Chasing the Dragon: the Resilience of a Species to Climate Change in the Wet Tropics, Australia

Home , Blanus, Common Collared Lizard, Hypsilurus, Lampropholis robertsi, Sphenomorphus

Chasing the Dragon: The Resilience of a Species to Climate Change in the Wet Tropics, Australia

Author Bernays, Sofie

Published 2015

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/126

Downloaded from http://hdl.handle.net/10072/367595

Griffith Research Online https://research-repository.griffith.edu.au

Chasing the dragon: The resilience of a species to climate change in the Wet Tropics, Australia

Sofie Bernays

Bachelor of Environmental Management (Honours)

Griffith School of Environment

Griffith Sciences

Griffith University

Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy

June 2015

Summary

Throughout history, climatic changes have caused environmental systems to shift and have influenced biotic assemblages. Most of these changes have occurred slowly, over millions of years, enabling species to either adapt to new conditions, endure the changes, or shift distributions to maintain their habitat requirements. Due to the fast rate at which climate change is currently occurring, it is unknown if species will be able to use these mechanisms to successfully respond to this rapidly changing environment. Areas that have small geographical extents, elevated uplands and high numbers of endemic species, such as the Wet Tropics in north-eastern Queensland, are expected to be particularly vulnerable to climate change. The endemic species in this at-risk area are also expected to be more susceptible to climate change. The endemic Boyd’s forest dragon (Hypsilurus boydii, Macleay) is a highly camouflaged, large lizard that inhabits lowland and upland forests from the northern to the southern boundary of the Wet Tropics. Determining how H. boydii has responded to previous climate change may give insight into how the species may respond to future climatic changes.

The main aims of this study were to understand how geographical features and climate have influenced the genetic makeup, morphology and distribution of H. boydii, and to use this information to determine how climate change may influence future populations. This study used genetic analyses to identify evolutionary and geographical relationships across the Wet Tropics (north vs. south of the Black Mountain corridor [BMC] and upland vs. lowland) and within each of these regions; explore morphological variation across the regions and examine conformity to three eco-geographical rules (Bergmann’s rule, Allen’s rule, and the isolation rule); and attempt to predict species distribution patterns of the species throughout the Wet Tropics during past, present and future climatic scenarios. Seventy- seven dragons were collected from nine sites across the Wet Tropics, with a blood sample (for genetic analyses) taken from each individual, 47 of these individuals, from eight of the sites, were sampled for morphological measurements. Due to the cryptic and ambush nature of the species, sample sizes were low and uneven.

Summary

To understand the influence that geography and climate have had on the genetic makeup of H. boydii, genetic patterns were examined using one mitochondrial (ND4) and three nuclear genes (PTGER4, MKL1 and BZW1; N = 76). Genetic diversity and genetic structure were high across the Wet Tropics, with differences detected between northern and southern regions, although not between upland and lowland regions. The deep divergence between populations north and south of the BMC was suggested to have occurred 0.90 – 3.26 million years ago. There was variation in the detected levels of divergence between mitochondrial and nuclear genes, with the mitochondrial gene and one nuclear gene reflecting the highest genetic structure. The findings from this study suggest long-term isolation between northern and southern populations with no evidence of secondary contact or cryptic species. There was also evidence for significant genetic differentiation among sites within the regions. It is therefore suggested that H. boydii has experienced restrictions to dispersal across the BMC but also within the northern and southern regions. The dispersal ability of the species appears to quite be limited, and may become negligible if further fragmentation of forest habitat occurs.

Influences of geography (latitude) and climate (altitude) on morphological variation were explored by testing three eco-geographical rules: Bergmann’s rule, Allen’s rule, and the isolation rule. The inverse of Bergmann’s rule and Allen’s rule suggest there may be evolutionary advantages for small reptiles in cool habitats, as small animals are expected to have a faster rate of heat absorption, whereas the isolation rule suggests different evolutionary pressures affect isolated populations differently. Due to warmer habits in lowlands and cooler habitats in the uplands, it was expected that H. boydii would: conform to the inverse of Bergmann’s rule (larger animals found in warmer lowlands than in cooler uplands); conform to Allen’s rule (larger limbed animals found in warmer lowlands than in cooler uplands); and show little effect from the isolation rule (no variation across the BMC). The morphological analysis found phenotypic differences between upland and lowland populations, with lowland individuals significantly larger than those found in the uplands, as well as slight evidence for male ‘limbs’ being larger in lowland regions compared with those

Summary found in upland regions. There were no morphological differences detected between northern and southern regions across the BMC and no evidence for cryptic species. The results conformed to the inverse of Bergmann’s rule, showed weak support for Allen’s rule in males, and did not support the isolation rule. The homogeneous morphology across the BMC supported findings from previous studies on other reptile species.

Relationships between environmental factors and species occurrences were detected which indicate how these features may have influenced past, present and future distributions of H. boydii. Species modelling was conducted on three climatic scenarios (HadGEM2-ES, ACCESS1-0, MIROC-ESM) using the RCP 8.5 emissions scenario (future atmospheric emissions release is ‘business as usual’). The species distribution modelling suggested the BMC latitudinal barrier may not have always been present, with suitable habitat predicted in the BMC approximately 22,000 years ago. Future modelling suggested that the BMC latitudinal barrier will become more pronounced as suitable habitat both sides of the BMC is reduced by 68 – 86 % by the year 2070.

Overall, genetic analysis suggested that the BMC has maintained a barrier to north-south dispersal. In contrast, species modelling suggested the BMC has not remained a firm barrier to north-south dispersal. With numerous possible explanations for the discrepancy between these findings, the most likely explanations are: the model is over predicting suitable habitat; the limited dispersal ability of H. boydii does not enable movement into all suitable habitat; or suitable habitat does not guarantee inhabitance. The slight morphological variation across altitudes is not expected to influence the resilience of the species in the future. However, the limited dispersal ability of the species may reduce the likelihood of H. boydii moving into new habitat as the fragmentation effects of future climate change take place.

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Statement of originality

"This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself."

Sofie Bernays

Table of Contents

Summary ...... i Statement of originality...... i Table of Contents ...... ii Table of Figures ...... v List of Tables ...... vii Acknowledgements ...... ix Chapter 1 General introduction ...... 1 1.1 Climate change ...... 2 1.3 Contemporary climate change ...... 3 1.4 At-risk regions ...... 3 1.4.1 Elevated regions ...... 3 1.4.2 Rainforests ...... 4 1.5 At-risk area – Wet Tropics ...... 4 1.6 At-risk species ...... 5 1.7 Genetics and historical climatic change ...... 6 1.7.1 Gene flow ...... 7 1.7.2 Phylogeography ...... 8 1.8 Morphological variation and historical climate change ...... 10 1.8.1 Sexual dimorphism ...... 12 1.8.2 Eco-geographical rules ...... 13 1.8.3 Geographical isolation ...... 14 1.8.4 Morphology as the first response historically ...... 14 1.8.5 Morphology as the first response to the future warming ...... 17 1.9 Species distribution ...... 17 1.9.1 Distribution shifts...... 18 1.10 Study species: Boyd’s forest dragon ...... 19 1.11 Scope and aims of the study ...... 20 1.11.1 Justification ...... 20 1.11.2 Study aims and structure ...... 21 Chapter 2 Study region, study species and sampling ...... 24 2.1 Study Area: Wet Tropics ...... 25 2.2 Study Species: Boyd’s Forest Dragon, Hypsilurus boydii ...... 27 2.3 Study design ...... 29 2.4 Field sampling technique ...... 32 Chapter 3 Phylogeographic patterns of H. boydii across the Wet Tropics ...... 34 3.1 Aim ...... 36 3.2 Methods ...... 36 3.2.1 Field sampling for genetic analysis ...... 36 3.2.2 Laboratory methods ...... 37 3.2.3 Statistical analyses ...... 42 3.3 Results ...... 45 3.3.1 Population structure ...... 45 3.3.2 Latitudinal barriers to dispersal ...... 53 3.3.3 Altitudinal barriers to dispersal ...... 58 3.4 Discussion ...... 59 ii

3.4.1 Population structure ...... 59 3.4.2 Latitudinal barriers to dispersal ...... 62 3.4.3 Altitudinal barriers to dispersal ...... 64 3.4.4 Genetic structure and historical climate change ...... 65 3.4.5 Conclusion ...... 67 Chapter 4 Morphological variation: Examining Bergmann’s rule, Allen’s rule and the isolation rule 68 4.1.1 Morphology and the Wet Tropics ...... 69 4.2 Aim ...... 71 4.3 Methods ...... 71 4.3.1 Field sampling for morphological analysis ...... 71 4.3.2 Museum samples ...... 73 4.3.3 Statistical analyses ...... 73 4.3.4 Statistical analysis of museum samples ...... 76 4.4 Results ...... 77 4.4.1 Males vs. females ...... 79 4.4.2 Bergmann and Allen’s rule: lowland vs. upland ...... 79 4.4.3 Isolation rule: northern vs. southern populations ...... 82 4.4.4 Bergmann’s, Allen’s and isolation rule: altitude-latitude ...... 84 4.4.5 Morphological & genetic comparisons ...... 92 4.5 Discussion ...... 93 4.5.1 Males vs. females ...... 93 4.5.2 Bergmann’s and Allen’s rule ...... 95 4.5.3 Isolation rule ...... 96 4.5.4 Interaction: Bergmann’s, Allen’s and isolation rule ...... 97 4.5.5 Morphology and genetics...... 97 4.5.6 Conclusion ...... 98 Chapter 5 Species’ modelling of past, present and future distributions of H. boydii ..... 100 5.1 Paleo and future distribution modelling ...... 101 5.1.1 Historical climate change ...... 101 5.1.2 Contemporary climate change ...... 102 5.1.3 Species Distribution Models ...... 103 5.1.4 Aim ...... 103 5.2 Methods ...... 104 5.2.1 MaxEnt modelling ...... 104 5.2.2 Species Occurrences ...... 104 5.2.3 Niche modelling ...... 105 5.2.4 Climate scenarios ...... 107 5.3 Results ...... 110 5.3.1 Current distribution ...... 110 5.3.2 Paleo and future distribution ...... 116 5.4 Discussion ...... 122 5.4.1 Current distribution ...... 122 5.4.2 Past distribution ...... 123 5.4.3 Future distribution ...... 125 5.4.4 Conclusion ...... 126 Chapter 6 General discussion ...... 128

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6.1 Overall chapter summaries ...... 129 6.2 The role of latitude on the genetic makeup, morphology and distribution of H. boydii 129 6.2.1 Evidence for latitudinal barrier causing isolation and genetic divergence ..... 129 6.2.2 Discordance between phylogeography, morphology and species modelling 131 6.2.3 Morphological differences did not support genetic and distribution breaks . 132 6.3 The role of altitude on the genetic makeup, morphology and distribution of H. boydii 133 6.4 Dragons in the future ...... 133 6.4.1 Management ...... 134 6.5 Further work ...... 135 6.6 Conclusion ...... 135 References ...... 136 Appendices ...... 164 Appendix A. H. boydii ND4 gene Analysis of Molecular Variance ...... 164 Appendix B. H. boydii PTGER4 gene Analysis of Molecular Variance ...... 164 Appendix C. H. boydii MKL1 gene Analysis of Molecular Variance ...... 165 Appendix D. H. boydii gene BZW1 Analysis of Molecular Variance ...... 165 Appendix E. Statistical analysis and results of museum samples ...... 166 Appendix F. ANOVA examining the effect of altitude and latitude on field and museum character measurements...... 173 Appendix G. ANOVA examining the effect of altitude and latitude on male and female character measurements...... 175

Table of Figures

Figure 1.1 Phenotypic and genetic response times to climate change...... 15 Figure 1.2. H. boydii ...... 19 Figure 2.1. The Wet Tropics bioregion and location within Australia...... 26 Figure 2.2 Average temperature ...... 27 Figure 2.3. Populations of H. boydii sampled across the Wet Tropics ...... 31 Figure 3.1. Blood extraction from a) a caudal vein puncture; and b) a nail clip...... 37 Figure 3.2. Cloning process ...... 41 Figure 3.3. Populations of H. boydii sampled across the Wet Tropics. Circles identify northern and southern partitioning where the dashed line identifies upland and lowland population partitioning. The Black Mountain Corridor (BMC) is identified by a solid line..... 47 Figure 3.4. Haplotype network and their geographical distribution across the Wet Tropics. 50

Figure 3.8. Slatkin's linearised ΦST (blue) and FST (red) illustrating the correlation between genetic distance (ΦST and FST) and geographic distance (Euclidian) for the nDNA BZW1 gene region...... 53 Figure 4.1. Images illustrate approximate measurement location take for each morphological measurement...... 72 Figure 4.2. Identifying the sex ...... 73 Figure 4.3. Museum samples of H. boydii ...... 77 Figure 4.4. Mantel's test illustrating the correlation between morphological distance (Mahalanobis distance) and geographical distance...... 84 Figure 4.5. NMDS plot of standardized measurements ...... 87 Figure 4.6. The mean male SVL for each altitude (lowland and upland) and latitude (north and south) combination...... 89 Figure 4.7. The mean male TW for each altitude (lowland and upland) and latitude (north and south) combination...... 91 Figure 4.8. The mean HW for each altitude (lowland and upland) and latitude (north and south) combination...... 92

Figure 4.9. Identify the correlation between Mahalanobis morphological distance and Slatkin's genetic distant ...... 93 Figure 5.1. Climate scenario comparisons ...... 108 Figure 5.2. The IPCC simulated time series (1905-2100) of temperature rise for the RCP8.5 scenario ...... 109 Figure 5.3. Jackknife of regularized training gain for H. boydii ...... 112 Figure 5.4. Jackknife of regularized training gain for H. boydii ...... 112 Figure 5.5. Response curves for each environmental variable...... 115 Figure 5.6. The minimum, average and maximum projected current distribution of H. boydii within the boundary of the Wet Tropics bioregion...... 116 Figure 5.7. Minimum, average and maximum HadGEM2-ES projected distribution of H. boydii during the mid-Holocene (approximately 6,000 years ago), in 2050 and in 2070. ... 118 Figure 5.8. Minimum, average and maximum ACCESS1.0 projected distribution of H. boydii in 2050 and in 2070...... 120 Figure 5.9. Minimum, average and maximum MIROC-ESM projected distribution of H. boydii during the last glacial maximum (approximately 22,000 years ago), mid-Holocene (approximately 6,000 years ago), and for 2050 and 2070...... 121

List of Tables

Table 2.1 Samples collected ...... 30 Table 3.1. Details of primer and PCR conditions for each genes analysed...... 39 Table 3.2. H. boydii genetic diversity of all genes of each population and region ...... 48

Table 3.3. Neutrality tests (Tajima’s D and Fu’s FS) for all four genes...... 49

Table 3.4. Neutrality tests (Tajima’s D and Fu’s FS) on the northern and southern regions .. 53 Table 3.5. H. boydii ND4 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions ...... 54 Table 3.6. ND4 F-statistic frequencies of H. boydii ...... 55 Table 3.7. H. boydii PTGER4 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions ...... 55 Table 3.8. PTGER4 F-statistic frequencies of H. boydii ...... 56 Table 3.9. H. boydii MKL1 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions ...... 56 Table 3.10. MKL1 F-statistic frequencies of H. boydii ...... 57 Table 3.11. H. boydii gene BZW1 Analysis of Molecular Variance results within northern and southern Wet Tropics regions ...... 57 Table 3.12. BZW1 F-statistic frequencies of H. boydii ...... 58

Table 3.13. Neutrality tests (Tajima’s D and Fu’s FS) on the lowland and upland regions for all four genes...... 58 Table 4.1. Morphological measurement, measurement code and description of the measurement...... 72 Table 4.2. Standardized morphological measurements averages for each population ...... 78 Table 4.3. Welch’s two sample t-test results between males and females ...... 79 Table 4.4. Welch’s two sample t-test results between lowland and upland for the total sample ...... 80 Table 4.5. Welch’s two sample t-test results between lowland and upland within each sex 81 Table 4.6. Welch’s two sample t-test results comparing latitude (north vs. south) in the total sample ...... 82 Table 4.7. Welch’s two sample t-test results between the north and south within each sex 83 Table 4.8. Standardized morphological measurements averages for lowland-north, lowland- south, upland-north, upland-south ...... 86 Table 4.9. SIMPER results ...... 88 Table 4.10. ANOVA examining the effect of altitude and latitude on male SVL ...... 89 Table 4.11. ANOVA examining the effect of altitude and latitude on male TW ...... 90 Table 4.12. Tukey's HSD test on the male morphological variable TW ...... 90 Table 4.13. ANOVA examining the effect of altitude and latitude on male HW ...... 91

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Table 4.14. Tukey's HSD test on the male morphological variable HW...... 91

Table 4.15. Correlation between genetic distance (FST and ΦST) and morphological distance (Mahalanobis distance) ...... 92 Table 5.1. Analysis of variable contributions to the current distribution of H. boydii ...... 111 Table 5.2. Summary of unsuitable and suitable habitat (km2) ...... 122

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Acknowledgements

"Beyond this place, there be Dragons." ~ Out of Africa, S. Pollack (1985)

This thesis feels like a team effort due to the support I have received from so many special people. Firstly, I would like to thank my principle supervisor, Professor Jane Hughes, whose support has been unwavering through this entire process. I have enjoyed your guidance during my PhD and I am so grateful for your tireless help through the trying times (and for allowing me to keep my thesis title). Dr. Daniel Schmidt, my associate supervisor, I would like you to know how much I appreciate your positive support over the years and your help with countless laboratory questions. I feel very honoured that I had you both as my supervisors.

I would like to thank the past and present “geeks” of the Molecular Ecology Laboratory at Griffith University. Thank you Kathryn Real for your assistance during the ‘clone wars’. You always had patience and advice, which gave me new hope when things were not working. A special thank you goes to Jemma Somerville, Joel Huey, Tim Page, Ana Dobson-Cox, Kate Masci and Ashlee Shipham for making the lab and Griffith such a great place to work. I would also like to thank Dr. Andrew Amey at the Queensland Museum for granting me access to the museum specimens.

I would like to thank Dr. Tariq Ezaz. Even though my project changed course, I appreciate the time you took introducing me to chromosome identification and teaching me blood sampling techniques. Thank you to Professor Peter Mather for sharing your knowledge on reptile sex identification.

Without the help of my dragon offsiders, dragon hunting would not have been possible. A huge thank you to Courtenay Mills, Kaye Stuart and Angus Coates for long stints in the field. A special mention to David Sternberg – two and a half weeks in the field with no dragon sightings really hurt my soul. I could not have asked for a better person to share the misery with. My gratitude goes out to all of those who accompanied or assisted me in the field: Mia Thompson, Kimberlee Bernays, Kelly Thompson, Louis Gettings, the Burns family, the Coates family, Ian McLeod, Win Phan, Bon Phan, James Biggs, Lara Biggs, Ross Bernays, Rachel Bernays and Elena McMaster.

To my mum and her partner Greg who willingly donated their 4WD and fridge to the dragon cause. Mum, even though you think I don’t do things in a normal way, you tell me you love me more for it – thank you. To my dad and his partner Ai, thank you for allowing your house to be turned into base camp and feeding the hungry hunters on our return from their long adventures. Dad, I appreciate our chats about science and that you are always interested in my stories, even if “boys are not meant to be on phones for more than two minutes”. To my sister Kimberlee, thank you for loaning your truck for the greater good. I appreciate the support you have given me over the years – I am lucky to have you as my sister.

Ceaira Cottle, I am so glad I sat next to you that the first week of uni. I treasure our friendship and could not have asked for a better person to share this journey with. Thank you for all your years of friendship, advice and of course proof-reading. Charlotte Hurry, thanks for the good times – you made the last months of writing more enjoyable. Thank you to Joe McMahon for your endless patience with my GIS questions. To Jimmy Fawcett, Ryan Woods, Andrew Bentley and Linden George, I appreciate your words of wisdom on my drafts.

The funding for this research was provided by Griffith University, the Skyrail Rainforest Foundation and the Australian Government (Australian Postgraduate Award scholarship). The field work would not have been possible without the endorsement from the traditional owners of the Wet Tropics. I was fortunate enough to receive assistance from the Beachcomber Coconut Caravan Village, Fitzroy Island Resort, PK's Jungle Village Resort, Kingfisher Park Birdwatchers Lodge and Palm Tree Caravan Park. Thank you all for deeming my project worthy of your support.

Lastly I must thank my partner Vas. Thank you for accompanying me on the inaugural hunt and lending your expertise even though being in a hot humid tropical forest in the middle of summer is at the very bottom of your list of things to do – it must be true love. Thank you for being supportive when I was away hunting dragons for months on end, and for holding down the fort in the finishing stages of my thesis. Most importantly, thank you for making me laugh throughout it all - I dedicate my most perilous dragon capture to you.

General introduction

Chapter 1 General introduction

“Marvellous creatures, dragons, aren't they.”

~ Harry Potter and the Goblet of Fire, J. K. Rowling (2000)

General introduction

1.1 Climate change

The Earth’s systems are in a constant state of climatic flux. Generally, the change in climatic conditions has been slow and gradual, although several more significant fluctuations have occurred throughout the Earth’s history (Zachos et al. 1993, Benton and Twitchett 2003). These changes have shaped both terrestrial and aquatic biota, and influenced biotic assemblages (Zachos et al. 1993). There have been some periods of rapid climatic change (e.g. late Permian to early Triassic), which have been too sudden for all biotic assemblages to remain intact (Benton and Twitchett 2003). Although these past events have been natural, rapid changes that have been identified in contemporary time are largely anthropogenically induced (Alley et al. 2003). As environmental systems shift under a changing environment, research and understanding of the impacts and outcomes of an anthropogenically-influenced warming climate is more vital than ever.

Historical climate change has largely determined contemporary species patterns. During warm post-glacial periods in the northern hemisphere, expansion and colonisation events often occurred (Taberlet et al. 1998), whereas during cooler glacial periods, many species distributions contracted (Hewitt 1996). These climatic periods would have contributed to the extinction and evolution of countless species. The Permian-Triassic extinction event, which occurred 251 million years ago, is thought to be the largest one in Earth’s history (Urban 2015). Over a period of approximately two million years, a predicted 80 – 96 % of aquatic species and ~62 % of terrestrial families (not taking into account the number of surviving or extinct species within each family) became extinct (Raup 1979, Benton 1995). It then took 100 million years for pre-extinction biodiversity to return to the earlier level (at the family level) (Beaugrand 2015). The climatic change affecting this mass extinction event is thought to be fallout from mass volcanism (asteroid impact is deemed less likely), which caused global temperatures to increase by 6 oC and large increases in atmospheric carbon (Urban 2015). Findings such as these identify that the Earth’s biota have experienced and recovered from drastic climatic shifts in the past, but over very long time periods.

General introduction

1.3 Contemporary climate change

Key environmental impacts from contemporary climate change include increased temperatures, increased CO2 levels, changes to hydrological processes, and habitat loss (IPCC 2007a). The rate at which these changes are occurring is predicted to be much greater than historical changes. According to the IPCC (2014a), temperatures are expected to increase by 4.6 oC (mean) but perhaps up to 7.8 oC by 2100 (IPCC 2014a). These temperature projections are approaching (and exceeding) the temperature rise that occurred during the late Permian-early Triassic mass extinction event (Urban 2015). The crucial differentiating factor between the historical change and contemporary climate change is the rate at which it is occurring. Compared to the potential 7.8 oC increase in 75 years projected by the IPCC, historical events saw a 6 oC temperature increase over two million years (IPCC 2014a).

1.4 At-risk regions

Although climate change is expected to affect almost all geological and biological systems, some areas will be more sensitive to change than others. These sensitive areas are expected to experience a disproportionately negative effect (Hughes 2011) because they contain high species diversity, high species abundance and often high endemic abundance (Hilbert et al. 2001, Borges et al. 2006, Chen et al. 2011, Dirnbock et al. 2011), while being geographically restricted (Thuiller et al. 2005). These sensitive areas include rainforests, montane forests, mountaintop communities, and island systems. Climatically sensitive regions in Australia include places such as the Great Barrier Reef, Kakadu wetlands, and the Wet Tropics (Steffen et al. 2009, Hughes 2011).

1.4.1 Elevated regions Mountains and mountain tops are documented as regions of vulnerability to climate change (Williams 2006) due to their geographical limits, climate gradients and isolation (Pounds et al. 1999, Raxworthy et al. 2008, Dirnbock et al. 2011). The disconnection of mountaintop habitats from neighbouring populations (a population being an interbreeding group of individuals that are geographically confined) (i.e. neighbouring mountain tops) by warm 3

General introduction valleys of inhospitable terrain means that many of these species can neither tolerate temperature rise nor disperse (Raxworthy et al. 2008, Marcus 2012). When species have populations at both high and low elevations, differences in morphology (e.g. body size and fat storage) and reproductive process (e.g. clutch size) can occur between the populations (Goldberg 1974). Large extinction events are expected for mountainous systems, as modelling has described a habitat loss of 50 % in montane rainforest, with a temperature increase of only 1 oC (Hilbert et al. 2001).

1.4.2 Rainforests The sustainability of rainforests over the next century is of concern because vegetation growth, community assemblage, environmental requirements and ecosystem services will not only be affected but will also create a flow-on impact on a vast number of ecosystems and biota (Hughes 2000). It is furthermore expected that shifts in geographical distributions and changes in forest function will change current ecosystems (Silver 1998, Hughes 2000, Hilbert et al. 2001). As cool temperatures retreat altitudinally and latitudinally, forests are expected to ‘migrate’ to keep up with their preferred environment (Hughes 2000). Forest functions will also undergo changes in CO2 levels, changing the nutritional value of vegetation and altering other ecosystem services (e.g. water quantity) (Lawler et al. 1997, Kanowski et al. 2001).

1.5 At-risk area – Wet Tropics

Some regions will feel the impact of climate change more severely than others as a consequence of the vulnerability of their ecosystems (e.g. elevated regions) and biota. It is predicted that because of this, Australia’s ecosystems will be more at risk and fare more poorly than other regions (e.g. New Zealand) over the next century (Hennessy et al. 2007, Rosenzweig et al. 2007). The Wet Tropics in Far North Queensland (FNQ) has been identified as one of these regions (Hennessy et al. 2007). The Wet Tropics region has high species richness and high endemism for its size (18,000 km2) in comparison to larger tropical forests (Moritz et al. 2001, Harrison et al. 2003, Bell et al. 2007). The small geographical region

General introduction incorporates a sharp altitudinal gradient between the narrow coastal lowlands and narrow inland rainforest uplands. This geography means that, under historical climate change, most species would have undergone extensive rainforest restrictions and expansion events (Williams and Pearson 1997, Schneider and Moritz 1999). Dramatic expansion and restriction events have also occurred historically across a region of dry habitat in the northern Wet Tropics the Black Mountain corridor (BMC) (Schneider et al. 1998). It is thought to be from expansion and contraction processes and the local geography that such a high level of species diversity and endemism has created both highly-restricted endemic species (e.g. Williams and Pearson 1997, Hurry et al. 2014, Bernays et al. 2015) and widely- spread endemic species (e.g. Schneider et al. 1998). Such climatic and geological processes are also expected to have influenced the level of diversity within rainforest species (Haffer 1997, Moritz et al. 2000).

1.6 At-risk species

The resilience of an organism to climate change is dependent upon its capability to endure, recover, and/or adapt to a changed environment (Woram et al. 2003). Organisms that will be resilient to climate change are those that can modify the timing of their life-cycle, shift home range margins, and adapt morphological characteristics to suit their changing environment (Rosenzweig et al. 2007). Limited dispersers with a sensitive biophysical range and lifespan longevity would therefore be expected to have low tolerance and be sensitive to climate change (Huey et al. 1991, Hughes 2000, Bagne et al. 2011). Endemic species and ectotherms are organisms that are expected to be sensitive, and have problems adapting or responding, to climate change (Huey et al. 2009, Dirnbock et al. 2011). For the former, this is because they usually occur within a limited geographical range and tend to have narrow niche requirements (Sekercioglu et al. 2008, Coetzee et al. 2009). The latter, organisms that do not produce their own body heat (e.g. lizards), tend to have morphological– environmental correlations, which may reflect the strong relationship these species have with their environment (Bull 1987, Adolph and Porter 1993, Losos et al. 1997, Huey et al. 2009). Ectothermic lizards, in particular, are anticipated to have low resilience to climate change (Araujo and Pearson 2005).

General introduction

Determining how a species may respond to contemporary climate change may be best predicted by exploring how it responded to historical climate change. Identifying how climatic and geographical features have influenced species biology may help to determine how a species may endure, adapt or disperse in response to future climate change.

1.7 Genetics and historical climatic change

One method that can be used to examine the influence of geography and climate on a species utilises genetic data (Avise et al. 1987). Genetic information can be used to identify evolutionary and geographical relationships, the dispersal potential of a species, and, to some extent, its resilience to a changing climate (Avise et al. 1987, Slatkin 1987, Freeland 2005, Bell et al. 2010). These factors can be explored by inferring genetic relationships among individuals and populations across the landscape to make inferences on historical dispersal (Slatkin 1987, Freeland 2005).

To examine genetic relationships, genetic markers (mitochondrial DNA and nuclear DNA) can be used to trace lineages through the landscape (Avise et al. 1987). Mitochondrial DNA (mtDNA) is maternally passed on from one generation to the next (Freeland 2005). This ‘simplistic’ maternal mode of inheritance is favoured in many population genetics studies (e.g. Martin et al. 2015, Petrova et al. 2015) due to its non-recombining mode of inheritance (which reduces complications involved with recombination during sexual reproduction of nuclear DNA), lack of introns and repetitive DNA, and the fast rate of mtDNA evolution compared to nuclear DNA (nDNA) (Avise et al. 1987). This rate is suggested to occur approximately four times more slowly in diploid nDNA than in haploid mtDNA due to the differences in effective population size (Hare 2001). Furthermore, coding and non-coding regions differ in genetic variability as non-coding genes do not code for proteins; therefore, they are generally not restricted by selection in the number of mutations that can occur (Griffiths et al. 2000). Nuclear DNA is maternally and paternally inherited, which means that the identified historical genetic patterns reflect that of the entire population. This is particularly important if sex-biased fitness or dispersal occurs in the general population

General introduction

(Hare 2001). By incorporating nDNA with mtDNA markers, “a more complete understanding of the historical, demographic and selective processes shaping phylogeographical patterns is emerging” (Hare 2001, p.700). Examining both mtDNA and nDNA is a comprehensive approach to assessing phylogeographic history, which minimises bias due to sex-limited dispersal and the limited resolution available with a single locus (mtDNA) (Avise et al. 1987, Hare 2001).

1.7.1 Gene flow With the use of genetic markers, genetic diversity and gene flow within and between populations can be examined. The extent of genetic differentiation can be used to infer dispersal (and subsequent mating of individuals); however, dispersal cannot infer gene flow, as the reproductive success of immigrant individuals does not necessarily coincide with migration success (Slatkin 1987). As dispersal (and successful mating) events occur, alleles are transferred among populations, which increases genetic diversity (the number of different alleles). When gene flow is high among populations, it is inferred that dispersal among populations is high. When gene flow is low or not significant among populations, it is inferred that dispersal is limited. Usually, populations exhibiting low gene flow exhibit low genetic diversity, and, if restrictions to gene flow occur over a period time, this can lead to inbreeding (the mating of closely related individuals) (Slatkin 1985, 1987).

The neutral theory, the platform on which molecular evolution is based, suggests that most genetic variation within a population is neutral (i.e. it does not strictly conform to Darwinian evolution by natural selection) (Kimura 1968). Under this scenario, that genetic variation is created largely by the neutral processes of genetic drift and mutation (Kimura 1968, 1983). Genetic drift is the random sampling of gene variants, creating fluctuations in gene frequencies within a population (Slatkin 1985). As the large number of genome mutations that arise from such processes are mostly neutral, nucleotide variations therefore do not affect fitness (sexual or physical) and are not a product of natural selection (Kimura 1968, 1983).

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Populations that are expected to be strongly affected by genetic drift are restricted species, such as endemic species, or species with small population sizes, as their alleles become fixed or become extinct more quickly (Freeland 2005). The allele fluctuation is much greater because, within a small population, an even smaller sample of the genetic variants is passed to the next generation. Thus, a small subset of genes can differ greatly from the original population (Ellstrand and Elam 1993). Small populations are also more likely to become fixed for rare alleles due to the greater effect of inbreeding. The likelihood of non-related mating in small populations is low, which tends to create a loss of allele heterozygosity and an increase in homozygote deleterious alleles (Lande 1988). Both processes (genetic drift and inbreeding) would be expected to create low genetic diversity (Lande 1988, Freeland 2005). With a lack of diversity, chances of having alleles that will be favoured in a ‘new’ warming environment are reduced (Jump et al. 2009).

1.7.2 Phylogeography To explain the pattern of dispersal and gene flow among populations, Wright’s (1943) isolation by distance model describes localised genetic variation as a result of geographical restrictions to gene flow. This means that genetic differentiation increases as geographical distance increases. Wright’s model is the basis for phylogeography (Avise et al. 1987).

Phylogeography combines molecular genetics and biogeography, which enables the study of the historical genetic variation in a species across a geographical landscape (Avise et al. 1987). Species that show high levels of gene flow across the landscape are expected to exhibit low levels of population structuring and be ‘free’ from barriers to dispersal. Species that show low levels of gene flow across the landscape are expected to exhibit high levels of population structuring and to be affected by barriers to dispersal (Avise et al. 1987).

Barriers that may limit or restrict gene flow can be physical barriers, such as geological structures (e.g. mountain ranges and water courses), or biological barriers, such as physiological constraints (e.g. habitat requirements). Over a long period of time, these

General introduction barriers may isolate populations, creating breaks in the genetic data. As populations on either side of a barrier continue to evolve separately, they become increasingly diverged from their common ancestor (vicariance) (Avise et al. 1987, Slatkin 1987). Divergence can not only occur genetically (within the DNA) but also morphologically (as a consequence of genetic differences or phenotypic plasticity) (Avise et al. 1987, West-Eberhard 1989). Species that often display restricted gene flow from barriers are those with sensitive habitat requirements (e.g. endemic species and mountain top specialists) and those that are geographically isolated and therefore have little opportunity or ability for dispersal (Henle et al. 2004). In a fragmented landscape, it is expected that isolated populations will incur a reduction in population size and thus are expected to experience an increase in the rate of genetic drift (Slatkin 1985, Williams et al. 2003a). This increased rate of drift is expected to increase genetic divergence between and reduce genetic variation within isolated populations (Slatkin 1985). This was evident in Amphibolurus nobbi (Nobbi Dragon), where isolated populations in remnant habitat exhibited reduced variation (Driscoll 2004).

Long-term barriers to gene flow can often be identifiable at both an intraspecies level (across multiple genes) (e.g. Kuo and Avise 2005) and an interspecies level (across multiple species) (Avise et al. 1987). Interspecies phylogeographic breaks are often concordant for species with similar life history strategies. At an intraspecies level, long-term barriers can create a genetic break across multiple genes. This can lead to speciation with a species diverging into sister species on either side of the barrier (Bickford et al. 2007). When morphological change does not occur with, or as a result from the genetic divergence, the sister species are classified as cryptic species as they are physically indistinguishable (Beebee and Rowe 2008). Cryptic species are often incorrectly identified as a single taxon (Bickford et al. 2007). For example, a genetic study on the Australian gecko genus, Diplodactylus, identified 16 cryptic species, which increased the species complex from 13 species to 29 species (Oliver et al. 2009). Study of the processes responsible for the geographic distributions of genetic lineages (i.e. phylogeography) enables understanding into historical events that occurred during previous warming events (Avise et al. 1987).

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Barriers to dispersal have been found to isolate species vertically (altitudinally) as well as horizontally (latitudinally and/or longitudinally) (Ohsawa and Ide 2008). Altitudinal barriers often result from the eco-gradients of montane system and limited dispersal areas (Finn et al. 2006). In the case of a Mexican tree species (Platanus mexicana), altitudinal barriers caused higher levels of genetic variation in upland regions than in lower elevations (Galvan- Hernandez et al. 2015). In contrast, a study on the western fence lizards (Sceloporus occidentalis) identified higher levels of genetic diversity in lowland populations than in highland populations (Leache et al. 2010). Latitudinal and/or longitudinal barriers often result from natural and anthropogenic habitat fragmentation (Avise et al. 1987, Fischer and Lindenmayer 2007). The BMC in the Wet Tropics is an example of a natural barrier of unsuitable habitat between two large regions of rainforest (Koetz et al. 2007, Mellick et al. 2014).

1.8 Morphological variation and historical climate change

Another method that explores how climatic and geography have influenced species examines correlations between geography, climate and morphological variants (phenotypes) of a species (Baldwin 1896, Davis and Shaw 2001). Morphological variation within a species can be used to explore the effect the environment has on the physical and behavioural features of a species (Johannesson and Johannesson 1996, Davis and Shaw 2001).

Variation in morphological traits can be attributed to natural selection, sexual selection or phenotypic plasticity (Iraeta et al. 2011), all of which can be influenced by environmental factors (Macedonia et al. 2002) or genetic drift (Slatkin 1987). Natural selection acts on morphology in the same manner that it acts on genetic variants (Kingsolver et al. 2001). Variation in morphological traits can occur within species and within populations. As not all individuals within a population will reproduce successfully, offspring of the surviving generation will generally inherit morphological traits from their parents. It is expected that

General introduction each subsequent generation will contain a higher proportion of individuals with the more advantageous morphological variant (Ridley 2004).

When these processes differ between males and females of the same species, it is called sexual dimorphism. The occurrence of sexual dimorphism has been attributed to numerous factors such as ecological causes, sexual selection and natural selection (Slatkin 1984). Sexual dimorphism resulting from natural selection, can occur when specific characteristics, such as body size, are influenced differently by natural selection in the two sexes; e.g. female biased (females are larger than males) snout-to-vent dimorphism in numerous species is thought to be the result of natural selection favouring large body size (body cavity) in females to accommodate for a larger number of offspring (Shine 1980, King 1989).

Sexual selection is a selection pressure that is purely concerned with achieving reproductive success (Emlen and Oring 1977). Often, these morphological variations that are sexually selected do not necessarily aid an individual’s fitness and survival within its environment (Kotiaho et al. 2001), but in intersexual (attracting mates and fertility) and intrasexual success (gaining and defending territories ) (Moore 1990). Sexual selection, rather than natural selection, was suggested to be responsible for complex colour patterns found in Australian dragon lizards (Chen et al. 2012).

Phenotypic plasticity is the change in morphology, behaviour or physiology of an individual within its lifetime as a response to its environment (Baldwin 1896, Stearns 1989). This physiological response is advantageous, with those individuals expressing plasticity often exhibiting a higher level of fitness (Price et al. 2003). These changes can lead to morphological divergence of an individual, population and/or species from their ancestor. Often, phenotypic plasticity is a response to encountering a new habitat (Robinson and Dukas 1999, Price et al. 2003).

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The influence of natural selection, sexual selection and phenotypic plasticity on morphological phenotypes in lizards most commonly influences body size, limb size, and/or colour (Allen 1877, cited in Millien and Jaeger 2001). These patterns have been identified in many studies of reptiles, birds, mammals, and other biota, which have validated the correlation between morphological variation and environmental pressures (Bedford and Christian 1996, Smith and Skulason 1996, Millien and Jaeger 2001, Stillwell et al. 2010). Species can express cryptic morphological diversity, where small variations in characteristics are only detected when explicitly measured, or can express more obvious variation (Dawson 2003).

1.8.1 Sexual dimorphism Sexual dimorphism is widespread in animals and plants, and can arise from both selection (natural and sexual) and phenotypic plasticity (Shine 1989, Stillwell et al. 2010). The level of differentiation and the direction (i.e. female bias or male bias) of phenotypic variation differs between species (Fairbairn 1997) and populations (Butler et al. 2000). The influencing process for the occurrence of dimorphism within a species also differs between environmental or evolutionary drivers. Some studies identify the cause of these drivers to be due to, but not limited to, the following: niche competition and resource competition (between and within sexes) (Butler et al. 2000); female fecundity; female mate preference; within-sex competition (Clutton-Brock 2009); offspring size (Fox and Czesak 2000); and energy conservation (Blanckenhorn 2000). Many of these causes of dimorphism are expected to be a response to habitats and the environmental variables (Shine 1989). Determining the mechanism responsible for sexual dimorphism within a species is difficult, because it could be the result of natural selection, sexual selection, phenotypic plasticity, or a combination thereof. In the tropical lizard Cnemidophoruso cellifer, sexual dimorphism between males and females was detected and expected to be the result of sexual selection (Vitt 1983).

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Eco-geographical rules Environmental factors such as temperature and altitude have long been correlated with selection pressures on morphological traits (Sinervo and Adolph 1989, Thorpe et al. 1995). Bergmann’s rule is a well-known eco-gradient selection correlation pattern (Mayr 1956, 1963, Angilletta et al. 2004). It states that in endotherms large body sizes are selectively favoured in cool environments, as a large body size means a small surface-to-volume ratio, which results in lower levels of heat loss (Mayr 1963, Blackburn et al. 1999). A large reptilian study on squamates (83 species; order: squamata), revealed the inverse of Bergmann’s expectation, with body size found to be larger at lower elevations and warmer temperatures (Ashton and Feldman 2003). These findings suggest that reptiles tend to express the inverse of Bergmann’s rule, which has been confirmed in other studies (e.g. Jin et al. 2007). A key explanation of this pattern being detected in ecothermic reptiles was suggested to be selective processes in cool environments favouring body size that allow rapid heat exchange. Rapid heat exchange is a process that can be achieved more effectively for small body sizes, as they exhibit a larger surface-to-volume ratio (Mayr 1956, Ashton and Feldman 2003).

The second well-known eco-gradient-selection correlation is Allen’s rule (Tilkens et al. 2007, Symonds and Tattersall 2010). It suggests correlations between limb length (any protruding appendage) and temperatures (latitude), leading to shorter limbs in colder environments (Allen 1877, cited in Millien and Jaeger 2001). It is thought that heat loss is lower through small appendages and heat loss is greater in larger appendages (e.g. small ears versus large ears) (MacArthur 1949). Although Allen’s rule has been studied in much less detail than Bergmann’s rule (Bidau and Marti 2008), conformity to the rule has been found in numerous animals, with a strong example being foxes. As habitat temperature increases, relative ear size also increases for populations of the artic fox (Alopex lagopus), the red fox (Vulpes vulpes), and the desert fox (Fennecus zerda) (Millien et al. 2006). A similar pattern was found when examining the ectotherm Dichroplus pratensis (South American grasshoppers), with appendage size decreasing as temperature decreased (exhibited by altitudinal and latitudinal increases) (Bidau and Marti 2008).

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Body size and limb length have other important life history trait interactions aside from heat loss rate. For example, body and limb size in reptiles have been shown to influence dispersal, age of sexual maturity, fecundity, fitness (Lorenzon et al. 2001), and energy and water requirements (Porter and Kearney 2009). Ultimately, variation in morphological characters has the potential to affect fitness, and therefore also the resilience, of a species (Gardner et al. 2011). An example of this is the invasion front of the cane toad (Bufo marinus) in Australia (Phillips et al. 2006a). Phillips et al. (2006a) showed that individuals at the front of the colonizing-invasion wave had longer legs and a faster locomotive speed than individuals in subsequent dispersal events and individuals in established populations.

1.8.2 Geographical isolation Geographically isolated and/or fragmented populations are expected to display similar patterns of morphological variation patterns to those seen in island populations (Millien et al. 2006). Under the island or isolation rule (Van Valen 1973), there is a pattern for small species to evolutionarily increase in size and large species to decrease in size on islands relative to the mainland. Geographic isolation due to habitat fragmentation was found to increase the size of mammals in Denmark (Schmidt and Jensen 2003), however, there are exceptions to this rule; for example, Meiri et al. (2005) suggested the pattern of snakes on islands did not reflect a geographical island – isolation correlation, but attributed the pattern to the availability of prey. The island model could also apply to populations geographically isolated by unsuitable habitat, such as that caused by the BMC.

There are other compounding causes for morphological variation apart from temperature and isolation. These often include biotic factors such as predation, competition, and displacement (McNab 1971, Adams 2004, Millien et al. 2006).

1.8.3 Morphology as the first response historically Barnosky et al. (2003) suggested that changes to morphology can be detected within a time scale of 100 years and thus, along with spatial distribution, morphology is deemed to be a

General introduction species first response to climate change (Figure 1.1). In recent history (20 - 100 years), there have been many examples demonstrating how the changing climate has resulted in changes in morphological characteristics (Grant and Grant 2002, Gardner et al. 2011). Studies on squamata have found that body size has been increasing in direct and indirect response to temperature (Wikelski and Romero 2003, Chamaille-Jammes et al. 2006). During a 20-year time period, common lizards (Lacerta vivipara) were found to exhibit larger body sizes when experiencing warmer temperatures during their first year of development (Chamaille- Jammes et al. 2006).

Figure 1.1 Phenotypic and genetic response times to climate change.“Temporal scales of biological responses to climate change. Phenotypical and phenological changes occur at the smaller time scales, from a few years up to several 100 years. Shifts in species distributions and extinctions are detectable at larger time scales “ (Millien et al. 2006).

With the use of fossil records, more historical eco-geographic-morphological correlations have been identified. These records show how historical climatic and geographical events have shaped morphological evolution in a species (Millien et al. 2006). Numerous studies examining Quaternary climatic events identified that during this time period, morphological characteristics of endotherms that had adapted to the changing climate were adhering to Bergmann’s rule (see Millien and Jaeger 2001). Woodrats (Neotoma) have shown a clear pattern of this, with larger individuals occurring during cold periods during the late Pleistocene epoch, and smaller individuals present during warm periods of the late Pleistocene (Smith and Betancourt 2006). The pattern continued during the Holocene, with high altitude populations conforming to Bergmann’s rule, although lowland populations displayed a more complex pattern, with varying responses over time (Smith and Betancourt

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2006). The isolation (island) model patterns have also been detected historically with many examples, two of which include a large mammal becoming dwarfed (Pleistocene) (Palombo 2003) and small rodents undergoing gigantism (late Miocene and the early Pliocene) (Millien and Jaeger 2001).

Not all species display these bio-gradient patterns and/or do not strictly conform to them over space or time (Dayan et al. 1991). Some morphological response patterns to climate change have been suggested to be a consequence of locally unique climatic and geographical interactions (Barnosky 2001). The same study also identified that many studies were suggesting that biotic interactions (e.g. predation and competition) have a greater influence on morphological characteristics than environmental factors. Such morphological- environmental correlations have been identified between morphological characteristics and habitat structure and/or substrate use (Pianka 1969, Losos et al. 2000, Kohlsdorf et al. 2001). In an agamid lizard, open habitat was found to be selecting for inconspicuous colouring (predation avoidance) and closed habitat for conspicuous colouring (Stuart-Fox and Ord 2004). It has been suggested that natural selection favours particular morphological traits (especially increased or decreased head and limb length) in specific structural habitats (Pianka 1969, Losos 1990, Schneider et al. 1999). In phrynosomatidae lizards, “short proximal hind limb segments” were found to correlate to open habitats (Herrel et al. 2001). Herrel et al. (2001) suggested this limb segment ratio aids acceleration speed – an important factor for escaping predation in a habitat with minimal shelter. Habitat-limb correlations have been found in various genera (e.g. Sphenomorphus and Ctenotus) (Pianka 1969), and it was further suggested by Pianka (1969) that long limbs are favoured in open habitats (utilizing solar and substrate heat sources)and shorter limbs in dense habitats (utilizing an air heat source).

When species or isolated populations are morphologically similar, it is likely that there has been either insufficient time to cause morphological variation (or detect morphological variation) and/or that selection processes or processes that lead to phenotypic plasticity are homogeneous throughout the entire population (Schneider and Moritz 1999). It has further

General introduction been suggested that morphological adaptability to future climate change may be restricted by lack of genetic variability (Millien et al. 2006).

1.8.4 Morphology as the first response to the future warming By identifying historical responses of species, a more accurate prediction of future climatic responses can be ascertained (Millien et al. 2006). Changes to morphological characteristics may occur as species and forest dynamics adapt to climate change. Whether species adapt to their changing environment and/or shift their distributional range to maintain current biophysical requirements, it is likely that changes to fitness and survival rates will occur (Millien et al. 2006, Pearman et al. 2008). It is unknown whether future morphological characteristics will remain stable or, if adaptation occurs, whether this will conform or not conform to eco-gradient hypotheses. If current morphological characteristics are not selectively favoured in a new environment, selection process or phenotypic plasticity may be a necessary component to resilience to future climate change. For a species to adapt morphological variables to suit the changing climate, genetic variability will give it a greater chance (Millien et al. 2006).

1.9 Species distribution

A further method that explores how climate and geography have influenced a species is to utilize patterns of environmental variables and species occurrences. These patterns can establish environmental and geographical criteria for a species, thus identifying how historical and future landscapes may influence its distribution (Pearson and Dawson 2003).

Many past, present and future factors influence the distribution of a species. One of concern in our warming environment is the availability of suitable habitat. Without taking into account physiological or dispersal processes, the geographical distribution of a species can be inferred based on areas classified as suitable habitat (Early et al. 2008). As the climate warms, suitable habitat is likely to do one, or both, of the following: modify (increase or

General introduction decrease) the area of suitable habitat (Rupp et al. 2000, Kearney et al. 2008); or shift the area of suitable habitat (Parmesan et al. 1999). Many species are expected to experience a combination of habitat shifts and decreases in geographical size (and thus their distribution), with many studies predicting that shifts will largely occur poleward and upward (Perry et al. 2005). This pattern is already occurring in some species (Parmesan et al. 1999, Perry et al. 2005).

Endemic species are susceptible to climate change because they tend to have limited geographical ranges, low dispersal ability, and exhibit habitat specialisation (Isaac 2008, Quinn et al. 2011). Reductions of suitable habitat are expected to further fragment habitats and isolate populations. This will leave species susceptible to loss of genetic variation, which further reduces their ability to adapt to a changing environment (Slatkin 1985, Isaac 2008). A study by Malcolm et al. (2006) on global endemism, revealed that 39–43 % of endemic plant and vertebrate species will become extinct under current climate models. This equates to some 56,000 endemic plant species and 3,700 endemic vertebrate species being lost. Studies on plants have shown that most endemic species will experience range reductions and others may experience range expansions (“at least in the short term”) (Thuiller et al. 2006, Williams 2006). Endemic vertebrates, such as the quokka (Setonix brachyurus) in Western Australia, and the riverine rabbit of the Karoo, South Africa, are expected to lose 100 % and 96 % of their habitats, respectively, by 2070 (Hughes et al. 2008, Gibson et al. 2010). In places like the Wet Tropics World Heritage area, 97 % of endemic vertebrates will have core habitat significantly reduced by 2030 (Rosenzweig et al. 2007).

1.9.1 Distribution shifts If species respond to climate change by shifting current distributions, successful dispersal will tend to be poleward or altitudinal in order to ‘chase’ retreating cooler temperatures. As climatic zones are compressed (retreating cooler temperature) over altitudinal gradients (Körner 2002), forests will constrict up the mountain side as temperature niches and biophysical climates shift altitudinally (Hughes 2000, Root et al. 2003). Various studies (e.g. Colwell et al. 2008, Raxworthy et al. 2008) have hypothesised that populations and species

General introduction assemblies have the potential to follow the bioclimate, with species altitudinally shifting home range boundaries to higher altitudes (Parmesan 1996, Hill et al. 2002, Peterson et al. 2002, Root et al. 2003). As hydrographic systems become altered, and species interactions change (predator-prey relationships food source), mountain assemblages are expected to undergo complex changes (Parmesan 1996, Pounds et al. 1999, Root et al. 2003, Gleason et al. 2008). Alternatively, areas of flat terrain where these altitudinal shifts are not possible are expected to incur larger extinction events or else biotic attrition will occur as physiological tolerance is exceeded (Colwell et al. 2008).

1.10 Study species: Boyd’s forest dragon

Of Australia’s 619 reptile species, 161 (26 %) inhabit the at-risk area of the Wet Tropics. Of these, 115 are lizards (Williams 2006). It is expected that these lizard species will experience population declines that have already been noted in other lowland forest lizard populations (e.g. Costa Rica) (Whitfield et al. 2007). One of the larger lizards in this region that may be at risk under a warmer climate is the Boyd’s forest dragon (Hypsilurus boydii, Macleay) (Figure 1.2) (Williams 2006).

a) b) c)

Figure 1.2. H. boydii a) unusually basking; b) camouflaged sit-and-wait position; and c) camouflaged sit-and- wait position on a thin trunk in common rainforest habitat.

Records show H. boydii is one of the Wet Tropics’ most widely distributed endemic lizards, which inhabits the northern reaches of the Wet Tropics (south of Cooktown) down to the Paluma Range in the south (Williams 2006). Unlike most endemic species of the Wet Tropics

General introduction that reside above elevations of 400m (and often above 600m), H. boydii inhabits low elevations as well, with elevations ranging from <100 m to > 900 m (this study) (mean elevation of 600m) (Covacevich and McDonald 1991, Williams 2006). These low elevation habitats usually have high rainfall (annual mean) and regular cloud cover (Covacevich and McDonald 1991, Nix and Switzer 1991).

It is not known how this ‘widely’ distributed endemic species would have reacted to past climate change events. If H. boydii can adapt and keep up with future environmental changes, it will exhibit a number of the following attributes: a) be a good disperser; b) be in a geographically appropriate locality to migrate poleward and altitudinally; and c) have suitable habitat in the future. The likelihood of these attributes could be best examined by determining genetic patterns, morphological variation and distribution of H. boydii (Avise et al. 1987, Collingham and Huntley 2000).

1.11 Scope and aims of the study

1.11.1 Justification Various studies have examined the phylogenetic patterns of Wet Tropics species with many studies focusing on Wet Tropics endemic species (e.g. Bowyer et al. 2002, Bell et al. 2007, Hurry et al. 2014). Of these studies, little focus has been given to widely distributed endemic biota – most of which would be expected to be less sensitive to climate change than narrow range endemics (e.g. Williams 2006, Bell et al. 2007). The importance of comparing phylogeographic patterns and species distributions is acknowledged and common in the literature (Hugall et al. 2002) but the inclusions of other species-environmental correlations is not as common but equally important (Bell et al. 2010). By incorporating phylogeographic, morphology and distribution analysis together, a more comprehensive understanding of the influence geography and climate has had on the species (Rodriguez-Gomez et al. 2013). This more comprehensive approach may give a better understanding how the species may have responded to past climatic changes which have led to the biological pattern present in the current population.

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Using these factors to determine if the widely distributed endemic H. boydii has and will respond to climatic change is important to understand for both the survival of the species and the maintenance of the biodiversity of the region. Examining these three biological factors, also increases the utility of the present study as a management tool for current and future populations. Management plans need knowledge of how species are linked to their environment in order to understand how these relationships might change in the future. The strongest evidence for this change is examining the role of geography and climate in the past, thus a management plan which incorporates a strong prediction of how the species may respond to climatic changes is produced.

1.11.2 Study aims and structure The goal of this study is to understand the role that geographical features and climate have played in the genetic makeup, morphology and distribution of the Wet Tropics endemic species, H. boydii, and to predict what this will mean for the survival of H. boydii in a changing environment. The research project aims to investigate this by using genetic analysis techniques, eco-morphological comparison methods and species distribution models. The structure of the thesis is as follows: Chapter One will explore the literature and Chapter Two will detail the study design and field collection methods that were used to test the processes.

The first of the data chapters, Chapter Three, investigates existing genetic pattern of H. boydii and aims to identify potential barriers using both mitochondrial and nuclear genes.

1. What is the genetic structure of Hypsilurus boydii populations across the Wet Tropics and how is this likely to have arisen historically?

This question will be answered by examining the historical phylogeographic pattern of H. boydii across the Wet Tropics; in particular whether there is divergence north and south of the BMC or between lowland and upland regions. Examining these divergences will enable exploration into the occurrence of genetic breaks across the region (including the BMC break) for H. boydii populations to determine if they are in line with the genetic findings of

General introduction previous studies (e.g. Schneider et al. 1998, Bell et al. 2010). Alternatively, given the wide altitudinal distribution of the species, it is possible it consists of a cryptic species complex. If so, genetic divergence between upland and lowland populations might be expected. This chapter aimed to answer this with two hypotheses: 1) in accordance with previous studies, historical genetic patterns of H. boydii populations will be explained by latitudinal partitioning (north vs. south - from either side of the BMC) as opposed to altitudinal partitioning (lowland vs. upland); and 2) as the species has been shown to disperse only short distances, there will be significant genetic differentiation among sites within blocks of forest. Given that the species is restricted to the Wet Tropics region, I also aimed to examine evidence for restricted dispersal within each of these regions. Such patterns would be likely to make the species more susceptible to any local extinctions or further habitat fragmentation.

Chapter Four investigates morphological patterns across the Wet Tropics by comparing body measurements and eco-gradient rules (Bergmann’s rule, Allen’s rule and isolation rule) (Mayr 1956, Van Valen 1973, Millien and Jaeger 2001) in order to answer:

1. What morphological patterns occur throughout the Wet Tropics and does Bergmann’s rule, Allen’s rule (altitude) and/or the isolation rule (latitude) explain this pattern?

This research question will examine the morphological characteristics of individuals and populations of H. boydii and identify any patterns in morphology and geographical features or climate (temperatures at altitudes). By examining the characteristics across latitudes and elevations, the influence of external forces can be explored and the possibility of cryptic diversity can be investigated. By incorporating the genetic findings from Chapter Three, this chapter will explore whether any patterns detected in the genes are reflected in the morphology. The research question will be explored with four main hypotheses: 1) morphological variation across the Wet Tropics will support the inverse of Bergmann’s rule (larger bodies in warmer lowlands than cool uplands) in line with patterns largely detected in other squamates; 2) morphological variation across the Wet Tropics will support Allen’s rule (larger limbs in warmer lowlands than cool uplands) on the basis of global patterns; 3)

General introduction morphological patterns will be best explained by Bergmann’s and Allen’s rules (explained by altitude) rather than the isolation rule (latitude) as lowland-upland temperature changes (upland ~7 - 25 oC and lowland ~18 - 32 oC) are more extreme than latitudinal temperature changes (northern region ~18 - 30 oC and southern region ~15 - 28 oC); 4) morphological phenotypes will be independent of latitudinal genetic patterns, i.e. altitudinal pressures (Bergmann’s and Allen’s rule) on morphology are more influential than latitudinal pressures (isolation island rule).

The final data chapter, Chapter Five, models the current distribution based on environmental factors and then using climate predictions forward and backward in time to see how this will have and will in the future affect lizard distributions.

1. What is the past, present and future distribution of Hypsilurus boydii across the Wet Tropics?

This chapter examines the past (22,000 and 6,000 years ago), present, and future (in the years 2050 and 2070) distributions of the Boyd’s Forest Dragon in order to examine how the species has responded and how it may respond in the future to changes to its environment. With the use of species distribution modelling, H. boydii distributions will be predicted by identifying current correlations between species presence-only occurrences and environmental features (geographic and climatic features). With the use of WorldClim 1.4’s databases historical and future climatic data, the occurrence-environmental correlations will be projected to determine distributions in the past and in the future.

Chapter Six is a general discussion on the findings of this study and will aim to determine the role that geographical and climatic features have had on the genetic makeup, morphology and distribution of H. boydii. The chapter will compare and contrast responses to altitudinal and latitudinal barriers as well as exploring how the species may respond in the future. Suggestions will be made on management and areas for future work.

General Methods

Chapter 2 Study region, study species and sampling

“If you see the dragon fly,

best you drink the flagon dry.”

~ The Riddler's Gift, G. Hamerton (2007)

General Methods

This chapter contains the study design and field methods that were used in the study. Laboratory and analysis techniques were described in the corresponding data chapter.

2.1 Study Area: Wet Tropics

The Wet Tropics in north eastern Australia is an area of approximately 18,000 km2 (Figure 2.1); ~ 9,000 km2 of which is protected by the Wet Tropics World Heritage Area (Wet Tropics Management Authority undated). Even though half of the region is protected (limiting the effects of habitat clearing e.t.c.), the region is predicted to be sensitive to climate change because of high levels of biodiversity, endemism and vulnerable species (Körner 2002). The Wet Tropics is vulnerable because: 1) the region is a relatively small, isolated, fragment of wet tropical rainforest; 2) the region is heavily dominated by altitudinal gradients and its complex topography makes temperature pressures more prevalent and detrimental; and 3) the endemic biota have adapted to an aseasonal niche environment of cool-wet weather (Nix and Switzer 1991, Williams et al. 1996, Williams and Pearson 1997). It is predicted that with a temperature rise of 5 oC, 60 of the 83 endemic species to the region will become extinct (Williams et al. 2003b). Escalating the pressure on remaining habitats, urbanisation is expected to encroach on habitats, fragmenting and isolating rainforest populations (Laurance et al. 2009).

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Figure 2.1. The Wet Tropics bioregion and location within Australia.

The mountain tops and high tableland plateaus of the Wet Tropics (often exceeding 1000 m) are cool-wet isolated refugia for many species that cannot survive the warm valleys and lowlands (Figure 2.2) (Nix and Switzer 1991, Hughes 2011). These cool ‘island’ populations have a limited area to which they can disperse and will become more restricted as warming temperatures encroach on their habitat (Hughes 2011). It has been suggested that historical constrictions to lowland rainforest are the reason for low endemism and low numbers of specialised species in lowland regions (Williams and Pearson 1997). Molecular data supports this, as extinction events were identified in lowland regions following Pleistocene rainforest constrictions (Williams and Pearson 1997, Schneider and Williams 2005, Bell et al. 2007).

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18.8 32.4 7.1 25.1

Figure 2 .2 Average temperature: a) minimum temperature for the coldest month; and b) maximum temperatures for the warmest month from the years (195 0 – 2000). Temperature data layers from (Hijmans et al. 2005 ).

2.2 Study Species: Boyd’s Forest Dragon, Hypsilurus boydii

Little is known about the diurnal Boyd’s Forest Dragon even though it is a poster species of the Wet Tropics (Wet Tropics Management Authority 2008, Queensland Government 2012, Williams undated). Phylogenetically, the species is most closely related to the rainforest dragon Hypsilurus dilophus of Indonesia and Papua New Guinea (Iskandar and Erdelen 2006, Ord and Stuart-Fox 2006). The only other Australian species belonging to the Hypsilurus monophyletic genus is Hypsilurus spinipes, which inhabits the wet moist forests of Southern Queensland and Northern New South Wales (Swan 2008).

It is thought that during its life time (approximately 10 years) (Just Lizards 2010), H. boydii is largely restricted to a home range (territory) and engages in little long distance dispersal (suggested daily average movement of approximately 63 - 95 m for males and 34 - 50 m for females) (Torr 1993). Territories tend not to overlap with dragons of the same sex.

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However, male territories do tend to contain/overlap with several female territories. A small study on a Mossman population measured male territories ranging between 67 m2 and 939 m2 and female territories ranging between 212 m2 and 787 m2 (Torr 1993). Within their territories, individuals usually have “home” perches to which they readily return during warmer monsoonal months (Torr 1993, 1997). Dragons are ambush predators and tend to adopt a sit-and-wait position on a vertical perch (Torr 1997). H. boydii’s predator avoidance technique (in addition to camouflage) is to ‘hide’ behind its vertical perch. It does this by slowly moving around the tree to keep the trunk between it and the threat. On the ground, H. boydii uses bipedal locomotion. With the exception of gravid females, H. boydii does not tend to bask. Little is known about behaviour during the remainder of the year when dragons are confined to the canopy, but it is expected that activity is severely reduced (Torr 1993, Cronin 2001).

Tropical lizards such as H. boydii which do not bask tend to have a small thermal safety region due to their low body temperatures and low heat tolerance. Small increases in temperature can exceed tolerance ranges and can shorten activities (foraging, social interactions and dispersal) (Grant 1990, Kearney et al. 2009), and decrease performance (Huey et al. 2009). Increased temperatures may also create a rise in competition and predation for lizards (Adolph and Porter 1993, Huey et al. 2009), adding to the stress expected under a changing climate. Whether this is applicable for a species that inhabits a large temperature range, is unknown. Unfortunately, it is expected that lizard populations of the Wet Tropics will experience the same population declines that have already been noted in other lowland forest lizard populations (e.g. Costa Rica) (Whitfield et al. 2007). However, it is not known if endemic generalists that already experience a range of temperatures will be impacted in the same way.

H. boydii is reported to be sexually dimorphic, although not overly conspicuously; males have been described as having a larger body and wider head (Wet Tropics Management Authority 1996, Torr 1997). Individuals are mainly green-grey in colour with heads of yellow, blue, green and white and a large crest and row of spines down the back (Dale 1973, Wilson

General Methods and Swan 2008). The dewlap (the loose skin hanging under the chin) which is bright yellow with a vertical row of white spines is usually inconspicuous as it is folded close to the body to aid in camouflage. The dewlap becomes prominently erect when threatened or courting (Torr 1993, Cronin 2001, Wilson and Swan 2008). As no large-scale study has been conducted on H. boydii, a significant portion of information on H. boydii is based on unpublished observations or on studies consisting of few study sites (Torr 1993).

2.3 Study design

This study was conducted in the Wet Tropics in north eastern Queensland, Australia (latitude -15.5S to -19.25S and longitude 145.83Eto 146.32E). The sample design aimed to examine the Wet Tropic by three regions, north (Cooktown to north of the black mountain corridor), central (south of the black mountain corridor to Koombooloomba Dam), and south (south of Koombooloomba Dam to Townsville), in order to identify any latitudinal patterns. Within each latitudinal partition, two upland sites and two lowland sites were identified in order to examine any altitudinal effects. There were two sites for each upland and lowland region to help determine if any outlier results were an altitude-latitude effect or the result of an unmeasured variable. From each site, the aim was to collect 20 individuals.

Upon commencing field work, it was evident that the sampling design could not be maintained. H. boydii individuals were difficult to spot, due to the elusive camouflaged nature of the sit-and-wait predator, with search times for one dragon taking an average of two days. This generated lengthy and intense survey periods during warm summer months when the dragons were in the forest understory and before monsoonal rains and cyclones hampered field work. Even with long search periods, H. boydii individuals were often not found at either preselected or ‘improvised’ (using local knowledge) study sites (Figure 2.3). If samples were not observed at a site, it was not assumed that H. boydii did not inhabit the area. The strongest example of this was in the far southern region of the Wet Tropics (Atlas of living Australia), which is known for H. boydii occurrences. Here, no individuals were

General Methods located despite the extensive search effort. The failure to detect individuals in this area may also be due to the severe destruction of rainforest habitat from recent cyclones Larry (March, 2006) and Yasi (February, 2011) (Wet Tropics Management Authority 2012).

A total of 78 individuals were successfully collected from sites north of the BMC (Daintree to the BMC; three populations) and south of the BMC (BMC to Koombooloomba Dam; six populations) (Table 2.1; Figure 2.3). The northern region consisted of one upland and two lowland sites and the southern region consisted of five upland and one lowland site. Lowland regions were classified as below 90 meters above sea level, and upland regions were classified as regions above 350 meters above sea level. Sample size and the number of populations differed between the phylogeography and morphology data chapters, as measurements were only taken in the second year of sampling when it became obvious that collecting larger numbers for extensive population genetic analyses would be impossible. For this study, populations are defined as a single sampling site. Samples were collected under the ethics permit GU Ref No: ENV/23/11/AEC, and collection permits WISP10513611 and WITK10510611, with Traditional Owner approval.

Table 2.1 Samples collected, giving the latitude (north of the BMC or south of the BMC), altitude (upland or lowland), population name, and code name of each sampling site. Sample size (N) for genetic analysis and morphological analysis were documented. Latitudes Altitude Population name Population Population Population Total (approximate code Latitude Longitude (N) latitude) North Lowland Daintree Dan -16.0888S 145.4633E 5 -15.5S – -16.5S Mossman Gorge Mos -16.4719S 145.3309E 16 Upland Mount Lewis MtL -16.5488S 145.2782E 4 South Lowland Crystal Creek Cry -16.962S 145.6796E 10 -16.5S – -17.0S Upland Lake Barrine Bar -17.2544S 145.632E 12 Mt Hypipamee Hyp -17.4244S 145.4835E 13 Palmerston Pal -17.6113S 145.7897E 3 Range Lake Eacham Eac -17.286S 145.6277E 14 Koombooloomba KOD -17.8406S 145.5956E 1

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NORTHERN REGION Dan

Mos MtL

Cry SOUTHERN REGION BMC Bar Eac Hyp Pal

KOD Eac

LOWLAND REGION UPLAND REGION

Figure 2.3. Populations of H. boydii sampled across the Wet Tropics(black dot). Sites where H. boydii was not found (blue cross). Dashed circles identify northern and southern partitioning, dashed line identifies upland and lowland populations partitioning. Code names correspond to those found in Table 2.1. The Black Mountain Corridor (BMC) is identified by a solid rectangle.

It is acknowledged that some aspects of this study may represent pseudo-replication (multiple measurements from each site), have uneven sample sizes, and small sample sizes; studies on rare or hard to capture species cannot always avoid these issues (Green 1979, Quinn and Keough 2002). Pseudo-replication is not an uncommon procedure to overcome

General Methods small sample size in rare species (Young 2003) although care in over interpreting the results must be taken (Seth et al. 2009).

2.4 Field sampling technique

Seventy-seven individual H. boydii dragons from across the Wet Tropics were spotted, caught, sampled and released. Indivduals were spotted using the stalk-capture method, which involved scanning tree trunks (truck diameters between 2cm and 20cm) at a height range of one to three meters from the ground. Once spotted, one person acted as a decoy and a second person would capture and restrain the dragon. Dragons were held ventral side up to induce tonic immobility (a fight or flight response in which the dragon lies still on its back) (Hoagland 1928) and were restrained by placing two fingers at the rear of the jaw and a hand on the hind legs. When possible, a cloth was gently draped over the dragon’s head and/or wrapped around the body to reduce stress – both methods reduce anxiety but do not impede breathing (de la Navarre 2008). These techniques are in line with the ethical guidelines and requirements for reptile research methods stipulated by the Animal Research Act 1985 (New South Wales Government 2010a), the Animal Research Regulation 2005 (New South Wales Government 2010b), the Animal Care and Protection Act 2001 (Queensland Government 2001) and Australian code of practice for the care and use of animals for scientific purposes (Australian Government 2004). After sampling was conducted, individuals were released on the same tree from which they were collected.

When individuals were out of reach, a noose pole was used to secure the lizard (Marcellini and Jenssen 1991). Spot light searching at night, while the dragons were sleeping on branches and tree trunks, was not as successful as diurnal search efforts.

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Phylogeography

Chapter 3 Phylogeographic patterns of H. boydii across the Wet Tropics

“It does not do to leave a live Dragon out of the equation.” ~ The Hobbit, J.R.R. Tolkien (1937)

Phylogeography

Historical phylogeographic patterns can reveal where geographical barriers have isolated and restricted gene flow. If these barriers are climatically driven (i.e. if habitat suitability is dependent on climatic conditions), reconnection of localised or isolated populations, and re- establishment of gene flow can occur under suitable conditions (Avise et al. 1987, Bell et al. 2010). Identifying localised isolation, colonisation, and re-colonisation events within a species, as a response to historical climate change, aids the understanding of how the species will respond to contemporary or future climate change (Bell et al. 2010). By incorporating phylogeographic techniques to identify the historical genetic structure of H. boydii, inferences can be made about how H. boydii may respond to current and future climate change. Furthermore, identifying the genetic structure across the region indicates the dispersal ability of the species, and hence allows predictions of the response of the species to extirpation and further habitat fragmentation (Williams et al. 2008).

By examining phylogeographic patterns of a species, responses of past populations to previous warming events can be revealed. This is evident in the molecular data of various organisms in the Wet Tropics (e.g. lizards, frogs, birds and mammals) (Williams and Pearson 1997, Schneider and Moritz 1999, Bell et al. 2010). Adam (1992) suggested the current species diversity patterns found in the Wet Tropics have been a result of Quaternary extinction events from rainforest contractions and expansions. There is limited paleoclimate information for the earlier Pliocene - Miocene epochs. However, it has been suggested that rainforests underwent significant contractions during the late Pliocene (dry period) and expansions (across the Australian continent) during the early Miocene (Adam 1992, Truswell 1993, Schneider et al. 1998). It is suggested that these earlier climatic events in the Pliocene and Miocene are responsible for the large scale vicariance occurring across the Wet Tropics, whereas the Quaternary climatic changes are more responsible for shallow divergences (a result of re-connection) (Schneider et al. 1998). Historical vicariance within the Wet Tropics has been identified in many rainforest species (vertebrate and invertebrate) with the BMC as one of the main biogeographical barriers creating restrictions to dispersal between northern and southern Wet Tropics populations during different historical time periods (Joseph et al. 1995). These studies focus on endemic species (Mellick et al. 2014) including reptiles (Edwards and Melville 2010). A study by Stuart-Fox et al. (2002) showed that

Phylogeography intraspecies divergence (Carlia rubrigularis) across the BMC was greater than interspecies divergence (Carlia rubrigularis and Carlia rhomboidalis ) in the southern region of the Wet Tropics. Results such as these have been the basis for many studies hypothesising the existence of cryptic species north and south of the BMC (e.g. chironomids and rainforest skinks) (Phillips et al. 2004, Krosch et al. 2009).

3.1 Aim

In order to understand the historical genetic structure of Hypsilurus boydii populations across the Wet Tropics, this chapter aimed to answer two hypotheses: 1) in accordance with previous studies, historical genetic patterns of H. boydii populations will be explained by latitudinal partitioning (north vs. south - either side of the BMC) as opposed to altitudinal partitioning (lowland vs. upland); and 2) as the species has been shown to disperse only short distances, there will be significant genetic differentiation among sites within blocks of forest.

3.2 Methods

See Chapter 2 for sampling design, site details and capture methods.

3.2.1 Field sampling for genetic analysis A blood sample was taken from each individual dragon for genetic analysis. The dragon was placed on its dorsal side and the upper tail was wiped with an alcoholic swab. Blood was extracted using a caudal vein puncture (see Powell and Knesel 1992) on the ventral midline of the tail using a 25 gauge hypodermic needle and 1 mL syringe (Figure 3.1a). The needle was inserted at a 45 degree angle approximately 2.5 cm from the vent (dependent on individual size), so as to not interfere with hemipenes (male genitalia which consist of two penes and are situated in an inverted position at the base of the tail) (Halliday 1994). If the syringe did not flush with blood, a slight suction was applied until flushing occurred. Between 0.1 – 0.2 mL of blood was extracted from each individual. Pressure was placed on the extraction site and an alcoholic swab was used to clean off any blood. The needle was

Phylogeography

removed from the syringe and the blood was added to 1 mL of blood buffer. The blood

buffer contained 100 mM Tris, 100 mM EDTA, 125 mL 2 % SDS, 0.1 mM NaCl and ddH2O to 500 mL. To prevent individuals being resampled, the toenail of the 3rd digit on the left hind leg was slightly clipped (not to draw blood) in order to identify sampled individuals (using

sterilised nail clippers).

a) b)

Figure 3.1. Blood extraction from a) a caudal vein puncture; and b) a nail clip.

Initially, a ‘nail clip’ blood extraction method (Kirkpatrick and Suthers 1988, Owen 2011) was trialled to collect blood from specimens (Figure 3.1b); however this method was not often successful. The method involved clipping the toenail of 3rd digit on the left hind leg (using sterilised nail clippers) in order to collect four drops of blood which were collected in a 1.5 mL Eppendorf tube (that contained 0.5 mL of blood buffer). The antiseptic Betadine® (Sanofi- aventis Healthcare Pty Ltd, Macquarie Park, NSW) was applied to the cut. Although this is a popular and successful avian blood extraction method (Owen 2011), it was often not successful at expelling more than one drop of blood. It is expected that the ectothermic nature of the lizard with the cool temperature of the rainforest did not enable blood flow from the cut site even with the use of an artificial heat source (heat pads).

3.2.2 Laboratory methods The genetic variation of H. boydii was examined using one mitochondrial DNA fragment and three nuclear DNA fragments. The mtDNA genome has a high mutation rate, is maternally inherited and is usually haploid (Jansen 2000), which makes it useful in phylogeographic

Phylogeography studies (Avise et al. 1987). The 670 base pair (bp) mitochondrially encoded NADH dehydrogenase 4 (ND4) fragment (Arevalo et al. 1994) was utilised in this study because it has a high mutation rate and is commonly used in reptilian genetic studies (Moussalli et al. 2005, Guo and Wang 2007, Sampaio et al. 2015). In order to minimize bias due to sex- limited dispersal and the limited resolution available with a single locus (mtDNA) (Avise et al. 1987, Ballard and Whitlock 2004) two coding genes, Prostaglandin E Receptor 4 Subtype EP4 (PTGER4) and Megakaryoblastic Leukemia Translocation 1 (MKL1) (Townsend et al. 2008) and one non coding nDNA gene, Basic leucine zipper and W2 domain-containing protein 1 (BZW1) (Fujita et al. 2010), were also examined.

Total genomic DNA was extracted using the spin column extraction protocol (The Qiagen

QIAamp® DNA Micro Kit) with the DNA pellet suspended in 60 μL of ddH2O and the sample stored at 4 oC. A polymerase chain reaction (PCR) process was used to amplify all four gene regions. All four genes (ND4, PTGER4, MKL1 and BZW1) were independently amplified in a 10 μL PCR reaction. The PCR recipes and chemical concentrations, details of primers, and the GeneAmp PCR System 2700 (Applied Biosystems) program conditions for the PCR process for each gene are shown in Table 3.1.

Phylogeography

Table 3.1. Details of primer and PCR conditions for each genes analysed. Gene Primer name and primer sequence (5’-3’) Recipe for a 10 µl PCR reaction Reference Program conditions ND4 Leu (fwd) 5 x polymerase reaction buffer, 4 mM (Arevalo et al. 1994) 5 mins @ 94 oC, 15 x(30 sec o o ACCACGTTTAGGTTCATTTTCATTAC of MgCl2, 0.2 mM of dNTPs, 0.33 U of @ 94 C, 30 sec @ 45 C, 30 o ND4 (rev) Red Taq, 0.2 μM of forward primer, sec @ 72 C), 25 x (30 sec 0.2 μM of reverse primer, 1.8 μg of @ 94 oC, 30 sec @ 50 oC, 30 CACCTATGACTACCAAAAGCTCATGTAGAAGC o Bovine Serum Albumin (BSA) (Bioline, sec @ 72 C), 7 mins @ 72 Sydney, NSW), ≤ 0.1 μg (0.50 μL) of oC, hold @ 4 oC template DNA PTGER4 PTGER4_f1 (fwd) 5 x polymerase reaction buffer, 2 mM (Townsend et al. 2008) 2 mins @ 94 oC, 34 x (30 sec o o GACCATCCCGGCCGTMATGTTCATCTT of MgCl2, 0.2 mM of dNTPs, 0.33 U of @ 94 C, 30 sec @ 62.1 C, o PTGER4_r5 (rev) Mango Taq, 0.2 μM of forward primer, 45 sec @ 72 C), mins @ 72 0.2 μM of reverse primer, 1.8 μg BSA, oC, hold @ 4 oC AGGAAGGARCTGAAGCCCGCATACA ≤ 0.1 μg (0.50 μL) of template DNA MKL1f MKL1_f1 (fwd) 5 x polymerase reaction buffer, 0.75 (Townsend et al. 2008) 2 mins 45 sec @ 94 oC, 35 x o GTGGCAGAGCTGAAGCARGARCTGAA mM of MgCl2, 0.3 mM of dNTPs, 0.33 (15 sec @ 94 C, 20 sec @ o o MKL1_r1 (rev) U of Mango Taq, 0.3 μM of forward 60 C, 1 min @ 72 C), 7 primer, 0.3 μM of reverse primer, 1.8 mins @ 72 oC, hold @ 4 oC GCRCTCTKRTTGGTCACRGTGAGG μg BSA, ≤ 0.1 μg (0.50 μL) of template DNA BZW1 Exon 2 (fwd) 10 x polymerase reaction buffer, 0.5 (Fujita et al. 2010) 3 mins @ 94 oC, 35 x (30 sec o o CTTCTGGAGCAAAGCTTGATTATCG mM of MgCl2, 0.25 mM of dNTPs, 0.33 @ 94 C, 30 sec @ 59.2 C, o Exon 3 (rev) U of Red Taq, 0.25 μM of forward 1 min @ 72 C), 10 mins @ primer, 0.25 μM of reverse primer, 72 oC, hold @ 4 oC ATCGTTTCTAGGTCTTCCTGTGCTG 1.8μg BSA, ≤ 0.1 μg (0.50 μL) of template DNA Cloning M13 (fwd) 5 x polymerase reaction buffer, 3.125 (Life Technologies, 4 mins @ 94 oC, 35 x (30 sec o o 5´-GTAAAACGACGGCCAG-3´ mM of MgCl2, 0.25 mM of dNTPs, 0.3 @ 94 C, 40 sec @ 53 C, 1 Mulgrave Victoria) o M13 (rev) U of Mango Taq, 0.5 μM of forward min @ 72 C), 4 mins @ 72 o o 5´-CAGGAAACAGCTATGAC-3´ primer, 0.5 μM of reverse primer, 2 μL C, hold @ 4 C of colony DNA

Phylogeography

The PCR products were purified using 0.25 μL of Exonuclease 1 and 1 μL of Shrimp alkaline phosphate to 5 μL of PCR product. Sequences were then either couriered (in a 96-well plate with primers) at room temperature to Macrogen Inc. in Korea to be sequenced on an Applied Biosystems 3130XL Genetic Analyser, or sequenced using BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) according to the manufacturers sequencing protocol (Thermo Fisher Scientific Inc 2014), with the DNA pellet sequenced at the School of Biomedical and Physical Sciences Griffith University DNA Sequencing Facility (Applied Biosystems 3130XL Genetic Analyser).

During the statistical analyses for both nDNA and mtDNA, many individuals were found to have alleles that contained multiple ambiguous base pairs, insertion and/or deletions. These sequencing problems in the mtDNA ND4 gene also occurred in a study of the agamid lizard Amphibolurus nobbi (Driscoll and Hardy 2005). Cloning was conducted in order to identify the genotype of individuals with multiple ambiguous base pairs and/or heterozygote length- variants. The PCR product of these individuals was cloned to correctly identify each base pair within the nucleotide sequence. Cloning was carried out following the TOPO® TA Cloning® Kit for Sub cloning, with One Shot® TOP10 Electrocomp™ E. coli protocol (Life Technologies, Mulgrave Victoria).

Each individual to be cloned was amplified using the appropriate gene PCR conditions from Table 3.1. A 6 μL transformation mix containing 3 μL of PCR product, 1 μL of dilute salt solution (50 mM) and 0.5 μL of TOPO® Vector was used to setup for the transformation process. Once gently mixed, incubated at room temperature for 10 min and stored on ice, cells were transformed by placing 2 μL of transformation mix, 25 μL of E. coli cells, and 30 μL of ddH2O in to a pre-chilled cuvette. The cells were shocked in the cuvette by using a voltage pulse of 1.85 kV (actual 3.5 kV) and adding 250 μL of S.O.C Medium (super optimal broth with catabolite repression). The transformed mix was then incubated and shaken for 2 hours at 37 oC. 10 μL of the incubated cells were combined with 230 μL of S.O.C medium and spread on to a set LB (Lysogeny broth) agar plate (Figure 3.2a) and incubated at 37 oC overnight. LB agar plates were prepared by dissolving 5.0 g Triptone, 2.5 g Yeast extract and

Phylogeography

2.5g NaCl in 500 mL of ddH2O, adding 7.5 g Bacto agar and once autoclaved and cooled, adding 100 μg/ mL ampicillin.

Six colonies from each LB agar plate (individual dragon) (Figure 3.2b & c) were picked and screened for inserts by conducting a PCR on each colony using the M13 forward and reverse primers (Table 3.1). Colonies for each individual were sequenced at Macrogen Inc. in Korea (couriered in a 96-well plate with primers at room temperature) on an Applied Biosystems 3130XL Genetic Analyser at Macrogen Inc. Six colonies were picked and sequenced per individual in order to ensure that at least one copy of each of the two haplotypes was identified in the statistical analyses.

a) b)

Figure 3.2. Cloning process a) warming set LB agar plates containing ampicillin (Bunsen burner flame is to keep the area sterile); b) & c) cloned colonies which have been incubated overnight and are ready for selection and subsequent analysis.

Phylogeography

3.2.3 Statistical analyses

3.2.3.1 Allele identification Individual sequences were edited and aligned in the software program SEQUENCHER v 4.9 (Sequencher®). The gene regions that contained large insertions and deletions were reduced to a single position for analysis. For each individual containing these insertions, a single gap was coded. This reduced complexity when phasing sequences (Driscoll and Hardy 2005) and minimised the chance of overestimating divergences (Salgueiro et al. 2004). The nuclear genes contained heterozygote individuals, and BZW1 and MKL1 also contained alleles of uneven lengths. These alleles required resolving in SeqPHASE (Flot 2010) and PHASE (v2.1.1) (Stephens et al. 2001, Stephens and Donnelly 2003) before further analysis could be conducted. SeqPHASE (Flot 2010) was used to convert the haplotype file into an input file compatible with PHASE (v2.1.1) (Stephens et al. 2001, Stephens and Donnelly 2003). Homozygote, cloned, and ‘fake’ (heterozygote individuals with length-variant sequences) input files from SeqPHASE (Flot 2010) were used to identify haplotypes using the PHASE software program (Stephens et al. 2001, Stephens and Donnelly 2003). PHASE is a Bayesian reconstruction method that treats ambiguous base pairs (and hence unknown haplotypes) as “unobserved random quantities” and uses genetic data to estimate the probability of phased haplotype pairs (Stephens et al. 2001). This software has been shown to outperform similar programs, as well as giving a phase call an uncertainty value in order to “avoid inappropriate overconfidence” in the reconstructed sequences (Stephens et al. 2001). It has been found to have a low false-positive rate (Garrick et al. 2010). A confidence probability threshold of 0.6 was used in this study and it has been shown in previous work that changing the threshold between 0.9 and 0.6 has little effect on the number of false- positives (Garrick et al. 2010). Individuals with thresholds below 0.6 (for either one or both alleles) were not included in further analysis. The resolved alleles were used in all proceeding analyses.

3.2.3.2 Neutrality tests

To test if the gene regions were evolving neutrally, Tajima’s D (Tajima 1989) and Fu’s FS (Fu 1997) tests were conducted in DnaSP v 5.10.1 (Librado and Rozas 2009). These analyses test for conformity to neutral expectations by examining if the genetic data differs from

Phylogeography expectations under Kimura’s (1968) neutral model. This is performed by comparing the expected diversity to the observed diversity, i.e. the frequency of mutations (Ramos-Onsins and Rozas 2002, Ramirez-Soriano et al. 2008). Tajima’s D (Tajima 1989) tests conformity within a region by comparing the number of segregating sites (within a gene region) to the average number of nucleotide differences (between sequences) in the region. When Tajima’s D is significantly positive, there is evidence the population has undergone population contractions or balancing selection (Mead et al. 2003). A significantly negative Tajima’s D is an indication that the population has experienced population expansion events or a selective sweep (Kreitman 2000). These contraction and expansion events are often consequences of historical geomorphological and climatic processes in the landscape (Excoffier et al. 2005). The evolution of a species can be affected by multiple factors, the likelihood of a single neutrality test detecting the presences of one of a range of evolutionary processes is low. It is therefore recommended by Fu (1997) to use more than one neutrality test. Fu’s FS (Fu 1997) has been found to be more sensitive than Tajima’s D in detecting populations that have experienced recent expansions (Fu 1997). This test examines the observed and expected haplotype frequencies (i.e. haplotype distribution) to test for a maintained population size against a growing population size (Fu 1997, Ramirez-

Soriano et al. 2008). When Fu’s FS is significantly negative, it suggests there is an excess of alleles, which can imply a recent population expansion event. When neutrality tests are non- significant, it is expected that the populations are upholding the neutrality model with genetic drift and mutations responsible for most of the genetic variation (Tajima 1989, Fu 1997).

The presence of other evolutionary processes on a species, such as selection, can lead to similar significant results in the neutrality tests (Ramos-Onsins and Rozas 2002). Genetic hitchhiking is a type of positive directional selection (Smith and Haigh 2007), which is expected to reduce nucleotide diversity, and thus produce similar results to a population undergoing a population expansion (Amos et al. 2008, Stephan 2010). A surplus of low frequency alleles detected from the Tajima’s D test (significant negative values) is a pattern expected for the presence of a population expansion or contraction event (Tajima 1989, Otto 2000) as well as positive selection processes (Tajima 1989, Biswas and Akey 2006) such 43

Phylogeography as purifying selection (Otto 2000, Biswas and Akey 2006) or background selection (Biswas and Akey 2006). On the other hand, processes such as intragenic recombination, can affect the power of tests, such as the Fu’s FS test, and leads to ineptly identified population growth or expansion signatures in the species (Ramos-Onsins and Rozas 2002). In order to identify the effects of demographic changes (expansions or contractions) vs. selection pressures, the demographic signature within the genetic data must be present across multiple genes (Przeworski et al. 2000, Pearse et al. 2006).

3.2.3.3 Population diversity To estimate time since divergence of populations, an Agamidae mutation rate (genus Phrynocephalus but originally estimated using data from iguanas, Cyclura) estimate of 1.13– 2.04 % (nucleotide substitution per million years) for the ND4 region was used (Malone et al. 2000, Pang et al. 2003). This rate of mutation was used with the pairwise sequence percent divergence to calculate time since divergence in MEGA (v5.2) (Tamura et al. 2011), using 1,000 bootstrap replicates, the p-distance method and default settings.

To examine genetic variation within H. boydii populations, molecular diversity, haplotype diversity and nuclear diversity were examined and calculated in ARLEQUIN v3.1 (Excoffier et al. 2005). For each gene region, statistical parsimony networks were used to visualise the evolutionary relationships among haplotypes using the software program TCS v1.21 (Clement et al. 2000), with plausible network connections given a 95 % confidence.

Wright’s F-statistic (Wright 1921) is a measure of genetic variation within and between populations. The proportion of genetic variation (heterozygosity) within each population in proportion to the genetic variation in the overall population is classified as the fixation index or the FST. An F-statistic varies between 0 - 1 with 0 reflecting complete mixing of the populations (panmixia) and 1 reflecting no variation within populations and no genetic sharing between the populations (Wright 1950). When the degree of genetic divergence is incorporated with the proportion of genetic diversity, a ΦST statistic is used (Excoffier et al.

Phylogeography

2005). The F-statistic was also incorporated into a hierarchical design in the form of an analysis of molecular variance (AMOVA). Partitioning the genetic variation to specific groupings allowed the exploration of hierarchical levels of genetic divergence among and within populations (Excoffier et al. 1992). To examine the effects of the BMC barrier, hierarchical partitioning patterns were explored between populations on the northern and southern sides of the BMC. The AMOVA examined this latitudinal partitioning between latitudinal regions, among populations within latitudinal regions, and within populations. In order to examine the effects of altitude, hierarchical patterns were explored between lowland populations and upland populations (altitudinal partitioning) (see Chapter 2 for site details). The AMOVA examined altitudinal partitioning between altitudes, among populations within altitudes, and within populations. The AMOVA comparisons were carried out for both FST and ΦST statistics using 10000 iterations of a non-parametric permutation process (Excoffier et al. 1992, Excoffier et al. 2005).

A Mantel’s test (Mantel 1967) was used to detect if genetic patterns conform to an isolation by distance model (IBD), in which genetic variation between populations increases as geographic distance (Euclidean) between populations increase (Slatkin 1993). The Mantel’s test examined if there was a positive correlation between geographic distance (Euclidian distance) and genetic distance (Slatkin’s linearised FST). Slatkin’s linearised genetic distance was measured for both FST (FST/1-FST) and ΦST (ΦST/1-ΦST) using the population pairwise comparisons. A positive correlation would suggest that the species conforms to the IBD model and that geographic distance best describes the genetic variation (Sokal 1979, Slatkin 1993). Population KOD was not included in population pairwise comparisons for Mantel’s test due to its small sample size.

3.3 Results

3.3.1 Population structure A 670 base pair (bp) fragment of the mtDNA gene, ND4, was examined for molecular diversity from 76 individuals from nine populations (Figure 3.3 and Table 3.2). The overall

Phylogeography haplotype diversity was high (h = 0.878) and 17 unique haplotypes were identified. There were 20 synonymous mutations and 5 nonsynonymous mutations detected in the sequence.

The Tajima’s D statistic and Fu’s FS statistic were not significantly different from results expected under the neutral mutation model when data was analysed as a whole (Table 3.3). When examining the nDNA, a 416bp fragment of the nuclear coding gene region PTGER4 was analysed from 68 individuals for genetic diversity (Table 3.2). The gene region did not have very high overall haplotype (0.659) or nucleotide diversity (0.002). The gene region contained 6 synonymous mutations but no nonsynonymous mutations. The neutrality tests were non-significant for the Tajima’s D statistic and Fu’s FS statistic (Table 3.3) (Tajima 1989). The second nuclear coding gene region analysed was a 923 bp fragment length of the MKL1 gene. The molecular diversity of this gene was examined from 66 individuals (Table 3.2). The fragment consisted of 22 haplotypes and the overall haplotype diversity was high at h = 0.851 and π = 0.004 (Table 3.2). There were 6 synonymous mutations and 4 nonsynonymous mutations identified in the sequence. The neutrality tests, Tajima’s D and

Fu’s FS were both non-significant (Table 3.3). A 953 bp fragment length of the BZW1 nuclear non-coding gene region was examined for molecular diversity (Table 3.2). Seventeen unique haplotypes were identified from 66 individuals. Although the haplotype diversity (h = 0.695) was not as high as the previous gene region (MKL1), both the nucleotide diversity (π = 0.0086) and the average number of pairwise differences were higher (k = 8.208). The MKL1 fragment conformed to the neutral model, as both Tajima’s D and Fu’s FS were non- significant when the gene was analysed as a whole (Table 3.3).

Phylogeography

NORTHERN REGION Dan

Mos BMC MtL Cry

SOUTHERN REGION Bar Eac Hyp Pal KOD Eac LOWLAND UPLAND REGION REGION

Figure 3.3. Populations of H. boydii sampled across the Wet Tropics. Circles identify northern and southern partitioning where the dashed line identifies upland and lowland population partitioning. The Black Mountain Corridor (BMC) is identified by a solid line.

Phylogeography

Table 3.2. H. boydii genetic diversity of all genes of each population and region (northern, southern, upland and lowland). N = total number of alleles; h = haplotype diversity; π = nucleotide diversity; and # = number of unique haplotypes (richness).

Population ND4 PTGER4 MKL1 BZW1 /Region N h π # N h π # N h π # N h π #

Dan 5 0.700 0.00209 3 10 0.940 0.00321 5 10 0.844 0.00308 6 10 0.778 0.00611 6

Mos 14 0.000 0.00000 1 26 0.655 0.00223 4 28 0.659 0.00316 6 22 0.771 0.01393 7

MtL 4 0.000 0.00000 1 6 0.333 0.00080 2 8 0.714 0.00387 3 8 0.857 0.01424 5

Cry 10 0.778 0.00179 4 18 0.940 0.00171 3 20 0.768 0.00322 6 20 0.468 0.00425 3

Bar 12 0.455 0.00100 4 22 0.567 0.00163 3 16 0.867 0.00304 8 16 0.442 0.00368 4

Hyp 13 0.526 0.00264 3 22 0.693 0.00216 3 20 0.895 0.00421 10 26 0.566 0.00593 5

Pal 3 0.667 0.00199 3 6 0.867 0.00353 4 6 0.867 0.00267 4 6 0.000 0.000 1

Eac 14 0.714 0.00323 5 24 0.940 0.94041 5 22 0.827 0.00348 8 22 0.455 0.00478 2

KOD 1 0.000 0.00000 1 2 1.000 0.0022 2 2 0.000 0.0000 1 2 0.000 0.00000 1

North 23 0.589 0.00136 4 42 0.655 0.00235 5 46 0.713 0.00330 9 40 0.783 0.01144 13

South 53 0.823 0.00424 13 94 0.656 0.00215 6 89 0.869 0.00036 17 92 0.566 0.00572 6

Lowland 29 0.744 0.00560 8 54 0.657 0.00229 6 58 0.813 0.00351 14 52 0.690 0.00966 13

Upland 47 0.808 0.00551 12 82 0.647 0.00215 5 74 0.848 0.00365 16 80 0.619 0.00680 9

Overall 76 0.878 0.00868 17 136 0.659 0.00226 9 132 0.851 0.00370 22 132 0.695 0.00864 17

Phylogeography

Table 3.3. Neutrality tests (Tajima’s D and Fu’s FS) for all four genes. Gene Tajima’s D P Fu’s FS P ND4 0.434 > 0.10 -0.293 > 0.10 PTGER4 -0.300 > 0.10 -2.855 > 0.10 MKL1 1.124 > 0.10 0.249 > 0.10 BZW1 0.948 > 0.10 3.553 > 0.10

The mtDNA ND4 haplotype network illustrates the 17 mtDNA haplotypes that were detected in the analysis (Figure 3.4a). There was a deep divergence between haplotypes in the northern region and those in the southern region. The network also showed evidence of higher genetic diversity in the southern populations, although the number of sampled individuals was also higher in the south. In contrast, for PTGER4, most haplotypes were shared across the northern and the southern region, although, there was a small clade that occurred only in the north and a small clade that occurred only in the south (Figure 3.4b). The deep divergence illustrated in the mtDNA network (Figure 3.4a) was not evident in the PTGER4 network (Figure 3.4b). The ambiguous loops (dotted lines) for MKL1 (Figure 3.4c) were resolved using the rule developed by Posada and Crandall (2001) to determine the most parsimonious network connections. There were a few small clades that were restricted to southern populations and a small clade built from an ambiguous loop that was restricted to northern populations. Although more structured than the PTGER4 gene, there was no deep divergence between northern and southern regions. In contrast, a very high divergence was observed within the BZW1 gene, with one clade, 12 mutational steps from the rest of the haplotypes (Figure 3.4d). This network identified a large divergence similar to that seen in the mtDNA network. However, the BZW1 major clades were not restricted to northern or southern regions. The two most divergent clades (24 mutational steps between them) both contained individuals from the north and south.

Phylogeography

a) b)

n=14-16 n=46-56 n=5 n=11 n=2-3 n=2-3 n=1 n=67 n=1 n=47 n=14 n=2-3 n=1

n=38 n=21-25 d) n=4-7 c) n=2-3 n=1 Figure 3.4. Haplotype network and their geographical distribution across the Wet Tropics. a) ND4 mtDNA gene; b) PTGER4 gene; c) MKL1 gene; d) BZW1 gene. Haplotype Network: haplotype size corresponds to the number of sequences with that haplotype; the colour corresponds to the geological locality where the haplotype was found; the pie diagrams illustrate population frequencies of each haplotype; the black line represents one base change between haplotypes; a black dot represents an extinct, or unsampled haplotype; the empty circles show sample sizes; and a dashed box identifies haplotypes with ambiguous bp that were resolved using the program PHASE (v2.1.1) (Stephens et al. 2001, Stephens and Donnelly 2003). Haplotypes which consisted only of resolved alleles are identified and haplotypes that contained both resolved and non-statistically resolve alleles were not identified in the haplotype network.

Morphology

The Mantel’s test, which tested the correlations between mtDNA ND4 genetic distance

(using both pairwise ΦST and FST values) and geographic distance (Euclidian), was significant for the pairwise ΦST (r = 0.446, P < 0.05) but non-significant for the pairwise FST comparisons (r = 0.328, P > 0.05) (Figure 3.5) (Slatkin 1993). This suggests that as geographic distance increases between populations, so does the genetic difference between them (when incorporating genetic divergence).

1.2 r = 0.446 1 P < 0.05

r = 0.328 0.8 P > 0.05

0.6

0.4 Genetic distance 0.2

0 0 20 40 60 80 100 120 140 160 180 200 Geographic distance (km)

Figure 3.5. Slatkin's linearised ΦST (blue) and FST (red) illustrating the correlation between genetic distance (ΦST and FST) and geographic distance (Euclidian) for the mtDNA ND4 gene region. The graph presents Mantel’s correlation (r), significance value (P) and the line of best fit. Population comparisons did not include KOD due to its small sample size.

The Mantel tests for all the nuclear genes were non-significant (Figure 3.6 – Figure 3.8).

Morphology

0.5 r = 0.278 0.45 P > 0.05 0.4 r = 0.015

0.35 P > 0.05 0.3 0.25 0.2

Genetic Distance 0.15 0.1 0.05 0 0 20 40 60 80 100 120 140 160 180 200 Geographic distance (km)

Figure 3.6. Slatkin's linearised ΦST (blue) and FST (red) illustrating the correlation between genetic distance (ΦST and FST) and geographic distance (Euclidian) for the nDNA PTGER4 gene region. The graph presents Mantel’s correlation (r), significance value (P) and the line of best fit. Population comparisons did not include KOD due to its small sample size.

0.5 r = 0.020 0.45 P > 0.05 0.4 r = 0.157

0.35 P > 0.05 0.3 0.25 0.2

Genetic distance 0.15 0.1 0.05 0 0 20 40 60 80 100 120 140 160 180 200 Geographic distance (km)

Figure 3.7. Slatkin's linearised ΦST (blue) and FST (red) illustrating the correlation between genetic distance (ΦST and FST) and geographic distance (Euclidian) for the nDNA MKL1 gene region. The graph presents Mantel’s correlation (r), significance value (P) and the line of best fit. Population comparisons did not include KOD due to its small sample size.

Morphology

3 r = 0.351 P > 0.05 2.5 r = 0.276

P > 0.05 2

1.5

1 Genetic distance

0.5

0 0 20 40 60 80 100 120 140 160 180 200 Geographic distance (km)

Figure 3.8. Slatkin's linearised ΦST (blue) and FST (red) illustrating the correlation between genetic distance (ΦST and FST) and geographic distance (Euclidian) for the nDNA BZW1 gene region. The graph presents Mantel’s correlation (r), significance value (P) and the line of best fit. Population comparisons did not include KOD due to its small sample size.

3.3.2 Latitudinal barriers to dispersal When northern and southern regions were tested separately for neutrality, all genes reported non-significant Tajima’s D statistic and Fu’s FS statistic values except for the MKL1 gene region (Table 3.4). MLK1 neutrality tests on the northern and southern regions identified a significant Tajima’s D (D = 2.448, P < 0.05) and a non-significant Fu’s FS (FS = 1.909, P > 0.10) in the northern region and a non-significant Tajima’s D (D = 0.942, P > 0.10) and Fu’s FS (FS = -2.909, P > 0.10) in the southern region.

Table 3.4. Neutrality tests (Tajima’s D and Fu’s FS) on the northern and southern regions for all four genes. Significant neutrality values are highlighted in bold. Gene Region Tajima’s D P Fu’s FS P ND4 North -0.44649 > 0.10 -0.084 > 0.10 South -0.59748 > 0.10 -2.529 > 0.10 PTGER4 North 0.11681 > 0.10 -0.349 > 0.10 South 0.29025 > 0.10 -0.767 > 0.10 MKL1 North 2.44845 < 0.05 1.909 > 0.10 South 0.94156 > 0.10 -2.909 > 0.10 BZW1 North 1.8630 > 0.10 2.992 > 0.10 South 0.19459 > 0.10 9.231 > 0.10

Morphology

When the ND4 sequence divergence rate detected in Mega, 1.2 % +\- 0.4 %, was used with the mutation rate 1.13 - 2.04 % per million years, to estimate when northern and southern populations diverged; the estimate was 0.90 – 3.26 million years ago.

The AMOVA based on the ND4 indicated high Φ-statistic and F-statistic values that were significant at each hierarchical partition with regions divided according to latitude (Table 3.5). Between regions (northern Wet Tropics versus southern Wet Tropics) accounted for the largest amount of genetic variation (75.32 %) when genetic divergence was incorporated. Both the ΦCT (ΦCT = 0.753, P < 0.05) and the FCT (FCT = 0.170, P < 0.05) were significant. Highly significant differences were also detected among populations within regions (P < 0.001) (Table 3.5). Pairwise comparisons indicated that these significant differences occurred in both regions (Table 3.6). KOD was the only population that did not differ significantly from other populations, but it should be noted the sample size for this population was small (Table 3.2).

Table 3.5. H. boydii ND4 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions based on haplotype frequencies. % Φ- % F- P P variation statistic variation statistic

Between north and south 75.32 0.753 < 0.05 16.99 0.170 < 0.05

Among populations within 13.95 0.565 < 0.001 36.93 0.445 < 0.001 regions

Within populations 10.74 0.893 < 0.001 46.08 0.540 < 0.001

Morphology

Table 3.6. ND4 F-statistic frequencies of H. boydii: bold indicates a significant value; the lower diagonal is FST values; and the upper diagonal is ΦST values. Dan Mos MtL Cry Bar Hyp Pal Eac KOD Dan * 0.809 0.245 0.893 0.922 0.821 0.862 0.803 0.825 Mos 0.809 * 1.000 0.958 0.973 0.909 0.982 0.891 1.000 MtL 0.518 1.000 * 0.918 0.949 0.837 0.940 0.813 1.000 Cry 0.254 0.662 0.484 * 0.132 0.685 0.760 0.475 0.793 Bar 0.457 0.788 0.672 0.253 * 0.704 0.824 0.468 0.871 Hyp 0.412 0.745 0.621 0.357 0.509 * 0.109 0.102 0.635 Pal 0.313 0.895 0.724 0.258 0.490 0.437 * 0.178 0.750 Eac 0.291 0.643 0.498 0.199 0.197 0.117 0.265 * 0.561 KOD 0.300 1.000 1.000 0.222 0.546 0.431 0.333 0.286 *

North – Lowland South – Lowland North – Upland South – Upland

There was no significant difference between latitudes for the PTGER4 gene (ΦCT = 0.022, P >

0.05; FCT = 0.000, P > 0.05) (Table 3.7). The ΦCT and FCT accounted for the smallest amount of genetic variation. There were significant differences for the lower hierarchical level, among populations within regions when genetic divergence was included (ΦSC = 0.082, P < 0.01), but not for FSC (FSC = 0.011, P > 0.05). The FST pairwise comparisons were mainly low with significant differences mostly involving comparisons with Hyp (P < 0.05) (Table 3.8). This pairwise differentiation pattern was also reflected in the ΦST comparisons. There were two significant differences involving KOD although, again these comparisons should be treated with caution as they had only two sequences (one individual).

Table 3.7. H. boydii PTGER4 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions based on haplotype frequencies. % Φ- % P F-statistic P variation statistic variation

Between north 2.20 0.022 > 0.05 -0.10 -0.000 > 0.05 and south Among populations within 8.01 0.082 < 0.01 3.26 0.011 > 0.05 regions

Within populations 89.79 0.102 < 0.001 96.84 0.319 > 0.05

Morphology

Table 3.8. PTGER4 F-statistic frequencies of H. boydii: bold indicates a significant value; the lower diagonal is FST values; and the upper diagonal is ΦST values Dan Mos MtL Cry Bar Hyp Pal Eac KOD Dan * -0.024 0.213 0.137 0.137 0.140 0.110 0.109 0.044 Mos -0.053 * 0.120 0.043 0.038 0.138 0.172 0.037 0.183 MtL 0.136 0.092 * -0.013 0.003 0.314 0.296 -0.001 0.642 Cry -0.018 -0.023 0.043 * -0.021 0.211 0.233 -0.014 0.373 Bar 0.028 0.006 -0.020 -0.022 * 0.150 0.173 -0.035 0.320 Hyp 0.039 0.078 0.221 0.090 0.087 * -0.060 0.144 -0.205 Pal 0.003 0.057 0.136 0.054 0.034 -0.061 * 0.134 -0.304 Eac 0.026 0.008 -0.029 -0.021 -0.039 0.107 0.035 * 0.262 KOD -0.073 0.061 0.461 0.116 0.164 -0.240 -0.215 0.179 *

North – Lowland South – Lowland North – Upland South – Upland

The AMOVA for the MKL1 gene identified a significant difference between the northern and southern regions based on the FCT (FCT = 0.068, P < 0.05) but not the ΦCT (Table 3.9). Significant differences were also detected among populations within latitudinal regions for both the ΦSC (ΦSC = 0.087, P < 0.01) and FSC (FSC = 0.043, P < 0.05). Pairwise population comparisons for the MKL1 gene region identified that most significant values were comparisons with population Cry (P < 0.05 FST and ΦST) (Table 3.10). The Cry comparisons were the only significant comparisons between populations in the southern region (with the exception of KOD, which has a low sample size). There were no significant differences between populations in the northern region. There were also significant differences (P < 0.05) between upland northern populations and upland southern populations.

Table 3.9. H. boydii MKL1 gene Analysis of Molecular Variance results within northern and southern Wet Tropics regions based on haplotype frequencies. % Φ- % F- P P variation statistic variation statistic Between north and 2.55 0.025 > 0.05 6.83 0.068 < 0.05 south Among populations 8.46 0.087 < 0.01 3.97 0.043 < 0.05 within regions

Within populations 88.99 0.110 < 0.001 89.20 0.108 < 0.001

Morphology

Table 3.10. MKL1 F-statistic frequencies of H. boydii: bold indicates a significant value; the lower diagonal is FST values; and the upper diagonal is ΦST values.

Dan MOS MtL Cry Bar Hyp Pal Eac KOD Dan * -0.054 0.087 0.223 0.076 0.047 0.005 0.020 0.227 MOS -0.009 * 0.071 0.191 0.097 0.051 0.030 0.032 0.237 MtL 0.023 0.043 * 0.046 0.289 0.107 0.250 0.168 0.414 Cry 0.168 0.221 0.098 * 0.322 0.145 0.289 0.211 0.439 Bar 0.074 0.156 0.162 0.149 * 0.019 -0.106 -0.014 -0.074 Hyp 0.016 0.066 0.057 0.099 -0.003 * -0.035 -0.030 0.023 Pal 0.014 0.088 0.143 0.157 -0.053 -0.048 * -0.081 -0.023 Eac 0.028 0.068 0.088 0.131 -0.010 -0.032 -0.062 * 0.012 KOD 0.340 0.446 0.441 0.314 0.061 0.143 0.082 0.129 *

North – Lowland South – Lowland North – Upland South – Upland

The BZW1 AMOVA results identified significant differences between northern and southern regions for both the ΦCT (ΦCT = 0.239, P < 0.05) and FCT (FCT = 0.148, P < 0.05) (Table 3.11).

Significant results were also detected among populations within regions (ΦSC = 0.187, P <

0.001; FSC = 0.152, P < 0.001). These differences were mostly among sites in the southern region (P < 0.05), with significant differences in the north between populations MOS and

Dan (ΦST = 0.20721, P < 0.05) (Table 3.12).

Table 3.11. H. boydii gene BZW1 Analysis of Molecular Variance results within northern and southern Wet Tropics regions based on haplotype frequencies.

% Φ- % F- P P variation statistic variation statistic

Among north and 23.92 0.239 < 0.05 14.75 0.148 < 0.05 south Among populations 14.20 0.187 < 0.001 12.97 0.152 < 0.001 within regions

Within populations 61.87 0.381 < 0.001 72.28 0.277 < 0.001

Morphology

Table 3.12. BZW1 F-statistic frequencies of H. boydii: bold indicates a significant value; the lower diagonal is FST values; and the upper diagonal is ΦST values.

Dan Mos MtL Cry Bar Hyp Pal Eac KOD Dan * 0.207 0.122 0.063 0.024 0.414 0.694 0.430 0.586 Mos -0.008 * -0.068 0.313 0.308 0.335 0.408 0.361 0.286 MtL 0.025 -0.034 * 0.273 0.269 0.343 0.475 0.378 0.279 Cry 0.058 0.130 0.236 * -0.047 0.289 0.622 0.278 0.543 Bar 0.037 0.117 0.235 -0.033 * 0.351 0.706 0.352 0.634 Hyp 0.240 0.243 0.259 0.211 0.291 * 0.067 -0.034 -0.134 Pal 0.538 0.476 0.525 0.568 0.655 0.120 * 0.145 0.000 Eac 0.298 0.296 0.336 0.240 0.337 -0.032 0.145 * -0.041 KOD 0.386 0.370 0.342 0.477 0.571 -0.067 0.000 -0.041 *

North – Lowland South – Lowland North – Upland South – Upland

3.3.3 Altitudinal barriers to dispersal When the data was partitioned into altitudinal regions, there were no significant Tajima’s D statistics or Fu’s FS statistics for any of the four genes in either the lowland or upland regions (Table 3.13).

Table 3.13. Neutrality tests (Tajima’s D and Fu’s FS) on the lowland and upland regions for all four genes. Gene Region Tajima’s D P Fu’s FS P ND4 Lowland 1.35731 > 0.10 -2.723 > 0.10 Upland -0.45581 > 0.10 -0.917 > 0.10 PTGER4 Lowland -0.31314 > 0.10 -1.101 > 0.10 Upland 0.90387 > 0.10 0.003 > 0.10 MKL1 Lowland 1.81206 > 0.10 -2.340 > 0.10 Upland 0.74023 > 0.10 -2.844 > 0.10 BZW1 Lowland -0.31314 > 0.10 -1.101 > 0.10 Upland 0.90387 > 0.10 0.003 > 0.10

When examining the AMOVA’s with altitudinal partitioning, no significant differences were observed between lowland and upland regions for any of the four genes (Appendix A - D). All genes had significant differences when examining among populations within altitudes (upland vs. lowland) (P < 0.05). This suggests that for each gene there is a difference in at least one altitudinal region, either between regions (north vs. south) or within a region

Morphology

(northern or southern region). The pairwise comparisons showed that although there were a lot of high significant comparisons, the majority was between the north and south (Table 3.6, Table 3.8, Table 3.10 and Table 3.12).

3.4 Discussion

This chapter revealed the genetic structure of H. boydii across the Wet Tropics and attempted to identify if geographical barriers have caused this structure. Having multiple genes in the analysis enabled a more in-depth evaluation of historical climatic processes that may be responsible for the genetic structure.

3.4.1 Population structure There was no evidence of population bottlenecks or recent expansions from the genetic data (Avise et al. 1987, Avise and Wollenberg 1997). Genetic diversity across the region was either very high (mtDNA an MKL1) or high (PTGER4 and BZW1) with nucleotide diversity levels high to low (BZW1, mtDNA, MKL1 and PTGER4 respectively). The pairwise ΦST and FST values were very high for the mtDNA and showed a lack of genetic sharing between the populations. The pairwise values for the nuclear genes ranged in value and many alleles were shared between populations. The Mantel test indicated that only the mtDNA gene showed an isolation-by-distance effect.

The BZW1 gene showed much stronger patterns of differentiation than the other two nuclear genes. The network showed deeply diverged haplotypes which were not confined to one region, superficially suggesting secondary contact. However, there was no evidence for secondary contact in the other genes – especially the mtDNA, so this explanation is unlikely. The most divergent BZW1 clade contained mostly cloned individuals and/or alleles resolved by phasing, and thus it is possible that this large divergence is due to a cloning artefact causing haplotype reconstruction errors (e.g. an incorrect base call during PCR recombination can become incorrectly ‘fixed’ in the allele, which can then be copied

Morphology multiple times in the cloning process) (Garrick et al. 2010). Alternatively, this gene or one closely linked to it may be affected by selection (Biswas and Akey 2006). If this clade is a cloning artefact and is removed from the analysis, the BZW1 network still illustrates a large divergence similar in size to the divergence illustrated in the mtDNA ND4 gene network.

H. boydii demonstrated a high level of genetic structure, which is expected for an endemic species, and even more so for an endemic inhabiting a small geographical range (Eberhart-

Phillips et al. 2015). High levels of genetic structure detected in the pairwise FST and AMOVA values, suggest a lack of gene flow and thus reduced dispersal throughout the region. As this was detected within each region as well as between the north and south, it may be suggested that H. boydii is a poor disperser between populations. Whether this is a reflection of dispersal ability, rather than preference for ‘closest available habitat’ is unknown. The latter could be explained by territory size (~ 1000 m2) and diurnal movement (~ 100 m per day in summer with winter activity expected to be negligible) (Torr 1997).

Lower levels of genetic structuring were detected in the two nDNA coding genes, PTGER4 and MKL1. This is expected due to the larger effective population size of nuclear genes. It may also be due to contemporary population size decline, sex-biased dispersal, strong gene flow, and/or inadequate time since isolation or selection. Demographic factors (population size) are unlikely to affect the genetic structuring of PTGER4 and MKL1, as demographic signatures within the DNA should be present across all genes (Przeworski et al. 2000, Pearse et al. 2006). Gene flow is also unlikely, as it would also be expected to be across all genes (Waples 1998). Sex-biased dispersal is also unlikely to be creating mtDNA and nDNA differences as one nDNA gene (BZW1) also displays a very structured network. Discrepancies between mtDNA and nDNA could be the result of selection on one or more nuclear genes (Tajima 1989, Biswas and Akey 2006). This is possible for one gene (MKL1), which seemed to show a different pattern than the other nuclear genes.

Morphology

As selection pressure only accounts for the unexpected results in one nuclear gene, it is unlikely the sole driving factor for discordance between mtDNA and nDNA. Inadequate time since isolation is most likely responsible for the lower genetic structuring in PTGER4 and MKL1 as these genes are slower evolving nuclear coding genes. The most likely explanation is this was a result of slower evolutionary rates. PTGER4 and MKL1 are coding and therefore would be subject to purifying selection, resulting in substitutions, therefore less likely to be affected by purifying selection. This suggests that the mtDNA reflects a more recent period of population isolation and that the nuclear genes reflects an older period of gene flow among populations and that there may have been a lack of evolutionary time to achieve divergence and a significant genetic-geographic distance correlation within the nuclear DNA (Thorne and Kishino 2002).

The overall high diversity in H. boydii may be explained by high diversity across the region, high diversity within each isolated population, or selection pressures. The genetic pattern detected in the H. boydii genes suggest higher genetic diversity in southern regions than northern regions, and although this pattern was also found in Wet Tropics skinks (Carlia) (Dolman and Moritz 2006), the most likely explanation for this variation pattern is that there were more sites and larger sample sizes in the southern region. This uneven sampling is not a flaw in the sampling design but rather an expression of sampling difficulties, abundance and the distribution of the species, with a wider distribution in the southern region.

There was one population in the north (Dan) and one in the south (Cry) that consistently identified a high level of private alleles across all genes. This high private diversity may be explained by the populations being in a historical refugial locality. There is mtDNA information from other Wet Tropics studies which also identified high diversity from populations in the Lamb Range rainforest (the Lamb Range separates southern lowland [Cry] and southern upland [Bar and Eac] populations). It was suggested that the Lamb Range has been a refugium and also received immigrants from the northern Wet Tropics (Williams 1997, Williams and Pearson 1997, Schneider et al. 1998). A similar explanation has also been

Morphology put forward for the Thornton Upland – a region west of Dan (Williams and Pearson 1997, Schneider et al. 1998).

The high levels of genetic diversity found across the Wet Tropics in H. boydii populations was not anticipated as genetic diversity is expected to be low in endemic species and high in widespread species (Frankham 1996, Mitton 1997). The high levels of overall diversity and within the partitioned regions (northern, southern, upland and lowland) were, however, similar to previous studies of other endemic lizards in the region. A study by Schneider and Moritz (1999) identified high genetic diversity within northern and southern regions but low variance between regions. It was suggested that this was an effect of long-term population size. In this chapter, the southern region was found to display higher levels of haplotype diversity in all genes, except BZW1. High levels of genetic diversity are expected in larger populations. This may superficially suggest that the population size of H. boydii is larger in the south than the north (Ellstrand and Elam 1993). However, smaller sample sizes (as seen in this chapter) tend to under estimate genetic variation (Leberg 2002). Having high genetic diversity would be advantageous in an endemic species giving it a greater chance of having gene variants that will aid survival in a changing environment (Jump et al. 2009).

3.4.2 Latitudinal barriers to dispersal The mtDNA network illustrated the isolation effect caused by the BMC with the north-south partitioning of populations. Avise et al. (1987) suggested that such a discontinuous pattern is suggestive of a long term extrinsic barrier to dispersal and subsequently a barrier to gene flow. It is unlikely that the missing haplotypes that separate the major clades were unsampled, but rather they have gone extinct due to the historical isolation.

This north-south partitioning across the BMC was also evident in the nuclear BZW1 network. The BZW1 network also revealed a pattern that may be indicative of secondary contact. However, secondary contact is an unlikely explanation in this case because no other gene identified secondary contact, even though such processes have been identified in other Wet

Morphology

Tropics species (beyond the current studies geographical extent) (Schneider and Moritz 1999). It is most likely that the secondary admixture pattern detected in BZW1 is due to different selection pressures or a result of cloning and/or phasing errors (as discussed previously). The fact that the BZW1 is the only nuclear gene to clearly identify a north-south divergence may be due to the fact that BZW1, although it is a slower evolving nuclear gene, is a non-coding gene. It would therefore be expected to be evolving at a faster rate than the PTGER4 or MKL1. Having a slower evolutionary rate would suggest that unstructured patterns expressed by the coding genes would be identifying the oldest genetic structure (of the four genes) of H. boydii. These patterns showed the common haplotypes that were geographically widespread across the Wet Tropics.

Although the PTGER4 and MKL1 networks did not illustrate clear phylogeographic structure across the BMC, the results from the AMOVA analysis detected significant differences between northern and southern regions for all genes except PTGER4. The fact that PTGER4 and MKL1 pairwise values consisted of few significant comparisons and none of these were among northern populations, suggests some structure in the southern regions. This contrasts with the large levels of structure identified in the mtDNA ND4 and BZW1 pairwise results, which identified high levels of structuring within and between regions.

The findings of this study are supported by previous work in the region. A study by Schneider et al. (1998), identified that response to the isolation created by the BMC differed between species. Some species (e.g. Carphodactylus laevis and Gnypetoscincus queenslandiae) sustained numerous populations with high diversity either side of the divide. Other species (e.g. Saltuarius cornutus and Litoria genimaculata) exhibited low diversity, star shape networks and shared haplotypes either side of the divide, indicating regional population contraction with a following event of recent expansion. In contrast to the H. boydii results, Schneider et al. (1998) suggested there was evidence for some species to have experienced recent colonization across the BMC and evidence from other species to have experienced speciation (Bell et al. 2007).

Morphology

3.4.3 Altitudinal barriers to dispersal One possible explanation for the apparent broad distribution of H. boydii across elevations was that it may consist of a species complex, with different cryptic species at low and high altitudes. There was clearly no evidence of this. None of the four genes showed a significant effect of altitude on genetic variation. AMOVA results suggested that there were no significant differences between upland and lowland populations. The pairwise comparisons suggested the majority of the significant difference occurring within the upland environment were comparisons between upland-northern and upland-southern populations. The same pattern was detected in the lowland region. It was unclear which region (upland or lowland) exhibited the higher level of genetic diversity, as results were not consistent across all genes. As most studies examining altitude effects within the Wet Tropics have focused on species richness (e.g. Williams et al. 1996) or genetic patterns within an altitude region (e.g. Bell et al. 2004, Nicholls and Austin 2005), there is little to compare with these results. The results are similar to global tropical studies that found gene flow was maintained across altitudinal gradients, e.g. Glyphorynchus spirurus (Mila et al. 2009), but differ from other studies which did identify genetic differences between altitudes e.g. Phyllastrephus debilis (Fuchs et al. 2011), Metrosideros polymorpha (Aradhya et al. 1993) and Nothofagus pumilio (Premoli 2003). These comparisons must be interpreted with caution as the altitudinal range (metres) for the upland region is much lower in this study. At present, there are limited studies that examine the genetic structure of terrestrial biota at various altitudes, within tropical regions.

From the findings, it may be speculated that high altitudinal refuges of H. boydii were not exempt from the isolation effect of the BMC and experienced dispersal restrictions. This may further suggest that individuals in upland regions did not encounter a continuous habitat the length of the Wet Tropics. This is in contrast to a mid-altitudinal study by Mellick et al. (2014), which identified that a migration of mid-altitude endemic rainforest tree species (Elaeocarpus foveolatus) across the BMC reduced the effect of isolation between populations north and south of the BMC. Furthermore, it was suggested that the dispersal

Morphology potential which resulted from a more continuous habitat was a consequence of historical climatic change.

3.4.4 Genetic structure and historical climate change The response of species to climate changes during the Quaternary and Tertiary in the Wet Tropics was largely determined by biology and biological requirements. These responses have had a large effect on the current distribution of the species (Schneider et al. 1998). In addition, climatic events in the region are believed to have created repetitive historical contractions and expansions of species’ distributions (Mellick et al. 2014). The rate of divergence calculated from the mtDNA suggested that the northern and southern populations of H. boydii in the Wet Tropics diverged 0.90 – 3.26 million years ago. This divergence time coincides with two other endemic rainforest lizards (Carphodactylus laevis and Gnypetoscincus queenslandiae). Their divergence may have been caused by climatic vicariance events across the Wet Tropics approximately 2 – 6 million years ago, during the late Miocene or Pliocene (Schneider et al. 1998). Although the study concentrated sampling to more of the upland regions, the latitudinal and altitudinal distributions are thought to be similar to H. boydii (Schneider et al. 1998, Williams 2006). When examining the divergence of populations north and south of the BMC, divergence times were also similar for a montane specialist skink (Lampropholis robertsi), which had an estimated divergence approximately 2.3–4.6 million years ago (Bell et al. 2010), but much earlier for a woodland dragon (Diporiphora australis) where divergence was estimated to have occurred 4.7– 9.2 million years ago (Edwards and Melville 2010). The divergence time estimates from this study coincided with the estimates for the rainforest restricted species but much later than the estimates for the drier woodland species. Due to H. boydii inhabiting both rainforest and drier sclerophyll forest, it was expected that its divergence time would be between that of a woodland dragon and of a montane specialist (Bell et al. 2010, Edwards and Melville 2010).

Mellick et al. (2014) suggested that Wet Tropics populations have had various opportunities for dispersal and gene mixing and opportunities to reduce the effects of divergence. The strong phylogeographic structuring of H. boydii, along with region specific haplotypes and

Morphology

high FST values, is concordant with a species that was restricted to various refugia during periods of habitat contraction (Schneider and Moritz 1999) and is not expected to have reduced these divergence effects through north-south dispersal in the last 0.90 – 3.26 million years. It may be suggested that restrictions to local dispersal are still occurring. Bottlenecks either side of the BMC have been previously suggested to have occurred to numerous biota, as a consequence of rainforest contraction during the Pleistocene epoch (Maruyama and Fuerst 1985, Schneider and Moritz 1999). As no bottlenecks were detected in my results, it may be surmised that historical climatic changes that created the north- south divergence were not extreme enough to create a detectable bottleneck in H. boydii populations. This may be a response of H. boydii’s wide (in relation to the Wet Tropics) latitudinal and altitudinal tolerances, as well as its inhabitance of both rainforest and sclerophyll forests.

Morphology

3.4.5 Conclusion This chapter illustrates the effects that historical climatic changes have had on the genetic structure of H. boydii. The BMC has acted as a barrier to latitudinal dispersal between populations in the northern and southern Wet Tropics. No evidence for cryptic species was identified, either north and south of the BMC, or between altitudes. Findings such as this identify the importance of cool rainforest habitats which act as refugia for biota in warming periods (Bell et al. 2010). If these refugia remain isolated, and experience further habitat contractions in the future, it may have greater effect on the northern region. As this study suggested, the northern region may have a smaller population size, which enables processes such as genetic drift to create further reductions to genetic diversity (Nei et al. 1975, Amos and Balmford 2001). This is detrimental to both the northern region and the species as a whole, as maintaining high levels of genetic diversity gives a species a greater chance at being resilient to current and future climatic changes (Jump et al. 2009).

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Chapter 4 Morphological variation: Examining Bergmann’s rule, Allen’s rule and the isolation rule

“Meddle not in the affairs of dragons… for you are crunchy and taste good with ketchup.” ~ The Woodchips, J. D. Roberts (2011)

Morphology

4.1.1 Morphology and the Wet Tropics Broad scale morphological variation has been shown to adhere to three main eco- geographical rules: Bergmann’s rule, Allen’s rule and the isolation (island) rule (Mayr 1956, Millien et al. 2006). The latitudinal difference across the Wet Tropics is unlikely to be large enough to have a significant effect on temperature (latitude ~ -15.5S [~18 - 30 oC] to 19.25S [~15 - 28 oC]) although temperature is likely to vary with altitude (upland ~7 - 25 oC and lowland ~18 - 32 oC) (see Figure 2.2). Evidence for adherence to eco-morphological rules has been largely tested for the BMC geographical isolation effect (isolation rule) with studies suggesting isolation had negligible effect on phenotypic divergence (Smith et al. 2001, Nicholls and Austin 2005, Moritz et al. 2009). To the knowledge of the author, there has been no study testing species adherence to Bergmann’s rule or Allen’s rule. Within the Wet Tropics, if altitudinal gradients are influencing morphological variation within a species, then it would be expected that comparisons between lowland populations and upland populations would reveal phenotypic differences with larger bodies and limbs in lowlands than uplands (Allen’s rule and the inverse of Bergman’s rule).

The suggested explanation as to why ectotherms, and particularly squamates, have these patterns is that small bodies and limbs (large surface to volume ratio) lose and gain heat faster than larger bodies and limbs (small surface to volume ratio) (Ashton and Feldman 2003). Gaining and loosing heat at a fast rate may be a favourable trait for ectotherms, particularly squamates, in cold environments as opposed to endotherms who tend to favour slow rates of heat loss (Mayr 1956, Ashton and Feldman 2003). Rapid heat absorption may aid diurnal activity, digestion time and egg development time (see Ashton and Feldman 2003). Faster rate of heat gain in small animals may be even more important for non- basking ectotherms such as H. boydii. This would suggest that temperature (and the other eco-gradient features that accompany altitude, e.g. forest structure) influence the morphology of H. boydii. Such findings could be the result of phenotypic plasticity or selection. Another explanation of size differences between temperature regions suggests reptiles with access to more radiant heat (i.e. individuals in warmer lowlands ) grow faster during development (Arnold and Peterson 1989). Other explanations suggest heavy competition in cool areas (small individuals) and less competition in warm areas (large

Morphology individuals) as well as “maintenance of preferred body temperature” (Ashton et al. 2000, Ashton and Feldman 2003)

If latitudinal isolation from the BMC has resulted in morphological differentiation (whether it is caused from natural selection, phenotypic plasticity or sexual selection) between the northern and southern Wet Tropics, the isolation rule would be supported. Of the few morphological studies that have examined this rule, no study has found large scale significant morphological differences between northern and southern populations, even though their genetic data often supported geographical isolation either side of the BMC, e.g. rainforest skinks (Carlia) (Schneider et al. 1999, Dolman and Moritz 2006), microhylid frogs (Cophixalus and Austrochaperina) (Hoskin 2004) and dung beetles (Temnoplectron) (Bell et al. 2007) . A study by Schneider (1999) suggested morphological variation of a Wet Tropics lizard was not influenced by geographical isolation but rather natural selection due to habitat type. Populations of similar habitat which were isolated by geographical barriers (including the BMC) were found to be less significantly different than the between habitat comparisons (Schneider et al. 1999). There is, however, evidence for minor morphological variation across the divide for otherwise morphologically cryptic sister-species of dung beetles (Temnoplectron), as well as speciation and pre-zygotic isolation just south of the BMC in frogs (Litoria) (Hoskin et al. 2005, Bell et al. 2007, Hoskin 2007). As genetic isolation was detected for H. boydii in Chapter 3, morphological differentiation between north and south may be expected.

It is possible that the sexes have responded differently to latitude and altitude, so males and females were analysed separately. Unpublished data suggest that there is a bias towards larger heads and overall body size in males than females (Wet Tropics Management Authority 1996, Torr 1997).

Morphology

4.2 Aim

The aim of this chapter was to determine how the geographical features and the biophysical climate of the Wet Tropics has influenced the morphological structure of H. boydii and to hypothesise what this might mean for the species in a changing environment. This will be explored by testing four main hypotheses: 1) morphological variation across the Wet Tropics will support the inverse of Bergmann’s rule (larger bodies in warmer lowlands than cool uplands) in line with patterns largely detected in other squamates; 2) morphological variation across the Wet Tropics will support Allen’s rule (larger limbs in warmer lowlands than cool uplands) on the basis of global patterns; 3) morphological patterns will be best explained by Bergmann’s and Allen’s rules (explained by altitude) rather than the isolation rule (latitude) due to the lowland-upland temperature changes (upland ~7 - 25 oC and lowland ~18 - 32 oC) being more extreme than latitudinal temperature changes (northern region ~18 - 30 oC and southern region ~15 - 28 oC); and 4) morphological phenotypes will be independent of latitudinal genetic patterns due to altitudinal pressures (Bergmann’s and Allen’s rule) on morphology being more influential than latitudinal pressures (isolation island rule). Alternatively, morphology might affect the genetic divergence detected between latitudes.

4.3 Methods

See Chapter 2 for sampling design, site details and capture methods.

4.3.1 Field sampling for morphological analysis Eleven morphological measurements were taken in the field for each individual captured (Table 4.1). Snout to vent was measured using a ruler (mm); limb, tail and head lengths were taken using a calliper (mm); and body mass (g) was measured using a Pesola spring balance (individual placed in a loosely woven bag which was attached to the balance) (Figure 4.1). These measurements were chosen as they are commonly used in morphological studies of squamates (Hews 1990, Losos 1990, Bedford and Christian 1996, Christian and Garland 1996).

Morphology

Table 4.1. Morphological measurement, measurement code and description of the measurement.

Measurement Measurement Measurement description code Snout to vent SVL from snout tip to vent length Tail length TL tip of tail to vent Tail height TH tallest part at base of tail Tail width TW widest part at base of tail Lower arm LA longest digit to elbow Forearm length Fore point where limb connects to body to elbow Hind limb length Leg point where limb connects to body to longest digit Lamellae width Lame at widest part Head width HW at widest part at jaw Head length HL from snout tip to behind jaw Body mass Weight weight

Figure 4.1. Images illustrate approximate measurement location take for each morphological measurement.

Each lizard was sexed from the ventral side using a thumb to slightly pull back the skin around the cloaca whilst applying gentle rubbing pressure at the base of the tail (Figure 4.2) (de Vosjoli 2007). Male hemipenes were never completely everted (erect) and were retracted (inverted position) immediately to avoid damage to the males. Females were identified by the lack of hemipenes.

Morphology a) b)

Figure 4.2. Identifying the sex a) 'bulge' often associated with male hemipenes; b) hemipenes starting to be everted.

4.3.2 Museum samples To increase sample size, 20 H. boydii specimens from the Queensland Museum (Appendix E) were measured using the same morphological characteristics and measuring technique as was used on the field samples, with the exception of body weight. Weight was excluded due to many individuals having been dissected and being filled with preservation liquid. The sex of museum samples could not always be determined due to preservation limiting the inversion of the hemipenes without damaging the specimen. Also many museum samples could not be used in the final analysis due to inexact latitude and longitude coordinates.

4.3.3 Statistical analyses The statistical analysis was conducted in R v3.1.3 (R Core Team 2015) except where alternative software is specifically stated. Morphological measurements were standardized in Microsoft Excel 2010 (Mircrosoft 2010) to remove the allometric effect of size and age on the morphological shape measurements. This was conducted using Thorpe’s (Thorpe 1976) -1 b theory of allometric modelling using the equation MS = MO (LSLO ) where MS is the

standardized morphological measurement, MO is the morphological measurement that is to

be standardized, LS is the overall mean length of the standardizing morphological

characteristic (i.e. snout-vent), and LO is the length of the standardizing morphological characteristic of the individual (snout-vent) (Lleonart et al. 2000). The allometric growth equation (M = aLb) was used to determine the value of parameter b by calculating the

regression of log10MO on log10LO using all H. boydii individuals in all groups (Lleonart et al.

Morphology

2000, Ferrito et al. 2007). The mean and standard error (SE) of each morphological measurement was calculated for: the overall, lowland, upland, northern, southern, upland- north, upland-south, lowland-north, lowland-south and individual populations in order to identify variation within each morphological measurement for each category. Even though SVL measurements would be strongly affected by age, a mean and SE was still calculated.

To determine if the sexes differed in response to altitude and/or latitude, analyses were conducted on the entire sample, male only sample and female only sample. When examining the males and females independently, the sample size decreased by one due to inconclusive sex determination of one individual.

Differences in morphological characteristics between geographical localities and sexes were examined using a Welch’s t-test (Welch 1938, 1947). Differences between males and females were examined in order to determine whether there was sexual dimorphism. The Welch’s t-test does not assume equal variance of the morphological characteristics. A Welch’s t-test was conducted on the snout-vent measurement in order to identify if individuals differed in length between north and south. Results of this test were cautiously interpreted. The snout-vent and the tail length (due to damage to tails) were not included in the multivariate analysis.

The data were further analysed using non-metric multidimensional scaling (NMDS). The NMDS is an indirect gradient analysis which uses a rank-based method to delegate dissimilarity ranks to the morphological distance measures, rather than distances themselves (Kantvilas and Minchin 1989). A rank-based approach is expected to be more robust (Spence and Lewandowsky 1989, Clarke 1993). The dissimilarity rank was calculated using Gower’s similarity coefficient (Gower 1971). Gower’s coefficient is a measurement for proximity which is popular in ecology due to its use on mixed data formats (Gower 1971, Harch et al. 1996). The NMDS produces stress values which measure the strength of the relationship by determining the amount of distortion that was introduced to enable the

Morphology plotting of the data in the specified dimensions (two dimensions in this study). High stress values, > 0.3, suggest an arbitrary ordination due to excess distortion, stress values approaching < 0.2 are thought to be fair, stress values < 0.1 are suggested to be good, and stress values < 0.05 suggest an excellent fit and have low chance of misinterpretation (Clarke 1993). The result was re-partitioned to illustrate patterns between the sexes, altitudes, latitudes and altitude-latitude.

To further examine the results, a dissimilarity matrix was calculated by comparing all the morphological measurements using the Gower’s similarity coefficient (Gower 1971) in order to conduct a two-way analysis of similarities (ANOSIM). The ANOSIM examined the differences in overall altitude, overall latitude, overall altitude-latitude (e.g. upland-north and upland-south), male altitude, male latitude, female altitude, and female latitude. As no significant results were identified in the female only t-tests, further analysis was not conducted. The ANOSIM calculates an R value between -1 and 1. A value of 1 suggests differences between individuals of different groups (e.g. lowland and upland) are greater than differences of individuals within groups. A value of 0 suggests no differences between groups. A value of -1 suggests differences between individuals of different groups (e.g. lowland and upland) are less than the differences of individuals within groups (Olden 2013). The R statistic is “based on the difference in the mean ranks between” regions and within regions (Poff et al. 2007).

To explore any differences identified among groups in the ANOSIM, similarity percentages (SIMPER) were performed to determine the contribution each morphological measurement made to the overall dissimilarity occurring among geographical localities (Clarke 1993). The percent contribution (%) was calculated using a Bray-Curtis similarity measure which compares all individuals within one group (e.g. lowland) to all individuals in another group (e.g. upland).

Morphology

A two-way analysis of variance (ANOVA) was performed on individual characters (Heiman 2010). The two-way ANOVA enables the effects of altitude (Bergmann’s and Allen’s rule) and latitude (isolation rule) to be examined individually as well as all altitude-latitude comparisons (lowland-north, lowland-south, upland-north and upland-south).

A Tukey’s honest significant difference (HSD) post hoc analysis was used for morphological characteristics where there was a significant interaction between altitude and latitude in the ANOVA (Tukey 1949). This was conducted in order to identify which of the four altitude- latitude combinations were significantly different from one another (Tukey 1949).

Mantel’s test (Mantel 1967), as was described in Chapter 3, explores the correlation between two distance measures by using similarity matrices. A correlation between geographical distance among populations (km) and morphological distance among populations as well as a correlation between morphological distance and genetic distance for each gene (Chapter 3: ND4, PTGER4, MKL1 and BZW1) were explored. To measure morphological distances between populations for all morphological variables, Mahalanobis distance (Mahalanobis 1936) was used. The Mahalanobis distance (D2) within each population was calculated from the equation, D2 = (x – m)T C-1 (x – m), where D2 is the distance between a given set of morphological variable for a population and a “new” set of variables, x is the morphological variable vector, m is the mean of x, T is identifying that the data is to be transposed, and C-1 is the covariance matrix inversed (DeVries 2005). The mean Mahalanobis of each population was calculated for the distance matrix. The Mantel’s test correlations, morphological distance (Mahalanobis) vs. geographic distance and morphological distance (Mahalanobis) vs. genetic distance (ΦST and FST) were graphed in Microsoft Excel (Mircrosoft 2010).

4.3.4 Statistical analysis of museum samples The statistical methods performed in this study were repeated with the inclusion of museum samples (where possible) in an attempt to increase the sample size. The combined

Morphology dataset, which now consisted of field and museum samples, was analysed separately from the pure field dataset due to concerns that museum morphological traits did not reflect their natural state as a result of their preservation method (e.g. dissected and contorted). No Mantel test was performed for museum samples because they did not consist of multiple samples from each site.

When the museum samples were incorporated into the data set, the overall results changed dramatically, so they were excluded from the main dataset (see Appendix E for the combination results). The differences caused by the inclusion of the museum samples could be due to: 1) the preservation of museum samples distorted (or in an unnatural position) measurements which influenced measurement technique (Figure 4.3); and 2) due to uneven sample size (the upland region subsequently contained twice as many individuals as the lowland region). The most likely explanation is due to museum sampling technique.

Figure 4.3. Museum samples of H. boydii which have been previously dissected and are in an unnatural position.

4.4 Results

The standard error values calculated from the standardized morphological measurements for each population and the overall population were small with the exception of SVL, Tail and Weight (Table 4.2).

Morphology

Table 4.2. Standardized morphological measurements averages for each population: population name, sample size (N), measurement mean, and standard error (SE) of each morphological measurement within each population and within the overall population. All measurements except SVL are standardized. Population (N) x̅ & SE SVL Tail TW TH Fore LA Leg Lame HL HW Weight Dan 5 x̅ 151.88 282.14 15.93 22.80 35.62 53.31 135.18 9.69 52.08 28.03 112.53 SE 12.93 14.99 0.42 0.84 0.61 0.81 1.57 0.42 1.62 0.96 6.81 Mos 8 x̅ v 145.75 262.90 16.12 21.64 33.89 53.58 140.58 8.35 48.73 27.69 112.46 SE 11.29 21.79 0.94 0.77 0.56 0.97 1.77 0.39 1.07 0.58 5.74 MtL 4 x̅ 142.90 278.71 16.24 22.55 34.05 52.63 134.92 9.81 51.96 28.95 101.61 SE 14.13 5.70 1.26 1.70 1.08 1.12 3.22 0.17 2.13 1.18 7.18 Cry 9 x̅ 161.88 271.39 17.86 23.63 35.24 54.66 141.85 9.15 51.98 29.33 121.26 SE 6.54 18.65 0.54 0.74 0.96 0.98 3.39 0.60 0.90 0.80 5.57 Bar 11 x̅ 129.91 273.12 16.42 22.08 33.70 54.58 140.23 9.00 50.78 27.80 107.22 SE 5.42 11.19 0.50 0.47 0.55 0.84 2.28 0.39 0.80 0.38 5.40 Hyp 5 x̅ 130.88 261.56 18.03 21.93 33.58 52.05 129.52 9.71 50.58 28.24 104.28 SE 10.88 15.89 0.29 0.79 0.41 1.44 2.49 0.22 1.21 1.12 5.54 Pal 3 x̅ 146.33 263.30 16.41 22.15 35.40 54.35 148.77 10.74 49.29 27.11 119.66 SE 9.84 18.75 1.81 2.09 2.41 4.31 7.40 0.65 2.83 1.87 21.11 Eac 3 x̅ 141.00 298.09 16.22 22.38 32.74 56.86 143.59 8.32 50.38 28.89 112.12 SE 3.51 8.77 0.69 1.83 0.79 2.31 6.96 0.47 1.97 0.82 9.96 Overall 47 x̅ 144.45 272.24 16.73 22.42 34.28 54.00 139.25 9.20 48.79 27.11 106.60 SE 3.56 6.03 0.28 0.32 0.31 0.46 1.25 0.19 0.46 0.29 2.55

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4.4.1 Males vs. females Mean values for most characters were lower for females than males (Table 4.3). These differences were significant for four morphological characteristics (Table 4.3). Males were significantly longer (SVL) than females (t = -2.6454, P < 0.02). Tail heights were also significantly larger in males than females (t = -4.6100, P < 0.001). Male heads were significantly longer (head length) (t = -3.8587, P < 0.001) and wider (head width) (t = -3.7247, P < 0.001) than females. No significant differences (P > 0.05) were found between the male and females for any limb measurements or weight (Table 4.3). The significant differences between the sexes were also identified in the ANOSIM results (R = 0.1364, P = 0.01).

Table 4.3. Welch’s two sample t-test results between males and females: sample size (N); measurement mean; and standard error (SE) for each sex. All measurements except SVL are standardized. Males Females Sex Variable (N) x̅ SE (N) x̅ SE t P SVL 23 152.55 4.17 24 134.92 5.25 -2.645 <0.02 Tail 23 275.58 8.67 24 268.90 8.53 -0.455 0.6516 TW 23 17.00 0.37 24 16.46 0.41 -0.967 0.3387 TH 23 23.65 0.38 24 21.19 0.36 -4.61 <0.001 FA 23 34.64 0.45 24 33.93 0.42 -1.172 0.2474 LA 23 54.48 0.67 24 53.51 0.63 -0.901 0.3724 Leg 23 139.78 1.91 24 138.72 1.65 -0.308 0.7600 Lame 23 9.30 0.28 24 9.10 0.25 -0.612 0.5440 HL 23 52.31 0.50 24 49.21 0.62 -3.859 <0.001 HW 23 29.23 0.34 24 27.29 0.38 -3.725 <0.001 Weight 23 115.78 3.83 24 107.40 3.22 -1.605 0.1158

4.4.2 Bergmann and Allen’s rule: lowland vs. upland The only significant morphological difference was body length (SVL) (t = 2.7186, P < 0.01) with lowland individuals being larger than upland individuals, as would be predicted by the inverse of Bergmann’s rule (Table 4.4).

Morphology

Table 4.4. Welch’s two sample t-test results between lowland and upland for the total sample: sample size (N), measurement mean, and standard error (SE) for lowland and upland regions for the total. All measurements except SVL are standardized. Total sample Lowland Upland Lowland vs. Upland Variable (N) x̅ SE (N) x̅ SE t P SVL 22 153.74 5.64 26 135.27 3.88 2.7186 <0.01 Tail 22 270.75 11.13 26 273.51 9.48 -0.1339 0.894 TW 22 16.79 0.44 26 16.68 0.55 0.2125 0.833 TH 22 22.72 0.47 26 22.17 0.66 0.9341 0.355 FA 22 34.83 0.47 26 33.82 0.60 1.6339 0.110 LA 22 53.96 0.55 26 54.03 1.10 0.0664 0.947 Leg 22 139.87 1.61 26 138.72 2.91 0.5661 0.574 Lame 22 8.98 0.31 26 9.39 0.35 -1.1418 0.260 HL 22 50.82 0.71 26 50.70 0.93 0.1541 0.878 HW 22 28.44 0.46 26 28.11 0.58 0.6447 0.523 Weight 22 116.08 3.42 26 107.79 5.53 1.7314 0.090

Examining the sexes individually, males were significantly larger in the lowland than in the upland region (t = 5.5494, P < 0.001), whereas females did not differ in size between the two altitudes (t = 0.9205, P > 0.05) (Table 4.5). These results must be interpreted with caution as they may just indicate differences in age between the samples. The ANOSIM results comparing lowland and upland samples revealed a non-significant difference in the total sample (R = -0.0008, P > 0.05), male only comparisons (R = 0.0306, P > 0.05) and female only comparisons (R = -0.0244, P > 0.05). Although non-significant, the positive R value in the male only ANOSIM suggests that individuals within altitudes are more similar than those between altitudinal groups.

Morphology

Table 4.5. Welch’s two sample t-test results between lowland and upland within each sex: sample size (N), measurement mean, and standard error (SE) for lowland and upland regions for each sex. All measurements except SVL are standardized. Males Females Lowland Upland Lowland vs. Upland Lowland Upland Lowland vs. Upland Variable (N) x̅ SE (N) x̅ SE t P (N) x̅ SE (N) x̅ SE t P SVL 10 170.51 3.63 13 139.40 4.27 5.5494 <0.001 12 139.77 7.949198784 12 130.07 6.905763 0.9205 0.368 Tail 10 269.36 18.41 13 278.51 7.95 -0.4563 0.656 12 271.90 14.229672 12 265.91 9.990743 1.7286 0.099 TW 10 16.97 0.73 13 17.03 0.42 -0.0704 0.945 12 16.64 0.571568908 12 16.27 0.622252 0.4321 0.670 TH 10 24.14 0.67 13 23.30 0.47 1.0328 0.316 12 21.53 0.444031585 12 20.84 0.579545 0.9578 0.349 FA 10 35.23 0.68 13 34.24 0.63 1.0642 0.300 12 34.51 0.661597434 12 33.35 0.50671 1.3823 0.182 LA 10 53.84 0.96 13 54.74 0.98 -0.6567 0.519 12 54.05 0.670278414 12 52.98 1.074679 0.8474 0.408 Leg 10 139.80 2.30 13 139.30 3.10 0.1301 0.898 12 139.93 2.347468829 12 137.51 2.380297 0.7233 0.477 Lame 10 8.91 0.53 13 9.66 0.32 -1.2182 0.242 12 9.04 0.374629246 12 9.16 0.347936 -0.2458 0.808 HL 10 52.96 0.80 13 51.87 0.69 1.0201 0.320 12 49.04 0.81628543 12 49.38 0.968338 -0.2648 0.794 HW 10 29.69 0.60 13 28.87 0.43 1.1094 0.283 12 27.40 0.514680919 12 27.17 0.572121 0.2955 0.770 Weight 10 121.01 6.47 13 111.49 4.93 1.1710 0.257 12 111.96 3.017684148 12 102.83 5.515578 1.4528 0.164

Morphology

4.4.3 Isolation rule: northern vs. southern populations No significant morphological differences were detected between northern and southern regions for the total sample (Table 4.6), the males only or the females only (P > 0.05) (Table 4.7). The non-significant differences between northern and southern populations were also identified in the ANOSIM analysis with non-significant results returned for the total sample (R = -0.0589, P > 0.05), males only (R = -0.1090, P > 0.05), and females only (R = -0.1254, P > 0.05).

Table 4.6. Welch’s two sample t-test results comparing latitude (north vs. south) in the total sample: sample size (N), measurement mean, and standard error (SE) northern and southern regions for the total. All measurements except SVL are standardized. Total sample North South North vs. South

Variable (N) x̅ SE (N) x̅ SE t P SVL 17 146.88 6.93 31 142.01 4.04 0.6101 0.547 Tail 17 272.28 10.99 31 272.22 7.28 0.0728 0.942 TW 17 16.09 0.51 31 17.08 0.31 -0.4585 0.650 TH 17 22.20 0.56 31 22.54 0.39 -1.6313 0.114 FA 17 34.44 0.42 31 34.20 0.42 0.3714 0.712 LA 17 53.27 0.55 31 54.39 0.64 -1.1931 0.239 Leg 17 137.66 1.33 31 140.12 1.79 -1.0004 0.323 Lame 17 9.09 0.28 31 9.26 0.25 -0.5358 0.595 HL 17 50.47 0.89 31 50.91 0.52 -0.4042 0.689 HW 17 28.09 0.46 31 28.35 0.37 -0.3855 0.702 Weight 17 109.93 3.72 31 112.50 3.41 -0.4577 0.650

Morphology

Table 4.7. Welch’s two sample t-test results between the north and south within each sex: sample size (N), measurement mean, and standard error (SE) for northern and southern regions for each sex. All measurements except SVL are standardized. Males Females North South North vs. South North South North vs. South Variable (N) x̅ SE (N) x̅ SE t P (N) x̅ SE (N) x̅ SE t P SVL 8 155.50 8.76 15 151.55 4.94 0.3922 0.702 9 139.23 10.32 15 132.34 5.88 0.5801 0.572 Tail 8 258.73 21.92 15 282.96 7.20 -1.0505 0.322 9 284.32 6.62 15 259.66 12.64 1.7286 0.099 TW 8 16.08 0.65 15 17.49 0.44 -0.4367 0.670 9 16.10 0.82 15 16.67 0.46 -0.1375 0.893 TH 8 23.41 0.75 15 23.80 0.47 -1.8004 0.094 9 21.12 0.67 15 21.23 0.44 -0.6078 0.554 FA 8 34.73 0.67 15 34.64 0.63 0.0998 0.922 9 34.18 0.56 15 33.78 0.60 0.4781 0.638 LA 8 53.12 0.63 15 55.01 0.96 -1.638 0.116 9 53.41 0.91 15 53.58 0.87 -0.1301 0.898 Leg 8 137.32 1.92 15 140.69 2.86 -0.9801 0.338 9 137.97 1.93 15 139.18 2.42 -0.3906 0.700 Lame 8 8.85 0.38 15 9.60 0.40 -1.3755 0.185 9 9.30 0.40 15 8.98 0.33 0.6159 0.546 HL 8 52.67 1.02 15 52.17 0.62 0.4158 0.685 9 48.52 1.09 15 49.62 0.75 -0.824 0.423 HW 8 29.09 0.63 15 29.29 0.45 -0.2674 0.793 9 27.20 0.54 15 27.34 0.52 -0.1941 0.848 Weight 8 113.23 5.91 15 116.91 5.36 -0.4607 0.651 9 107.00 4.76 15 107.64 4.41 -0.0987 0.922

Morphology

The Mantel’s test examining evidence for a correlation between morphological distance (Mahalanobis distance) and geographic distance (km) was not significant (r = 0.2622, P > 0.05) (Figure 4.4). Males and females were not tested individually due to small sample sizes.

25 r = 0.2622 P > 0.05

10 Mahalanobisdistance (morphologicaldistance) 5

0 0 20 40 60 80 100 120 140 160 180 200 Geographical distance (km)

Figure 4.4. Mantel's test illustrating the correlation between morphological distance (Mahalanobis distance) and geographical distance. All measurements except SVL are standardized.

4.4.4 Bergmann’s, Allen’s and isolation rule: altitude-latitude When partitioning samples into lowland-north, lowland-south, upland-north, upland-south, the sample size bias becomes more evident (Table 4.8). The smaller sample sizes tended to have much larger standard errors.

The rank order NMDS plot based on the dissimilarity matrix calculated a fair stress value of 0.1343, which suggests the configuration is a fair fit between the ordination distance and the dissimilarities (Figure 4.5). Males and females overlapped, although they were significantly different [ANOSIM results: R value (R = 0.1364, P = 0.01)]. The NMDS was drawn to examine differences between altitudes which suggested that the upland region (blue)

Morphology was more variable than the lowland region (Figure 4.5b, c and d) and that males differed slightly more between upland and lowland regions than females. These altitudinal partitioning graphs were supported by the ANOSIM results. When the plots were re-drawn to explore differences between latitudes, northern and southern individuals overlapped considerably, although males appear to be more similar to each other than females, even though sample sizes were similar (Figure 4.5e, f and g).

Morphology

Table 4.8. Standardized morphological measurements averages for lowland-north, lowland-south, upland-north, upland-south: population name, sample size (N), measurement mean, and standard error (SE) of each morphological measurement. All measurements except SVL are standardized. Lowland-north Lowland-south Upland-north Upland-south Variable (N) x̅ SE (N) x̅ SE (N) x̅ SE (N) x̅ SE SVL 13 148.11 8.24 9 161.88 6.54 4 142.90 14.13 20 134.52 4.20 Tail 13 271.52 15.60 9 285.45 21.37 4 277.51 16.28 20 265.01 9.17 TW 13 16.26 0.87 9 18.99 0.85 4 16.06 1.32 20 16.07 0.35 TH 13 22.24 0.80 9 24.62 1.00 4 22.39 1.77 20 21.53 0.52 Fore 13 34.60 0.50 9 35.67 0.91 4 33.96 0.91 20 33.69 0.46 LA 13 53.47 0.64 9 54.70 0.98 4 52.62 1.09 20 54.46 0.88 Leg 13 138.48 1.50 9 141.45 3.36 4 135.00 3.34 20 140.07 2.35 Lame 13 8.90 0.35 9 9.52 0.63 4 9.76 0.39 20 9.26 0.30 HL 13 50.17 1.25 9 53.07 1.17 4 51.77 2.31 20 49.74 0.70 HW 13 27.90 0.62 9 29.88 0.85 4 28.84 1.16 20 27.76 0.41 Weight 13 102.96 3.64 9 112.53 3.87 4 100.40 6.88 20 117.97 4.65

Morphology

a) Total b) Total

c) Males d) Females

e) Total f) Males

Females g) h) Total

Figure 4.5. NMDS plot of standardized measurements for a) females and males; b) altitudes for the total sample; c) altitudes for males; d) altitudes for females; e) latitudes for the total sample; f) latitudes for males; g) latitudes for females; and h) altitude-latitude comparison for the total sample.Not including SVL or Tail measurements. 87

Morphology The SIMPER results suggest that either three or four morphological characteristics were most important and contributed to 70 - 78 % of the differences for the total data (Table 4.9). Three key factors contributed to 70 – 80 % of the differences between males and females. The results for the altitude-latitude comparisons identified comparisons between lowland-north vs. upland-south as having the highest percentage, whereas the lowest percentage was identified between lowland-south vs. upland-south. The third and fourth influential factor inter-changes between head length and lower arm.

Table 4.9. SIMPER results with partitioning between: males and females; altitudes (total sample, males only and females only); latitudes (total sample, males only and females only); the altitude- latitude combination with the highest percent contribution (lowland-north vs. upland-south for the total sample); the altitude-latitude combination with the lowest percent contribution (lowland-south vs. upland-south for the total sample). Contributing factors show the percent contribution. All measurements were standardized. Tail and SVL not included. Partitioning Contribution factors Males & females Weight Leg length Head length Lower arm (40 %) (20 %) (9 %) (7 %) Altitude: Weight Leg length Head length Lower arm Total sample (42 %) (22 %) (8 %) (7 %) Altitude: Weight Leg length Lower arm _ Males (44 %) (21 %) (7 %) Altitude: Weight Leg length Head length _ Females (42 %) (20 %) (8 %) Latitude: Weight Leg length Head length Lower arm Total sample (40 %) (21 %) (8 %) (7 %) Latitude: Weight Leg length Lower arm _ Males (42 %) (24 %) (7 %) Latitude: Weight Leg length Lower arm _ Females (48 %) (21 %) (11 %) Altitude-latitude (highest percent Weight Leg length Head length Lower arm contribution combination): (42 %) (20 %) (8 %) (8 %) Lowland-north vs. upland-south Total sample Altitude-latitude (lowest percent Weight Leg length Lower arm _ contribution combination): (41 %) (22 %) (7 %) Lowland-south vs. upland-south Total sample

To further examine if morphological patterns reflected a more complex altitude- latitude pattern, lowland-north, lowland-south, upland-north and upland-south

Morphology partitioning were further analysed. Due to the low power of these tests (from sample sizes), results were interpreted cautiously. The two-way ANOVA results examining the effects of altitude and latitude on morphological measurements only identified significant differences (P < 0.05) between means in the male only data (see Appendix F and G for non-significant results). The ANOVA examining the influence of altitude and latitude on male SVL identified a significant altitude effect with no influence from latitude (Table 4.10). This was supported by large difference in the boxplot between lowland (lowland-north and lowland-south) and upland regions (upland-north and upland-south) (Figure 4.6).

Table 4.10. ANOVA examining the effect of altitude and latitude on male SVL and identifies the degrees of freedom (Df), sample size (N), mean (x̅) sum of squares (SS) and mean sum of squares (MS). Males: SVL Df Partitioning (N) x̅ SS MS F-statistic P Altitude 1 Lowland 10 153.74 5472.7 5472.7 26.7659 < 0.001 Upland 13 135.27 Latitude 1 North 8 146.88 149.1 149.1 0.7294 0.404 South 15 142.01 Altitude x 1 Lowland-north 5 148.11 0.1 0.1 0.0005 0.982 Latitude Lowland-south 5 161.88 Upland-north 3 142.90 Upland-south 10 134.52 Error 1 23 3884.8 204.5 9

SVL (cm)

Lowland Lowland Upland Upland North South North South Figure 4.6. The mean male SVL for each altitude (lowland and upland) and latitude (north and south) combination.

Morphology There was a significant interaction between latitude and altitude for male TW (Table 4.11). This was because there was a significant difference between north and south in the lowland but not in the upland (Table 4.12 and Figure 4.7). Male HW also revealed a significant interaction between altitude and latitude (Table 4.13). This was again due to significant differences between northern and southern lowland samples but not upland (Table 4.14 and Figure 4.8).

Table 4.11. ANOVA examining the effect of altitude and latitude on male TW and identifies the degrees of freedom (Df), sample size (N), mean (x̅) sum of squares (SS) and mean sum of squares (MS). Males: TW Df Partitioning (N) x̅ SS MS F-statistic P Altitude 1 Lowland 10 16.79 0.02 0.0197 0.0083 0.928 Upland 13 16.68 Latitude 1 North 8 16.09 10.99 10.9902 4.647 < 0.05 South 15 17.08 Altitude x Latitude 1 Lowland-north 5 16.26 19.004 19.004 8.0354 < 0.02 Lowland-south 5 18.99 Upland-north 3 16.06 Upland-south 10 16.07 Error 19 23 44.935 2.365

Table 4.12. Tukey's HSD test on the male morphological variable TW identifying the difference between the means (Diff), lower (Lower) and upper (Upper) 95 % confidence interval of the mean, and the Tukey adjusted P value. Region Comparison of male TW Diff Lower Upper P Altitude Upland vs. Lowland 0.059 -1.295 1.413 0.928 Latitude South vs. North 1.393 -0.016 2.802 0.052 Altitude x Upland-north vs. Lowland-north 2.199 -0.959 5.357 0.238 Latitude Lowland-south vs. Lowland-north 3.422 0.687 6.157 < 0.02 Upland-south vs. Lowland-north 1.641 -0.727 4.010 0.242 Lowland-south vs. Upland-north 1.223 -1.935 4.381 0.700 Upland-south vs. Upland-north -0.557 -3.404 2.289 0.945 Upland-south vs. Lowland-south -1.781 -4.149 0.588 0.184

Morphology

TW (cm)

Lowland Lowland Upland Upland North South North South

Figure 4.7. The mean male TW for each altitude (lowland and upland) and latitude (north and south) combination.

Table 4.13. ANOVA examining the effect of altitude and latitude on male HW and identifies the degrees of freedom (Df), sample size (N), mean (x̅) sum of squares (SS) and mean sum of squares (MS). Males: HW Df Partitioning (N) x̅ SS MS F-statistic P Altitude 1 Lowland 10 28.44 3.789 3.7888 1.7359 0.203 Upland 13 28.11 Latitude 1 North 8 28.09 1.122 1.1221 0.5141 0.482 South 15 28.35 Altitude x Latitude 1 Lowland-north 5 27.90 18.411 18.4108 8.4352 < 0.01 Lowland-south 5 29.88 Upland-north 3 28.84 Upland-south 10 27.76 Error 19 23 41.47 2.1826

Table 4.14. Tukey's HSD test on the male morphological variable HW identifying the difference between the means (Diff), lower (Lower) and upper (Upper) 95 % confidence interval of the mean, and the Tukey adjusted P value. Region Comparison of male HW Diff Lower Upper P Altitude Upland vs. Lowland -0.819 -2.119 0.482 0.203 Latitude South vs. North 0.445 -0.909 1.799 0.500 Altitude x Upland-north vs. Lowland-north 1.558 -1.476 4.592 0.489 Latitude Lowland-south vs. Lowland-north 2.363 -0.264 4.991 0.087 Upland-south vs. Lowland-north 0.004 -2.271 2.280 1.000 Lowland-south vs. Upland-north 0.805 -2.229 3.839 0.877 Upland-south vs. Upland-north -1.554 -4.288 1.181 0.403 Upland-south vs. Lowland-south -2.359 -4.634 -0.084 < 0.05

Morphology

HW (cm) HW

Lowland Lowland Upland Upland North South North South

Figure 4.8. The mean HW for each altitude (lowland and upland) and latitude (north and south) combination.

The ANOSIM results for the total sample suggested similar findings with no region differing from any other (R = 0.0436, P > 0.05) [lowland-north (N = 13), lowland-south (N = 9), upland-north (N = 4) and upland-south (N = 20)].

4.4.5 Morphological & genetic comparisons The only significant correlation identified by the Mantel’s test were between morphological distance (Mahalanobis) and BZW1 genetic distance (FST and ΦST) (Table 4.15; Figure 4.9), although this marginal P value would not be significant if corrected for multiple testing. There were no other significant genetic distance and morphological distance correlations identified (Table 4.15).

Table 4.15. Correlation between genetic distance (FST and ΦST) and morphological distance (Mahalanobis distance) for the mtDNA and nDNA genes.

Mahalanobis vs. FST Mahalanobis vs. ΦST Gene r value P r value P ND4 0.2656 0.125 0.1615 0.201 PTGER4 0.3000 0.081 0.3872 0.071 MKL1 -0.1358 0.702 -0.1310 0.731 BZW1 0.5327 < 0.01 (0.01) 0.4429 < 0.05 (0.044)

Morphology 3 ND4 (FST)

2.5 ND4 (ΦST )

PTGER4 (FST) 2

PTGER4 (ΦST ) 1.5 MKL1 (FST)

1 MKL1 (ΦST )

Genetic distance (Slakin's) BZW1 (FST) 0.5 BZW1 (ΦST ) 0 0 5 10 15 20 25 Mahalanobis morphological distance

Figure 4.9. Identify the correlation between Mahalanobis morphological distance and Slatkin's genetic distant (ΦST and FST) for ND4 (blue), PTGER4 (green), MKL1 (pink) and BZW1 (black). The graph presents the line of best fit for ΦST (dashed line) and FST (solid line). Population comparisons did not include KOD or Eac due to small sample size.

4.5 Discussion

4.5.1 Males vs. females The findings suggest that H. boydii males are larger than females. Males had significantly taller tails (at the base of tail), longer and wider heads, and longer bodies. As the snout-vent measurement was not standardized, this cannot necessarily be interpreted as meaning that males are larger than females for the entire population but rather, of the individuals utilised in this study, the males were significantly longer than the females. The presence of sex dimorphism was also supported by the significant ANOSIM value which suggested that there were significant differences between males and females. The snout-vent measurement was not included in the ANOSIM analysis, so the head measurements and the tail height are sufficient to distinguish between males and females. Of the three measurements (head length, head width and tail height) only head length was identified in the SIMPER results as being an influential contributor (20 %) to the differences between males and females. Weight was identified as being the most influential character causing differences. This is not unexpected as SIMPER results can often identify the most variable character

Morphology (Warton et al. 2012). It is the large variation occurring in the weight measurements that is being identified in the SIMPER results. This may be an artefact of meal time coinciding with capture time, it may reflect the health of the individual (weight is often used to imply health), or females being gravid (Hahn and Tinkle 1965). Morphological characteristics for males contained consistently less variation than females.

There are various explanations for phenotypic variation in lizard tail morphology. The fact that H. boydii has a laterally compressed tail (tail height was larger than tail width) is in concordance with the findings from a study on two other dragon species (Chlamydosaurus kingii and Physignathus lesueurii) (Bedford and Christian 1996). As H. boydii has a laterally compressed body, tail height may be correlated to or an extension of snout-vent length. Alternatively, tail height has been positively associated with mate selection (“capture and retention of females”) in Taricha granulosa (Janzen and Brodie 1989) and habitat (e.g. semi-aquatic – arboreal lizards had laterally compressed ‘fin’ tails and lizards inhabiting scree slopes had course – spiny tails) (Bedford and Christian 1996).

A larger head and body bias for males is concordant with other dragons and agamid species (Smith et al. 2001, Stuart-Smith et al. 2007). In many male agamid species, a larger head has been suggested to favour factors such as fitness, dominance (Vitt et al. 1985), territory rank (Hews 1990), reproductive success (Anderson and Vitt 1990), and weaponry (Husak et al. 2006). Although the results found in this study are in support of such findings, not all agamid species have larger males (female bias is not uncommon) (Braña 1996, Zamudio 1998, Kratochvil and Frynta 2002), size does not always reflect combat victors (strong bite force was not necessarily correlated to head size), and head size might not be linked to fitness (it is not completely understood how a larger head size is an advantage) (Husak et al. 2006).

Another possible but unlikely reason for detecting sexual dimorphism would be due to a false positive male identification rate if smaller males were wrongly identified as 94

Morphology females. This may have arisen as smaller males have smaller and less enlarged hemipenes, which are more difficult to invert during the sex identification process.

The field sample sex-ratio was almost even, for the total and regional samples. This may suggest that both sexes are just as difficult to locate and that sex ratios in the wild populations may be even. This is in contrast to a prickly skink study from the upland region which found a significantly female biased sex-ratio in their sampling (Sumner et al. 1999). The low male sample sizes were explained by higher male mortality rates or males were more difficult to find.

4.5.2 Bergmann’s and Allen’s rule The study predicted that individual lizards would be larger in warmer lowland regions in support of the inverse of Bergmann’s rule (Mayr 1956). This was supported in the pooled sample and the male-only sample but not in the females. When examining the SVL lowland-upland result for each individual sex, males were significantly larger in the lowland than in the upland region. Again, this can only be interpreted to mean that longer individuals were sampled in the lowlands and not that the lowland individuals are necessarily longer. Whether this result reflects an actual phenotypic difference rather than just a sampling artefact is unknown. There was a tendency for more variation in morphological characteristics in upland than lowland samples. These results may suggest that either: 1) by chance older animals were sampled in lowland areas; or 2) individuals are larger in the lowland areas. This could be due to selection favouring larger size in warm areas (selection), or that they grow faster in warmer temperatures (phenotypic plasticity).

Similar patterns were found in Wet Tropics skinks (Carlia rubrigularis) with male-only samples suggesting phenotypic variation across the region and female-only samples suggesting no morphological variation. A similar dissimilarity finding was detected in Galápagos lava lizards (Tropidurus albemarlensis) with locomotive speed differing between regions for males but not females (Snell et al. 1988). 95

Morphology 4.5.3 Isolation rule There was more variation in the morphological variables in the northern sample than in the southern sample. However, the only significant difference detected between northern and southern samples (with and without museum samples) was in the altitude-latitude interaction effects (TW and HW). If multiple testing was accounted for using a statistical correction such as a Bonferroni adjustment, the result would no longer be significant which would suggest the result is due to chance from repetitive testing (Benjamini and Hochberg 1995). No significant isolation effects were detected. The lack of north-south differences was also supported by the non-significant Mantel’s test. This suggests that morphological dissimilarities did not increase with increasing geographic distance (dispersal distance) between the isolated northern and southern regions. The lack of evidence for morphological divergence, or even cryptic species either side of the BMC is in support of the majority of morphological studies in the region (Schneider et al. 1999, Singhal and Moritz 2013).

From the evidence of Chapter 3, it is suggested that the BMC has isolated the northern and southern populations of the dragons, but morphological analysis suggest that the only detectable influence on morphological traits is an altitude-latitude interaction effect on two characters. The cause of this is unlikely to be insufficient time since isolation for morphological differences to develop. Firstly, the genetic data, especially from the mitochondrial gene, identified restrictions to dispersal across the BMC with no secondary contact (secondary contact may have enabled any dilution of morphological north-south divergences). Secondly, the genetic divergence of the northern and southern populations was expected to have occurred 0.90 – 3.26 million years ago. Phenotypic changes tend to arise as the first response to environmental change (0 – 1000 years) (Millien et al. 2006). Genetic divergence has already occurred, suggesting that there has been sufficient time for latitudinal morphological differences to arise. Probably, the habitats of the two regions, despite being isolated, exert similar selection pressures thus resulting in no detectable morphological divergence. The molecular changes have probably arisen purely as a result of genetic drift in isolation. These results are in support of previous studies in the Wet Tropics which identified genetic divergence across the BMC but did not identify morphological divergence, e.g. 96

Morphology Nyctimystes dayi, Glaphyromorphus fuscicaudis, and Saltuarius cornutus (Moritz et al. 2009)

4.5.4 Interaction: Bergmann’s, Allen’s and isolation rule There was evidence for interaction between altitude and latitude on two male morphological traits. Tail widths were found to be significantly larger in lowland-south than lowland-north regions as well as head width was found to be larger in lowland- south regions than upland-south regions. The lowland-south region had a small sample size and from only one locality. Apart from sample size, no other explanation can be safely interpreted from the dataset as to what would create this significant interaction between altitude and latitude in order to cause lowland southern samples to be larger than other samples. If tail width and head width are not considered an extension of the body but rather an independent limb, then the results also supports Allen’s rule (tail and head have been classified as both) (Millien et al. 2006) and the prediction of larger limbs occurring in the warmer Wet Tropics lowlands. If the sample size was larger, the data would not have been analysed with categories pooled. This pooling could have biased conclusions as suggested by the ANOVA analysis which indicated significant interactions between latitude and altitude for some characteristics.

4.5.5 Morphology and genetics

The only significant morphological-genetic distance correlation was between morphological (Mahalanobis) distance and BZW1 gene. This gene is more likely identifying isolation effects rather than having anything to do with the morphological characteristics that were measured. It is suggested that a significant test is likely to be detected due to chance from multiple testing (e.g. if the repetitiveness of eight tests were corrected for using Bonferroni correction, the P-values would not be significant) (Benjamini and Hochberg 1995).

Morphology 4.5.6 Conclusion The study found no evidence of morphological divergence either side of the BMC (isolation rule), but did identify weak evidence for the inverse of Bergmann’s rule with larger animals being found in the lowlands than the uplands. These results were supported with evidence for Allen’s rule (if head and tail are classified as limbs and not an extension of body length) with larger “limbs” in lowland males than upland males. Both patterns could be explained by the various proposed explanations: enables rapid heat adsorption in uplands (which can aid such things as digestion); excess of latent heat enables faster growth rate in lowlands; increased competition in colder uplands causes smaller sizes; and helps maintain a preferred body temperature across its range (Arnold and Peterson 1989, Ashton et al. 2000, Ashton and Feldman 2003). Rapid heat absorption in upland individuals (small bodies have a greater surface to volume ratio) may be even more important for H. boydii, as it is an ectotherm that does not bask (usually) (Torr 1993).

It is not known if the larger size and larger ‘limbs’ in the lowland have arisen from natural selection or sexual selection. If natural selection and/or phenotypic plasticity are responsible, phenotypes are likely to be favoured by specific environmental conditions (both historical and current conditions). Sexual selection may be driving upland – lowland differences due to diurnal activity requirements of males in upland areas. Fast heat absorption in males may enable high rates of diurnal activities such as competing for mates, maintaining territories, and feeding (digesting) (Ashton et al. 2000, Ashton and Feldman 2003).

It may be assumed that if environmental conditions shift under future climate change, shifts will largely occur altitudinally as upland regions begin to express conditions currently occurring in lowlands. Populaitons would either need to endure in the new conditions, adapt their morphological characteristics to their new environment or disperse to maintain morphological-ecological correlations (Cook et al. 2008). This may see all dragons increase in size (increased temperatures may enable all regions to grow larger); upland dragons increase in size as upland temperatures increase; larger 98

Morphology lowland dragons dispersing into upland dragon habitat (in order to maintain morphological-ecological patterns); no change in dragon sizes (size differences may be caused by an untested variable and not temperature); or extinction events may occur if H. boydii cannot endure, adapt or disperse with climate changes. With the expected changes to the climate and forest structure (Phillips et al. 2002, Hughes 2011), and the significant sexual dimorphism detected within H. boydii, males and females may even responded to climate change differently (Hoye et al. 2009).

Distribution modelling Chapter 5 Modelling of past, present and future distributions of H. boydii

“It may be that dragons really did exist and they were so valuable they were simply hunted to extinction.” ~ Carole Fontaine

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Distribution modelling 5.1 Paleo and future distribution modelling

5.1.1 Historical climate change Historically, climates have not been static and have always experienced change (Reeves et al. 2013). Fossil records have shown that climatic changes in the Wet Tropics, especially through the Quaternary period, have had a large impact on the current distribution of organisms (e.g. effects of the Black Mountain Corridor) (Schneider et al. 1998, Hewitt 2000, Jansson and Dynesius 2002). Species have had to adapt to these historical changes in order to survive through to the current climate (Kershaw and van der Kaars 2012). Understanding fluctuation patterns of environmental variables is key to understanding how species have historically responded to changes. By including historical responses to these fluctuations, our understanding of how a species may respond in the future will be improved (Bystriakova et al. 2014).

Over the Quaternary period, Wet Tropics taxa have shown different habitat expansion and restriction responses that are largely dependent on a taxon’s niche (e.g. highland versus lowland) (Moussalli et al. 2009). Some studies have shown that during the cold- dry period of the last glacial maximum (LGM), approximately 22 kya (thousand years ago) (Reeves et al. 2013), as well as during warm-wet periods of the mid-Holocene (5- 3.6 kya) period, restrictions to suitable rainforest habitat were common for many species (Kershaw and Nix 1988, Nix 1991, Hilbert et al. 2007, Moussalli et al. 2009, Bell et al. 2010). These studies also showed that cold-wet periods (e.g. mid-Holocene, 7.6-6 kya) have allowed expansions of suitable habitat (Moussalli et al. 2009). During this epoch, such species would have experienced habitat expansions. In contrast, they would have undergone habitat restrictions during cool-dry periods such as those experienced in the LGM (Moussalli et al. 2009, Bell et al. 2010). Mousasalii et al. (2009) suggested this pattern is most expected for lowland specialists, whereas Bell et al. (2010) suggests this is much more consistent among rainforest specialists with an expansive altitudinal range.

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Distribution modelling It has been suggested that species prone to extinction during the warm-wet mid- Holocene and cool-wet LGM would be species that inhabit montane habitats that would have experienced high levels of isolation (Moussalli et al. 2009). A more recent study on Wet Tropics endemic rainforest trees showed the largest continuous distributions occurred during the LGM and showed a steady contraction of habitat during the mid-Holocene with a recent slight expansion until current day (Mellick et al. 2014).

5.1.2 Contemporary climate change In the last 50 years, the rate of warming has nearly doubled (0.13°C ± 0.03°C per decade) from that which occurred over the previous 100 years (IPCC 2007c). The temperature rise is largely attributed to the increase in greenhouse emissions, which, at 49 (± 4.5) GtCO2eq /yr, is the highest in human history (IPCC 2014a). Consequently, the Intergovernmental Panel on Climate Change (IPCC ) has proposed that hotspots, such as the Wet Tropics in far northern Australia, are already at risk of losing biodiversity by 2050 (Reisinger et al. 2014). The 5th IPCC report predicted both temperature and extreme daily precipitation increases for the region, although there is uncertainty in predicting the overall rainfall changes (Reisinger et al. 2014). Some sources have suggested that the Wet Tropics will experience an increase in precipitation, which is opposite to what is expected in subtropical regions of Australia (Scheff and Frierson 2012). It is predicted that without strategies to dramatically reduce greenhouse gas emissions, temperatures and the subsequent impacts will continue to rise (IPCC 2014a).

As temperatures warm, there is concern for habitat loss and fragmentation as either direct or indirect effects of climate change. Changes to habitat structure due to physiological constraints, urbanisation or restrictions to dispersal have already caused habitats to become unsuitable (Opdam and Wascher 2004). Moreover, the major problem for tropical species, and especially tropical endemic species, is that they are usually sensitive to climate fluctuations due to their narrow environmental tolerances. This often reduces their ability to disperse into alternative habitat (Janzen 1967, 102

Distribution modelling McCain 2009, Hermant et al. 2013). Within the Wet Tropics, the expected reduction of montane habitats (50 % reduction by 2020) is correlated to a predicted loss of species (Hilbert et al. 2001, Hennessy et al. 2007). These species losses will affect almost all ecosystems from those inhabiting the lowland semi-deciduous mesophyll vine forests to the simple microphyll vine-fern thicket in the high elevations (Nix 1991). Across Australia, there has already been an average increase of approximately 1oC (IPCC 2007b), with projected temperature increases of 1 to 5 oC to occur over the next 60 years (CSIRO 2007). With a 1oC increase in temperature, it is predicted that most regional endemic lizards will lose significant core habitat, with one endemic species losing all habitat. With an increase of 3.5oC, the number of endemic species in the Wet Tropics will be dramatically reduced to less than half, and be limited to a few remnant highland habitats (Williams et al. 2003b). Williams et al. (2003b) predicted that with an increase of 7oC, no endemic species will remain in the Wet Tropics.

5.1.3 Species Distribution Models By forecasting areas of future suitable habitat, predictions of future distributions of species are inferred. In order to examine the future suitable habitat of a species, it is necessary to know what environmental variables influence the current habitat and in what way these variables will most likely change in the future. This is achieved by using a species distribution model (SDM) (Elith and Leathwick 2009). A SDM identifies the relationship between a species occurrence record at a specific geographical location and the physical features (environmental or physical) of that location. By identifying this relationship, an estimation of the current distribution at each point in the landscape can be made (Elith et al. 2011). SDMs have been used to accurately identify current distributions as well as predicting future and historical distributions. This forecasting is with the assumption that a species niche remains constant throughout time (Cordellier and Pfenninger 2009).

5.1.4 Aim The aim of the work described in this chapter was to examine the past, present and future distributions of the Boyd’s Forest Dragon (Hypsilurus boydii) in order to examine 103

Distribution modelling how the species has responded and how it may respond in the future to changes to its environment.

5.2 Methods

5.2.1 MaxEnt modelling Types of SDMs differ largely by the style of species data they use. There is a growing trend (as a result of a growing need) to use presence-only models (Elith et al. 2006). Using only a species presence record is particularly important in tropical studies as absence records often do not exist and/or the species has not been exhaustively sampled across its entire suspected distribution (Phillips et al. 2006b). A presence-only method also allows for museum records (presence-only data) to be incorporated into the study data set (Elith et al. 2011). One such presence-only approach is MaxEnt (MaxEnt v3.3.3k) (Phillips et al. 2006b), which uses maximum entropy modelling. This machine learning approach allows the algorithm to calculate a species probability distribution based on the most influential variables from “all reasonable predictors” (Merow et al. 2013). MaxEnt has been shown to outperform other presence-only models (Dupin et al. 2011, Chetan et al. 2014) as well as being useful for studies with small sample sizes (Chetan et al. 2014), irregular sampling, and minor geographical location errors (Kramer-Schadt et al. 2013). For all these reasons, MaxEnt was chosen as the species distribution model for this study.

5.2.2 Species Occurrences The study used a total of 36 unique non-overlapping occurrence records obtained from both field sampling for this project and contemporary presence records (Atlas of living Australia). Only records that had a geographic coordinate uncertainty of less than 500m were included in the study. Occurrence records generated from this study represent a site at which H. boydii was found rather than the location of every individual within a site. This ensures there is no bias due to sampling effort, as the model assumes that sampling has been random across the landscape in order to

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Distribution modelling sample under a range of environmental conditions (Phillips et al. 2006b, Merow et al. 2013).

5.2.3 Niche modelling Twenty-one current day environmental variables (averaged from the years 1950–2000) with a resolution of 1 km (30 arc-seconds) were used in this study (Hijmans et al. 2005). They consisted of 19 bioclimatic variables, 12 hydrological variables, 24 temperature variables, a vegetation variable and an altitude variable. These variables were projected to the Geocentric Datum of Australia 1994 and were formatted so all layers expressed the same grid cell size, alignment and spatial boundaries within the Australian Wet Tropics Bioregion ArcMap (Esri ArcMap v.10.1). Using ENTtools (Warren et al. 2011), a Pearson correlation coefficient was conducted on the environmental variables to identify which variables were independent. Only environmental variables with pairwise values between r < 0.8 and r > -0.8 were deemed independent and used in further analysis. These variables were as follows: vegetation; altitude; BIO-1 (annual mean temperature); BIO-2 (mean diurnal range); BIO-3 (isothermality); BIO-4 (temperature seasonality); BIO-5 (maximum temperature of warmest month); BIO-6 (minimum temperature of coldest month); BIO-7 (temperature annual range); BIO-8 (mean temperature of wettest quarter); BIO-9 (mean temperature of driest quarter); and BIO-12 (annual precipitation).

To model current distribution predictions of H. boydii, the environmental variables along with the unique geographical localities of H. boydii were modelled using the maximum entropy algorithm in MaxEnt with all 12 environmental layers. A raw output was used as no post-processing assumptions are made on this output, making the data more sensitive (Merow et al. 2013). The regularisation parameter was set at 0.5 as this is recommended for smaller data sets. Random seed was used in order to force the model to use different optimizing environmental samples in each run (Phillips and Dudik 2008). Maximum iterations were set at 5000 in order to reduce the model over or under fitting the data (Young et al. 2011). The remaining settings were set using MaxEnt default. Response curves and jackknife measurements were also selected. 105

Distribution modelling Current distribution predictions were run with and without the vegetation layer to evaluate both models to see how vegetation is influencing the data. This is because there was no vegetation layer available for future scenario modelling. The models were initially run with a random test percentage of 25, which enables the model to retain 25 % of the data for testing the model and 75 % for training the model. The model was then run with 5-fold replicates to examine the variability within the small dataset.

To assess the accuracy of the model and its predictions, a true skill statistic (TSS) was calculated. This test does not have the reliance on prevalence that other performance measures have (e.g. kappa statistic) and has been shown to be more suitable and accurate for ecological studies (Allouche et al. 2006, Hodd et al. 2014). The TSS uses the model’s output background predictions, sample predictions and 10 percentile threshold to measure its performance (Allouche et al. 2006) by comparing the number of accurate forecasts (minus the forecasts that are a result of random guessing) with “a hypothetical set of perfect forecasts” (Allouche et al. 2006). The TSS value lies between 0 and 1, with 0 being poor and 1 being a perfect prediction (a value of 0.5 – 0.7 is classified as a good prediction) (Allouche et al. 2006, Silva et al. 2014).

A secondary accuracy test is the MaxEnt-produced area under the receiver operating characteristic (ROC) curve (the AUC value). The AUC evaluates the relationship between species occurrence records and the models distribution predictions (Phillips et al. 2006b). This is to measure how accurately the test was able to correctly discriminate between locations where H. boydii were present and absent (Hanley and McNeil 1982). A test that cannot distinguish between absence and presence any better than random chance is given an area value of 0.5. A test that has an area value < 0.5 is worse at distinguishing between absence and presence than would happen with random chance. Although there is some contention to the weight of this test (Lobo et al. 2008), AUC values > 0.70 are deemed useful for such studies. A test that has no false positives and no false negatives has an area value of 1 (Fan et al. 2006, Moussalli

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Distribution modelling et al. 2009). Standard deviations were also calculated as the curve is an average of the 5-fold replicates.

5.2.4 Climate scenarios Future projections of the 10 bioclimatic variables from the WorldClim 1.4 database were incorporated into the model for three climatic scenarios – Hadley Centre Global Environmental Earth-System Model Version 2 (HadGEM2-ES), Australian Community Climate and Earth System Simulator coupled model 1 (ACCESS1-0), and the Model for Interdisciplinary Research on Climate (MIROC-ESM) – in order to project future distributions of H. boydii. The first projected climatic scenario, HadGEM2-ES (Collins et al. 2011), was developed at the Met Office Hadley Centre and is featured in various climatic prediction studies (Li et al. 2014, Perez et al. 2014, York et al. 2014). This complex model is said to accurately represent future projections as it incorporates terrestrial ecosystems, ocean ecosystems, and tropospheric chemistry schemes as well as having the benefit of including biogeochemical feedbacks (Collins et al. 2011). The second climatic scenario, ACCESS1-0, is the result of a collaborative development between the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Bureau of Meteorology at the Centre for Australian Weather and Climate Research (Bi et al. 2013). ACCESS1-0 is a more advanced earth-systems scenario and has been specifically developed using Australian climatic data for use on Australia’s climate research (Bi et al. 2013, Smith et al. 2013). MIROC-ESM was the third scenario used in this study. It was developed by the University of Tokyo, Japan Agency for Marine-Earth Science and Technology and the National Institute for Environmental Studies to be a comprehensive advanced earth-systems scenario that contains individual aerosol, sea ice and land surface models (Watanabe et al. 2011). Again, this model has been shown to perform well when tested (Sueyoshi et al. 2013, Boysen et al. 2014).

Using three separate climatic scenarios reduces the uncertainty of climatic responses that coincide with projecting in a geographical area with higher unpredictability (northern Australia) as well as with studies that have small sample sizes (IPCC 2007b, 107

Distribution modelling Smith et al. 2013). These three climatic scenarios were developed and used in the IPCC 5th Assessment Report (IPCC 2007b) as well as contributing to the Coupled Model Inter-comparison Project (CMIP5) (Figure 5.1). All future climatic scenarios were conducted for the Representative Concentration Pathway baseline scenario (RCP8.5) for the year 2050 (average for 2041–2060) and 2070 (average for 2061–2080) (Hijmans et al. 2005). The RCP8.5 scenario is the “worst case” representative concentration pathway and is based on high greenhouse gas emissions that continue to rise throughout the 21st century (Riahi et al. 2011, IPCC 2014b). This pathway predicts that as greenhouse gases rise, temperatures are very likely to continue to rise and exceed a 2oC increase by the end of the 21st century (Figure 5.2) (IPCC 2007c). The RCP8.5 pathway is considered a “ business as usual” scenario (Riahi et al. 2011).

Figure 5.1. Climate scenario comparisons “(a) Equilibrium climate sensitivity (ECS) against the global mean surface temperature of CMIP5 models, both for the period 1961–1990 … (b) Equilibrium climate sensitivity against transient climate response (TCR)…” (Flato et al. 2013, p817).

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Figure 5.2. The IPCC simulated time series (1905-2100) of temperature rise for the RCP8.5 scenario when compared against the “best case scenario” RCP2.6. “...change in global annual mean surface temperature relative to 1986–2005… Time series of projections and a measure of uncertainty (shading) are shown for scenarios RCP2.6 (blue) and RCP8.5 (red). Black (grey shading) is the modelled historical evolution using historical reconstructed forcings. The mean and associated uncertainties averaged over 2081−2100 are given for all RCP scenarios as colored vertical bars. The numbers of CMIP5 models used to calculate the multi-model mean is indicated…“ (IPCC 2013, p21)

Historical data simulations were also conducted using the altitudinal layer and 10 bioclimatic layers in order to project the distribution of H. boydii during the mid- Holocene epoch, approximately 6,000 years ago (Phillips et al. 2006b) and the LGM, approximately 22,000 years ago. The associated historical environmental layers from WorldClim were only available for the HadGEM2-ES scenario (mid-Holocene) and the MIROC-ESM scenario (mid-Holocene and LGM) at a spatial resolution of 2.5 minutes (approximately 5km) (Hijmans et al. 2005). Due to the model constraints, sea level was held constant and land inundation or exposure was not included (e.g. sea level during the LGM was 125 m lower than current sea level height) (Yokoyama et al. 2001).

The layer processing and formatting of the MaxEnt output files were carried out in ArcMap (Esri). As MaxEnt predicts species occurrences from bioclimatic data, the information can be used to imply suitable or unsuitable habitats. Grids from the probability of occurrence distribution maps were classified as either suitable or unsuitable habitat. The distinction between suitable and unsuitable habitats was created by classifying the bottom 10 % of possible occurrences (E) as unsuitable (Pearson et al. 2007, Peterson et al. 2011). By eliminating E, any errors created in the

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Distribution modelling data collection, bias file, model and scenario are reduced. The unsuitable and suitable habitat maps were produced in ArcMap (Esri).

In order to report an approximate area (km2) of total suitable habitat, the 30-second resolution was converted to kilometres after statistical tests (0.93 x 0.93 = 0.86 km2 at the equator). Only a minor level of accuracy is lost on conversion as the Wet Tropics region is small and close to the equator (Hijmans et al. 2005).

5.3 Results

5.3.1 Current distribution The TSS value of the model with vegetation was 0.2924, which suggests the model performed poorly at predicting the current distribution of H. boydii. The distribution model without the vegetation layer performed much more strongly, with a TSS value of 0.5814, which is classified as a good model or a good degree of agreement to the distribution map (Monserud and Leemans 1992). The test also revealed an overall accuracy value of 0.8313, a sensitivity of 0.75, and a specificity of 0.8314. The sensitivity is the proportion of presence records that have been predicted correctly, whereas the specificity is the proportion of observed absences that have been correctly predicted. These variables are independent from each other (Allouche et al. 2006). All these results support the performance of the model.

The average ROC curve from the 5 fold-replicates produced an average AUC value of 0.742 with a standard deviation of 0.047. This value classifies the model as a useful test at discriminating between species-present locations and species-absent locations for H. boydii (Hanley and McNeil 1982). Although this is classified as a good AUC value, there is dispute in the literature of the relevance of standalone AUC values (Lobo et al. 2008, Elith et al. 2010, Warren and Seifert 2011).

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Distribution modelling An analysis of variable contributions was also calculated by MaxEnt (Table 5.1), which identified the average contribution of each environmental variable to the current distribution of H. boydii. The permutation importance is the contribution of each variable determined by the level of decrease created in the training AUC when randomly permuting the variables training points. When a large decrease occurs, it indicates that the model relies heavily on a variable (Phillips et al. 2006b). BIO-9 (mean temperature at driest quarter) made the largest contribution and BIO-12 (annual precipitation) had the largest permutation importance. As the permutation importance is calculated from the overall model (as opposed to the percent contribution, which is calculated from each run), it is therefore a more important statistic (Songer et al. 2012).

Table 5.1. Analysis of variable contributions to the current distribution of H. boydii Variable Variable Percent Permutation I.D. contribution importance BIO-9 mean temperature at driest quarter 42.4 18.3 BIO-5 maximum temperature of warmest month 17.4 3.4 BIO-12 annual precipitation 14.4 34.7 BIO-4 temperature seasonality 10.4 10.2 alt altitude 4.6 13.9 BIO-6 min temperature of coldest month 4.3 5.3 BIO-1 annual mean temperature 3.3 3 BIO-3 isothermality 1.2 2.1 BIO-2 mean diurnal range 1.1 5.3

The jackknife test of variable importance on the training data (Figure 5.3) identified BIO-4 (temperature seasonality) as the variable that seems to singularly have the most information as it has the highest isolated gain. BIO-9 (mean temperature of driest quarter) has the least shared information with other variables as it reduces the gain the most when omitted.

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Jackknife of regularised training gain for H. boydii

Environmental variables Environmental

Test gain

Figure 5.3. Jackknife of regularized training gain for H. boydii

The jackknife test of variable importance on the test data (Figure 5.4) showed a slight change as it identified BIO-5 (maximum temperature of warmest month) as the variable that by itself has the most information as it increases the gain the most. BIO-9 again identified as having the most information that does not occur in the other

variables as it decreases the gain the most when omitted from the run.

Environmentalvariables

Test gain

Figure 5.4. Jackknife of regularized training gain for H. boydii

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Distribution modelling The marginal response curves (Figure 5.5) produced by MaxEnt illustrate how the species distribution prediction is influenced by each variable. Each curve shows the response when a single variable is manipulated and all other variables are kept constant at their average value (Phillips et al. 2006b). The BIO4 variable response curve (temperature seasonality) suggests that predicted probability of suitable conditions increases with the more extreme temperatures. The BIO5 variable response curve (maximum temperature of warmest month) suggests predicted suitability is loosely positively correlated with warmer temperatures. The response curve for the BIO9 variable (mean temperature of wettest quarter) suggests that predicted suitability is negatively correlated with mean temperature (wettest quarter). As seasonality would include temperature data that would be included in “maximum temperature of warmest month” and “mean temperature of wettest quarter”, the curves may not be completely independent.

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Raw output Raw

Raw output Raw Raw output Raw

Altitude (alt) annual mean temperature (BIO-1) Mean diurnal range (BIO-2)

Raw output Raw

Raw output Raw Raw output Raw

Isothermality (BIO-3) Temperature seasonality (BIO-4) Maximum temperature of warmest month (BIO-5) Response of H. boydii to isothermality Response of H. boydii to annual mean temperature

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Raw output Raw

Raw output Raw Raw output Raw

Minimum temperature of coldest month (BIO-6) Temperature Annual Range (BIO-7) Mean Temperature of Wettest Quarter (BIO-8)

Raw output Raw Raw output Raw

Mean Temperature of Driest Quarter (BIO-9) Annual Precipitation (BIO-12) Figure 5.5. Response curves for each environmental variable. The red response curve is the mean over the 5-fold replicates and the blue response curve is the response ± one standard deviation (Phillips et al. 2006b). Predicted probability of suitable conditions is on the y-axis and the environmental variables measure on the x-axis. MaxEnt exponent is pairwise linear.

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Distribution modelling The distribution map produced by MaxEnt identifies areas of high probability of H. boydii occurrence (dark green) to areas of low probability of H. boydii occurrence (maroon). The small raw relative probability values are because the data are calculated from the probability of H. boydii being present (0 – 1) in each cell, with the sum of the training pixels adding to 1 (Phillips et al. 2006b). For each time projection, a point-wise average, minimum and maximum distribution probability map of the model was created in order to illustrate the variation in the model – a result most likely due to the small sample size. The current probability distribution maps (Figure 5.6) identified the highest probability to the higher altitude and mid latitude region of the Wet Tropics. Using the 10 % E threshold, the average current distribution has an unsuitable habitat area of 13,344 km2 and a suitable habitat area of 18,617 km2.

Current distribution

Minimum distribution Average distribution Maximum distribution

Figure 5.6. The minimum, average and maximum projected current distribution of H. boydii within the boundary of the Wet Tropics bioregion. Dark green is high habitat suitability and maroon is very poor suitability.

5.3.2 Paleo and future distribution The HadGEM2-ES distribution maps of the mid-Holocene epoch all identified two heavy probability areas (Mt Lewis region and the central tableland region (Figure 5.7)). The average unsuitable habitat and suitable habitat count for this epoch was

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Distribution modelling calculated at 30,734 km2 and 1,226 km2 respectively. Although the minimum output map showed a large high probability area (dark green), the maximum output had a larger suitable total area due to the larger amount of lower probability area (light green). The projected future distributions in 2050 showed a reduced version of the current distribution for the 2050 distribution maps. The average unsuitable habitat, 28,104 km2, had increased and was much larger than the projected suitable habitat, 3,654 km2. Projecting to 2070, the probability distributions’ maps were difficult to interpret. The average unsuitable habitat, 29,385 km2, and suitable habitat, 2,531 km2, did show the same decreasing trend in suitable habitat.

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Mid-Holocene 2050 2070

Minimum distribution Minimum

Average distribution

Maximumdistribution

Figure 5.7. Minimum, average and maximum HadGEM2-ES projected distribution of H. boydii during the mid- Holocene (approximately 6,000 years ago), in 2050 and in 2070. Dark green is high habitat suitability and maroon is very poor suitability.

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The ACCESS-1.0 scenario was the only scenario without historical projections. The distribution maps for 2050 (Figure 5.8) showed a similar probability to the previous scenario. The ACCESS-1.0 scenario also projected a large increase in the average unsuitable habitat of 27,863 km2 and a subsequent decrease in suitable habitat, at 4,050 km2. For 2070, the ACCESS1.0 scenario has reduced the occurrence probability in the northern Wet Tropics (Figure 5.8). Interestingly, the unsuitable habitat, 27,224 km2, has decreased slightly, with the suitable habitat, 4,591 km2, increasing since 2050.

The MIROC-ESM projection for the LGM illustrated a widely spread projected distribution (Figure 5.9). This was evident with the low unsuitable habitat at 12,173 km2 and the high suitable habitat at 19,786 km2. During the mid-Holocene era, the MIROC-ESM scenario projected the average unsuitable habitat of 22,775 km2 to increase and suitable habitat of 9,185 km2 to decrease. This was illustrated in the probability distribution maps.

When projected into the future for 2050, the MIROC-ESM distributions (Figure 5.9) were similar to the previous models. The average unsuitable habitat, 23,925 km2, and suitable habitat, 6,911 km2, supported this. For 2070, the MIROC-ESM scenario projected the average unsuitable habitat of 26,053 km2 to be further increased and the suitable habitat of 5,863 km2 to be decreased. This pattern was illustrated with the projected distribution maps. Of all the projections, the MIROC- ESM distribution map for 2070 suggests the most southern extension of H. boydii.

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2050 2070

Minimum distribution Minimum

Average distribution Average

Maximum distribution Maximum

Figure 5.8. Minimum, average and maximum ACCESS1.0 projected distribution of H. boydii in 2050 and in 2070. Dark green is high habitat suitability and maroon is very poor suitability.

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LGM Mid-Holocene 2050 2070

Minimum distribution Minimum

Average distribution

Maximumdistribution

Figure 5.9. Minimum, average and maximum MIROC-ESM projected distribution of H. boydii during the last glacial maximum (approximately 22,000 years ago), mid-Holocene (approximately 6,000 years ago), and for 2050 and 2070. Dark green is high habitat suitability and maroon is very poor suitability.

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The expected unsuitable and suitable habitats for all scenarios across all time periods (Table 5.2) identified a trend of decreasing available habitat in the future.

Table 5.2. Summary of unsuitable and suitable habitat (km2) within the Wet Tropics calculated from projected distribution maps for all climate scenarios. Climate scenario LGM mid-Holocene Current 2050 2070 HadGEM2-ES Unsuitable habitat - 30,734 13,344 28,104 29,385 Suitable habitat - 1,226 18,617 3,654 2,531 ACCESS-1.0 Unsuitable habitat - - 13,344 27,863 27,224 Suitable habitat - - 18,617 4,050 4,591 MIROC-ESM Unsuitable habitat 12,173 22,775 13,344 23,925 26,053 Suitable habitat 19,786 9,185 18,617 6,911 5,863

5.4 Discussion

This chapter expands on previous paleodistribution and future climate change work for the Wet Tropics. By examining the past, present and future distributions of H. boydii, I was able to examine how the species may have responded to historical climatic changes as well as how it is most likely to respond to continuing climate change in the future. The historical distribution results showed a distribution history for H. boydii, starting from 22,000 kya as a species that inhabited most of the Wet Tropics, which then experienced severe contractions during the mid-Holocene era, and has since reinhabited much of the region. Continuing climate change is predicted to result in considerable reduction of suitable habitat of H. boydii, with future populations restricted to refugia largely in the central highlands. The effect of habitat fragmentation or other physiological limits on dispersal have not been taken into account in this chapter.

5.4.1 Current distribution The current distribution map of H. boydii identifies a large continuous suitable habitat from the northern extent to the central region, with a disconnection to the southern Wet Tropics habitat. What is most surprising is the suggested connectivity between the north and central

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Distribution modelling regions across the BMC. Although it has been a suggested historical barrier between north- central dispersal, it is also thought to be a current barrier (Bell et al. 2010). The probability map also projected suitable habitat in low-, mid- and high-elevation regions, such as on Hinchinbrook Island (Figure 5.6). The current distribution map was similar to Williams’ map (2006). Visually comparing the two projections, my model suggested a larger area of suitable habitat than Williams (2006). Williams’ (2006) scenarios used increases in temperature to predict distributions (details in Williams et al. 2003b) as opposed to the comprehensive earth systems scenario used in this study. Another cause for the difference in distribution maps may be due to over-fitting of my model. Although MaxEnt has been shown to deal with this issue well, over-fitting can occur for small sample sizes (Elith et al. 2011). If this has occurred, it would mean that the model has over-predicted suitable habitat for all time periods; rather, this would mean that future (2050 and 2070) distributions would be even smaller than reported here.

5.4.2 Past distribution Identifying the predicted historical distribution of H. boydii up to and including the LGM is significant because it illustrates environmental and biological patterns during what has been suggested as one of the most important climatic progressions in the past 100,000 years (Reeves et al. 2013). Although there was only one species distribution map for the period of the LGM, it suggested the region supported a much larger suitable habitat than any other time period. This expanded habitat pattern agrees with studies on a midland species (altitudinal distribution 750-250 m above sea level) (Mellick et al. 2014) and a montane rainforest species (altitudinal distribution 1000 – 1600 m) (Bell et al. 2010) for this period. It is however, in contrast to other Wet Tropics taxa studies that have shown the LGM to be a period of habitat restriction with the mid-Holocene as a period of habitat expansion. The latter patterns were revealed for both highland species ( > 800 m and > 600m) (Moussalli et al. 2009, Mellick et al. 2014), a low to midland species (< 500m) (Moussalli et al. 2009) and altitudinal generalists (~50 – 1300 m) (Moussalli et al. 2009, Bell et al. 2010). The model suggests a continuous connected habitat from the northern Wet Tropics to the southern Wet Tropics with the BMC not in effect.

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The subsequent interglacial warming that occurred in the mid-Holocene era suggested a habitat contraction for H. boydii. This agrees with the mid-altitude endemic tree distribution pattern of Mellick et al. (2014) but is not consistent with the mid-Holocene pattern suggested by Bell at al. (2010). Although there was a large difference between the two mid- Holocene scenarios maps for H. boydii, both scenarios showed a large habitat reduction from the LGM, which does support the overall findings. The isolated altitudinal refugia are most prominent in the HadGEM2-ES scenario with the scenario having a slightly higher ‘lower average’ temperature range (HadGEM2-ES 18.5-25oC and MIROC-ESM 17.9-25.2 oC). This may indicate that temperature is a strongly influencing predictor in the HadGEM2-ES scenario as cool temperature occurrences correspond to the scenarios projected distribution. During this period, the Black Mountain corridor would have had the largest effect on distribution, with north-central habitats being completely isolated in one scenario and extremely restricted in the other.

The LGM and mid-Holocene dispersal patterns detected may have differed from previous studies for three main reasons: 1) recent bioclimatic models have different calculating methods and abilities; 2) uncertainty surrounding distribution models; and 3) H. boydii physiological requirements.

The development of niche models is continuingly evolving (e.g. transition from emission SRES scenarios to earth-system scenarios) (IPCC 2007c, Collins et al. 2013) and the availability of environmental variables (climatic layers) is increasing (Hijmans et al. 2005). At the time of this study, there were few published studies on Wet Tropics taxa using the newly updated CMIP5 earth-system models with the WorldClim variables (Hijmans et al. 2005). For this reason, directly comparing model outputs that have used different approaches, scenarios and environmental data sets may not be very meaningful. For example, Hijmans et al. (2005) used a “thin-plate smoothing spline algorithm” on the raw environmental data, whereas Hilbert et al. (2007) used a back-propagation learning algorithm (Hilbert and Van Den Muyzenberg 1999) and both studies suggested differing paleo-distributions.

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As paleo and climatic change modelling extrapolate from current conditions, each step of the projecting process contains a level of uncertainty (Laurance et al. 2011). There is uncertainty in the compilation of the environmental raw data as it is averaged over 50 years, the unavailability of records or actual weather stations and the scale at which raw data are projected (Hijmans et al. 2005, Cook et al. 2010). The accuracy of the paleoclimate estimates and extrapolations themselves (Wolfe 1995), the selection of environmental variables for the model as well as the “modelling procedures” (Elith et al. 2006) all carry uncertainty. There is uncertainty in the interference that physiological and dispersal capabilities will have on the projected distribution and that they stay constant over time (Swenson 2008). However, these uncertainties are largely mitigated by the models and by using multiple scenarios.

Similar or dissimilar paleo-distribution models may simply be the result of similarities in physiological requirements of the study species (e.g. H. boydii and the midland species in Mellick et al. (2014)). Unlike many endemic species that have been modelled in the Wet Tropics (Bowyer et al. 2002, Hugall et al. 2002), H. boydii occurs throughout a large altitudinal gradient. Physiological features might not be restricted by cool-dry (LGM) or warm-wet periods (5 – 3.6 kya) but rather experience some biophysical constraint during the cold-wet mid-Holocene period (7.5 – 6 kya).

5.4.3 Future distribution In the future, it is expected that the distribution of H. boydii will be significantly reduced. This is in line with numerous other climatic studies on future distributions on Wet Tropics endemic species (Williams et al. 2003b, Williams 2006, Isaac et al. 2009). The current suitable habitat is predicted to be reduced to less than half or even one-fifth in just 35 years (2050), with future estimates ranging between approximately 6,911 km2 and 3,654 km2. The north, central and southern Wet Tropics habitats are now isolated and extremely fragmented. In 55 years (2070), future scenarios predict less than ~15-30 % (2,531 – 5,863 km2) of the current day distribution will remain. Both ACCESS-1.0 and HadGEM2-ES predict that no habitat will remain in the south, with very little habitat remaining in the north. The

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MIROC-ESM suggests suitable habitat persisting in the southern Wet Tropics with the distribution expanding past the current southern boundary. Although there are differences in the statistics, they all show a significant and alarming reduction in suitable habitat for what could be regarded as a Wet Tropics endemic generalist.

5.4.4 Conclusion In conclusion, although the Boyd’s Forest Dragon has been resilient to historical climate change, future distributions are not predicted to be as resilient to change. The ~54 % reduction in suitable habitat that occurred over a 16 kya time span between the LGM and mid-Holocene era is less than the projected loss over the next 35 years (57 – 80 %) and over the next 55 years (68 –86 %). With such large changes to the distribution of H. boydii in such a short amount of time, there may be further flow-on effects to community composition and even ecosystem function (Wardle et al. 2011).

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Chapter 6 General discussion

What are you going to do about your dragon?

~ Harry Potter and the Goblet of Fire, J. K. Rowling (2000)

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6.1 Overall chapter summaries

The phylogeographic analysis suggests H. boydii is comprised of two main genetic groups with both private and shared haplotypes. Although there were differences between mtDNA and nDNA genes, haplotypes were mainly geographically restricted to either north or south of the BMC. The morphological analysis, however, suggested slight phenotypic differences between upland and lowland populations but no discernible differences across the BMC. The species distribution models suggest these latitudinal barriers may not have always been present and future modelling suggests that this barrier will become more pronounced. Altitudinal barriers may have always been present but it is unknown how contractions of lowland habitat would have and/or will affect morphological patterns.

6.2 The role of latitude on the genetic makeup, morphology and distribution of H. boydii

The climate and geography of the Wet Tropics has, at differing time periods, enabled both dispersal throughout the region and created barriers to dispersal. The mtDNA and nuclear data together suggest that the species was probably more widely distributed historically but, due to dry periods during the Pleistocene, the BMC has acted as a barrier, which is more obvious in the mtDNA because of its faster evolutionary rate.

6.2.1 Evidence for latitudinal barrier causing isolation and genetic divergence Species distribution modelling examined abiotic factors that may be influencing phylogeographic patterns (Kidd and Ritchie 2006). In this study, species distribution modelling revealed that the geographical limits of the two genetic groups (i.e. the northern and southern haplotypes) appear to coincide with a break in current suitable habitat (BMC). This supports the suggestion that there is no or limited gene flow between northern and southern regions (Rissler and Apodaca 2007).

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The species distribution modelling and genetic analyses suggest that climatic conditions have not always maintained this barrier. Two nuclear genes suggested gene flow occurred across the BMC during an early time period. This suggests the northern and southern populations were not always isolated, with isolation and the following divergence occurring 0.90 – 3.26 million years ago (as detected in the mtDNA). The modelling identified a much later barrier fluctuation with suitable habitat occurring in the BMC 22,000 years ago but not 6,000 years ago. Results from previous studies in the region support the evidence of a fluctuating barrier. Schneider et al. (1998), identified a fluctuating barrier by detecting certain taxa had experienced more contemporary dispersal, thus suggesting secondary contact during a cooler wetter period (e.g. Carphodactylus laevis – chameleon gecko and Litoria nannotis – waterfall frog).

It has been found that as the difference between divergent but morphologically similar groups increases, so does their chance of reproductive isolation (Singhal and Moritz 2013). This suggests that species containing divergent populations are on a spectrum of species divergence. It is possible H. boydii has persisted as two separate groups for long enough for reproductive isolation to occur, although this was not tested in this study. Based on mtDNA divergence the northern and southern groups have been isolated for 0.90 – 3.26 million years. This time has been sufficient for the development of reproductive isolation in other groups (e.g. BMC: dung beetle Temnoplectron speciation events 1.7 – 2.7 million; tropics globally: ring tail possums Pseudocheirus 2.06 – 12.65 and tanagers Rhamphocelus 0.8 – 4.16) (Moritz et al. 2000, Bell et al. 2007). It is important to note that the dung beetle has a different generation time to H. boydii, therefore speciation would be much faster.

However, it is possible that the fluctuating barrier effect of the BMC has inhibited reproductive isolation between northern and southern populations (Avise 2000, McGuire et al. 2007). The lack of strong divergence within H. boydii (suggested by the nDNA and morphology), could be attributed to the absence of long-term stability, and supports previous studies (Singhal and Moritz 2013). Singhal and Moritz (2013) suggested that the southern Wet Tropics is an unstable system in which it is expected that cryptic lineages

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General discussion rather than cryptic species, are more likely found. Singhal and Moritz (2013) further stated that “broad-range introgression and discordance are exceedingly rare” in the Wet Tropics. This may suggest that populations either side of the BMC are large enough to not incur rapid effects from genetic drift.

These conclusions are based on two important assumptions: 1) the predicted species distribution represents a true representation of the species niche which is viable for a population (Graham et al. 2004, Rissler and Apodaca 2007); and 2) that the niche requirements of H. boydii 0.90 – 3.26 million years ago did not differ significantly from current day occurrence-environmental correlations (Pearson and Dawson 2003).

6.2.2 Discordance between phylogeography, morphology and species modelling Since divergence occurred approximately 0.90 – 3.26 million years ago, the BMC was found to be a continuous barrier to dispersal despite evidence in the distribution models that suggested potential for latitudinal dispersal approximately 22,000 thousand years ago. As there was no phylogeographic evidence for secondary contact of northern and southern populations, these differences may be explained by: models over predicting suitable habitat; modelling not occurring at a fine enough scale to visibly identify unsuitable habitat; genetic sampling was inadequate and shared haplotypes were not sampled; shared haplotypes have gone extinct; or dispersal across the landscape is slow (Gonzalez et al. 2011). Over predicting from models has been suggested to be an issue for both endangered and rare species (Preston et al. 2008). It is unlikely that the results are the consequence of inadequate sampling (refer to 3.3.2), although slow colonizing dispersal rate may be reflected in the high levels of structure found in most populations. Studies such as Bell et al. (2010) supported this study’s findings of discordance between phylogeographic evidence and opportunities for dispersal (specialist skink, Lampropholis robertsi). L. robertsi was however found to morphologically differ between the northern and southern regions unlike H. boydii.

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Discordance was also found between the model predictions of extensive habitat during the LGM followed by contractions during the mid-Holocene and the lack of recent demographic event signatures in the genetic analyses. This may suggest either the demographic events were too recent to be detected in the genetic data or the demographic events did not drastically affect the genetic data. This may mean that dispersal within the northern and southern region during periods of habitat contraction is strong enough to maintain gene flow so that processes such as bottlenecks do not occur. It may also reflect a higher rate of dispersal, i.e. a strong ability to disperse. However, this explanation does not support the high levels of divergence between most populations detected. Another explanation is that dispersal into the BMC did occur (Schneider et al. 1998), but individuals within the BMC may have become isolated and gone extinct during mid-Holocene contractions (in support of secondary contact in other reptiles). Hoskin et al. (2011) suggested it is likely north-south hybrid populations did occur in the contact zone, but would have gone extinct each time a habitat contraction event occurred before introgression (backcrossing of the linage hybrids to create gene flow between the north-south linages) could have been achieved.

A further explanation for the apparent contradictions between the genetic analysis and the species distribution models may be due to H. boydii not actually inhabiting the entire projected suitable habitat (Hutchinson 1957). As well as this, H. boydii’s interaction with other species has not been taken into account (e.g. prey insect assemblages) (Hutchinson 1957, Araújo and Guisan 2006).

6.2.3 Morphological differences did not support genetic and distribution breaks Despite the indication from the genetic analyses and species distribution models, there appears to have been no morphological divergence across the BMC. As proposed in Chapter 4, this suggests that selective pressures are similar across the region which has not enabled cryptic morphological diversity to develop.

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6.3 The role of altitude on the genetic makeup, morphology and distribution of H. boydii

Despite the wide range of altitudes in which the species was found, there were only small morphological differences between upland and lowland and no genetic differences.

6.4 Dragons in the future

To reduce the effects of rapid climate change on the Wet Tropics biota (e.g. increase of stress, decrease in fitness, reduced reproductive fitness or extinction events), most species will either endure, shift distributions to maintain current environmental tolerances and/or adapt to new environments (Meehl et al. 2007, Nelson et al. 2007, Kearney et al. 2009, Chen et al. 2011, Dubey and Shine 2011). The modelling has suggested that suitable dragon habitat will shift in the future, as conditions change. Whether populations either side of the BMC can persist in spite of these shifts, as they have in previous warming events, is unknown. With severe habitat isolation between northern and southern populations projected in the future, latitudinal regions would need to maintain genetic diversity in order to have the greatest chance of resilience to climate change. This may not be possible as H. boydii may already be showing signs of dispersal limitations.

It is expected that lowland tropical populations may be more susceptible to warming events (Laurance et al. 2011) and even though the modelling identifies dispersal avenues, the model does not take into account anthropogenic barriers nor whether suitable vegetation and prey will also be sustained. A large assumption of the modelling, and thus the extrapolation of the response of the species in the future, is that H. boydii populations respond in a similar linear way to current patterns (Stenseth and Mysterud 2002, Chamaille- Jammes et al. 2006). If the populations respond differently to future climatic changes, then the modelling predictions for 2050 and 2070 will be inaccurate (Stenseth and Mysterud 2002).

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6.4.1 Management When conserving a species, prioritising which populations warrant conservation status is often required, as efforts to conserve every population throughout its range is rarely feasible. As not all populations are deemed evolutionarily equal, it has been suggested that conservation priority should be given to evolutionarily significant populations (Evolutionarily Significant Unit) whose genetic attributes are and will be significant in present and future generations (Ryder 1986). Although the definition of what determines an Evolutionarily Significant Unit (ESU) is controversial, ESU status is usually given to historically isolated populations whose nDNA is significantly divergent and whose mtDNA is reciprocally monophyletic (originating from a common ancestor) (Ryder 1986, Moritz 1994b, Palsboll et al. 2007), as is the case for the populations of H. boydii north and south of the BMC.

ESUs in the Wet Tropics should reflect the present genetic lineages as well as be able to sustain the identified evolutionary process in the region (Moritz 2002). Long term management involves managing levels of genetic diversity within H. boydii (Lande 1988). The BMC has also been found to separate ESUs of endemic skinks and bird species on either side of the divide (Moritz 1994b, 2002). Moritz (1994a) suggested that the conservation status either side of the BMC is “obvious” as conservation priority should be given to regions with high numbers of ESUs. Clearly the groups of populations north and south should be given ESU status.

The findings of this study suggest that conservation strategies and management plans should aim to treat dragons in the northern and southern region of the Wet Tropics as separate ESUs. If these regions included an area with an altitudinal gradient, this should enable dispersal of lowland individuals in order to maintain current conditions. However, anthropogenic barriers have not been accounted for and the availability of habitat does not ensure dispersal into the habitat – especially if dispersal ability is low (Hughes 2007).

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6.5 Further work

The future study of H. boydii would benefit from obtaining samples from the far southern region of the Wet Tropics which would include the areas around Paluma National Park (146.152842E, -18.962547S) and Mission Beach (145.080853E, -17.910828S). The rainforest environment leading south into the Paluma area is narrow and often discontinuous, which may lead to high levels of unique diversity in H. boydii populations (Hilbert et al. 2007).

In order to examine the two major genetic lineages of H. boydii that inhabit the northern and southern regions, species distribution modelling or rather lineage modelling (modelling of genetic groups within a species) should be conducted independently on the northern and southern genetic groups, as well as lowland and upland samples (Rissler et al. 2006). By modelling the niche requirements of the groups independently, specific niche requirements of altitudes and latitudes can be tested and accounted for. It may determine if it is the unsuitable habitat of the BMC that is isolating these two groups or if other niche requirements have developed as a by-product from the BMC isolation affect. Identifying niche differences between lowland and upland population would aid in explaining morphological differences. To test these niche differences, a much greater species occurrence data set would be required.

6.6 Conclusion

Climate and geographical features have largely influenced the genetic makeup and species distribution of H. boydii in a similar way with a fluctuating latitudinal barrier throughout history. Variation in dragon morphology however, has not responded and has been influenced by altitude. There has been little morphological variation across the barrier, with instead slight differentiation between lowland and upland populations.

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Appendices

Appendix A. H. boydii ND4 gene Analysis of Molecular Variance results between lowland and upland Wet Tropics altitudinal regions based on haplotype frequencies. % Φ- % P F-statistic P variation statistic variation

Among altitudinal 17.10 0.171 > 0.05 7.65 0.076 > 0.05 regions Among populations within 67.01 0.808 < 0.001 43.71 0.473 < 0.001 regions Within 15.89 0.841 < 0.001 48.65 0.514 < 0.001 populations

Appendix B. H. boydii PTGER4 gene Analysis of Molecular Variance results between lowland and upland Wet Tropics altitudinal regions based on haplotype frequencies. % Φ- % P F-statistic P variation statistic variation

Among altitudes 2.20 0.022 > 0.05 1.75 0.017 > 0.05

Among populations within 7.89 0.081 < 0.01 2.19 0.022 > 0.05 altitudes Within 89.91 0.101 < 0.001 96.06 0.039 > 0.05 populations

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Appendix C. H. boydii MKL1 gene Analysis of Molecular Variance results within upland and lowland Wet Tropics regions based on haplotype frequencies (without KOD). % Φ- % F- P P variation statistic variation statistic

Among regions 2.83 0.028 > 0.05 1.54 0.015 > 0.05

Among populations 8.18 0.084 < 0.01 6.96 0.071 < 0.001 within regions

Within populations 89.98 0.110 < 0.01 91.52 0.085 < 0.001

Appendix D. H. boydii gene BZW1 Analysis of Molecular Variance results within upland and lowland Wet Tropics regions based on haplotype frequencies (without KOD).

% Φ- % F- P P variation statistic variation statistic

Among altitudinal 7.48 0.075 > 0.05 7.60 0.076 > 0.05 regions

Among populations 24.53 0.265 < 0.001 16.96 0.184 < 0.001 within regions

Within populations 67.99 0.320 < 0.001 75.44 0.246 < 0.001

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Appendix E. Statistical analysis and results of museum samples

Museum samples used in the study were from the Queensland Museum collection (Table E.1). Some sampling co-ordinates did not contain seconds. These samples were included in the study as long as the degrees and minutes located the sampling region distinctly in an altitude and latitude region. Sampling localities were highly biased to southern upland regions

Table E.1. Museum samples used in analysis. The museum identification number (ID), latitude region, altitude region and geographical co-ordinates are given for each sample. The number of decimal places differs due to inconsistent and incomplete coordinates. Region Co-ordinate (Decimal Degrees) Museum Latitude Altitude Latitude Longitude sample ID region region J65679 North Lowland 15.94166667 145.325 J27146 North Upland 15.8 145.3 J65780 North Upland 16.56527778 145.2736111 J60641 North Upland 15.9 145.2 J6263 South Lowland 17.6 145.9 J13510 South Upland 18.4 145.8 J12397 South Upland 17.9 145.9 J1005 South Upland 18.2 145.5 J48353 South Upland 18.225 145.8083333 J1001 South Upland 18.2 145.5 J90417 South Upland 17.68611111 145.5222222 J80468 South Upland 17.5 145.6 J81594 South Upland 19 146.2 J78236 South Upland 17.24722222 145.6388889 J20692 South Upland 17.4 145.7 J74704 South Upland 16.7 145.4 J90437 South Upland 17.43361111 145.4875 J1004 South Upland 18.2 145.5 J79899 South Upland 19.0 146.2 J70769 South Upland 18.20833333 145.775

When museum samples were included in the dataset the x̅ values and SE values changed substantially from the field samples (Table E.2).

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Table E.2. The population name, sample size (N), measurement mean, and standard error (SE) of each morphological measurement of the field sample as well as the combined field and museum samples. Field samples only Combined field and museum samples Variable (N) x̅ SE (N) x̅ SE Snout-vent 47 144.45 3.56 68 146.12 2.60 Tail (mm) 47 272.24 6.03 68 284.06 5.82 Tail Width (mm) 47 16.73 0.28 68 17.63 0.34 Tail Height (mm) 47 22.42 0.32 68 23.59 0.37 Fore arm (mm) 47 34.28 0.31 68 36.05 0.38 Lower Arm (mm) 47 54 0.46 68 58.21 0.74 Leg (mm) 47 139.25 1.25 68 148.89 1.77 Lamellae (mm) 47 9.2 0.19 68 10.07 0.23 Head Length (mm) 47 48.79 0.46 68 54.05 0.73 Head Width (mm) 47 27.11 0.29 68 30.35 0.46

Bergmann’s and Allen’s rule

When the museum data is incorporated into the total dataset, the two-sampled t-tests results identified significant differences between lowlands and uplands for three morphological characteristics (Table E.3). Significantly longer lower arm length (t = -2.7159, P < 0.05), lamellae length (t = -2.3857, P < 0.05) and leg length (t = -2.1521, P < 0.05) were all identified in the upland region. It should be noted that the sample size in the southern region is more than double of the northern region. The ANOSIM however, did not identify significant results between lowland and upland populations (R = 0.0035, P > 0.05). Snout- vent length was no longer significant and the lowland region no longer identified the larger individuals.

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Table E.3. Welch’s two sample t-test results between lowland and upland populations including museum samples. Museum and field samples Lowland Upland Total sample Variable (N) x̅ SE (N) x̅ SE t P SVL 24 153.64 5.18 44 142.02 2.70 1.9888 0.054 Tail 24 282.39 11.76 44 284.96 6.42 -0.1919 0.849 TW 24 17.69 0.63 44 17.60 0.40 0.1115 0.912 TH 24 23.82 0.65 44 23.47 0.46 0.4484 0.656 FA 24 35.83 0.51 44 36.17 0.52 -0.46 0.644 LA 24 55.84 0.92 44 59.51 0.99 -2.7159 < 0.01 Leg 24 144.23 2.39 44 151.43 2.34 -2.1521 < 0.05 Lame 24 9.40 0.32 44 10.44 0.29 -2.3857 < 0.02 HL 24 52.90 1.04 44 54.68 0.98 -1.2543 0.215 HW 24 29.88 0.77 44 30.61 0.58 -0.7532 0.455

Isolation rule

The t-test results did not reveal any significant differences between northern and southern populations for any of the morphological variables (Table E.4). Again it should be noted that the sample size in the southern region is more than double of the northern region. The ANOSIM results produced a non-significant value when comparing northern and southern populations (R = -0.0094, P > 0.05).

Table E.4. Welch’s two sample t-test results between northern and southern populations including museum samples. North South Total sample

Variable (N) x̅ SE (N) x̅ SE t P SVL 21 147.43 5.59 47 145.54 2.84 0.3011 0.765 Tail 21 285.38 11.34 47 283.47 6.82 0.1444 0.886 TW 21 17.21 0.70 47 17.82 0.38 -0.7755 0.444 TH 21 23.36 0.71 47 23.70 0.44 -0.4016 0.690 FA 21 36.18 0.70 47 36.00 0.46 0.2173 0.829 LA 21 56.70 1.32 47 58.89 0.89 -1.3768 0.176 Leg 21 145.70 3.10 47 150.32 2.15 -1.2234 0.228 Lame 21 9.76 0.37 47 10.21 0.29 -0.9552 0.345 HL 21 53.34 1.34 47 54.37 0.88 -0.6422 0.525 HW 21 30.21 0.95 47 30.42 0.52 -0.1973 0.845

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Bergmann’s, Allen’s and isolation rule

Examining the interaction between altitude and latitude highlighted the unbalanced sample sizes in the four regions (Table E.5).

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Table E.5. Partitioning the combined museum and field samples into lowland-north, lowland-south, upland-north, upland-south and identifying population name, sample size (N), measurement mean, and standard error (SE) of each morphological measurement. Lowland-north Lowland-south Upland-north Upland-south Variable (N) x̅ SE (N) x̅ SE (N) x̅ SE (N) x̅ SE SVL 14 147.89 7.63 10 161.69 5.85 7 146.52 7.76 35 141.95 2.96 Tail 14 276.73 15.01 10 290.31 19.51 7 302.66 15.32 35 282.83 7.32 TW 14 16.69 0.85 10 19.09 0.78 7 18.25 1.24 35 17.55 0.43 TH 14 22.79 0.79 10 25.27 0.96 7 24.50 1.40 35 23.36 0.51 Fore 14 35.49 0.64 10 36.32 0.84 7 37.57 1.64 35 36.14 0.55 LA 14 55.24 1.17 10 56.68 1.49 7 59.60 3.06 35 59.86 1.07 Leg 14 142.26 2.35 10 147.00 4.73 7 152.59 7.74 35 151.98 2.50 Lame 14 9.14 0.36 10 9.75 0.59 7 11.01 0.61 35 10.51 0.33 HL 14 51.44 1.30 10 54.94 1.55 7 57.14 2.64 35 54.42 1.09 HW 14 29.23 1.13 10 30.80 0.93 7 32.15 1.58 35 30.55 0.63

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The NMDS stress value was fairly low suggested that the interpretation of the results were reliable (stress value = 0.0797) (Figure E.1). The NMDS was partitioning for both north-south and lowland-upland showed little difference and suggested that there was no difference between the regions.

Figure E.1. NMDS plots with data (combined field and museum) partitioned by: a) lowland and upland regions; and b) northern and southern regions.

The SIMPER results identified the same four most influential variables in lowland-upland and the north-south comparisons. These were leg length, lower arm, head length and head width (Table E.6). When percentages were rounded to the nearest whole number, they revealed the same percent value.

Table E.6 SIMPER results with partitioning between: altitudes (combined field & museum samples); latitudes (combined field & museum samples). Partitioning Contribution factors Altitude: Leg length Lower arm Head length Head width Field & museum samples (35 %) (15 %) (14 %) (9 %) Latitude: Leg length Lower arm Head length Head width Field & museum samples (35 %) (15 %) (14 %) (9 %)

The ANOVA results identified significant altitude-latitude interaction for the TH data (Table E.7) but no further significant comparison was identified in the Tukey HSD test (Table E.8 and Figure E.2). The remaining morphological measurement ANOVA’s were non-significant (Appendix F: a -.j)

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Table E.7. ANOVA examining the effect of altitude and latitude on TH (combining field and museum samples) and identifies the degrees of freedom (Df), sample size (N), mean (x)̅ sum of squares (SS) and mean sum of squares (MS). Field & Df Partitioning (N) x̅ SS MS F-statistic P museum: TH Altitude Lowland 24 23.82 1 Upland 44 23.47 1.99 1.991 0.2153 0.644 Latitude North 21 23.36 1 South 47 23.70 4.47 4.469 0.4833 0.489 Altitude x Lowland-north 14 22.79 Latitude Lowland-south 10 25.27 Upland-north 7 24.50 1 Upland-south 35 23.36 40.41 40.412 4.3703 < 0.05 Error 64 591.79 9.247

Table E.8. Tukey's HSD test on the morphological variable TH from the combined field and museum samples identifying the difference between the means (Diff), lower (Lower) and upper (Upper) 95 % confidence interval of the mean, and the Tukey adjusted P value. Region Comparison of TH Diff Lower Upper P Altitude Upland vs Lowland -0.358 -1.900 1.183 0.644 Latitude South vs North 0.499 -1.096 2.093 0.534 Altitude x Upland-north vs Lowland-north 1.712 -2.002 5.425 0.619 Latitude Lowland-south vs Lowland-north 2.483 -0.838 5.804 0.209 Upland-south vs Lowland-north 0.481 -2.036 2.997 0.958 Lowland-south vs Upland-north 0.771 -3.182 4.724 0.955 Upland-south vs Upland-north -1.231 -4.537 2.075 0.760 Upland-south vs Lowland-south -2.002 -4.861 0.857 0.261

TH TH (cm)

Lowland Lowland Upland Upland

North South North South Figure E.2. The mean TH for each altitude (lowland and upland) and latitude (north and south) combination.

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Appendix F. ANOVA examining the effect of altitude and latitude on field and museum character measurements and identifies the degrees of freedom (Df), sample size, sum of squares (SS) and mean sum of squares (MS). Morphological characteristics: a) SVL; b) Tail; c) TW; d) FA; e) LA; f) Leg; g) Lame; h) HL; and i) HW a) Field & museum: SVL Df SS MS F- P e) Field & museum: LA Df SS MS F-statistic P statistic Altitude 1 208.56 208.564 5.786 < 0.02 Altitude 1 2094.9 2094.95 4.9078 < 0.05 Latitude 1 5.1 5.1 0.1415 0.708 Latitude 1 205.6 205.65 0.4818 0.490 Altitude x Latitude 1 7 7.004 0.1943 0.661 Altitude x Latitude 1 1074.4 1074.39 2.517 0.118 Error 64 2306.96 36.046 Error 64 27318.9 426.86 f) Field & museum: Leg Df SS MS F-statistic P b) Field & museum: Tail Df SS MS F-statistic P Altitude 1 804.6 804.59 3.8491 0.054 Altitude 1 103 102.7 0.0436 0.835 Latitude 1 32.4 32.43 0.1551 0.695 Latitude 1 170 170.3 0.0723 0.789 Altitude x Latitude 1 109.6 109.57 0.5242 0.472 Altitude x Latitude 1 3513 3512.8 1.4913 0.227 Error 64 13377.9 209.03 Error 64 150759 2355.6 g) Field & museum: Df SS MS F- P c) Field & museum: TW Df SS MS F-statistic P Lame statistic Altitude 1 0.11 0.1069 0.0142 0.906 Altitude 1 16.858 16.8578 4.9717 < 0.05 Latitude 1 7.71 7.7077 1.0251 0.315 Latitude 1 0.015 0.0146 0.0043 0.948 Altitude x Latitude 1 29.4 29.4001 3.9103 0.052 Altitude x Latitude 1 4.846 4.8461 1.4292 0.236 Error 64 481.19 7.5187 Error 64 217.007 3.3907

d) Field & museum: FA Df SS MS F-statistic P h) Field & museum: HL Df SS MS F-statistic P Altitude 1 1.79 1.7909 0.1783 0.674 Altitude 1 49.56 49.556 1.3957 0.242 Latitude 1 2.04 2.0407 0.2032 0.654 Latitude 1 0.85 0.853 0.024 0.877 Altitude x Latitude 1 18.12 18.118 1.804 0.184 Altitude x Latitude 1 120.8 120.803 3.4022 0.070 Error 64 642.78 10.0434 Error 64 2272.45 35.507

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i) Field & museum: HW Df SS MS F-statistic P Altitude 1 8.13 8.13 0.5655 0.455 Latitude 1 0.24 0.238 0.0165 0.898 Altitude x Latitude 1 34.01 34.007 2.3655 0.129 Error 64 920.08 14.376

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Appendix G. ANOVA examining the effect of altitude and latitude on male and female character measurements and identifies the degrees of freedom (Df), sample size (N), mean (x̅) sum of squares (SS) and mean sum of squares (MS). Morphological characteristics: a) Male Tail; b) Male TH c) Male FA; d) Male LA; e) Male Leg; f) Male Lame; g) Male HL ;h) Male Weight; and i) Female SVL; j) Female Tail; k) Female TW; l) Female TH; m) Female FA; n) Female LA; 0) Female Leg; p) Female Lame; q) Female HL; r) Female HW; s) Female Weight. a) Males: Tail Df SS MS F-statistic P e) Males: Leg Df SS MS F-statistic P Altitude 1 473 473.39 0.2567 0.618 Altitude 1 1.43 1.425 0.0142 0.906 Latitude 1 2633 2632.73 1.4278 0.247 Latitude 1 70.13 70.129 0.6996 0.413 Altitude x Latitude 1 2707 2707.36 1.4682 0.241 Altitude x Latitude 1 2.1 2.102 0.021 0.886 Error 19 35035 1843.95 Error 19 1904.54 100.239 b) Males: TH Df SS MS F-statistic P f) Males: Lame Df SS MS F-statistic P Altitude 1 4.028 4.0284 1.2919 0.270 Altitude 1 3.171 3.1709 1.7002 0.208 Latitude 1 2.266 2.2656 0.7266 0.405 Latitude 1 1.605 1.6052 0.8607 0.365 Altitude x Latitude 1 13.495 13.4947 4.3276 0.051 Altitude x Latitude 1 3.545 3.5446 1.9006 0.184 Error 19 59.247 3.1182 Error 19 35.435 1.865 c) Males: FA Df SS MS F-statistic P g) Males: HL Df SS MS F-statistic P Altitude 1 5.518 5.5178 1.0182 0.326 Altitude 1 6.626 6.6256 1.1514 0.297 Latitude 1 0.218 0.2178 0.0402 0.843 Latitude 1 0.185 0.1849 0.0321 0.860 Altitude x Latitude 1 0.404 0.4042 0.0746 0.788 Altitude x Latitude 1 23.6 23.5998 4.1012 0.057 Error 19 102.966 5.4193 Error 19 109.333 5.7544 d) Males: LA Df SS MS F-statistic P h) Males: Weight Df SS MS F-statistic P Altitude 1 4.573 4.5733 0.402 0.534 Altitude 1 512.5 512.5 1.3371 0.262 Latitude 1 15.006 15.0061 1.3192 0.265 Latitude 1 235.8 235.82 0.6153 0.443 Altitude x Latitude 1 0.89 0.89 0.0782 0.783 Altitude x Latitude 1 34.1 34.07 0.0889 0.769 Error 19 216.128 11.3752 Error 19 7282.4 383.28

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i) Females: SVL Df SS MS F-statistic P Altitude 1 563.7 563.67 0.8808 0.359 n) Females: LA Df Df SS MS F-statistic Latitude 1 6.5 6.51 0.0102 0.921 Altitude 1 6.911 6.9114 0.702 0.412 Altitude x Latitude 1 1829.9 1829.87 2.8592 0.106 Latitude 1 6.108 6.1077 0.6204 0.440 Error 20 12799.7 639.98 Altitude x Latitude 1 8.75 8.7502 0.8888 0.357 Error 20 196.898 9.8449 j) Females: Tail Df SS MS F-statistic P Altitude 1 216 215.7 0.1216 0.731 o) Females: Leg Df SS MS F-statistic P Latitude 1 3869 3868.8 2.1819 0.155 Altitude 1 35.09 35.087 0.5089 0.484 Altitude x Latitude 1 571 571.3 0.3222 0.577 Latitude 1 65.12 65.12 0.9445 0.343 Error 20 35463 1773.2 Altitude x Latitude 1 31.18 31.179 0.4522 0.509 Error 20 1378.99 68.949 k) Females: TW Df SS MS F-statistic P Altitude 1 0.8 0.7998 0.2022 0.658 p) Females: Lame Df SS MS F-statistic P Latitude 1 5.646 5.6459 1.427 0.246 Altitude 1 0.095 0.09476 0.0573 0.813 Altitude x Latitude 1 9.457 9.4565 2.3901 0.138 Latitude 1 1.399 1.39942 0.8469 0.368 Error 20 79.131 3.9565 Altitude x Latitude 1 0.057 0.05739 0.0347 0.854 Error 20 33.049 1.65244 l) Females: TH Df SS MS F-statistic P Altitude 1 2.934 2.9339 0.9641 0.338 q) Females: HL Df SS MS F- P Latitude 1 2.63 2.6299 0.8642 0.364 statistic Altitude x Latitude 1 6.87 6.8705 2.2578 0.149 Altitude 1 0.675 0.6749 0.0673 0.798 Error 20 60.86 3.043 Latitude 1 6.932 6.9322 0.6909 0.416 Altitude x Latitude 1 4.123 4.1225 0.4109 0.529 m) Females: FA Df SS MS F-statistic P Error 20 200.673 10.0337 Altitude 1 7.962 7.9618 1.7595 0.200 Latitude 1 0.929 0.9293 0.2054 0.655 Altitude x Latitude 1 0.24 0.2398 0.053 0.820 Error 20 90.501 4.525

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r) Females: HW Df SS MS F-statistic P s) Females: Weight Df SS MS F-statistic P Altitude 1 0.31 0.3102 0.0822 0.777 Altitude 1 500.6 500.58 2.1511 0.158 Latitude 1 0.727 0.7273 0.1928 0.665 Latitude 1 353.2 353.18 1.5177 0.232 Altitude x Latitude 1 1.98 1.9803 0.5248 0.477 Altitude x Latitude 1 210.3 210.33 0.9038 0.353 Error 20 75.465 3.7733 Error 20 4654.2 232.71

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