus merikin Rubiaceae Sunda 1 Atractocarpus hirtus Rubiaceae Sunda 1 Atractocarpus fitzalaniitenuipes Rubiaceae Sunda 1 Atractocarpus fitzalaniifitzalanii Rubiaceae Sunda 1 Atractocarpus chartaceus Rubiaceae Sunda 1 Atractocarpus benthamianus Rubiaceae Sunda 1 Antirhea tenuiflora Rubiaceae Sunda 1 Antirhea putaminosa Rubiaceae Sunda 1 Antirhea ovatifolia Rubiaceae Sunda 1 Antirhea MtLewis Rubiaceae Sunda 1 Antirhea LowerDowney Rubiaceae Sunda 1 Aidia racemosa Rubiaceae Sunda 1 Aidia MtLewis Rubiaceae Sunda 1 Aidia GapCreek Rubiaceae Sunda 1 Zanthoxylum veneficum Rutaceae Sunda 1 Zanthoxylum rhetsa Rutaceae Sunda 1 Zanthoxylum parviflorum Rutaceae Sunda 1

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Zanthoxylum ovalifolium Rutaceae Sunda 1 Zanthoxylum nitidum Rutaceae Sunda 1 Zanthoxylum brachyacanthum Rutaceae Sunda 1 Sarcomelicope simplicifolia Rutaceae Sunda 1 Pitaviaster haplophyllus Rutaceae Sunda 1 Phebalium squamulosum Rutaceae Sunda 1 Phebalium longifolium Rutaceae Sunda 1 Phebalium distans Rutaceae Sunda 1 Murraya paniculata Rutaceae Sunda 1 Murraya ovatifoliolata Rutaceae Sunda 1 Micromelum minutum Rutaceae Sunda 1 Melicope xanthoxyloides Rutaceae Sunda 3 Melicope vitiflora Rutaceae Sunda 3 Melicope rubra Rutaceae Sunda 3 Melicope peninsularis Rutaceae Sunda 3 Melicope micrococca Rutaceae Sunda 3 Melicope jonesii Rutaceae Sunda 3 Melicope hayesii Rutaceae Sunda 3 Melicope fellii Rutaceae Sunda 3 Melicope elleryana Rutaceae Sunda 3 Melicope broadbentiana Rutaceae Sunda 3 Melicope bonwickii Rutaceae Sunda 3 Melicope affinis Rutaceae Sunda 3 Medicosma sessiliflora Rutaceae Sunda 1 Medicosma riparia Rutaceae Sunda 1 Medicosma PeterBotte Rutaceae Sunda 1 Medicosma MtMellum Rutaceae Sunda 1 Medicosma glandulosa Rutaceae Sunda 1 Medicosma fareana Rutaceae Sunda 1 Medicosma elliptica Rutaceae Sunda 1 Medicosma EastMulgrave Rutaceae Sunda 1 Medicosma cunninghamii Rutaceae Sunda 1 Lunasia amara Rutaceae Sunda 4 Leionema ellipticum Rutaceae Sunda 1 Leionema elatiusbeckleri Rutaceae Sunda 1 Harrisonia brownii Rutaceae Sunda 4 Glycosmis trifoliata Rutaceae Sunda 1 Euodia pubifolia Rutaceae Sunda 1 Euodia hylandii Rutaceae Sunda 1

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Dinosperma stipitatum Rutaceae Sunda 1 Dinosperma melanophloia Rutaceae Sunda 1 Dinosperma longifolium Rutaceae Sunda 1 Dinosperma erythrococcum Rutaceae Sunda 1 Clausena smyrelliana Rutaceae Sunda 1 Clausena brevistyla Rutaceae Sunda 1 Citrus inodora Rutaceae Sunda 1 Citrus garrawayae Rutaceae Sunda 1 Citrus australasica Rutaceae Sunda 1 Brombya platynema Rutaceae Sunda 1 Brombya GapCreek Rutaceae Sunda 1 Bosistoa transversa Rutaceae Sunda 1 Bosistoa selwynii Rutaceae Sunda 1 Bosistoa pentacoccaconnaricarpa Rutaceae Sunda 1 Bosistoa pentacocca Rutaceae Sunda 1 Bosistoa medicinalis Rutaceae Sunda 1 Bosistoa floydii Rutaceae Sunda 1 Boronia lanceolata Rutaceae Sunda 1 Boronia excelsa Rutaceae Sunda 1 Boronia alulata Rutaceae Sunda 1 Acronychia wilcoxiana Rutaceae Sunda 3 Acronychia vestita Rutaceae Sunda 3 Acronychia suberosa Rutaceae Sunda 3 Acronychia pubescens Rutaceae Sunda 3 Acronychia pauciflora Rutaceae Sunda 3 Acronychia parviflora Rutaceae Sunda 3 Acronychia octandra Rutaceae Sunda 3 Acronychia oblongifolia Rutaceae Sunda 3 Acronychia littoralis Rutaceae Sunda 3 Acronychia laevis Rutaceae Sunda 3 Acronychia imperforata Rutaceae Sunda 3 Acronychia eungellensis Rutaceae Sunda 3 Acronychia crassipetala Rutaceae Sunda 3 Acronychia chooreechillum Rutaceae Sunda 3 Acronychia BataviaDowns Rutaceae Sunda 3 Acronychia baeuerlenii Rutaceae Sunda 3 Acronychia acuminata Rutaceae Sunda 3 Acronychia acronychioides Rutaceae Sunda 3 Acronychia acidula Rutaceae Sunda 3

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Acronychia aberrans Rutaceae Sunda 3 Xylosma terraereginae Salicaceae Sunda 4 Xylosma TempleBay Salicaceae Sunda 4 Xylosma ovatum Salicaceae Sunda 4 Xylosma MtLewis Salicaceae Sunda 4 Scolopia braunii Salicaceae Sunda 4 Ryparosa javanica Salicaceae Sunda 4 Homalium JohnstoneRiver Salicaceae Sunda 4 Homalium circumpinnatum Salicaceae Sunda 4 Homalium brachybotrys Salicaceae Sunda 4 Homalium alnifolium Salicaceae Sunda 4 Flacourtia territorialis Salicaceae Sunda 4 Flacourtia ShiptonsFlat Salicaceae Sunda 4 Casearia multinervosa Salicaceae Sunda 4 Casearia MissionBeach Salicaceae Sunda 4 Casearia grewiifolia Salicaceae Sunda 4 Casearia grayi Salicaceae Sunda 4 Casearia dallachii Salicaceae Sunda 4 Casearia costulata Salicaceae Sunda 4 Baileyoxylon lanceolatum Salicaceae Sunda 4 Palaquium galactoxylon Sapotaceae Sunda 1 Chrysophyllum roxburghii Sapotaceae Sunda 1 Quassia TozerRange Simaroubaceae Sunda 1 Quassia MtNardi Simaroubaceae Sunda 1 Quassia MtGoonanaman Simaroubaceae Sunda 1 Quassia bidwillii Simaroubaceae Sunda 1 Quassia Barong Simaroubaceae Sunda 1 Quassia baileyana Simaroubaceae Sunda 1 Brucea javanica Simaroubaceae Sunda 1 Ailanthus triphysa Simaroubaceae Sunda 1 Ailanthus integrifoliaintegrifolia Simaroubaceae Sunda 1 Symplocos thwaitesii Symplocaceae Sunda 2 Symplocos stawelliistawellii Symplocaceae Sunda 2 Symplocos stawelliimontana Symplocaceae Sunda 2 Symplocos paucistaminea Symplocaceae Sunda 2 Symplocos NorthMary Symplocaceae Sunda 2 Symplocos MtFinnigan Symplocaceae Sunda 2 Symplocos hylandii Symplocaceae Sunda 2 Symplocos hayesii Symplocaceae Sunda 2

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Symplocos harroldii Symplocaceae Sunda 2 Symplocos graniticola Symplocaceae Sunda 2 Symplocos cyanocarpa Symplocaceae Sunda 2 Symplocos crassiramifera Symplocaceae Sunda 2 Symplocos Symplocaceae Sunda 2 cochinchinensisthwaitesiipilosiuscula Symplocos Symplocaceae Sunda 2 cochinchinensisthwaitesiiglaberrima Symplocos Symplocaceae Sunda 2 cochinchinensisthwaitesiigittonsii Symplocos Boonjee Symplocaceae Sunda 2 Symplocos BigTableland Symplocaceae Sunda 2 Symplocos baeuerlenii Symplocaceae Sunda 2 Symplocos ampulliformis Symplocaceae Sunda 2 Ternstroemia cherryi Theaceae Sunda 4

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Appendix 2 Re-analysis of Sunda and Sahul species

Species assigned ancestry with high confidence yielded 453 Sunda species and 410 Sahul species. This excludes species from lineages (genera) associated with an ancestry but for which some taxa might have returned to Sahul more recently (i.e. 50 Sunda species and 308 Sahul species), species with some doubt about ancestry (i.e. 62 Sunda species and 21 Sahul species) species from genera for which we have little information (i.e. 39 Sunda species and 56 Sahul species).

Fig. S2.1 shows the species richness pattern for Sunda species pool is still consistent with the latitudinal expansion pattern observed in the original analysis (Fig. 2.2a). Similarly, the phylogenetic endemism pattern remains largely unchanged (Fig. 2.2b).

Fig. S2.2 shows the re-analysis of functional traits primarily yielded no significant difference whether all or selected assigned species with high confidence were used. Using both ANOVA and Tukey HSD post-hoc test, Sahul taxa were only slightly significantly taller (P-value < 0.05) when the full dataset was used.

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Figure S2.1 Spatial and phylogenetic patterns across the Australian continent for Sunda and Sahul using only species assigned ancestry with high confidence (see Materials and Methods in Chapter 2) (A) Species richness, based on the number of species in each grid cell, (B) Phylogenetic endemism, represents millions of years (“My”) of evolutionary history partitioned across species range. For instance, PE of 8 represent branches summing to 8 My now restricted to a single 5 km cell, hence 40 My across five cells with PE of 8. Grid cell (50 x 50 km) view on continental species pools of Sunda and Sahul ancestry. Lighter colours (orange to light yellow) = lower values; darker colours = greater concentrations (black to red).

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Figure S2.2 Functional trait comparison for species of Sunda (black boxplots) and Sahul (white boxplots) ancestry using all assigned species (Species group “0”) and only species that were assigned ancestry with high confidence (Species group “1”).

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Appendix 3 Additional data on functional traits

Both the continuous and categorical traits were taken from published floras and other sources for all species (Bootle, 1983; Stanley & Ross, 1983-1989; Harden, 1990-2002; Hyland et al., 2003; http://www.anbg.gov.au/cpbr/cd-keys/rfk/index.html; Cornelissen et al., 2003; Cooper & Cooper, 2004; Floyd, 2008; Chave et al., 2009). Detailed information about the traits can be found in previous published papers (Kooyman et al., 2011, 2013).

Table S3.1 summarises the statistic outputs of Fig. 2.4, using species-level trait data (leaf area, height, wood density and fruit size (log10-transformed)) for the Sunda (“Sunda”), Sahul (“Sahul”) and all Australian woody rainforest species pools (“All”). “LCL” is the lower confidence limit, “UCL” is the upper confidence limit, “std” is standard deviation, “max” is the maximum value for the trait, “min” is the minimum value for the trait, and “mean” is the average trait value.

Table S3.1 Summary statistics for Sunda and Sahul species functional trait analysis

Statistical Sunda Sahul measures Leaf Wood Fruit Leaf Wood Fruit Height Height area density size area density size LCL 3.67 1.07 2.72 1.19 3.58 1.2 2.76 1.3 UCL 3.76 1.12 2.75 1.25 3.67 1.25 2.78 1.36 std 0.64 0.37 0.09 0.36 0.55 0.32 0.09 0.37 max 4.8 1.78 2.92 2.69 5.19 1.7 2.92 2.48 min 0.32 0 2.48 0.48 0 0 2.38 0.3 mean 3.72 1.09 2.74 1.22 3.62 1.22 2.77 1.33

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Statistical All measures Leaf Wood Fruit Height area density size LCL 3.61 1.08 2.74 1.24 UCL 3.66 1.11 2.76 1.28 std 0.61 0.38 0.09 0.42 max 5.19 1.78 2.94 2.78 min 0 0 2.31 0.18 mean 3.64 1.1 2.75 1.26

Fig. S3.3 shows weak correlation between each pair of the continuous traits (i.e. “LA” or Leaf Area, Height, “Fs” or Fruit Size, “WD” or Wood density) for both Sunda and Sahul components. This result was based on species-level traits obtained from 795 Sahul- and 604 Sunda-derived species.

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Figure S3.3 Correlation analysis for four continuous traits. Correlation matrices were produced for (A) Sunda component (N = 604), (B) Sahul component (N = 795). For each component, the bottom of the diagonal shows the scatter plots of each pair of traits, on the top of the diagonal shows the value of the correlation, and on the diagonal is the distribution of each variable (i.e. “LA” or Leaf Area, Height, “Fs” or Fruit Size, “WD” or Wood density. 136

References

Bootle, K. R. (1983). Wood in Australia: types, properties and uses. McGraw-Hill, Sydney, NSW.

Harden, G. J. (1990-2002 with revisions). Flora of New South Wales. (vol. 1-4). University of New South Wales Press, Sydney, NSW.

Stanley, T. D., & Ross, E. M. (1983-1989). Flora of south-eastern Queensland. (vol. 1-3). Queensland Department of Primary Industries, Brisbane, QLD.

Chave, J., Coomes, D., Jansen S., Lewis S. L., Swenson N. G., & Zanne A. E. (2009). Towards a worldwide wood economics spectrum. Ecology Letters, 12, 351-366.

Cooper, W., & Cooper, W.T. (2004). Fruits of the Australian Tropical Rainforest. Nokomis Editions, Melbourne, Australia.

Cornelissen J. H. C, Lavorel, S., Gardnier E., Diaz S., Buchmann N., Gurvich D. E., … & Poorter, H. (2003). A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany, 51, 335–380.

Floyd, A. G. (2008). Rainforests of mainland south-eastern Australia. Terania Publishing, Lismore, NSW.

Hyland, B. P. M., Whiffin, T., Christophel, D. C., Gray, B., & Ellick, R. W. (2003). Australian tropical rainforest plants. Trees, Shrubs and Vines. CSIRO, Melbourne, VIC.

Kooyman, R. M., Rossetto, M., Cornwell, W., & Westoby, M. (2011). Phylogenetic tests of community assembly across regional to continental scales in tropical and subtropical rain forests. Global Ecology and Biogeography, 20, 707-716.

Kooyman, R. M., Rossetto, M., Sauquet, H., & Laffan, S.W. (2013). Landscape patterns in rainforest phylogenetic signal: isolated islands of refugia or structured continental distributions?. PloS ONE, 8, e80685.

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Appendix 4 Statistical outputs and additional analysis of plot and climate data

Figure S4.1 The regional areas of this study’s plots. The tropical areas are Cape York and the Wet Tropics which are in the Queensland (“Qld”) state, and the subtropical areas are Nightcap Border (i.e. Nightcap-Border Ranges), Washpool, and Dorrigo which are in the New South Wales (“NSW”) state. (Figure was adopted from Kooyman et al., 2011).

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Fig. S4.2 shows that the Sunda and Sahul components have significantly different species richness across regional plots. For both components species richness in the AWT is significantly higher than Nightcap-Border Ranges (Sunda: P-value < 0.001, Sahul: P-value < 0.005), and richness in Nightcap- Border Ranges is significantly higher compared than Dorrigo (Sunda: P-value < 0.001, Sahul: P- value < 0.05).

Figure S4.2 Richness boxplot from Sunda and Sahul Plot data. Species richness of Sunda (black boxplots) and Sahul (grey boxplots) components in plot samples for each regional area (“CY” = Cape York, “AWT” = Australian Wet Tropics, “Night” = Nightcap-Border Ranges, “Dorri” = Dorrigo, “Wash” = Washpool). Significance in the comparisons was identified using ANOVA and Tukey HSD post-hoc tests, and the resulting significant comparisons were labelled as follows P-value < 0.001 as “***”, P-value < 0.005 as “**” and P-value < 0.05 as “*”. Comparison between Sunda and Sahul components within each regional area resulted in highly significant results (P-value < 0.001).

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Table S4.1 shows that only the Australian Wet Tropics (AWT) has a significant relationship between altitude and Sunda proportions (P-value < 0.001), and between minimum monthly temperature (bio06) and Sunda proportions (P-value < 0.001). Both Cape York (CY) and Dorrigo (Dorri) had R2 of zero, suggesting no relationship. Both Nightcap-Border Ranges (Night) and Washpool (Wash) show relatively low R2 values (as compared to the AWT) and low P-values indicating weak relationships between altitude and Sunda proportions, and bio06 and Sunda proportions.

Table S4.1 Relationship between altitude and Sunda proportions (left hand side table), and between bio06 and Sunda proportions (right hand side table) in regional plots: Cape York (“CY”), Australian Wet Tropics (“AWT”), Nightcap-Border Ranges (“Night”), Washpool (“Wash”) and Dorrigo (“Dorri”)

Altitude R2 P-value Slope bio06 R2 P-value Slope CY 0 1 0 CY 0 1 0 AWT 0.2933 <0.001 -1440.1 AWT 0.2544 <0.001 9.423 Night 0.01544 0.07673 -337.37 Night 0.01716 0.066 -2.026 Wash 0.09234 0.02684 -781.82 Wash 0.1177 0.014 2.81 Dorri 0 0.6857 -95.09 Dorri 0 0.491 -0.809

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AWT

NNSW

Figure S4.3 The relationship between altitude (m) and minimum monthly temperature (bio06) in each region using plot data. A least square regression line is plotted for each of the regions, Cape York (CY), the Australian Wet Tropics (AWT) and Nightcap-Border Ranges, Washpool and Dorrigo in Northern New South Wales (NNSW).

Fig. S4.3 is supplemented by a summary table of statistics (Table S4.2). In the table, R2 is a measure of explanatory power of variables, P-value < 0.001 indicates significance in the relationship between the variables, X-intercept indicates the minimum altitude for temperature to decline, and the slope indicates the strength in relationship between the two variables (i.e. the larger the number the steeper the slope).

Table S4.2 Statistical summary on the relationship between altitude (m) and bio06 in each region.

Regions R2 P-value X-intercept |Slope| CY 0.153 574.556 26.891 <0.001 AWT 0.5597 <0.001 1829.8 106.1 Night 0.381 <0.001 1030.51 110.26 Wash 0.8253 <0.001 1331.57 236.86

Dorri 0.6885 <0.001 1089.87 164.91

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We also implemented additional modelling to observe which models have the most important explanatory variables (e.g. altitude, bio06) influencing the proportion of Sunda species in plot data. Model selection was performed using AICc (the second order Akaike information criterion corrected for small or finite samples) from the MuMin package (Barton, 2016) in R.

Table S4.3 shows Model 1 is the best out of six models by having an Akaike weight of 0.558. However, Model 2 which includes additional variable of bio06 has a similar Akaike weight and in fact model 1 is only 1.26 times more likely to be the best model than Model 2.

Table S4.3 Output of the AICc analysis to select the best model that explains variation in proportion of Sunda species per plot (“Prop”).*

Model Model K L AICc delta weight no. 1 Prop ~ Region + Altitude 7 471.19 -928.19 0 0.56 Prop ~ Region + Altitude + 2 8 471.99 -927.73 0.47 0.44 bio06 3 Prop ~ Region + bio06 7 450.17 -886.15 42.05 <0.001 4 Prop ~ Region 6 439.77 -867.40 60.79 <0.001 5 Prop ~ bio06 3 400.83 -795.63 132.57 <0.001 6 Prop ~ Altitude 3 313.77 -621.49 306.70 <0.001

* K is the number of parameters in the model, L denotes the maximization of a likelihood function, AICc is an extension of AIC suitable for the case of small sample sizes or when the number of fitted parameters is moderate to large (Hurvich & Tsai, 1989; Johnson & Omland, 2004).

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References

Barton K. (2016) MuMIn: multi-model inference. R package version 1.15. 6. Accessed 08 September 2017.

Hurvich, C. M., & Tsai, C-L. (1989). Regression and time series model selection in small samples. Biometrika, 76, 297-307.

Johnson, J. B., & Omland, K. S. (2004). Model selection in ecology and evolution. Trends in ecology & evolution, 19, 101-108.

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Appendix 5 Summary of Sunda and Sahul families, genera and species.

Table S5.1 Summary of the families, number of species (“N Species”) and proportion of species (“Proportion”) with Sunda and Sahul ancestry (in parentheses).

N species N species Proportion Proportion Family (Sunda) (Sahul) (Sunda) (Sahul)

Actinidiaceae 1 0 1 0 Anacardiaceae 6 2 0.75 0.25 Annonaceae 30 0 1 0 Apocynaceae 0 8 0 1 Aquifoliaceae 3 0 1 0 Araliaceae 0 3 0 1 Araucariaceae 0 6 0 1 Atherospermataceae 0 10 0 1 Bignoniaceae 2 0 1 0 Burseraceae 6 0 1 0 Cannabaceae 8 0 1 0 Cardiopteridaceae 2 0 1 0 Casuarinaceae 0 1 0 1 Celastraceae 2 8 0.2 0.8 Clusiaceae 18 0 1 0 Combretaceae 20 0 1 0 Cornaceae 3 0 1 0 Corynocarpaceae 0 3 0 1 Cunoniaceae 0 30 0 1 Cupressaceae 5 0 1 0 Datiscaceae 1 0 1 0 Dilleniaceae 1 3 0.25 0.75 Ebenaceae 2 0 1 0 Elaeocarpaceae 0 43 0 1 Euphorbiaceae 18 1 0.95 0.05 Eupomatiaceae 0 3 0 1 Fabaceae 0 9 0 1 Gentianaceae 4 0 1 0 Gesneriaceae 1 0 1 0 Hamamelidaceae 3 0 1 0 Hernandiaceae 0 4 0 1 Himantandraceae 0 1 0 1 Icacinaceae 4 0 1 0 Lauraceae 20 102 0.16 0.84 Lecythidaceae 6 0 1 0 Lythraceae 2 0 1 0 Malvaceae 27 17 0.61 0.39 144

Melastomataceae 7 0 1 0 Meliaceae 44 0 1 0 Monimiaceae 0 26 0 1 Moraceae 47 0 1 0 Myristicaceae 4 0 1 0 Myrtaceae 0 188 0 1 Nothofagaceae 0 3 0 1 Paracryphiaceae 1 0 1 0 Pennantiaceae 1 0 1 0 Phyllanthaceae 60 0 1 0 Podocarpaceae 0 10 0 1 Primulaceae 1 4 0.2 0.8 Proteaceae 0 87 0 1 Rhamnaceae 0 14 0 1 Rosaceae 2 0 1 0 Rubiaceae 110 0 1 0 Rutaceae 82 18 0.82 0.18 Salicaceae 19 0 1 0 Sapindaceae 0 144 0 1 Sapotaceae 2 28 0.07 0.93 Simaroubaceae 9 0 1 0 Symplocaceae 19 0 1 0 Theaceae 1 0 1 0 Thymelaeaeceae 0 7 0 1 Winteraceae 0 12 0 1

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Appendix 6 Endemic Sunda-derived species

Eight Sunda-derived species were identified to contribute to the strong phylogenetic endemism pattern south of the subtropics (i.e. latitude -36 ° and higher; or Southern New South Wales and Victoria; see Fig. 2.2) based on the output generated by BIODIVERSE 1.1 (http://purl.org/biodiverse; Laffan et al., 2010).

Table S6.1 lists the species, along with the oldest Australian fossil record representing each clade / genus of interest. While some Sunda species did not have any fossil data, the remaining species either belonged to clades from the Eocene suggesting the antiquity of these lineages on Sahul or from the late Oligocene / early Miocene before the Sunda-Sahul collision.

Table S6.1 Sunda species contributing to the strong phylogenetic endemism pattern south of the subtropics and the oldest Australian fossil record representing the clade / genus of interest (Sniderman & Jordan, 2011, and references therein)

Age classes for fossils Species Genus Family (genus) late Oligocene/early Acronychia oblongifolia Acronychia Rutaceae Miocene late Oligocene/early Acronychia wilcoxiana Acronychia Rutaceae Miocene Atractocarpus benthamianus Atractocarpus Rubiaceae Absent from fossil record Cinnamomum virens Cinnamomum Lauraceae Absent from fossil record Cyclophyllum longipetalum Cyclophyllum Rubiaceae Absent from fossil record Dysoxylum fraserianum Dysoxylum Meliaceae late Eocene Ficus macrophylla Ficus Moraceae Absent from fossil record Litsea australis Litsea Lauraceae early Eocene

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The output in Fig. S6.1 is from a reanalysis of the plot data only using species endemic to the Australian Wet Tropics (N(Sunda)=71, N(Sahul)=141; Kooyman unpublished data). Similar to the original AWT plot results, there was a significant (P-value < 0.001) relationship between altitude and Sunda proportions and between bio06 and Sunda proportions although R2 remained weak.

Altitude R2 P-value Slope bio06 R2 P-value Slope 0.29 <0.001 -1022.7 0.25 <0.001 9.69

Figure S6.1 Endemic species plot analysis for Sunda (Black dots / lines) and Sahul (White dots / lines) components. The scatterplots show the proportion of species per plot vs. altitude (left) or bio06 (minimum monthly temperature; plot on the right). The table below each scatterplot indicates the linear regression output to describe the relationship between altitude and proportion per plot, and bio06 and proportion per plot.

References

Laffan, S. W., Lubarsky, E., & Rosauer, D. F. (2010). Biodiverse, a tool for the spatial analysis of biological and related diversity. Ecography, 33, 643-647.

Sniderman, J. K., & Jordan, G. J. (2011). Extent and timing of floristic exchange between Australian and Asian rain forests. Journal of Biogeography, 38, 1445-1455. 147

Appendix 7 Work-flow for the whole chloroplast genomic based multiple species study across the Australian rainforests

Figure S7.1 A presentation of the work-flow used in Chapter 3.

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Appendix 8 DNA extraction method for various Australian rainforest species

Total genomic DNA extraction was performed using a modified cetyl trimethylammonium bromide (CTAB) method (Doyle & Doyle, 1990) and DNA purification was performed using a method based on Alexander et al. (2007).

Solutions required for the DNA extraction  Lysis Buffer (autoclaved) (500 mL, pH 8.0: 0.2 M TrisHCl, 0.05 M EDTA, 2 M NaCl, 2% CTAB)  Extraction buffer (autoclaved) (500 mL, pH 8.0: 0.35 M sorbitol, 0.1 M TrisHCl, 5 mM EDTA)  5% Sarkosyl (autoclaved)  Fresh Working Extraction Buffer (change every 3-4 days) (contains 0.5% sodium metabisulfite and 2% PVP-40) (120 mL, 0.6g sodium metabisulfite, 2.4g PVP, 20 mL 5% Sarkosyl, 50 mL Lysis Buffer, 50 mL extraction Buffer)  Proteinase K (0.1 mg/mL of fresh working buffer).  Chloroform:isoamyl (24:1)  Cold Isopropanol  Cold 70% ethanol  Binding Buffer (2M Guanidine-HCL, 95%v/v 100% ethanol)  Cleaning Buffer (70% ethanol)

Extraction 1. Extract using 100-150 mg of freeze dried leaf with mortar and pestle. Grind the leaf bits in 1,000 uL of fresh working buffer 2. Add 2.5 uL of Proteinase k to tube. Mix well and incubate on a 50 ºC metal plate for 15 min. 3. Invert tube several times before placing in a 65 ºC water bath for 30 min. 4. Add 1,000 uL of chloroform:isoamyl (24:1). Invert 10 times and spin at 3,000 x g for 10 minutes. 5. Transfer aqueous phase into a 1.5 ml tube and estimate the volume transferred. 6. Add 1 vol. of ice cold isopropanol. Invert 10 times and place tubes at -20 ºC for 1 hr. 7. Centrifuge at 3,000 x g for 30 minutes. Carefully remove supernatant.

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8. Add 500 uL of ice cold 70% ethanol. Centrifuge at 13,000 x g for 10 min. Carefully remove supernatant. 9. Dry pellet by tipping the tube on paper towel (~30 min wait). 10. Resuspend pellet in 100 ul water.

Cleaning (before starting, put a small bottle of water in the 65 ºC water bath) 1. Add 1.5 vol. of binding buffer. Mix well and transfer to filter column. 2. Centrifuge 6,000 x g for 2 min. Make sure all liquid pass through silica-based filter column, if not spin at top speed. 3. Discard flow through, add 500 ul of washing buffer and spin at 13,000 x g for 2 min. 4. Discard flow through and repeat step 4. 5. Discard flow through and spin the column at 13,000 x g for 1 min to remove all traces of ethanol. 6. Transfer filter to 1.5 ml tube, add 40 uL of water to the filter and let it sit for 20 min at room temp. 7. Spin column at 13,000 x g for 1 min and repeat step 6. 8. Store DNA in fridge.

References

Alexander, P. J., Rajanikanth, G., Bacon, C. D., & Bailey, C. D. (2007). Recovery of plant DNA using a reciprocating saw and silica‐based columns. Molecular Ecology Notes, 7, 5-9.

Doyle, J. J., & Doyle, J. L. (1990). Isolation of plant DNA from fresh tissue. Focus, 12, 13-15.

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Appendix 9 Next generation sequencing (NGS) data summary

120 NGS libraries representing 20 species in the Tropics and 39 species in the Subtropics were analysed in this study (Table S9.1). Within each region, there were two libraries for each of the two populations for each species. Number of raw reads ranged between 1,402,308 (Pittosporum revolutum in Nightcap-Border Ranges) to 27,825,700 (Rhodamnia blairiana in Mt Lewis), and quality trimming removed between 0.41 to 9.55% of reads resulting in a range of 1,396,473 to 27,825,689 trimmed reads. The length of the new chloroplast references ranged between 91,859 bp for Doryphora aromatica and 133,892 bp for Stenocarpus salignus.

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Table S9.1 NGS data summary for Australian rainforest species populations in the Tropics (Mt Lewis or “ML” and Mt Baldy-Tinaroo or “MB-T”) and the Subtropics (Nightcap-Border Ranges or “N-BR” and Dorrigo or “D”). “N raw reads” means number of raw reads (includes paired and unpaired reads), “N trimmed reads” is the number of quality trimmed paired reads, “% read removed” is the percentage of poor quality reads (i.e. unpaired or below the quality threshold in the Quality Trimming tool in CLC), “Ave. trimmed read length (bp)” is the average length of the quality read, “Length of chloroplast sequence (bp)” is the length of a chloroplast genome reference specific to the species, “N chloroplast trimmed reads” is the number of quality trimmed reads mapped to a chloroplast reference, “% chloroplast trimmed reads” is the percentage of quality reads mapped to the chloroplast reference.

(bp) (bp) reads reads length length Species Population N trimmedN N rawNreads N chloroplast chloroplast N % chloroplast sequence(bp) trimmed readstrimmed readstrimmed %removed read Ave. trimmedreadAve. Raw read length (bp)lengthread Raw Lengthof chloroplast Alangium villosum D 16,331,956 16,308,269 0.15% 101.0 99.6 105,561 567,284 3.48 Alangium villosum N-BR 5,394,880 5,382,519 0.23% 101.0 99.6 105,561 180,062 3.35 Archirhodomyrtus beckleri ML 6,271,748 6,271,336 0.01% 147.5 147.5 121,508 161,541 2.58 Archirhodomyrtus beckleri MB-T 23,594,370 23,594,358 0.00% 150.6 150.3 121,508 592,156 2.51 Argyrodendron D 16,618,294 16,618,216 0.00% 150.6 150.4 95,799 261,961 1.58 trifoliolatum Argyrodendron N-BR 20,070,804 20,070,801 0.00% 150.6 150.3 95,799 267,127 1.33 trifoliolatum Argyrodendron ML 5,396,018 5,395,632 0.01% 149.9 149.9 108,641 65,807 1.22 trifoliolatum Argyrodendron MB-T 6,025,734 6,025,053 0.01% 147.2 147.2 108,641 90,243 1.50 trifoliolatum Atractocarpus D 10,023,226 3,264,922 67.43% 101.0 98.8 118,560 127,363 3.90 benthamianus 152

Atractocarpus N-BR 2,907,900 2,899,409 0.29% 101.0 98.5 118,560 70,408 2.43 benthamianus Austrobuxus swainii D 1,470,628 1,467,694 0.20% 101.0 99.8 103,749 128,675 8.77 Austrobuxus swainii N-BR 5,193,334 5,185,517 0.15% 101.0 99.7 103,749 149,422 2.88 Beilschmeidia bancroftii ML 5,532,086 5,531,246 0.02% 148.7 148.6 124,985 125,849 2.28 Beilschmeidia bancroftii MB-T 5,216,368 5,212,097 0.08% 146.4 146.4 124,985 110,467 2.12 Brachychiton acerifolius D 6,901,662 6,888,232 0.19% 101.0 93.7 101,522 144,371 2.10 Brachychiton acerifolius N-BR 7,570,688 7,341,935 3.02% 101.0 99.8 101,522 34,882 0.48 Breynia cernua ML 5,926,624 5,926,164 0.01% 150.0 150 125,767 123,885 2.09 Breynia cernua MB-T 8,398,234 8,397,692 0.01% 149.9 149.8 125,767 146,336 1.74 Callicoma serratifolia D 2,626,370 2,621,626 0.18% 101.0 99.5 123,919 82,189 3.14 Callicoma serratifolia N-BR 5,798,718 5,785,979 0.22% 101.0 99.6 123,919 159,442 2.76 Cardwellia sublimis ML 4,152,502 4,151,880 0.01% 148.9 148.9 127,147 187,265 4.51 Cardwellia sublimis MB-T 25,180,392 25,180,382 0.00% 150.6 150.2 127,147 844,444 3.35 Ceratopetalum apetalum D 5,578,162 5,503,688 1.34% 101.0 100 127,605 227,668 4.14 Ceratopetalum apetalum N-BR 6,806,428 6,696,616 1.61% 101.0 99.9 127,605 350,277 5.23 Cinnamomum laubatii ML 6,717,882 6,716,852 0.02% 149.1 149.1 129,613 243,210 3.62 Cinnamomum laubatii MB-T 8,395,172 8,392,342 0.03% 147.4 147.4 129,613 297,233 3.54 Cinnamomum oliveri D 12,521,010 12,470,003 0.41% 101.0 92.9 129,613 164,674 1.32 Cinnamomum oliveri N-BR 8,879,046 8,420,776 5.16% 101.0 98.7 129,613 348,402 4.14 Claoxylon australe D 13,847,170 13,811,192 0.26% 101.0 93.6 131,734 188,467 1.36 Claoxylon australe N-BR 5,981,726 5,790,509 3.20% 101.0 99.8 131,734 153,096 2.64 Cryptocarya meissneriana D 11,127,646 11,070,049 0.52% 101.0 99.2 118,164 94,662 0.86 Cryptocarya meissneriana N-BR 12,260,042 12,224,613 0.29% 101.0 98.7 118,164 153,702 1.26 Cryptocarya obovata D 7,560,916 7,456,266 1.38% 101.0 100 127,662 53,614 0.72 Cryptocarya obovata N-BR 5,624,730 5,524,901 1.77% 101.0 99.9 127,662 59,713 1.08 Cryptocarya rigida D 5,197,656 5,186,790 0.21% 101.0 98.8 131,059 68,299 1.32 Cryptocarya rigida N-BR 6,436,308 6,403,776 0.51% 101.0 99.2 131,059 94,149 1.47

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Darlingia darlingiana ML 3,463,084 3,462,661 0.01% 149.5 149.4 101,551 92,264 2.66 Darlingia darlingiana MB-T 11,893,856 11,893,180 0.01% 149.6 149.6 101,551 216,785 1.82 Diploglottis australis D 5,453,042 5,436,189 0.31% 101.0 99.1 114,621 36,099 0.66 Diploglottis australis N-BR 5,801,710 5,436,189 6.30% 101.0 99.1 114,621 268,212 4.93 Doryphora aromatica ML 12,252,428 11,958,083 2.40% 101.0 99.4 91,859 107,126 0.90 Doryphora aromatica MB-T 3,600,630 3,600,408 0.01% 149.4 149.4 91,859 30,411 0.84 Doryphora sassafras D 13,531,834 13,388,022 1.06% 101.0 90.8 128,919 127,109 0.95 Doryphora sassafras N-BR 11,585,966 10,868,971 6.19% 101.0 100.1 128,919 213,740 1.97 Duboisia myoporoides D 10,608,096 10,546,763 0.58% 101.0 98.6 131,382 197,236 1.87 Duboisia myoporoides N-BR 9,943,554 9,897,927 0.46% 101.0 98.5 131,382 440,424 4.45 Ehretia acuminata D 3,735,032 3,727,095 0.21% 101.0 99.6 129,845 124,026 3.33 Ehretia acuminata N-BR 2,175,170 2,171,598 0.16% 101.0 99.7 129,845 74,875 3.45 Elaeocarpus reticulatus D 19,091,756 18,967,285 0.65% 101.0 100.1 129,270 186,939 0.99 Elaeocarpus reticulatus N-BR 6,237,746 6,176,310 0.98% 101.0 100 129,270 406,045 6.57 Elaeocarpus sericopetalus ML 5,186,816 5,185,536 0.02% 148.0 148 119,838 222,587 4.29 Elaeocarpus sericopetalus MB-T 21,367,812 21,367,806 0.00% 150.6 150.3 119,838 1,058,020 4.95 Endiandra bessaphila ML 4,368,208 4,367,717 0.01% 149.4 149.4 124,594 110,737 2.54 Endiandra bessaphila MB-T 2,511,500 2,511,092 0.02% 140.6 140.6 124,594 58,759 2.34 Endiandra crassiflora D 4,656,444 4,584,755 1.54% 101.0 99.8 117,352 103,546 2.26 Endiandra crassiflora N-BR 13,111,590 12,873,394 1.82% 101.0 99.9 117,352 52,472 0.41 Endiandra muelleri D 7,084,414 6,700,856 5.41% 101.0 100.4 118,408 41,853 0.62 Endiandra muelleri N-BR 6,798,692 6,700,856 1.44% 101.0 100 118,408 42,074 0.63 Eupomatia laurina D 5,631,596 5,584,596 0.83% 101.0 100.3 107,554 72,473 1.30 Eupomatia laurina N-BR 2,405,488 2,373,661 1.32% 101.0 100.4 107,554 39,109 1.65 Eupomatia laurina ML 4,050,942 4,050,490 0.01% 149.9 149.9 107,554 197,066 4.87 Eupomatia laurina MB-T 3,669,250 3,668,863 0.01% 149.6 149.6 107,554 92,555 2.52 Ficus coronata D 4,397,778 4,394,695 0.07% 101.0 98.8 131,282 161,804 3.68 Ficus coronata N-BR 3,567,166 3,562,863 0.12% 101.0 99.3 131,282 156,985 4.41 154

Homolanthus populifolius D 11,641,492 11,613,691 0.24% 101.0 99.3 125,435 139,556 1.20 Homolanthus populifolius N-BR 11,355,538 11,320,828 0.31% 101.0 99.4 125,435 251,763 2.22 Litsea leefeana ML 22,078,002 22,077,998 0.00% 150.6 150.2 132,461 567,643 2.57 Litsea leefeana MB-T 9,267,848 9,265,941 0.02% 149.8 149.8 132,461 158,883 1.71 Neolitsea dealbata D 24,330,232 24,330,225 0.00% 150.6 150.1 131,442 361,479 1.49 Neolitsea dealbata N-BR 1,793,238 1,769,353 1.33% 101.0 100.2 131,442 97,244 5.50 Neolitsea dealbata ML 2,099,230 2,098,597 0.03% 148.6 147.7 131,442 29,887 1.42 Neolitsea dealbata MB-T 11,775,606 11,498,028 2.36% 101.0 99.3 131,442 370,153 3.22 Nothofagus moorei D 2,034,724 1,996,521 1.88% 101.0 99.9 122,881 47,924 2.40 Nothofagus moorei N-BR 18,643,700 18,577,533 0.35% 101.0 99.9 122,881 237,981 1.28 Pennantia cunninghamii D 2,343,450 2,316,550 1.15% 101.0 100.4 131,466 107,232 4.63 Pennantia cunninghamii N-BR 2,235,550 2,213,842 0.97% 101.0 100.3 131,466 172,341 7.78 Pittosporum revolutum D 11,469,798 11,430,542 0.34% 101.0 98.7 101,890 261,014 2.28 Pittosporum revolutum N-BR 1,402,308 1,396,473 0.42% 101.0 98.7 101,890 106,757 7.64 Polyscias murrayi D 20,470,794 20,470,793 0.00% 150.4 130,032 1,362,828 6.66 Polyscias murrayi N-BR 2,931,228 2,904,468 0.91% 101.0 100.5 130,032 203,134 6.99 Polyscias murrayi ML 4,796,956 4,795,690 0.03% 149.4 149.4 130,032 453,338 9.45 Polyscias murrayi MB-T 16,618,906 16,615,564 0.02% 150.0 150 130,032 1,586,313 9.55 Pouteria australis D 2,279,132 2,273,119 0.26% 101.0 99.7 118,849 72,387 3.18 Pouteria australis N-BR 9,467,882 9,453,800 0.15% 101.0 99.3 118,849 84,025 0.89 Prunus turneriana ML 18,370,314 18,370,308 0.00% 150.6 150.3 114,494 409,461 2.23 Prunus turneriana MB-T 1,751,522 1,751,055 0.03% 150.4 150.4 114,494 46,182 2.64 Quintinia sieberi D 7,368,002 7,357,731 0.14% 101.0 99.4 131,038 200,552 2.73 Quintinia sieberi N-BR 5,088,818 5,082,294 0.13% 101.0 99.5 131,038 89,464 1.76 Rhodamnia blairiana ML 27,825,700 27,825,689 0.00% 150.6 150.3 130,010 1,246,732 4.48 Rhodamnia blairiana MB-T 6,958,044 6,956,973 0.02% 149.2 149.2 130,010 126,300 1.82 Rhodamnia rubescens D 2,713,988 2,709,813 0.15% 101.0 99.7 130,010 118,974 4.39 Rhodamnia rubescens N-BR 14,763,612 14,712,194 0.35% 101.0 99.7 130,010 386,203 2.63 155

Sarcopteryx stipata D 10,600,012 10,562,603 0.35% 101.0 98.3 128,799 124,153 1.18 Sarcopteryx stipata N-BR 12,649,230 12,593,280 0.44% 101.0 98.5 128,799 171,217 1.36 Schizomeria ovata D 1,518,904 1,517,370 0.10% 101.0 99.5 123,641 114,898 7.57 Schizomeria ovata N-BR 2,244,382 2,243,151 0.05% 101.0 99.3 123,641 49,779 2.22 Scolopia braunii ML 6,988,898 6,987,942 0.01% 147.9 147.9 117,947 234,055 3.35 Scolopia braunii MB-T 3,577,882 3,577,494 0.01% 147.2 147.2 117,947 139,016 3.89 Sloanea australis D 3,065,366 3,061,088 0.14% 101.0 98.6 110,617 42,960 1.40 Sloanea australis N-BR 15,863,274 15,784,161 0.50% 101.0 98.4 110,617 79,406 0.50 Sloanea australis ML 4,014,174 4,011,723 0.06% 149.2 149.2 110,617 104,054 2.59 Sloanea australis MB-T 8,095,160 8,094,028 0.01% 150 150 110,617 132,900 1.64 Stenocarpus salignus D 9,674,892 9,658,897 0.17% 101.0 98.8 133,892 239,917 2.48 Stenocarpus salignus N-BR 13,456,894 13,413,442 0.32% 101.0 98.9 133,892 335,145 2.50 Synoum glandulosum ML 3,484,100 3,483,671 0.01% 148.8 148.8 116,551 77,853 2.23 Synoum glandulosum MB-T 4,177,058 4,175,275 0.04% 147.8 147.8 116,551 46,909 1.12 Syzygium oleosum D 4,192,666 4,189,047 0.09% 101.0 99 107,831 46,625 1.11 Syzygium oleosum N-BR 2,440,898 2,437,985 0.12% 101.0 98.7 107,831 42,848 1.76 Tabernaeamontana D 4,997,776 4,967,542 0.60% 101.0 99 120,956 153,553 3.09 pandacaqui Tabernaeamontana N-BR 6,733,488 6,705,904 0.41% 101.0 98.6 120,956 165,944 2.47 pandacaqui Toona ciliata D 26,061,440 26,061,437 0.00% 150.6 150.4 111,835 861,921 3.31 Toona ciliata N-BR 7,809,438 7,405,795 5.17% 101.0 94.83 111,835 369,210 4.99 Toona ciliata ML 5,423,028 5,422,680 0.01% 149.3 149.3 111,835 237,477 4.38 Toona ciliata MB-T 25,566,970 25,566,958 0.00% 150.6 150.4 111,835 1,360,921 5.32 Tristaniopsis collina D 23,608,168 23,592,926 0.06% 101.0 99.6 126,455 190,823 0.81 Tristaniopsis collina N-BR 2,300,242 2,296,676 0.16% 101.0 98.6 126,455 65,646 2.86 Trochocarpa laurina D 3,217,484 3,210,075 0.23% 101.0 99.2 104,121 57,235 1.78 Trochocarpa laurina N-BR 2,174,214 2,170,125 0.19% 101.0 99 104,121 74,437 3.43

156

Wilkiea huegeliana D 4,905,062 4,871,529 0.68% 101.0 97.9 131,847 127,059 2.61 Wilkiea huegeliana N-BR 7,047,582 7,033,495 0.20% 101.0 97.5 131,847 85,065 1.21

157

Table 9.2 Summary of chloroplast genomic diversity data for populations in the for the Tropics (ML and MB-T) and Subtropics (N-BR and D) and overall within each region.

Genomic diversity Species ML MB-T N-BR D Tropics Subtropics Alangium villosum 1.92 x 10-5 0 1.20 x 10-5 Archirhodomyrtus beckleri 1.37 x 10-5 0 8.23 x 10-5 Argyrodendron trifoliolatum 8.70 x 10-5 4.87 x 10-5 0 2.78 x 10-5 3.31 x 10-5 1.39 x 10-5 Atractocarpus benthamianus 6.75 x 10-5 4.39 x 10-5 5.57 x 10-5 -5 -5 -5 Austrobuxus swainii 2.31 x 10 1.93 x 10 2.12 x 10 -5 -5 -5 Beilschmeidia bancroftii 1.33 x 10 1.60 x 10 1.47 x 10 Brachychiton acerifolius 2.96 x 10-5 1.18 x 10-5 1.97 x 10-5 -5 -4 -5 Breynia cernua 2.92 x 10 1.22 x 10 7.55 x 10 -5 -6 -5 Callicoma serratifolia 5.49 x 10 3.23 x 10 2.91 x 10 Cardwellia sublimis 1.21 x 10-4 5.24 x 10-5 8.65 x 10-5 -6 -6 -6 Ceratopetalum apetalum 6.27 x 10 3.13 x 10 4.70 x 10 -6 -5 -5 Cinnamomum laubatii 5.02 x 10 2.51 x 10 1.50 x 10 Cinnamomum oliveri 1.93 x 10-5 1.85 x 10-4 9.04 x 10-5 -5 -5 -5 Claoxylon australe 2.28 x 10 2.28 x 10 2.28 x 10 Cryptocarya meissneriana 2.17 x 10-4 2.67 x 10-4 2.69 x 10-4 Cryptocarya obovata 1.03 x 10-4 2.82 x 10-5 6.58 x 10-5 -5 -6 -5 Cryptocarya rigida 3.05 x 10 3.05 x 10 1.68 x 10 Darlingia darlingiana 3.25 x 10-4 2.95 x 10-5 1.77 x 10-4 Diploglottis cunninghamii 0 3.49 x 10-6 1.94 x 10-6 -4 -6 -4 Doryphora aromatica 2.66 x 10 7.26 x 10 1.25 x 10 Doryphora sassafras 2.87 x 10-4 4.34 x 10-5 1.52 x 10-4 Duboisia myoporoides 3.04 x 10-5 3.04 x 10-5 3.04 x 10-5 -6 -6 -6 Ehretia acuminata 9.24 x 10 6.16 x 10 7.70 x 10 Elaeocarpus reticulatus 3.40 x 10-5 2.07 x 10-4 1.21 x 10-4 -5 -5 -5 Elaeocarpus sericopetalus 3.06 x 10 5.28 x 10 4.17 x 10

158

-6 -6 -6 Endiandra bessaphila 8.03 x 10 8.03 x 10 8.03 x 10 Endiandra crassiflora 8.52 x 10-6 2.39 x 10-5 1.70 x 10-5 -6 -6 Endiandra muelleri 6.76 x 10 1.27E-05 9.38 x 10 Eupomatia laurina 2.39 x 10-4 7.75 x 10-5 0 1.45 x 10-4 1.58 x 10-4 7.25 x 10-5 -5 Ficus coronata 7.31E-05 0 4.57 x 10 -5 -6 Homolanthus populifolius 1.59 x 10 0 8.86 x 10 -5 -5 -4 Litsea leefeana 8.56 x 10 8.81 x 10 1.74 x 10 Neolitsea dealbata 3.35 x 10-5 1.22 x 10-5 1.22 x 10-5 0 2.28 x 10-5 6.76 x 10-6 -6 -5 -5 Nothofagus moorei 9.77 x 10 3.26 x 10 2.12 x 10 Pennantia cunninghamii 6.09 x 10-6 5.07 x 10-6 5.70 x 10-6 -6 -5 -5 Pittosporum revolutum 3.93 x 10 1.96 x 10 1.18 x 10 Polyscias murrayi 1.51 x 10-4 1.54 x 10-5 0 9.26 x 10-6 8.33 x 10-5 4.21 x 10-6 -4 -4 Pouteria australis 2.66 x 10 0 1.33 x 10 Prunus turneriana 7.44 x 10-5 6.47 x 10-6 8.10 x 10-5 -4 -5 -4 Quintinia sieberi 3.39 x 10 9.77 x 10 2.18 x 10 Rhodamnia blairiana 1.88 x 10-5 5.11 x 10-5 3.50 x 10-5 -5 -5 -5 Rhodamnia rubescens 6.46 x 10 1.54 x 10 4.00 x 10 Sarcopteryx stipata 0 0 0 Schizomeria ovata 0 2.91 x 10-5 1.46 x 10-5 -5 -5 -5 Scolopia braunii 5.37 x 10 5.09 x 10 5.23 x 10 Sloanea australis 1.36 x 10-5 4.34 x 10-5 3.62 x 10-5 0 3.01 x 10-5 1.61 x 10-5 Stenocarpus salignus 7.47 x 10-6 6.57 x 10-5 3.98 x 10-5 -6 -6 Synoum glandulosum 5.72 x 10 0 2.86 x 10 -6 -6 -6 Tabernaeamontana pandacaqui 8.27 x 10 3.31 x 10 5.51 x 10 Toona ciliata 1.19 x 10-5 0 0 0 5.96 x 10-6 0 -6 -5 -5 Tristaniopsis collina 6.33 x 10 5.06 x 10 2.85 x 10 -5 -5 -5 Trochocarpa laurina 7.30 x 10 1.92 x 10 4.61 x 10 Wilkiea huegeliana 2.28 x 10-5 3.64 x 10-5 2.28 x 10-5

159

Table S9.3 Chloroplast genomic distance (number of chloroplast SNPs over the length of chloroplast genomic sequence) between populations within the Tropics (ML vs. MB-T) and Subtropics (N-BR vs. D).

Genomic distance Species Tropics Subtropics Alangium villosum 4.74 x 10-6 Archirhodomyrtus beckleri 1.73 x 10-4 Argyrodendron trifoliolatum 1.20 x 10-3 2.09 x 10-5 Atractocarpus benthamianus 1.32 x 10-3 Austrobuxus swainii 1.88 x 10-3 Beilschmeidia bancroftii 1.46 x 10-3 Brachychiton acerifolius 2.27 x 10-3 Breynia cernua 1.91 x 10-4 Callicoma serratifolia 6.70 x 10-4 Cardwellia sublimis 2.83 x 10-4 Ceratopetalum apetalum 7.60 x 10-4 Cinnamomum laubatii 1.20 x 10-4 Cinnamomum oliveri 1.54 x 10-5 Claoxylon australe 2.88 x 10-4 Cryptocarya meissneriana 2.79 x 10-4 Cryptocarya obovata 6.89 x 10-4 Cryptocarya rigida 2.52 x 10-4 Darlingia darlingiana 7.58 x 10-4 Diploglottis cunninghamii 1.61 x 10-3 Doryphora aromatica 7.51 x 10-4 Doryphora sassafras 3.34 x 10-4 Duboisia myoporoides 5.33 x 10-5 Ehretia acuminata 1.31 x 10-4 Elaeocarpus reticulatus 9.28 x 10-5 Elaeocarpus sericopetalus 9.18 x 10-5 Endiandra bessaphila 2.97 x 10-4 Endiandra crassiflora 8.18 x 10-4 Endiandra muelleri 1.78 x 10-3 Eupomatia laurina 7.44 x 10-5 1.42 x 10-3 Ficus coronata 6.09 x 10-5 Homolanthus populifolius 3.99 x 10-4 Litsea leefeana 1.13 x 10-4 Neolitsea dealbata 5.33 x 10-5 7.61 x 10-6 Nothofagus moorei 1.68 x 10-3 Pennantia cunninghamii 7.61 x 10-5 Pittosporum revolutum 2.29 x 10-3 Polyscias murrayi 2.70 x 10-4 5.40 x 10-5 Pouteria australis 7.15 x 10-4 Prunus turneriana 2.36 x 10-4 Quintinia sieberi 4.50 x 10-4 Rhodamnia blairiana 2.58 x 10-4 Rhodamnia rubescens 3.62 x 10-4 Sarcopteryx stipata 2.33 x 10-5 160

Schizomeria ovata 2.59 x 10-4 Scolopia braunii 5.43 x 10-4 Sloanea australis 1.36 x 10-4 8.59 x 10-4 Stenocarpus salignus 8.74 x 10-4 Synoum glandulosum 1.79 x 10-3 Tabernaeamontana pandacaqui 7.69 x 10-4 Toona ciliata 1.79 x 10-5 1.79 x 10-5 Tristaniopsis collina 1.37 x 10-3 Trochocarpa laurina 1.26 x 10-3 Wilkiea huegeliana 3.31 x 10-3

161

Table S9.4 Estimated species ages (years) in populations across the Tropics (ML and MB-T) and Subtropics (N-BR and D). Branch rates were inferred from third codon positions of chloroplast genomes by van der Merwe et al. (2019).

Estimated species ages Branch rate Species ML MB-T N-BR D base pair/ 107 years Alangium villosum 51,000 5,900 0.045 Archirhodomyrtus beckleri 37,600 3,700 0.047 Argyrodendron trifoliolatum 196,800 106,300 3,800 60,700 0.050 Atractocarpus benthamianus 75,300 44,700 0.119 Austrobuxus swainii 20,100 16,500 0.142 Beilschmeidia bancroftii 34,900 41,800 0.050 Brachychiton acerifolius 65,300 86,300 0.038 Breynia cernua 66,100 259,300 0.058 Callicoma serratifolia 197,600 14,300 0.034 Cardwellia sublimis 265,200 109,000 0.059 Ceratopetalum apetalum 31,200 15,700 0.024 Cinnamomum laubatii 13,000 64,900 0.051 Cinnamomum oliveri 60,500 532,600 0.034 Claoxylon australe 16,800 17,900 0.148 Cryptocarya meissneriana 1,261,900 1,635,700 0.021 Cryptocarya obovata 404,300 122,000 0.039 Cryptocarya rigida 63,100 7,400 0.061 Darlingia darlingiana 475,600 53,100 0.073 Diploglottis cunninghamii 3,300 11,600 0.064 Doryphora aromatica 432,300 9,400 0.074 Doryphora sassafras 842,700 141,600 0.037 Duboisia myoporoides 74,400 75,200 0.049 Ehretia acuminata 23,400 13,500 0.056 Elaeocarpus reticulatus 60,800 370,700 0.067 Elaeocarpus sericopetalus 85,800 148,300 0.047 Endiandra bessaphila 22,000 23,300 0.045 Endiandra crassiflora 13,100 24,800 0.111 Endiandra muelleri 14,500 19,200 0.099 Eupomatia laurina 562,500 184,000 12,500 380,500 0.047 Ficus coronata 98,200 2,900 0.089 Homolanthus populifolius 23,500 2,600 0.085 Litsea leefeana 156,700 161,200 0.072 Neolitsea dealbata 57,400 2,600 15,600 51,700 0.069 Nothofagus moorei 19,700 25,800 0.077 Pennantia cunninghamii 5,600 16,800 0.033 Pittosporum revolutum 13,900 18,400 0.103 162

Polyscias murrayi 340,700 34,700 5,300 31,600 0.053 Pouteria australis 1,201,000 8,500 0.033 Prunus turneriana 170,600 15,700 0.051 Quintinia sieberi 696,900 190,400 0.061 Rhodamnia blairiana 35,700 102,400 0.075 Rhodamnia rubescens 114,600 32,400 0.066 Sarcopteryx stipata 4,800 5,000 0.044 Schizomeria ovata 9,600 122,300 0.028 Scolopia braunii 104,100 100,800 0.034 Sloanea australis 25,700 112,700 52,500 5,500 0.066 Stenocarpus salignus 19,000 187,400 0.043 Synoum glandulosum 25,300 6,400 0.029 Syzygium oleosum 29,900 6,200 0.041

Tabernaeamontana pandacaqui 10,700 5,000 0.086 Toona ciliata 4,300 34,700 4,800 4,700 0.041 Tristaniopsis collina 25,300 158,200 0.038 Trochocarpa laurina 64,300 23,400 0.149 Wilkiea huegeliana 11,600 171,800 0.026

References

Van der Merwe, M., Yap, J. S., Cristofolini, C., Foster, C. S. P., Ho, S. Y. W., & Rossetto, M. (2019). A framework for resolving assemblage accumulation in biological communities using chloroplast genome sequences. Methods in Ecology and Evolution.

163

Appendix 10 Plot data within the Tropics and Subtropics

Plot data for the Tropics and Subtropics were obtained from Kooyman et al. (2011) and Metcalfe and Ford (2008). These plots are in the proximity of the study sites: Mt Lewis (16° 34' 46.4556" S, 145° 18' 58.572" E), Mt Baldy-Tinaroo (Mt Baldy: 17° 16' 36.48" S, 145° 25' 43.1106" E; Tinaroo: 17° 10' 38.8626" S, 145° 39' 32.9112" E), Nightcap-Border Ranges (Nightcap: 28° 38' 25.3464" S, 153° 20' 4.47" E; Border Ranges: 28° 29' 21.825" S, 153° 9' 20.073" E) and Dorrigo (30° 20' 21.2598" S, 152° 48' 51.5628" E).

Table S10.1 Summary of study plots in the Tropics and Subtropics. This table lists the number of plots (“N plots”) and the average proportions of Sahul (“Proportion Sahul”) and Sunda (“Proportion Sunda”) species per plot, and the number of Sahul (“N Sahul species”) and Sunda ("N Sunda species”) species at each study site.

Proportion Proportion N Sahul N Sunda Study Site N plots Sahul Sunda species species Tropics Mt Lewis 11 0.7 0.3 169 93 Mt Baldy-Tinaroo 13 0.59 0.41 162 109 Subtropics Nightcap-Border 12 0.63 0.37 95 52 Ranges Dorrigo 12 0.79 0.21 75 24

References

Metcalfe, D. J., & Ford, A. J. (2008). Floristic biodiversity in the Wet Tropics. In N. Stork & S. Turton (Eds.), Living in a Dynamic Tropical Forest Landscape (pp. 123-132). Blackwell, Oxford, UK.

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Appendix 11 Distribution, sampling and study design of Doryphora sassafras and Toona ciliata

Figure S11.1 Current species occurrences of (a) Doryphora sassafras (green dots) and (b) Toona ciliata (red dots) in Australia. The occurrence data was obtained from the Atlas of Living Australia (ALA; http://ala.org.au), and this data has been cleaned using the procedure that was described in the methods in Chapter 4.

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Fig S11.2 Work flow for the landscape-level study of D. sassafras and T. ciliata across NSW in Chapter 4. GT denotes high throughput genotyping data generated using the DArTseq platform. Refer to the methods in Chapter 4 for details on the quality filter process.

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Appendix 12 Additional population genetic analyses for Doryphora sassafras and Toona ciliata

Figure S12.1 Within population genetic diversity and latitude relationship for Doryphora sassafras (a, c and e respectively) and Toona ciliata (b, d and f respectively) using different diversity measures: allelic richness (“ar”), expected heterozygosity (“He”) and observed heterozygosity (“Ho”). In each scatterplot the points represent populations in this study. A linear relationship test was conducted for each scatterplot, and this resulted in all relationships between genetic diversity measures and latitude being significant for D. sassafras, but not for T. ciliata. Spearman rank correlation tests were performed on the relationships between genetic diversity measures and latitude, and similar outcomes were resulted for both species (see Table S12.1).

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Table S12.1 Summary of the Spearman rank correlation test on the relationships between genetic diversity measures (“ar” = allelic richness, “He” = expected heterozygosity, “Ho” = observed heterozygosity) and latitude for both species. For each species, the Spearman coefficient (Rs) is listed, the closer the value is to +1 or -1, the stronger the likely correlation. The P-value is calculated in the same way as linear regression and correlation.

Doryphora sassafras Toona ciliata Genetic diversity P-value R P-value R measure s s ar 0.029 -0.447 0.124 0.337 He 0.003 -0.578 0.202 0.283 Ho 0.003 -0.580 0.312 0.157

Figure S12.2 Study sites of Doryphora sassafras (green triangles) and Toona ciliata red dots) on an elevation map of New South Wales (NSW), Australia with the yellow bar showing the location oft the Hunter River Corridor. Refer to Table S12.2 for population details.

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Figure S12.3 Discriminant analysis of principal components (DAPC) results for Doryphora sassafras (a,c) and Toona ciliata (b,d). (a,b) The optimal number of clusters (K) as determined by 'k- means'. For D. sassafras the graph shows a clear decrease of Bayesian information criterion (BIC) at K = 2 clusters to be the most likely value of K, with further decrease until K = 4 to indicate the presence of subpopulations. For T. ciliata the decrease of BIC was continuous suggesting K = 1 is the most likely value. (c,d) Scatterplot based on the DAPC output for K = 4, each cluster is indicated by a different colour and dots with the clusters represent sampled individuals. In D. sassafras (c), populations fall into four geographical clusters: “NNSW” consists of populations from Border Ranges to Dorrigo in Northern New South Wales (NNSW), “W-BT” consists of populations from Werrikimbe to Barrington Tops, “CC” consists of populations from the Central Coast and “RNP-S” consists of populations from the Illawarra escarpment and SNSW (populations belonging to each cluster are listed in Table 4.1). For T. ciliata (d), a main cluster of individuals and two slightly differentiated populations, CarawiryCreek and WatagansNP were observed. In order to compare population structure results, T. ciliata populations were approximately grouped into clusters as in D. sassafras but based on their geographical location (Table S12.2).

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Table S12.2 List of sampled populations of Doryphora sassafras and Toona ciliata with elevation listed in meters (m), latitude / longitude information, and number of samples collected per population (“N”) included. Altitude was represented by a colour gradient with higher altitudes indicated in red and lower altitudes indicated in blue, and this colour scheme was only partitioned within each species.

Doryphora sassafras Toona ciliata Altitude Longitud Altitude Clusters Population names Latitude N Population names Latitude Longitude N (m) e (m) AirdropRd 731 -28.3936 153.0579 6 Sheepstationcreek 386 -28.4117 153.0239 6 Nightcap 575 -28.574 153.3374 6 BorderRanges 362 -28.5042 153.1165 6 BigScrub 168 -28.6389 153.332 6 RummeryRd 366 -28.5872 153.3726 6 Washpool 862 -29.4719 152.3315 6 BigScrub 152 -28.6406 153.3353 6 NNSW NymboiBinderay 496 -30.1802 152.8007 6 Washpool 774 -29.2825 152.3962 6 DorrigoNP 549 -30.3515 152.7896 6 NymboiBinderay 766 -30.1667 152.6762 6 Darkwood 84 -30.4152 152.7605 6 WilliWilliNP 597 -31.289 152.5203 6 Werrikimbe 479 -31.2369 152.5368 6 Knodingbull 816 -31.5492 152.1506 6 OxleyHwy 905 -31.3893 152.1077 5 UpperAllynfootofhill 418 -32.1471 151.7644 6 Werrikimb Knodingbull 808 -31.5818 152.1454 6 SkimmingGapRd 435 -32.2387 151.726 6 e Plateau to Boorganna 480 -31.6154 152.3975 6 CarawiryCreek 203 -32.2807 151.8007 6 Barrington StarrsBigNellie 433 -31.6898 152.5185 5 Tops GloucesterTops 489 -32.0626 151.6828 6 JerusalemTrack 435 -32.2387 151.726 6 WalkersRidgeOlneySF 429 -33.063 151.2668 6 WatagansNP 141 -33.021 151.431 6 Central PalmGrove 71 -33.3171 151.3003 6 KatandraReserve 63 -33.4065 151.3776 4 Coast KatandraReserve 58 -33.4151 151.3941 5 Wahronga 144 -33.7286 151.0084 5 StanwellPark 204 -34.2283 150.9735 6 Wombarra 251 -34.2797 150.9467 6 MtKeira 287 -34.3863 150.8671 5 SublimePtBulli 222 -34.3002 150.9249 6 Illawarra MacquariePass 211 -34.5676 150.6666 6 MtKeira 294 -34.4073 150.8504 6 escarpment FitzroyFalls 603 -34.6447 150.4837 6 MtKembla 428 -34.4426 150.7693 5 and SNSW Broughton 212 -34.7316 150.724 5 MacquariePass 177 -34.5697 150.6715 5 Cambewarra 486 -34.7988 150.5775 5 MinnamurraNP 231 -34.6345 150.7263 5 170

McDonaldSF 26 -35.2123 150.4175 6 Nowra 9 -34.8633 150.5939 4 ClydeMountain 779 -35.5486 149.9536 4

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Figure S12.4 Estimation of Individual ancestry coefficients or snmf results for Doryphora sassafras (a,c) and Toona ciliata (b,d). (a,b) Histogram of adjusted P-values to determine the optimal value of K. For D. sassafras the graph shows a clear decrease in cross-entropy at K = 2 as the most likely value of K, with further decrease until K = 4 to indicate the presence of subpopulations. For T. ciliata K = 1 had the lowest cross-entropy. (c,d) snmf barplots (K = 4) show estimated ancestry proportions of sampled populations sorted by increasing latitude, with different colors to indicate different ancestries. In (c) and (d), labels below the barplot indicate where each geographically structured genetic cluster begins. In T. ciliata (d), admixture was generally observed throughout the sampled population. Two T. ciliata populations, CarawiryCreek in W-BT and WatagansNP in CC stood out slightly from the rest of the populations as well as from each other at K = 4.

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Figure S12.5 Estimates (FST, GST and G’ST) of between-population genetic differentiation vs. distance for Doryphora sassafras (A, C, E) and Toona ciliata (B, D, F). Within each pairwise FST scatterplot, each dot represents genetic distance between paired populations, and results from a correlation test for significant isolation by distance (Mantel test) was indicated in each scatterplot (r of 0.8 and higher indicates strong positive correlation between the level of between-population differentiation and geographic distance, and the corresponding two-tailed P-value = 0.001 indicates the results are statistically significant at an alpha of 0.05 from 999 permutations, i.e. the population structure of species is characterised by a pattern of isolation by distance).

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Figure S12.6 Pairwise Fst plots of Doryphora sassafras (a:d) and Toona ciliata (e:h) populations grouped according to clusters as in Table 4.1 based on results from Fig. S12.3-12.4. A mantel test with up to 999 random permutations was conducted for each cluster, resulting in almost all pairwise Fst vs. distance relationships being significant for D. sassafras (P-values ranged between 0.004 and 0.012) except in Central Coast where only three populations were available for pairwise comparisons. None of the pairwise Fst relationships were significant for T. ciliata.

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Appendix 13 Environmental Niche Modelling data outputs Table S13.1 Description of each bioclimatic variable Permutation importance (%) of environmental variables used in the Maxent modeling. The predictive power of the models was tested using area under receiver operating curve (AUC; Swetts, 1988). The AUC and TSS of D. sassafras are 0.835 and 0.515, and the AUC and TSS of T. ciliata are 0.768 and 0.392, which are considerable.

Permutation importance (%) Environmental variables Description Doryphora sassafras Toona ciliata Bio01 Annual mean temperature (°C) 28.5 Bio02 Mean diurnal temperature range (mean (period max-min)) (°C) 24 1 Bio03 Isothermality (Bio02 ÷ Bio07) Bio04 Temperature seasonality (Coefficient of variance) 66.5 Bio05 Max temperature of warmest week (°C) Bio06 Min temperature of coldest week (°C) Bio07 Temperature annual range (Bio05-Bio06) (°C) Bio10 Mean temperature of warmest quarter (°C) 17.2 Bio11 Mean temperature of coldest quarter (°C) Bio12 Annual precipitation (mm) 23.2 4.4 Bio13 Precipitation of wettest week (mm) Bio14 Precipitation of driest week (mm) 18.5 Bio15 Precipitation seasonality (Coefficient of variance) 5.8 Bio16 Precipitation of wettest quarter (mm) Bio17 Precipitation of driest quarter (mm) TPI Topographic Position Index TWI Topographic Wetness Index 10.9

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Table S13.2 List of selected General Circulation Models (GCMs) for which past (Last Glacial Maximum and Mid-Holocene) and future (2070 based on both, RCP4.5 and RCP 8.5) climate scenarios. The selection of GCMs was based upon skill score assessments undertaken by Whetton et al. (2015).

Last Glacial Maximum Mid-Holocene 2070 (RCP 4.5) 2070 (RCP 8.5) CNRM-CM5 BCC-CSM1-1 ACCESS1-0 ACCESS1-0 COSMOS-ASO CNRM-CM5 CanESM2 CanESM2 MPI-ESM-P MPI-ESM-P GFDL-CM3 GFDL-CM3 MRI-CGCM3 MRI-CGCM3 MPI-ESM-LR MPI-ESM-LR MPI-ESM-MR MPI-ESM-MR MRI-CGCM3 MRI-CGCM3 NorESM1-M NorESM1-M

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(a) 120000 100.00%

100000 80.00% 80000 60.00% 60000 40.00% 40000

20000 20.00%

0 0.00% LGM Mid-Holocene Current Area of optimal suitability %overlap with current sp. occurrence

(b) 120000 100.0%

100000 80.0% 80000 60.0% 60000 40.0% 40000

20000 20.0%

0 0.0% LGM Mid-Holocene Current Area of optimal suitability %overlap with current sp. occurrence

Figure S13.1 Area of optimal habitat suitability (km2, grey barplots) for (a) Doryphora sassafras and (b) Toona ciliata during the Last Glacial Maximum (LGM), mid-Holocene (MH) and the current period. Percentage of species occurrence that overlap with the distribution of optimal habitat for each period was measured (orange line). For each period, the percentage was generated by dividing the number of species occurrences that overlap with the area of optimal habitat by the total number of species occurrences in NSW (D. sassafras has a total of 1,686 occurrences, T. ciliata has a total of 913 occurrences). T. ciliata has a larger area of optimal habitat than D. sassafras across all three periods. For D. sassafras, there was an increase in area of optimal habitat from the LGM to the current period, and highest overlap in species occurrences during the current period (74.61%). For T. ciliata, area of suitable habitat dramatically increased during the MH, and contracted slightly towards the current period. Species occurrences of T. ciliata overlapped slightly more with the area of suitable habitat predicted for the MH (76.3%) than the current period (73.6%).

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Reference

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285-1293.

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