Conservation Genetics of a Relict , moorei (F.Muell.) Krasser

Lee Schultz B.Sc. (Hons)

School of Natural Resource Sciences University of Technology Brisbane,

This dissertation is submitted as a requirement of the Doctor of Philosophy Degree February 2008

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Keywords

Nothofagus moorei; antarctic ; relictual; cool ; conservation genetics; AFLPs; microsatellites; chloroplast DNA; historical genetic diversity and structure; clonality.

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Abstract

Nothofagus moorei is a long-lived, Gondwana relict cool temperate rainforest tree. Nothofagus-dominated were widespread across much of eastern Australia during the mid-Tertiary but today, N. moorei occurs only as a series of disjunct, isolated populations in south-east Queensland and northern . Clonal regeneration via coppicing is reported to be a common feature of most N. moorei populations, while successful sexual regeneration is believed to be rare, occurring largely only in niches with high light levels and limited competition. While clonal propagation enables population persistence and individual longevity, it cannot generate novel genotypes. Isolated populations, potentially high levels of clonality, low-potential for successful sexual regeneration, long-lived individuals and predicted global warming effects make N. moorei vulnerable to local, if not total, population extinction.

The current study aimed to assess the relative conservation status of extant N. moorei populations in order to develop appropriate conservation management strategies for long-term population persistence. Levels of genetic diversity and population structure were examined across the remaining natural distribution of N. moorei using nuclear amplified fragment length polymorphism (AFLP), microsatellite and chloroplast DNA markers. In total 607 individuals were sampled from 20 populations and 5 geographical regions: Lamington/Border Ranges, Ballow, Dorrigo/New England, Werrikimbe and Barrington. Genetic results were then analysed to assess conservation status of each population and geographical region.

Microsatellite and AFLP data identified comparatively high levels of genetic diversity in all remnant populations sampled. The prevalence of coppicing in the northern Lamington/Border Ranges populations appears to have had little impact on relative levels of genetic diversity, heterozygosity or population structure. Population differentiation was limited, with the majority of genetic variation retained within populations, no regional structuring and high levels of admixture. Analysis of cpDNA variation showed that the three Dorrigo/New England populations were divergent from all other populations, suggesting an ancient divergence in N. moorei prior to Pleistocene glaciations.

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While levels of genetic diversity were essentially the same across all populations, Bayesian analysis of genetic structure did identify four populations with differing gene pool proportions which would be important to include in conservation efforts in addition to individuals from other populations. Similarly, individuals from four significantly differentiated groups identified using traditional F-statistics suggests individuals from each of these four groups should be included in future conservation plans. In order to maintain ancient chloroplast lineages, populations from the Dorrigo/New England region should also be assigned special conservation value.

Populations of N. moorei appear to have retained significant levels of genetic diversity and show little population divergence in spite of marked reductions in the natural distribution since the Early Miocene. Sampling of these ancient however, suggests current levels of diversity in N. moorei actually reflect past diversity and differentiation, and that there have been insufficient generations since the historical contraction in distribution for genetic diversity to be adversely affected and regional differentiation to evolve.

Long-term persistence of N. moorei is still threatened by future accelerated climate change and the limited preferred habitat that remains where N. moorei can expand its range. While the ability to regenerate clonally may enable long-term persistence of N. moorei, populations are still likely to continue to decline as climatic conditions will increasingly favour sub-tropical and warm temperate across much of N. moorei’s northern distribution. Southern populations of N. moorei, in contrast, could expand their ranges into eucalypt woodlands as predicted climate becomes warmer and wetter. However, this will ultimately be determined by the frequency of fires, with increased fire frequencies favouring the expansion of eucalypts and contraction and possible local population extinction of N. moorei dominated cool temperate rainforests.

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Keywords...... ii Abstract...... iii Table of Contents...... v List of Figures...... x List of Tables...... xii List of Appendices...... xiv List of General Abbreviations...... xv List of Population Abbreviations...... xviii Statement of Original Authorship...... xix Acknowledgements...... xx

Table of Contents

Chapter 1: General Introduction...... 1 1.1 Historical climates and vegetation change in Australia...... 1 1.2 The ancient genus Nothofagus...... 4 1.3 Conservation genetics concepts...... 6 1.4 Habitat fragmentation effects...... 7 1.5 Molecular markers used in conservation genetics...... 10 1.5.1 Allozymes...... 10 1.5.2 Random Amplified Polymorphic DNA...... 11 1.5.3 Amplified Fragment Length Polymorphisms...... 12 1.5.4 Inter-Simple Sequence Repeats...... 13 1.5.5 Microsatellites (nuclear)...... 13 1.5.6 Chloroplast DNA molecular markers...... 14 1.5.6.1 Chloroplast PCR-RFLPs...... 15 1.5.6.2 Chloroplast microsatellites...... 15 1.5.7 Choosing an appropriate molecular marker...... 16 1.6 Case study: Nothofagus moorei...... 17 1.6.1 The current distribution of Nothofagus moorei’...... 17 1.6.2 Systematics...... 18 1.6.3 Morphology...... 20 1.6.4 Breeding system...... 22

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1.6.5 Effects of fire on N. moorei populations...... 24 1.6.6 Absence of N. moorei from climatically suitable sites...... 25 1.6.7 Conservation status of N. moorei...... 25 1.6.8 Threatened species associated with N. moorei dominated cool temperate rainforests...... 26 1.7 Predicted future climate change and the fate of N. moorei...... 29 1.8 Thesis structure and aims...... 31

Chapter 2: General Methods...... 34 2.1 Study sites and sampling...... 34 2.1.1 Study sites...... 34 2.1.2 Sampling strategy...... 35 2.2 DNA extractions...... 39 2.3 AFLP analysis...... 40 2.3.1 Restriction digestion of genomic DNA and ligation to adaptors..40 2.3.2 Pre-amplification reactions...... 41 2.3.3 Primer labelling and selective amplification...... 41 2.3.4 Electrophoresis...... 42 2.3.5 Error rate...... 42 2.4 Microsatellite analysis...... 42 2.4.1 Initial screening...... 43 2.4.2 Microsatellite procedure...... 43 2.5 Chloroplast DNA analysis...... 45 2.5.1 PCR-RFLP cpDNA analysis...... 45 2.6 Statistical analyses – AFLPs and microsatellites...... 47 2.6.1 AFLP specific analysis...... 47 2.6.2 Microsatellite specific analysis – Independence of loci...... 48

Chapter 3: How extensive is clonality across N. moorei’s natural distribution?...49 3.1 Introduction...... 49 3.2 Methods...... 53 3.2.1 AFLP and microsatellite methods...... 53 3.2.2 Geographic distance determination...... 53

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3.2.3 Statistical analysis...... 53 3.3 Results...... 55 3.3.1 Levels of clonality revealed with AFLPs...... 55 3.3.2 Levels of clonality revealed with microsatellites...... 61 3.3.3 Comparison of the effectiveness of AFLPs and microsatellites in clonal identity...... 73 3.4 Discussion...... 74 3.4.1 Clonality revealed with AFLPs...... 74 3.4.2 Clonality revealed with microsatellites...... 76 3.4.3 The role of disturbance in levels of sexual regeneration in N. moorei populations...... 77 3.4.4 Conclusion...... 79

Chapter 4: Contemporary genetic diversity and structure in Nothofagus moorei populations...... 80 4.1 Introduction...... 80 4.2 Methods...... 84 4.2.1 AFLP and microsatellite methodology...... 84 4.2.2 Statistical analysis...... 84 4.2.2.1 Genetic diversity within populations...... 84 4.2.2.2 Isolation by distance within and among populations...... 86 4.2.2.3 Population differentiation and relationships among populations...... 87 4.2.2.4 Regional genetic diversity and population structure patterns...... 88 4.2.2.5 Bottleneck detection based on microsatellite data...... 90 4.3 Results...... 91 4.3.1.1 Genetic diversity within populations – AFLPs...... 91 4.3.1.2 Genetic diversity within populations – Microsatellites...... 91 4.3.2.1 Isolation by distance within and among populations – AFLP data...... 100 4.3.2.2 Isolation by distance within and among populations – Microsatellite data...... 108

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4.3.3.1 Population differentiation and relationships among populations – AFLPs...... 116 4.3.3.2 Population differentiation and relationships among populations – microsatellites ...... 121 4.3.4 Regional population structure...... 125 4.3.5 Bottleneck detection...... 130 4.4 Discussion...... 132 4.4.1 Genetic diversity within N. moorei populations...... 132 4.4.2 Population structure and gene flow within N. moorei – AFLPs ...... 135 4.4.3 Population structure and gene flow within N. moorei – microsatellite data...... 136 4.4.4 Comparison of AFLP and microsatellite data for population and regional differentiation...... 139 4.4.5 Bottleneck detection...... 139 4.4.6 Implications for conservation...... 140 4.4.7 Conclusion...... 142

Chapter 5: Historical structuring among Nothofagus moorei regions...... 143 5.1 Introduction...... 143 5.2 Methods...... 147 5.2.1 DNA extraction and cpDNA PCR-RFLP methodology...... 147 5.3 Results...... 147 5.3.1 Ancient regional population differentiation...... 147 5.4 Discussion...... 152 5.4.1 Chloroplast haplotype diversity...... 152 5.4.2 Comparison of AFLP, microsatellite and cpDNA data to identify population differentiation in N. moorei...... 155 5.4.3 Implications for conservation...... 156 5.4.4 Conclusion...... 156

Chapter 6: General Discussion...... 157 6.1 Genetic diversity and population differentiation in extant N. moorei populations – Summary of findings...... 157

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6.2 Comparative studies of Australian rainforest species...... 159 6.3 Habitat fragmentation effects...... 162 6.4 tree model – an alternative explanation for high within population diversity and low population divergence in N. moorei...... 164 6.5 Significance of clonality to the ongoing persistence of N. moorei population ...... 166 6.6 Conservation management of N. moorei populations...... 168 6.7 Future research directions...... 170 6.8 Conclusion...... 171

Appendices...... 173 References...... 188

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List of Figures

Figure 1.1 Distribution of extant N. moorei populations...... 18 Figure 1.2 classification...... 19 Figure 1.3 morphology in N. moorei...... 20 Figure 1.4 Characteristic new red produced in spring in N. moorei...... 21 Figure 1.5 Seed pods of N. moorei...... 21 Figure 1.6 Immature tree of N. moorei invading open eucalypt forest ecotone...... 22 Figure 1.7 Multi-stemmed N. moorei individual...... 23 Figure 1.8 Characteristic swollen basal burl of N. moorei...... 24 Figure 1.9 Growth of on trunk of N. moorei...... 27 Figure 1.10 Vulnerable rufous scrub bird (Antrichornis rufescens)...... 28 Figure 2.1 Nothofagus moorei study sites and regions...... 37 Figure 2.2 Difficult access to Beech Lookout population of N. moorei...... 39 Figure 4.1 Spatial autocorrelation graphs based on 100 AFLP loci...... 101 Figure 4.2 Mantel test for isolation by distance across 20 populations based on 100 AFLP loci...... 108 Figure 4.3 Spatial autocorrelation graphs based on 4 microsatellite loci...... 109 Figure 4.4 Mantel test for isolation by distance across 20 populations based on 4 microsatellite loci...... 116

Figure 4.5 Neighbour-joining phylogenetic tree using AFLP population pairwise FST values for N. moorei...... 119 Figure 4.6 Scatter plot of PCA for 491 N. moorei individuals from 20 populations based on data from 100 AFLP loci...... 120 Figure 4.7 Neighbour-joining phylogenetic tree using microsatellite population

pairwise FST values for N. moorei...... 123 Figure 4.8 Scatter plot of PCA for 491 N. moorei individuals from 20 populations based on data from 4 microsatellite loci...... 124 Figure 4.9 Values of log likelihood of the multilocus genotypic data as a function of the number of clusters using Bayesian STRUCTURE...... 128 Figure 4.10 Fractions of ancestry within each population of N. moorei based on the Bayesian approach of Pritchard et al. 2000...... 129

Figure 5.1 Representative screening gel of TaqI digest of the cpDNA psaA-trnS2r

(AS2) fragment in N. moorei and N. cunninghamii...... 148

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Figure 5.2 Diagrammatic representation of two N. moorei haplotypes 1 and 2 and haplotype 3 in N. cunninghamii...... 149 Figure 5.3 Geographic distributions of cpDNA haplotypes in N. moorei...... 150

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List of Tables

Table 1.1 Classification and distribution of the 35 extant Nothofagus species...... 5 Table 2.1 Geographical location of N. moorei populations within each of the five main regions and sample size for each population...... 38 Table 2.2 AFLP adapter and primer sequences used in study of N. moorei...... 41 Table 2.3 Repeat motif, primer sequences, number of alleles (A), annealing

temperature (Ta) and magnesium concentration for each of the microsatellite loci used in the study...... 44 Table 2.4 Primer pairs, fragment sizes and annealing temperatures used in study of cpDNA variation in N. moorei...... 47 Table 3.1 Similarity Dice values for each population of N. moorei using AFLPs....57 Table 3.2 Similarity Dice values for putative clones from Tullawallal and Mt Wanungra populations using AFLPs...... 58 Table 3.3 Similarity Dice values for coppice and canopy from Link Trail population using AFLPs...... 59 Table 3.4 Similarity Dice values for coppice and canopy from Gloucester Tops population using AFLPs...... 60 Table 3.5 Euclidean distance average and range for 491 N. moorei individuals from 20 populations based on 4 microsatellite loci...... 63 Table 3.6 Number of clones identified for each population of N. moorei using microsatellites...... 64 Table 3.7 Distances between clonal individuals for N. moorei populations...... 66

Table 3.8 Probability (Pgen) values to discriminate between identical genotypes by chance versus genuine clones...... 68 Table 3.9 Euclidean distances for putative clones from Tullawallal and Mt Wanungra populations using microsatellites...... 70 Table 3.10 Euclidean distances for coppice and canopy for Link Trail population using microsatellites...... 71 Table 3.11 Euclidean distances for coppice and canopy for Gloucester Tops population using microsatellites...... 72 Table 4.1 Genetic diversity indices for each population and region of N. moorei based on 100 AFLP loci...... 94

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Table 4.2 Linkage disequilibrium for each microsatellite locus pair tested on 491 individuals of N. moorei from 20 populations...... 95 Table 4.3 Genetic diversity indices for each population and region using four microsatellite loci...... 96 Table 4.4 Estimated null allele frequencies for the 4 microsatellite loci tested in 491 individuals from 20 populations of N. moorei...... 97

Table 4.5 Mean values of allelic richness (Rs), observed heterozygosity (Ho), Nei’s

expected heterozygosity (He), within sample gene diversity (Hs), and

fixation index (FIS) for each population and region...... 98 Table 4.6 Summary of genetic diversity values of 20 populations of N. moorei at four microsatellite loci...... 99 Table 4.7 Mantel test results for correlation Rxy between the pairwise geographic and genetic distance matrices within each population of N. moorei and probability P for tests of significance by random permutation for AFLP data...... 107 Table 4.8 Mantel test results for correlation Rxy between the pairwise geographic and genetic distance matrices within each population of N. moorei and probability P for tests of significance by random permutation for microsatellite data...... 115 Table 4.9 Global population differentiation and gene flow estimates based on data from 100 AFLP loci and 4 microsatellite loci for 491 N. moorei individuals from 20 populations...... 117

Table 4.10 Population pairwise FST values based on data from 100 AFLP loci for 491 N. moorei individuals from 20 populations...... 118

Table 4.11 Population pairwise FST values based on data from 4 microsatellite loci for 491 N. moorei individuals from 20 populations...... 122 Table 4.12 Analysis of molecular variance (AMOVA) for 20 populations of N. moorei using AFLPs...... 125

Table 4.13 SAMOVA FCT values for different groupings of 20 N. moorei populations using microsatellites...... 126 Table 4.14 Spatial analysis of molecular variance (SAMOVA) for 20 N. moorei populations using microsatellites for 4 groups...... 126 Table 4.15 Analysis of molecular variance (AMOVA) for 20 populations of N. moorei split into sexual and asexual regions...... 127

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Table 4.16 Wilcoxon signed ranks test for demographic equilibrium under the infinite allele, stepwise mutation and two-phase mutation models...... 131 Table 5.1 cpDNA haplotype for each population of N. moorei within each of the

geographical regions based on restriction digest patterns of AS2 with TaqI...... 151

List of Appendices

Appendix 4.1 Kruskall-Wallis test values...... 174 Appendix 4.2 Allelic frequencies of N. moorei at 4 microsatellite loci...... 176 Appendix 4.3 Pilot study chapter: Population and regional structure in N. moorei based on 2 northern and 2 southern populations...... 182

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List of General Abbreviations

AFLP Amplified fragment length polymorphism AMOVA Analysis of molecular variance APS Ammonium persulfate a.s.l Above sea level ATP Adenosine triphosphate atpB β subunit of ATPase in chloroplast genome bp Base pairs BSA Bovine serum albumin cm Centimetres cpDNA Chloroplast DNA CTAB Hexadecyltrimethylammonium bromide DNA Deoxyribose nucleic acid dNTP Deoxynucleotidetriphosphate df Degrees of freedom EDTA Ethylenediamenetetraacetic acid EtOH Ethanol

FIS Fixation index

FST Wright’s F-statistic, subpopulations relative to total population

GST Equivalent to FST h Nei’s gene diversity index

HE Expected heterozygosity HEX Hexachlorofluorescein

HO Observed heterozygosity hrs Hours

HS Within sample gene diversity I Shannon’s Index of genetic diversity IAM Infinite allele model Indel Insertion or deletion ISSR Inter simple sequence repeat ITS Internal transcribed spacer region of ribosomal DNA K Potassium km Kilometre

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M Molar m metre µg Microgram µL Microlitre µM Micromolar Mg Magnesium MgCl Magnesium chloride mL Millilitres mm Millimetre mM Millimolar Mt Mount Mtn Mountain Myr Million years

Na Number of alleles

Ne Effective number of alleles NaCl Sodium chloride ncutas06 Microsatellite loci designed for at the University of Tasmania

Nm Number of migrants (gene flow) NSW New South Wales P Probability PCA Principle coordinate analysis PCR Polymerase chain reaction PCR-RFLP Polymerase chain reaction – restriction fragment length polymorphism PVP Polyvinylpropylene QLD Queensland R Regression coefficient from Mantel test RAPD Random amplified polymorphic DNA rbcL Large subunit of the ribulose 1, 5-bisphosphate carboxylase gene in the chloroplast genome

RS Allelic richness SAMOVA Spatial analysis of molecular variance spp. Species SS Sum of squares

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SSR Simple sequence repeat (alternative name for microsatellites) TAMARA Carboxytetramethylrhodamine Taq Enzyme extracted from Thermus aquaticus used for PCR amplification TBE Tris-Borate-EDTA buffer TE Tris-EDTA buffer TEMED N,N,N',N'-tetramethylethylenediamine Tris Trishydroxymethylaminomethane Tris-HCl Trishydroxymethylaminomethane Hydrochloride U Units, defined amount of enzyme V Volts W Watts w/v Weight per volume

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List of Population Abbreviations

ABPW Antarctic Beech Picnic Walk BA Mt Ballow BAL Best of All Lookout BM Bar Mtn DUMP Duramlee and Mowburra Peaks EF Elabana Falls EP Echo Point GT Gloucester Tops HL Helmholtzia Loop KG Kilungoondie LF Lightning Falls LT Link Trail MB Mt Banda MH Mt Hobwee MM Mt Moombil MtW Mt Wanungra NO Nothofagus Mtn PB Plateau Beech TW Tullawallal WR Weeping Rock

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements of an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written except where due reference is made.

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Lee Schultz

February 2008

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Acknowledgements

I would like to thank my supervisor Associate Professor Peter Mather for all his time and encouragement throughout my PhD candidature. I especially thank Peter for taking over the role of principal supervisor and for his efficient feedback on thesis drafts. A big thank you to Tanya Scharaschkin who stepped up to the task of reading thesis drafts for me, giving me great feedback and advice, and many enthusiastic conversations. To Noel Meyers, thank you for your initial inspiration for the project, encouragement and understanding during those tough times of my candidature.

Thank you to fellow candidate, Peter Prentis, for help on field trips to and the Border Ranges and a big thank you for your expertise in data analysis and running my STRUCTURE input file. To Greg Jordan from the University of Tasmania, thank you for providing leaf material of N. cunninghamii for chloroplast analysis. To David Haliczer and friends from the Queensland Bushwalking Club, thank you for guiding me to the remote populations of N. moorei at Nothofagus Mtn, Mt Ballow and Durramlee and Mowburra Peaks. I must admit I was temporarily put off bushwalking after those field trips! Especially the rather steep and slippery bushwalk up precipitous grassy cliffs to Mt Widgee!

Thank you Rebecca Jones for providing the N. cunninghamii microsatellite primer details prior to publication. Thanks to Dave Hurwood for his help with SAMOVA analysis. To Angela Duffy, thank you for helping me in the lab, especially teaching me how to use the Gelscan.

Most importantly I would like to thank all my family for all their support and encouragement throughout my rather lengthy and interrupted candidature. It might have taken a bit longer than I’d planned but I’ve finally done it! A very big thank you to my loving partner Andy. You’ve been my rock throughout these final hard, writing times and have been so patient, understanding and encouraging. Thank you. Finally, thank you to my late step-dad John who was always so supportive and thoughtful. This work is dedicated to him.

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Chapter 1. General Introduction

1.1 Historical climates and vegetation change in Australia

Over 135 Myr ago, Australia was part of the Gondwana that also included , , , , , South Africa, Madagascar and India (Sanmartín and Ronquist 2001). Australia’s climate and vegetation has changed substantially since the initial separation from Antarctica some 97 Myr ago (Hill 2004). During the Late (~98 – 65 Myr ago), oceanic currents circulated water from the warm tropics to high latitudes resulting in very little temperature differential between the equator and the poles (Hill 2004). Climate in southern Australia was most likely seasonal with mild summers with long days and reliable, high rainfall due to the warm oceanic waters creating high evaporation rates and condensation at high latitudes (Read and Brown 1996, Hill 2004). Winters were moderately cold and humidity was high (Hill 2004). During the Early Cenozoic, the climate became warmer and more humid with abundant year round rainfall at middle to high latitudes as a result of the rapid movement of the Australian continent to lower latitudes (Hill 2004). By 45 Myr ago, Australia had effectively separated from Antarctica, shallow seas still linked the two continents however, allowing warm currents to continue flowing south (Floyd 1990). Over millions of years, seas that separated the southern continents deepened as Australia drifted to the north, resulting in development of the circum-polar oceanic current that circulates water around the north and south poles thus confining a large mass of water at high latitudes, increasing the equator-to-pole temperature gradient and causing rapid cooling and formation of an ice cap in Antarctica (Read and Brown 1996). Changed oceanic current circulation patterns also affected levels of rainfall, substantially reducing and making rainfall more seasonal and increasing aridity (Martin 1990, Read and Brown 1996). Short intervals of increased rainfall also occurred in southern Australia between 5 and 1.8 Myr ago (Hill 2004). Major climatic oscillations occurred during the Quaternary glacial cycles with cool and dry glacial periods interspersed with warm, wet interglacial periods (Hill 2004).

During the early Cretaceous, vegetation in Australia consisted of a canopy of Araucariaceae and Podocarpaceae, with the occasional Ginkgo canopy tree and an understorey of pteridosperms, cycads, bennettialeans and cryptogams (Hill et al. 1999).

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The first angiosperms entered Australia ~120 Myr ago and by 86-84 Myr ago canopy angiosperms were intermingled with podocarp and araucarian (Hill 2004). Nothofagus appeared along with the ~84 Myr ago. Prior to the isolation of Australia from Gondwana, vegetation consisted primarily of Nothofagus-dominated rainforest as well as a minor component of sclerophyll and monsoonal tropical vegetation (Crisp et al. 2004). From the Eocene (58-37 Mya) through to the early Miocene (~25 Myr ago) there was a gradual expansion of Nothofagus forests across eastern Australia. Reduced rainfall and increased aridity during the early Miocene (~25 Myr ago), resulted in Nothofagus-dominated rainforests being replaced by drier rainforest dominated by and Casuarinaceae species (Hill 2004). By the Late Miocene (11 to 5 Myr ago), Nothofagus forests decreased further in distribution in central New South Wales, with the complete extinction of the Brassospora subgenus from Australia (Hill 2004). With greatly reduced rainfall, Myrtaceae species increased and fire frequency also increased (Hill 2004).

During the early (5 to 1.8 Myr ago) there was a brief resurgence of Nothofagus forests in eastern Australia, although the resurgence did not include species within the Brassospora subgenus, as a result of an intermittent wet period whereby rainforests were able to expand westwards for a brief interval (Martin 1990). Across the remainder of the Pliocene, aridity increased due to an expanding latitudinal temperature gradient, expansion of the Antarctic ice cap and intensification of the sub-tropical high pressure ridge (Crisp et al. 2004). During this period there was a dramatic shift from temperate rainforest to dominance by the Asteraceae and Gramineae (Poaceae) (Hill 2004).

Onset of the Quaternary glaciations 1.8 Myr ago saw a further dramatic reduction in rainforests and expansion of scrubland and grassland dominated by Asteraceae and Poaceae (Trustwell 1990, Markgraf et al. 1995, Hill 2004). Rainforests became restricted to areas of orographic rainfall on the coastal escarpment in eastern Australia, the extreme south-west of Western Australia and the western coast and isolated mountains in Tasmania (Markgraf et al. 1995). Across the Quaternary (1.8 Myr ago to present), there were frequent climatic oscillations as the earth underwent a series of glacial and inter-glacial periods (Markgraf et al. 1995). This resulted in major range shifts for Nothofagus species as populations expanded and contracted to more suitable environments (Adam 1992, Hewitt 2000). During the interglacial period of climate warming, warm-adapted tree species expanded from refugia while cold-adapted tree

2 Chapter 1. General Introduction species remained trapped and either went extinct or persisted when forced to higher altitudes (Petit et al. 2003). During the Pleistocene (1.8 Myrs -11, 550 years ago), Nothofagus became extinct in southern NSW presumably as a result of environments becoming more unfavourable (Markgraf et al. 1995). The arrival of humans 60,000 to 40,000 years ago also saw an increase in fire frequency (Hill 2004) that further reduced Nothofagus-dominated rainforest distributions as more sclerophyllous, fire-adapted vegetation expanded. Rainforests with modern taxa composition and geographical distributions did not become established until 5,000 to 3,000 years ago (Markgraf et al. 1995). Thus, while rainforests were once dominated in Australia by Nothofagus species, modern distributions represent relictual populations confined to the limited favourable habitat patches that remain.

The reconstruction of historical climate and vegetation in Australia is based on fossil records and geological data. Plant fossil records include fossil leaves, pollen, , wood and charcoal (Truswell 1990, Hill 2004). It must be noted therefore, that climate and vegetation reconstructions are continually being modified as new fossil evidence presents itself. While there is no disagreement that the aforementioned vegetation and climate changes definitely occurred, the timing of the events is still open to debate and there may be vegetation types that were present during certain geological times, but there is simply no fossil record as evidence. In particular, the occurrence of fossil pollen in a specific region does not necessary mean that species inhabited that region, as long distance pollen dispersal may have transported the pollen to that site (Truswell 1990). In contrast, the presence of fossil leaves is more indicative of a species inhabiting a site in which the fossil leaf was found. Unfortunately, the fossil record for fossil leaves is quite poor throughout Australia and leaves are not the best morphological character by which to identify species (Truswell 1990). There are an increasing number of fossil flowers being recovered that provides clearer evidence for the presence of a particular species/lineage (Truswell 1990).

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1.2 The ancient genus Nothofagus

Nothofagus is considered a living relict of Gondwanan forests as this genus was present in eastern Gondwana prior to continental break-up (Dettmann et al. 1990). Fossil pollen records demonstrate conclusively that ancestral Nothofagus originated during the Cretaceous, approximately 84 Million years ago (Myr ago) and that the genus was more widely distributed in the past than it is today (Trustwell 1990, Hill 2001, Hill 2004). Pollen fossil records of Nothofagus are present within Antarctica from 80-83 Myr ago up until the time the genus became extinct in Antarctica during the Pliocene (Hill and Dettmann1996). No autochthonous Nothofagus fossils however, have been recorded from Africa, Madagascar or India that all separated from Gondwana prior to 105 Myr ago (Swenson et al. 2001). Today, the modern distribution of Nothofagus is considered to represent refugia of past extensive Gondwanan forests (Read and Brown 1996).

There are 35 known Nothofagus extant species (and several hybrids) distributed across South America, New Guinea, New Caledonia, New Zealand and Australia (Rix and Jackson 2004). The 35 species belong to four distinct subgenera: Lophozonia, Fuscopsora, Nothofagus and Brassospora (Table 1.1) (Hill and Read 1991, Hill and Jordan 1993). The division of Nothofagus species into subgenera is based on four main pollen grain morpho-types: menziesii, fusca type a, fusca type b and brassii, respectively (Hill 2001). Phylogenetic analyses of Nothofagus based on morphological, chloroplast DNA (rbcL) and nuclear DNA (ITS) data all show strong congruence and suggest that the four subgenera have been distinct monophyletic entities for tens of millions of years (Hill and Jordan 1993, Hill and Dettmann 1996, Manos 1997, Setoguchi et al. 1997, Jordan and Hill 1999). Major radiations within Nothofagus are estimated to have occurred 55 and 40 Myr ago, respectively. This is hypothesised to have resulted in the modern four subgenera prior to the break-up of eastern Gondwana, but since this time evolutionary change within Nothofagus has apparently been very slow (Hill 1991, Linder and Crisp 1995).

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Table 1.1 Classification and distribution of the 35 extant Nothofagus species (from Manos 1997, Setoguchi et al . 1997)

Subgenus Species Distribution

Nothofagus N. antarctica South America N. betuloides South America N. dombeyi South America N. nitida South America N. pumilo South America

Fuscospora N. gunnii Australia (Tasmania) N. solandri New Zealand N. truncata New Zealand N. fusca New Zealand N. alessandri South America

Lophozonia N. cunninghamii Australia N. moorei Australia N. menziesii New Zealand N. alpina South America N. glauca South America N. obliqua South America

Brassospora N. aequilateralis New Caledonia N. balansae New Caledonia N. baumanniae New Caledonia N. codonandra New Caledonia N. discoidea New Caledonia N. brassii New Guinea N. carrii New Guinea N. crenata New Guinea N. flaviramea New Guinea N. grandis New Guinea N. nuda New Guinea N. perryi New Guinea N. pseudoresinosa New Guinea N. pullei New Guinea N. resinosa New Guinea N. rubra New Guinea N. starkenborghii New Guinea N. stylosa New Guinea N. womersley New Guinea

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1.3 Conservation genetic concepts

Genetic variation is a key component for species survival as genetic diversity allows species to evolve and cope with environmental change including new diseases, pests and parasites; new competitors, new predators, pollution, climatic cycles such as El- Nino and La-Nina and human-induced global climate change (Frankham et al. 2002). In general, naturally outbreeding species with large populations possess extensive gene pools allowing them to adapt to environmental change. Small populations however, typically have lower levels of genetic diversity and theoretically over time this will continue to decline (Frankham et al. 2002).

It has long been accepted that inbreeding usually produces deleterious effects (Frankel and Soulé 1981). Darwin initially demonstrated this when he assessed the progeny of 57 plant species that were both self-fertilised and cross-fertilised. On average, selfing reduced seed production by 41% and plant height by 13% compared with outcrossing (Darwin 1876). Subsequent experiments have often shown similar results in both laboratory and domestic animals and (Frankham et al. 2002). The deleterious effects of inbreeding can dramatically increase risk of extinction. For example in the plant species Clarkia pulchella, highly inbred populations (Fis = 0.08-0.09) exhibited an extinction rate of 69% while less inbred populations (Fis = 0.04) exhibited an extinction rate of only 25% (Newman and Pilson 1997). This illustrates how a slight increase in inbreeding rate can dramatically increase risk of extinction.

Small populations can also result in mutation accumulation (Lande 1995, Lynch et al. 1995). In small populations, mildly deleterious alleles may become selectively neutral and as a result increase in frequency, ultimately reducing the reproductive fitness of a population. Over time, these alleles may become fixed via genetic drift and result in negative population growth and extinction. This phenomenon is known as mutational meltdown. (Lande 1995, Lynch et al. 1995).

Conservation genetics involves the use of “genetics to preserve species as dynamic entities that can evolve to cope with environmental change and thus minimise their risk of extinction” (Frankham et al. 2002). Determining genetic diversity is a key component in conservation genetics. The partitioning of genetic diversity within and among

6 Chapter 1. General Introduction populations is of great importance in assessing population viability for conservation. Assessment of the levels of gene flow allows identification of populations that are genetically depauperate or fragmented and can characterise their relative relationships. This can then be translated into identifying populations in need of new individuals, populations that can “donate” individuals to more vulnerable populations or populations in need of further consideration at the demographic and environmental level (Haig 1998). With a sound knowledge of the levels of genetic diversity present within and among populations, appropriate management strategies can be implemented to maximise a population’s evolutionary potential.

1.4 Habitat fragmentation effects

Natural populations can be fragmented by movements of continents, sea level changes, glacial periods and/or climate change so that populations and species may become geographically isolated. Human impacts on natural ecosystems including habitat fragmentation in recent times also have been large. Land used for agriculture, forestry, cities and roads has resulted in significant natural habitat loss and/or fragmentation and this has had the consequence of producing geographical isolation for many plant and animal populations (Spellerberg and Sawyer 1999). The temperate ruil (cool temperate rainforest dominated by Nothofagus alessandrii and N. nervosa) forest in central represents an example of natural forest that has been impacted significantly by human- mediated activities (Bustamante and Castor 1998).

Habitat fragmentation often leads to a reduction in natural population size and an increase in population isolation and may result theoretically in deleterious genetic affects (inbreeding) due to associated reduced levels of genetic diversity and gene flow (Rossetto et al. 2004b, Bacles et al. 2005). There is increasing evidence, however, that plant species can respond to habitat fragmentation in diverse ways that may or may not result in restricted gene flow, reductions in genetic diversity or population divergence (Bacles et al. 2005).

7 Chapter 1. General Introduction

Microsatellite analysis of logged and unlogged sites for a Costa Rican tree (Swietenia macrophylla) identified no evidence for differentiation between fragmented and continuous sites (Céspedes et al. 2003). In contrast, population differentiation increased as a result of habitat fragmentation and population reduction in an endangered Brazilian tropical tree (Caesalpinia echinata) (Cardoso et al. 2005).

Results of some studies have been counterintuitive with habitat fragmentation actually increasing inter-population gene flow and breaking down local population structure, as is the case for populations of the wind-pollinated and wind-dispersed Acer saccharum (Fore et al. 1992, Young et al. 1993). Similarly, pollen movements in the wind- dispersed temperate tree Fraxinus exelsior increased in a fragmented population relative to a continuous population, with associated low inter-population differentiation (Bacles et al. 2005). Such studies demonstrate that the theoretical assumptions about the impact of population isolation on genetic structure and diversity do not necessarily apply universally to all species.

Relative individual life span plays a particularly important role since the effects of genetic drift associated with habitat fragmentation are influenced directly by the number of generations during which population size remains small (Young et al. 1996). As such, short lived species like most herbs may show greater loss of genetic variation than will many relatively long lived species, such as trees (Young et al. 1996).

Populations may be extensively fragmented yet still display low levels of population genetic differentiation due to historical gene flow (Lowe et al. 2005). This genetic continuity may be an historical artefact and remains simply because insufficient generations have passed for populations to diverge (Hartl 1987). Long life cycles in many forest tree species may also conceal or delay the potential effects of habitat fragmentation on levels of genetic variation. There are also greater chances of gene flow among populations in long-lived species due to the extended time scales involved that may counteract short term impacts of drift (Loveless and Harmrick 1984). Molecular genetic studies can therefore be misleading about contemporary population processes in species that have undergone rapid changes in population size and/or migration rate (Moritz 1994). The real effects of genetic erosion may not be revealed for hundreds or even thousands of years in very long-lived species (Bekessy et al. 2002).

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Microsatellite analysis of fragmented populations of the common ash, Fraxinus excelsior (Oleaceae) revealed high levels of genetic diversity, compared with those from continuous populations and low population divergence, suggesting historical gene flow among populations (Bacles et al. 2005).

Another example of how historical processes can affect modern levels of population diversity is apparent in Austromyrtus gonoclada (Myrtaceae), one of Queensland’s rarest rainforest plants known from just 27 individuals. Individuals of A. gonoclada retained higher levels of heterozygosity and overall high genetic diversity compared with other more widespread Austromyrtus species (Shapcott and Playford 1996). It is believed that the 27 remaining individuals of A. gonoclada are representatives of a former more widespread population and modern population diversity is essentially an historical legacy (Shapcott and Playford 1996). There were, however, very few seedlings of A. gonoclada compared with other Austromyrtus species and most seeds fell from the parent plant while green and were non-viable, indicative either of self- incompatibility or climate change from land clearing that resulted in poor rates of (Shapcott and Playford 1996).

The breeding system of a plant can also play a major role in determining how habitat fragmentation will affect population connectivity. A wide variety of sexual and asexual breeding systems are recognised in plant species and the relative importance of sexual versus clonal regeneration varies among plant species and among populations within species. There are also anecdotal reports that some species have apparently abandoned sexual reproduction in favour of clonal regeneration in some habitats (Eckert 2002). Clonality may allow populations to persist in habitats or regions where sexual reproduction has been unsuccessful (Eckert 2002). Interestingly, broad comparisons of the relative amount of genetic diversity in clonal versus sexual species have failed to reveal much difference in relative levels of population differentiation (Hamrick and Godt 1990). This apparent lack of difference may be attributed to the fact that only one or few successfully established seedlings are needed to increase genetic diversity of a predominantly clonally regenerating population (Alfonso-Corrado et al. 2004).

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The variety of modes of sexual reproduction that occur in plant species can affect relative levels of genetic diversity and influence plant population structure (Loveless and Hamrick 1984). Outcrossing plant species have on average, higher overall genetic diversity and reduced population divergence compared with selfing or inbred plant species (Loveless and Hamrick 1984, Hamrick and Godt 1996). Thus, the relationships between breeding system, distribution patterns and relative levels of genetic diversity and population structure can be complex and therefore need to be taken into consideration when implementing conservation management strategies for any species.

1.5 Molecular markers used in conservation genetics

There are a variety of molecular markers available for assessing population genetic diversity and structure in plant species. Over the past decade there have been huge advances in development of molecular markers. Historically allozymes were typically used in assessments of population genetic diversity and structure. Since this time, there has been development of many PCR-based DNA methods. The main nuclear DNA methods used in population genetics studies include RAPDs, AFLPs, ISSRs, and microsatellites. In addition, chloroplast DNA is used to assess historical population structure and gene flow, with PCR-RFLPs most often employed, while chloroplast microsatellites have only recently been developed.

1.5.1 Allozymes

The use of allozymes began extensively in 1966 after the development of the electrophoretic technique in the 1950s (Parker et al. 1998). Allozyme refers to different allelic forms of nuclear-encoded enzymes. Essentially the technique involves running ground up tissue samples through an inert gel (starch or polyacrylamide) with an electrical gradient. Enzymes are separated by size, shape and/or charge. To visualise the different allozyme band positions, enzyme-specific stains are applied to the gel (Parker et al. 1998). The main advantages to using allozymes is that they are relatively inexpensive and relatively straightforward to set up. Furthermore most allozymes represent codominant Mendelian loci so individuals can be identified as homozygous or heterozygous for each locus. The main limitation to using allozymes is the relatively

10 Chapter 1. General Introduction low levels of polymorphism, with less than half of all loci polymorphic, and loci with greater than 3 alleles, relatively uncommon (Parker et al. 1998). In addition, species that have experienced bottlenecks or have narrow, endemic distributions often do not display any polymorphic loci (Parker et al. 1998). Furthermore, allozymes can differ in metabolic function and therefore may not be selectively neutral. Allozyme loci or the traits to which they are genetically linked, may also be under natural selection (Parker et al. 1998).

There have been numerous studies that have used allozyme markers to assess population genetic diversity and structure in endangered plant species and the implications for conservation of the species (Prober and Brown 1994, Shapcott 1994, Shapcott 1997, Starr and Carthew 1998, Auler et al. 2002, Shapcott 2002). In particular, there have been several studies that have used allozymes to investigate population genetic diversity and structure in South American and New Zealand Nothofagus species (Haase 1992, 1993, Premoli 1997, Marchelli and Gallo 2001, Marchelli and Gallo 2004, Premoli and Kitzberger 2005, Torres-Díaz et al. 2007).

1.5.2 Random Amplified Polymorphic DNA (RAPD)

RAPD markers are produced by PCR using short arbitrary oligonucleotide primers (Parker et al. 1998). There are more than 400 different 10-base RAPD primers available commercially (Parker et al. 1998). The resulting amplification products are run on an agarose or polyacrylamide gel. Most of the fragments result from the amplification of a single locus, and can be simply scored as present or absent. The intensity of the band can also vary and has been used in some studies to distinguish dominant homozygote from heterozygotes, however, this practice is not recommended as many other artefacts can cause differences in band intensity (Semagn et al. 2006). The main limitations of RAPDs are their lack of reproducibility, dominant inheritance and homology of bands (Jones et al. 1997, Rajput et al. 2006, Semagn et al.2006). The main advantages of RAPDs is their relatively low cost when using agarose gels and no knowledge of the target species genome is required.

Despite the problems of irreproducibility, RAPDs have been used extensively to assess population genetic diversity and structure in endangered plant species (Rossetto et al.

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1995, Bushakra et al. 1999, Rossetto et al. 1999, Bekessy et al. 2002, Pye and Gadek 2004). RAPDS have also been used commonly in conjunction with other marker systems such as microsatellites, AFLPs and ISSRs to identify clonal diversity in endangered plant species (Kjølner et al. 2004, Esselman et al. 1999, Rossetto et al. 2004a).

1.5.3 Amplified Fragment Length Polymorphisms (AFLPs)

The AFLP technique, like RAPDs, is a DNA fingerprinting method; however, AFLP analysis requires additional steps compared with RAPD analysis. Initially genomic DNA is digested with a rare cutter (EcoRI or PstI) and a frequent cutter (MseI or TaqI) restriction enzyme, then double-stranded oligonucleotide adaptors are ligated to these restriction fragments. This is followed by a pre-amplification round of PCR and finally selective PCR amplification (Semagn et al. 2006). AFLP fragments can then be visualised on polyacrylamide gels with autoradiography or automatic DNA sequencers using fluorescent detection (Semagn et al. 2006). Amplified fragment length polymorphism markers (AFLPs) have been used widely to assess genetic diversity and population structure in plant species (Escaravage et al. 1998, Cardoso et al. 2000, Pornon et al. 2000, Larson et al. 2004, Zawko et al. 2001, Llorens et al. 2004, Prentis et al. 2004, Zartman et al. 2006). AFLP markers are particularly favoured in population genetic studies as they require no species-specific sequence information; many loci are available and they tend to be distributed randomly across the genome. AFLP markers are also reliable, reproducible and require only micrograms of DNA for analysis (Mariette et al. 2001, Cardoso et al. 2005). Additionally, high resolution of AFLP markers often make them very useful for identifying clones and therefore this permits inferences about relative levels of asexual and sexual reproduction within populations to be made (Mueller and Wolfenbarger 1999). One of the main disadvantages of AFLP markers however, is that they are inherited in a dominant fashion and as such genotypic information obtained from each locus is limited (Mariette et al. 2001). Fine scale genetic structure and mating systems are difficult to assess with dominant markers because assumptions of random mating under Hardy-Weinberg equilibrium must be made without any ability to test the assumptions (Mueller and Wolfenbarger 1999, Llorens et al. 2004).

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1.5.4 Inter-Simple Sequence Repeats (ISSRs)

Inter-simple sequence repeat (ISSR) markers amplify DNA segments in between two identical microsatellite repeat regions i.e. this method uses microsatellites as primers. The microsatellite primers are longer than RAPD primers, usually 15-30 mers, which allows higher stringency amplification and less template-primer mismatch artefacts (Semagn et al. 2006). The main advantages of ISSRs are that they exhibit the specificity of microsatellite markers yet no sequence information from the target species genome is required and they generally display high levels of polymorphism (Semagn et al. 2006). Unfortunately, like RAPD markers, ISSRs are limited by their dominant mode of inheritance and varying levels of reproducibility have been reported ranging from 99% to only 86-94% (Semagn et al. 2006).

ISSRs have been used extensively in studies of cultivar diversity in crop species (Wolfe et al. 1998, Joshi et al. 2000, Matos et al. 2001, Quian et al. 2001, Amel et al. 2005, Terzopolous et al. 2005). More recently, ISSRs have been used for analysis of population genetic structure in natural plant populations (Li and Ge 2001, Ge et al. 2003, Cariaga et al. 2005, Taylor et al. 2005, Chung et al. 2006, Xia et al. 2007).

1.5.5 Microsatellites (nuclear)

Microsatellites are variable length, tandemly repeated mono-, di-, tri-, tetra- or penta- nucleotide units that are widely and randomly distributed across the nuclear and chloroplast genome (Powell et al. 1996). They are highly polymorphic, neutral and co- dominantly inherited molecular markers that allow detection of departures from Hardy- Weinberg equilibrium (Galeuchet et al. 2005). Microsatellites are therefore sensitive indicators of subtle changes in genetic diversity and allow specific assessments of levels of inbreeding within small, isolated habitat fragments (Rossetto et al. 2004b) and as such are ideal for studying genetic diversity and structure within and among isolated populations. The biggest limitation of microsatellites is the time spent developing specific primers for each new species. Microsatellite primers require a high degree of homology to the target sequence in order to function and as such species-specific markers need to be developed (Parker et al. 1998). However, there are an increasing

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number of studies reporting successful cross-species amplification of microsatellite loci (Isagi and Suhandono 1997, Steinkellner et al. 1997, Jones et al. 2002, Ainsworth et al. 2003, Barker et al. 2003, Engel et al. 2003, Stamati et al.2003, Jones et al. 2004).

Microsatellites have been used extensively for conservation genetics studies of endangered tree species. Some studies include assessment of genetic diversity and structure in the endangered and/or fragmented species Elaeocarpus grandis, Vouacapoua Americana, Eucalyptus bentahmii and Pinus elliotti var. densa (Rossetto et al. 2004b, Dutech et al. 2004, Butcher et al. 2005, Williams et al. 2007). Microsatellites are also increasingly being used in studies of clonal diversity in tree species including scrub oak (Quercus geminata), poplar (Populus nigra) and western red cedar (Thuja plicata) (Ainsworth et al. 2003, Barsoum et al. 2004, O’Connell and Ritland 2004). Microsatellites are commonly used in combination with AFLPs or RAPDs for assessment of genetic diversity. Some studies on tree species include Pinus pinaster, Quercus petraea, Quercus robur, Malus sylvestris and Elaeocarpus williamsianus, (Mariette et al. 2001, 2002, Coart et al. 2003, Rossetto et al. 2004).

1.5.6 Chloroplast DNA molecular markers

Due to their generally slow rate of evolution, chloroplast DNA (cpDNA) markers can often provide a better picture of more ancient historical factors that may have influenced a species genetic diversity patterns than is possible from equivalent nuclear DNA markers (Newton et al. 1999). In general, the chloroplast genome has a much slower rate of evolution compared with the nuclear genome (Wolfe et al. 1987) and is therefore more appropriate for studying genetic processes and change over very long time scales (Cavers et al. 2003a). In most angiosperms, cpDNA is also maternally inherited via seeds, thus the potential for seed-mediated gene flow is limited and so cpDNA variation tends to be more structured geographically than is nuclear DNA (Newton et al. 1999, Cavers et al. 2003a). Additionally, the chloroplast genome is haploid, which halves the effective population size and increases susceptibility to genetic drift and so increases the rate of population differentiation (Schaal et al. 1998). Overall, differences in chloroplast sequence can persist over long time frames allowing for population differentiation and reconstruction of post-glaciation migration routes (Petit et al. 1993, Dumolin-Lapegue et al. 1997).

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1.5.6.1 Chloroplast PCR-RFLPs

PCR-RFLPs or CAPS (cleaved amplified polymorphic sequences) involves amplification of specific cpDNA loci using universal primers. Primers are anchored within the highly conserved tRNA region or the large single copy region of the chloroplast genome (Demesure et al. 1995, Dumolin-Lapegue et al. 1997). The PCR-RFLP method involves three steps: 1. PCR amplification of non-coding regions of the chloroplast genome using universal primers designed in the conserved coding sequences 2. Restriction digestion of PCR fragment with a variety of restriction enzymes 3. Analysis of polymorphic banding patterns through gel electrophoresis

PCR-RFLP analysis of chloroplast DNA has become routine for assessing phylogenetic relationships within and among plant species. There have been numerous studies on temperate forest trees using cpDNA PCR-RFLPs to investigate population differentiation and to infer glacial refugia and post-glacial migratory paths (Demesure et al. 1996, Marchelli et al.1998, Petit et al. 2002a, 2002b, Jimenez et al.2004, Rowden et al. 2004, Vettori et al. 2004, Lumaret et al. 2005, Magni et al. 2005, Marchelli and Gallo 2006).

1.5.6.2 Chloroplast microsatellites

Chloroplast microsatellites are variable repeat regions within the chloroplast genome. Development of chloroplast microsatellites has only been practiced in recent times (Powell et al. 1995a,1995b). Chloroplast microsatellites are typically <15 mononucleotide repeats but are highly polymorphic, making them ideal for evaluating population genetic structure and phylogeographical patterns (Gugerli et al. 2001, Provan et al. 2001). The major drawback of chloroplast microsatellites lies in their development. Unlike conventional nuclear microsatellites that can be developed via screening genomic libraries, complete sequence data for the species of interest or a closely related species is required for development of chloroplast microsatellites. This is due to the nature of the chloroplast microsatellites being short mononucleotide

15 Chapter 1. General Introduction

repeats that are highly specific (Provan et al. 2001). However, once developed for a single species, chloroplast microsatellites regularly cross-amplify into related species (Provan et al. 2001).

To date, studies that have utilised chloroplast microsatellites have identified far greater levels of diversity than with traditional cpDNA PCR-RFLPs and have been used extensively in studies of gymnosperms (Vendramin et al. 1999, 2000, Ribeiro et al. 2002, Cuenca et al. 2003, Gomez et al. 2003) and in angiosperms (Palmé and Vendramin 2002, Rendell and Ennos 2002, Lira et al. 2003, Rendell and Ennos 2003, Deguilloux et al.2004, Heuertz et al. 2004).

1.5.7 Choosing an appropriate molecular marker

Choosing the molecular marker type to use in a study largely depends on the research question being asked. Assessments of historical population structure, contemporary gene flow and diversity or clonal identity all require differing levels of marker resolution and polymorphism. Additionally, the time and cost of materials needs to be taken into consideration (Parker et al. 1998). Each molecular marker is powerful on its own but is often more informative when used in conjunction with other marker systems (Esselman et al. 1999, Mariette et al. 2001, Zawko et al.2001, Cavers et al. 2003b, Coart et al. 2003, Rossetto et al. 2004b) and with ecological and demographic data from the field (Haig 1998). However, the cost and time of collecting field data within a certain time frame often means molecular markers can provide the fastest evaluation of conservation issues (Haig 1998).

In general, allozymes provide a quick, cheap method for investigating genetic structure in plant species, provided sufficient variability can be detected. Similarly, RAPDs are fast and relatively cheap to use to quickly evaluate population genetic structure, however, RAPDs are increasingly becoming outdated and many journals will now not accept RAPD data for publication (Pérez et al. 1998, Jones et al. 1997, Rajput et al. 2006). If greater polymorphism levels are required, especially when discriminating clones, then AFLPs and ISSRs are far more informative. Microsatellites are undoubtedly the best available markers to use at present, when looking to address questions of paternity and clonal identity; however, the time and money involved in

16 Chapter 1. General Introduction developing species-specific markers when no cross-species markers are available, may make microsatellites non-viable for some studies.

1.6 Case study: Nothofagus moorei

1.6.1 The current distribution of Nothofagus moorei

Nothofagus moorei is one of the most geographically isolated Australian Nothofagus species, and is found only in disjunct habitats in eastern Australia, separated by almost 1,000km from its closest living relative in the south (N. cunninghamii) and by more than 2,000km from its northern relatives in New Guinea (Bale and Williams 1993). Nothofagus moorei’s distribution spans just four degrees of latitude and occurs as a series of disjunct, isolated populations in five separate regions within New South Wales and Queensland: Barrington Tops, Dorrigo/New England, and Werrikimbe in NSW and along the New South Wales/Queensland border at Lamington/Border Ranges and Ballow, respectively (Figure 1.1) (Read and Brown 1996).

Nothofagus-dominated cool temperate rainforests in northern New South Wales and southern Queensland are floristically simple forests with only one to three common tree species present forming a single or two-layered canopy of dense, even and uniform height (Floyd 1990). Stranglers and palms are absent from the understorey of these forests while ground ferns and tree ferns are very common and mossy epiphytes and are abundant (Adam 1992). Cool temperate rainforests dominated by N. moorei occur at altitudes of 900 to 1500 metres above sea level in cool, reliably moist areas such as those areas along the coastal escarpment where mists are frequent and annual rainfall is usually between 1750 and 3000mm (Floyd 1990, Adam 1992).

Surrounding vegetation types vary from region to region. Within the Lamington, Ballow and Werrikimbe regions, N. moorei cool temperate rainforests are bordered by warm temperate and sub-tropical forests. In contrast, the N. moorei forests of New England and Barrington have distinct ecotones with eucalypt woodlands (Read and Hill 1985, Bale and Williams 1993).

17 Chapter 1. General Introduction

N

20ºS

Lamington/Border Ballow Ranges 28ºS 30ºS Dorrigo/New England 32ºS Werrikimbe

Barrington

40ºS

0 400km

Figure 1.1 Distribution of extant N. moorei populations across South-East Queensland and northern New South Wales. The limited latitudinal range of N. moorei is shown by the dashed blue lines.

1.6.2 Systematics

Nothofagus moorei belongs to the Order Fagales and monogenetic Family Nothofagaceae. Traditionally, all Nothofagus species were placed in the Family along with the (Fagus) and oaks (Quercus) due to morphological similarities with these genera (Rix and Jackson 2004). Sequencing of the chloroplast matK and nuclear rbcL genes within the Fagales Order however, suggested that Nothofagus should be assigned to a monophyletic family (Nothofagaceae) (Manos and

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Steele 1997) (Figure 1.2). Further molecular analysis within Nothofagus using chloroplast atpB-rbcL, nuclear ITS and analysis of pollen morphology resulted in four subgenera being recognised: Lophozonia, Fucospora, Nothofagus and Brassospora (Setoguchi et al. 1997). Nothofagus moorei is a member of the Lophozonia subgenus, and based on sequence data from the chloroplast atpB-rbcL intergenic spacer region, the nuclear rbcL region and the nuclear ribosomal ITS region and morphology, is most closely related to N. cunninghamii (Manos 1997, Setoguchi et al. 1997). Nothofagus moorei is commonly known as Antarctic Beech.

Figure 1.2 Phylogeny of the Fagales showing the monophyletic Nothofagaceae family. This phylogeny is based upon sequence data of the chloroplast matK gene and nuclear rbcL gene (reproduced from Stevens, P. F. {2001 onwards}. Angiosperm Phylogeny Website. Version 7, May 2006 http://www.mobot.org/MOBOT/research/APweb/).

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1.6.3 Morphology

Mature N. moorei individuals vary in height from 35 to 50 metres with a diameter of individual stems reaching up to 2 metres (Poole 1987, Rix and Jackson 2004). Individual trees can consist of either single stems or multiple stems that arise from an epicormic basal burl (Howard 1981). Bark of the trunk is rough and scaly (Rix and Jackson 2004). Leaves are , toothed, and ovate and grow in an alternating pattern (Figure 1.3). Leaf length varies from 3.5 to 5 cm on mature or flowering shoots to 5 to 10 cm long on vigorous shaded or coppice shoots (Rix and Jackson 2004). Nothofagus moorei has one major leaf growth flush of red leaves each year in early spring, with an occasional second minor flush in summer (Figure 1.4) (Poole 1987, Read and Brown 1996). Leaf fall occurs in autumn and spring with leaf life spans of approximately two years (Poole 1987, Read and Brown 1996). The trunks of N. moorei trees provide habitat for many species of and the beech orchid (Dendrobium falcarostrum) (Poole 1987).

Figure 1.3 Leaf morphology in N. moorei. (Photo reproduced with permission from Australian National Botanic Gardens website: www.anbg.gov.au).

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Figure 1.4 Characteristic new red leaves produced in spring in N. moorei. (Photo reproduced with permission from Mount Tomah Botanical Gardens website. © Botanic Gardens Trust / Simone Cottrell).

Flowers of N. moorei are unisexual. Male flowers are stalked in clusters of 5 to 7, forming below and maturing before the female flowers. The female flowers are sessile in groups of 3 surrounded by bracts. Winged achenes are small (8-10mm) with narrow wings (Figure 1.5).

Figure 1.5 Seed pods of N. moorei. (Photo reproduced with permission from: www.bio.mq.edu.au/ecology/abasden/Research.html).

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1.6.4 Breeding system

Nothofagus moorei is monoecious, wind-pollinated and exhibits masting (irregularly spaced years of heavy flowering followed by years of little/no flowering). Flowering occurs in spring from August to October while (2-3 winged achenes) are released from December to March (Read and Brown 1996). Seeds germinate and establish rapidly, but few mature under the closed rainforest canopy (Poole 1987, Floyd 1990, Read and Brown 1996). Seedlings are shade intolerant with continuous seedling regeneration in undisturbed, closed canopy forests not reported, although seedling regeneration is quite common in large canopy gaps, landslip areas and ecotones within eucalypt-dominated forests where light intensities are high (Figure 1.6) (Simpson 1976, Howard 1981, Read and Hill 1985). Seed dispersal is believed to be poor in this species, with large gravity-dispersed achenes poorly adapted for long-distance dispersal (Read and Brown 1996). There is potential however, for pollen-mediated long-distance gene flow to provide connectivity among disjunct populations owing to N. moorei’s monoecious, wind-pollinated habit (Read and Brown 1996).

N. moorei closed canopy forest

Young N. moorei sapling

Open eucalypt woodland

Figure 1.6 Immature tree of N. moorei invading open eucalypt forest ecotone. (Photo reproduced with permission from www.bio.mq.edu.au/ecology/abasden/Research.html).

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Nothofagus moorei exhibits vigorous asexual regeneration via coppicing from an epicormic burl (Poole 1987, Floyd 1990). The epicormic burl develops as a corrugated, gnarled bulge up to twice the basal stem diameter from below the level and extends two to three metres up the trunk (Figures 1.7 and 1.8). and coppice shoots grow at random from this woody mass of dormant buds even when the crown is intact (Howard 1981). Coppicing allows N. moorei to regenerate continuously and self-replace and so retain a position within the forest (Turner 1976, Poole 1987, Read and Brown 1996).

Immature stem

Mature stem

Decayed central parent

Coppicing leaves

Figure 1.7 Multi-stemmed N. moorei individual. Central parent gave rise to numerous coppicing stems over time. The different stem sizes reflect the age differences in coppicing.

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Swollen basal burl

Figure 1.8 Characteristic swollen basal burl of N. moorei. (Photo reproduced with permission from Wikipedia).

1.6.5 Effects of fire on N. moorei populations

Dense, vigorously growing stands of N. moorei prevent deep penetration of fire into the forests due to high humidity and low quantity and flammability of fuel (Read and Brown 1996). When fire penetrates around the edges and into the less vigorously growing stands of N. moorei however, eucalypts can invade and a mixed forest will result (Poole 1987). The establishment of eucalypts on the boundaries of N. moorei dominated cool temperate rainforests will increase the likelihood of future fires due to the increased flammability and promotion of fire by sclerophyllous eucalypts (Read and Brown 1996). Coppicing enables individual N. moorei trees damaged by fire to remain viable and hence to retain their place in the forest. A transient balance exists therefore, between N. moorei dominated cool temperate rainforest and eucalypt-dominated sclerophyll largely determined by the frequency and intensity of fires (Poole 1987, Read and Brown 1996). This effect is seen within the Barrington Tops populations in particular where stands of N. moorei grow in a matrix of forests dominated by eucalypts (Poole 1987, Read and Brown 1996). Cool temperate rainforests are currently expanding into eucalypt woodlands at Barrington Tops, although recent fires in 2003 may have swung the balance in favour of eucalypts since then. Recent fires (in the last 20 years), are

24 Chapter 1. General Introduction also thought to be the cause of the disappearance of an advancing N. moorei forest on Mt Widgee along the McPherson Range in Lamington National Park. Young N. moorei trees were observed to be colonising surrounding sclerophyllous forest dominated by Eucalyptus and species (Herbert 1936, Simpson 1976). Both Herbert (1936) and Simpson (1976) noted the advance of young N. moorei trees into areas occupied formerly by more open eucalypt dominated forest. Under the new N. moorei canopy sclerophyllous Xanthorrhoea arborea (grass trees) persisted, covered with the same species of epiphytic ferns, mosses and orchids on their trunks as seen on the trunks of N. moorei (Herbert 1936). On the exposed northern ridge of Mt Widgee scattered seedlings of N. moorei were also observed and it was predicted that they would eventually suppress growth of Xanthorrhoea and Eucalyptus as they matured and formed a closed canopy (Herbert 1936).

1.6.6 Absence of N. moorei from climatically suitable sites

Nothofagus forests are absent currently in many sites that are apparently, climatically suitable. The absence of N. moorei from the Bulga Plateau in northern NSW is thought to be due to land clearing in the early 20th century. Nothofagus moorei is also absent from the in NSW however, presence of a small population of the rufous scrub bird (Atrichornis rufescens), a species commonly associated with cool temperate rainforests, suggest that N. moorei may have previously inhabited the area but has since become extinct (Adam 1992).

1.6.7 Conservation status of N. moorei

Surprisingly, N. moorei is not classified as endangered or vulnerable. Given the current disjunct distribution of N. moorei, a predominantly asexual mode of regeneration and global warming effects, this species probably should have been listed as vulnerable.

According to the Environmental Protection and Biodiversity Conservation Act 1999 (EPBC Act), threatened species are classified as either extinct, extinct in the wild, critically endangered, endangered, vulnerable or conservation dependent. Species can

25 Chapter 1. General Introduction be nominated for consideration by any member of the public annually. According to section 179 of the EPBC Act, a native species is considered critically endangered, endangered or vulnerable if it meets any one of the five selection criteria outlined in the EPBC regulations 2000. Briefly these five selection criteria include: (1) The species has undergone, or is suspected to have undergone, or is likely to undergo a reduction in numbers. (2) The species geographic distribution is precarious for the survival of the species and is very restricted to limited (3) The estimated total number of mature individuals will continue to decline OR will likely continue to decline and its geographic distribution is precarious for its survival (4) The estimated total number of mature individuals is extremely low to low. (5) The probability of its extinction in the wild is at least 50% in the immediate future to 10% in the medium-term future.

When considering the status of N. moorei against these selection criteria, the species most certainly warrants protection as a vulnerable species, especially when we consider that approximately two thirds of N. moorei dominated rainforest are within secure reserves in NSW and all of the approximate 700 hectares of N. moorei forests in QLD are within secure reserves. Reserves are not all National Parks however, with some being forestry reserves and with one third of NSW forests still vulnerable to exploitation (Read and Brown 1996).

1.6.8 Threatened species associated with N. moorei dominated cool temperate rainforests

The cool temperate rainforests of northern NSW and south-east QLD provide an important habitat for many other unique plant and animal species. Nothofagus moorei is considered a “keystone species”, that is, a species that is an important component to the cool temperate rainforest community; and reduced genetic diversity in this keystone species may also decrease the resilience of associated species to environmental change (Williams et al. 2007). There are at least three well known species closely associated with N. moorei-dominated cool temperate rainforests: the rufous scrub bird (Atrichornis rufescens), the beech orchid (Dendrobium falcorostrum) and Novacastria

26 Chapter 1. General Introduction nothofagi (beech beetle) (Selman and Lowman 1983, Poole 1987, Dockrill 1992, Ryan 1995). Additionally, the trunks of N. moorei trees provide a complex intergrading microhabitat for several species of corticolous bryophytes (Franks and Bergstrom 2000).

The beech orchid (Dendrobium falcorostrum) grows almost exclusively on the trunks and branches of N. moorei trees (Figure 1.9) (Dockrill 1992). This orchid is distributed in the same regions as N. moorei from Barrington to Lamington at altitudes above 750m, although rarely it may be found at lower altitudes and attached to rocks (Dockrill 1992). Although the beech orchid is not listed as vulnerable, a reduction in the number of mature individuals of N. moorei over time could simultaneously result in a reduction in number of beech orchids.

Figure 1.9 The characteristic growth of Dendrobium falcorostrum on the upper trunk of Nothofagus moorei. (Photo reproduced with permission from the Australian Native Orchid Society website: http://www.ourshopfront.com/kabi/Natives/Dendrobium%20falcorostrum.php).

27 Chapter 1. General Introduction

The rufous scrub bird (Atrichornis rufescens) (Figure 1.10) is listed as a vulnerable species in NSW according to the NSW Threatened Species Conservation Act 1995. It inhabits the sub-tropical, warm and cool temperate rainforests at high altitude along the NSW/QLD border. It is often best observed in Lamington National Park along the Main Border walking track between Mt Bithongabel and Mt Hobwee (Ryan 1995).

Figure 1.10 The vulnerable rufous scrub bird (Antrichornis rufescens) inhabits N. moorei dominated cool temperate rainforests along the QLD/NSW border. (Photo reproduced with permission from School of Natural and Rural Systems Management, University of Queensland: http://lamington.nrsm.uq.edu.au/MainMenu.html. Photographer: Glenn Threlfo).

The most closely associated species with N. moorei is the beetle Novacastria nothofagi (Coleoptera: Chrysomelidae). The larvae of this species feed exclusively on the young leaves of N. moorei. Experimental tests revealed that when given the choice of leaves from N. moorei and two other understorey species associated with N. moorei forests, larvae would feed preferentially on N. moorei leaves. Most significantly, larvae would not eat the leaves of the other species even in the absence of N. moorei leaves, and consequently died (Selman and Lowman 1983).

28 Chapter 1. General Introduction

When considering the close association of the aforementioned species, it becomes obvious that preservating N. moorei is important not only for a single species, but for the unique cool temperate rainforest community that depends on N. moorei for habitat.

1.7 Predicted future climate change and the fate of N. moorei

Global warming has already seen average surface temperatures rise by 0.6 ±0.2ºC since 1900 but in Australia the average temperature increase has been 0.8ºC since 1910 (Hughes 2003). The majority of temperature increase has occurred post 1950, with the 1980’s and the 1990’s the warmest and second warmest decades on record, respectively (Hughes 2003). It is predicted that by 2030, average temperatures will increase by 0.4 to 2.0ºC relative to 1990. Rainfall is predicted to increase in some areas of Australia but decrease in south-western Australia and south-east Queensland (Hughes 2003). This rapid climate change puts many species at risk of extinction by shifting the climatic range within which species can persist outside of their current geographical range (McLachlan et al. 2005).

Level of extinction risk will depend on the rate at which species can extend, adapt or expand their natural ranges (McLachlan et al. 2005). The current accelerated climate projections necessitate range shifts in many North American tree populations at rates of 100 – 1000 metres/year (Davis and Zabinski 1992, Iverson and Prasad 2002). Rapid postglacial migration has become widely accepted for many Northern hemisphere temperate forest trees with estimated migration rates in North American trees of more than 100 metres/year. This suggests that temperate trees have the capacity to track future climate change via rapid migration (McLachlan et al. 2005). Indeed during the Tertiary, the distributions of cool temperate rainforests dominated by Nothofagus expanded and contracted in response to ongoing climate change (Adam 1992, Hewitt 2000).

Although most tree species have been subjected to large-scale global environmental changes many times during their evolutionary histories and many species have survived, there are major differences today from historical climate change effects (Hamrick 2004). Rapid climate change now is occurring at a time when landscapes are

29 Chapter 1. General Introduction highly fragmented as a result of urbanisation and agriculture (Davis and Shaw 2001). Consequently, the ability of species to shift their ranges in response to environmental changes will be restricted by available habitat to colonise (Hamrick 2004). Additionally, the rapid rate at which climate is changing, may result in tree species that possess long generation times, being unable to adapt sufficiently quickly (Hamrick 2004).

Global environmental changes are certain to affect tree species distributions and individual abundance with some species expanding and others contracting their ranges (Hamrick 2004). If environmental changes continue over long time periods or if climate change occurs too rapidly for long-lived species to adapt, some species are likely to face extinction (Hamrick 2004). Tree species with restricted ranges and/or reduced levels of genetic variation are most likely to be prone to extinction under this scenario (McLaughlin et al. 2002).

Read and Brown (1996) predicted that the most vulnerable species in an environment exposed to changed climate and disturbance will be those with restricted distributions and poor vagility. On this basis, Nothofagus moorei can be considered to be vulnerable as populations are disjunct and isolated, seed dispersal is poor and successful sexual regeneration appears to depend on habitats being disturbed (Hunter 1988, Read and Brown 1996). While the majority of N. moorei populations are currently secure within National Parks and reserves (Read and Brown 1996), effects of climate change are increasing and will likely have a growing negative impact on N. moorei populations in the future.

The populations most threatened with extinction are those located in the closed canopy forests within the Lamington/Border Ranges region. Seedling regeneration within these populations is apparently rare and populations are maintained largely via vegetative coppicing (Adam 1992). Large canopy gaps created as a result of dieback and tree-fall are replaced by competitively superior sub-tropical and warm temperate tree species, an effect that could eventually result in N. moorei population extinction. Predictions of increased ambient temperatures and reduced rainfall will further encourage replacement of N. moorei dominated cool temperate rainforests by warm temperate and sub-tropical tree species.

30 Chapter 1. General Introduction

Southern populations of N. moorei are faced with additional threats associated with increased fire frequencies. Populations in the Barrington, Dorrigo/New England and Werrikimbe regions are surrounded by fire-prone Eucalyptus forests. Increased temperatures and reduced rainfall are predicted to increase fire frequencies that are likely to result in gradual encroachment of eucalypt forest into the remaining cool temperate rainforests currently dominated by N. moorei.

1.8 Thesis structure and aims

The ability of N. moorei populations to persist during future rapid climatic change remains unknown. The presence of disjunct and isolated populations, limited successful sexual regeneration, predominant clonal regeneration and limited capacity for long distance dispersal effectively makes N. moorei very vulnerable at least to local population extinctions, if not total extinction. Since future evolutionary potential will depend on adequate levels of genetic diversity remaining, an assessment of the levels and patterns of genetic diversity within and among all extant populations of N. moorei is a necessary precursor if predictions are to be made about the ability of extant populations to persist into the future and for development of appropriate conservation management plans for the species.

Prior to the current study there has been only a single study published on genetic diversity levels and population structure in N. moorei populations. Taylor et al. (2005) examined 146 individuals from 20 populations from the northern and southern limits of N. moorei’s distribution in the McPherson and Barrington Ranges using Inter-Sequence Simple Repeats (ISSRs). Genetic diversity was substantially higher in northern compared with southern populations and there was also strong regional differentiation between northern and southern populations (Taylor et al. 2005). Furthermore, no evidence for clonal regeneration outside the multi-stemmed basal burls was apparent in northern populations as had previously been reported by Howard (1981) and Floyd (1990) (Taylor et al. 2005). Since this study only sampled populations from the southern Barrington region and northern QLD/NSW border region (see Figure 1.1) and only sampled 1-10 individuals from each population, no strong conclusions could be made about the levels of diversity or significance of regional differentiation in extant

31 Chapter 1. General Introduction

N. moorei populations. Furthermore, analysis of clonal regeneration was restricted to only 13 individuals from just a single population.

The current study aimed to investigate the relative conservation value of extant populations of N. moorei across the species’ entire extant range. This study aimed to answer the following specific questions:

(1) How extensive is clonality across N. moorei’s natural distribution?

This question was addressed by comparing individual similarity coefficients within each population for nuclear AFLPs and microsatellite marker data. Northern populations of N. moorei have been reported to be regenerating primarily via coppice (Howard 1981, Floyd 1990). Genetic assessment of clonality may provide additional evidence for this observation. The levels of clonality within populations have implications for the long term persistence of N. moorei populations.

(2) How genetically diverse are extant N. moorei populations?

Nuclear AFLPs and microsatellite markers were used to address this question. Given that many N. moorei populations apparently reproduce clonally and that there has been historical expansions and contractions in distribution, reduced levels of genetic diversity and high levels of inbreeding may be predicted. Depauperate levels of genetic diversity and high inbreeding may reduce N. moorei population fitness, evolutionary potential and long-term population persistence.

(3) Do N. moorei populations show population differentiation and regional population structuring?

Nuclear AFLPs and microsatellite markers were used to assess recent population differentiation while chloroplast PCR-RFLP markers were used to assess ancient, pre- glacial population divergence. Describing population structure allows inferences to be made about the levels of dispersal among populations, and the evolutionary history and potential for diversification among populations (Avise 1992). Assessment of population

32 Chapter 1. General Introduction structure will allow the potential for dispersal among populations and the impact of geographical barriers on dispersal to be quantified.

33 Chapter 2. General Methods

Chapter 2. General Methods

2.1 Study sites and sampling

2.1.1 Study sites

To investigate the pattern of genetic structure and diversity in N. moorei, twenty populations were sampled from across the species’ entire natural distribution (Figure 2.1 and Table 2.1). Nothofagus moorei has a much larger distribution in the northern region of Lamington, Border Ranges and Ballow compared with the area occupied by N. moorei in the remaining regions of New England/Dorrigo, Werrikimbe and Barrington (Noel Meyers, pers. communication). More populations were sampled therefore from the northern region compared with other regions so as to sample representative populations across the species’ natural distribution. While there are several accessible populations within the Barrington region, only two populations were sampled as this region was initially only sampled as part of a pilot study. Additional populations from the Barrington region were not sampled later in the study, as the two previously sampled populations were believed to be representative of the southern region.

Only two populations were sampled from the Werrikimbe region as these were the only populations known to be accessible in this region. Similarly, within the Dorrigo/New England region, only three populations were sampled as these were the only known easily accessible populations here. Two populations within the Dorrigo/New England region of N. moorei’s distribution were also unable to be sampled, due to poor accessibility. A remote population of N. moorei occurs within a very small area in Cathedral Rock National Park, north-west of New England National Park. Cathedral Rock National Park is predominantly covered in bare granite rock and poor , with vegetation predominantly mallee heath and eucalypts. Nothofagus moorei survives in this unusual landscape by occupying wetter gullies where it is protected from fire by granite tors, making this population quite unique (Williams 1997).

Similarly, a population in Cannawarra National Park, south-west of New England National Park, could not be sampled due to accessibility problems. This particular

34 Chapter 2. General Methods population is located at Beech Lookout on Styx Valley Way, and is found at the base of cliffs (Figure 2.2).

A field trip to sample N. moorei trees at Mt Widgee on the eastern McPherson Range of Lamington National Park proved unsuccessful. The Mt Widgee population was reported to be the only population on the eastern McPherson range actively undergoing sexual regeneration and expanding into the open sclerophyllous woodland (Herbert 1936, Simpson 1976). Fires have swept through this population in recent times as evident by the presence of charred eucalypts and grass trees and N. moorei was apparently absent from this site.

2.1.2 Sampling strategy

To assess population genetic diversity and structure, between 21-41 individuals were sampled from each of 20 populations (see Table 2.1 for detail). Within each population, a single individual tree was selected randomly and all surrounding neighbours sampled. In some populations, such as Tullawallal, this resulted in the entire population being sampled, while in other populations, this sampling strategy resulted in a sub-sample of the total population. Sampling of neighbouring individuals was adopted so that assessment of genetic similarity of nearest neighbours could be made in addition to sub-sampling genetic diversity within populations.

An individual was defined as a multi- or single stemmed tree visibly separated from surrounding individuals above the ground level. Approximately 10 coppicing leaves were sampled from the basal burl of each individual. Coppicing leaves were sampled due to ease of access; however, this resulted in only old trees being sampled. In some populations, younger age cohorts were present (based on trunk diameter); however, coppicing leaves were not available for sampling from young trees.

To determine if multi-stemmed plants were clonal or if they represented discrete and hence genetically variable individuals, coppice samples were taken from each stem of a multi-stemmed tree for the Tullawallal and Mt Wanungra populations.

35 Chapter 2. General Methods

To ascertain whether seeds had established in the basal burl, coppicing shoots and canopy leaves from the same parental individual were sampled for 30 individuals from the Link Trail and Gloucester Tops populations.

Leaf samples were stored at ambient temperature during sampling in the field then stored at -20ºC until DNA extraction. The distance between each sampled individual was recorded and an XY co-ordinate map later drawn up for each population to show the spatial distribution of individuals sampled within each population (data not shown). The XY co-ordinates were later used in spatial autocorrelation and in a Mantel test to assess isolation by distance within individual populations.

36 Chapter 2. General Methods

N Lamington

Border Ranges

Ballow

Dorrigo/ New England

Werrikimbe

Barrington

Figure 2.1 Map illustrating location of Nothofagus moorei study sites (black dots) and regions (red arrows and text).

37 Chapter 2. General Methods

Table 2.1 Geographical location of N. moorei populations within each of the 5 main regions and sample size (N) for each population. Population abbreviations in brackets.

Region /Population Latitude Longitude N SE

Lamington/Border Ranges Tullawallal (TW) 28°12’30” 153°11’30” 26 Mt Wanungra (MtW) 28°15’10” 153°10’35” 34 Echo Point (EP) 28°16’50” 153°10’30” 25 Elabana Falls (EF) 28°14’35” 153°09’10” 30 Lightning Falls (LF) 28°15’30” 153°09’40” 33 Mt Hobwee (MH) 28°15’20” 153°12’05” 39 Best of All Lookout (BAL) 28°13’28” 153°14’45” 27 Antarctic Beech Picnic area (ABPW) 28°22’47” 153°05’28” 21 Bar Mtn (BM) 28°23’48” 153°06’59” 31 Helmholtzia Loop (HL) 28°22’38” 153°03’56” 41

Ballow Durramlee and Mowburra Peaks (DUMP) 28°15’05” 152°37’45” 30 Mt Ballow (BA) 28°16’30” 152°36’30” 30 Nothofagus Mtn (NO) 28°17’05” 152°36’50” 30

Dorrigo/New England Kilungoondie (KG) 30°21’30” 152°43’20” 30 Mt Moombil (MM) 30°28’30” 152°51’20” 29 Weeping Rock (WR) 30°30’37” 152°25’21” 33

Werrikimbe Mt Banda (MB) 31°10’20” 152°24’06” 29 Plateau Beech (PB) 31°00’47” 152°14’41” 29

Barrington Gloucester Tops (GT) 32°05’30” 151°30’30” 30 Link Trail (LT) 32°05’00” 151°35’05” 30

38 Chapter 2. General Methods

Cliff Edge

Canopy of N. moorei tree

Figure 2.2 Photo illustrating the difficult access to the Beech Lookout population of N. moorei in Cannawarra National Park, part of the New England/Dorrigo region.

2.2 DNA extractions

Total genomic DNA was extracted using a modified CTAB protocol (Doyle and Doyle 1987). Approximately 1-2 leaves (0.5g) were combined with liquid nitrogen and samples ground to a fine powder using a mortar and pestle. The leaf powder was homogenised with 3ml of CTAB extraction buffer (100mM Tris-HCl pH 8.0, 1.4M NaCl, 20mM EDTA, 2% w/v CTAB, 1.5% w/v PVP {poly-vinyl-pyrolidone MW 40000}) in a mortar and pestle then transferred to a 5ml graduated tube and 60µl of β- mercaptoethanol was added and mixed. Samples were incubated at 60ºC for 30- 60min then extracted twice with chloroform:isoamyl alcohol (24:1). DNA was precipitated with an equal volume of 100% EtOH and 1/10th volume 7.5M Ammonium Acetate at -20ºC overnight. DNA was then pelleted via centrifugation at 6000rpm for 30min at -20ºC and the DNA pellet subsequently washed with 70% EtOH, air-dried and resuspended in 100µl of TE buffer. DNA was quantified spectrophotometrically at 260nm using a Biophotometer (Eppendorf) (Sambrook and Russel 2001).

39 Chapter 2. General Methods

2.3 AFLP analysis

Amplified fragment length polymorphism (AFLP) analysis is a DNA fingerprinting technique based on the detection of genomic restriction fragments by PCR amplification (Vos et al. 1995). The AFLP technique involves five steps: 1. restriction digestion 2. ligation of specific adaptors 3. pre-selective amplification 4. selective amplification 5. gel electrophoresis

AFLP markers were used as they show high levels of polymorphism and are cost effective, with the same primer sets amplifying across species (Vos et al. 1995). Furthermore, primers and adaptors were readily available. AFLPs have been used extensively in studies that have investigated genetic diversity and structure in threatened plant species (Cardoso et al. 2000, Larson et al. 2004, Zawko et al. 2001, Llorens et al. 2004, Prentis et al. 2004, Zartman et al. 2006), in addition to studies on clonal diversity and identity (Escaravage et al. 1998, Pornon et al. 2000, Suyama et al. 2000, Moriguchi et al. 2001, Douhovnikoff and Dodd 2003). These attributes therefore made AFLP markers ideal for assessing not only the levels of genetic diversity and structure within N. moorei populations, but also for identification of clonal individuals within populations.

2.3.1 Restriction digestion of genomic DNA and ligation to adaptors

Approximately 400ng of genomic DNA was digested with the restriction enzyme MseI (5 units MseI, 2.5 µl buffer A {Roche; 33mM Tris-Acetate, 10mM Mg-Acetate, 66mM K- Acetate, 0.5mM dithiothreitol, pH 7.9}, 12.5 µl 100 ng/µl BSA, total volume 25 µl) at 65ºC for 1 hour. The reaction was stopped by icing samples for 10 minutes. This digest mix was then further digested with the restriction enzyme EcoRI (5 units EcoRI, 1.5 µl buffer A {Roche}, 7.5 µl 100 ng/µl BSA, total volume 40 µl) at 37ºC for 1 hour. After digestion, adaptor sequences (Table 2.2) were ligated to the sticky ends of the fragments using T4 DNA ligase (1 µl ligase, 1 µl of each adaptor, 0.5 µl 1000 ng/µl BSA, 1 µl 1mM ATP, 1 µl buffer A {Roche}, total volume 50 µl ) at 37ºC for 3 hours.

40 Chapter 2. General Methods

Table 2.2 AFLP adaptor and primer sequences used in study of Nothofagus moorei.

Adaptor/Primer Sequence

MseI adaptors 5’ – GAC GAT GAG TCC TGA G - 3’ 5’ – TAC TCA GGA CTC AT - 3’

MseI primers M01 5’ – GAT GAG TCC TGA GTA AC – 3’

M49 5’ – GAT GAG TCC TGA GTA A CAG – 3’

M55 5’ – GAT GAG TCC TGA GTA A CGA – 3’ EcoRI adaptors 5’ – CTC GTA GAC TGC GTA CC – 3’ 5’ – AAT TGG TAC GCA GTC TAC – 3’

EcoRI primers E01 5’ – GAC TGC GTA CCA ATT CA – 3’

E33 5’ – GAC TGC GTA CCA ATT CAA G – 3’

E45 5’ – GAC TGC GTA CCA ATT CAT G – 3’

E42 5’ – GAC TGC GTA CCA AAT CAG T – 3’

2.3.2 Preamplification reactions

Ligated genomic DNA was diluted with 450 µl TE buffer. 5 µl of this ligation mixture was preamplified with a non-selective primers E01 and M01 (Table 2.2) in a PCR master mix consisting of 5 µl 10 x buffer, 0.2 µl Taq {Roche}, 1 µl 10 mM dNTPs, 1 µl 75ng/µl

E01, 1 µl 75 ng/µl M01, total volume 50 µl). The reaction was amplified in a thermocycler, using 30 cycles of 94ºC for 30 sec, 56ºC for 1 min, 72ºC for 1 min, followed by a final extension at 72ºC for 10 min. PCR reactions were diluted 1:50 with TE buffer at completion of PCR.

2.3.3 Primer labelling and selective amplification

For selective AFLP-PCR EcoRI and MseI primers that have an additional three arbitrary nucleotides at the 3’ end were used to amplify an aliquot of the library of preamplified fragments. Selective AFLP-PCR reaction consisted of 5 µl of diluted preamplified DNA, 1.2 µl 25mM MgCl, 2 µl 10x buffer, 2.5 µl dNTPs {- dATP}, 1 µl each of selective primer

(Table 2.2), 0.2 µl Taq {Roche}, 0.1 µl ATP33 in a total volume of 20 µl. Selective primer combinations used were: E42-M55, E45-M49, E33-M55. The reaction was amplified for one cycle of 94ºC for 30 sec, 61.5ºC for 30 sec, 72ºC for 1 min. This was repeated 30 times

41 Chapter 2. General Methods with a lowering of the annealing temperature by 0.7ºC per cycle. This was followed by a final extension at 72ºC for 5 min.

2.3.4 Electrophoresis

The radio-labelled, selective PCR reaction was mixed with loading buffer (1:1) {98% v/v formamide, 10mM EDTA, 0.025% w/v bromophenol blue, 0.25% w/v xylene cyanol}, heated to 90ºC for 3 min and then cooled on ice. Samples were then loaded on a 5% denaturing polyacrylamide gel and electrophoresed for 2 hrs 45 min at a constant power of 75W in 1X TBE running buffer. The gel mix of 5% v/v acrylamide:bis (19:1), 1 x TBE buffer and 8M urea was polymerised by the addition of 450 µl of 10% APS and 300 µl TEMED to 250ml of gel mix (Vos et al. 1995). After electrophoresis, gels were transferred onto 3MM Whatman chromatography paper, covered with plastic wrap and dried under a heated vacuum for ~2hrs. Dried gels were then exposed to Kodak autoradiographic film for 1-3 days and the film developed in an automatic developer machine. Bands were visualised on the autoradiographic film using a light box and scored as present or absent (see section 2.6.1).

2.3.5 Error rate

To determine the error rate associated with AFLP fingerprinting of N. moorei samples, DNA was extracted 6 times from 6 different individuals and processed as if they were independent samples. The average number of differences across the 6 replicated samples was used as an indicator of experimental and total error.

2.4 Microsatellite Analysis

Microsatellite markers were used in addition to AFLPs so that two independent indicators of genetic diversity and structure could be obtained. Like AFLPs, microsatellites are highly polymorphic and they have been used successfully in numerous population genetic diversity and structure studies on endangered and/or fragmented tree species (Rossetto et al. 2004b, Dutech et al. 2004, Butcher et al. 2005, Williams et al. 2007). Additionally, microsatellites have been used increasingly for

42 Chapter 2. General Methods assessment of clonal diversity in tree species (Ainsworth et al. 2003, Barsoum et al. 2004, O’Connell and Ritland 2004). Thus, the use of microsatellites in addition to AFLPs enabled confirmation of clones identified with AFLPs. Microsatellites are also more informative than AFLPs due to their codominant mode of inheritance, thus they allow for assessment of allele frequencies, levels of heterozygosity and inbreeding within and among populations (Jarne and Lagoda 1996). Microsatellite primers were available for N. cunninghamii and were shown to successfully cross-amplify in N. moorei (Jones et al. 2004), so this eliminated the cost and time of developing species- specific primers for N. moorei.

2.4.1 Initial screening

Six sets of microsatellite primers developed for N. cunninghamii by Jones et al. (2004) were screened in 30 N. moorei individuals to test for their ability to produce a single, clear PCR product on a 1.0% agarose gel in 1X TBE buffer. Loci were then screened for polymorphisms using a GelScan2000 (Corbett Research) with fluorescently labelled primers (5’ end of reverse primer only labelled with HEX {Hexachlorofluorescein}). Four loci were selected for analysis that showed relatively high levels of polymorphism, no excess of homozygotes and produced distinct bands (ncutas06, ncutas12, ncutas13 and ncutas20). Table 2.3 details microsatellite primer sequences and repeat motifs. Jones et al. (2004) reported successful cross-amplification of the other two loci (ncutas02 and ncutas04) in N. moorei however, in the current study, nctuas02 produced multiple PCR products even after optimisation of magnesium concentrations and annealing temperatures; and ncutas04 produced only monomorphic bands. Consequently, these two primer sets were excluded from further analyses. Initial screening followed the microsatellite protocol in section 2.4.2.

2.4.2 Microsatellite procedure

PCR was performed in 96-well plates using a 25µl reaction mix containing: 1x Taq reaction buffer (Roche), 1.5-2.0mM MgCl2 (Fisher Biotech), 0.2mM of dNTPs (Roche pre-mix), 0.2µM of forward and reverse primers (Geneworks- reverse primers HEX- labelled), 1 Unit of Taq polymerase (Roche) and ~50ng of DNA template. Amplification was carried out using an Eppendorf Mastercycler. PCR conditions were: 5 mins

43 Chapter 2. General Methods

denaturing at 94ºC, then 35 cycles of 30 secs at 94ºC, 30 secs at 48ºC or 55ºC, 30 secs at 72ºC; followed by a final extension phase of 5min at 72ºC. Optimal annealing temperatures and magnesium concentrations for each primer are shown in Table 2.3. PCR samples were denatured with 1:1 formamide loading dye (98% v/v formamide, 10mM EDTA, 0.025% w/v bromophenol blue, 0.25% w/v xylene cyanol) for 3 min at 94ºC and samples immediately cooled on ice before loading 0.5-1.0µl of each sample into each well of an 5% acrylamide denaturing gel (5% acrylamide:bis {19:1}, 7M Urea, 0.6X TBE). Gels were run with 0.6X TBE buffer at 40ºC and 1200V on the GelScan200 DNA analyser (Corbett Research). The commercial standard GeneScan TAMRA-350 (TAMRA {carboxytetramethylrhodamine} dye-labelled size standard; Applied Biosystems) was loaded into the two end lanes and middle lane of all gels to ensure consistency in allele scoring. In addition, an identical reference sample was run on all gels to ensure consistent scoring. Gels were scored using the One-DScan program (v 2.03 Scanalytics Inc). Bands for each individual were sized against the TAMRA-350 size standard that includes 12 TAMRA-labelled bands ranging in size from 35 to 350bp. Output text files included band sizes for each individual, which were then entered into an Excel spreadsheet for later analysis.

Table 2.3 Repeat motif, primer sequences, number of alleles (A), annealing temperature (Ta) and magnesium concentration for each of the microsatellite loci used in this study.

Locus Repeat motif Primer Sequences A Allele size Ta Mg (5’ to 3’) range (bp) (˚C) (mM)

ncutas06 (CT)14 Fwd TTTCCCTCCATGAATACTTG 24 310 - 386 48 1.5 Rev AATGGCTTGATATTGTTACC

ncutas12 (CA)16 Fwd GCATCATCCCATCCTAAGTTAT 7 193 - 213 55 2.0 Rev CTGAACACTGGCATCTTTAATG

ncutas13 (CT)16 Fwd TAACCCACCACTCTTGCCGAAGT 16 293 - 341 55 2.0 Rev GGAACGGCCTCCACATCTCA

ncutas20 (CT)10 Fwd ATTTAGCGTCGTTTAGTAGTTTT 8 222 - 236 55 2.0 Rev AAGCGATGAGTTACATTCAA

44 Chapter 2. General Methods

2.5 Chloroplast DNA analysis

To assess ancient genetic divergence of N. moorei populations, chloroplast DNA PCR- RFLP markers were employed. Chloroplast DNA evolves at a much slower rate than nuclear DNA, so polymorphisms in chloroplast DNA are more likely to reflect ancient divergence (Newton et al. 1999). Additionally, the maternal inheritance of the chloroplast genome in angiosperms allows for assessment of geographical structure among populations (Newton et al. 1999, Cavers et al. 2003a). PCR-RFLP analysis of chloroplast DNA has become routine for assessing phylogenetic relationships in tree species and for inferring glacial refugia (Demesure et al. 1996, Marchelli et al.1998, Petit et al. 2002a, 2002b, Jimenez et al.2004, Rowden et al.2004, Vettori et al. 2004, Lumaret et al. 2005, Magni et al. 2005 Marchelli and Gallo 2006). The PCR-RFLP method essentially involves three steps: (1) PCR amplification of non-coding regions of the chloroplast genome using universal primers designed in the conserved coding sequences; (2) restriction digestion of PCR fragment with a variety of restriction enzymes; and (3) analysis of polymorphic banding patterns via gel electrophoresis (Demesure et al. 1995, Dumolin-Lapegue et al. 1997). Given past contractions and expansions of N. moorei’s distribution, several chloroplast haplotypes may exist corresponding with possible glacial refugia in the past.

2.5.1 PCR-RFLP cpDNA analysis

PCR amplification was undertaken using 15 primer pairs as described by Demesure et al. (1995) and Dumonlin-Lapegue et al. (1997) (Table 2.4). PCR reactions were carried out in a total volume of 25.0µl containing ~50ng total genomic DNA, 1 x Taq reaction buffer (Roche), 2.0mM MgCl2 (Fisher Biotech), 0.2mM of dNTPs (Roche pre-mix), 0.2µM of forward and reverse primers (Geneworks); and 1 Unit of Taq polymerase (Roche). Amplification was carried out using an Eppendorf Mastercycler. PCR conditions were: 4mins denaturing at 94ºC, then 35 cycles of 45 secs at 94ºC, 45 secs at 47ºC to 62ºC, 2-5min at 72ºC; followed by a final extension phase of 10min at 72ºC. Optimal annealing temperatures and fragment sizes for each primer are shown in Table 2.4. PCR products were checked on 1.8% agarose gels stained with ethidium bromide in order to verify amplification. The size marker IX (Roche) was run in each of

45 Chapter 2. General Methods the end lanes on each gel to estimate size of fragments. To maximise detection of chloroplast variation, initial screening was carried out using 28 individuals from 14 populations (2 individuals from each population) from across N. moorei’s natural range. A total of 6 primer sets produced single bright PCR bands that required little optimisation. To screen for polymorphisms, 10µl of PCR products were digested independently using 4 different restriction enzymes (AluI, HaeIII, HinfI and TaqI, Roche). Restriction digests were carried out in total volume of 20µl using 5 U of restriction enzyme at 37ºC for 3hrs to overnight for AluI, HaeIII and HinfI enzymes and at 65ºC for 3hrs to overnight for TaqI. Digested fragments were run on a 1.8% agarose gel containing ethidium bromide in 1X TBE buffer for 60-90min at 90V. Marker IX (Roche) was run in the end lanes to verify size of fragments. Gels were photographed with a gel documentation system under ultraviolet light. From the initial screening, only 1 primer pair-restriction enzyme combination produced polymorphic length variants/point variation. The psaA-trnS2r (AS2) fragment digested with TaqI produced point mutation variation. A total of 498 individuals from 20 populations across N. moorei’s range were then screened using this primer pair-restriction enzyme combination as detailed above for the initial screening.

46 Chapter 2. General Methods

Table 2.4 Primer pairs, fragment sizes and annealing temperatures (Ta) used in study of cpDNA variation in N. moorei.

Primer 1 Primer 2 Abbreviation ~Size Annealing temperature Reference (bp) (°C)

trnH trnKr HK 1690 62.0 Demesure et al. 1995

trnK1 trnK2r K1K2 2580 53.5 Demesure et al . 1995 trnC trnDr CD 3000 58.0 Demesure et al . 1995 trnD trnTr DT 1800 54.5 Demesure et al . 1995 psbC trnSr psbCS 1680 57.0 Demesure et al . 1995 trnS trnfMr SfM 1700 62.0 Demesure et al . 1995

psaA trnS2rAS2 3700 58.0 Demesure et al . 1995

trnS2 trnT2rS2T2 1500 57.5 Demesure et al . 1995 trnM rbcLr ML 2900 59.0 Demesure et al . 1995

trnK2 trnQr K2Q 3075 47.5 Dumolin-Lapegue et al . 1997 trnQ trnRr QR 3086 56.5 Dumolin-Lapegue et al . 1997 rpoC1 trnCr rpoCC 4795 47.5 Dumolin-Lapegue et al . 1997 trnT3 psbCr TC 3236 52.5 Dumolin-Lapegue et al . 1997 trnfM psaAr fMA 5108 47.5 Dumolin-Lapegue et al . 1997 trnF trnVr FV 3492 57.5 Dumolin-Lapegue et al . 1997 trnV rbcLr VL 3850 57.5 Dumolin-Lapegue et al . 1997

2.6 Statistical analyses - AFLPs and microsatellites

2.6.1 AFLP specific analysis

Statistical analyses that examined variation in AFLP phenotypes were based on the assumptions that AFLP markers are dominant, diploid markers that conform to Hardy- Weinberg equilibrium expectations, and in which, fragments do not co-migrate. Amplification products were scored as discrete character states (present/absent) by eye, with 2 people scoring each gel to ensure objective results. A binary (0/1) data matrix comprising all individuals for all fragments was constructed and used for analysis. Only data from intense, unambiguous, clear bands were used for statistical analysis.

47 Chapter 2. General Methods

2.6.2 Microsatellite specific analysis - Independence of microsatellite loci

Microsatellite loci were scored individually and the various alleles at each locus identified according to the length of the fragment. Single bands were taken to indicate the presence of two identical alleles (homozygotes).

To determine if each microsatellite locus was independent, genotypic tests for linkage disequilibrium were calculated among pairs of loci in each population using Fisher’s Exact tests in GENEPOP (web version 3.4 Raymond and Rousset 1995; http://wbiomed.curtin.edu.au/genepop/index.html). Unbiased P-values were derived by a Markov chain method. The significance value for these multiple significance tests and all subsequent multiple significance tests was set using the sequential Bonferroni procedure (Rice 1989). Sequential Bonferroni reduces the probability of making a Type 1-error (probability of rejecting the null hypothesis when it is actually true) by adjusting the table-wide p-value to keep it at a constant 0.05 (Rice 1989).

48 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.1 Introduction

Levels of sexual and clonal reproduction vary widely within and among populations of plant species (Eckert 2002). Clonal reproduction occurs when no meiotic recombination occurs and results in offspring that are genetically identical to each other and to their immediate parent plant (Eckert 2002). Consequently, a high level of clonal regeneration will in theory reduce genetic variation within and among populations. It can also reduce effective population sizes, and influence the role that natural selection may play within populations (Eckert 2002, Bengtsson 2003). One main disadvantage associated with extensive clonality in species that also reproduce sexually is that pollen movement within a clone may result in inbreeding depression for self-compatible plants or reduce seed formation in self-incompatible plants due to the close proximity of genetically identical individuals (Bushakra et al. 1999). Clonal regeneration can also be highly advantageous however, because it may allow plant populations to persist in habitats where sexual reproduction is otherwise unsuccessful (Eckert 2002). Therefore, different environments can favour or reduce the selective advantage of clonal reproduction.

Some Australian cool temperate rainforest species, including N. moorei, N. cunninghamii and moorei are known to exhibit clonal regeneration via coppicing (Johnston and Lacey 1983). The prevalence of extensive coppicing in these species has been hypothesised to offer a regeneration strategy that maintains population numbers in closed canopy stands (Turner 1976). Similarly, extensive coppicing reported within pure stands of Doryphora sassafras at Sassafras and E. moorei at Pinkwood in south-eastern NSW is believed to be a response to the very limited habitat disturbance that occurs at these sites (Johnston and Lacey 1983). Since sexually-reproduced offspring in these species compete with adult trees for light and space to develop and grow, habitats that have low disturbance regimes offer unfavourable conditions for ongoing regeneration via sexual reproduction/seed growth. A number of other Australian cool temperate rainforest species also exhibit coppicing including: moschatum, Ceratopetalum apetalum, Eucryphia lucida and Schizomeria ovata (Johnston and Lacey 1983). The ability of cool temperate rainforest

49 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

species to develop multiple stems in the absence of injury or fire enables them to persist within the rainforest where disturbance is rare for long periods of time, without the necessity for seedling regeneration (Johnston and Lacey 1983). A coppicing regeneration strategy is particularly important when seed production is spasmodic or inadequate; seedlings are shade-intolerant; or may be subject to browsing; or if seedlings are competitively inferior to invading species if the canopy is disturbed (Johnston and Lacey 1983). Johnston and Lacey (1983) also hypothesised that the presence of persistent clones within cool temperate rainforests can delay the progress of succession and preserve genotypes, effectively insulating species against genetic drift.

The rare and threatened Wollemi pine (Wollemia nobilis) is one such species where clonal regeneration may have preserved genotypes and delayed succession over very long time frames. Wollemia nobilis was believed to be extinct until 1994 when two populations were discovered in the Wollemi National Park in the Blue Mountains, 150 km north-west of Sydney, Australia (Offord et al. 1999). Together the two populations comprise approximately 40 large multi-stemmed adult trees with small seedlings within the immediate vicinity of their trunks (Offord et al. 1999). Seedlings grow very slowly and mortality appears high, thus few saplings are present within the remaining populations (Offord et al. 1999). The Wollemi pine is unusual compared with other Araucariaceae conspecifics in that it exhibits coppicing not just in response to damage or injury but consistently as a form of ongoing regeneration (Hill 1997). Regular coppicing behaviour has been suggested as perhaps the sole reason for why this species has persisted (Offord et al. 1999).

Effective conservation of threatened and endangered tree species requires that population demography is well understood. It is of utmost importance therefore to identify and quantify the extent of clonality in endangered species in order to develop appropriate conservation and management strategies (Rossetto et al. 2004a). Identifying clonal individuals in some plants however, can be difficult. If physical connections among individuals such as roots remain intact then clones are easily identified. In long-lived plants however, connections may often become severed above ground so apparently isolated individuals may in fact still be clonal (Ainsworth et al. 2003). Genetic analysis is the most efficient means to define members of a clone

50 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? because discrete individuals are rarely, if ever, identical for multi-locus genotypes unless they are clones (Bushakra et al. 1999). Additionally, genetic analysis eliminates the need for destructive excavations to identify clones, which is of particular importance when researching rare and threatened species.

Molecular markers have been used to identify clonal individuals within many plant species. AFLP markers have been used successfully in several plant species to elucidate clonal diversity including for: Populus nigra, Rhododendron ferrugineum, Populus nigra subsp. betulifolia, Sasa senanensis, and Salix exigua, (Arens et al. 1998, Escaravage et al.1998, Winfield et al. 1998, Pornon et al. 2000, Suyama et al. 2000, Douhovnikoff and Dodd 2003). Similarly RAPDs have been used successfully in clonal identity of plant species such as Lyonothamnus floribundus, Eucalyptus phylacis and E. dolorosa (Bushakra et al. 1999, Rossetto et al. 1999). The most powerful identification of clones however, is obtained via the use of a combination of molecular markers (Bligh et al. 1999, Esselman et al. 1999, Rossetto et al. 2004). The combined use of AFLPs and RAPDs has been used to assess clonal diversity and structure in Crypotermeria japonica and Saxifraga cernua (Moriguchi et al. 2001, Kyølner et al. 2004).

As the development of more species-specific primers increases and cross-species amplification proves more and more successful, microsatellites are becoming the marker of choice for assessing clonal diversity in plant populations. Clonal diversity in Zostera marina, Quercus geminata and Populus tremula were determined via use of highly polymorphic microsatellite markers (Reusch et al. 2000, Ainsworth et al. 2003, Suvanto and Latva-Karjanmaa 2005).

RAPD analysis of the relictual endemic ironwood (Lyonothamnus floribundus) from the Californian Channel Islands confirmed that distinct plant groves constituted in fact, independent clones and that the total number of genotypically unique individuals was far less than had previously been thought, due to extensive clonality (1125 individuals rather than previously estimated 32000 based upon the number of trunks present) (Bushakra et al. 1999). Lyonothamnus floribundus previously had a widespread distribution in the western United States during the Miocene 6-18 Myr ago, but then populations contracted significantly, to the extant that relictual populations comprise

51 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? isolated groves containing one to more than 100 trunks (Bushakra et al. 1999). Extensive clonality may impact sexual reproduction in ironwood negatively due to limited pollinator movements among flowers within a single individual (Bushakra et al. 1999).

Analysis of the endangered rainforest tree Elaeocarpus williamsianus (Hairy Quandong) assessed using four microsatellite loci and seven RAPD primer sets identified very extensive clonality, with most populations consisting of single clones (Rossetto et al. 2004a). In addition to extensive clonality, sexual reproduction had become compromised in E. williamsianus with sterile very common in all but two trees of different genets producing viable seed. Habitat fragmentation is believed to have removed the balance between the relative levels of clonal propagation and sexual regeneration (Rossetto et al. 2004a).

For the Wollemi pine, analysis of the two extant populations using 13 allozyme loci, more than 800 AFLP loci and 20 microsatellite loci revealed no polymorphism whatsoever, suggesting that genetic variation was probably completely absent and that clonality was approaching unity (Peakall et al. 2003).

Field observations have suggested that northern populations of N. moorei probably regenerate predominantly via coppicing (Howard 1981, Floyd 1990). Floyd (1990) postulated that northern populations were most likely at their environmental limit and that individuals may be coppicing as a survival response strategy to stressful environments. If this is true, northern populations of N. moorei may exhibit relatively low genetic diversity and fixed genotypes due to extensive clonal regeneration relative to other populations. If so, these populations may be at greater risk of extinction when faced with changing environmental factors like increasing levels of fragmentation and climate change. In order to develop appropriate conservation management strategies for N. moorei, the extent of clonality across the species’ limited extant distribution needs to be determined. The current study aimed to investigate levels of clonality within 20 populations across N. moorei’s distribution by screening variation in three AFLP primer sets and at four microsatellite loci.

52 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.2 Methods

3.2.1 AFLP and microsatellite methods

DNA was extracted and analysed using AFLP and microsatellite markers as described previously in sections 2.2 – 2.4 (Chapter 2).

3.2.2 Geographic distance determination

Physical distances between all individuals were estimated and recorded during sampling in the field and later an XY co-ordinate map was drawn by hand on graph paper for each population to show the spatial distribution of individuals sampled within each population. Using these hand-drawn maps, distances between clonal individuals were estimated.

Geographical distance information was not recorded for the Tullawallal, Antarctic Beech Walk/Picnic, Duramlee/Mowburra Peaks, Mt Ballow and Nothofagus Mtn populations and therefore, these populations were not included in the table showing distance between clones.

3.2.3 Statistical analyses

To test for presence of clones within populations using AFLP data, a Dice similarity matrix (Sneath and Sokal 1973) was calculated for each population using SPSS for Windows (v 11.5.1). The Dice similarity coefficient (D) was calculated using the following equation:

D(i1,i2) = 2a/(2a + b + c)

Where i1 and i2 are any two individuals; a = number of shared bands; b = number of bands present in i1 and absent in i2; c = number of bands present in i2 and absent in i1 (Dice 1945, Sorensen 1948). Dice values range from 0 to 1, with 0 being 100% non- identical and 1 being 100% identical. The Dice similarity coefficient was chosen as it

53 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? produces the most robust results and it does not consider the absence of a band in two samples as a match (Murgúia and Villaseñor 2003, Meyer et al. 2004).

To test for presence of clones within populations using microsatellite data, a Euclidean distance dissimilarity matrix was calculated for each population using SPSS for Windows (v 11.5.1). Euclidean distance is a simple count of the number of differences between two genotypes. Euclidean distance (d) was calculated using the following equation:

2 d(x,y) = √∑i(xi-yi)

Where x and y are any two individuals and i is a corresponding element (different microsatellite alleles). A euclidean distance of 0.000 indicates no differences between two individuals genotypically which are therefore considered putative clones while a value of >1.000 indicates individuals are maximally dissimilar. Clonal individuals were represented by a single genotype for the remaining analyses.

In order to estimate the error rate during AFLP analysis, six independent DNA isolations from six different leaf samples from a single individual were processed as if they were independent samples. The average number of differences across the six replicated samples was used as an indicator of experimental and total error. Artefacts of the AFLP technique such as incomplete digestion of genomic DNA and scoring error can generate differences in replicate samples. Error rate associated with these potential artefacts is then applied to the Dice similarity coefficient values using the following equation:

Actual Dice value = (Dice value x Error rate) + Dice value

Applying the error rate to the Dice values ensures potential clones are not misidentified. This technique has been used previously in studies of clonal diversity for AFLP markers (Arens et al. 1998, Escaravage et al. 1998, Douhovnikoff and Dodd 2003).

To determine the power of the microsatellite markers to identify clones, the probability

(Pgen) of individuals with the same genotype being clones or being the same by chance

54 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? was calculated for each 4-locus clonal genotype, for each population that containined clones, using the following equation:

h Pgen = { πpiqi }2

Where piqi is the product of allele frequencies at each locus in the population (two frequencies per locus) and h is the number of heterozygous loci (Parks and Werth

1993). Pgen values <0.05 were indicative of genuine clones rather than identity by chance (Parks and Werth 1993).

3.3 Results

3.3.1 Levels of clonality revealed with AFLPs

In total, 491 individuals were screened with 3 primer sets generating 100 unique DNA fragments, of which 85% of fragments were polymorphic. The average error rate for six replicates from six different individuals was calculated at 8% across the three primer pairs tested. Error rate was associated with inconsistent resolution of AFLP gels and the subjective nature of scoring by eye, despite two experimenters scoring all AFLP gels independently. Additionally, sheared genomic DNA may have caused some poor reproducibility among replicate samples.

All individuals screened showed very similar but unique AFLP banding profiles with average Dice similarity values that ranged from 0.8196 at Kilungoondie to 0.9143 at Best of All Lookout (Table 3.1). This result suggests that all individuals possessed unique genotypes and were not clonal. Application of the 8% error rate to Dice values yielded a Dice value threshold of 0.9260, such that any two individuals with a Dice value ≥ 0.9260 should be considered clones. When this threshold value was applied to the similarity Dice matrix for each population, many clonal individuals were identified (data not shown). There was however, no consistency using this threshold level such that in any given population, individual “A” and “B” had Dice values >0.9260 and

55 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? likewise for individuals “B” and “C”, yet individuals “A” and “C” had Dice values much less than the threshold value suggesting non-clonality, yet individuals A, B and C should have all belonged to the same clonal genotype and hence all had Dice values ≥ 0.9260. This result illustrates the inconsistency of using AFLP data, in isolation, to identify clonal individuals in N. moorei populations.

Putative multi-stemmed clones from the same basal burl within the Tullawallal and Mt Wanungra populations were not clonal according to raw Dice similarity matrix results (Dice similarity value <1.000), however when the 8% error rate was applied to the Dice similarity values, all Mt Wanungra putative clones were scored as clonal and two of three putative clones within Tullawallal also scored as clonal (Table 3.2). In contrast, 11/30 individuals from the Link Trail population and 22/30 individuals from the Gloucester Tops population showed non-identity between coppice and canopy leaf samples from the same apparent individual plant even after application of the 8% error rate (Table 3.3 and 3.4).

56 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.1 Average and range of Dice values for 491 individuals from 20 populations across N. moorei’s distribution based on data from 100 AFLP loci. A Dice value of 1.000 indicates 100% identity while a value of 0.000 represents 100% non-identity.

Region /Population Dice Dice Average Range

Lamington/Border Ranges Tullawallal 0.8715 0.7568 - 0.9677 Mt Wanungra 0.8857 0.7903 - 0.9929 Echo Point 0.8978 0.8217 - 0.9760 Elabana Falls 0.8743 0.7727 - 0.9524 Lightning Falls 0.8736 0.7344 - 0.9855 Mt Hobwee 0.8998 0.8217 - 0.9921 Best of All Lookout 0.9143 0.7840 - 0.9927 Antarctic Beech Walk/Picnic 0.8952 0.8217 - 0.9673 Bar Mtn 0.8869 0.7840 - 0.9701 Helmholtzia Loop 0.8995 0.8276 - 0.9706

Ballow Durramlee and Mowburra Peaks 0.8768 0.7967 - 0.9612 Mt Ballow 0.8696 0.7705 - 0.9474 Nothofagus Mtn 0.8577 0.7541 - 0.9393

Dorrigo/New England Kilungoondie 0.8196 0.7761 - 0.9787 Mt Moombil 0.8544 0.7586 - 0.9606 Weeping Rock 0.8910 0.7907 - 0.9545

Werrikimbe Mt Banda 0.9067 0.8189 - 0.9714 Plateau Beech 0.8776 0.7586 - 0.9587

Barrington Gloucester Tops 0.8389 0.6408 - 0.9636 Link Trail 0.8649 0.7288 - 0.9767

57 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.2 Dice values for putative clones of N. moorei from Tullawallal (TW) and Mt Wanungra (MtW) populations based on data from 100 AFLP loci. A Dice value of 1.000 indicates 100% identity while a value of 0.000 represents 100% non-identity. Samples were taken from multi-stemmed trees and hence assigned as putative clones. Samples were considered clonal if after application of the 8% error rate, the Dice value was equal to 0.000 (Clonal? 8% error).

Sample 1 Sample 2 AFLP Clonal? Dice measure 8% error

TW4 TW5 0.982 Yes TW8 TW9 0.882 No TW23 TW24 0.969 Yes W5 W6 0.938 Yes W8a W8b 0.968 Yes W10a W10b 0.939 Yes W11a W11b 0.969 Yes W11b W11c 0.957 Yes W11a W11c 0.963 Yes W12a W12b 0.958 Yes W14a W14b 0.988 Yes W14b W14c 0.982 Yes W14a W14c 0.994 Yes W19a W19b 0.988 Yes W23 W24 0.936 Yes W24 W25 0.97 Yes W23 W25 0.945 Yes

58 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.3 Dice values for coppice and canopy leaves for individuals from the Link Trail (LT) population of N. moorei, based on data from 100 AFLP loci. Sample 1 (LT#C) represent coppicing leaves while Sample 2 (LT#) represents canopy leaves from the same individual stem. A Dice value of 1.000 indicates 100% identity while a value of 0.000 represents 100% non-identity. Samples were considered clonal if after application of the 8% error rate, the Dice value was equal to 0.000 (Clonal? 8% error).

Sample 1 Sample 2 AFLP Clonal? Dice measure 8% error

LT1C LT1 0.924 Yes LT2C LT2 0.948 Yes LT3C LT3 0.962 Yes LT4C LT4 NA NA LT5C LT5 0.896 No LT6C LT6 0.974 Yes LT7C LT7 0.962 Yes LT8C LT8 Missing data NA LT9C LT9 0.899 No LT10C LT10 0.899 No LT11C LT11 0.925 Yes LT12C LT12 0.873 No LT13C LT13 0.821 No LT14C LT14 0.946 Yes LT15C LT15 0.975 Yes LT16C LT16 0.91 No LT17C LT17 0.907 No LT18C LT18 0.889 No LT19C LT19 0.936 Yes LT20C LT20 0.972 Yes LT21C LT21 0.951 Yes LT22C LT22 0.957 Yes LT23C LT23 0.98 Yes LT24C LT24 0.838 No LT25C LT25 0.908 No LT26C LT26 0.908 No LT27C LT27 0.923 Yes LT28C LT28 0.955 Yes LT29C LT29 0.961 Yes LT30C LT30 0.939 Yes

59 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.4 Dice values for coppice and canopy leaves for individuals from the Gloucester Tops (GT) population of N. moorei, based on data from 100 AFLP loci. Sample 1 (GT#C) represent coppicing leaves while Sample 2 (GT#) represents canopy leaves from the same individual stem. A Dice value of 1.000 indicates 100% identity while a value of 0.000 represents 100% non-identity. Samples were considered clonal if after application of the 8% error rate, the Dice value was equal to 0.000 (Clonal? 8% error).

Sample 1 Sample 2 AFLP Clonal? Dice measure 8% error

GT1C GT1 Data missing NA GT2C GT2 0.671 No GT3C GT3 0.923 Yes MISSING GT4 NA NA GT5C GT5 0.896 No GT6C GT6 0.93 Yes GT7C GT7 0.895 No GT8C GT8 0.923 Yes GT9C GT9 0.885 No GT10C GT10 0.892 No GT11C GT11 0.805 No GT12C GT12 0.897 No GT13C GT13 0.88 No GT14C GT14 0.941 Yes GT15C GT15 0.846 No GT16C GT16 0.756 No GT17C GT17 0.818 No GT18C GT18 0.805 No GT19C GT19 0.887 No GT20C GT20 0.857 No GT21C GT21 0.782 No GT22C GT22 0.808 No GT23C GT23 0.879 No GT24C GT24 0.787 No GT25C GT25 0.822 No GT26C GT26 0.805 No GT27C GT27 0.823 No GT28C GT28 0.859 No GT29C GT29 0.92 Yes GT30C GT30 0.913 Yes

60 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.3.2 Levels of clonality revealed with microsatellites

Among the 491 individuals tested with 4 microsatellite loci, a total of 55 unique alleles were detected, ranging from 7 alleles at ncutas12 to 24 alleles at ncutas06 (Chapter 2, Table 2.3).

Individuals with identical 4-locus genotypes and hence a Euclidean distance of 0.000 were assumed to be clones while individuals with non-identical genotypes displayed a Euclidean distance >0.000. Average Euclidean distance for populations ranged from 6.6003 at Mt Wanungra to 13.4193 at Mt Moombil (Table 3.5), with all but four populations containing clones (Table 3.5). Clonal individuals were not observed in any populations from the Werrikimbe region (Table 3.6). Only two of the three populations within the Ballow region and a single population within the Barrington region exhibited a single clonal genotype (Table 3.6). Within the Dorrigo-New England region only a single discrete clonal genotype was present in each of the Kilungoondie and Weeping Rock populations, while four unique clonal genotypes were present in the Mt Moombil population, respectively (Table 3.6). In contrast, populations in the Lamington/Border region displayed relatively high numbers of clones, with the number of clonal genotypes per population varying from one (Antarctic Beech Walk/Picnic and Echo Point) to eight (Mt Wanungra) (Table 3.6).

The majority of putative clones within each population were found within close geographical distance to each other (Table 3.7). The mean physical distance between putative clones within populations varied from 1.3m for clones at Mt Moombil to 7.5m for clones at Best of All Lookout (Table 3.7). Overall mean distance between clones across all populations was 4.7m (+/-3.6m), indicating that most identical genotypes (clonal individuals) occurred in clusters directly physically adjacent to one another. Four clonal individuals from four populations were however, separated by distances from 20 to 50m (Table 3.7), suggesting that either these individuals were identical by chance rather than by fragmentation of clonal stems from a basal burl, or there was a sampling error.

To test the theory that putative clones were genetically identical by chance, Pgen values were calculated for all clonal genotypes for each population containing putative clones.

61 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

The probability that clones possessed the same genotype by chance was statistically

very small across all clonal genotypes for all populations (Pgen<0.027, Table 3.8), therefore all individuals possessing identical microsatellite genotypes were considered genuine clones.

Four microsatellite loci were sufficient to confirm that individuals from the same basal burl were in fact clones for the two of the three Tullawallal putative clones and all of Mt Wanungra putative clones (Table 3.9).

Coppicing shoots and canopy leaves from the same parental individual had identical genotypes for 29 individuals in the Gloucester Tops population, however the Link Trail population exhibited differences between coppice and canopy leaves for six individuals (Table 3.10 and 3.11). Different genotypes between coppice and canopy leaves in the Link Trail population may also have been due to a sampling problem where canopy leaves from a different individual were accidentally sampled instead of the canopy leaves from the identical individual as coppicing leaves. In some cases the density of stems and associated leaves were so high, and the individual trees so tall, that it was difficult to be 100% confident that a particular leaf sample came from a specific individual from which a basal burl sample had also been removed. Overall, identical genotypes were displayed for most coppice and canopy leaves from the same individual indicating that seedling establishment on the basal burls is a rare event, if it occurs at all.

62 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.5 Euclidean distance average and range for 491 individuals from 20 populations across N. moorei’s distribution based on data from 4 microsatellite loci. A Euclidean distance of 0.000 is indicative of 100% identity while a value >1.000 indicates genetic dissimilarity.

Region /Population Euclidean Distance Average Range

Lamington/Border Ranges Tullawallal 8.7614 0.000 - 20.248 Mt Wanungra 6.6003 0.000 - 13.379 Echo Point 10.6630 0.000 - 23.043 Elabana Falls 6.9941 0.000 - 15.264 Lightning Falls 9.2558 0.000 - 24.062 Mt Hobwee 8.6966 0.000 - 16.583 Best of All Lookout 9.0255 0.000 - 18.735 Antarctic Beech Walk/Picnic 8.6607 0.000 - 14.832 Bar Mtn 7.7507 0.000 - 16.062 Helmholtzia Loop 10.4794 0.000 - 25.573

Ballow Durramlee and Mowburra Peaks 7.3462 0.000 - 15.716 Mt Ballow 10.2517 0.000 - 18.894 Nothofagus Mtn 9.2277 2.000 - 24.597

Dorrigo/New England Kilungoondie 12.4072 0.000 - 27.662 Mt Moombil 13.4193 0.000 - 29.462 Weeping Rock 12.6750 0.000 - 13.379

Werrikimbe Mt Banda 9.0553 1.000 - 17.176 Plateau Beech 9.7309 1.000 - 20.712

Barrington Gloucester Tops 8.3427 1.414 - 20.322 Link Trail 7.6209 0.000 - 18.303

63

Table 3.6 Number of clones identified from 20 populations of N. moorei based on data from 4 microsatellite loci. Individuals were in in clonality Howextensive 3. Chapter clonal if Euclidean distance = 0.000. The corrected number of individuals is calculated by subtraction of individuals missing data and counting all individuals from a clonal genotype as only 1 individual.

Region/Population # individuals # individuals # clonal groups # individuals/ Corrected # individuals sampled missing data clonal group

Lamington, Border Ranges Tullawallal 26 3 6 2-3 17 Mt Wanungra 34 1 7 2-7 15 Echo Point 25 1 1 2 23 moorei N. Elabana Falls 30 2 3 2 25 Lightning Falls 33 1 5 2-3 26

Mt Hobwee 39 1 6 2-4 26 populations? Best of All Lookout 27 1 6 2-5 14 Antarctic Beech Walk/Picnic 21 1 1 2 18 Bar Mtn 31 1 8 2 22 Helmholtzia Loop 41 1 3 2-6 26

Ballow Durramlee and Mowburra Peaks 30 0 0 na 30 Mt Ballow 30 1 1 2 28 Nothofagus Mtn 30 1 1 2 28

Dorrigo and New England Kilungoondie 30 2 1 2 27 Mt Moombil 29 0 4 2 25 Weeping Rock 33 2 1 3 29

64

in in clonality Howextensive 3. Chapter Table 3.6 cont. Number of clonal genotypes identified from 20 populations of N. moorei based on data from 4 microsatellite loci. Individuals were clonal if Euclidean distance = 0.000. The corrected number of individuals is calculated by subtraction of individuals missing data and counting all individuals from a clonal genotype as only 1 individual.

Region/Population # individuals # individuals # clonal groups # individuals/ Corrected # individuals sampled missing data clonal group

Werrikimbe moorei N. Mt Banda 29 0 0 na 29 Plateau Beech 29 1 0 na 28

Barrington Tops populations? Link Trail 30 2 1 2 27 Gloucester Tops 30 2 0 na 28

Total no. indivduals sampled 607 491

65

Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.7 Distances between clonal individuals across 20 N. moorei populations. Individuals were considered clones if they displayed the same 4 locus microsatellite genotype (Euclidean distance = 0.000). # indicates questionable distance and was not included in calculating mean distances.

Region /Population Clonal indidviduals Distance (m)

Lamington/Border Ranges Mt Wanungra W1, W2 5 W5, W6 3 W10, W11 10 W12, W13,W14, W15 4 W17, W18 5 W19, W20 10 W23, W24, W25 2 Mean 5.6

Best of All Lookout BAL1, BAL2, BAL3 3 BAL6, BAL8 3 BAL9, BAL14, BAL15 7 BAL10, BAL12, BAL13, BAL16, BAL17 9 BAL23, BAL24 3 BAL25, BAL26 20 Mean 7.5

Mt Hobwee MH6, MH7, MH8, MH9 2 MH10, MH11, MH12, MH13 4 MH15, MH16 2 MH25, MH26 2 MH31, MH32 5 MH35, MH36, MH37, MH38 8 Mean 3.8

Echo Point EP23, EP24 3

Elabana Falls EF13, EF14 10 EF16, EF18 20# EF22, EF23 3 Mean 6.5

Lighnting Falls LF3, LF4 2 LF7, LF8 2 LF10, LF11 2 LF19, LF20, LF21 2 LF32, LF33 5 Mean 2.6

66 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.7 cont. Distances between clonal individuals across 20 N. moorei populations. Individuals were considered clones if they displayed the same 4 locus microsatellite genotype (Euclidean distance = 0.000). # indicates questionable distance and was not included in calculating mean distances.

Region /Population Clonal indidviduals Distance (m)

Lamington/Border Ranges Bar Mtn BM1, BM2 7 BM3, BM4 5 BM7, BM8 7 BM10, BM11 2 BM16, BM17 2 BM18, BM19 2 BM25, BM26 10 BM29, BM30 6 Mean 5.1

Helmholtzia Loop HL1, HL2 5 HL4, HL10 50# HL11, HL12, HL13 7 Mean 6

Dorrigo/New England Kilungoondie KG13, KG14 5

Mt Moombil MM2, MM3 1 MM9, MM10 1 MM14, MM16 20# MM26, MM27 2 Mean 1.3

Weeping Rock WR18, WR19, WR20 2

Barrington Link Trail LT4, LT5 1

OVERALL MEAN 4.7(3.6)

67 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.8 Probability (Pgen) that N. moorei individuals within the same population have the same clonal genotype by chance based on allele frequency and heterozygosity for 4 microsatellite loci. Pgen values <0.05 indicate genuine clones.

Region /Population Genotype Clonal indidviduals/genotype Pgen

Lamington/Border Ranges Tullawallal A TW2, TW3 0.017 BTW6, TW7 2.032 x10-4 C TW9, TW10 0.016 D TW13, TW15 0.023 E TW18, TW23, TW24 0.004 F TW25, TW26 0.027

Mt Wanungra AW1, W2 5.624 x10-4 B W5, W6 1.790 x10-5 C W10, W11 0.001 D W12, W13,W14, W15 3.284 x10-4 E W17, W18 6.568 x 10-4 F W19, W20 6.379 x10-4 G W23, W24, W25 8.983 x10-4

Best of All Lookout A BAL1, BAL2, BAL3 2.479 x10-5 B BAL6, BAL8 0.001 C BAL9, BAL14, BAL15 0.006 D BAL10, BAL12, BAL13, BAL16, BAL17 0.003 E BAL23, BAL24 1.765 x10-4 F BAL25, BAL26 7.953 x10-4

Mt Hobwee A MH6, MH7, MH8, MH9 6.469 x10-5 B MH10, MH11, MH12, MH13 6.798 x10-4 C MH15, MH16 4.651 x10-4 D MH25, MH26 6.236 x10-5 E MH31, MH32 6.843 x10-4 MH35, MH36, MH37, MH38 5.535 x10-6

Echo Point A EP23, EP24 3.725 x10-4

Elabana Falls A EF13, EF14 2.109 x10-4 B EF16, EF18 6.326 x10-4 C EF22, EF23 2.346 x10-5

Lighnting Falls A LF3, LF4 0.002 BLF7, LF8 7.239 x10-4 C LF10, LF11 0.002 D LF19, LF20, LF21 3.073 x10-4 E LF32, LF33 9.248 x10-4

68 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.8 cont. Probability (Pgen) that N. moorei individuals within the same population have the same clonal genotype by chance based on allele frequency and heterozygosity for 4 microsatellite loci. Pgen values <0.05 indicate genuine clones.

Region /Population Genotype Clonal indidviduals/genotype Pgen

Lamington/Border Ranges Bar Mtn ABM1, BM2 9.093 x10-5 BBM3, BM4 1.352 x10-4 CBM7, BM8 7.675 x10-4 D BM10, BM11 1.894 x10-4 E BM16, BM17 1.300 x10-5 F BM18, BM19 2.604 x10-4 G BM25, BM26 5.980 x10-5 H BM29, BM30 6.926 x10-6

Helmholtzia Loop AHL1, HL2 1.393 x10-5 B HL4, HL10 0.004 C HL11, HL12, HL13 0..001

Antarctic Beech Walk/Picnic A ABP7, ABP10 3.008 x10-6

Ballow Duramlee/Mowburra Peaks A DU3, DU15 0.002

Mt Ballow A BA20, BA29 0.001

Dorrigo/New England Kilungoondie A KG13, KG14 1.819 x10-4

Mt Moombil A MM2, MM3 6.402 x10-4 B MM9, MM10 4.742 x10-5 C MM14, MM16 0.001 D MM26, MM27 1.143 x10-5

Weeping Rock A WR18, WR19, WR20 0.002

Barrington Link Trail A LT4, LT5 4.584 x10-4

69 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.9 Euclidean distances for putative clones of N. moorei from Tullawallal (TW) and Mt Wanungra (MtW) populations based on data from 4 microsatellite loci. A Euclidean distance of 0.000 is indicative of 100% genetic identity while a value >1.000 indicates genetic dissimilarity. Samples were taken from multi-stemmed trees and hence assigned as putative clones. Samples were considered clonal if they had a Euclidean distance of 0.000.

Sample 1 Sample 2 Euclidean distance Clonal?

TW4 TW5 0.000*2 loci Yes TW8 TW9 1.414 No TW23 TW24 0.000 Yes W5 W6 0.000 Yes W8a W8b 0.000 Yes W10a W10b 0.000 Yes W11a W11b 0.000 Yes W11b W11c 0.000 Yes W11a W11c 0.000 Yes W12a W12b 0.000 Yes W14a W14b 0.000 Yes W14b W14c 0.000 Yes W14a W14c 0.000 Yes W19a W19b 0.000 Yes W23 W24 0.000 Yes W24 W25 0.000 Yes W23 W25 0.000 Yes

70 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.10 Euclidean distance for coppice and canopy leaves for individuals from the Link Trail (LT) population of N. moorei, based on data from 100 AFLP loci. Sample 1 (LT#C) represent coppicing leaves while Sample 2 (LT#) represents canopy leaves from the same individual stem. A Euclidean distance of 0.000 is indicative of 100% genetic identity while a value >1.000 indicates genetic dissimilarity. Samples were considered clonal if they had a Euclidean distance of 0.000.

Sample 1 Sample 2 Euclidean distance Clonal?

LT1C LT1 4.2430 No LT2C LT2 0.0000 Yes LT3C LT3 0.0000 Yes LT4C LT4 0.0000 Yes LT5C LT5 0.0000 Yes LT6C LT6 0.0000 Yes LT7C LT7 0.0000 Yes LT8C LT8 0.0000 Yes LT9C LT9 0.0000 Yes LT10C LT10 0.0000 Yes LT11C LT11 0.0000 Yes LT12C LT12 0.0000 Yes LT13C LT13 0.0000 Yes LT14C LT14 0.000 *3 loci Yes LT15C LT15 0.0000 Yes LT16C LT16 8.1850 No LT17C LT17 0.000 *3 loci Yes LT18C LT18 1.0000 No LT19C LT19 0.0000 Yes LT20C LT20 0.0000 Yes LT21C LT21 0.0000 Yes LT22C LT22 0.0000 Yes LT23C LT23 1.0000 No LT24C LT24 0.000 *3 loci Yes LT25C LT25 5.0990 No LT26C LT26 11.9160 No LT27C LT27 0.0000 Yes LT28C LT28 0.0000 Yes LT29C LT29 0.0000 Yes LT30C LT30 0.0000 Yes

71 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

Table 3.11 Euclidean distance for coppice and canopy leaves for individuals from the Gloucester Tops (GT) population of N. moorei, based on data from 100 AFLP loci. Sample 1 (GT#C) represent coppicing leaves while Sample 2 (GT#) represents canopy leaves from the same individual stem. A Euclidean distance of 0.000 is indicative of 100% genetic identity while a value >1.000 indicates genetic dissimilarity. Samples were considered clonal if they had a Euclidean distance of 0.000.

Sample 1 Sample 2 Euclidean distance Clonal?

GT1C GT1 0.000 *3 loci Yes GT2C GT2 0.000 *2 loci Yes GT3C GT3 0.0000 Yes MISSING GT4 NA NA GT5C GT5 0.0000 Yes GT6C GT6 0.0000 Yes GT7C GT7 0.0000 Yes GT8C GT8 0.0000 Yes GT9C GT9 0.0000 Yes GT10C GT10 0.0000 Yes GT11C GT11 0.0000 Yes GT12C GT12 0.0000 Yes GT13C GT13 0.0000 Yes GT14C GT14 0.0000 Yes GT15C GT15 0.0000 Yes GT16C GT16 0.000 *1 loci Yes GT17C GT17 0.000 *3 loci Yes GT18C GT18 0.0000 Yes GT19C GT19 0.0000 Yes GT20C GT20 0.000 *3 loci Yes GT21C GT21 0.0000 Yes GT22C GT22 0.0000 Yes GT23C GT23 0.0000 Yes GT24C GT24 0.0000 Yes GT25C GT25 0.0000 Yes GT26C GT26 0.000 *3 loci Yes GT27C GT27 0.0000 Yes GT28C GT28 0.0000 Yes GT29C GT29 0.0000 Yes GT30C GT30 0.0000 Yes

72 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.3.3 Comparison of the effectiveness of AFLPs and microsatellites in clonal identity

Overall, AFLPs were unable to identify clones consistently within N. moorei populations due to a high scoring error rate; yet microsatellites effectively identified clonal individuals in 16/20 populations.

AFLP and microsatellite data for putative clones from the Tullawallal and Mt Wanungra populations did show congruence. That is, when the 8% error rate was applied to the AFLP generated Dice values for putative Tullawallal and Mt Wanungra clones, the same results were obtained when microsatellites were used to compare the same individuals. Both AFLPs and microsatellites revealed that Tullawallal samples TW8 and TW9 were in fact not clonal despite appearing to be connected to the same basal burl. The two other putative Tullawallal clones and all Mt Wanungra putative clones were shown to be clonal based on both AFLP and microsatellite marker sets (Table 3.2 and Table 3.9).

Comparison of AFLP and microsatellite results for coppice and canopy samples showed less congruence, with only four coppice and canopy samples from Link Trail showing congruence for AFLP and microsatellite data, with microsatellites discriminating coppice from canopy for samples LT1 and LT23 while AFLPs discriminated coppice from canopy for samples LT5, LT9, LT10, LT12, LT13, LT17 and LT24 (Table 3.3 and Table 3.10). Similarly, there was a lack of congruence between AFLP and microsatellite data when discriminating coppice and canopy samples in the Gloucester Tops population (Table 3.4 and Table 3.11). Microsatellites clearly demonstrated that coppice and canopy samples were genetically identical for all individuals while AFLP results indicated that 22/30 individuals had genetically distinct coppice and canopy leaf samples (Table 3.4 and Table 3.11).

73 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.4 Discussion

3.4.1 Clonality revealed with AFLPs

The main objective here was to estimate the extent of clonality within N. moorei populations, and specifically to determine whether multiple stems from the same basal burl constitute clonal individuals. Overall, AFLP markers appear to be ineffective for clonal discrimination in N. moorei due to the high error rate associated with the technique. AFLPs could not identify putative clonal individuals consistently even when a threshold Dice value of 0.9260 was applied to Dice similarity matrices for all populations. Without use of this threshold value AFLPs did not identify any clones, with all individuals in each population possessing a unique AFLP banding pattern.

Several studies have shown that AFLP profiles quite commonly do not produce 100% identity for two individuals from the same plant or genet (Tohme et al. 1996, Arens et al. 1998, Winfield et al. 1998, Cabrita et al. 2001). Preliminary work by Douhovnikoff and Dodd (2003) also showed that AFLP fingerprints from clones of the narrow-leaved willow, Salix exigua, were non-identical. Similarly, assessment of clonal diversity in Rhodendron ferrugineum identified large genetic differences between putative clones, with similarity values ranging from 0.52 to 0.94 for all (Escaravage et al. 1998). These results raise ambiguity when identifying non-identical genotypes as genetically distinct individuals or clones.

Non-identity between coppice and canopy leaves from the same individual within the Link Trail and Gloucester Tops populations even after application of an 8% error rate could however, result from genetic mosaicism. Genetic mosaicism within individual trees may result in non-identity of coppice and canopy leaf samples in the same individual (Antolin and Strobeck 1985, Gill et al. 1995). Although theoretical models predict that genetic mosaics should be rare (~5%) and that genetic variation within a clonal unit should be difficult to detect, if somatic mutation rates are high and there are large associated selective benefits of mosaicism, then genetic differences within clones may be apparent (Gill et al. 1995). Somatic mutations can increase genetic variability both within an individual tree and in a tree population more than via gametic mutations,

74 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? particularly in very long-lived trees, thereby increasing plant evolutionary rates (Atonlin and Strobeck 1985, Gill et al.1995).

An alternative explanation for the non-identity between coppice and canopy samples and the high error rate observed here could be that genomic DNA was sheared during analysis and/or there was incomplete digestion due to poor DNA quality. Several other studies have noted problems with AFLP reproducibility due to incomplete digestion of genomic DNA (Bligh et al. 1999, Goulão et al. 2001). Leaves of N. moorei, like many rainforest tree species, contain high levels of phenolics that can interfere with restriction enzyme activity resulting in incomplete digestion (Scott and Playford 1996). Additionally, vortexing the DNA pellet to aid resuspension in TE buffer may have sheared some genomic DNA and led to the relatively high error rate in replicate samples.

Another possible explanation for non-identity of coppice and canopy could be contamination by fungi or other micro-organisms (Douhovnikoff and Dodd 2003). The non-specific nature of AFLP analysis can result in amplification of non-target DNA. Interestingly, only two N. moorei clonal individuals were identified using ISSR markers from a total of 146 sampled individuals from 20 populations from the northern and southern extremes of N. moorei’s distribution (Taylor et al. 2005). ISSR markers are also non-specific and the lack of clonality reported in both studies may simply be a reflection of the marker system utilized. Analysis of clonal diversity in the rare Calamagrostis porteria ssp. insperata (Poaceae) using ISSRs identified several multi- locus genotypes within populations. Given the long-lived, highly clonal nature of Calamagrostis porteria ssp. insperata however, it was not known whether the high clonal genotypic diversity represented distinct genets or somatic mutations in the neutral, hyper-variable regions amplified with ISSR primers (Esselman et al. 1999).

Given the relatively high error rate associated with the AFLP profiles in the study and the non-identity of coppice and canopy samples from the same individuals, AFLP analysis may not be a reliable marker system for clonal identification in this species, so an alternative approach was adopted.

75 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

3.4.2 Clonality revealed with microsatellites

Microsatellites have been used successfully to identify clonal individuals in many plant species including eelgrass (Zostera marina), scrub oak (Quercus geminata), western red cedar (Thuja plicata), Hairy Quandong (Elaeocarpus willamsiansus) and aspen (Populus tremula) (Reusch et al. 1998, 2000, Ainsworth et al. 2003, O’Connell and Ritland 2004, Rossetto et al. 2004a, Suvanto and Latva-Karjanmaa 2005). In contrast to the AFLP results, assessment of clonality in N. moorei populations using microsatellites identified clonal individuals in 16/20 populations. Furthermore, clonal individuals occurred in clusters directly physically adjacent to one another thus supporting their likelihood of clonality rather than chance genetic identity. In contrast to AFLP results, microsatellites appear to have discriminated clones successfully in N. moorei. It could be argued that four microsatellite loci may be insufficient to discriminate clones; however studies of Quercus geminata, Q. robus and Q. petraea (Ainsworth et al. 2003, Bakker et al. 2001) have shown that six to seven microsatellite loci were sufficient to detect clonal individuals. In Q. geminata 28 unique genotypes were discriminated from among 47 individuals (Ainsworth et al. 2003). Furthermore, analysis of the endangered Australian rainforest tree Elaeocarpus williamsianus (Hairy Quandong) using four microsatellite loci and seven RAPD primer sets identified very extensive clonality with single clones present within most of the populations (Rossetto et al. 2004a). The best discrimination of clonal individuals is achieved with microsatellite loci that possess a large numbers of alleles, each at relatively low frequencies. Across the four microsatellite loci used here, a total of 55 alleles were identified, varying from 7 to 24 alleles per locus, with most alleles at relatively low frequencies. Furthermore, the probability that individuals with the same clonal genotype were genetically identical by chance was statistically very small for all putative clones in each clonal population

(Pgen<0.027).

Importantly, this study has revealed low to high levels of clonality outside the multi- stemmed basal burl for 16/20 populations across N. moorei’s distribution. This finding is in stark contrast to the results of Taylor et al. (2005) that suggested little evidence of clonality outside the multi-stemmed basal burls. There may be several reasons for the lack of clonality evident in Taylor’s study including that only 13 adjacent individuals were sampled from a single population in the Border Ranges region; using the

76 Chapter 3. How extensive is clonality across N. moorei’s natural distribution? dominant ISSR marker system that may have the same inherent artefact problems as AFLPs. In addition, only 42 ISSR fragments were scored that may have been insufficient to discriminate clones where they existed. Furthermore, the lack of clonality detected by Taylor et al. (2005) in the remaining populations may well have been due to the fact that only 5-10 individuals were sampled randomly from each of the 11 northern Lamington/Border Ranges populations and 9 Barrington populations, thus the likelihood of sampling putative clones in these populations was probably quite low. The current study sampled 21 to 41 individuals from each of 20 populations however, across N. moorei’s entire distribution range, and the random sampling strategy involved sampling of nearest neighbours, so the chance of detecting clones was higher.

Analysis of Euclidean distances among individuals within each population revealed relatively higher clonality levels in the northern Lamington/Border populations compared with other regions (Table 3.6). These results support the field observations by Howard (1981) who observed that N. moorei populations on the McPherson Range (Lamington region) were undergoing predominantly asexual coppicing with little sexual regeneration except on the higher elevations at Mt Nothofagus (Ballow region). Higher elevations may reduce warm-temperate and sub-tropical tree growth, eliminating seedling competition with N. moorei, and allowing seedling establishment and maturation (Howard 1981). Floyd (1990) also suggested populations on the northern McPherson range exhibited higher levels of coppicing.

3.4.3 The role of disturbance in levels of sexual regeneration in N. moorei populations

Floyd (1990) hypothesised that increasingly unfavourable conditions for growth can result in many tree species adopting coppicing as an adaptive strategy to enhance survival. Floyd (1990) went on to argue that the scarcity of high elevation peaks above 1000m along the McPherson range (Lamington/Border Ranges and Ballow regions) favourable to N. moorei has resulted in the species being effectively ‘trapped’, ecologically, and so coppicing may have evolved as a survival strategy. Results presented here contrast with Floyd’s hypothesis. Successful sexual regeneration appears to occur in the western McPherson range (Ballow region) based on the low levels of clonality in this region. Furthermore, coppicing occurs both in sexually regenerating populations and sexually-derived seedlings are produced in populations

77 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

that regenerate clonally (Howard 1981, Meyers 1993). The key difference between the western and eastern McPherson Range (Ballow region and Lamington/Border Ranges region, respectively) N. moorei populations are the relative levels of successful sexual regeneration, based on the levels of clonal diversity reported here. Successful sexual regeneration may depend primarily on disturbance events rather than altitude and ecotonal vegetation type effects. All population sites in the current study were between 900 and 1500 m a.s.l with surrounding vegetation type varying from warm temperate/sub-tropical to open Eucalypt woodland. Regeneration strategy appears not to be correlated with altitude or surrounding vegetation type (data not shown), but rather with historical disturbance regime. Higher sexual regeneration is apparent in the Barrington, Ballow, Werrikimbe and Dorrigo-New England regions. All of these regions have undergone substantial disturbance in the past. Barrington has been exposed to repeated fires; Werrikimbe has been heavily logged in the past; and the Dorrigo - New England and Ballow regions were cleared extensively for farmland. In contrast, the Lamington region where there are high levels of coppicing, is a continuous strip of intact warm-temperate/sub-tropical and cool temperate rainforest that has been exposed to very little present or past disturbance. Previous studies have suggested successful seedling establishment beneath N. moorei forests is rare except in disturbed areas such as beside tracks (Howard 1981, Adam 1987).

Similar arguments have been made for South American Nothofagus species where sexual regeneration in lowland, mid-altitude sites depends on infrequent, catastrophic disturbances due to factors like landslides and volcanism. Nothofagus species in Chile and are all relatively shade-intolerant and hence tend to be competitively excluded from lowland forests in the absence of disturbance (Veblen and Ashton 1978, Veblen et al. 1981, Veblen 1989, Pollmann 2005, Premoli and Kitzberger 2005). Similarly, regeneration of New Zealand Nothofagus species, N. fusca, N. solandri var cliffortiodes and N. truncata, all depend on infrequent large disturbance events including landslides following earthquakes or heavy rainfall (Veblen et al. 1996). In contrast, N. menziesii in New Zealand is able to regenerate sporadically on logs in canopy gaps from minor treefalls in addition to mass sexual regeneration following catastrophic landslides and wind-throw (Stewart 1986, Stewart and Rose 1990). The Tasmanian species, N. cunninghamii, can also regenerate continually in canopy gaps

78 Chapter 3. How extensive is clonality across N. moorei’s natural distribution?

due to the poor generation capacity of the co-occurring, Atherosperma moschatum (Read and Busby 1990).

3.4.4 Conclusion

Levels of clonal regeneration via coppicing appear highest within the northern Lamington/Border Ranges region, part of the eastern McPherson Range, supporting observations by Howard (1981). Coppicing is not as prevalent in the northern Ballow region, part of the western McPherson Range, suggesting a west-east division in clonal regeneration rates. Low levels of coppicing were also apparent in all southern regions. The different levels of clonal regeneration were attributed to different levels of disturbance and competition. Given the higher clonality within the northern Lamington/Border Ranges region, levels of genetic diversity are predicted to be lower in this region relative to other regions.

79 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.1 Introduction

Future evolutionary adaptation, in theory, depends on the existence and maintenance of adequate levels of genetic variation (Milligan et al. 1994, Newton et al. 1999). Small isolated populations often possess relatively low levels of genetic diversity and high levels of inbreeding; factors that increase homozygosity and that can lead to reductions in individual fitness (Milligan et al. 1994, Geburek 1997, Petit et al. 1998, Soh et al. 2000).

Breeding systems exert a major influence on the levels of genetic diversity that are present within and among populations (Loveless and Hamrick 1984, Hamrick and Godt 1996). Sexual reproduction generates variation, allowing greater recruitment opportunities for genetically variable offspring in spatially and temporally heterogeneous environments. however, cannot generate variation or novel genotypes rather it simply multiplies the numbers of existing genotypes (Loveless and Hamrick 1984). Hermaphroditism is the most common sexual system in plants and appears to be adaptive for a sessile state (Freeman et al. 1997). For hermaphrodites, in practical terms self-fertilisation is the easiest way to find a suitable distant mate. In fact, many plant individuals will mate simultaneously with numerous sexual partners as well as with themselves over their individual lifetimes. There are often strong genetic reasons however, to avoid self-fertilisation. Inbreeding depression arising from selfing through the expression of recessive, deleterious alleles in homozygous individuals is a common negative outcome of selfing (Darwin 1876; Lloyd 1979; Lande and Schemske 1985; Charlesworth and Charlesworth 1987).

Gene flow refers to the relative contribution that genes make to their own or different populations and is achieved via dispersal of male and/or female sex cells (gametes) or asexual cells (vegetative clones or apomictic seeds). The primary form of gene flow in most plants is via pollen dispersal and pollen is dispersed via vectors such as wind, water or animals. Gene flow can also occur via seeds but for most plant species gene flow via seeds tends to be limited, with many seeds only being dispersed short

80 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations distances from the parental population. Gene flow via vegetative propagules is common in hydrophytes (water plants) where only a small fragment of plant material needs to be transported to another water body via the stream or via water fowl, to establish a new population (Richards 1986; Crawley 1997). Vegetative dispersal is also quite effective in rhizomatous weeds eg Carex arenaria can travel up to 10m per year via rhizomes over bare stable sand. Similarly, wild strawberry (Fragaria vesa) moves up to 5 m per year via stolons (Richards 1986). Apomictic seeds can also result in gene flow among populations.

Levels of gene flow within and among populations directly affects the genetic structure of populations. When gene flow is limited, populations are likely to diverge genetically and undergo independent evolution, whereas if gene flow is high, populations are likely to remain connected reproductively and hence evolve together (Slatkin 1985, 1987). Patterns of genetic variation in long-lived plant species can result from a combination of both historical and contemporary processes. Populations that share a recent common ancestor are likely to be more genetically similar than will those having more distant common ancestry (Schaal et al. 1998). If genetic exchange between two populations ceases however, shared common ancestry will be the only determinant of genetic similarity between them. Genetic exchange across a plant’s natural range can be severely restricted either by the presence of large geographical distributions and/or limited pollen and/or seed dispersal. In these cases, historical events such as range expansion, range fragmentation and past population bottlenecks can be strong determinants of a species’ population genetic structure (Marchelli and Gallo 2004).

Historical gene flow can be reflected in the current levels of genetic differentiation in long-lived species. (Hartl 1987). Populations may be extensively fragmented yet display low genetic differentiation due to past gene flow. This genetic continuity may be an historical artefact and is retained simply because insufficient generations have passed for populations to diverge (Hartl 1987).

Climate change and habitat fragmentation can cause populations to contract in size during which time genetic drift occurs and gene flow decreases, resulting in divergence between isolated populations (Nei et al. 1975). Expansion of populations by just a few founders can cause population bottlenecks and decrease genetic diversity (Cormuet

81 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations and Luikart 1996). Of particular importance is the effect of glacial isolation and post- glacial range expansion on genetic diversity and structure in temperate tree species throughout the Quaternary period (Demsesure et al. 1996, Petit et al. 2002, Palmé et al. 2003). These past contraction and expansion events in temperate tree species have often resulted in a genetic signature being present in species showing the postglacial recolonisation routes from glacial refugia (Heuertz et al. 2004).

Identifying population structure and relative gene flow is crucial for effective conservation management of vulnerable or endangered species (Balloux and Lugon- Moulin 2002). Limited gene flow and population differentiation can result in loss of genetic diversity critical to the long-term survival of any population (Sork and Smouse 2006). By documenting the levels of genetic diversity both within and among populations, inferences can be made about the levels and patterns of dispersal among populations, the potential for diversification and differentiation among populations and the evolutionary history of populations (Frankham et al. 2002).

The distribution of N. moorei has reduced dramatically over its evolution, with numerous population expansions and contractions during Quaternary glaciations (Adam 1992). Modern N. moorei populations are restricted essentially to five disjunct geographical regions with long distance seed dispersal among them likely to be limited (Read and Brown 1996). Regional populations are likely to be genetically differentiated therefore, and gene flow is probably limited among them.

Levels of genetic diversity in N. moorei populations are most likely constrained when individuals regenerate via asexual coppicing, as has been shown for the Lamington/Border Ranges region (see Chapter 3). Future accelerated climate change is predicted to be an order of magnitude faster than climate change during the recent geological past (Houghton et al. 1996). The potential for N. moorei populations to respond to predicted climate change remains unknown, but can be affected by the limitations placed on genetic diversity levels by a predominantly asexual mode of reproduction in the Lamington/Border Ranges region.

The disjunct, isolated distribution of N. moorei populations and its inferred breeding system make the species vulnerable to extinction; particularly in the Lamington/Broder

82 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Ranges region where clonality levels are highest (see Chapter 3). Hence, investigation of the levels and structure of genetic diversity in N. moorei populations is essential to provide managers with tools to make informed decisions on appropriate conservation management strategies that will assist in long term species persistence.

To date only a single study has examined genetic diversity and population differentiation in N. moorei (Taylor et al. 2005). Analysis of 146 individuals from 20 populations taken from the northern and southern limits of the natural distribution that examined dominant inter-simple-sequence-repeat (ISSR) markers revealed moderate levels of overall genetic diversity and significant regional structuring between southern Barrington Tops and northern Lamington/Border Ranges populations (Taylor et al. 2005). Sample sizes were small (n = 1 - 10) however, and intervening natural populations from the New England/Dorrigo and Werrikimbe regions were not examined (Taylor et al. 2005). Thus the significance of the structure observed could not be assessed rigorously because it was unknown if the variation was the result of independent evolution or if it may reflect clinal variation across an environmental gradient. Furthermore, the study of Taylor et al. 2005 used dominant markers so levels of inbreeding, heterozygosity, allelic richness etc could not be assessed. Further genetic analysis of N. moorei populations using both dominant and co-dominant markers and larger sample sizes, combined with sampling from across the species entire extant distribution is required to provide a more comprehensive understanding of population diversity in this ancient endemic tree species and to relate this to relative conservation status of each extant population.

In this chapter, genetic variation and spatial structure within and among 20 extant N. moorei populations (491 individuals), distributed across the species’ entire latitudinal range were analysed using three AFLP primer sets and four microsatellite loci. The main aim was to estimate the level of intra and inter-population genetic diversity and the level of population and regional differentiation in the target species and relate this information to possible conservation management strategies.

83 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.2 Methods

4.2.1 AFLP and microsatellite methodology

DNA was extracted and analysed using AFLP and microsatellite markers as previously described in sections 2.2, 2.3 and 2.4 (Chapter 2).

4.2.2 Statistical analysis

4.4.2.1 Genetic diversity within populations

For all analyses using microsatellite data, clonal genotypes were represented by only a single individual per clonal genotype, so as to not to confound results. In total 491 individuals from 20 populations and 5 regions were analysed for all statistical tests that follow.

To confirm the independence of the four microsatellite loci, genotypic (linkage) disequilibrium was tested for all locus pairs in each population by Exact tests in GENEPOP (web version 3.4 Raymond and Rousset 1995). P values (=0.05) were adjusted applying a sequential Bonferroni correction (Rice 1989) to avoid false correlations between loci.

For each population-microsatellite locus combination, departure from Hardy-Weinberg equilibrium was assessed using Exact tests with unbiased P-values estimated via a Markov chain method in GENEPOP (web version 3.4 Raymond and Rousset 1995). The Hardy-Weinberg principle states: “in a large, randomly breeding (diploid) population allelic frequencies will remain the same from generation to generation; assuming no unbalanced mutation, gene migration, selection or genetic drift”. When a population meets all of the Hardy-Weinberg conditions it is said to be in Hardy-Weinberg equilibrium (Stern 1943). Populations in their natural environment however, rarely, if ever conform to Hardy-Weinberg equilibrium due to external processes such as genetic drift, gene flow, selection, mutation and non-random mating (Emigh 1980). The extent of deviation from Hardy-Weinberg equilibrium is an indication of the intensity of these external factors on genetic diversity in the target species.

84 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

The program MICROCHECKER was used to infer whether heterozygote deficits identified using Exact tests (see above) may have been due to presence of null alleles, scoring difficulties due to stutter or large allele dropout (Van Oosterhout et al. 2004). When null alleles were present, the frequency was estimated using Brookfield’s equation that assumes non-amplifications are a result of null allele homozygotes (Brookfield 1996).

To assess levels of genetic diversity within populations, Shannon’s Index of diversity (Lewontin 1972) and Nei’s (1973) gene diversity index were calculated for each population in POPGENE 1.32 (Yeh et al. 1997) for both AFLP and microsatellite data, using the following equations:

Shannons’s Index (I) = ∑pi log2.pi

Nei’s gene diversity (He) = 1 - ∑pi

Where pi is the frequency of presence or absence of each band.

Shannon’s Index has been recommended for use with dominant data sets such as AFLPs but has also been used for estimates of genetic diversity using microsatellite data. Values for Shannon’s Index range from 0 to 1.06 for each locus. Nei’s (1973) gene diversity is a measure of total heterozygosity with the values ranging from 0 to 0.5 for each locus. Additionally, the percentage of polymorphic AFLP loci (%P) was calculated in POPGENE 1.32 (Yeh et al. 1997). To determine whether levels of genetic diversity where significantly different among populations and among regions, Kruskall-Wallis tests were applied to all diversity indices using SPSS or Windows (v 11.5.1).

Genetic polymorphisms for the four microsatellite loci in each population were assessed by calculating the effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He) and Wright’s Fixation index (Fis) in POPGENE 1.32 (Yeh et al. 1997). Measurement of the effective number of alleles provides a meaningful comparison of allelic diversity across loci where allele frequencies vary. Wright’s

85 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

fixation index (Fis) measures past inbreeding in populations and allows inference about breeding system to be made.

Fis is calculated from the equation:

Fis = He – Ho/He

Values close to zero are expected under random mating while positive values indicate inbreeding or undetected null alleles. Negative values indicate an excess of heterozygosity due to assortative mating and/or selection.

Microsatellite genetic diversity, across all populations and for each population, were estimated separately for allelic richness (Rs), Ho and gene diversity within populations

(Hs). Rs and Hs were calculated using FSTAT v2.9.3 (Goudet 2001). Allelic richness is a measure of the number of alleles independent of sample size, and is based on the rarefaction method developed by Hurlbert (1971).

4.2.2.2 Isolation by distance within and among populations

To determine any isolation by distance patterns within populations, spatial autocorrelation analysis for each population was conducted in GenAlEx V 5.1 (Peakall and Smouse 2001) using geographic XY co-ordinates for the location of each individual within each population and a genetic distance matrix. The distance between each individual was recorded at the time of sampling and an XY co-ordinate map drawn up for each population to show the spatial distribution of individuals sampled within each population (data not shown). These XY co-ordinates were then used for the spatial autocorrelation analysis and Mantel tests. The outcome of the spatial autocorrelation analysis was summarised using a correlogram that displays the genetic correlation as a function of distance. Correlation values (r) outside the confidence interval of the upper (U) and lower (L) confidence limits, generated by 999 random permutations, were considered to be statistically significant (P<0.05) and the null hypothesis of no spatial structure was rejected.

Isolation by distance patterns were also evaluated using Mantel tests (Mantel 1967). Within populations, matrices of geographical and pairwise genetic distance (PhiPT)

86 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

were generated and compared using a Mantel test in GenAlEx V 5.1 (Peakall and Smouse 2001) to determine any significant correlations between geographic and genetic distances. Among populations, isolation by distance was investigated by Mantel tests in GenAlEx V 5.1 using matrices of the natural logarithm of geographical

distance and genetic distance in terms of FST/(1-FST) values. Under the isolation by distance model, values of the FST/(1-FST) ratio are expected to increase linearly with the logarithm of distance (Rousset 1997).

4.2.2.3 Population differentiation and relationships among populations

For all analyses using microsatellite data, clonal genotypes were represented by only a single individual per clonal genotype, so as to not to confound results on genetic structuring. In total 491 individuals from 20 populations and 5 regions were analysed for all statistical tests that follow.

Genetic differentiation among populations was evaluated by estimating population

pairwise FST values in ARLEQUIN Version 2.000 (Schneider et al. 2000). Significance

of pairwise population FST values were tested by comparison with 95% confidence

intervals from 1023 permutations. FST values indicate the proportion of genetic variation present among populations relative to the variation found within populations.

Values for FST range from 0 to 1; with 1 indicating complete population genetic differentiation and 0 indicating no population divergence (Nei 1977, Hartl and Clark 1997).

Overall population differentiation (Gst or Fst) and gene flow among populations (Nm) were calculated in POPGENE 1.32 (Yeh et al. 1997). GST is equivalent to Wright’s FST (Nei 1977, 1986).

87 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

The average gene flow among populations (Nm) was estimated from FST and GST values using the formulas:

Nm = 0.25(1 – FST)/FST

Nm = 0.5(1-GST)/GST (Slatkin and Barton 1989).

Phylogenetic analysis was conducted to investigate patterns of divergence and to describe evolutionary relationships among populations. The population pairwise FST matrix generated in ARLEQUIN Version 2.000 was used to construct a neighbour- joining phylogenetic tree using the NEIGHBOR program in PHYLIP, version 3.6 (Felsenstein 1989, 2005). The neighbour-joining method does not assume that all lineages evolve at the same rate () and produces an unrooted tree (Saitou and Nei 1987).

To illustrate how individuals clustered from different populations a Principal Coordinate Analysis (PCA) was conducted in GenAlEx V 5.1 (Peakall and Smouse 2001).

4.2.2.4 Regional genetic diversity and population structure patterns

For all analyses using microsatellite data, clonal genotypes were represented by only a single individual per clonal genotype, so as to not to confound results on genetic structuring. In total 491 individuals from 20 populations and 5 regions were analysed for all statistical tests that follow.

To determine the extent of regional genetic differentiation, AFLP data were analysed using analysis of molecular variance (AMOVA) in ARLEQUIN Version 2.000 using a genetic distance matrix (Schneider et al. 2000). AMOVA partitions variation in the data according to predetermined hierarchical levels and compares variation within and among groups. (Excoffier et al. 1992). Genetic variation was partitioned among individuals within populations, among populations within regions and among regions. Regions were defined based on the extant geographical structure of populations:

88 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Lamington/Border Ranges, Ballow, Dorrigo/New England, Werrikimbe and Barrington. Population subdivision analyses were assessed using Ф-statistics generated in AMOVA under the permutational procedures of the ARLEQUIN program.

Defining regions based on geographical location may not reflect the true extent of differentiation among populations. To determine the optimal number of significantly differentiated groups from the 20 extant populations analysed for microsatellite variation, a spatial analysis of molecular variance (SAMOVA) was conducted using the freeware SAMOVA 1.0 (Dupanloup et al. 2002). SAMOVA defines groups of populations that are geographically homogeneous and maximally differentiated from each other using a simulated annealing approach (Dupanloup et al. 2002). SAMOVA analysis was performed several times increasing the user-defined number of groups (K) from K = 2 through to K = 7 using 100 simulated annealing steps each time. The maximum indicator of differentiation (FCT) was examined in each analysis as the determinant of the optimal number of groupings.

Regional genetic structure was also estimated from microsatellite data using the Bayesian clustering program, STRUCTURE 2.0 (Pritchard and Wen 2002). STRUCTURE 2.0 assigns individual multi-locus genotypes probabilistically to a user- defined number of clusters (K), achieving linkage equilibrium within clusters (Pritchard et al. 2000). STRUCTURE uses a model that does not assume a particular mutation process and is able to detect hidden substructure via pooling of sets of individuals independently of the actual sample structure (Pritchard et al. 2000). Results from STRUCTURE enable the user to graph the levels of ancestry for each population based on the maximal number of clusters (K). STRUCTURE was run 6 times for each K {1 to 20} for 106 Monte Carlo Markov Chain (MCMC) iterations after a burn-in period of 105, without any prior information on the population of origin of each sampled individual. The admixture model was used, in which the fraction of ancestry from each cluster is estimated for each individual. The parameter of individual admixture, α was selected to be the same for all clusters and it was given a uniform prior. Allele frequencies were kept independent among clusters to avoid overestimating the number of clusters (Heurtz et al. 2004). Estimation of k was based on the average computed posterior probabilities for each k, that were then used to calculate Uk (see Evanno et al. 2005). The variable Uk is an ad hoc quantity based on the second order rate of change of the

89 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations likelihood function with respect to k (ln[Pr(X | k)]). The Uk was used as it is proposed to have superior ability for estimating the true k (Evanno et al. 2005). The fractions of ancestry were averaged over individuals within each of the 20 N. moorei populations and a corresponding bar graph plotted.

To test whether the reported predominantly sexually regenerating regions were differentiated from the predominantly clonally regenerating region (based on results from Chapter 3) an AMOVA analysis was conducted in GenAlEx V 5.1 (Peakall and Smouse 2001) with two regions defined a priori as:

1. Ballow, Dorrigo/New England, Werrikimbe and Barrington 2. Lamington/Border Ranges

4.2.2.5 Bottleneck detection based on microsatellite data

When populations experience a recent reduction in their effective population size (bottleneck) there is a correlated reduction in the number of alleles and gene heterozygosity, however, the reduction in allele number occurs faster than the reduction in heterozygosity, therefore in a recently bottlenecked population, the observed heterozygosity is higher than the expected equilibrium heterozygosity (Cornuet and Luikart 1996). When a population is at mutation-drift equilibrium (i.e. the effective population size has remained constant in the recent past) there is approximately an equal probability that a locus will exhibit a heterozygosity excess or deficit (Cornuet and Luikart 1996).

To determine whether any N. moorei populations had experienced recent bottlenecks, Wilcoxon signed rank tests were performed using BOTTLENECK version 1.2.02 software (Piry et al. 1999) under all three mutation model assumptions: infinite allele model (IAM), stepwise mutation model (SMM) and the two-phase mutation model (TPM). Under the TPM mutations were set as 95% single-step and 5% multi-step, with a variance among multiple steps of 12, as suggested for microsatellite data by Piry et al. 1999. The Wilcoxon signed rank tests the significance of the difference between observed heterozygosity and expected heterozygosity and is the most appropriate test

90 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

when only a few polymorphic loci are analysed (Piry et al. 1999). It must be noted that the tests for heterozygosity excess here do NOT refer to tests for Hardy-Weinberg equilibrium (Luikart and Cornuet 1998). Clonal genotypes were represented by only a single individual per clonal genotype, so as to not to confound results.

4.3 Results

4.3.1.1 Genetic diversity within populations - AFLPs

AFLP analysis of 491 individuals from 20 populations using 3 primer pairs provided a total of 100 unique DNA fragments (loci), 85 of which were polymorphic (Table 4.1). Number of polymorphic loci varied among populations and ranged from 41% in the Echo Point population to 58% in the Nothofagus Mtn, Gloucester Tops and Link Trail populations (Table 4.1). Overall genetic diversity measured with Shannon's Index varied from 0.2002 at Echo Point to 0.2980 at Link Trail with an overall Shannon's Index value of 0.3770, suggesting moderate levels of genetic diversity within populations (Table 4.1). Similarly, moderate levels of genetic diversity were obtained using Nei's gene diversity index, with values ranging from 0.1318 at Echo Point to 0.1995 at Nothofagus Mtn and an overall index across all populations of 0.2488 (Table 4.1). While levels of genetic diversity varied among populations and among regions, Kruskall-Wallis tests indicated there were no significant differences among populations and among regions for all genetic diversity parameters (P>0.05; Appendix 4.1).

4.3.1.2 Genetic diversity within populations – Microsatellites

Only a small number of cases of significant linkage disequilibrium were detected among loci and no obvious pattern was evident so each locus provided an independent assessment of genetic variation (Table 4.2).

Single locus Exact tests for Hardy-Weinberg equilibrium indicated significant deviations for some populations (Table 4.3). For locus ncutas06, 6 out of 20 populations

displayed significant positive Fis values indicative of a heterozygote deficit (P = 0.05 for

91 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

sequential Bonferonni; Table 4.3). Similarly, 3 out of 20 populations exhibited significant heterozygote deficits at locus ncutas13 (Table 4.3). In contrast, 5 out of 20

populations showed significant negative Fis values at locus ncutas12, indicating a significant excess of heterozygotes (Table 4.3). Reduced heterozygote levels (positive

Fis values) are often attributed to the presence of null alleles or inbreeding, whilst an excess of heterozygotes can result from to non-random mating or selection. Since only a small number of populations displayed reduced heterozygosity at only two loci, it is unlikely that inbreeding was high within N. moorei. Tests for null alleles however, did reveal the presence of null alleles at the ncutas06 and ncutas13 loci (Table 4.4). Ten of the 20 populations displayed null alleles at the ncutas06 locus while 5/20 populations contained null alleles at the ncutas13 locus (Table 4.4). Therefore, null alleles rather than inbreeding most likely produced the heterozygote deficit in these populations.

Since only a few populations at a single locus exhibited a heterozygote excess

(negative Fis value) selection would be an unlikely explanation for this excess. A more plausible explanation for these results may be that they represent statistical anomalies or that individuals may practice a form of non-random mating. Across all loci Weeping

Rock was the only population that displayed a significant positive Fis value (0.265; Table 4.5) however, this heterozygote deficit was attributable to the high frequency of null alleles at the ncutas06 locus (Table 4.4).

Overall, relatively high levels of genetic variability were detected in the 491 individuals sampled from 20 populations that included most of N. moorei’s natural distribution (Table 4.6). Measures of Nei’s gene diversity (Nei 1973) varied among populations and ranged from 0.4883 in the Tullawallal population to 0.7265 at Bar Mtn, with an overall highest gene diversity of 0.7277 (Table 4.6). Similarly, Shannon’s Index values were high, ranging from 0.8581 to 1.6346 (Table 4.6). Kruskall-Wallis tests revealed however, no significant differences among populations for both Shannon’s Index and Nei’s genetic diversity indices (P=0.457; Appendix 4.1). A total of 55 unique alleles were detected across the 4 loci, ranging from 7 alleles at ncutas20 to 24 at ncutas06

(Chapter 2, Table 2.3). The effective number of alleles (Ne) per locus per population ranged from 1.1846 at nctuas20 to 10.4143 at ncutas06 (Table 4.3). Three of the 55 unique alleles were restricted to the Lamington/Border region, 2 alleles were restricted to the Dorrigo-New England region and a single allele was restricted to the Barrington

92 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

region. Additionally, eight private alleles were restricted to individual populations

(Appendix 4.2). Observed levels of heterozygosity (Ho) ranged from 0.1667 at locus ncutas20 to 1.0000 at loci ncutas12 and ncutas13 (Table 4.3). Mean levels of observed heterozygosity were high across all loci with 0.6867, 0.7751, 0.7470 and 0.4217 at locus ncutas06, ncutas12, ncutas13and ncutas20 respectively (Table 4.3).

Genetic diversity at the population level was assessed from estimates of allelic richness

(Rs), observed heterozygosity (Ho) and within sample gene diversity (Hs) (Table 4.5). Allelic richness provides a measure of genetic diversity independent of sample size so that populations can be compared directly. Overall allelic richness (Rs), observed heterozygosity (Ho) and gene diversity (Hs) were 8.136, 0.658 and 0.663, respectively (Table 4.5). Most of the populations appear to contain relatively similar levels of abundant genetic variation. Allelic richness varied from 3.192 in Tullawallal to 7.259 in Mt Moombil; observed heterozygosity ranged from 0.5147 in Tullawallal to 0.8304 in Mt Banda; and gene diversity varied from 0.503 in Tullawallal to 0.754 in Plateau Beech (Table 4.5). Within regions, allelic richness was highest within the Dorrigo/New England and Werrikimbe regions and lowest in the Lamington and Barrington regions (Table 4.5), however, there was no significant difference among populations or regions (P>0.05; Appendix 4.1).

93 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.1 Genetic diversity indices calculated according to Shannon’s Index and Nei’s gene diversity and percentage of polymorphic loci across 491 individuals from 20 populations of N. moorei using 100 AFLP loci.

Population/Region Shannon's Nei's gene Polymorphic loci (%) Index (I) diversity (1973)

Tullawallal 0.2543 0.1679 52.0 Mt Wanungra 0.2797 0.1879 54.0 Echo Point 0.2002 0.1318 41.0 Elabana Falls 0.2507 0.1611 55.0 Lightning Falls 0.2681 0.1784 54.0 Mt Hobwee 0.2086 0.1358 45.0 Best of All Lookout 0.2005 0.1329 42.0 Antarctic Beech Walk/Picnic 0.2521 0.1699 47.0 Bar Mtn 0.2415 0.1607 49.0 Helmholtzia Loop 0.2443 0.1623 50.0 Lamington/Border Ranges 0.3430 0.2263 75.0

Durramlee and Mowburra Peaks 0.2613 0.1718 54.0 Mt Ballow 0.2761 0.1853 51.0 Nothofagus Mtn 0.2991 0.1995 58.0 Ballow 0.3217 0.2124 66.0

Kilungoondie 0.2515 0.1666 53.0 Mt Moombil 0.2743 0.1843 55.0 Weeping Rock 0.2387 0.1568 51.0 Dorrigo/New England 0.3076 0.2041 67.0

Mt Banda 0.2458 0.1657 47.0 Plateau Beech 0.2418 0.1598 50.0 Werrikimbe 0.2806 0.1849 58.0

Gloucester Tops 0.2936 0.1948 58.0 Link Trail 0.2980 0.1914 58.0 Barrington 0.3301 0.2187 68.0

Overall 0.3770 0.2488 85.0

94 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.2 Linkage disequilibrium for each microsatellite locus pair tested on 491 individuals of N. moorei from 20 populations and 5 regions.

Locus Pair Population ncutas06 ncutas06 ncutas12 ncutas06 ncutas12 ncutas13 ncutas12 ncutas13 ncutas13 ncutas20 ncutas20 ncutas20

Tullawallal Mt Wanungra Antarctic Beech Picnic/Walk Bar Mtn * Best of All Lookout ** Durramlee/Mowburra Peaks Echo Point Elabana Falls Helmholtzia Loop * Lightning Falls * Mt Ballow * Mt Hobwee * Nothofagus Mtn Kilungoondie Mt Moombil Weeping Rock * Mt Banda Plateau Beech Link Trail Gloucester Tops

* denotes significance after sequential Bonferroni correction of α

95

Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter

Table 4.3 Summary of genetic values for 20 populations of N. moorei at 4 microsatellite loci. Effective number of alleles (Ne), observed

heterozygosity (Ho), Nei’s expected heterozygosity (He) and Wright’s fixation index (Fis) are listed for each population and locus. Statistically significant deviations from Hardy-Weinberg expectations are indicated by * (P<0.05 for Bonferroni sequential correction).

ncutas06 ncutas12 ncutas13 ncutas20 Population Ne Ho He Fis Ne Ho He Fis Ne Ho He Fis Ne Ho He Fis Tullawallal 3.6352 0.7647 0.7249 -0.0549 1.8408 0.3529 0.4567 0.2273 2.5463 0.7647 0.6073 -0.2593 1.1967 0.1765 0.1644 -0.0737 Mt Wanungra 5.1136 0.8667 0.8044 -0.0773 2.8481 0.6000 0.6489 0.0753 5.0000 0.8667 0.8000 -0.0833 1.4107 0.3333 0.2911 -0.1450 Echo Point 5.2118 0.6087 0.8081 0.2468 2.4778 0.7826 0.5964 -0.3122 4.2151 0.8690 0.7628 -0.1400 1.6794 0.4348 0.4045 -0.0748 Elabana Falls 5.1230 0.4000 0.8048 *0.5030 2.7964 0.9200 0.6424 -0.4321 5.2521 0.8400 0.8096 0.0375 1.7241 0.4000 0.4200 -0.0476 Lightning Falls 9.5211 0.6923 0.8950 0.2264 1.7955 0.5769 0.8950 -0.3022 2.5655 0.6154 0.6102 -0.0085 2.3513 0.6154 0.5747 -0.0708 Mt Hobwee 9.0133 0.6923 0.8891 0.2213 2.9391 0.9231 0.6598 -0.3991 4.0723 0.8077 0.7544 -0.0706 1.7333 0.4615 0.4231 -0.0909 Best of All Lookout 8.5217 0.6429 0.8827 *0.2717 2.0851 0.5000 0.5204 0.0392 4.7805 1.0000 0.7908 -0.2645 1.3243 0.2857 0.2449 -0.1667 Antarctic Beech WP 6.6804 0.7222 0.8503 0.1506 3.5410 0.8889 0.7176 -0.2387 5.1840 1.0000 0.8071 -0.2390 1.1846 0.1667 0.1559 -0.0693 Helmholtzia Loop 6.6601 0.3846 0.8499 *0.5474 2.6050 0.9231 0.8499 -0.4982 3.0658 0.8462 0.6738 -0.2558 1.6172 0.4615 0.3817 -0.2093 Bar Mtn 9.2190 0.7727 0.8915 0.1333 2.3667 0.8636 0.5775 -0.4955 4.6095 0.9545 0.7831 -0.2190 2.8896 0.5455 0.6539 0.1659 Lamington/Border 14.3913 0.6368 0.9305 *0.3157 2.9062 0.7547 0.6559 *-0.1506 7.2896 0.8443 0.8628 *0.0214 1.7646 0.4104 0.4333 0.0529 Durramlee Peaks 6.3740 0.4643 0.8431 *0.4493 2.0390 0.4643 0.8431 -0.5419 4.1155 0.7857 0.7570 -0.0379 1.4586 0.2857 0.3144 0.0913 Mt Ballow 9.2235 0.8929 0.8597 -0.0014 2.1421 1.0000 0.5631 *-0.8756 7.0314 0.4286 0.7813 *0.5004 1.7062 0.3571 0.3887 0.1317 Nothofagus Mtn 9.4737 0.7667 0.8944 0.1429 2.6826 1.0000 0.6272 *-0.5943 3.7113 0.7667 0.7306 -0.0494 1.5693 0.4000 0.3628 -0.1026 Ballow 12.0358 0.7093 0.9269 *0.2264 2.3368 0.9302 0.5721 *-0.6261 5.2232 0.6628 0.8085 *0.1803 1.5785 0.3488 0.3665 *0.0481 Kilungoondie 10.4143 0.9259 0.9040 -0.0243 2.0918 0.6667 0.9040 -0.2773 8.4767 0.5556 0.8820 *0.3701 1.4155 0.3393 0.2936 -0.1355

Mt Moombil 9.7656 0.6000 0.8976 *0.3316 2.2202 0.9600 0.5490 *-0.7467 8.9928 0.9600 0.8888 -0.0801 1.2279 0.2000 0.1856 -0.0776 moorei Nothofagus Weeping Rock 8.5816 0.4138 0.8835 *0.5316 2.1157 0.4483 0.5273 0.1499 7.2189 0.7241 0.8615 0.1594 1.4242 0.3448 0.2979 -0.1577 Dorrigo/NE 15.1001 0.6420 0.9338 0.3125 2.1881 0.6790 0.5430 *-0.2505 10.2837 0.7407 0.9028 *0.1795 1.3684 0.2963 0.2692 *-0.1005 Mt Banda 9.2235 0.8929 0.8916 -0.0014 2.3488 1.0000 0.5733 *-0.7442 6.7879 0.7500 0.8527 0.1204 2.3368 0.6788 0.5721 -0.1862 Plateau Beech 8.7150 0.7931 0.8853 0.1041 2.1372 1.0000 0.5321 *-0.8793 8.5816 0.7931 0.8835 0.1023 1.6019 0.3793 0.3757 -0.0095 Werrikimbe 10.6875 0.8421 0.9064 0.0710 2.2415 1.0000 0.5539 *-0.8055 9.2696 0.7719 0.8621 0.1347 1.9455 0.5263 0.4860 -0.0830 Link Trail 6.4222 0.7353 0.8443 0.1291 1.8843 0.5000 0.4693 -0.0654 3.8727 0.4412 0.7418 *0.4052 2.7656 0.6471 0.6384 -0.0136 Gloucester Tops 7.0314 0.7500 0.8578 0.1257 2.1687 0.5714 0.5389 -0.0604 4.3799 0.6071 0.7717 0.2132 2.5290 0.6071 0.6046 -0.0042 populations Barrington 7.2324 0.7419 0.8617 0.1390 2.0168 0.5323 0.5042 -0.0557 4.6174 0.5161 0.7834 *0.3412 2.9210 0.6290 0.6576 0.0435

OVERALL 17.0925 0.6867 0.9415 0.2706 2.6109 0.7751 0.617 -0.2563 8.0125 0.747 0.8752 0.1465 1.9126 0.4217 0.4772 0.1163

96

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.4 Estimated null allele frequencies for the 4 microsatellite loci tested in 491 individuals from 20 populations of N. moorei. The presence of null alleles was determined with MICROCHECKER and the null allele frequency estimated as per Brookfield’s equation that assumes non-amplifications are a result of null allele homozygotes (Brookfield 1996).

Locus Region /Population ncutas06 ncutas12 ncutas13 ncutas20

Lamington/Border Ranges Tullawallal na na na na Mt Wanungra na na na na Echo Point 0.1103 na na na Elabana Falls 0.2243 na na na Lightning Falls 0.1069 na na na Mt Hobwee 0.1042 na na na Best of All Lookout 0.1274 na na na Antarctic Beech Walk/Picnic na na na na Bar Mtn nananana Helmholtzia Loop 0.2669 na na na

Ballow Durramlee and Mowburra Peaks 0.2055 na na na Mt Ballow na na 0.2249 na Nothofagus Mtn 0.0674 na na na

Dorrigo/New England Kilungoondie na na 0.1735 na Mt Moombil 0.1568 na na na Weeping Rock 0.2494 na 0.0738 na

Werrikimbe Mt Banda na na na na Plateau Beech na na na na

Barrington Gloucester Tops na na 0.0929 na Link Trail na na 0.2183 na

97 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.5 Mean values of allelic richness (Rs), observed heterozygosity (Ho), Nei’s expected heterozygosity (He), within sample gene diversity (Hs) and fixation index

(Fis) from the analysis of 491 individuals sampled from 20 populations of N. moorei using 4 microsatellite loci. Fis value of 0 = random mating, negative value = excess heterozygotes (selection), positive value = inbreeding. Statistically significant deviations from Hardy-Weinberg expectations are indicated by * (P<0.05 for Bonferoni sequential correction).

Population/Region Rs Ho He Hs Fis

Tullawallal 3.192 0.5147 0.4883 0.503 -0.024 Mt Wanungra 5.665 0.6667 0.6361 0.658 -0.014 Echo Point 5.700 0.6739 0.6430 0.657 -0.026 Elabana Falls 6.159 0.6400 0.6692 0.684 0.064 Lightning Falls 6.066 0.6250 0.6307 0.643 0.029 Mt Hobwee 6.169 0.7212 0.6816 0.695 -0.038 Best of All Lookout 6.000 0.6071 0.6097 0.633 0.041 Antarctic Beech WP 6.105 0.6944 0.6327 0.650 -0.069 Bar Mtn 5.807 0.7841 0.7265 0.743 -0.056 Helmholtzia Loop 5.104 0.6538 0.6304 0.643 0.004 Lamington/Border Ranges 5.596 0.6616 0.7206 0.656 -0.008 Durramlee Peaks 5.790 0.5804 0.6060 0.618 0.060 Mt Ballow 6.395 0.6696 0.6741 0.687 0.025 Nothofagus Mtn 6.033 0.7333 0.6537 0.664 -0.105 Ballow 6.073 0.6628 0.6660 0.656 -0.010 Kilungoondie 7.312 0.6204 0.6504 0.663 0.065 Mt Moombil 7.259 0.6800 0.6304 0.643 -0.058 Weeping Rock 6.765 0.4828 0.6425 0.657 *0.265 Dorrigo/NE 7.112 0.5895 0.6622 0.655 0.100 Mt Banda 6.632 0.8304 0.7224 0.680 -0.091 Plateau Beech 7.274 0.7414 0.6691 0.754 -0.102 Werrikimbe 7.103 0.7851 0.7096 0.716 -0.096 Link Trail 4.789 0.5809 0.6734 0.685 0.152 Gloucester Tops 5.449 0.6339 0.6932 0.707 0.104 Barrington 5.119 0.6048 0.7017 0.695 0.130

OVERALL 8.136 0.658 0.728 0.663 -0.013

98 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.6 Genetic diversity indices calculated according to Shannon’s Index and Nei’s gene diversity for 491 individuals from 20 populations of N. moorei using 4 microsatellite loci.

Population/Region Shannon's Index Nei's gene diversity (1973) Ih

Tullawallal 0.8581 0.4883 Mt Wanungra 1.3345 0.6361 Echo Point 1.3410 0.6430 Elabana Falls 1.4511 0.6692 Lightning Falls 1.3813 0.6307 Mt Hobwee 1.4801 0.6816 Best of All Lookout 1.3268 0.6097 Antarctic Beech Walk/Picnic 1.3975 0.6327 Bar Mtn 1.5123 0.7265 Helmholtzia Loop 1.2818 0.6304 Lamington/Border Ranges 1.7695 0.7206 Durramlee and Mowburra Peaks 1.3113 0.6060 Mt Ballow 1.5069 0.6741 Nothofagus Mtn 1.4328 0.6537 Ballow 1.5660 0.6660 Kilungoondie 1.5595 0.6504 Mt Moombil 1.5196 0.6304 Weeping Rock 1.4847 0.6425 Dorrigo/New England 1.7032 0.6622 Mt Banda 1.6346 0.7224 Plateau Beech 1.5481 0.6691 Werrikimbe 1.6873 0.7096 Gloucester Tops 1.4278 0.6932 Link Trail 1.3346 0.6734 Barrington 1.4600 0.7017

Overall 1.8353 0.7277

99 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.3.2.1 Isolation by distance within and among populations – AFLP data

Significant and positive correlation values up to 15 to 40 metres were observed for autocorrelation analysis within all populations based on AFLP data for 491 individuals from 20 populations (Figure 4.1). This suggests that within a 15-40 metre radius, individual trees within a population are more closely related to each other than they are to more geographically distant trees. Spatial autocorrelation was not determined for Tullawallal, Antarctic Beech Walk/Picnic and Durramlee/Mowburra Peaks because geographical data were not available. Mantel tests also confirmed a significant, positive correlation between genetic and geographical distance within populations except for Elabana Falls, Mt Ballow and Kilungoondie populations (Table 4.7).

Among populations there was no correlation between geographical distance and genetic distance (R2 = 0.0116, P = 0.166; Figure 4.2), suggesting that at larger spatial scales, isolation by distance is not evident and that gene flow may be extensive among populations.

100 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Mt Wanungra

0.4

0.3

0.2 r

r 0.1 U

0 L

-0.1 -0.2 10 20 30 40 50 60 70 80 90 100 Distance

Echo Point

0.3 0.25 0.2 0.15 0.1 r r 0.05 U 0 L -0.05 -0.1 -0.15 -0.2

0 0 0 0 0 1 3 5 7 9 110 130 150 170 190

Distance

Elabana Falls

0.2

0.15

0.1 r 0.05

r U 0 L -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Distance

Figure 4.1 Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Mt Wanungra, Echo Point and Elabana Falls populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

101 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Lightning Falls

0.4

0.3

0.2 r

r 0.1 U

0 L

-0.1

-0.2

0 0 0 0 0 0 0 10 30 5 7 9 7 9 1 3 110 130 150 1 1 2 2 Distance

Mt Hobwee

0.4

0.3 0.2 r 0.1

r U 0 L -0.1 -0.2

-0.3

0 0 0 0 0 1 3 50 70 90 1 3 5 1 1 1 170 190 210 230

Distance

Best of All Lookout

0.4 0.3 0.2 r 0.1

r U 0 L -0.1 -0.2 -0.3

10 30 50 70 90 110 130 150 170 190 Distance

Figure 4.1 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Lightning Falls, Mt Hobwee and Best of All Lookout populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

102 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Bar Mtn

0.35 0.3 0.25 0.2 r 0.15

r 0.1 U 0.05 L 0 -0.05 -0.1 -0.15

0 0 0 0 0 0 0 1 30 5 70 9 1 7 1 1 130 15 1 190 2 Distance

Helmholtzia Loop

0.25 0.2 0.15 0.1 r

r 0.05 U 0 L -0.05 -0.1 -0.15

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Distance

Mt Ballow

0.2

0.15 0.1 r 0.05 r U 0 L -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 Distance

Figure 4.1 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Bar Mtn, Helmholtzia Loop and Mt Ballow populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

103 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Nothofagus Mtn

0.12 0.1 0.08 0.06 0.04 r 0.02 r U 0 -0.02 L -0.04 -0.06 -0.08 -0.1 10 20 30 40 50 60 70 80 90 100 110 Distance

Kilungoondie

0.25 0.2 0.15 0.1 r 0.05

r U 0 L -0.05 -0.1 -0.15 -0.2 10 20 30 40 50 60 70 80 90 100 Distance

Mt Moombil

0.4 0.3 0.2 r

r 0.1 U 0 L -0.1 -0.2 0 0 0 0 0 0 0 0 0 0 0 0 100 300 500 700 900 110 130 150 170 190 210 230 250 27 29 Distance

Figure 4.1 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Nothofagus Mtn, Kilungoondie and Mt Moombil populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

104 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Weeping Rock

0.15

0.1

0.05 r

r U 0 L -0.05 -0.1 10 30 50 70 90 110 130 150 170 190

Distance

Mt Banda

0.3 0.2 0.1 r

r U 0 L -0.1

-0.2 5 152535455565758595 Distance

Plateau Beech

0.15

0.1

0.05 r

r 0 U L -0.05

-0.1

-0.15 20 60 100 140 180 220 260 300 340 380 420 Distance

Figure 4.1 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Weeping Rock, Mt Banda and Plateau Beech populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

105 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Gloucester Tops

0.1 0.08 0.06 0.04 r 0.02

r U 0 L -0.02 -0.04 -0.06

-0.08 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 Distance

Link Trail

0.15

0.1

0.05 r

r 0 U

-0.05 L -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 130

Distance

Figure 4.1 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Gloucester Tops and Link Trail populations based on 100 AFLP loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

106 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.7 Mantel test results for correlation (Rxy) between the pairwise geographic (x) and genetic distance (y) matrices for 491 individuals from 20 populations of N. moorei using data from 100 AFLP loci. Probability (P) for tests of significance were conducted using 999 random permutations. Positive and significant correlation (Rxy) values indicate significant correlation of geographic and genetic distance.

Region/ Population Rxy P

Lamington/Border Ranges Tullawallal ND ND Mt Wanungra 0.220 0.001 Echo Point 0.256 0.001 Elabana Falls 0.104 0.067 Lightning Falls 0.366 0.001 Mt Hobwee 0.406 0.001 Best of All Lookout 0.614 0.001 Antarctic Beech Walk/Picnic ND ND Bar Mtn 0.228 0.010 Helmholtzia Loop ND ND

Ballow Durramlee and Mowburra Peaks ND ND Mt Ballow 0.068 0.173 Nothofagus Mtn 0.227 0.001

Dorrigo/New England Kilungoondie 0.112 0.085 Mt Moombil 0.292 0.002 Weeping Rock 0.219 0.001

Werrikimbe Mt Banda 0.346 0.001 Plateau Beech 0.281 0.001

Barrington Gloucester Tops 0.134 0.017 Link Trail 0.173 0.003

ND: no geographic data

107 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

1.200

1.000 R2 = 0.0116

) 0.800 ST 0.600 (1-F ST

F 0.400

0.200

0.000 0.000 1.000 2.000 3.000 4.000 5.000 6.000 logn Geographic Distance

Figure 4.2 Correlation between geographic and AFLP-based genetic distances 2 {FST/(1- FST)} for 491 individuals from 20 N. moorei populations. The correlation (R ) value is indicated and was not significant (P = 0.166).

4.3.2.2 Isolation by distance within and among populations – Microsatellite data

Correlograms generated using GenAlex V5.1 (Peakall and Smouse 2001) based on microsatellite data for 491 individuals from 20 populations revealed positive and significant correlation values up to 15 to 40 metres in all populations except Mt Ballow, Mt Banda and Gloucester Tops where no correlation was observed (Figure 4.3). This suggests that within a 15-40m radius, individual trees at a site are more closely related to each other than they are to more geographically distant individuals in all populations except Mt Ballow, Mt Banda and Gloucester Tops. Spatial autocorrelation was not determined for Tullawallal, Antarctic Beech Walk/Picnic and Durramlee/Mowburra Peaks populations because geographical data were not available. Mantel tests were conducted to confirm the correlation between genetic and geographical distance for each population. Positive correlations were not significant however, in Nothofagus Mtn, Mt Ballow, Kilungoondie, Mt Moombil, Mt Banda or Plateau Beech populations (Table 4.8).

Among populations there was no significant correlation between geographical and genetic distance (R2 = 0.0274; P = 0.111; Figure 4.4), suggesting that at larger spatial scales there is no isolation by distance affect and that gene flow among regions is present.

108 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Mt Wanungra

1

0.8 0.6 r 0.4 r U 0.2 L 0 -0.2 -0.4 10 20 30 40 50 60 70 80 90 100

Distance

Echo Point

0.4 0.3

0.2

0.1 r

r U 0 L -0.1 -0.2 -0.3 10 30 50 70 90 110 130 150 170 190 Distance

Elabana Falls

0.5 0.4

0.3 r 0.2

r U 0.1 L 0

-0.1 -0.2 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Distance

Figure 4.3 Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Mt Wanungra, Echo Point and Elabana Falls populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

109 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Lightning Falls

0.8 0.6 0.4 r

r 0.2 U L 0 -0.2 -0.4 10 30 50 70 90 110 130 150 170 190 210 230 Distance

Mt Hobwee

0.5

0.4 0.3 0.2 r

r 0.1 U

0 L -0.1 -0.2

-0.3 10 30 50 70 90 110 130 150 170 190 210 230 Distance

Best of All Lookout

0.8 0.6 0.4 r 0.2

r U 0 L -0.2 -0.4 -0.6

10 30 50 70 90 110 130 150 170 190 Distance

Figure 4.3 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Lightning Falls, Mt Hobwee and Best of All Lookout populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

110 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Bar Mtn

0.7 0.6 0.5 0.4 r 0.3

r 0.2 U 0.1 L 0 -0.1 -0.2 -0.3 10 30 50 70 90 110 130 150 170 190 Distance

Helmholtzia Loop

0.7 0.6 0.5 0.4 r 0.3

r 0.2 U 0.1 L 0 -0.1 -0.2 -0.3 10 30 50 70 90 110 130 150 170 Distance

Mt Ballow

0.25 0.2 0.15 0.1 r

r 0.05 U 0 L -0.05

-0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Distance

Figure 4.3 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Bar Mtn, Helmholtzia Loop and Mt Ballow populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

111 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Nothofagus Mtn

0.2 0.15 0.1 r 0.05

r U 0 L -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100

Distance

Kilungoondie

0.2 0.15

0.1 r

r 0.05 U

0 L

-0.05 -0.1 10 20 30 40 50 60 70 80 90 100 Distance

Mt Moombil

0.6 0.5

0.4 0.3 r 0.2

r U 0.1 L 0 -0.1 -0.2

-0.3 10 30 50 70 90 110 130 150 170 190 Distance Figure 4.3 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Nothofagus Mtn, Kilungoondie and Mt Moombil populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

112 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Weeping Rock

0.35 0.3 0.25 0.2 0.15 r 0.1 r U 0.05 0 L -0.05 -0.1 -0.15 -0.2 10 30 50 70 90 110 130 150 170 190 Distance

Mt Banda

0.25 0.2 0.15 0.1 r

r 0.05 U 0 L -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 Distance

Plateau Beech

0.3 0.25 0.2 0.15 r 0.1

r U 0.05 L 0 -0.05 -0.1 -0.15 10 30 50 70 90 110 130 150 170 190

Distance

Figure 4.3 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Weeping Rock, Mt Banda and Plateau Beech populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

113 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Gloucester Tops

0.2 0.15 0.1 r 0.05

r U 0 L -0.05

-0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Distance

Link Trail

0.3 0.25 0.2 0.15 r 0.1

r U 0.05 L 0 -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 Distance

Figure 4.3 cont. Genetic spatial autocorrelation (r) of genetic distance with geographic distance (m) for N. moorei individuals from Gloucester Tops and Link Trail populations based on 4 microsatellite loci. Dashed lines are the upper (U) and lower (L) 95% confidence intervals around r = 0. Vertical lines are 95% bootstrapped confidence intervals around each calculated r value.

114 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.8 Mantel test results for correlation (Rxy) between the pairwise geographic (x) and genetic distance (y) matrices for 491 individuals from 20 populations of N. moorei using data from 4 microsatellite loci. Probability (P) for tests of significance were conducted using 999 random permutations. Positive and significant correlation (Rxy) values indicate significant correlation of geographic and genetic distance.

Region /Population Rxy P

Lamington/Border Ranges Tullawallal ND ND Mt Wanungra 0.361 0.001 Echo Point 0.115 0.019 Elabana Falls 0.251 0.001 Lightning Falls 0.271 0.002 Mt Hobwee 0.289 0.001 Best of All Lookout 0.525 0.001 Antarctic Beech Walk/Picnic ND ND Bar Mtn 0.209 0.003 Helmholtzia Loop ND ND

Ballow Durramlee and Mowburra Peaks ND ND Mt Ballow 0.038 0.286 Nothofagus Mtn 0.050 0.256

Dorrigo/New England Kilungoondie 0.008 0.449 Mt Moombil 0.020 0.407 Weeping Rock 0.348 0.001

Werrikimbe Mt Banda 0.118 0.085 Plateau Beech 0.120 0.072

Barrington Gloucester Tops -0.102 0.890 Link Trail 0.237 0.001

ND: no geographic data

115 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

0.350 2 0.300 R = 0.0274 0.250 )

ST 0.200

(1-F 0.150 ST F 0.100 0.050 0.000 0.000 1.000 2.000 3.000 4.000 5.000 6.000 logn Geographic Distance

Figure 4.4 Correlation between geographic and microsatellite-based genetic distances {FST/(1-FST)} for 491 individuals from 20 N. moorei populations. The correlation (R2) value is indicated and was not significant (P = 0.111).

4.3.3.1 Population differentiation and relationships among populations – AFLPs

There was considerable divergence among populations (GST = 0.3062; Table 4.9) and low but sufficient levels of gene flow to maintain genetic connectivity among populations (Nm = 1.1330; Table 4.9). All population pairwise FST values were significant (P<0.05; Table 4.10) with the greatest differentiation between Gloucester Tops and Mt Banda populations (0.507; Table 4.10) and the least differentiation evident between Nothofagus Mtn and Mt Ballow populations (0.087; Table 4.10). Levels of differentiation among populations from the highly clonal Lamington/Border Ranges region and populations from less clonal regions (as identified in Chapter 3) were similar to levels of differentiation among populations within regions (Table 4.10).

While there was significant differentiation among populations, the neighbour-joining phylogenetic tree based on pairwise FST values revealed no apparent geographical structuring of sampled populations. Two populations from the Lamington region (Tullawallal {TW} and Mt Wanungra {MtW}) and the two Barrington populations (Link Trail ({LT} and Gloucester Tops {GT}) formed a unique clade (Figure 4.5). A separate clade grouped populations from the eastern McPherson Range of Lamington (Echo Point {EP}, Lightning Falls {LF}, Elabana Falls {EF}) with populations from the western McPherson Range of Ballow (Mt Ballow {BA},

116 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Nothofagus Mtn {NO} and Durramlee/Mowburra Peaks {DUMP}) (Figure 4.5). A third clade comprised all remaining populations from the Lamington region and all populations from the Werrikimbe and Dorrigo/New England regions (Figure 4.5).

Principal coordinate analysis identified a similar separate cluster of the Gloucester Tops, Link Trail, Tullawallal and Mt Wanungra populations however; there was no strong clustering of individuals within each of these populations (Figure 4.6). Individuals from all other populations were clustered together, with no distinct population or regional sub-clusters within this main cluster (Figure 4.6).

Table 4.9 Global population differentiation (GST/FST) and gene flow (Nm) estimates based on data from 100 AFLP loci and 4 microsatellite loci for 491 N. moorei individuals from 20 populations. Nm represents the number of migrants per generation.

Molecular marker FST/GST Nm

AFLPs 0.3062 1.133 Microsatellites 0.1055 2.1197

117

Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter

Table 4.10 Population pairwise FST values based on data from 100 AFLP loci for 491 N. moorei individuals from 20 populations. FST values are given below diagonal while the significance (*) is given above diagonal. * indicates significant difference (P<0.05) based on 1023 permutations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 10******************* 20.2260****************** 3 0.269 0.268 0 * * * * * * * * * * * * * * * * 4 0.288 0.188 0.202 0 * * * * * * * * * * * * * * * 5 0.45 0.449 0.4 0.414 0 * * * * * * * * * * * * * * 6 0.41 0.466 0.41 0.455 0.191 0 * * * * * * * * * * * * * 7 0.428 0.411 0.36 0.393 0.303 0.253 0 * * * * * * * * * * * * 8 0.495 0.457 0.419 0.445 0.344 0.3 0.252 0 * * * * * * * * * * * 9 0.469 0.457 0.432 0.454 0.361 0.345 0.387 0.28 0 * * * * * * * * * * 10 0.464 0.452 0.383 0.406 0.331 0.285 0.268 0.21 0.265 0 * * * * * * * * * 11 0.476 0.474 0.423 0.464 0.357 0.29 0.269 0.13 0.29 0.183 0 * * * * * * * * 12 0.488 0.458 0.41 0.413 0.32 0.274 0.239 0.202 0.322 0.134 0.239 0 * * * * * * * 13 0.465 0.467 0.416 0.416 0.202 0.176 0.283 0.298 0.331 0.27 0.281 0.269 0 * * * * * * 14 0.426 0.433 0.374 0.42 0.234 0.187 0.247 0.34 0.373 0.281 0.311 0.302 0.246 0 * * * * * 15 0.425 0.428 0.34 0.385 0.159 0.164 0.232 0.302 0.33 0.242 0.294 0.277 0.184 0.087 0 * * * * Nothofagus moorei Nothofagus 16 0.452 0.412 0.429 0.42 0.295 0.219 0.259 0.198 0.272 0.214 0.222 0.262 0.258 0.262 0.26 0 * * * 17 0.421 0.431 0.382 0.458 0.348 0.218 0.27 0.254 0.289 0.204 0.252 0.255 0.303 0.313 0.291 0.211 0 * * * 18 0.491 0.467 0.46 0.47 0.349 0.254 0.233 0.235 0.337 0.273 0.239 0.217 0.251 0.289 0.278 0.234 0.241 0 * * 19 0.507 0.486 0.449 0.473 0.345 0.251 0.291 0.183 0.321 0.116 0.158 0.166 0.247 0.29 0.286 0.205 0.209 0.199 0 * 20 0.451 0.417 0.451 0.451 0.331 0.241 0.299 0.197 0.283 0.241 0.231 0.246 0.252 0.311 0.283 0.133 0.189 0.155 0.203 0

populations 1 - Gloucester Tops 6 - Elabana Falls 11 - Bar Mtn 16 - Kilungoondie 2 - Link Trail 7 - Lightning Falls 12 - Helmholtzia Loop 17 - Mt Moombil 3 - Tullawallal 8 - Mt Hobwee 13 - Durramell/Mowburra Peaks 18 - Weeping Rock 4 - Mt Wanungra 9 - Best of All Lookout 14 - Mt Ballow 19 - Mt Banda 5 - Echo Point 10 - Antarctic Beech Walk/Picnic 15 - Nothofagus Mtn 20 - Plateau Beech 118

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Figure 4.5 Neighbour-joining phylogenetic tree using AFLP population pairwise FST values for 491 individuals of N. moorei from 20 populations. See list of abbreviations for population abbreviations.

Lamington/Border Ranges

Ballow

Dorrigo/New England

Werrikimbe

Barrington

119

Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter

Gloucester Tops Link Trail Tullawallal Mt Wanungra Echo Point Elabana Falls Lightning Falls ) Mt Hobwee Best of All Lookout Antarctic Beech Walk/Picnic Bar Mtn

Coordinate 2 (16.30% Helmholtzia Loop Duramlee/Mowburra Peaks Mt Ballow

Nothofagus Mtn moorei Nothofagus Kilungoondie Mt Moom bil Weeping Rock Coordinate 1 (42.13%) Mt Banda populations Plateau Beech

Figure 4.6 Scatter plot of 491individuals of N. moorei from 20 populations based on genetic distance matrix generated from 100 AFLP loci.

Each symbol represents a different population. Coordinates 1 and 2 explain 42.13% and 16.30% of the total variation observed, respectively. 120

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.3.3.2 Population differentiation and relationships among populations – microsatellites

Microsatellites detected moderate levels of genetic divergence among populations

(FST = 0.1055; Table 4.9) and quite high levels of gene flow with more than 2 migrants per generation (Table 4.9). Population pairwise FST values were significant among all populations (P<0.05; Table 4.11), indicating significant population differentiation. Greatest population differentiation was evident among the Antarctic

Beech Walk/Picnic and Link Trail populations (FST = 0.231, Table 4.11) while least population differentiation was evident among the Mt Hobwee and Mt Banda populations (FST = 0.013; Table 4.11). Comparison of the highly clonal populations from the Lamington/Border Ranges region with less clonal populations from other regions (as identified in Chapter 3) identified no patterns, with differentiation among populations within the Lamington/Border Ranges region similar to differentiation among populations among different regions (Table 4.11).

Despite significant population differentiation among all populations, the neighbour- joining phylogenetic tree based on pairwise FST values failed to identify any strong geographical structuring of populations (Figure 4.7). Two populations from the Lamington region (Best of All Lookout and Mt Hobwee) clustered with all populations from the Werrikimbe and Dorrigo/New England regions and a single population from the Ballow region (Mt Ballow) (Figure 4.7). The Antarctic Beech Walk/Picnic and Elabana Falls populations from the Lamington/Border Ranges region formed a separate group from the remaining Lamington/Border Ranges populations and Ballow populations (Figure 4.7). The two Barrington populations formed a unique clade with the Bar Mtn population falling intermediate between the Barrington clade and other clades (Figure 4.7). The principal coordinate analysis revealed no distinct clustering of individuals within regions, with individuals from different populations clustering randomly (Figure 4.8).

121

Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter

Table 4.11 Population pairwise FST values based on data from 4 microsatellite loci for 491 N. moorei individuals from 20 populations. FST values are given below diagonal while the significance (*) is given above diagonal. * indicates significant difference (P<0.05) based on 1023 permutations. 1234567891011121314151617181920 10******************* 20.0360****************** 30.1750.0860***************** 40.1860.0980.1340**************** 50.1780.090.0980.0670*************** 60.1090.050.0870.0840.0940************** 70.1640.0850.0660.070.070.0660************* 80.0940.0530.1390.1240.1070.0640.0890************ 90.0890.0790.1510.130.170.0680.120.0680*********** 100.1660.0840.0940.1220.1160.1010.0980.0660.1020********** 110.0870.0470.1170.1180.0950.0460.0910.0320.0430.0670********* 120.0990.0490.0930.0630.0570.0490.0550.0390.0950.0630.0450******** 130.0920.050.1280.0870.0750.0550.0890.0240.0670.0710.0180.0280******* 140.1160.0440.1110.0770.050.050.0690.0520.1260.0790.0630.0140.0430****** 150.1140.0560.0910.0940.0480.0740.0580.0750.1370.10.0680.0260.0590.0220***** 160.0950.040.0850.0760.0520.0580.0630.0710.0950.0760.0450.0260.0530.0340.0280**** Nothofagus moorei Nothofagus 17 0.117 0.035 0.101 0.074 0.06 0.06 0.063 0.043 0.096 0.046 0.031 0.013 0.029 0.016 0.03 0.027 0 * * * 18 0.121 0.061 0.112 0.041 0.053 0.066 0.057 0.062 0.073 0.077 0.044 0.035 0.034 0.045 0.052 0.043 0.028 0 * * 19 0.229 0.152 0.231 0.073 0.151 0.142 0.165 0.151 0.17 0.192 0.167 0.134 0.124 0.141 0.174 0.177 0.136 0.082 0 * 20 0.207 0.123 0.22 0.056 0.148 0.127 0.156 0.142 0.15 0.158 0.156 0.11 0.114 0.122 0.161 0.156 0.125 0.084 0.039 0

1 - Tullawallal 6 - Duramlee/Mowburra Peaks 11 - Mt Ballow 16 - Weeping Rock populations 2 - Mt Wanungra 7 - Echo Point 12 - Mt Hobwee 17 - Mt Banda 3 - Antarctic Beech Walk/Picnic 8 - Elabana Falls 13 - Nothofagus Mtn 18 - Plateau Beech 4 - Bar Mtn 9 - Helmholtzia Loop 14 - Kilungoondie 19 - Link Trail 122 5 - Best of All Lookout 10 - Lightning Falls 15 - Mt Moombil 20 - Gloucester Tops

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Figure 4.7 Neighbour-joining phylogenetic tree using microsatellite population pairwise FST values for 491 individuals of N. moorei from 20 populations. See list of abbreviations for population abbreviations.

Lamington/Border Ranges

Ballow

Dorrigo/New England

Werrikimbe

Barrington

123

Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter

Tullawallal Mt Wanungra Antarctic Beech Bar Mtn Best of All Lookout Durramlee and Mowburra EchoPeaks Point Elabana Falls Helmholtzia Loop Lightning Falls Mt Ballow Mt Hobwee

Coordinate 2 (19.19%) Nothofagus Mtn Kilungoondie Mt Moombil Nothofagus moorei Nothofagus Weeping Rock Mt Banda Plateau Beech Link Trail Coordinate 1 (28.37%) Gloucester Tops populations

Figure 4.8 Scatter plot of 491individuals of N. moorei from 20 populations based on genetic distance matrix generated from 4 microsatellite loci. Each symbol represents a different population. Coordinates 1 and 2 explain 28.37% and 19.19% of the total variation

observed, respectively. 124

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.3.4 Regional population structure

AMOVA based on 100 AFLP loci indicated that a significant proportion of the variation was held among populations within regions (24.32%, P<0.001; Table 4.12), although most variation was present within populations (62.95%, P<0.001; Table 4.12). A small but significant proportion of variation was held among regions (12.73%; P<0.001)

Spatial analysis of molecular variance (SAMOVA) based on 4 microsatellite loci indicated that optimum (highest FCT) population structure was present within four groups that did not correlate with extant geographical regional structuring. One group consisted of only the Mt Moombil population; the second group - Mt Ballow: the third group consisted of four of the Lamington populations: Mt Wanungra, Echo Point, Elabana Falls and Lightning Falls and the fourth group consisted of the remaining populations from across all regions (FCT = 0.10410; P<0.001; Table 4.13). Greater differentiation was evident with just two groups with one group consisting of

Mt Ballow and the other group consisting of all other populations, however, this FCT value was not significant (Table 4.13). Overall FCT differentiation values were very similar across the two to seven defined groupings (Table 4.13) suggesting low population differentiation. Furthermore SAMOVA results for the above four defined groups revealed that the majority of genetic variation was present within populations (87.50%; P<0.001; Table 4.14) suggesting extensive ongoing gene flow occurs among all populations.

Table 4.12 Analysis of molecular variance (AMOVA) for 491 N. moorei individuals from 20 populations across 5 regions based on data from 100 AFLP loci. Significance values (P) were calculated using 1023 permutations.

Source of variation Df SS Variance % of variation P Components

Among regions 4 1187.65 1.60 12.73 <0.001

Among pop/regions 15 1557.60 3.05 24.32 <0.001

Within pops 625 4940.31 7.90 62.95 <0.001

Degrees of freedom (Df), sum of squares (SS).

125 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Table 4.13 SAMOVA FCT values for different groupings (K) for 491 N. moorei individuals from 20 populations across 5 regions using data from 4 microsatellite loci. FCT values represent levels of differentiation, such that the highest FCT value represents the grouping with the greatest differentiation. Significance values (P) were calculated using 1023 permutations.

# groups defined (K) FCT P

2 0.13201 P>0.05 3 0.10285 P<0.001 4 0.10410 P<0.001 5 0.10050 P<0.001 6 0.09597 P<0.001 7 0.09422 P<0.001

Table 4.14 Spatial analysis of molecular variance (SAMOVA) for 491 N. moorei individuals from 20 populations across 5 regions based on data from 4 microsatellite loci. Significance values (P) were calculated using 1023 permutations.

Source of variation df SS Variance % of variation P components

Among groups 3 21002.772 35.11182 10.41 <0.001

Among pops/groups 16 11323.399 7.04837 2.09 <0.001

Within populations 1150 339408.506 295.13783 87.50 <0.001

Degrees of freedom (Df), sum of squares (SS).

126 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Based on the microsatellite results from Chapter 3, the Lamington/Border Ranges region exhibited a higher level of clonality than other regions, however, AMOVA failed to detect any significant differentiation between this highly clonal region and the other predominantly sexual regions, with the majority of genetic variation present within individual populations (91%, Table 4.15).

Table 4.15 Analysis of molecular variance (AMOVA) for 491 N. moorei individuals from 20 populations across 5 regions based on data from 4 microsatellite loci. Significance values (P) were calculated using 1023 permutations.

Source of variation df SS Variance % of variation P components

Among regions 4 60.448 0.03333 2.26 <0.001

Among pops/regions 15 124.841 0.12026 8.17 <0.001

Within pops 1150 1516.302 1.31852 89.57 <0.01

Degrees of freedom (Df), sum of squares (SS).

Using the Bayesian clustering approach of Pritchard et al. (2000) the log (Ln) probability of the data increased from K = 1 to K =4 and decreased thereafter suggesting that the optimal number of clusters or gene pools (K) was 4 (Figure 4.9). There was no geographical structuring of gene pools based on the fractions of ancestry within each of the 20 populations; however, there were some distinct patterns evident (Figure 4.10). Gene pool 2 was predominant in the Barrington populations (LT and GT). The Antarctic Beech Walk/Picnic (ABW/P) population contained a high proportion of gene pool 1, while Mt Hobwee (MH) from the same Lamington/Border Ranges region, contained a high proportion of gene pool 2 (Figure 4.10). All other populations appeared to have similar levels of ancestry from the four gene pools, suggesting high levels of admixture, possibly reminiscent of post-glacial expansions.

127 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

-6600 -6700 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 -6800 -6900 -7000 -7100 Ln P(D) -7200 -7300 -7400 -7500 K

Figure 4.9 Values of log likelihood of the multilocus genotypic data [Ln P(D)] as a function of the number of clusters (K), used with the STRUCTURE method of Pritchard et al. 2000 applied to 491 N. moorei individuals from 20 populations. STRUCTURE analysis based on 4 microsatellite loci.

128

100% Chapter 4. “Contemporary” genetic diversity and structure in andstructure diversity genetic “Contemporary” 4. Chapter 90%

80%

70%

60%

50% Gene pool 4 40% Gene pool 3 Gene pool 2 Gene pool 1 30%

20%

10% moorei Nothofagus

0% LT LF EF HL EP BA PB GT KG BM MB NO MH TW MM WR BAL MtW DU/MP ABW/P

populations Lamington/Border Ranges Ballow Dorrigo/NE Werrikimbe Barrington

129 Figure 4.10 Fractions of ancestry within each population of N. moorei based on the Bayesian approach of Pritchard et al. 2000.

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.3.5 Bottleneck detection

Under the infinite allele model (IAM) 8 out of 20 N. moorei populations exhibited a significant excess of heterozygosity (Table 4.16), which suggests exposure to recent bottlenecks in these populations. In contrast, under the single stepwise mutation model (SMM) only a single population exhibited a significant heterozygote excess (Link Trail; P<0.03125) and one population exhibited a significant heterozygote deficit (Echo Point; P<0.03125). When considering the two-phase mutation model (TPM) only the Link Trail population displayed a significant heterozygote excess (P<0.03125), indicative of a recent bottleneck in this population. The different results obtained for the three mutation models most likely arises from the different assumptions for each model. Under the IAM, mutations at each locus result in a new mutant state, whereas under the strict SMM, mutations change the state of an allele by one step forward or backward with equal probability (Luikart and Cornuet 1998, Piry et al. 1999). The IAM has been recommended for allozyme data whereas the SMM has been considered more appropriate for microsatellite data. The recent development of the TPM however, has shown to be better suited for analysis of most microsatellite data (Piry et al. 1999). Given this information, it is probably best to discount the results from the IAM and SMM and consider only those results from the TPM. This then suggests that only the Link Trail population displayed a significant heterozygote excess and thus most probably had been exposed to a recent bottleneck.

130

Table 4.16 Wilcoxon signed ranks test for demographic equilibrium (Cornuet and Luikart 1996) under the infinite allele, in andstructure diversity genetic “Contemporary” 4. Chapter stepwise mutation and two-phase mutation models based on data from 4 microsatellite loci for 20 populations of N. moorei.

Region /Population Infinite Allele Model Stepwise mutation model Two-phase mutation model

Lamington/Border Ranges Tullawallal Equilibrium Equilibrium Equilibrium Mt Wanungra Equilibrium Equilibrium Equilibrium

Echo Point He excess - P = 0.03125 He deficiency - P = 0.03125 Equilibrium Elabana Falls Equilibrium Equilibrium Equilibrium Lightning Falls Equilibrium Equilibrium Equilibrium

Mt Hobwee He excess - P = 0.03125 Equilibrium Equilibrium Best of All Lookout Equilibrium Equilibrium Equilibrium Antarctic Beech Walk/Picnic Equilibrium Equilibrium Equilibrium

Bar Mtn He excess - P = 0.03125 Equilibrium Equilibrium

Helmholtzia Loop He excess - P = 0.03125 Equilibrium Equilibrium

Ballow Durramlee and Mowburra Peaks Equilibrium Equilibrium Equilibrium Mt Ballow Equilibrium Equilibrium Equilibrium

Nothofagus Mtn He excess - P = 0.03125 Equilibrium Equilibrium Nothofagus moorei Nothofagus

Dorrigo/New England Kilungoondie Equilibrium Equilibrium Equilibrium Mt Moombil Equilibrium Equilibrium Equilibrium Weeping Rock Equilibrium Equilibrium Equilibrium

Werrikimbe

Mt Banda Equilibrium Equilibrium Equilibrium populations

Plateau Beech He excess - P = 0.03125 Equilibrium Equilibrium

Barrington

Gloucester Tops He excess - P = 0.03125 Equilibrium Equilibrium

Link Trail He excess - P = 0.03125 He excess - P = 0.03125 He excess - P = 0.03125 131 Equilibrium, He excess and He defiency indicate mutation-drift equilibrium, significant heterozygosity excess and significant heterozygosity deficiency, respectively. P is the probability of rejecting the null hypothesis of mutation-drift equilibrium.

Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.4 Discussion

4.4.1 Genetic diversity within N. moorei populations

AFLP analysis indicates overall genetic diversity within N. moorei populations was moderate (I =0.3770, h = 0.2488), while microsatellite analyses indicate that overall genetic diversity was relatively high (I = 1.8353; h = 0.7277). The genetic diversity indices are not directly comparable however, due to the different marker systems utilised. A direct comparison of Nei’s (1973) gene diversity or Shannon’s Index (Lewontin 1972) estimates between highly allelic microsatellites and dominant bi-allelic ISSRs could lead to an over- or under-estimation of real diversity levels. Importantly, both AFLPs and microsatellites have identified moderate to high levels of genetic diversity based on Shannon’s Index and Nei’s gene diversity within all N. moorei populations and with no significant differences evident among populations.

A separate study of N. moorei diversity that used dominant ISSR markers revealed substantially lower genetic diversity estimates (I = 0.2731, h = 0.1680) among 20 populations collected from only the southern and northern distribution regions (Taylor et al. 2005). Results here based on comparable dominant AFLP data were variable for both Shannon’s Index and Nei’s gene diversity values across the different regions sampled (Table 4.1). Greatest diversity was evident within the Lamington/Border Ranges region (I = 0.3430; h = 0.2263) and lowest estimates within the Werrikimbe region (I = 0.2806, h = 0.1849), however; these differences were not significant. The study by Taylor et al. (2005) identified greater diversity present within the Lamington region (I = 0.2656; h = 0.1613) compared with the Barrington region (I = 0.1877; h = 0.1159), however, the significance of these values was not reported.

The majority of population genetic diversity studies undertaken on other Nothofagus species have utilised allozyme markers (Haase 1992, 1993, Premoli 1997, Marchelli and Gallo 2001). While both allozymes and microsatellites are codominant markers, allozymes often tend to exhibit low levels of polymorphism as they are the products of functional genes with associated constraints on variation. Consequently, genetic diversity indices for the two different marker systems are often not directly comparable. Microsatellites have recently been developed for the Australian Nothofagus species, N.

132 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

cunninghamii and South American Nothofagus species N. nervosa, N. obliqua, N. dombeyi and N. glauca, however, there are no published population genetic studies of these species to date (Jones et al. 2004, Azpilicueta et al. 2004). Levels of expected heterozygosity for the 15 N. cunninghamii individuals analysed during development of the microsatellite primers (from a single population) ranged from 0.46 to 0.90 across 14 polymorphic loci; while the number of alleles ranged from four to 12 (Jones et al. 2004). While levels of heterozygosity were not calculated for microsatellite loci developed for South American Nothofagus species, the number of alleles per locus ranged from only two to six across three polymorphic loci (Azpilicueta et al. 2004). Comparison of expected heterozygosity levels and number of alleles with that observed here for N.

moorei (He = 0.728; A = 7 to 24 for four loci) suggests levels of genetic diversity in N. moorei are relatively high.

In contrast to Nothofagus species, there have been several population genetic studies that have used microsatellites to assess levels of genetic diversity in temperate Quercus (oaks) and Fagus (beech) species. Levels of observed heterozygosity in temperate white oak species, Q. petraea and Q. robur were 0.820 and 0.799, respectively (Strieff et al. 1998, Bakker et al. 2001). The six microsatellite loci were highly polymorphic with allele numbers ranging from 12-29 per locus. (Strieff et al. 1998). In contrast, Q. geminata (scrub oak) exhibited much lower levels of observed heterozygosity (0.170) with just two to seven alleles per locus across seven microsatellite loci (Ainsworth et al. 2003). Levels of observed heterozygosity in the European beech (F.sylvatica) in Lower Saxony, Germany, were moderately high in

comparison with the aforementioned Quercus studies (Ho = 0.572) while the number of alleles observed across four microsatellite loci was also comparatively high (5-21 alleles/locus) (Vornam et al. 2004). Comparison of the levels of observed heterozygosity and number of alleles per locus in Quercus and Fagus species with that

here for N. moorei (Ho = 0.6576, A = 7-24) indicate that in comparative terms, genetic diversity is relatively high for N. moorei populations.

In general, there have been relatively few studies of Australian rainforest tree diversity that have employed microsatellite markers to date. Microsatellite analysis of the tropical/sub-tropical blue quandong (Elaeocarpus grandis) in northern New South Wales revealed overall higher levels of diversity than those for closely related endemic

133 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

species (A = 3.4, He = 0.568) (Rossetto et al. 2004b). The French Guianan Neotropical tree Vouacapoua americana populations exhibited mean allelic richness

and gene diversity (He) values of 4.1 and 0.506, respectively (Dutech et al. 2004). In contrast, N. moorei showed considerably higher microsatellite allelic richness and gene diversity values of 5.998 and 0.728, respectively. Therefore, based on the limited number of microsatellite studies of temperate European tree species and other Australian and non-Australian rainforest species, it appears that overall microsatellite genetic diversity may be comparatively high in N. moorei.

The relatively high level of genetic diversity present within N. moorei populations from the Lamington/Border Ranges region was surprising given the inferred high levels of clonal propagation in this region (see Chapter 3). It has been observed however, that many other tree species with clonal propagation can retain relatively high levels of genetic diversity over time (Ellstrand and Roose 1987). The long-lived Mexican oaks Quercus eduardii and Q. potosonia both regenerate primarily clonally and only reproduce sexually once every four years (Alfonso-Corrado et al. 2004). In spite of this, both species showed higher levels of genetic diversity than other related clonal and non-clonal Quercus species (Alfonso-Corrado et al. 2004). Broad comparisons of genetic diversity in clonal versus sexual species also failed to reveal significant differences in relative levels of population differentiation (Hamrick and Godt 1990). This apparent lack of difference may result from the observation that only one or few seedlings that establish are needed to increase genetic diversity of a predominantly clonally regenerating population (Alfonso-Corrado et al. 2004).

It was also particularly surprising that there was no significant level of inbreeding detected. Only one population (Weeping Rock) displayed a significant and positive Fis value (0.265) and this was attributable to null alleles at the ncutas06 locus. Given that in the Lamington/Border Ranges region clonality appears to be a prevalent form of regeneration, one might assume that when successful sexual regeneration does occur inbreeding may result from cross-fertilisation of clonal individuals. Allelic fixation (Fis) values for all Lamington/Border Ranges populations were low (-0.014 to 0.069) and none significantly departed from Hardy-Weinberg equilibrium (Table 4.4). It is most likely that the monoecious habit of N. moorei flowers has helped maintain outcrossing

134 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

and that the present levels of heterozygosity are perhaps the consequence of historical outcrossing.

4.4.2 Population structure and gene flow within N. moorei - AFLPs

An overall GST estimate of 0.3062 for AFLP markers indicates that populations across the extant range of N. moorei sampled here were differentiated. Comparisons with other AFLP and RAPD population studies suggest that relative population differentiation was substantially lower in N. moorei than for tree species with similar ecologies (Cardoso et al. 2000, 2005). AFLP analysis of the Heart-of-Palm (Euterpe edulis) from the Atlantic rainforest in Brazil identified significant population differentiation (Fst = 0.426) and suggested that sampled populations had been affected by historical forest fragmentation (Cardoso et al. 2000). Similarly, populations of the Brazilian coastal, tropical tree Caesalpinia echinata were highly differentiated with 51.67% of total variation held among the three main geographical areas where populations were distributed, suggesting that either historical or recent fragmentation events had influenced population structure in this species (Cardoso et al. 2005). However, when compared to the average GST for long lived plant species (0.213; Harmick and Godt 1989), N. moorei displays a considerably higher level of population differentiation (0.3062).

Despite the moderate level of population divergence, gene flow appears to have remained sufficient to maintain genetic connectivity among populations with more than one migrant per generation (Nm = 1.1330). The life history traits in N. moorei of outcrossing, wind-pollination, late succession and long life cycle all tend to encourage gene flow and inhibit development of high population differentiation (Hamrick and Godt 1996).

AMOVA revealed that while a significant proportion of the total genetic variation for N. moorei was held among populations within regions (24.32%, P<0.001), most variation was held within populations (62.95%, P<0.001), with only 12.73% of variation present among geographical regions, suggesting that ongoing gene flow was substantial. In contrast, 29% of total variation was held among northern and southern regions for ISSR

135 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

analysis of N. moorei populations (Taylor et al. 2005). Sampling of southern and northern populations without sampling intermediate populations is the probable reason why estimates of regional differentiation were higher rather than there existing real regional differences, as similar levels of differentiation between southern and northern populations was evident here when only southern and northern populations were analysed in a pilot study (Appendix 4.3). Phylogenetic analysis of populations (Neighbour-joining tree) supports a lack of regional structure for N. moorei populations across the natural range with no strong geographical pattern evident (Figure 4.5). Furthermore, PCA results illustrated the lack of structuring of individuals from different populations (Figure 4.6). Results here illustrate the importance of sampling across a species entire distribution in order to fully assess the levels and structuring of genetic diversity within populations, so that appropriate conservation management strategies can be implemented.

4.4.3 Population structure and gene flow within N. moorei – Microsatellite data

The majority of microsatellite genetic variation was also held within populations (87.50%; P<0.001). Results here for N. moorei are consistent with a study of a New Zealand species of Nothofagus (N. truncata) in which 95.1% of allozyme gene diversity was held within populations (Haase 1992). Similarly, studies of South American Nothofagus species N.betuloides, N.dombeyi, N. nitida (Premoli 1997) and N. nervosa (Marchelli and Gallo 2001) revealed that the majority of genetic diversity was partitioned within populations. According to Hamrick and Godt (1996), most wind-pollinated, outcrossed, long-lived, woody perennials retain most of their genetic variation within populations. It is therefore not surprising that the majority of genetic variation is retained within populations of Nothofagus species as all are monoecious, wind- pollinated species with high outcrossing rates. As discussed above, Taylor et al. 2005 reported that while the majority of variation in N. moorei was retained within populations (60.30%; <0.001), a significant proportion of genetic variation was also evident among regions (29.32%; P<0.001). The difference in partitioning of genetic variance in the two studies most likely reflects the marker systems utilized. Taylor et al. 2005 screened N. moorei diversity with ISSRs that are known often to over-estimate population differentiation due to their dominant mode of inheritance. AFLP results for this current

136 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

study also revealed 62% of variation was held within populations however, only 12% was held among regions reflecting the effect of sampling intermediate populations in the distribution. In contrast, microsatellites generally show lower population differentiation due to their high mutation rates (Jenczewski et al. 1999, Mariette et al. 2001).

Global population differentiation (FST = 0.1055) in N. moorei appears to be relatively similar to that reported for other Australian cool temperate rainforest species. Isozyme analysis identified only moderate population differentiation in the temperate rainforest trees Atherosperma moschatum (Sassafras) and Lagarostrobus franklinii (Huon Pine)

(FST = 0.178 and 0.095, respectively) (Shapcott 1994, 1997). Levels of differentiation in N. moorei are also comparable with those identified for other Nothofagus species, ranging from 0.257 to 0.047 in N. alessandrii and N. nitida, respectively (Torres-Díaz et al. 2007).

Phylogenetic analysis and PCA of populations showed no evidence of significant population structure (Figures 4.7 and 4.8). Interestingly, the Barrington populations clustered separately from other regions (Figure 4.7). A possible explanation for this divergence may be that there has been extensive range expansion of N. moorei via seedling dispersal in the Barrington region over the past 1500 years (Dodson et al. 1986). Extensive range expansion of N. moorei forests into areas where eucalypts have dominated via sexual regeneration may have resulted in significant changes in allele frequencies.

SAMOVA results indicate that greatest population differentiation was evident among four groups comprising sites that were not concordant with relative geographical locations. Maximal differentiation FCT values were comparatively similar from K = 2 through to K = 7 groups, suggesting limited differentiation of populations overall. Similarly, the lack of geographical structuring of the four gene pools identified with STRUCTURE, also suggests a lack of differentiation and high levels of admixture. The lack of substantial genetic divergence among regions may result from short periods of isolation and/or low but constant ongoing gene flow (Dutech et al. 2004). Throughout the Quaternary (1.8 Myr ago) there were at least 17 glacial/interglacial cycles of about 100, 000 years duration each, resulting in frequent and rapid climate fluctuations and

137 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

associated episodic contraction and expansion of Nothofagus-dominated rainforests (Adam 1992). It is conceivable that extant populations of N. moorei have not been isolated from each other for sufficient time frames for significant regional differentiation to develop, especially given the longevity of N. moorei individuals. While estimates of gene flow varied from 1.1330 to 2.1197 for AFLPs and microsatellite data, respectively, contemporary gene flow among regions seems an unlikely cause of low divergence among extant populations as seed dispersal in N. moorei across large distances is probably a relatively rare event. Spatial autocorrelation analysis revealed that individuals within a 15-40m radius were most closely related, suggesting that seedlings commonly establish within close proximity to parental plants. This is not surprising since seed of N. moorei possess poorly developed wings and are hence not suited to long distance dispersal (Read and Brown 1996). A previous study of N. cunninghamii seed dispersal has shown that the majority of seed were dispersed close to the parent tree with seed often non-viable when dispersed longer distances (Hickey et al. 1983). Similarly, New Zealand Nothofagus species have seed with poorly developed wings and limited potential for long distance dispersal. This attribute, i.e. seeds with apparent poor adaptations for long distance dispersal, appears to be a conserved character in the genus Nothofagus. Experiments have shown that the majority of seed falls within 30-40 metres of the parent plant with a maximum limit of ~ 200 metres (Allen 1987). These results would suggest that N. moorei seeds can colonise only over short distances. Recent evidence from sequencing of a ~7.2-kb chloroplast genome fragment from 11 species of three Nothofagus subgenera (Lophozonia, Fuscospora and Nothofagus) however, has suggested that trans-oceanic seed dispersal rather than vicariance may explain extant trans-Tasman Sea Nothofagus distributions (Knapp et al. 2005), thus negating the inference that the seed of extant Nothofagus species is not suited to long distance dispersal. Based upon these recent findings, long distance dispersal in N. moorei cannot be discounted and perhaps only rare successful long distance dispersal is required to prevent population divergence of extant populations in this long-lived tree.

138 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

4.4.4 Comparison of AFLP and microsatellite data for population and regional differentiation

Overall, results from AFLP and microsatellite data are congruent. Both AFLPs and microsatellites revealed that the majority of genetic variation was contained within populations (62.95% for AFLPs versus 87.5% for microsatellites). There was however,

some population differentiation evident, with AFLPs estimating a global GST of 0.3062 while microsatellites estimated a global FST of 0.1055 and all population-pairwise comparisons of FST were significant. Despite the differentiation present the neighbour- joining trees based on AFLPs and microsatellite data both revealed a lack of geographic structure among populations, although the phylogeny of each population tree was different. Similarly, principal coordinate analysis of AFLP and microsatellite data identified a lack of spatial structuring of populations. The differences in the actual levels of genetic differentiation estimated with AFLPs and microsatellites is most likely due to the dominant nature of AFLPs that can tend to over-estimate differentiation while in contrast, the high mutation rates and potential for homoplasy of microsatellites can tend to underestimate real levels of real regional differentiation (Mariette et al. 2001). Overall, AFLP and microsatellite data showed congruence for evidence of isolation by distance within most populations and a lack of isolation by distance at larger spatial scales.

4.4.5 Bottleneck detection

Population bottlenecks occur when there’s been a substantial decrease in effective population size. Bottlenecks can result in increased genetic drift and inbreeding, reduced genetic diversity and fixation of deleterious alleles, ultimately reducing the adaptive potential and increasing the probability of population extinction (Luikart and Cornuet 1998). Given the past range shifts of N. moorei during Quaternary glacial and interglacial periods; it is likely that population bottlenecks have occurred. It is of great importance to identify any population bottlenecks as these populations may be more susceptible to extinction in the future.

Surprisingly, under the TPM only one population (Link Trail) displayed a significant excess in heterozygosity indicative of a bottleneck. Given that the Link Trail population

139 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

is surrounded by sclerophyllous eucalypt woodland, it is quite likely that fire events and short inter-fire intervals could be responsible for past population reductions and associated bottlenecks.

Under the IAM several populations showed a significant heterozygosity excess, however, the assumptions of the IAM are unlikely to hold true for most microsatellite loci and as such the results under this model may be unreliable. Indeed when looking at those populations with a significant heterozygosity excess, there is no real explanation for why those populations should have undergone bottlenecks compared with other populations. For example, within the Lamington/Border Ranges region Echo Point, Mt Hobwee, Bar Mtn and Helmholtzia Loop all displayed a significant heterozygosity excess, while other populations, like Best of All Lookout and Tullawallal that have small effective population sizes (based on observation), do not appear to have been exposed to bottlenecks. Thus the results under the IAM appear to be inconsistent with what would be expected given the history of N. moorei expansions and contractions.

4.4.6 Implications for conservation

Variation in population size has been identified as the most important variable for explaining relative differences in allozyme variation among populations (Frankham 1996). A recent meta-analysis of population genetic studies that were published between 1987 and 2005 using both isozyme and DNA-based markers, added further confirmation of a positive correlation between population size, fitness and genetic variation (Leimu et al. 2006). Population size should therefore be considered in any study of plant genetic variation (Ellstrand and Ellam 1993). Unfortunately, the current study was unable to quantify population sizes for N. moorei due to the nature of the spatial distribution of individuals within populations hence the relationship between genetic diversity indices and population size could not be assessed directly.

To identify those populations of greatest priority for conservation, several diversity measurements should be considered. Microsatellite markers allow more diversity indices to be estimated due to their co-dominant nature and will therefore be considered in more detail here with regard to conservation management of N. moorei populations.

140 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Levels of observed heterozygosity were highest within the Werrikimbe region (Ho =

0.7851) and lowest within the Dorrigo region (Ho = 0.5895). Allelic richness values identified different regions of high and low diversity with Dorrigo exhibiting highest allelic richness (Rs = 7.112) while the Lamington/Border Ranges region displayed the lowest allelic richness (Rs = 5.596). Within sample gene diversity (Hs) in contrast, was very similar across all regions (Table 4.5). Despite these variations however, no differences were significant for all diversity measures, thus suggesting all populations have equal conservation status in terms of levels of genetic diversity.

Shannon’s Index and Nei’s gene diversity index estimated from both AFLP and microsatellite data showed very similar values among most populations and were not significantly different from one another. Additionally, there was no apparent inbreeding in any population, with high levels of heterozygosity prevalent in all populations, including populations from the Lamington/Border Ranges region which displays high levels of clonality based on results from Chapter 3.

Conservation theory suggests that populations with the highest genetic diversity should be assigned higher conservation priority to maintain a species’ potential for evolutionary change (Zawko et al. 2001). On this basis, results here overall, suggest that all populations of N. moorei are largely equivalent in terms of genetic diversity levels and inbreeding and as such all populations would probably demand equal conservation status. Thus germplasm for ex-situ propagation and conservation should be collected largely at random from across N. moorei’s natural distribution to ensure the greatest diversity is maintained. When considering the results of the spatial autocorrelation however, care should be taken to collect seed from individual trees that are at least 40 metres apart, so as to maximise collection of high genetic diversity for restoration efforts.

While results of population and regional differentiation overall displayed a lack of regional structuring, population differentiation was significant for all pairwise population estimates of FST, suggesting that it would be best to include seed from individuals from each population in restoration efforts. Additionally, while the SAMOVA analysis only detected very weak structuring, seed should be collected from the four differentiated groups identified (1 - Mt Moombil; 2 - Mt Ballow; 3 – Mt Wanungra, Echo Point, Elabana

141 Chapter 4. “Contemporary” genetic diversity and structure in Nothofagus moorei populations

Falls, Lightning Falls; 4 – All remaining populations) in order to maximise genetic diversity. Furthermore, local adaptation, while not detected, may exist, as populations sampled inhabit very variable environments ranging from sub-tropical/warm temperate ecotones to eucalypt ecotones and varying soil types and rainfall.

Results from the Bayesian clustering analysis suggest that the Antarctic Beech Walk/Picnic, Mt Hobwee and Barrington populations warrant special attention as these populations displayed differential ancestry proportions for each of the four identified gene pools. In order to maximise genetic diversity across all gene pools, it would be important to include these populations in addition to other populations in any conservation management plan.

The detection of a putative bottleneck in the Link Trail population suggests that this population warrants monitoring in the future, as genetic diversity may decline and inbreeding increase as a delayed result of this putative bottleneck.

4.4.7 Conclusion

Overall levels of genetic diversity were high in N. moorei compared with other Nothofagus and temperate tree species. Levels of genetic diversity were comparable among all populations. The highly clonal northern Lamington/Border Ranges region displayed equally high levels of diversity as other less clonal regions, suggesting sexual recruitment is still sufficient to retain high genetic diversity within this region. Levels of population differentiation were significant, but low, indicating past levels of gene flow have been sufficient to maintain connectivity of populations, with essentially four gene pools identified from which populations have expanded.

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Chapter 5. Historical structuring among Nothofagus moorei regions

5.1 Introduction

Due to their generally slow rate of evolution, chloroplast DNA (cpDNA) markers can often provide a clearer picture of more ancient historical factors that may have influenced a species genetic diversity patterns than is possible from equivalent nuclear DNA markers (Newton et al. 1999). In general, the chloroplast genome has a much slower rate of evolution compared with the nuclear genome (Wolfe et al. 1987) and is therefore more appropriate for studying genetic processes and change over very long time scales (Cavers et al. 2003a). In most angiosperms, cpDNA is also maternally inherited via seeds, thus the potential for seed-mediated gene flow is limited and so cpDNA variation tends to be more structured geographically than nuclear DNA (Newton et al. 1999, Cavers et al. 2003a). Additionally, the chloroplast genome is haploid, which halves the effective population size and increases susceptibility to genetic drift and so increases the rate of population differentiation (Schaal et al. 1998). Overall, differences in chloroplast sequence can persist over long time frames allowing for population differentiation and reconstruction of post-glaciation migration routes (Petit et al. 1993, Dumolin-Lapegue et al. 1997). Re-colonization of pre-glaciated areas from different glacial refugia is likely to be reflected in the geographical pattern of genetic variation (Marchelli and Gallo 2004). Latitudinal and altitudinal range shifts during Quaternary glaciations (1.64 Mya) have been shown to have had both stochastic and selective effects on the patterns of genetic variation in many species (Hewitt 2004). During glacial/inter-glacial cycles and range shifts, many populations and lineages may become extinct; alleles may be lost as a result of population bottlenecks and founder events; and mutations can accumulate and may spread by selection and subsequent population expansion (Hewitt 2004).

In the past, due to a slow evolutionary rate, chloroplast DNA markers have largely been used to examine plant systematics and evolution above the species level (Soltis et al. 1992). The mutation rate of the cpDNA varies for different regions of the genome with most variation residing in the large single-copy regions (Schaal et al. 1998). Development of universal chloroplast primers for conserved chloroplast DNA sequences flanking more variable regions and the development of the PCR-RFLP

143 Chapter 5. Historical structuring among Nothofagus moorei regions technique has enabled studies of cpDNA variation at the intra-specific level to identify population differentiation and to relate variation to past glacial cycles (Newton et al. 1999). Utilising these PCR-RFLP techniques, over 50% of the cpDNA variation detected apparently results from small insertion/deletion mutations (indels) (Demesure et al. 1996). Currently, it is acceptable to use cpDNA indels as phylogenetic markers provided their level of divergence is low (Demesure et al. 1996, Marchelli and Gallo 2006). Given that the average rate of divergence of chloroplast DNA ranges from 0.024 to 0.116 percent per million years (Hewitt 2000), the data from cpDNA phylogenetic studies may reflect past glacial contractions and post-glacial demographic change that occurred during the Quaternary period (2.4Mya) (Hewitt 2000, 2004).

There have been several studies on temperate forest trees from the Fagales (Order to which N. moorei belongs – see Chapter 1) that have utilised a cpDNA PCR-RFLP approach to investigate population differentiation and to infer glacial refugia and post- glacial migratory paths. A study of chloroplast DNA diversity of 85 populations of the common beech, Fagus sylvatica (Fagaceae), across its extant distribution in continental Europe revealed widespread distribution of a single haplotype in the north of Europe and many different haplotypes across the south. Geographical structuring was correlated with post-glacial recolonisation. The main glacial refugia were located in southern Europe and populations were considered to have expanded subsequently, post-glacially into the north (Demesure et al. 1996). A similar study on cpDNA genetic variation in Italian populations of F. sylvatica revealed large population differentiation, geographical structuring and high rates of fixation of unique haplotypes among populations (Vettori et al. 2004). Geographical structuring of the 14 identified cpDNA haplotypes were correlated with two main refugia during the in the south of the Italian and Balkan peninsulas, respectively (Vettori et al. 2004). Similarly, assessment of cpDNA variation in the endangered Mexican Fagus grandifolia var. mexicana (Fagaceae) revealed three haplotypes of which two were restricted to two isolated populations while the third haplotype was found in two additional populations in Mexico and the USA, with all populations fixed for a single haplotype (Rowden et al. 2004). Geographical structuring suggests that Fagus populations in Mexico retreated northward and expanded southward repeatedly during alternate glacial and interglacial periods during the Pleistocene (Rowden et al. 2004).

144 Chapter 5. Historical structuring among Nothofagus moorei regions

One genus that has received much attention in relation to cpDNA variation and past glacial refugia and colonization routes is Quercus (oaks - Fagaceae). A large scale study of chloroplast DNA variation in eight species of European white oaks (Quercus spp.) revealed extensive chloroplast diversity within all species with a total of 32 haplotypes identified (Petit et al. 2002a). Haplotypes were shared among the eight species when in sympatry but diversity was partitioned differentially among species due to different ecological and life history attributes (Petit et al. 2002a). High population differentiation was evident with geographical structuring of related haplotypes. Three main glacial refugia were identified in southern Europe in the Iberian, Italian and Balkan peninsulas, respectively (Petit et al. 2002b).

A similar level of high chloroplast diversity was exhibited in the North American deciduous northern red oak (Quercus rubra) populations, with 12 haplotypes identified. In contrast to European oaks, Q.rubra population differentiation was low with no spatial structuring identified. Quercus rubra populations were believed to have had an extensive natural distribution range during the glacial periods but had undergone very limited post-glacial expansion (Magni et al. 2005). This study highlighted differences in the evolutionary histories of Quercus populations on different continents related to their respective glacial cycles (Magni et al. 2005).

Chloroplast PCR-RFLP studies of three Mediterranean evergreen oaks: Q.suber, Q.ilex and Q.coccifera revealed very different patterns to European deciduous oaks with no common haplotypes present in these two groups (Jimenez et al. 2004). A high level of cpDNA variation was observed in the Mediterranean evergreen oaks with 81 haplotypes identified. A separate study of Q.suber identified nine haplotypes that showed clear phylogeographical patterns corresponding with three potential glacial refugia in Italy, north Africa and Iberia, respectively (Lumaret et al. 2005).

Although studies have been conducted that have utilised chloroplast DNA data to reconstruct evolutionary events in the Nothofagus genus (Manos and Steele 1997, Jordan and Hill 1999); there have only been three published studies that have examined cpDNA variation within Nothofagus species. PCR-RFLP analysis of South American N. nervosa populations in identified two unique haplotypes (Marchelii et al. 1998). The two haplotypes were differentiated among populations with

145 Chapter 5. Historical structuring among Nothofagus moorei regions eight southern populations geographically separated from three northern populations by the Huechulafquen mountain chain (Marchelli et al. 1998). The distribution of the two haplotypes suggested that N. nervosa dispersed from at least two different refugia after the last glaciation (Marchelli et al. 1998). A further, more extensive study of N. nervosa chloroplast variation assessed 26 populations across the species’ entire geographical range in Chile and Argentina. Seven indels described five haplotypes with distributions that were highly structured, geographically (Marchelli and Gallo 2006). Two unique haplotypes were restricted to Pacific Coastal Mountains populations while the Andes Mountains exhibited north-south variation in the distribution of the three alternative haplotypes (Marchelli and Gallo 2006). Results from this study support the earlier hypothesis that there were two different glacial refugia in the Andes Mountains (Marchelli et al. 1998) in addition to a proposed glacial refugium in the Pacific Coastal Mountains (Marchelli and Gallo 2006).

Analysis of chloroplast diversity in an Australian Nothofagus species (N. cunninghamii) revealed strong population differentiation with five haplotypes present within Tasmania and Victoria (Worth 2003). Limited biogeographical conclusions could be made however, due to the occurrence of both a common and an ancestral haplotype across much of the species natural range. Based on the presence of endemic haplotypes in southern Victoria and Tasmania, glacial refugia were suggested to have been present in both these areas (Worth 2003).

There have been no published studies that have investigated chloroplast DNA diversity across the distributional range of N. moorei. Given that modern N. moorei populations are restricted essentially to five disjunct regions and seed-mediated long distance gene flow is likely to be rare, it is possible that sufficient time has elapsed since the last glacial for populations to have diverged and they may now consist of monophyletic lineages. Ancient chloroplast variation may exist among regions, potentially identifying past glacial refugia for N. moorei populations. The current study aimed to investigate the pattern of historical chloroplast DNA variation within and among N. moorei populations across the entire extant distribution to determine if chloroplast haplotypes relate to possible glacial refugia.

146 Chapter 5. Historical structuring among Nothofagus moorei regions

5.2 Methods

5.2.1 DNA extraction and cpDNA PCR-RFLP methodology

DNA was extracted and analysed using cpDNA PCR-RFLP markers as detailed in Chapter 2, sections 2.2 and 2.5.

5.3 Results

5.3.1 Ancient regional population differentiation

Of the 15 primer pairs tested (as detailed in section 2.5, Chapter 2) only six produced single, bright PCR products that were analysed further by restriction enzyme digestion. All other primer pairs produced multiple bands, low intensity bands or no bands at all and hence were excluded from further analyses. Initial restriction enzyme analysis screening of two individuals from each of 14 populations identified variation only within the AS2 fragment when samples were digested with TaqI. All other enzyme and primer combinations provided identical results even when patterns from both N. cunninghamii and N. moorei individuals were compared (data not shown). Full screening of all 491 individuals from the 20 populations across N. moorei’s range with AS2/TaqI identified two unique haplotypes (Figures 5.1 and 5.2). A 3rd unique haplotype was apparent for the outgroup N. cunninghamii sample (Figures 5.1 and 5.2). The different banding patterns are explained by the gain of a restriction site in both haplotype 2 and 3. The gain of a TaqI restriction site in haplotype 2 resulted in the loss of a 700bp fragment and gain of 300 and 400bp fragments, while in haplotype 3 the gain of TaqI restriction site saw the loss of the 1000bp fragment and gain of a 600bp and doublet 400bp fragments (Figure 5.2).

The two haplotypes identified were structured spatially. Haplotype 1 occurred in all individuals in all populations within the Lamington/Border Ranges, Ballow, Werrikimbe and Barrington regions, while haplotype 2 occurred in all individuals from populations within the Dorrigo/New England region (Figure 5.3, Table 5.1).

147 Chapter 5. Historical structuring among Nothofagus moorei regions

2 2 2 2 2 2 2 1 1 2 1 X

1636bp

1018bp

517bp

298bp 220bp

154bp

1 2 2 2 2 3

Figure 5.1 Representative screening gel of TaqI digest of the cpDNA psaA-trnS2r (AS2) fragment in N. moorei and N. cunninghamii. Two N. moorei samples from 14 populations across the 5 regions and 1 N. cunninghamii sample were screened to identify possible polymorphisms. Empty lanes denote samples that did not amplify. Yellow numbers above each digest sample indicate the different haplotypes. Numbers next to red arrows on right indicate the fragment size for the molecular marker (DNA molecular marker X 0.07 to 12.2kb – Roche).

148 Chapter 5. Historical structuring among Nothofagus moorei regions

Haplotype 1 Haplotype 2 Haplotype 3 N. moorei N. moorei N. cunninghamii (All other regions) (Dorrigo/New England region)

~1000bp

~700bp

~600bp

~400bp

~300bp

~200bp

Figure 5.2 Diagrammatic representations of N. moorei haplotypes 1 and 2 and closely related N. cunninghamii haplotype 3 based on TaqI digest of the cpDNA psaA-trnS2r

(AS2) fragment from 491 N. moorei individuals across 20 populations and 1 N. cunninghamii individual (outgroup). Each line represents a DNA fragment. The solid blue and red lines in Haplotypes 2 and 3 represent additional DNA fragments, respectively. Dashed blue and red lines represent the loss of DNA fragment in Haplotypes 2 and 3, respectively. Fragment sizes are shown for the different fragments in haplotypes 2 and 3 compared with haplotype 1.

149 Chapter 5. Historical structuring among Nothofagus moorei regions

N

Ballow – Haplotype 1

Lamington/Border Ranges – Haplotype 1

Dorrigo/New England – Haplotype 2 Werrikimbe – Haplotype 1

Barrington – Haplotype 1

0 400km

Figure 5.3 Regional structuring of cpDNA haplotypes 1 and 2 in N. moorei. Haplotypes were based on different banding patterns of TaqI digests of the cpDNA psaA-trnS2r

(AS2) fragment from 491 individuals across 20 populations and 5 regions. Haplotype 1 is represented by red and haplotype 2 is represented by blue.

150 Chapter 5. Historical structuring among Nothofagus moorei regions

Table 5.1 cpDNA haplotype for each population of N. moorei within each of the 5 geographical regions based on restriction digest pattern of AS2 with TaqI for 491 individuals.

Region Population cpDNA haplotype

Lamington/Border Ranges Tullawallal 1 Mt Wanungra 1 Echo Point 1 Elabana Falls 1 Lightning Falls 1 Mt Hobwee 1 Best of All Lookout 1 Antarctic Beech Picnic area 1 Bar Mtn 1 Helmholtzia Loop 1

Ballow Durramlee and Mowburra Peaks 1 Mt Ballow 1 Nothofagus Mtn 1

Dorrigo/New England Kilungoondie 2 Mt Moombil 2 Weeping Rock 2

Werrikimbe Mt Banda 1 Plateau Beech 1

Barrington Gloucester Tops 1 Link Trail 1

151 Chapter 5. Historical structuring among Nothofagus moorei regions

5.4 Discussion

5.4.1 Chloroplast haplotype diversity

Only two unique cpDNA haplotypes were identified after screening of 15 universal primer pairs suggesting that chloroplast DNA variation is generally very low in N. moorei populations. Haplotype 1 was distributed in all populations in the Lamington, Border Ranges, Werrikimbe and Barrington Tops regions, while haplotype 2 was found only in the Dorrigo/New England populations (Figure 5.3). This low variation may be attributable to insufficient numbers of primer pairs tested. Of the 15 primer pairs tested, only 6 pairs yielded single band products that were analysed further for variation via restriction enzyme digestion. For those primer pairs that did not work, there was either no product or multiple PCR products. Indeed PCR conditions were not optimised for these primers, but rather they were excluded from restriction enzyme analysis. Had the PCR conditions been optimised, perhaps restriction digestion of these PCR products may have yielded additional unique haplotypes. Restriction analysis only utilised four restriction enzymes: HaeIII, HinfI, TaqI and AluI. Further studies should optimise PCR conditions for those primer pairs that did not yield product or yielded multiple bands and restriction digestions performed to determine if any further variation can be detected. Another option that should be considered for future studies is the use of polyacrylamide gels instead of agarose gels. Polyacrylamide gels have greater resolution and therefore can detect more subtle variation (Sambrook et al. 1989). Most of the previous studies of chloroplast PCR-RFLP have utilised polyacrylamide gels over agarose for this reason. Agarose gels were utilised in this study due to ease of the methodology.

Studies to date on cpDNA variation in Nothofagus species indicate generally high conservation of the Nothofagus chloroplast genome (Manos 1997, Marchelli et al. 1998, Worth 2003). An extensive study of chloroplast DNA variation in the closely related N. cunninghamii also revealed relatively low variation in spite of the fact that 19 universal cpDNA primers were trialled (Worth 2003). Only four mutations were detected that defined five unique haplotypes that were strongly structured geographically (Worth 2003). Similarly, analysis of chloroplast DNA variation using 16 universal chloroplast DNA primer pairs in 37 populations of the South American N. nervosa across the species’ entire range in Argentina and Chile also revealed only five unique haplotypes

152 Chapter 5. Historical structuring among Nothofagus moorei regions

(Marchelli et al. 1998, Marchelli and Gallo 2006). Considered together, this suggests that the Nothofagus chloroplast genome may in general, be highly conserved across the genus. Worth (2003) observed high conservation between N. cunninghamii and N. moorei, with haplotypes that differed by only three base pairs.

Low chloroplast variation across the natural distribution of the temperate common ash (Fraxinus excelsior) in Europe has also been detected using cpDNA PCR-RFLPs. A total of 68 cpDNA fragments were generated but only one fragment digested with TaqI displayed a polymorphism that yielded two haplotypes that differed by a single base pair mutation (Heuertz et al. 2004). In contrast, studies in other northern hemisphere temperate Fagales tree species have revealed substantially higher chloroplast variation using fewer universal primer pairs. Haplotype numbers varied from 11 to 32 for Alnus glutinosa (Betulaceae), Castanea sativa (Fagaceae), Betula pendula (Betulaceae) and numerous Quercus species (Fagaceae) using 4 to 16 primer pairs (King and Ferris 1998, Fineschi et al. 2000, Palme et al. 2003, Petit et al. 2002a). Care must be taken however, when comparing cpDNA variation among different studies due to differences in life history and historical events that may have influenced cpDNA differentiation and differences in the number of PCR fragments and restriction enzymes that were employed in the analysis (Worth 2003).

Studies on cpDNA variation in European and North American tree species have used data on differentiation patterns to infer glacial refugia and post-glaciation dispersal routes (King and Ferris 1998). Of particular relevance to this study is the level of population subdivision observed in Fagaceae. Fagus sylvatica, Quercus petraea, Q.pubescens and Q.robur all exhibited high population subdivision with distribution of haplotypes related to the geographical location of glacial refugia (Demesure et al. 1996, Dumolin-Lapegue et al. 1997). There have been very few equivalent studies on chloroplast genetic diversity and relationships in southern hemisphere temperate tree species. Three independent studies of cpDNA diversity in Nothofagus species in Tasmania (N. cunninghamii) and South America (N. nervosa) identified unique chloroplast haplotypes from adjacent populations and hypothesised that associations may exist with glacial refugia (Marchelli et al. 1998, Worth 2003, Marchelli and Gallo 2006).

153 Chapter 5. Historical structuring among Nothofagus moorei regions

The current distribution of Australian Nothofagus species has been suggested to be refugial relative to what was present in Gondwanan forests (Floyd 1990, Poole 1987). Floristic linkages and the prevalence of locally endemic temperate species suggest the Upper Dorrigo/New England and Hastings (Werrikimbe) regions were probably core refugia for cool temperate rainforests dominated by N. moorei in the past (Bale and Williams 1993, Floyd 1990). Floristic affinities between the Barrington region, the Upper Dorrigo/New England and Hastings suggest Barrington may also have been part of these core refugial areas (Bale and Williams 1993). The McPherson range in the NSW/QLD border region is hypothesised to have been a separate refugium since Nothofagus-dominated communities are much more fragmented and contain fewer temperate species (Bale and Williams 1993). A refugium in the McPherson range concurs with Floyd's (1990) hypothesised Mt Warning refugium.

Unlike the extensive northern hemisphere fossil pollen record for Quercus and Fagus species, the available pollen and macrofossil record in Australia for Nothofagus species has limited ability to confirm whether extant Nothofagus species survived in glacial refugia in the regions they now occupy (Worth 2003). While the pollen fossil record for Nothofagus is extensive, distinction between the local presence of a species and pollen from outside the region remains difficult due to the masting, wind-pollinated habit of Nothofagus species (Willis et al. 2000). Macrofossils provide the most direct evidence for the identification of refugia as the presence of macrofossils in an area suggests the species must have once inhabited the area however; the macrofossil record of Nothofagus is at best, fragmentary (Hill 1991). Molecular markers provide a convenient tool for indirectly inferring the likely location of refugia because they often harbour higher genetic diversity and/or contain unique haplotypes (Hewitt 1996, King and Ferris 1998).

Given the very limited level of chloroplast diversity identified for N. moorei here, the geographical structuring of haplotypes needs to be interpreted with care. Given this limitation, however, the vegetation history of the east coast of Australia and the proposed locations of glacial refugia; one scenario could explain the current chloroplast haplotype distribution in N. moorei. Nothofagus distribution during the Tertiary period was extensive and continuous along the eastern coastal fringe of Australia. Prior to the Quaternary (1.8 Myr ago) potentially all individuals may have been ancestral chloroplast

154 Chapter 5. Historical structuring among Nothofagus moorei regions haplotype 1. The onset of the first Quaternary glacial period saw populations contract to four main refugia: Mt Warning, Dorrigo/New England, Hastings (Werrikimbe) and Barrington. The following first inter-glacial period resulted in expansion of N. moorei populations from glacial refugia. A local mutation within the Dorrigo/New England region prior to this expansion, that produced haplotype 2, may then have colonized the entire Dorrigo/New England region, while all other regions maintained their ancestral haplotype 1 status. The following glacial-inter-glacial periods saw expansion and contraction from these glacial refugia with no further mutations. This hypothesis requires extinction of haplotype 1 from the Dorrigo/New England region. An alternative, possibly more plausible scenario would involve a mutation in the widespread ancestral haplotype 1 to haplotype 2 prior to the contraction of N. moorei to the four main refugia with the onset of the Quaternary glaciations.

Future research on chloroplast variation in N. moorei should utilise more PCR fragments, restriction enzymes and polyacrylamide gels and investigate chloroplast microsatellite variation. The use of chloroplast microsatellites in combination with PCR- RFLP techniques has increased the number of haplotypes identified in previous studies (Provan et al. 2001, Palme and Vendramin 2002, Grivet and Petit 2003). With a more thorough investigation of chloroplast variation, more specific evidence about historical refugial area may be available that could identify the source populations from which modern populations were derived.

5.4.2 Comparison of AFLP, microsatellite and cpDNA data to identify population differentiation in N. moorei

Several studies have identified similar population structuring when chloroplast and nuclear marker data were combined. Analysis of population structuring in Cedrela odorata (Spanish Cedar) using cpDNA PCR-RFLPs and AFLPs identified two highly differentiated groups with variation at both marker types showing congruence (Cavers et al. 2003b). Similarly, AFLPs and chloroplast microsatellites identified a north/south divide in European populations of Spiranthes romanzoffiana (Forrest et al. 2004); and French and Portuguese populations of Pinus pinaster were also highly differentiated (Ribeiro et al. 2002). In contrast, results from the current study using nuclear AFLP and

155 Chapter 5. Historical structuring among Nothofagus moorei regions microsatellite data (see Chapter 4) suggest very low population divergence in N. moorei combined with limited ongoing gene flow among populations; while chloroplast variation identified divergence of populations in the Dorrigo/New England region from all other populations. The lack of congruence between nuclear AFLP and microsatellite data versus chloroplast haplotype data is not surprising given their very different rates of mutation and modes of inheritance. Variation in chloroplast PCR-RFLPs is better able to identify more ancient divergence particularly in long-lived species like N. moorei. Given that the divergence of N. moorei and N. cunninghamii (N. moorei’s closest relative) has been estimated to have occurred 20 Myr ago (Hill 1991) and that other studies of cpDNA variation in other Nothofagus species have also identified very little intra-specific variation, it would appear that the Nothofagus chloroplast genome is highly conserved. The differentiation of the populations in the Dorrigo/New England region may reflect an ancient divergence that occurred early on and prior to recent Pleistocene glacial cycles.

5.4.3 Implications for conservation

The identification of an ancient population divergence of the Dorrigo/New England populations from other regions suggests that any conservation management strategy should aim to include populations from the Dorrigo/New England region in addition to populations from the other regions, so that the two distinct chloroplast lineages are conserved.

5.4.4 Conclusion

Chloroplast markers identified an apparent ancient population divergence of the Dorrigo/New England populations from other regions. This ancient population divergence is predicted to have occurred during the contraction of N. moorei’s distribution during the Pleistocene glacial cycles. Further research on chloroplast variation in N. moorei needs to be conducted so that all ancient lineages are identified and appropriate conservation strategies can be implemented to retain maximum genetic diversity within the species.

156 Chapter 6. General Discussion

Chapter 6. General Discussion

6.1 Genetic diversity and population differentiation in extant N. moorei populations – Summary of findings

The changing distribution of cool temperate rainforests where N. moorei was a dominant species influenced N. moorei’s evolution dramatically approximately 40 Myr ago (Hill 1991, Linder and Crisp). From the Eocene to the Miocene (~40-25 Myr ago) cool temperate rainforests dominated by N. moorei expanded across eastern Australia, since this time however, the distribution has seen several expansions and contractions as a result of climate change. During the Quaternary glaciations (1.8 Myr ago) there were dramatic reductions and expansions of N. moorei’s distribution as the climate oscillated between cold and arid glacial periods and warmer, wetter interglacial periods (Adam 1992, Hewitt 2000). The distribution of N. moorei was then further compromised by expansion of dry, fire-loving, sclerophyllous vegetation some 40, 000 -60, 000 years ago after Aboriginal people colonised the continent (Hill 2004). Consequently, the modern distribution of N. moorei dominated cool temperate rainforests is restricted to relictual patches of rainforests in North East Australia where relatively small patches of environment remain favourable.

Based on the inferred evolutionary history of N. moorei and the assumed high levels of clonal coppicing reported in the species, particularly in the northern populations, genetic diversity levels are likely to be comparatively low and populations differentiated regionally. Additionally, genetic bottlenecks and genetic drift affects would likely have impacted several populations given the past contractions and expansions of rainforest during the Quaternary glaciations.

Surprisingly, microsatellite and AFLP data generated here for N. moorei identified comparatively high genetic diversity across all remnant populations of N. moorei analysed in the study. Furthermore, predominantly clonally regenerating populations from the Lamington/Border Ranges region contained equivalent levels of genetic diversity as that present in other regions with higher rates of successful sexual regeneration. Population differentiation, while significant in some comparisons (pairwise population comparisons), was generally limited with the majority of genetic

157 Chapter 6. General Discussion variation retained within populations and gene flow among populations sufficient to maintain apparent effective connectivity among modern populations. Furthermore only a single population from the Barrington region (Link Trail) appeared to have been exposed to a recent bottleneck, most likely as a consequence of increased fire frequency and aridity favouring expansion of dry sclerophyllous eucalypt vegetation and a consequential contraction of cool temperate N. moorei rainforest (Dodson et al. 1986).

Analysis of regional population structure based on nuclear DNA using both conventional F-statistics and Bayesian statistical approaches revealed no evidence for regional structuring of populations and suggested high levels of admixture. Analysis of cpDNA variation however, identified differentiation of the three Dorrigo-New England populations from all other populations, perhaps suggesting a more ancient divergence of N. moorei populations prior to Pleistocene glacial cycles.

Microsatellite and AFLP results support the observation by Howard (1981) that northern populations in the McPherson range appear to be regenerating predominantly via clonal coppicing with limited successful sexual regeneration except on the higher peaks within the Ballow region. Results here may negate Floyd’s (1990) argument that higher rates of coppicing are present along the McPherson Range due to the scarcity of high elevation peaks. Patterns of microsatellite variation suggest extensive clonality in all northern populations from the eastern McPherson Range (Lamington/Border Ranges). Clonal regeneration was far less prominent however, in the western McPherson Range (Ballow region) with only two clonal individuals identified in a single population. In general, disturbance regimes rather than the availability of high elevation peaks may be the primary factor influencing the relatively high levels of successful sexual regeneration in this species.

Comparisons of results achieved here with that of Taylor et al. (2005) identified different estimates for levels of clonality and regional structuring. This is most likely explained by the very small sample sizes (n = 1 – 10) and limited sampling scale (samples only being taken from the southern and northern limits) of N. moorei’s distribution in the earlier study (Taylor et al. 2005). An initial pilot study conducted here that sampled two populations from the northern region and two populations from the southern region,

158 Chapter 6. General Discussion respectively, suggested significant regional divergence between southern and northern populations as had been reported by Taylor et al. (2005). When intermediate populations were sampled in between the southern and northern limits however, little population divergence was evident at a regional scale. Furthermore, the current study found no significant differences for genetic diversity indices among the sampled populations, while the earlier study had failed to conduct statistical tests on genetic diversity indices, yet still stated that northern and southern regions were substantially different from one another (Taylor et al. 2005).

Additionally, results here identified high levels of clonality in the Lamington/Border Ranges region. In contrast, Taylor et al. (2005) failed to identify any clonal individuals in their study. This is most likely due to the sampling design adopted, where only 13 adjacent individuals from a single population in the Border Ranges were assessed for clonality (Taylor et al. 2005). Here however, 21 to 41 individuals per population were sampled from across 20 populations to assess levels of clonality. Results highlight the importance of undertaking adequate sampling from extensive areas of a species’ distribution and equally the importance of sampling an adequate number of individuals from each population to obtain reliable estimates of clonality, population divergence and population structure.

6.2 Comparative studies of Australian rainforest species

There have been several studies that have investigated genetic diversity and population structure in other Nothofagus species and in other Australian cool temperate and sub- tropical/tropical rainforest trees in general using a variety of marker systems including isozymes, RAPDs and microsatellites (Hasse 1992, Haase 1993, Shapcott 1994, Veblen et al. 1996, Marchelli et al. 1998, Premoli 1997, Shapcott 1997, Azpilceta et al. 2004, Pye and Gadek 2004).

Besides the study by Taylor et al. (2005), another study investigated genetic diversity and structure in an Australian Nothofagus species. Worth (2003) screened cpDNA variation in N. cunninghamii across the species’ entire extant range in southern Victoria and Tasmania. Overall cpDNA variation detected was low with only four base pair

159 Chapter 6. General Discussion mutations characterising four unique haplotypes (Worth 2003). Limited biogeographical inferences however, could be made from the spatial distribution of the haplotypes due to the distribution pattern of the ancestral haplotype (Worth 2003).

Isozyme analysis of New Zealand’s N. truncata suggested that this species had comparatively low total genetic diversity (He = 0.054) with the majority of variation held within populations (95.1%) (Hasse 1992). Partitioning of genetic variation in N. truncata was attributed to successive range contractions/expansion events during Pleistocene glacial cycles (Haase 1992). Similar low levels of isozyme diversity were exhibited in other New Zealand Nothofagus species N. fusca, N. solandri and N. cliffortioides (He = 0.027, 0.027and 0.034, respectively) while N. menziesii exhibited significantly higher levels of expected heterozygosity (0.099) (Haase 1993).

In contrast, South American Nothofagus species have received much more attention in relation to population genetic structure. Nothofagus temperate forests occur on both sides of the Andes Mountains, in Chile and Argentina across a wide latitudinal range from 33ºS to 56ºS (Veblen et al. 1996). South American Nothofagus forests have been exploited in the past because they yield high quality wood, with N. nervosa and N. obliqua having been significantly overexploited (Azpilceta et al. 2004). One of the first published studies that investigated genetic variation in the geographically restricted N. nitida and widespread N.betuloides and N. dombeyi used isozymes (Premoli 1997).

This study identified higher total genetic variation in the widespread N. betuloides (HT =

0.301) and N. dombeyi (HT = 0.228) species compared with the geographically isolated

N. nitida (HT = 0.156). This result supports the hypothesis that widespread species often consist of historically large and continuously distributed populations that harbour relatively higher levels of genetic diversity (Premoli 1997). The study also revealed that the majority of genetic variation was held within populations with very little population differentiation evident (GST levels of 0.120, 0.074 and 0.047 for N. betuloides, N. dombeyi and N. nitida respectively) (Premoli 1997).

Analysis of cpDNA variation using PCR-RFLPs identified only two unique haplotypes in the deciduous South American N. nervosa. The two unique haplotypes were separated by the Huechulafquen mountain chain, part of the main Andes mountain range

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(Marchelli et al. 1998). Distributions of the two haplotypes were separated by a major mountain chain suggesting the existence of at least two different glacial refugia from where N. nervosa perhaps expanded during the inter-glacial period (Marchelli et al. 1998). Additional studies of genetic diversity in N. nervosa using allozyme markers provided more evidence for existence of northern and southern glacial refugia in this species (Marchelli and Gallo 2004). A recent, extensive study of N. nervosa chloroplast variation assessed 26 populations across the species entire geographical range in Chile and Argentina (Marchelli and Gallo 2006). Two unique haplotypes were restricted to the Pacific Coastal Mountains populations while the Andes Mountains exhibited a north-south variation in the distribution of three additional unique haplotypes (Marchelli and Gallo 2006). Results from this study support the hypothesis for two discrete glacial refugia in the Andes Mountains (Marchelli et al.1998) in addition to a proposed glacial refugium in the Pacific Coastal Mountains (Marchelli and Gallo 2006).

In general, few studies have been conducted on genetic diversity in temperate rainforest tree species in Australia. Atherosperma moschaturm (Sassafras - Atherospermataceae) is a cool temperate rainforest tree distributed from Tasmania across Victoria to Barrington Tops in New South Wales (Floyd 1989). Seedlings of A. moschatum germinate rapidly under the canopy of closed forests, but few seedlings survive due to herbivory, water stress, insect attack and fungal infection (Read 1985, Neyland 1991). Atherosperma moschatum is reported to regenerate clonally via coppicing that permits population persistence. Unlike N. moorei, populations of A. moschatum have been reported to show a latitudinal gradient in coppicing, with extensive coppicing in Tasmanian populations, only limited coppicing in Victoria and no evidence for coppicing in NSW populations (Johnston and Lacey 1983, Read and Hill 1988, Shapcott 1994). Isozyme analysis of populations from across the geographical range identified low population differentiation (FST = 0.178) with genetic diversity spread evenly across the distribution; and no ecological or genetic differences were apparent between small isolated populations and larger continuous ones (Shapcott 1994).

In contrast, an isozyme analysis of 32 sites for the Tasmanian endemic rainforest tree Lagarostrobus franklinii (Huon Pine - Podocarpaceae) identified low genetic diversity both within and among populations and relatively high levels of inbreeding (Shapcott 1997). Despite low levels of genetic diversity and high inbreeding estimates, however,

161 Chapter 6. General Discussion

L. franklinii has persisted for long evolutionary periods via clonal regeneration (Shapcott 1997).

In contrast to the patterns in disjunct populations of N. moorei, RAPD analysis of bidwillii (Bunya Pine - Araucariaceae) across the species’ disjunct natural distribution in north and south Queensland identified high population divergence between northern and southern populations (FST = 0.2932) (Pye and Gadek 2004). This strong regional population structure was attributed to major evolutionary changes that resulted from climate change during the Pliocene (Pye and Gadek 2004). Despite regional differentiation, genetic diversity was high within all populations of A. bidwilli (Pye and Gadek 2004).

Thus, comparative studies of temperate rainforest trees have not identified common patterns for genetic diversity and population structure and illustrate the importance of assessing patterns in each species individually to allow potential causal mechanisms for observed patterns to be developed and explored in more detail.

6.3 Habitat fragmentation effects

Habitat fragmentation often leads to reductions in population size and increases in population isolation and may theoretically result in deleterious genetic affects (inbreeding) due to associated reduced levels of genetic diversity and gene flow (Rossetto et al. 2004b, Bacles et al. 2005). There is increasing evidence, however, that plant species can respond to habitat fragmentation in diverse ways that may or may not result in restricted gene flow, reductions in genetic diversity and/or population divergence (Bacles et al. 2005).

Populations may be extensively fragmented yet still display low levels of population genetic differentiation due to historical gene flow (Lowe et al. 2006). This apparent genetic continuity may be an historical artefact and remains simply because insufficient generations have passed for populations to diverge signficantly (Hartl 1987). Long life cycles in many forest tree species may also conceal or delay the potential effects of habitat fragmentation on levels of genetic variation. There are also greater chances of

162 Chapter 6. General Discussion gene flow being re-established following isolation between populations of long-lived species due to the extended time scales of their existence that may counteract any short term impacts of drift (Loveless and Harmrick 1984). Molecular genetic studies can therefore be misleading with respect to contemporary population processes in species that have undergone rapid changes in population size and/or migration rate (Moritz 1994). The real effects of genetic erosion may not be revealed for hundreds or even thousands of years in very long-lived species (Bekessy et al. 2002).

Microsatellite analysis of fragmented populations of the common ash, Fraxinus excelsior (Oleaceae) revealed high levels of genetic diversity, comparable with those from continuous populations and low population divergence, suggesting extensive historical gene flow among populations (Bacles et al. 2005).

Another example of how historical processes can affect modern levels of population diversity is apparent in Austromyrtus gonoclada (Myrtaceae), one of Queensland’s rarest rainforest plants known from just 27 individuals. Individuals of A. gonoclada retained higher levels of heterozygosity and overall high genetic diversity compared with other more widespread Austromyrtus species (Shapcott and Playford 1996). It is believed that the 27 remaining individuals of A. gonoclada are representatives of a former more widespread population and modern population diversity is essentially an historical legacy (Shapcott and Playford 1996). There were, however, very few seedlings of A. gonoclada compared with other Austromyrtus species and most seeds fell from the parent plant while green and were non-viable indicative of either self- incompatibility or impacts of climate change from land clearing that resulted in poor rates of sexual reproduction (Shapcott and Playford 1996).

The relatively high levels of genetic diversity retained within modern N. moorei populations and limited population differentiation evident suggests that historical processes may still influence current levels of genetic diversity and structure in this species. The current patterns of genetic diversity and differentiation in N. moorei identified most likely reflect the historical widespread distribution of N. moorei prior to Pleistocene glaciations. Alternatively, current gene flow among extant isolated populations may occur at a level sufficient to prevent significant differentiation of modern populations. This latter scenario seems unlikely given that N. moorei’s seed is

163 Chapter 6. General Discussion poorly dispersed (Read and Brown 1996). Pollen-mediated gene flow may be more likely given the greater ability for long-distance wind-mediated pollen movement. Based on the levels of pollen flow in other wind-pollinated temperate forest trees however, the majority of pollen dispersal in N. moorei is likely to be localised within tens to hundreds of metres of parent trees with only rare long distance pollen dispersal events over several kilometres (Hamrick 2004, Bacles et al. 2005). Pollen flow therefore would be unlikely to be sufficient to prevent population divergence among geographically distant regions where N. moorei now occurs.

In this study, leaf material sampled from N. moorei populations was taken from the coppicing shoots of mature individuals. One observation made during sampling was that younger (based on the smaller diameter of the trunk) N. moorei trees did not coppice, so here older individuals tended to be sampled. No seedlings were available to sample in any populations except for Mt Banda where 3 seedlings were collected. Sampling coppicing leaves from older individuals means that current genetic diversity and gene flow is not being assessed since these mature individuals were presumably established hundreds to thousands of years ago. In effect, this study may thus have sampled only historical levels of gene flow and genetic diversity, not current gene flow among young trees.

6.4 Forest tree model – an alternative explanation for high within population diversity and low population divergence in N. moorei

Forest trees are characterised in general by relatively high levels of genetic diversity within populations and limited differentiation among populations (Hamrick and Godt 1990, Hamrick and Godt 1996). This pattern has long been attributed to very large effective population sizes and/or high gene flow occurring among populations. More recent models of forest trees however, have suggested that high within population diversity and low population divergence is a result of the genetic stability of forest trees due to large founder populations and overlapping generations (Austerlitz et al. 2001, Austerlitz and Garnier-Géré 2003). Most forest trees have long juvenile periods and overlapping generations which effectively results in almost no founder effects. Each new population retains essentially all of the genetic diversity of the ancestral population

164 Chapter 6. General Discussion

(Austerlitz et al. 2001). This is because it takes many years for the initial founders to reach sexual maturity and during this time more migrants arrive, thus increasing the size of the founding population (Austerlitz et al. 2001). Additionally, there are minimal effects of genetic drift in forest trees. Presence of overlapping generations means that even without gene flow FST estimates among populations are unlikely to increase after colonisation and genetic relationships between source and founder populations can remain effectively unchanged even after several thousands of years of separation (Austerlitz et al. 2001, Austerlitz and Garnier-Géré 2003). Based on this model, over a 100, 000 year glacial cycle no significant genetic divergence may be evident between refugia even after 80, 000 years of isolation unless population sizes declined to very small sizes (<100 trees) and by the end of the glacial period each newly colonised population would be genetically similar to its source (Austerlitz et al. 2001, Austerlitz and Garnier-Géré 2003). A lack of substantial among-population differentiation and high within population diversity in N. moorei may be explained by this alternative forest tree model.

The same model has been offered to explain the very high levels of genetic diversity evident within, and little genetic differentiation among, provenances across broad regions for Araucaria cunninghamii (Scott 2004). Araucaria cunninghamii is a rainforest emergent that is distributed from Irian Jaya through , the east coast of Australia to northern New South Wales. (Scott 2004). The genus Araucaria was widespread prior to the (210 Myr ago). Palaeogeographical evidence indicates that populations remained large, stable and continuous for >200 Myrs (Scott 2004). Like many rainforest trees, Araucaria’s distribution declined in south-east Australia during Pleistocene glacial cycles and then retreated to small refugia in Victoria. Evolutionary consequence of isolation in glacial refugia and later expansion during inter-glacial periods produced population genetic divergence, often resulting in species radiations (Crisp et al. 1995). Evolution of A. cunninghamii however, appears to have been delayed, maintaining relatively high genetic diversity and apparent connectivity between very distant populations (Scott 2004).

It is interesting to note however, that there appears to be no single pattern that can explain levels of genetic diversity and the extent of population divergence in all long- lived trees. The bunya pine (Araucaria bidwilli), like A. cunninghamii, was once

165 Chapter 6. General Discussion previously widespread but populations then contracted as a result of historical climate change. Today populations of A. bidwillii are differentiated regionally between the north and south in eastern Australia (Pye and Gadek 2004).

6.5 Significance of clonality to the ongoing persistence of N. moorei populations

The ability to regenerate clonally via vigorous coppicing is a unique characteristic of many Australian cool temperate rainforest trees (Johnston and Lacey 1983). Coppicing ability may be the sole reason N. moorei individuals can persist in the northern Lamington/Border Ranges region. Without this ability, N. moorei individuals may not be replaced in this region and populations may eventually become extinct as individuals die or are destroyed as a result of storm damage and/or are replaced by competitively superior, warm temperate and sub-tropical species.

Survival of the ancient Wollemi pine (Wollemia nobilis) has been attributed to an ability to coppice (Offord et al. 1999). Much like N. moorei populations in the Lamington/Border Ranges region, the Wollemi pine shows limited successful sexual regeneration and depends on clonal regeneration to maintain an already depauperate population (Offord et al. 1999). Although coppicing may enable the Wollemi pine to persist, coppicing does not allow for range expansion, but only for individuals to maintain their current geographical positions.

The prevalence of coppicing in the northern Lamington/Border Ranges populations appears to have had little effect on levels of population diversity, heterozygosity or population structure. Clonal regeneration of extant individuals can allow persistence of remaining genetic diversity into the future, conserving genotypes, unless there is mass destruction of multiple populations from within this region. Thus, coppicing may effectively protect populations from genetic erosion (Johnston and Lacey 1983). In terms of relative fitness estimates, conservation of current levels of genetic diversity may not however, necessarily be sufficient to allow future adaptation to climate change. The current gene pool may not contain sufficient diversity of genotypes to allow populations to respond to the full extent of climate change effects that has been predicted for the next 50 to 100 years. Furthermore, any successful sexual

166 Chapter 6. General Discussion regeneration that does occur may result in highly inbred individuals due to cross- fertilisation among clones, thus further reducing the evolutionary potential of the species in this region.

While coppicing in the Lamington/Border Ranges populations may allow persistence of extant populations, there is a concern that shade-tolerant under-canopy species such as Doryphora sassafras and Ceratopetalum apetalum could replace N. moorei as old individuals die or are blown down in wind storms (Read and Hill 1985). Over time, gradual replacement by sub-tropical and warm temperate species could result in local population extinctions.

While all extant populations of N. moorei coppice to some degree, the trait is more common in the northern Lamington/Border Ranges populations presumably as a response to the closed canopy forests present there. Should climate change result in expansion of closed canopy forests in other regions, coppicing may increase there also and become the predominant regeneration strategy that maintains N. moorei populations. The current expansion of N. moorei into open eucalypt woodland at Barrington Tops may see the development of closed canopy N. moorei-dominant cool temperate rainforests provided the frequency and intensity of fire remains low. Nothofagus moorei individuals within closed canopy forests may shift to predominantly clonal regeneration due to insufficient light on the forest floor for sexually produced young trees to survive. Results here suggest that coppicing in N. moorei does not occur as a response to unsuccessful sexual regeneration; rather coppicing is an ongoing regeneration strategy particularly where the canopy is closed and in low light environments such as is present in the Lamington/Border Ranges region. While sexual regeneration apparently occurs in all populations within all regions, it may be unsuccessful in many populations due to a lack of suitable sites for seedling development and maturation. Seed from northern populations is equally viable when compared with that from southern populations, however, seed size from clonal individuals within the Lamington/Border Ranges region is significantly smaller than seed from non-clones, suggesting that clonal individuals are allocating greater resources to clonal, rather than, sexual regeneration (Meyers 1993). Over time, selection may favour those individuals that adopt a largely clonal regeneration strategy and seeds may become non-viable. Clonal selection has been seen in many other plant species

167 Chapter 6. General Discussion

(Rosetto et al. 2004; Honnay and Bossuyt 2005). Unfortunately, this process limits the evolutionary potential of populations and they may have ultimately been selected to decline given the predictions for accelerated climate change.

6.6 Conservation management of N. moorei populations

There are a number of ways to estimate genetic diversity including: allelic richness, heterozygosity and percent of polymorphic loci. Of these, allelic richness has been argued to be the most informative measure of genetic diversity with relevance to conservation issues (Marshall and Brown 1975). While heterozygosity is proportional to the amount of genetic variance at a locus and is related to the immediate response to selection, allelic composition limits the potential response to selection across generations regardless of allele frequencies (Petit et al. 1998). A steady decline in allelic richness was observed during postglacial recolonisation of Europe by beech (Comps et al. 2001). Founder events commonly result in significant allele losses and therefore initial allelic richness is likely to be highest in glacial refugia (Comps et al. 2001). If allelic richness is the best measure of overall fitness and a species’ ability to respond to future environmental change, then all extant populations of N. moorei appear to show similar potential to respond to any predicted climate change in the future, given that all populations share similar levels of allelic richness. The similar level of adaptive genetic diversity present currently within each discrete population would indicate therefore, that all sampled populations can be considered essentially equal in terms of conservation status. The low but significant population differentiation detected, however, suggests ideally representatives from all populations should be conserved. Furthermore, although no regional population structure was detected, individuals from the four groups identified as significantly differentiated should be conserved. These groups are (1) Mt Moombil; (2) Mt Ballow; (3) Mt Wanungra, Echo Point, Elabana Falls and Lightning Falls; and (4) all remaining populations. The Bayesian method while also failing to detect any regional structure did identify differing gene pool proportions in the two Barrington populations, Mt Hobwee and Antarctic Beech Walk/Picnic populations, thus suggesting conservation of these four populations in addition to other populations would be necessary to maintain the most genetic diversity in wild populations.

168 Chapter 6. General Discussion

Assessment of chloroplast DNA variation identified divergence of populations within the Dorrigo/New England region from all other regions. This pattern implies that it would be important in any conservation strategy for N. moorei to include populations from the Dorrigo/New England region as well as populations from other regions in order to conserve both unique cpDNA haplotypes.

Fortunately, all populations of N. moorei sampled here occur within National Parks or forest reserves, so there is little danger of land-clearing destroying these extant populations. Nevertheless, rapid climate change, continued northward continental drift of Australia to the warmer tropics and fire are all unavoidable and ultimately threaten the long term persistence of this species. The ability to regenerate clonally via coppicing however, may enable long-term persistence of N. moorei but populations are likely to continue to decline as climatic conditions favour more competitive sub-tropical and warm temperate forests in much of the species’ northern distribution. While populations in northern New South Wales may actually increase in size if climate becomes gradually warmer and wetter in this region, as predicted, this will be at the expense however, of Eucalyptus dominated woodlands. Frequency of fire may be the most important determinant of long term persistence of N. moorei populations in the south. Should fire frequency increase, there is likely to be a shift to more Eucalyptus dominated woodland at the expense of Nothofagus forests in this region.

The past contraction of extant N. moorei populations to high altitude, high rainfall mountain tops as a result of climate change across the Holocene may have ultimately effectively ‘trapped’ N. moorei. Poole (1987) described N. moorei as a species ‘hanging on to the last few favourable sites’. This statement may hold true. Nothofagus moorei is already absent from several apparently climatically suitable sites as populations went extinct from these sites or individuals were incapable of dispersing to them. In fact, during field trips for this study, extinction of the Mt Widgee population was recognised. This was previously reported to be the only site in the Eastern McPherson range (Lamington/Border Ranges) where sexual regeneration was occurring (Herbert 1936). The presence of charred Eucalypt and Xanthorrhoea species suggested that the young expanding N. moorei forest had been killed by fire some time ago.

169 Chapter 6. General Discussion

Given the inherent extinction risks for N. moorei it would be advisable that conservation efforts concentrate on establishment of ex-situ germplasm banks. Ex situ germplasm banks could include cultivation of N. moorei in a dedicated conservation facility, a field gene bank or cultivation in display or reference collections such as botanical gardens (Cochrane 2004, Maunder et al. 2004). There are already several botanical gardens that possess N. moorei specimens including the National Botanic Garden in Canberra, the Mt Tomah Botanic Gardens in the Blue Mountains of NSW and the Mt Lofty Botanic Gardens in Adelaide, South Australia. Another option could be establishment of a seed bank; however, the viability of long term storage of N. moorei seed would need to be assessed first, as many plant species have seed that is not suitable for seed banks. Experiments on South American Nothofagus species have shown their seed remain viable over a 2 year period when stored at low moisture content and cool temperatures (León-Lobos and Ellis 2005), therefore, these species may be conserved in an ex situ seed bank quite successfully. Commercial cultivation of N. moorei, similar to that for Wollemi pine, would also enhance conservation of the species, provided variable genotypes are cultivated commercially. Several nurseries already sell N. moorei seedlings to the public.

Finally, the conservation status of N. moorei should be upgraded to vulnerable. Currently N. moorei is not classified as vulnerable, yet according to section 179 of the EPBC Act, N. moorei meets all criteria for this level of conservation status (see Chapter 1 for details of criteria). Reclassification of N. moorei as a vulnerable species is particularly important since one third of N. moorei’s distribution in NSW is within forestry reserves and thus populations are vulnerable to exploitation from logging.

6.7 Future research directions

Given that this study sampled coppicing leaf tissue from older N. moorei individuals, it may be argued that levels of genetic diversity and structure reflect only historical processes. In order to fully assess contemporary levels of genetic diversity and structure, leaf samples need to be taken from seedlings and immature single-stemmed individuals. Sampling stems from within a large basal burl is likely to only reflect past

170 Chapter 6. General Discussion genetic diversity and structure since coppicing from a basal burl simply replicates genotypes.

The development of additional microsatellite loci specific for N. moorei would be highly useful for assignment testing within and among populations to determine exactly how much current dispersal is occurring and over what distance dispersal events are occurring. It would be particularly important to compare the highly clonal Lamington/Border Ranges region with other less clonal regions.

The remote isolated population at Cathedral Rock National Park near New England National Park in NSW warrants urgent assessment of genetic diversity with both nuclear and chloroplast markers. This single isolated population occurs in the wet gullies among granite tors. Granite outcrops such as those in Cathedral Rock National Park represent isolated island habitats and may harbour populations/species with complex evolutionary histories (Byrne and Hopper 2008). Thus the Cathedral Rock N. moorei population may harbour a unique evolutionary lineage that would be of the utmost importance to conserve, particularly as this isolated population may be highly susceptible to local extinction.

In order to determine whether N. moorei seedlings are capable of germination and maturation under predicted future climate change to increased temperature and reduced/increased rainfall, glasshouse germination studies should be considered.

A transplant study of N. moorei seeds and/or seedlings to other climatically suitable sites where N. moorei is currently absent would provide an interesting assessment of the species capacity to colonise new habitats in the future. Current absence of N. moorei from climatically suitable sites may simply be a reflection of its limited dispersal ability and the requirement for high light environments for seedling maturation.

6.8 Conclusion

Despite marked reductions in N. moorei’s distribution since the Early Miocene, populations appear to have retained significant levels of genetic diversity and show little

171 Chapter 6. General Discussion evidence of population divergence. Levels of clonality however, are substantially greater in the northern Lamington/Border Ranges region, suggesting that these may be remnant populations surviving in environments of low quality.

In relictual, long-lived forest trees like N. moorei, where generations overlap, changes in genetic diversity and gene frequencies are difficult to detect. We are effectively looking back in time when assessing modern populations. The only way to assess habitat fragmentation effects in such long-lived tree species is to examine levels of genetic diversity and gene flow in seedling populations. Unfortunately, in N. moorei at least, such populations do not occur naturally. This study has highlighted the important role life history traits play in influencing levels of genetic diversity and structure in long-lived relictual tree species.

172 Appendices

Appendix 4.1 Kruskall Wallis test results for comparison of Shannon’s Index (I) and Nei’s gene diversity index (Nei) based on 100 AFLP loci and 491 N. moorei individuals from 20 populations and 5 regions. Test results for both populations and regions were not significant, suggesting gene diversity indices were the same across populations and regions.

AFLP population comparisons

I Nei Chi-Square 19.000 19.000 df 19 19 Asymp. Sig. 0.457 0.457 Degrees of freedom (df)

AFLP region comparisons

I Nei Chi-Square 4.000 4.000 df 4 4 Asymp. Sig. 0.406 0.406 Degrees of freedom (df)

173 Appendices

Appendix 4.1 cont. Kruskall Wallis test results for comparison of Shannon’s Index (I),

Nei’s gene diversity index (Nei), allelic richness (RS), observed heterozygosity (HO), expected heterozygosity (HE) and within sample gene diversity (HS) based on 4 microsatellite loci and 491 N. moorei individuals from 20 populations and 5 regions. Test results for both populations and regions were not significant, suggesting gene diversity indices were the same across populations and regions.

Microsatellite population comparisons

I Nei RS HO HE HS Chi-Square 19.000 19.000 19.000 19.000 19.000 19.000 df 19 19 19 19 19 19 Asymp. 0.457 0.457 0.457 0.457 0.457 0.457 Sig. Degrees of freedom (df)

Microsatellite region comparisons

I Nei RS HO HE HS Chi-Square 4.000 4.000 4.000 4.000 4.000 4.000 df 4 4 4 4 4 4 Asymp. 0.406 0.406 0.406 0.406 0.406 0.406 Sig. Degrees of freedom (df)

174

Appendix 4.2 Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from 20 Appendices populations. Population abbreviations as per list of abbreviations at front of thesis.

Lamington/Border Ranges

Locus Band size (bp) TW MtW MH BAL EP EF LF HL ABW/P BM

ncutas06 310 0.0882 0.1 0.0192 0 0.1739 0.02 0.0962 0 0 0.0227 3400000000000 342 0 0 0.0769 0 0 0 0.0577 0 0 0 344 0 0 0.0769 0.1429 0.087 0 0.0385 0.0577 0.1111 0 346 0 0 0.0385 0 0.0217 0.12 0.1154 0.0962 0 0.1136 348 0 0 0.0962 0.1429 0.0217 0.06 0.0769 0.2692 0 0.0909 352 0 0 0 0.0357 0.1304 0.34 0.0385 0.0962 0.0278 0.0909 354 0.3824 0.3667 00000.0577 0.1154 0 0.1364 356 0 0.1333 0 0 0 0.12 0000 358 0 0 0 0.0357 0.0217 0.04 0 0 0.2222 0 360 0.0588 0.0333 0.0385 0.0714 0 0.16 0.0769 0 0.0833 0.1136 362 0 0 0 0.0357 0.0217 00000 364 0 0.0333 0.0962 0.0714 0.3478 0.14 0.1923 0.0962 0.25 0.0682 366 0 0 0.0769 0000000 368 0 0.0667 0.1538 0.2143 0.0217 0 0.1154 0 0.0556 0.0909 370 0 0.0667 0.1923 0 0 0 0.0962 0.0385 0.0556 0.0909 372 0 0.1333 0.0962 0.0714 0 0 0 0.1731 0 0.0227 374 0 0 0 0.0357 000000.1591 376 0 0 0 0.0714 0.1087 0 0.0385 0.0192 0.0278 0 378 0 0 0.0385 0.0714 0.0435 0 0 0 0.0556 0 3800000000000 382 0.2941 0.0667 000000.0385 0.0833 0 386 0.1765 00000000.0278 0 388 000000000

175

Appendices Appendices Appendix 4.2 cont. Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from 20 populations. Population abbreviations as per list of abbreviations at front of thesis.

Lamington/Border Ranges

Locus Band size (bp) TW MtW MH BAL EP EF LF HL ABW/P BM

ncutas12 1930000000000 205 0 0 0 0.1429 0.0435 0.04 0 0.0769 0 0.1591 207 0.3529 0.3667 0.1923 0.6429 0.4565 0.42 0.2115 0.4423 0.1111 0.5682 209 0.6471 0.4333 0.4423 0.2143 0.4348 0.4 0.7115 0.4231 0.3611 0.2727 211 0 0.0333 0.3269 00000.0577 0.1667 0 213 0 0.1667 0.0192 0 0.0652 0.14 0.0769 0 0.3611 0 215000.01920000000

ncutas13 293 0.2059 0.1667 0.0769 0.25 0.0217 0.02 0.0192 0 0.0556 0 295 0 0 0.0769 0 0.1087 0.02 0.0385 0 0 0.0455 297 0 0 0 0 0 0.08 0.0192 0 0.0278 0 299 0 0.1333 0.3846 0 0.1304 0.1 0.0385 0.2692 0.0833 0.0227 301 0.2647 0.2667 0.0192 0.0714 0.3696 0.06 0.1538 0.0577 0.2222 0.3182 303 0 0.0333 0 0.1071 0.0217 0.12 0.0192 0 0.1667 0.0682 305 0 0 0.2692 0 0 0.06 0.0769 0.1923 0 0.0682 307 0 0.0333 0.0192 0.2857 0.0217 0.04 0 0 0.1667 0.2273 309 0.5294 0.2667 0.0962 0.0357 0.2609 0.04 0.5962 0.4615 0 0 311 0 0 0.0577 0.2143 0.0435 0.38 0.0192 0 0.2778 0.2273 3130000.0357000000 31700000000.0192 0 0 321 0 0 0 0 0 0.06 0.0192 0 0 0.0227 325 0 0.1 0 0 0.0217 0.02 0 0 0 0 3330000000000 3410000000000

176

Appendices Appendices Appendix 4.2 cont. Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from 20 populations. Population abbreviations as per list of abbreviations at front of thesis.

Lamington/Border Ranges

Locus Band size (bp) TW MtW MH BAL EP EF LF HL ABW/P BM

ncutas20 2180000000000 2220000000000 2240000000000 226 0.9118 0.8333 0.7308 0.8571 0.7391 0.74 0.5769 0.7692 0.9167 0.4091 228 0.0588 0.1 0.0769 0 0.2174 0.16 0.1731 0.1154 0.0556 0.25 230 0.0294 0.0667 0.1923 0.1429 0.0435 0.08 0.25 0 0.0278 0.3409 2320000000000 236000000.0200.1154 0 0

177

Appendices Appendices Appendix 4.2 cont. Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from 20 populations. Population abbreviations as per list of abbreviations at front of thesis.

BallowDorrigo/New England Werrikimbe Barrington

Locus Band size (bp) DUMP BA NO KG MM WR MB PB LT GT

ncutas06 310 0 0 0 0.1296 0.04 0.069 0 0.0179 0 0.1071 34000000.20.069000.1471 0.125 342 0 0 0.0167 0 0 0.0345 0.0517 0.0179 0 0 3440000.037000000 346 0.0179 0.0893 0.0833 0.0185 0.04 0.1724 0.0172 0.0179 0 0 348 0.0714 0.1071 0.0833 0.1667 0.12 0.0517 0.069 0.1071 0 0 352 0.0179 0.0357 0.05 0.037 0 0.0517 0.0345 0.1071 0 0.0357 354 0.0536 0 0 0.0556 0.1 0.1034 0.069 0.0536 0.0882 0.1607 356 0.0179 000000.0690.0536 0 0.0357 35800000.0200.0345 0 0.0294 0 360 0.0714 0 0.0167 000000.1471 0.0714 362 0.0179 0 0.0167 0.0185 0.08 00000 364 0.2143 0 0 0 0.02 0.0345 0.0345 0.0357 0 0 366 0 0.0179 0.05 0.1296 0.04 0 0.0862 0.0357 0 0 368 0.2679 0 0.15 0.0185 0 0.2241 0.1207 0.2143 0.1471 0.0179 370 0.125 0.0893 0.1833 0.0556 0.04 0.0345 0.2241 0.0893 0 0 372 0.0357 0.1071 0.0167 0.1111 0 0 0.1379 0 0.2647 0.25 374 0.0893 0.0893 0.1333 0 0.08 0 0.0517 0.1071 0.0294 0 376 0 0.0714 0.0667 0.0185 0.1 0 0 0.0536 0.0588 0.0536 378 0 0.1071 0.05 0.0185 0 0.0345 0 0 0 0.0179 380 0 0 0 0.0556 000000 382 0 0.1071 0.0667 0.0741 0.08 0.069 0 0.0893 0 0 386 0 0.1786 0.0167 0.0556 0.04 0.0172 0 0 0.0882 0.125 388000000.0345 0000

178

Appendix 4.2 cont. Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from Appendices 20 populations. Population abbreviations as per list of abbreviations at front of thesis.

Ballow Dorrigo/New England Werrikimbe Barrington

Locus Band size (bp) DUMP BA NO KG MM WR MB PB LT GT

ncutas12 193000.01670000000 205 0.0179 0.0357 0.1667 0.0556 0.06 0.0345 0.0345 0.0893 0.2059 0.1786 207 0.4286 0.5 0.4833 0.5926 0.52 0.431 0.5 0.5 0.6912 0.625 209 0.5536 0.4643 0.3333 0.3519 0.42 0.5345 0.4655 0.4107 0.1029 0.1964 2110000000000 2130000000000 2150000000000

ncutas13 293 0 0 0.0333 0.0185 0.02 0.0345 0.0345 0.0179 0.0147 0.0357 295 0.0357 0.0179 0 0.1481 0 0.0172 0 0 0 0 297 0 0.0536 0.0167 0.1111 0.04 0.0172 0 0.0893 0 0 299 0.2143 0.2321 0.2 0.1481 0.08 0.0862 0.2069 0.0714 0.0294 0.25 301 0.125 0.1786 0.1667 0.1852 0.1 0.2414 0.1034 0.0536 0.2353 0.3036 303 0.0536 0.0357 0 0.037 0.1 0.0345 0.069 0.2679 0.2941 0.0893 305 0.0536 0.0714 0.1 0.0741 0.02 0.1034 0.1379 0.0714 0.1029 0 307 0.0179 0.1429 0 0.0556 0.08 0.1897 0.0862 0.0893 0 0.0179 309 0.4107 0.125 0.4333 0.0556 0.04 0.069 0.0862 0.1964 0.3235 0.25 311 0 0.0893 0 0 0.16 0.1034 0 0.0357 0 0 313 0 0 0 0.0185 0.02 0 0.0172 0 0 0 317 0 0 0 0 0 0 0.069 0 0 0 321 0.0714 0.0536 0.05 0.037 0.18 0.0172 0.1034 0.0714 0 0.0357 325 0 0 0 0.1111 0.12 0.0862 0.0862 0.0357 0 0.0179 33300000.0400000 341 0.0179 0 0 0000000

179

Appendix 4.2 cont. Relative frequency of microsatellite alleles per population per locus for 491 N. moorei individuals from Appendices 20 populations. Population abbreviations as per list of abbreviations at front of thesis.

Ballow Dorrigo/New England Werrikimbe Barrington

Locus Band size (bp) DUMP BA NO KG MM WR MB PB LT GT

ncutas20 21800000.0600000 2220000000.0172 0 0 0 224000000.1207 0 0.0714 0 0 226 0.8214 0.75 0.7833 0.8333 0.9 0.8276 0.7759 0.6071 0.2647 0.25 228 0.0536 0.125 0.1 0.0926 0.02 0 0.1379 0.125 0.4706 0.2143 230 0.0714 0.0714 0.1167 0.0556 0 0.0517 0.0517 0.1964 0.2647 0.5357 232 0 0 0 0.0185 0.02 0 0.0172 0 0 0 236 0.0536 0.0536 00000000

180

Appendices

Appendix 4.3 Pilot Study Chapter

Population and regional structure in N. moorei based on 2 northern and 2 southern populations.

Aim

A pilot study was conducted to determine the extent of clonality and population and regional structure in N. moorei and therefore to determine the level of scale to pursue in further research. Four populations were sampled: two populations from N. moorei’s northern distribution in Lamington National Park (Tullawallal and Mt Wanungra) and two populations from the southern distribution in Barrington Tops National Park (Barrington Tops and Gloucester Tops).

NOTE: Results presented here relate only to population and regional structure. The clonality results were presented in Chapter 3.

Methods

Study sites and sampling

One hundred and nineteen individuals of Nothofagus moorei from 4 populations in 2 regions were sampled. Two populations consisting of 60 individuals (36 from Mt Wanungra and 24 from Tullawallal) were sampled from Lamington National Park in south-east Queensland and 2 populations consisting of 59 individuals (30 from Link Trail and 29 from Gloucester Tops) were sampled from Barrington Tops National Park in New South Wales. These populations represent the two northern and southern extreme limits of N. moorei’s distribution in Australia. Each population within the 2 regions were separated by approximately 6km. Populations were sampled randomly within 50 x 50m or 100 x 50m quadrat, with distances between each tree recorded and mapped. Only coppice leaves were sampled from the Lamington National Park populations, while both coppice and associated canopy leaves of the same individual were sampled in the Barrington Tops National Park populations.

181 Appendices

AFLP and microsatellite methodology

DNA was extracted and analysed using AFLP and microsatellite markers as previously described in section 2.4 (Chapter 2).

Data analysis

To determine the extent of among-population genetic differentiation data were analysed using analysis of molecular variance (AMOVA) in ARLEQUIN Version 2.000 using a genetic distance matrix (Schneider et al. 2000). AMOVA partitions variation in the data according to predetermined hierarchical levels and compares variation within and among groups. (Excoffier et al. 1992). Genetic variation was partitioned among individuals within populations, among populations within regions and among regions. Regions were defined based on the extant geographical structure of populations: Lamington/Border Ranges, Ballow, Dorrigo/New England, Werrikimbe and Barrington. Population subdivision analyses were assessed using Ф-statistics generated in AMOVA under the permutational procedures of the ARLEQUIN program.

The genetic differentiation among populations was also evaluated by calculating population pairwise FST values in ARLEQUIN Version 2.000 (Schneider et al. 2000).

The significance of the pairwise population FST values was tested by comparison to 95% confidence intervals acquired by 1023 permutations.

Relationships among populations were investigated using a principle co-ordinate analysis (PCA) to show the clusters of relatedness among N. moorei individuals from the 2 southern and 2 northern populations. PCA was performed in GenAlEx V 5.1 (Peakall and Smouse 2001).

182 Appendices

Results

Based on AFLP data, AMOVA results clearly show that most of the genetic variation observed was within populations (57.56%), with very little variation among populations within a region (8.94%) (Table A4.2.1). There was also substantial but not significant genetic variation among regions (33.49%). The lack of significance for this variation may be attributable to the high variation observed within populations masking the variation among regions. These results suggest gene flow is occurring at both small and large spatial scales i.e. within and among populations. Similarly, AMOVA results based on microsatellite data revealed overwhelmingly that the majority of genetic variation was retained within populations (82.34%, Table A4.2.2) with only 4.73% retained among populations within regions. There was a substantial amount of variation among regions (12.92%) but this was not significant.

Population pairwise FST values were significant for all pairwise estimates and most interestingly, the level of differentiation among southern (Gloucester Tops and Link Trail) and northern (Tullawallal and Mt Wanungra) populations was much greater than that within southern and northern populations (Tables A4.2.3 and A4.2.4), suggesting there is regional differentiation among southern and northern populations. This is further supported by the PCA graph based on AFLP data which shows a distinct differentiation of southern and northern populations (Figure A4.2.1). The PCA graph based on microsatellite data also reveals regional differentiation, although it appears a lot weaker, as northern and southern populations overlap (Figure A4.2.2).

183 Appendices

Table A4.2.1 Analysis of molecular variance (AMOVA) for 94 N. moorei individuals from 4 populations across 2 regions based on data from 124 AFLP loci. Significance values (P) were calculated using 1023 permutations.

Source of variation Df SS Variance % of variation P value Components

Among regions 1 584.23 3.35125 33.49 >0.05

Among pops/regions 2 171.844 1.69601 8.94 <0.001

Within pops 174 1899.409 10.92614 57.56 <0.001

Degrees of freedom (Df); Sum of squares (SS)

Table A4.2.2 Analysis of molecular variance (AMOVA) for 87 N. moorei individuals from 4 populations across 2 regions based on data from 4 microsatellite loci. Significance values (P) were calculated using 1023 permutations.

Source of variation Df SS Variance % of variation P value Components

Among regions 1 37.78 0.20594 12.92 >0.05

Among pops/regions 2 15.146 0.07534 4.73 <0.001

Within pops 332 435.399 1.31144 82.34 <0.001

Degrees of freedom (Df); Sum of squares (SS)

184 Appendices

Table A4.2.3 Pairwise FST values based on 124 AFLP loci for 2 northern and 2 southern populations of N. moorei. All FST values were significant P<0.001.

Tullawallal Mt Wanungra Link Trail Gloucester Tops

Tullawallal * Mt Wanungra 0.17293 * Link Trail 0.45038 0.49645 * Gloucester Tops 0.35691 0.39497 0.12236 *

Table A4.2.4 Pairwise FST values based on 4 microsatellite loci for 2 northern and 2 southern populations of N. moorei. All FST values were significant P<0.001.

Tullawallal Mt Wanungra Link Trail Gloucester Tops

Tullawallal * Mt Wanungra 0.06048 * Link Trail 0.22468 0.16491 * Gloucester Tops 0.19775 0.13686 0.05207 *

185 Appendices

Principal Coordinates

Tullawallal Mt Wanungra Link Trail Coord. 2 Gloucester Tops

Coord. 1

Figure A4.2.1 Scatter plot of 100 individuals of N. moorei based on the genetic similarity matrix generated from124 AFLP loci. Coordinates (Coord.) 1 and 2 represent 23.75% and 6.59% of the variance, respectively. Different symbols correspond to the 4 different populations.

Principal Coordinates

Tullawallal Mt Wanungra Link Trail Coord. 2 Gloucester Tops

Coord. 1

Figure A4.2.2 Scatter plot of 93 individuals of N. moorei based on the genetic similarity matrix generated from 4 microsatellite loci. Coordinates (Coord.) 1 and 2 represent 21.08% and 12.00% of the variance, respectively. Different symbols correspond to the 4 different populations.

186 Appendices

Discussion

Results have shown putative regional structuring among northern (Tullawallal and Mt Wanungra) and southern (Gloucester Tops and Link Trail) populations based on AFLP and microsatellite data. AFLP data revealed stronger differentiation of northern and southern populations compared with microsatellites however, this likely reflects the differing marker systems, with dominant AFLPs tending to over-estimate population differentiation while microsatellites generally under-estimate population differentiation due to their high mutation rates.

Given that only two populations from both regions were sampled and no intervening populations sampled, north-south differentiation needs to be confirmed in a larger study. The results do however, provide a direction in which to focus future sampling. Ideally populations will be sampled from intervening populations from the Dorrigo/New England and Werrikimbe regions, in addition to populations from the isolated western McPherson (Ballow) region in the north of N. moorei’s distribution. Sampling strategies should aim to sample representative genetic diversity from several populations from each region across N. moorei’s distribution. Levels of clonality in addition to diversity and spatial structure should be investigated so a complete assessment of N. moorei’s evolutionary potential and conservation status can be made.

187

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